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Proceedings Track Presentations

Papers will be presented in the COSIs track program

3DSIG: Structural Bioinformatics and Computational Biophysics

3DSIG: Structural Bioinformatics and Computational Biophysics

QDeep: distance-based protein model quality estimation by residue-level ensemble error classifications using stacked deep residual neural networks
Date: July 15 and 16

  • Md Hossain Shuvo, Auburn University, United States
  • Sutanu Bhattacharya, Auburn University, United States
  • Debswapna Bhattacharya, Auburn University, United States

Presentation Overview: Show

Motivation: Protein model quality estimation, in many ways, informs protein structure prediction. Despite their tight coupling, existing model quality estimation methods do not leverage inter-residue distance information or the latest technological breakthrough in deep learning that has recently revolutionized protein structure prediction.

Results: We present a new distance-based single-model quality estimation method called QDeep by harnessing the power of stacked deep residual neural networks (ResNets). Our method first employs stacked deep ResNets to perform residue-level ensemble error classifications at multiple predefined error thresholds, and then combines the predictions from the individual error classifiers for estimating the quality of a protein structural model. Experimental results show that our method consistently outperforms existing state-of-the-art methods including ProQ2, ProQ3, ProQ3D, ProQ4, 3DCNN, MESHI, and VoroMQA in multiple independent test datasets across a wide-range of accuracy measures; and that predicted distance information significantly contributes to the improved performance of QDeep.

Availability: https://github.com/Bhattacharya-Lab/QDeep

Boosting the accuracy of protein secondary structure prediction through nearest neighbor search and method hybridization
Date: July 15 and 16

  • Spencer Krieger, University of Arizona, United States
  • John Kececioglu, University of Arizona, United States

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Motivation: Protein secondary structure prediction is a fundamental precursor to many bioinformatics tasks. Nearly all state-of-the-art tools when computing their secondary structure prediction do not explicitly leverage the vast number of proteins whose structure is known. Leveraging this additional information in a so-called template-based method has the potential to significantly boost prediction accuracy.

Method: We present a new hybrid approach to secondary structure prediction that gains the advantages of both template- and non-template-based methods. Our core template-based method is an algorithmic approach that uses metric-space nearest neighbor search over a template database of fixed-length amino-acid words to determine estimated class-membership probabilities for each residue in the protein. These probabilities are then input to a dynamic programming algorithm that finds a physically-valid maximum-likelihood prediction for the entire protein. Our hybrid approach exploits a novel accuracy estimator for our core method, that estimates the unknown true accuracy of its prediction, to discern when to switch between template- and non-template-based methods.

Results: On challenging CASP benchmarks the resulting hybrid approach boosts the state-of-the-art Q8 accuracy by more than 2-10%, and Q3 accuracy by more than 1-3%, yielding the most accurate method currently available for both 3- and 8-state secondary structure prediction.

Availability: A preliminary implementation in a new tool we call Nnessy is available free for non-
commercial use at http://nnessy.cs.arizona.edu.

Geometric Potentials from Deep Learning Improve Prediction of CDR H3 Loop Structures
Date: July 15 and 16

  • Jeffrey A. Ruffolo, Johns Hopkins University, United States
  • Carlos Guerra, George Mason University, United States
  • Sai Pooja Mahajan, Johns Hopkins University, United States
  • Jeremias Sulam, Johns Hopkins University, United States
  • Jeffrey J. Gray, Johns Hopkins University, United States

Presentation Overview: Show

Antibody structure is largely conserved, except for a complementarity-determining region featuring six variable loops. Five of these loops adopt canonical folds which can typically be predicted with existing methods, while the remaining loop (CDR H3) remains a challenge due to its highly diverse set of observed conformations. In recent years, deep neural networks have proven to be effective at capturing the complex patterns of protein structure. This work proposes DeepH3, a deep residual neural network that learns to predict inter-residue distances and orientations from antibody heavy and light chain sequence. The output of DeepH3 is a set of probability distributions over distances and orientation angles between pairs of residues. These distributions are converted to geometric potentials and used to discriminate between decoy structures produced by RosettaAntibody and predict new CDR H3 loop structures de novo. When evaluated on the Rosetta antibody benchmark dataset of 49 targets, DeepH3-predicted potentials identified better, same, and worse structures (measured by root-mean-squared distance [RMSD] from the experimental CDR H3 loop structure) than the standard Rosetta energy function for 33, 6, and 10 targets, respectively, and improved the average RMSD of predictions by 32.1% (1.4 Å). Analysis of individual geometric potentials revealed that inter-residue orientations were more effective than inter-residue distances for discriminating near-native CDR H3 loops. When applied to de novo prediction of CDR H3 loop structures, DeepH3 achieves an average RMSD of 2.2 ± 1.1 Å on the Rosetta antibody benchmark.

BioVis: Biological Data Visualizations

BioVis: Biological Data Visualizations

Interactive visualization and analysis of morphological skeletons of brain vasculature networks with VessMorphoVis
Date: July 15

  • Marwan Abdellah, Blue Brain Project / EPFL, Switzerland
  • Nadir Román Guerrero, Blue Brain Project / EPFL, Switzerland
  • Samule Lapere, Blue Brain Project / EPFL, Switzerland
  • Jay S. Coggan, Blue Brain Project / EPFL, Switzerland
  • Benoit Coste, Blue Brain Project / EPFL, Switzerland
  • Snigdha Dagaer, Blue Brain Project / EPFL, Switzerland
  • Daniel Keller, Blue Brain Project / EPFL, Switzerland
  • Jean-Denis Courcol, Blue Brain Project / EPFL, Switzerland
  • Henry Markram, Blue Brain Project / EPFL, Switzerland
  • Felix Schurmann, Blue Brain Project / EPFL, Switzerland

Presentation Overview: Show

Motivation: Accurate morphological models of brain vasculature are key to modeling and simulating cerebral blood flow (CBF) in realistic vascular networks. This in silico approach is fundamental to revealing the principles of neurovascular coupling (NVC). Validating those vascular morphologies entails performing certain visual analysis tasks that cannot be accomplished with generic visualization frameworks. This limitation has a substantial impact on the accuracy of the vascular models employed in the simulation. Results: We present VessMorphoVis, an integrated suite of toolboxes for interactive visualization and analysis of vast brain vascular networks represented by morphological graphs segmented originally from imaging or microscopy stacks. Our workflow leverages the outstanding potentials of Blender, aiming to establish an integrated, extensible and domain-specific framework capable of interactive visualization, analysis, repair, high-fidelity meshing and high-quality rendering of vascular morphologies. Based on the initial feedback of the users, we anticipate that our framework will be an essential component in vascular modeling and simulation in the future, filling a gap that is at present largely unfulfilled.
Availability and implementation: VessMorphoVis is freely available under the GNU public license on Github at https://github.com/BlueBrain/VessMorphoVis. The morphology analysis, visualization, meshing and rendering modules are implemented as an add-on for Blender 2.8 based on its Python API (Application Programming Interface). The add-on functionality is made available to users through an intuitive graphical user interface (GUI), as well as through exhaustive configuration files calling the API via a feature-rich CLI (command line interface) running Blender in background mode.

ClonArch: Visualizing the Spatial Clonal Architecture of Tumors
Date: July 15

  • Jiaqi Wu, University of Illinois at Urbana Champaign, United States
  • Mohammed El-Kebir, University of Illinois at Urbana Champaign, United States

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Motivation: Cancer is caused by the accumulation of somatic mutations that lead to the formation of distinct populations of cells, called clones. The resulting clonal architecture is the main cause of relapse and resistance to treatment. With decreasing costs in DNA sequencing technology, rich cancer genomics datasets with many spatial sequencing samples are becoming increasingly available, enabling the inference of high-resolution tumor clones and prevalences across different spatial coordinates. While temporal and phylogenetic aspects of tumor evolution, such as clonal evolution over time and clonal response to treatment, are commonly visualized in various clonal evolution diagrams, visual analytics methods that reveal the spatial clonal architecture are missing.

Results: This paper introduces ClonArch, a web-based tool to interactively visualize the phylogenetic tree and spatial distribution of clones in a single tumor mass. ClonArch uses the marching squares algorithm to draw closed boundaries representing the presence of clones in a real or simulated tumor. ClonArch enables researchers to examine the spatial clonal architecture of a subset of relevant mutations at different prevalence thresholds and across multiple phylogenetic trees. In addition to simulated tumors with varying number of biopsies, we demonstrate the use of ClonArch on a hepatocellular carcinoma tumor with 280 sequencing biopsies. ClonArch provides an automated way to interactively examine the spatial clonal architecture of a tumor, facilitating clinical and biological interpretations of the spatial aspects of intra-tumor heterogeneity.

Availability: https://github.com/elkebir-group/ClonArch

CAMDA: Critical Assessment of Massive Data Analysis

CAMDA: Critical Assessment of Massive Data Analysis

Improved survival analysis by learning shared genomic information from pan-cancer data
Date: July 13 and 14

  • Sunkyu Kim, Korea University, South Korea
  • Keonwoo Kim, Korea University, South Korea
  • Junseok Choe, Korea University, South Korea
  • Inggeol Lee, Korea University, South Korea
  • Jaewoo Kang, Korea University, South Korea

Presentation Overview: Show

Motivation: Recent advances in deep learning have offered solutions to many biomedical tasks. However, there remains a challenge in applying deep learning to survival analysis using human cancer transcriptome data. Since the number of genes, the input variables of survival model, is larger than the amount of available cancer patient samples, deep learning models are prone to overfitting. To address the issue, we introduce a new deep learning architecture called VAECox. VAECox employs transfer learning and fine tuning.
Results: We pre-trained a variational autoencoder on all RNA-seq data in 20 TCGA datasets and transferred the trained weights to our survival prediction model. Then we fine-tuned the transferred weights during training the survival model on each dataset. Results show that our model outperformed other previous models such as Cox-PH with LASSO and ridge penalty and Cox-nnet on the 7 of 10 TCGA datasets in terms of C-index. The results signify that the transferred information obtained from entire cancer transcriptome data helped our survival prediction model reduce overfitting and show robust performance in unseen cancer patient samples.
Availability: Our implementation of VAECox is available at https://github.com/SunkyuKim/VAECox

IDMIL: An alignment-free interpretable deep multiple instance learning (MIL) for predicting disease from whole-metagenomic data
Date: July 13 and 14

  • Mohammad Arifur Rahman, George Mason University, United States
  • Huzefa Rangwala, George Mason University, United States

Presentation Overview: Show

The human body hosts more microbial organisms than human cells. Analysis of this microbial diversity provides key insight into the role played by these microorganisms on human health. Metagenomics is the collective DNA sequencing of coexisting microbial organisms in a host. This has several applications in precision medicine, agriculture, environmental science, and forensics. State-of-the-art predictive models for phenotype predictions from microbiome rely on alignments, assembly, extensive pruning, taxonomic profiling along with expert-curated reference databases. These processes are time-consuming and they discard the majority of the DNA sequences for downstream analysis limiting the potential of whole-metagenomics. We formulate the problem of predicting human disease from whole-metagenomic data using Multiple Instance Learning (MIL), a popular supervised learning paradigm. Our proposed alignment-free approach provides higher accuracy in prediction by harnessing the capability of deep convolutional neural network (CNN) within a MIL framework and provides interpretability via neural attention mechanism.

The MIL formulation combined with the hierarchical feature extraction capability of deep-CNN provides significantly better predictive performance compared to popular existing approaches. The attention mechanism allows for the identification of groups of sequences that are likely to be correlated to diseases providing the much-needed interpretation. Our proposed approach does not rely on alignment, assembly and manually curated databases; making it fast and scalable for large-scale metagenomic data. We evaluate our method on well-known large-scale metagenomic studies and show that our proposed approach outperforms comparative state-of-the-art methods for disease prediction.

AITL: Adversarial Inductive Transfer Learning with input and output space adaptation for pharmacogenomics
Date: July 13 and 14

  • Hossein Sharifi Noghabi, Simon Fraser University, Canada
  • Shuman Peng, Simon Fraser University, Canada
  • Olga Zolotareva, Bielefeld University, Germany
  • Colin Collins, University of British Columbia, Canada
  • Martin Ester, Simon Fraser University, Canada

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Motivation: the goal of pharmacogenomics is to predict drug response in patients using their single- or multi-omics data. A major challenge is that clinical data (i.e. patients) with drug response outcome is very limited, creating a need for transfer learning to bridge the gap between large pre-clinical pharmacogenomics datasets (e.g. cancer cell lines), as a source domain, and clinical datasets as a target domain. Two major discrepancies exist between pre-clinical and clinical datasets: 1) in the input space, the gene expression data due to difference in the basic biology, and 2) in the output space, the different measures of the drug response. Therefore, training a computational model on cell lines and testing it on patients violates the i.i.d assumption that train and test data are from the same distribution.
Results: We propose Adversarial Inductive Transfer Learning (AITL), a deep neural network method for addressing discrepancies in input and output space between the pre-clinical and clinical datasets. AITL takes gene expression of patients and cell lines as the input, employs adversarial domain adaptation and multi-task learning to address these discrepancies, and predicts the drug response as the output. To the best of our knowledge, AITL is the first adversarial inductive transfer learning method to address both input and output discrepancies. Experimental results indicate that AITL outperforms state-of-the-art pharmacogenomics and transfer learning baselines and may guide precision oncology more accurately.

CompMS: Computational Mass Spectrometry

CompMS: Computational Mass Spectrometry

MutCombinator: Identification of mutated peptides allowing combinatorial mutations using nucleotide-based graph search
Date: July 15

  • Seunghyuk Choi, Department of Computer Science, Hanyang University, South Korea
  • Eunok Paek, Department of Computer Science, Hanyang University, South Korea

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Proteogenomics has proven its utility by integrating genomics and proteomics. Typical approaches use data from next generation sequencing to infer proteins expressed. A sample-specific protein sequence database is often adopted to identify novel peptides from matched mass spectrometry-based proteomics; nevertheless, there is no software that can practically identify all possible forms of mutated peptides suggested by various genomic information sources. We propose MutCombinator, which enables us to practically identify mutated peptides from tandem mass spectra allowing combinatorial mutations during the database search. It uses an upgraded version of a variant graph, keeping track of frame information. The variant graph is indexed by nine nucleotides for fast access. Using MutCombinator, we could identify more mutated peptides than previous methods, because combinations of point mutations are considered, and also because it can be practically applied together with a large mutation database such as COSMIC. Furthermore, MutCombinator supports in-frame search for coding regions and three-frame search for noncoding regions.

Deep multiple instance learning classifies subtissue locations in mass spectrometry images from tissue-level annotations
Date: July 15

  • Dan Guo, Northeastern University, United States
  • Melanie Christine Föll, University of Freiburg, Germany
  • Veronika Volkmann, University of Freiburg, Germany
  • Kathrin Enderle-Ammour, University of Freiburg, Germany
  • Peter Bronsert, University of Freiburg, Germany
  • Oliver Schilling, University of Freiburg, Germany
  • Olga Vitek, Northeastern University, United States

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Motivation: Mass spectrometry imaging (MSI) characterizes molecular composition of tissues at spatial resolution, and has a strong potential for distinguishing tissue types, or disease states. This can be achieved by supervised classification, which takes as input MSI spectra, and assigns class labels to subtissue locations. Unfortunately, developing such classifiers is hindered by the limited availability of training sets with subtissue labels as the ground truth. Subtissue labeling is prohibitively expensive, and only rough annotations of the entire tissues are typically available. Classifiers trained on data with approximate labels have sub-optimal performance.
Results: To alleviate this challenge, we contribute a semi-supervised approach mi-CNN. mi-CNN implements multiple instance learning with a convolutional neural network (CNN). The multiple instance aspect enables weak supervision from tissue-level annotations when classifying subtissue locations. The convolutional architecture of the CNN captures contextual dependencies between the spectral features. Evaluations on simulated and experimental datasets demonstrated that mi-CNN improved the subtissue classification as compared to traditional classifiers. We propose mi-CNN as an important step towards accurate subtissue classification in MSI, enabling rapid distinction between tissue types and disease states.

Evolution and Comparative Genomics

Evolution and Comparative Genomics

Phylogenetic double placement of mixed samples
Date: July 14

  • Metin Balaban, University of California San Diego, United States
  • Siavash Mirarab, University of California San Diego, United States

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Motivation: Consider a simple computational problem. The inputs are (i) the set of mixed reads generated from a sample that combines two organisms and (ii) separate sets of reads for several reference genomes of known origins. The goal is to find the two organisms that constitute the mixed sample. When constituents are absent from the reference set, we seek to phylogenetically position them with respect to the underlying tree of the reference species. This simple yet fundamental problem (which we call phylogenetic double-placement) has enjoyed surprisingly little attention in the literature. As genome skimming (low-pass sequencing of genomes at low coverage, precluding assembly) becomes more prevalent, this problem finds wide-ranging applications in areas as varied as biodiversity research, food production and provenance, and evolutionary reconstruction.
Results: We introduce a model that relates distances between a mixed sample and reference species to the distances between constituents and reference species. Our model is based on Jaccard indices computed between each sample represented as k-mer sets. The model, built on several assumptions and approximations, allows us to formalize the phylogenetic double-placement problem as a non- convex optimization problem that deconvolutes mixture distances and performs phylogenetic placement simultaneously. Using a variety of techniques, we are able to solve this optimization problem numerically. We test the resulting method, called MISA, on a varied set of simulated and biological datasets. Despite all the assumptions used, the method performs remarkably well in practice.
Availability: The sofware and data are available at https://github.com/balabanmetin/misa.

EvoLSTM: Context-dependent models of sequence evolution using a sequence-to-sequence LSTM
Date: July 14

  • Dongjoon Lim, McGill University, Canada
  • Mathieu Blanchette, McGill University, Canada

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Motivation: Accurate probabilistic models of sequence evolution are essential for a wide variety of bioinformatics tasks, including sequence alignment and phylogenetic inference. The ability to realistically simulate the evolution of sequence is also at the core of many benchmarking strategies. Yet mutational processes have complex context dependencies that remain poorly modeled and understood.
Results: We introduce EvoLSTM, a recurrent neural network-based evolution simulator that captures mutational context dependencies. EvoLSTM uses a sequence-to-sequence LSTM model trained to predict mutation probabilities at each position of a given descendant ancestral sequence, taking into consideration the 14 flanking nucleotides. EvoLSTM can realistically simulate primate DNA sequence and reveals unexpectedly strong long-range context dependencies in mutation rates.
Conclusion: EvoLSTM brings modern machine learning approaches to bear on sequence evolution. It will serve as a useful tool to study and simulate complex mutational processes.

FastMulRFS: Fast and accurate species tree estimation under generic gene duplication and loss models
Date: July 14

  • Erin Molloy, University of Illinois at Urbana-Champaign, United States
  • Tandy Warnow, the university of illinois at urbana-champaign, United States

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Motivation:Species tree estimation is a basic part of biological research but can be challenging because of gene duplication and loss (GDL), which results in genes that can appear more than once in a given genome. All common approaches in phylogenomic studies either reduce available data or are error-prone, and thus, scalable methods that do not discard data and have high accuracy on large heterogeneous datasets are needed.

Results: We present FastMulRFS, a polynomial-time method for estimating species trees without knowledge of orthology. We prove that FastMulRFS is statistically consistent under a generic model of GDL when adversarial GDL does not occur. Our extensive simulation study shows that FastMulRFS matches the accuracy of MulRF (which tries to solve the same optimization problem) and has better accuracy than prior methods, including ASTRAL-multi (the only method to date that has been proven statistically consistent under GDL), while being much faster than both methods.

Sampling and Summarizing Transmission Trees with Multi-strain Infections
Date: July 14

  • Palash Sashittal, University Of Illinois at Urbana-Champaign, United States
  • Mohammed El-Kebir, University Of Illinois at Urbana-Champaign, United States

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Motivation: The combination of genomic and epidemiological data hold the potential to enable accurate pathogen transmission history inference. However, the inference of outbreak transmission histories remains challenging due to various factors such as within-host pathogen diversity and multi-strain infections. Current computational methods ignore within-host diversity and/or multi-strain infections, often failing to accurately infer the transmission history. Thus, there is a need for efficient computational methods for transmission tree inference that accommodate the complexities of real data.
Results: We formulate the Direct Transmission Inference (DTI) problem for inferring transmission trees that support multi-strain infections given a timed phylogeny and additional epidemiological data. We establish hardness for the decision and counting version of the DTI problem. We introduce TiTUS, a method that uses SATISFIABILITY to almost uniformly sample from the space of transmission trees. We introduce criteria that prioritizes parsimonious transmission trees that we subsequently summarize using a novel consensus tree approach. We demonstrate TiTUS’s ability to accurately reconstruct transmission trees on simulated data as well as a documented HIV transmission chain.
Availability: https://github.com/elkebir-group/TiTUS

Inference of Population Admixture Network from Local Gene Genealogies: a Coalescent-based Maximum Likelihood Approach
Date: July 14

  • Yufeng Wu, Computer Science and Engineering Department, University of Connecticut, United States

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Population admixture is an important subject in population genetics. Inferring population demographic history with admixture under the so-called admixture network model from population genetic data is an established problem in genetics. Existing admixture network inference approaches work with single genetic polymorphisms. While these methods are usually very fast, they don't fully utilize the information (e.g., linkage disequilibrium or LD) contained in population genetic data.

In this paper, we develop a new admixture network inference method called GTmix. Different from existing methods, GTmix works with local gene genealogies that can be inferred from population haplotypes. Local gene genealogies represent the evolutionary history of sampled alleles and contain the LD information. GTmix performs coalescent-based maximum likelihood inference of admixture networks with inferred genealogies based on the well-known multispecies coalescent (MSC) model. GTmix utilizes various techniques to speed up likelihood computation on the MSC model and optimal network search. Our simulations show that GTmix can infer more accurate admixture networks with much smaller data than existing methods, even when these existing methods are given much larger data. GTmix is reasonably efficient and can analyze genetic datasets of current interests.

Copy Number Evolution with Weighted Aberrations in Cancer
Date: July 14

  • Ron Zeira, Princeton University, United States
  • Benjamin J. Raphael, Princeton University, United States

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Motivation: Copy number aberrations (CNAs), which delete or amplify large contiguous segments of the genome, are a common type of somatic mutation in cancer. Copy number profiles, representing the number of copies of each region of a genome, are readily obtained from whole-genome sequencing or microarrays. However, modeling copy number evolution is a substantial challenge, since CNAs alter contiguous segments of the genome and different CNAs may overlap with one another. A recent popular model for copy number evolution is the Copy Number Distance (CND), defined as the length of a shortest sequence of deletions and amplifications of contiguous segments that transforms one profile into the other. All events contribute equally to the CND; however, CNAs are observed to occur at different rates according to their length or genomic position and also vary across cancer type.
Results: We introduce a weighted copy number distance that allows events to have varying weights, or probabilities, based on their length, position and type. We derive an efficient algorithm to compute the weighted copy number distance as well as the associated transformation, based on the observation that the constraint matrix of the underlying optimization problem is totally unimodular. We demonstrate the utility of the weighted copy number distance by showing that the weighted CND: improves phylogenetic reconstruction on simulated data where copy number aberrations occur with varying probabilities; aids in the derivation of phylogenies from ultra low-coverage single-cell DNA sequencing data; helps estimate CNA rates in a large pan-cancer dataset.

Function SIG: Gene and Protein Function Annotation

Function SIG: Gene and Protein Function Annotation

The Ortholog Conjecture Revisited: the Value of Orthologs and Paralogs in Function Prediction
Date: July 13 and 14

  • Moses Stamboulian, Indiana University, United States
  • Rafael Guerrero, Indiana University, United States
  • Matthew Hahn, Indiana University, United States
  • Predrag Radivojac, Northeastern University, United States

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The computational prediction of gene function is a key step in making full use of newly sequenced genomes. Function is generally predicted by transferring annotations from homologous genes or proteins for which experimental evidence exists. The ''ortholog conjecture'' proposes that orthologous genes should be preferred when making such predictions, as they evolve functions more slowly than paralogous genes. Previous research has provided little support for the ortholog conjecture, though the incomplete nature of the data cast doubt on the conclusions. Here we use experimental annotations from over 40,000 proteins, drawn from over 80,000 publications, to revisit the ortholog conjecture in two pairs of species: (i) Homo sapiens and Mus musculus and (ii) Saccharomyces cerevisiae and Schizosaccharomyces pombe. By making a distinction between questions about the evolution of function versus questions about the prediction of function, we find strong evidence against the ortholog conjecture in the context of function prediction, though questions about the evolution of function remain difficult to address. In both pairs of species, we quantify the amount of data that must be ignored if paralogs are discarded, as well as the resulting loss in prediction accuracy. Taken as a whole, our results support the view that the types of homologs used for function transfer are largely irrelevant to the task of function prediction. Aiming to maximize the amount of data used for this task, regardless of whether it comes from orthologs or paralogs, is most likely to lead to higher prediction accuracy.

Discovery of multi-operon colinear syntenic blocks in microbial genomes
Date: July 13 and 14

  • Dina Svetlitsky, Ben Gurion University of the Negev, Israel
  • Tal Dagan, Kiel University, Germany
  • Michal Ziv-Ukelson, Ben Gurion University of the Negev, Israel

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An important task in comparative genomics is to detect functional units by analyzing gene-context patterns. Colinear syntenic blocks (CSBs) are groups of genes that are consistently encoded in the same neighborhood and in the same order across a wide range of taxa. Such colinear syntenic blocks are likely essential for the regulation of gene expression in prokaryotes. Recent results indicate that colinearity can be conserved across multiple operons, thus motivating the discovery of multi-operon CSBs. This computational task raises scalability challenges in large datasets.

We propose an efficient algorithm for the discovery of cross-strand multi-operon CSBs in large genomic datasets. The proposed algorithm uses match-point arithmetic, which is scalable for large datasets of microbial genomes in terms of running time and space requirements. The algorithm is implemented and incorporated into a tool with a graphical user interface, denoted CSBFinder-S. We applied CSBFinder-S to data mine 1,485 prokaryotic genomes and analyzed the identified cross-strand CSBs. Our results indicate that most of the syntenic blocks are exclusively colinear. Additional results indicate that transcriptional regulation by overlapping transcriptional genes is abundant in bacteria. We demonstrate the utility of CSBFinder-S to identify common function of the gene-pair PulEF in multiple contexts, including Type 2 Secretion System, Type 4 Pilus System, and DNA uptake.

Benchmarking Gene Ontology Function Predictions Using Negative Annotations
Date: July 13 and 14

  • Alex Warwick Vesztrocy, University of Lausanne, Switzerland
  • Christophe Dessimoz, University of Lausanne, Switzerland

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With the ever-increasing number and diversity of sequenced species, the challenge to characterise genes with functional information is even more important. In most species, this characterisation almost entirely relies on automated electronic methods. As such, it is critical to benchmark the various methods. The CAFA series of community experiments provide the most comprehensive benchmark, with a time-delayed analysis leveraging newly curated experimentally supported annotations. However, the definition of a false positive in CAFA has not fully accounted for the Open World Assumption (OWA), leading to a systematic underestimation of precision. The main reason for this limitation is the relative paucity of negative experimental annotations. This paper introduces a new, OWA-compliant, benchmark based on a balanced test set of positive and negative annotations. The negative annotations are derived from expert-curated annotations of protein families on phylogenetic trees. This approach results in a large increase in the average information content (IC) of negative annotations. The benchmark has been tested using the naïve and BLAST baseline methods, as well as two orthology-based methods. This new benchmark could complement existing ones in future CAFA experiments.

HitSeq: High-throughput Sequencing

HitSeq: High-throughput Sequencing

Distance Indexing and Seed Clustering in Sequence Graphs
Date: July 15 and 16

  • Xian Chang, University of California Santa Cruz, United States
  • Jordan Eizenga, University of California Santa Cruz, United States
  • Adam Novak, University of California Santa Cruz, United States
  • Jouni Sirén, University of California Santa Cruz, United States
  • Benedict Paten, University of California Santa Cruz, United States

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Graph representations of genomes are capable of expressing more genetic variation and
can therefore better represent a population than standard linear genomes. However, due to the greater
complexity of genome graphs relative to linear genomes, some functions that are trivial on linear genomes
become much more difficult in genome graphs. Calculating distance is one such function that is simple in a
linear genome but complicated in a graph context. In read mapping algorithms such distance calculations
are fundamental to determining if seed alignments could belong to the same mapping.
We have developed an algorithm for quickly calculating the minimum distance between positions
on a sequence graph using a minimum distance index. We have also developed an algorithm that uses the
distance index to cluster seeds on a graph. We demonstrate that our implementations of these algorithms
are efficient and practical to use for a new generation of mapping algorithms based upon genome graphs

Hopper: A Mathematically Optimal Algorithm for Sketching Biological Data
Date: July 15 and 16

  • Benjamin DeMeo, Massachusetts Institute of Technology, United States
  • Bonnie Berger, Massachusetts Institute of Technology, United States

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Single-cell RNA-sequencing (scRNA-seq) has grown massively in scale since its inception, presenting substantial analytic and computational challenges. Even simple downstream analyses, such as dimensionality reduction and clustering, require days of runtime and hundreds of gigabytes of memory for today's largest datasets. In addition, current methods often favor common cell types, and miss salient biological features captured by small cell populations. Here we present Hopper, a single-cell toolkit that both speeds up the analysis of single-cell datasets and highlights their transcriptional diversity by intelligent subsampling, or sketching. Hopper realizes the optimal polynomial-time approximation of the Hausdorff distance between the full and downsampled dataset, ensuring that each cell is well-represented by some cell in the sample. Unlike prior sketching methods, Hopper adds points iteratively and allows for additional sampling from regions of interest, enabling fast and targeted multi-resolution analyses.
In a dataset of over 1.3 million mouse brain cells, we detect a cluster of just 64 macrophages expressing inflammatory tissues (0.004% of the full dataset) from a Hopper sketch containing just 5,000 cells, and several other small but biologically interesting immune cell populations invisible to analysis of the full data. On an even larger dataset consisting of ~2 million developing mouse organ cells, we show even representation of important cell types in small sketch sizes, in contrast with prior sketching methods. By condensing transcriptional information encoded in large datasets, Hopper grants the individual user with a laptop the same analytic capabilities as large consortium.

The String Decomposition Problem and its Applications to Centromere Analysis and Assembly
Date: July 15 and 16

  • Tatiana Dvorkina, Saint Petersburg State University, Saint Petersburg, Russia, Russia
  • Andrey V. Bzikadze, Graduate Program in Bioinformatics and Systems Biology, University of California, San Diego, CA, USA, United States
  • Pavel A. Pevzner, epartment of Computer Science and Engineering, University of California, San Diego, CA, USA, United States

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Motivation: Recent attempts to assemble extra-long tandem repeats (such as centromeres) faced the challenge of translating long error-prone reads from the nucleotide alphabet into the alphabet of repeat units. Human centromeres represent a particularly complex type of high-order repeats (HORs) formed by chromosome-specific monomers. Given a set of all human monomers, translating a read from a centromere into the monomer alphabet is modeled as the String Decomposition Problem. The accurate translation of reads into the monomer alphabet turns centromere assembly into a more tractable problem than the notoriously difficult problem of assembling centromeres in the nucleotide alphabet.

Results: We describe a StringDecomposer algorithm for solving this problem, benchmark it on the set of long error-prone reads generated by the Telomere-to-Telomere consortium, and identify a novel (rare) monomer that extends the set of known X-chromosome specific monomers. Our identification of a novel monomer emphasizes the importance of identification of all (even rare) monomers for future centromere assembly efforts and evolutionary studies. To further analyze novel monomers, we applied StringDecomposer to the set of recently generated long accurate Pacific Biosciences HiFi reads. This analysis revealed that the set of known human monomers and HORs remains incomplete. StringDecomposer opens a possibility to generate a complete set of human monomers and HORs for using in the ongoing efforts to generate the complete assembly of the human genome.

Availability: StringDecomposer is available on https://github.com/ablab/stringdecomposer.

Identification of Conserved Evolutionary Trajectories in Tumors
Date: July 15 and 16

  • Ermin Hodzic, Simon Fraser University, Canada
  • Raunak Shrestha, University of California, San Francisco, United States
  • Salem Malikic, Indiana University Bloomington, United States
  • Colin Collins, University of British Columbia, Canada
  • Kevin Litchfield, Francis Crick Institute, United Kingdom
  • Samra Turajlic, Francis Crick Institute; The royal Marsden NHS Foundation Trust, United Kingdom
  • Cenk Sahinalp, National Cancer Institute, NIH, United States

Presentation Overview: Show

As multi-region, time-series, and single cell sequencing data become more widely available, it is becoming clear that certain tumors share evolutionary characteristics with others. In the last few years, several computational methods have been developed with the goal of inferring the subclonal composition and evolutionary history of tumors from tumor biopsy sequencing data. However, the phylogenetic trees that they report differ significantly between tumors (even those with similar characteristics).

In this paper, we present a novel combinatorial optimization method, CONETT, for detection of recurrent tumor evolution trajectories. Our method constructs a consensus tree of conserved evolutionary trajectories based on the information about temporal order of alteration events in a set of tumors. We apply our method to previously published datasets of 100 clear-cell renal cell carcinoma and 99 non-small-cell lung cancer patients and identify both conserved trajectories that were reported in the original studies, as well as new trajectories.

Weighted minimizer sampling improves long read mapping
Date: July 15 and 16

  • Chirag Jain, National Institutes of Health, United States
  • Arang Rhie, National Institutes of Health, United States
  • Haowen Zhang, Georgia Institute of Technology, United States
  • Claudia Chu, Georgia Institute of Technology, United States
  • Brian Walenz, National Institutes of Health, United States
  • Sergey Koren, National Institutes of Health, United States
  • Adam Phillippy, National Institutes of Health, United States

Presentation Overview: Show

In this era of exponential data growth, minimizer sampling has become a standard algorithmic technique for rapid genome sequence comparison. This technique yields a sub-linear representation of sequences, enabling their comparison in reduced space and time. A key property of the minimizer technique is that if two sequences share a substring of a specified length, then they can be guaranteed to have a matching minimizer. However, because the k-mer distribution in eukaryotic genomes is highly uneven, minimizer-based tools (e.g., Minimap2, Mashmap) opt to discard the most frequently occurring minimizers from the genome in order to avoid excessive false positives. By doing so, the underlying guarantee is lost and accuracy is reduced in repetitive genomic regions.

We introduce a novel weighted-minimizer sampling algorithm. A unique feature of the proposed algorithm is that it performs minimizer sampling while taking into account a weight for each k-mer; i.e, the higher the weight of a k-mer, the more likely it is to be selected. By down-weighting frequently occurring k-mers, we are able to meet both objectives: (i) avoid excessive false-positive matches, and (ii) maintain the minimizer match guarantee. We tested our algorithm, Winnowmap, using both simulated and real long-read data and compared it to a state-of-the-art long read mapper, Minimap2. Our results demonstrate a reduction in the mapping error-rate from 0.14% to 0.06% in the recently finished human X chromosome (154.3 Mbp), and from 3.6% to 0% within the highly repetitive X centromere (3.1 Mbp). Winnowmap improves mapping accuracy within repeats and achieves these results with sparser sampling, leading to better index compression and competitive runtimes.

Artificial-Cell-Type Aware Cell Type Classification in CITE-seq
Date: July 15 and 16

  • Qiuyu Lian, Tsinghua University, China
  • Hongyi Xin, University of Pittsburgh, United States
  • Jianzhu Ma, Purdue University, United States
  • Liza Konnikova, University of Pittsburgh, United States
  • Wei Chen, University of Pittsburgh, United States
  • Jin Gu, Tsinghua University, China
  • Kong Chen, University of Pittsburgh, United States

Presentation Overview: Show

Motivation: Cellular Indexing of Transcriptomes and Epitopes by sequencing (CITE-seq), couples the measurement of surface marker proteins with simultaneous sequencing of mRNA at single cell level, which brings accurate cell surface phenotyping to single cell transcriptomics. Unfortunately, multiplets in CITE-seq datasets create artificial cell types and complicates the automation of cell surface phenotyping.
Results: We propose CITE-sort, an artificial-cell-type aware surface marker clustering method for CITE-seq. CITE-sort is aware of and is robust to multiplet-induced artificial cell types. We benchmarked CITE-sort with real and simulat-ed CITE-seq datasets and compared CITE-sort against canonical clustering methods. We show that CITE-sort produces the best clustering performance across the board. CITE-sort not only accurately identifies real biological cell types but also consistently and reliably separates multiplet-induced artificial-cell-type droplet clusters from real biological-cell-type droplet clusters. In addition, CITE-sort organizes its clustering process with a binary tree, which facilitates easy interpretation and verification of its clustering result and simplifies cell type annotation with domain knowledge in CITE-seq.

Mutational Signature Learning with Supervised Negative Binomial Non-Negative Matrix Factorization
Date: July 15 and 16

  • Xinrui Lyu, Department for Computer Science, ETH Zürich, Switzerland
  • Jean Garret, Department of Mathematics, ETH Zürich, Switzerland
  • Gunnar Rätsch, Department for Computer Science, ETH Zürich, Switzerland
  • Kjong-Van Lehmann, Department for Computer Science, ETH Zürich, Switzerland

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Understanding the underlying mutational processes of cancer patients has been a long standing goal in the community and promises to provide new insights that could improve cancer diagnoses and treatments. Mutational signatures are summaries of the mutational processes, and improving the derivation of mutational signatures can yield new discoveries previously obscured by technical and biological confounders. Existing mutational signature extraction methods depend on the size of patient- cohort available and solely focus on the analysis of mutation count data without considering the exploitation of available metadata.
Here we present a supervised method that utilizes cancer type as metadata to extract more distinctive signatures. More specifically, we use a Negative Binomial Non-Negative Matrix Factorization and add a Support Vector Machine loss. We show that mutational signatures extracted by our proposed method have a lower reconstruction error and are designed to be more predictive of cancer type than those generated by unsupervised methods. This design reduces the need for elaborate post-processing strategies in order to recover most of the known signatures unlike the existing unsupervised signature extraction methods. Signatures extracted by a supervised model used in conjunction with cancer type labels are also more robust, especially when using small and potentially cancer-type limited patient cohorts. Finally, we adapted our model such that molecular features can be utilized to derive an according mutational signature. We used APOBEC expression and MUTYH mutation status to demonstrate the possibilities that arise from this ability. We conclude that our method, which exploits available metadata, improves the quality of mutational signatures as well as helps derive more interpretable representations.

REINDEER: efficient indexing of k-mer presence and abundance in sequencing datasets
Date: July 15 and 16

  • Camille Marchet, Université de Lille, CRIStAL, France
  • Zamin Iqbal, EBI, United Kingdom
  • Daniel Gautheret, Université Paris-Sud, Orsay, France
  • Mikaël Salson, Université de Lille, CRIStAL, France
  • Rayan Chikhi, Pasteur Institute, CNRS, France

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Analyzing abundances of sequences within large collections of sequencing datasets is of prime importance for biological investigations, such as the detection and quantification of variants in genomes and transcriptomes. In this work we present REINDEER, a novel computational method that performs indexing of k-mers and records their counts across a collection of datasets. We demonstrate that REINDEER is able to index counts within 2,585 human RNA-seq datasets using only 36 GB of RAM and 60 GB of disk space during construction, and 75 GB of RAM during random access queries. To the best of our knowledge, REINDEER is the first practical method that can index k-mer counts in large dataset collections. Briefly, REINDEER constructs the compacted de Bruijn graph (DBG) of each dataset, then implicitly represents a merged DBG of all datasets. Indexing all the k-mers present in the merged DBG would be too expensive; instead REINDEER indexes a set of minitigs, which are sequences of coherently grouped k-mers. Minitigs are then associated to vectors of counts per dataset. Software is available at github.com/kamimrcht/REINDEER.

TandemTools: mapping long reads and assessing/improving assembly quality in extra-long tandem repeats
Date: July 15 and 16

  • Alla Mikheenko, Saint Petersburg State University, Russia
  • Andrey Bzikadze, University of California San Diego, United States
  • Alexey Gurevich, Center for Algorithmic Biotechnology, St. Petersburg State University (Saint Petersburg, Russia), Russia
  • Karen Miga, UC Santa Cruz Genomics Institute, University of California Santa Cruz, United States
  • Pavel Pevzner, University of California San Diego, United States

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Extra-long tandem repeats (ETRs) are widespread in eukaryotic genomes and play an important role in fundamental cellular processes, such as chromosome segregation. Although emerging long-read technologies have enabled ETR assemblies, the accuracy of such assemblies is difficult to evaluate since there are no tools for their quality assessment. Moreover, since the mapping of error-prone reads to ETR remains an open problem, it is not clear how to polish draft ETR assemblies. To address these problems, we developed the TandemTools package that includes the TandemMapper tool for mapping reads to ETRs and the TandemQUAST tool for polishing ETR assemblies and their quality assessment. We demonstrate that TandemTools not only reveals errors in ETR assemblies but also improve the recently generated assemblies of human centromeres.

Identifying tumor clones in sparse single-cell mutation data
Date: July 15 and 16

  • Matthew Myers, Princeton University, United States
  • Simone Zaccaria, Princeton University, United States
  • Benjamin Raphael, Princeton University, United States

Presentation Overview: Show

Motivation: Recent single-cell DNA sequencing technologies enable whole-genome sequencing of hundreds to thousands of individual cells. However, these technologies have ultra-low sequencing coverage (<0.5x per cell) which has limited their use to the analysis of large copy-number aberrations (CNAs) in individual cells. While CNAs are useful markers in cancer studies, single-nucleotide mutations are equally important, both in cancer studies and in other applications. However, ultra-low coverage sequencing yields single-nucleotide mutation data that is too sparse for current single-cell analysis methods.

Results: We introduce SBMClone, a method to infer clusters of cells, or clones, that share groups of somatic single-nucleotide mutations. SBMClone uses a stochastic block model to overcome sparsity in ultra-low coverage single-cell sequencing data, and we show that SBMClone accurately infers the true clonal composition on simulated datasets with coverage at low as 0.2x . We applied SBMClone to single-cell whole-genome sequencing data from two breast cancer patients obtained using two different sequencing technologies. On the first patient, sequenced using the 10X Genomics CNV Solution with sequencing coverage 0.03x, SBMClone recovers the major clonal composition when incorporating a small amount of additional information. On the second patient, where pre- and post-treatment tumor samples were sequenced using DOP-PCR with sequencing coverage 0.5x , SBMClone shows that tumor cells are present in the post-treatment sample, contrary to published analysis of this dataset.

PhISCS-BnB: A Fast Branch and Bound Algorithm for the Perfect Tumor Phylogeny Reconstruction Problem
Date: July 15 and 16

  • Erfan Sadeqi Azer, Indiana University, United States
  • Farid Rashidi Mehrabadi, Indiana University, National Cancer Institute, National Institutes of Health, United States
  • Salem Malikic, Indiana University, United States
  • Xuan Cindy Li, University of Maryland, National Cancer Institute, National Institutes of Health, United States
  • Osnat Bartok, Weizmann Institute of Science, Israel
  • Kevin Litchfield, Lung Cancer Centre of Excellence, United Kingdom
  • Ronen Levy, Weizmann Institute of Science, Israel
  • Yardena Samuels, Weizmann Institute of Science, Israel
  • Alejandro A. Schäffer, National Cancer Institute, National Institutes of Health, United States
  • E. Michael Gertz, National Cancer Institute, National Institutes of Health, United States
  • Chi-Ping Day, National Cancer Institute, National Institutes of Health, United States
  • Eva Pérez-Guijarro, National Cancer Institute, National Institutes of Health, United States
  • Kerrie Marie, National Cancer Institute, National Institutes of Health, United States
  • Maxwell P. Lee, National Cancer Institute, National Institutes of Health, United States
  • Glenn Merlino, National Cancer Institute, National Institutes of Health, United States
  • Funda Ergun, Indiana University, United States
  • S. Cenk Sahinalp, National Cancer Institute, National Institutes of Health, United States

Presentation Overview: Show

Motivation: Recent advances in single cell sequencing (SCS) offer an unprecedented insight into tumor emergence and evolution. Principled approaches to tumor phylogeny reconstruction via SCS data are typically based on general computational methods for solving an integer linear program (ILP), or a constraint satisfaction program (CSP), which, although guaranteeing convergence to the most likely solution, are very slow. Others based on Monte Carlo Markov Chain (MCMC) or alternative heuristics not only offer no such guarantee, but also are not faster in practice. As a result, novel methods that can scale up to handle the size and noise characteristics of emerging SCS data are highly desirable to fully utilize this technology.
Results: We introduce PhISCS-BnB, a Branch and Bound algorithm to compute the most likely perfect phylogeny (PP) on an input genotype matrix extracted from a SCS data set. PhISCS-BnB not only offers an optimality guarantee, but is also 10 to 100 times faster than the best available methods on simulated tumor SCS data. We also applied PhISCS-BnB on a recently published large melanoma data set derived from the sub-lineages of a cell line involving 20 clones with 2367 mutations, which returned the optimal tumor phylogeny in less than 4 hours. The resulting phylogeny agrees with and extends the published results by providing a more detailed picture on the clonal evolution of the tumor.

Terminus enables the discovery of data-driven, robust transcript groups from RNA-seq data
Date: July 15 and 16

  • Hirak Sarkar, University of Maryland, United States
  • Avi Srivastava, Stony Brook University, United States
  • Hector Corrada Bravo, University of Maryland, United States
  • Michael I. Love, University of North Carolina-Chapel Hill, Chapel Hill, United States
  • Rob Patro, University of Maryland, United States

Presentation Overview: Show

Motivation: Advances in sequencing technology, inference algorithms and differential testing methodology have enabled transcript-level analysis of RNA-seq data. Yet, the inherent inferential uncertainty in transcript-level abundance estimation, even among the most accurate approaches, means that robust transcript level analysis often remains a challenge. Conversely,
gene-level analysis remains a common and robust approach for understanding RNA-seq data, but it coarsens the resulting analysis to the level of genes, even if the data strongly support specific transcript-level effects.

Results: We introduce a new data-driven approach for grouping together transcripts in an experiment based on their inferential uncertainty. Transcripts that share large numbers of ambiguously-mapping fragments with other transcripts, in complex patterns, often cannot have their abundances confidently estimated. Yet, the total transcriptional output of that group of transcripts will have greatly-reduced inferential uncertainty, thus allowing more robust and confident downstream analysis. Our approach, implemented in the tool terminus, groups together transcripts in a data-driven manner allowing transcript-level analysis where it can be confidently supported, and deriving transcriptional groups where the inferential uncertainty is too high to support a transcript-level result.

Availability: Terminus is implemented in Rust, and is freely-available and open-source. It can be obtained from https://github.com/COMBINE-lab/Terminus

Improved Design and Analysis of Practical Minimizers
Date: July 15 and 16

  • Hongyu Zheng, Carnegie Mellon University, United States
  • Carl Kingsford, Carnegie Mellon University, United States
  • Guillaume Marcais, Carnegie Mellon University, United States

Presentation Overview: Show

Minimizers are methods to sample k-mers from a sequence, with the guarantee that similar set of k-mers will be chosen on similar sequences. It is parameterized by the k-mer length k, a window length w and an ordering on the k-mers. Minimizers are used in a large number of softwares and pipelines to improve computation efficiency and decrease memory usage. Despite the method’s popularity, many theoretical questions regarding its performance remain open. The core metric for measuring performance of a minimizer is the density, which measures the sparsity of sampled k-mers. The theoretical optimal density for a minimizer is 1/w, provably not achievable in general. For given k and w, little is known about asymptotically optimal minimizers, that is minimizers with density O(1/w).
We derived a necessary and sufficient condition for existence of asymptotically optimal minimizers. We also provide a randomized algorithm, called the Miniception, to design minimizers with the best theoretical guarantee to date on density in practical scenarios. Constructing and using the Miniception is as easy as constructing and using a random minimizer, which allows the design of efficient minimizers that scale to the values of k and w used in current bioinformatics software programs.

iRNA: Integrative RNA Biology

iRNA: Integrative RNA Biology

LinearPartition: Linear-Time Approximation of RNA Folding Partition Function and Base Pairing Probabilities
Date: July 13 and 14

  • He Zhang, Baidu Research USA, United States
  • Liang Zhang, Oregon State University, United States
  • David Mathews, University of Rochester, United States
  • Liang Huang, Baidu Research USA; Oregon State University, United States

Presentation Overview: Show

RNA secondary structure prediction is widely used to understand RNA function. Recently, there has been a shift away from the classical minimum free energy (MFE) methods to partition function-based methods that account for folding ensembles and can therefore estimate structure and base pair probabilities. However, the classical partition function algorithm scales cubically with sequence length, and is therefore a slow calculation for long sequences. This slowness is even more severe than cubic-time MFE-based methods due to a larger constant factor in runtime. Inspired by the success of our recently proposed LinearFold algorithm that predicts the approximate MFE structure in linear time, we design a similar linear-time heuristic algorithm, LinearPartition, to approximate the partition function and base pairing probabilities, which is shown to be orders of magnitude faster than Vienna RNAfold and CONTRAfold (e.g., 2.5 days vs. 1.3 minutes on a sequence with length 32,753 nt). More interestingly, the resulting base pairing probabilities are even better correlated with the ground truth structures. LinearPartition also leads to a small accuracy improvement when used for downstream structure prediction on families with the longest length sequences (16S and 23S rRNA), as well as a substantial improvement on long-distance base pairs (500+ nt apart).

The locality dilemma of Sankoff-like RNA alignments
Date: July 13 and 14

  • Teresa Müller, University Freiburg, Bioinformatics, Germany
  • Milad Miladi, University of Freiburg, Germany
  • Frank Hutter, University of Freiburg, Germany
  • Ivo Hofacker, University of Vienna, Austria
  • Sebastian Will, École polytechnique, Austria
  • Rolf Backofen, Albert-Ludwigs-University Freiburg, Germany

Presentation Overview: Show

Elucidating the functions of non-coding RNAs by homology has been strongly limited due to fundamental computational and modeling issues. While existing simultaneous alignment and folding (SA&F) algorithms successfully align homologous RNAs with precisely known boundaries (global SA&F), the more pressing problem of finding homologous RNAs in the genome (local SA&F) is intrinsically more difficult and much less understood. Typically, the length of local alignments is strongly overestimated and alignment boundaries are dramatically mispredicted. We hypothesize that local SA&F approaches are compromised this way due to a score bias, which is caused by the contribution of RNA structure similarity to their overall alignment score.
In the light of this hypothesis, we study local SA&F for the first time systematically—based on a novel local RNA alignment benchmark set and quality measure. First, we vary the relative influence of structure similarity compared to sequence similarity. Putting more emphasis on the structure component leads to overestimating the length of local alignments. This clearly shows the bias of current scores and strongly hints at the structure component as its origin. Second, we study the interplay of several important scoring parameters by learning parameters for local and global SA&F. The divergence of these optimized parameter sets underlines the fundamental obstacles for local SA&F. Thirdly, by introducing a position-wise correction term in local SA&F, we constructively solve its principal issues.

A Bayesian framework for inter-cellular information sharing improves dscRNA-seq quantification
Date: July 13 and 14

  • Avi Srivastava, Stony Brook university, United States
  • Laraib Iqbal Malik, Stony Brook University, United States
  • Hirak Sarkar, University of Maryland, United States
  • Robert Patro, University of Maryland, United States

Presentation Overview: Show

Motivation: Droplet based single cell RNA-seq (dscRNA-seq) data is being generated at an unprecedented pace, and the accurate estimation of gene level abundances for each cell is a crucial first step in most dscRNA-seq analyses. When preprocessing the raw dscRNA-seq data to generate a count matrix, care must be taken to account for the potentially large number of multi-mapping locations per read. The sparsity of dscNRA-seq data, and the strong 3’ sampling bias, makes it difficult to disambiguate cases where there is no uniquely mapping read to any of the candidate target genes.
Results: We introduce a Bayesian framework for information sharing across cells within a sample, or across multiple modalities of data using the same sample, to improve gene quantification estimates for dscRNA-seq data. We use an anchor-based approach to connect cells with similar gene expression patterns, and learn informative, empirical priors which we provide to alevin’s gene multi-mapping resolution algorithm. This improves the quantification estimates for genes with no uniquely mapping reads (i.e. when there is no unique intra-cellular information). We show our new model improves the per cell gene level estimates and provides a principled framework for information sharing across multiple modalities. We test our method on a combination of simulated and real datasets under various setups.
Availability: The information sharing model is included in alevin and is implemented in C++14. It is available as open-source software, under GPL v3, at https://github.com/COMBINE-lab/salmon as of version 1.1.0.

Finding the Direct Optimal RNA Barrier Energy and Improving Pathways with an Arbitrary Energy Model
Date: July 13 and 14

  • Hiroki Takizawa, The University of Tokyo, Japan
  • Junichi Iwakiri, The University of Tokyo, Japan
  • Goro Terai, The University of Tokyo, Japan
  • Kiyoshi Asai, The University of Tokyo, Japan

Presentation Overview: Show

RNA folding kinetics plays an important role in the biological functions of RNA molecules. An important goal in the investigation of the kinetic behavior of RNAs is to find the folding pathway with the lowest energy barrier. For this purpose, most of the existing methods employ heuristics because the number of possible pathways is huge even if only the shortest (direct) folding pathways are considered. In this study, we propose a new method using a best-first search strategy to efficiently compute the exact solution of the minimum barrier energy of direct pathways. Using our method, we can find the exact direct pathways within a Hamming distance of 20, while the previous methods even miss the exact short pathways. Moreover, our method can be used to improve the pathways found by existing methods for exploring indirect pathways. The source code and datasets created and used in this research are available at https://github.com/eukaryo/czno.

Graph neural representational learning of RNA secondary structures for predicting RNA-protein interactions
Date: July 13 and 14

  • Zichao Yan, McGill University, Canada
  • William Hamilton, McGill University, Canada
  • Mathieu Blanchette, McGill University, Canada

Presentation Overview: Show

Motivation: RNA-protein interactions are key effectors of post-transcriptional regulation. Significant experimental and bioinformatics efforts have been expended on characterizing protein binding mechanisms on the molecular level, and on highlighting the sequence and structural traits of RNA that impact the binding specificity for different proteins. Yet our ability to predict these interactions in silico remains relatively poor.
Results: In this study, we introduce RPI-Net, a graph neural network approach for RNA-protein interaction prediction. RPI-Net learns and exploits a graph representation of RNA molecules, yielding significant performance gains over existing state-of-the-art approaches. We also introduce an approach to rectify particular type of sequence bias present in many CLIP-Seq data sets, and we show that correcting this bias is essential in order to learn meaningful predictors and properly evaluate their accuracy. Finally, we provide new approaches to interpret the trained models and extract simple, biologically-interpretable representations of the learned sequence and structural motifs.



ganon: precise metagenomics classification against large and up-to-date sets of reference sequences
Date: July 15

  • Vitor C. Piro, Hasso Platner Institute, Germany
  • Temesgen Hailemariam Dadi, Freie Universität Berlin, Germany
  • Enrico Seiler, Freie Universität Berlin, Germany
  • Knut Reinert, Freie Universität Berlin, Germany
  • Bernhard Renard, Hasso Platner Institute, Germany

Presentation Overview: Show

The exponential growth of assembled genome sequences greatly benefits metagenomics studies. However, currently available methods struggle to manage the increasing amount of sequences and their frequent updates. Indexing the current RefSeq can take days and hundreds of GB of memory on large servers. Few methods address these issues thus far, and even though many can theoretically handle large amounts of references, time/memory requirements are prohibitive in practice. As a result, many studies that require sequence classification use often outdated and almost never truly up-to-date indices. Motivated by those limitations we created ganon, a k-mer based read classification tool that uses Interleaved Bloom Filters in conjunction with a taxonomic clustering and a k-mer counting/filtering scheme. Ganon provides an efficient method for indexing references, keeping them updated. It requires less than 55 minutes to index the complete RefSeq of bacteria, archaea, fungi and viruses. The tool can further keep these indices up-to-date in a fraction of the time necessary to create them. Ganon makes it possible to query against very large reference sets and therefore it classifies significantly more reads and identifies more species than similar methods. When classifying a high-complexity CAMI challenge dataset against complete genomes from RefSeq, ganon shows strongly increased precision with equal or better sensitivity compared with state-of-the-art tools. With the same dataset against the complete RefSeq, ganon improved the F1-Score by 65% at the genus level. It supports taxonomy- and assembly-level classification, multiple indices and hierarchical classification. The software is open-source and available at: https://gitlab.com/rki_bioinformatics/ganon

Topological and kernel-based microbial phenotype prediction from MALDI-TOF mass spectra
Date: July 15

  • Caroline Weis, ETH Zurich, Switzerland
  • Max Horn, ETH Zurich, Switzerland
  • Bastian Rieck, ETH Zurich, Switzerland
  • Aline Cuenod, University of Basel, Switzerland
  • Adrian Egli, University Hospital Basel, Switzerland
  • Karsten Borgwardt, ETH Zurich, Switzerland

Presentation Overview: Show

Motivation: Microbial species identification based on Matrix-assisted laser desorption ionization time-of-flight (MALDI-TOF) mass spectrometry has become a standard tool in biomedicine and microbiology. MALDI-TOF mass spectra harbour the potential to deliver prediction results for other phenotypes, such as antibiotic resistance. Machine learning algorithm development specifically for MALDI-TOF MS based phenotype prediction is still in its infancy. Current spectral pre-processing typically involves a parameter-heavy chain of operations without analysis of their influence on the prediction results. In addition, classification algorithms lack quantification of
uncertainty, which is indispensable for predictions potentially influencing patient treatment.

Results: We present a novel prediction method for
antimicrobial resistance based on MALDI-TOF mass spectra. First, we compare the complex conventional pre-processing to a new approach that exploits topological information and requires only a single parameter, namely the number of peaks of a spectrum to keep. Second, we introduce PIKE, the Peak Information Kernel, a similarity measure specifically tailored to MALDI-TOF mass
spectra which combined with a Gaussian Process classifier provides well calibrated uncertainty estimates about predictions. We demonstrate the utility of our approach by predicting antibiotic resistance of three highly-relevant bacterial species.
Our method consistently out-performs competitor approaches, while demonstrating improved performance and security by rejecting out-of-distribution samples, such as bacterial species not represented in the training data. Ultimately, our method could contribute to an earlier and precise antimicrobial treatment in clinical patient care.

Availability: We make our code publicly available as an easy-to-use Python package at https://github.com/BorgwardtLab/maldi_PIKE.

caroline.weis@bsse.ethz.ch, karsten.borgwardt@bsse.ethz.ch

MetaBCC-LR: Metagenomics Binning by Coverageand Composition for Long Reads
Date: July 15

  • Anuradha Wickramarachchi, Research School of Computer Science, Australian National University, Australia
  • Vijini Mallawaarachchi, Research School of Computer Science, Australian National University, Australia
  • Vaibhav Rajan, School of Computing, National University of Singapore, Singapore
  • Yu Lin, Research School of Computer Science, Australian National University, Australia

Presentation Overview: Show

Motivation: Metagenomics studies have provided key insights into the composition and structure of microbial communities found in different environments. Among the techniques used to analyze metagenomic data, binning is considered a crucial step to characterise the different species of microorganisms present. The use of short-read data in most binning tools poses several limitations, such as insufficient species-specific signal, and the emergence of long-read sequencing technologies offers us opportunities to surmount them. However, most current metagenomic binning tools have been developed for short reads. The few tools that can process long reads either do not scale with increasing input size or require a database with reference genomes that are often unknown. In this paper, we presentMetaBCC-LR, a scalable reference-free binning method which clusters long reads directly based on their k-mer coverage histograms and oligonucleotide composition.

Results: We evaluate MetaBCC-LR on multiple simulated and real metagenomic long-read datasets with varying coverages and error rates. Our experiments demonstrate that MetaBCC-LR substantially outperforms state-of-the-art reference-free binning tools, achieving∼13% improvement in F1-score and∼30% improvement in ARI compared to the best previous tools. Moreover, we show that using MetaBCC-LR before long read assembly helps to enhance the assembly quality while significantly reducing the assembly cost in terms of time and memory usage. The efficiency and accuracy of MetaBCC-LR pave the way for more effective long-read based metagenomics analyses to support a wide range of applications.

Availability: The source code is freely available at: https://github.com/anuradhawick/MetaBCC-LR.

MLCSB: Machine Learning in Computational and Systems Biology

MLCSB: Machine Learning in Computational and Systems Biology

Unsupervised Topological Alignment for Single-Cell Multi-Omics Integration
Date: July 13 and 14

  • Kai Cao, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, China
  • Xiangqi Bai, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, China
  • Yiguang Hong, Chinese Academy of Science, China
  • Lin Wan, Academy of Mathematics and Systems Science, CAS, China

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Motivation: Single-cell multi-omics data provide a comprehensive molecular view of cells. However, single- cell multi-omics datasets consist of unpaired cells measured with distinct unmatched features across modalities, making data integration challenging.
Results: In this study, we present a novel algorithm, termed UnionCom, for the unsupervised topological alignment of single-cell multi-omics integration. UnionCom does not require any correspondence information, either among cells or among features. It first embeds the intrinsic low-dimensional structure of each single-cell dataset into a distance matrix of cells within the same dataset and then aligns the cells across single-cell multi-omics datasets by matching the distance matrices via a matrix optimization method. Finally, it projects the distinct unmatched features across single-cell datasets into a common embedding space for feature comparability of the aligned cells. To match the complex nonlinear geometrical distorted low-dimensional structures across datasets, UnionCom proposes and adjusts a global scaling parameter on distance matrices for aligning similar topological structures. It does not require one-to-one correspondence among cells across datasets, and it can accommodate samples with dataset-specific cell types. UnionCom outperforms state-of-the-art methods on both simulated and real single-cell multi-omics datasets. UnionCom is robust to parameter choices, as well as subsampling of features.
Availability: UnionCom software is available at https://github.com/caokai1073/UnionCom.

MHCAttnNet: Predicting MHC-Peptide Bindings for MHC Alleles Classes I & II Using An Attention-Based Deep Neural Model
Date: July 13 and 14

  • Gopalakrishnan Venkatesh, International Institute of Information Technology, Bangalore, India
  • Aayush Grover, International Institute of Information Technology, Bangalore, India
  • G Srinivasaraghavan, International Institute of Information Technology, Bangalore, India
  • Shrisha Rao, International Institute of Information Technology, Bangalore, India

Presentation Overview: Show

Motivation: Accurate prediction of binding between an MHC allele and a peptide plays a major role in the synthesis of personalized cancer vaccines. The immune system struggles to distinguish between a cancerous and a healthy cell. In a patient suffering from cancer who has a particular MHC allele, only those peptides that bind with the MHC allele with high affinity help the immune system recognize the cancerous cells.
Results: MHCAttnNet is a deep neural model that uses an attention mechanism to capture the relevant subsequences of the amino acid sequences of peptides and MHC alleles. It then uses this to accurately predict the MHC-peptide binding. MHCAttnNet achieves an AUC-PRC score of 94.18% with 161 class I MHC alleles which outperforms the state-of-the-art models for this task. MHCAttnNet also achieves a better AUC-ROC score in comparison to the state-of-the-art models while covering a greater number of class II MHC alleles. The attention mechanism used by MHCAttnNet provides a heatmap over the amino acids thus indicating the important subsequences present in the amino acid sequence. This approach also allows us to focus on a much smaller number of relevant trigrams corresponding to the amino acid sequence of an MHC allele, from 9251 possible trigrams to about 258. This significantly reduces the number of amino acid subsequences that need to be clinically tested.

TinGa: fast and flexible trajectory inference with Growing Neural Gas
Date: July 13 and 14

  • Helena Todorov, Ghent University, Belgium
  • Wouter Saelens, Ghent University, Belgium
  • Robrecht Cannoodt, Ghent University, Belgium
  • Yvan Saeys, Ghent University, Belgium

Presentation Overview: Show

Motivation: During the last decade, trajectory inference methods have emerged as a novel framework to model cell developmental dynamics, most notably in the area of single-cell transcriptomics. At present, more than 70 trajectory inference methods have been published, and recent benchmarks showed that, while some methods perform well for certain trajectory types, overall there is still a lot of room for improvement.
Results: In this work we present TinGa, a new trajectory inference model that is fast and flexible, and that is based on growing neural graphs. This allows TinGa to model both the most simple as well as most complex trajectory types. We performed an extensive comparison of TinGa to the five best existing methods for trajectory inference on a set of 250 datasets, including both synthetic as well as real datasets. Overall, TinGa obtained better results than all other methods on all ranges of data complexity, from the simplest linear datasets to the most complex disconnected graphs. In addition, TinGa obtained the fastest run times, showing that our method is thus one of the most versatile methods up to date.
Availability: R scripts for running TinGa, comparing it to top existing methods and generating the figures of this paper are available at https://github.com/Helena-todd/researchgng

Factorized embeddings learns rich and biologically meaningful embedding spaces using factorized tensor decomposition
Date: July 13 and 14

  • Assya Trofimov, IRIC - Université de Montréal, Canada
  • Joseph Paul Cohen, University of Montreal, Canada
  • Yoshua Bengio, U. Montreal, Canada
  • Claude Perreault, IRIC - Université de Montréal, Canada
  • Sebastien Lemieux, IRIC / Université de Montréal, Canada

Presentation Overview: Show

The recent development of sequencing technologies revolutionised our understanding of the inner workings of the cell as well as the way disease is treated. A single RNA sequencing (RNA-Seq) experiment, however, measures tens of thousands of parameters simultaneously. While the results are information rich, data analysis provides a challenge. Dimensionality reduction methods help with this task by extracting patterns from the data by compressing it into compact vector representations.

We present the factorized embeddings (FE) model, a self-supervised deep learning algorithm that learns simultaneously, by tensor factorization, gene and sample representation spaces. We ran the model on RNA-Seq data from two large-scale cohorts and observed that the sample representation captures information on single-gene and global gene expression patterns. Moreover, we found that the gene representation space was organized such that tissue-specific genes, highly correlated genes as well as genes participating in the same GO terms were grouped. Finally, we compared the vector representation of samples learned by the FE model to other similar models on 49 regression tasks. We report that the FE-trained representations rank first or second in all of the tasks, surpassing, sometimes by a considerable margin, other representations.

Towards Heterogeneous Information Fusion: Bipartite Graph Convolutional Networks for In Silico Drug Repurposing
Date: July 13 and 14

  • Zichen Wang, University of California, Los Angeles, United States
  • Mu Zhou, SenseBrain Research, United States
  • Corey Arnold, University of California, Los Angeles, United States

Presentation Overview: Show

Motivation: Mining disease and drug association and their interactions are essential for developing computational models in drug repurposing and understanding underlying biological mechanisms. Recently, large-scale biological databases are increasingly available for pharmaceutical research, allowing for deep characterization for molecular informatics and drug discovery. In this study, we propose a bipartite graph convolution network model that integrates multi-scale pharmaceutical information. Especially the introduction of protein nodes serve as a bridge of message passing, which provides insights into the protein-protein interaction (PPI) network for improved drug repositioning assessment.

Results: Our approach combines insights of multi-scale pharmaceutical information by constructing a multi-relational graph of protein–protein, drug–protein and disease-protein interactions. Specifically, our model offers a novel avenue for message passing among diverse domains that we learn useful feature representations for all graph nodes by fusing biological information across interaction edges. Then the high-level representation of drug-disease pairs are fed into a multi-layer perceptron decoder to predict therapeutic indications. Unlike conventional graph convolution networks that assume the same node attributes in a global graph, our model is domain-consistent by modeling inter-domain information fusion with bipartite graph convolution operation. We offered an exploratory analysis for finding novel drug-disease associations. Extensive experiments showed that our approach achieves improved performance than multiple baseline approaches.

Cancer mutational signatures representation by large-scale context embedding
Date: July 13 and 14

  • Yang Zhang, Carnegie Mellon University, United States
  • Yunxuan Xiao, Shanghai Jiao Tong University, China
  • Muyu Yang, Carnegie Mellon University, United States
  • Jian Ma, Carnegie Mellon University, United States

Presentation Overview: Show

The accumulation of somatic mutations plays critical roles in cancer development and progression. However, the global patterns of somatic mutations, especially non-coding mutations, and their roles in defining molecular subtypes of cancer have not been well characterized due to the computational challenges in analyzing the complex mutational patterns. Here we develop a new algorithm, called MutSpace, to effectively extract patient-specific mutational features using an embedding framework for larger sequence context. Our method is motivated by the observation that the mutational rate at megabase scale and the local mutational patterns jointly contribute to distinguishing cancer subtypes, both of which can be simultaneously captured by MutSpace. Simulation evaluations demonstrate that MutSpace can effectively characterize mutational features from bona fide patient subgroups and achieve superior performance compared with previous methods. As a proof-of-principle, we apply MutSpace to 560 breast cancer patient samples and demonstrate that our method achieves high accuracy in subtype identification. In addition, the learned embeddings from MutSpace reflect intrinsic patterns of breast cancer subtypes and other features of genome structure and function. MutSpace is a promising new framework to better understand cancer heterogeneity based on somatic mutations.

NetBio: Network Biology

NetBio: Network Biology

GLIDE: Combining Local Methods and Diffusion State Embeddings to Predict Missing Interactions in Biological Networks
Date: July 15 and 16

  • Kapil Devkota, Tufts University, United States
  • James Murphy, Tufts University, United States
  • Lenore Cowen, Tufts University, United States

Presentation Overview: Show

Motivation: One of the core problems in the analysis of biological networks is the link prediction problem. In particular, existing interactions networks are noisy and incomplete samples of the true network, with many true links missing because those interactions have not yet been experimentally observed. Methods to predict missing links have been more extensively studied for social than for biological networks; a recent paper of Kovacs et al. argued that there is some special structure in PPI network data that might mean that alternate methods may outperform the best methods for social networks.
Based on a generalization of the diffusion state distance (DSD), we design a new embedding-based link prediction method called GLIDE (Global and Local Integrated Diffusion Embedding). GLIDE is designed to effectively capture global network structure, combined with alternative network type-specific customized measures that capture local network structure. We test GLIDE on a classical version of the yeast PPI network as well as a collection of three recently curated human biological networks derived from the 2016 DREAM disease module identification challenge in rigorous cross validation experiments.

Results: We indeed find that different local network structure is dominant in different types of biological networks. We find that the simple local network measures are dominant in the highly connected network core between hub genes, but that GLIDE's global embedding measure adds value in the rest of the network. For example, we make GLIDE-based link predictions from genes known to be involved in Crohn's disease, to genes that are not known to have an association, and make some new predictions, finding support in other network data and the literature.

Availability: GLIDE can be downloaded at:

Identifiability and experimental design in perturbation studies
Date: July 15 and 16

  • Torsten Gross, IRI Life Sciences, Humboldt University, Berlin, Germany, Germany
  • Nils Blüthgen, Charité – Universitätsmedizin Berlin, Germany

Presentation Overview: Show

Motivation: A common strategy to infer and quantify interactions between components of a biological system is to deduce them from the network’s response to targeted perturbations. Such perturbation experiments are often challenging and costly. Therefore, optimising the experimental design is essential to achieve a meaningful characterisation of biological networks. However, it remains difficult to predict which combination of perturbations allows to infer specific interaction strengths in a given network topology. Yet, such a description of identifiability is necessary to select perturbations that maximize the number of inferable parameters.
Results: We show analytically that the identifiability of network parameters can be determined by an intuitive maximum flow problem. Furthermore, we used the theory of matroids to describe identifiability relationships between sets of parameters in order to build identifiable effective network models. Collectively, these results allowed to device strategies for an optimal design of the perturbation experiments. We benchmarked these strategies on a database of human pathways. Remarkably, full network identifiability was achieved with on average less than a third of the perturbations that are needed in a random experimental design. Moreover, we determined perturbation combinations that additionally decreased experimental effort compared to single-target perturbations. In summary, we provide a framework that allows to infer a maximal number of interaction strengths with a minimal number of perturbation experiments.
Availability: IdentiFlow is available at github.com/GrossTor/IdentiFlow.

Prediction of cancer driver genes through network-based moment propagation of mutation scores
Date: July 15 and 16

  • Anja Gumpinger, Eidgenoessische Technische Hochschule Zuerich (ETH), Switzerland
  • Kasper Lage, Havard Medical School, United States
  • Heiko Horn, BROAD Institute, United States
  • Karsten Borgwardt, Eidgenoessische Technische Hochschule Zuerich (ETH), Switzerland

Presentation Overview: Show

Motivation: Gaining a comprehensive understanding of the genetics underlying cancer development and progression is a central goal of biomedical research. Its accomplishment promises key mechanistic, diagnostic and therapeutic insights. One major step in its direction is the identification of genes that drive the emergence of tumors upon mutation. Recent advances in the field of computational biology have shown the potential of combining genetic summary statistics that represent the mutational burden in genes with biological networks, such as protein-protein interaction networks, to identify cancer driver genes. Those approaches superimpose the summary statistics on the nodes in the network, followed by an unsupervised propagation of the node scores through the network. However, this unsupervised setting does not leverage any knowledge on well-established cancer genes, a potentially valuable resource to improve the identification of novel cancer drivers.
Results: We develop a novel node embedding that enables classification of cancer driver genes in a supervised setting. The embedding combines a representation of the mutation score distribution in a node’s local neighborhood with network propagtion. We leverage the knowledge of well-established cancer driver genes to define a positive class, resulting in a partially-labeled data set, and develop a cross validation scheme to enable supervised prediction. The proposed node embedding followed by a supervised classification improves the predictive performance compared to baseline methods, and yields a set of promising genes that constitute candidates for further biological validation.

Network-based characterization of disease–disease relationships in terms of drugs and therapeutic targets
Date: July 15 and 16

  • Midori Iida, Kyushu Institute of Technology, Japan
  • Michio Iwata, Kyushu Institute of Technology, Japan
  • Yoshihiro Yamanishi, Kyushu Institute of Technology, Japan

Presentation Overview: Show

Pathogenesis is generally considered as disease-specific, yet characteristic molecular features are often common to various diseases. Disease states are often characterized by altered gene expression levels. Thus, similarities between diseases can be explained by characteristic gene expression patterns. However, most disease-disease relationships remain uncharacterized. In this study, we proposed a novel approach for network-based characterization of disease–disease relationships in terms of drugs and therapeutic targets. We performed large-scale analyses of omics data and molecular interaction networks for 79 diseases, including adrenoleukodystrophy, leukemia, Alzheimer's disease, asthma, atopic dermatitis, breast cancer, cystic fibrosis, and inflammatory bowel disease. We quantified disease–disease similarities based on proximities of abnormally expressed genes in various molecular networks and showed that similarities between diseases could be explained by characteristic molecular net-work topologies. Furthermore, we developed a kernel matrix regression algorithm to predict the commonalities of drugs and therapeutic targets among diseases. Our comprehensive prediction strategy indicated many new associations among phenotypically diverse diseases.

Network-principled deep generative models for designing drug combinations as graph sets
Date: July 15 and 16

  • Mostafa Karimi, Texas A&M University, United States
  • Arman Hasanzadeh, Texas A&M University, United States
  • Yang Shen, Texas A&M University, United States

Presentation Overview: Show

Motivation: Combination therapy has shown to improve therapeutic efficacy while reducing side effects. Importantly, it has become a indispensable strategy to overcome resistance in antibiotics, anti-microbials, and anti-cancer drugs. Facing enormous chemical space and unclear design principles for small-molecule combinations, computational drug-combination design has not seen generative models to meet its potential to accelerate resistance-overcoming drug combination discovery.

Results: We have developed the first deep generative model for drug combination design, by jointly embedding graph-structured domain knowledge and iteratively learning a reinforcement learning-based chemical graph-set designer. First, we have developed Hierarchical Variational Graph Auto-Encoders (HVGAE) trained end-to-end to jointly embed gene-gene, disease-disease and gene-disease networks. Novel attentional pooling is introduced here for learning disease-representations from associated genes' representations. Second, targeting diseases in learned representations, we have recast the drug-combination design problem as graph-set generation and developed a deep learning-based model with novel rewards. Specifically, besides chemical validity rewards, we have introduced novel generative adversarial award, being generalized sliced Wasserstein, for chemically diverse but distributionally similar molecules to known drug-like compounds or drugs. We have also designed a network principle-based reward for drug combinations. Numerical results indicate that, compared to state-of-the-art graph embedding methods, HVGAE learns more generalizable and informative disease representations in disease-disease graph reconstruction. Results also show that the deep generative models generate drug combinations following the principle across diseases. A case study on melanoma shows that generated drug combinations collectively cover the disease module similar to FDA-approved drug combinations and could also suggest promising novel systems-pharmacology strategies. Our method allows for examining and following network-based principle or hypothesis to efficiently generate disease-specific drug
combinations in a vast chemical combinatorial space.

Chromatin network markers of leukemia
Date: July 15 and 16

  • Noel Malod-Dognin, Barcelona Supercomputing Center (BSC), Spain
  • Vera Pancaldi, Centre de Recherches en Cancerology de Toulouse (CRCT), France
  • Alfonso Valencia, Barcelona Supercomputing Center (BSC), Spain
  • Natasa Przulj, Barcelona Supercomputing Center (BSC), Spain

Presentation Overview: Show

Motivation: The structure of chromatin impacts gene expression. Its alteration has been shown to coincide with the occurrence of cancer. A key challenge is in understanding the role of chromatin structure in cellular processes and its implications in diseases.

Results: We propose a comparative pipeline to analyze chromatin structures and apply it to study chronic lymphocytic leukemia (CLL). We model the chromatin of the affected and control cells as networks and analyze the network topology by state-of-the-art methods.
Our results show that chromatin structures are a rich source of new biological and functional information about DNA elements and cells that can complement protein-protein and co-expression data. Importantly, we show the existence of structural markers of cancer-related DNA elements in the chromatin. Surprisingly, CLL driver genes are characterized by specific local wiring patterns not only in the chromatin structure network of CLL cells, but also of healthy cells. This allows us to successfully predict new CLL-related DNA elements. Importantly, this shows that we can identify cancer-related DNA elements in other cancer types by investigating the chromatin structure network of the healthy cell of origin, a key new insight paving the road to new therapeutic strategies. This gives us an opportunity to exploit chromosome conformation data in healthy cells to predict new drivers.

Combining phenome-driven drug target prediction with patients’ electronic health records-based clinical corroboration towards drug discovery
Date: July 15 and 16

  • Mengshi Zhou, Case Western Reserve University, United States
  • Chunlei Zheng, Case Western Reserve University, United States
  • Rong Xu, Case Western Reserve University, United States

Presentation Overview: Show

Predicting drug-target interaction (DTIs) using human phenotypic data has the potential in eliminating the translational gap between animal experiments and clinical outcomes in humans. One challenge in human phenome-driven DTI predictions is integrating and modeling diverse drug and disease phenotypic relationships. Leveraging large amounts of clinical observed phenotypes of drugs and diseases and electronic health records (EHRs) of 72 million patients, we developed a novel integrated computational drug discovery approach by seamlessly combining DTI prediction and clinical corroboration.
We developed a network-based DTI prediction system (TargetPredict) by modeling 855,904 phenotypic and genetic relationships among 1430 drugs, 4251 side effects,1059 diseases, and 17,860 genes. We systematically evaluated TargetPredict in de novo cross-validation and compared it to a state-of-art phenome-driven DTI prediction approach. We applied TargetPredict in identifying novel repositioned candidate drugs for Alzheimer’s disease (AD), a disease affecting over 5.8 million people in the United States. We evaluated the clinical efficiency of top repositioned drug candidates using EHRs of over 72 million patients.
Results: The area under the receiver operating characteristic (ROC) curve was 0.97 in the de novo cross-validation when evaluated using 910 drugs. TargetPredict outperformed a state-of-art phenome-driven DTI prediction system as measured by precision-recall curves (MAP: 0.28 versus 0.23, p-value<0.0001). The EHR-based case-control studies identified top-ranked repositioned drugs that are significantly associated with lower odds of AD. For example, we showed that liraglutide, a type 2 diabetes drug, is significantly associated with decreased risk of AD (AOR: 0.76; 95% CI (0.70,0.82), p-value < 0.0001).
In summary, our integrated approach that seamlessly combines computational DTI prediction and large-scale patient EHRs-based clinical corroboration has high potential in rapidly identifying novel drug targets and drug candidates for complex diseases.

RegSys: Regulatory and Systems Genomics

RegSys: Regulatory and Systems Genomics

Fully Interpretable Deep Learning Model of Transcriptional Control
Date: July 15 and 16

  • Yi Liu, University of Chicago, United States
  • Kenneth Barr, University of Chicago, United States
  • John Reinitz, University of Chicago, United States

Presentation Overview: Show

The universal expressibility assumption of Deep Neural Networks (DNNs)
is the key motivation behind recent works in the system biology community to employ DNNs to solve important problems in functional genomics and molecular genetics. Typically, such investigations have taken a "black box" approach in which the internal structure of the model used is set purely by machine learning considerations with little consideration of representing the internal structure of the biological system by the mathematical structure of the DNN. DNNs have not yet been applied to the detailed modeling of transcriptional control in which mRNA production is controlled by the binding of specific transcription factors to DNA, in part because such models are in part formulated in terms of specific chemical equations that appear different in form from those used in neural networks. In this paper, we give an example of a DNN which can model the detailed control of transcription in a precise and predictive manner. Its internal structure is fully interpretable and is faithful to underlying chemistry of transcription factor binding to DNA. We derive our DNN from a systems biology model that was not previously recognized as having a DNN structure. Although we apply our DNN to data from the early embryo of the fruit fly Drosophila, this system serves as a testbed for analysis of much larger data sets obtained by systems biology studies on a genomic scale.

TopicNet: a framework for measuring transcriptional regulatory network change
Date: July 15 and 16

  • Shaoke Lou, Yale University, United States
  • Tianxiao Li, Yale University, United States
  • Xiangmeng Kong, Yale University, United States
  • Jing Zhang, Yale University, United States
  • Jason Liu, Yale University, United States
  • Donghoon Lee, Yale University, United States
  • Mark Gerstein, Yale University, United States

Presentation Overview: Show

Next generation sequencing data highlights comprehensive and dynamic changes in the human gene regulatory network. Moreover, changes in regulatory network connectivity (network “rewiring”) manifest different regulatory programs in multiple cellular states. However, due to the dense and noisy nature of the connectivity in regulatory networks, directly comparing the gains and losses of targets of key TFs is not that informative. Thus, here, we seek a abstracted lower-dimensional representation to understand the main features of network change. In particular, we propose a method called TopicNet that applies latent Dirichlet allocation (LDA) to extract meaningful functional topics for a collection of genes regulated by a TF. We then define a rewiring score to quantify the large-scale changes in the regulatory network in terms of topic change for a TF. Using this framework, we can pinpoint particular TFs that change greatly in network connectivity between different cellular states. This is particularly relevant in oncogenesis. Also, incorporating gene-expression data, we define a topic activity score that gives the degree that a topic is active in a particular cellular state. Furthermore, we show how activity differences can highlight differential survival in certain cancers.

MAGGIE: leveraging genetic variation to identify DNA sequence motifs mediating transcription factor binding and function
Date: July 15 and 16

  • Zeyang Shen, University of California San Diego, United States
  • Marten Hoeksema, University of California San Diego, United States
  • Zhengyu Ouyang, University of California San Diego, United States
  • Christopher Benner, University of California San Diego, United States
  • Christopher Glass, University of California San Diego, United States

Presentation Overview: Show

Motivation: Genetic variation in regulatory elements can alter transcription factor (TF) binding by mutating a TF binding motif, which in turn may affect the activity of the regulatory elements. However, it is unclear which TFs are prone to be affected by a given variant. Current motif analysis tools either prioritize TFs based on motif enrichment without linking to a function or are limited in their applications due to the assumption of linearity between motifs and their functional effects.
Results: We present MAGGIE, a novel method for identifying motifs mediating TF binding and function. By leveraging measurements from diverse genotypes, MAGGIE uses a statistical approach to link mutation of a motif to changes of an epigenomic feature without assuming a linear relationship. We benchmark MAGGIE across various applications using both simulated and biological datasets and demonstrate its improvement in sensitivity and specificity compared to the state-of-the-art motif analysis approaches. We use MAGGIE to reveal insights into the divergent functions of distinct NF-κB factors in the pro-inflammatory macrophages, showing its promise in discovering novel functions of TFs. The Python package for MAGGIE is freely available at https://github.com/zeyang-shen/maggie.

Text Mining

Text Mining

PEDL: Extracting protein-protein associations using deep language models and distant supervision.
Date: July 13

  • Leon Weber, Humboldt-Universität zu Berlin, Germany
  • Kirsten Thobe, Max Delbrück Center for Molecular Medicine in the Helmholtz Association, Germany
  • Oscar Arturo Migueles Lozano, Max Delbrück Center for Molecular Medicine in the Helmholtz Association, Germany
  • Jana Wolf, Max Delbrück Center for Molecular Medicine in the Helmholtz Association, Germany
  • Ulf Leser, Humboldt-Universität zu Berlin, Germany

Presentation Overview: Show

Motivation: A significant portion of molecular biology investigates signalling pathways and thus depends on an up-to-date and complete resource of functional protein-protein associations (PPAs) that constitute such pathways. Despite extensive curation efforts, major pathway databases are still notoriously incomplete. Relation extraction can help to gather such pathway information from biomedical publications. Current methods for extracting PPAs typically rely exclusively on rare manually labelled data which severely limits their performance.

Results: We propose PEDL, a method for predicting PPAs from text that combines deep language models and distant supervision. Due to the reliance on distant supervision, PEDL has access to an order of magnitude more training data than methods solely relying on manually labelled annotations. We introduce three different data sets for PPA prediction and evaluate PEDL for the two subtasks of predicting PPAs between two proteins, as well as identifying the text spans stating the PPA. We compared PEDL with a recently published state-of-the-art model and found that on average PEDL performs better in both tasks on all three data sets. An expert evaluation demonstrates that PEDL can be used to predict PPAs that are missing from major pathway data bases and that it correctly identifies the text spans supporting the PPA.

Availability: PEDL is freely available at https://github.com/leonweber/pedl. The repository also includes scripts to generate the used data sets and to reproduce the experiments from this paper.

Contact: leser@informatik.hu-berlin.de or jana.wolf@mdc-berlin.de

Supplementary information: Supplementary data are available at Bioinformatics online.

TransMed: Translational Medical Informatics

TransMed: Translational Medical Informatics

Identifying diagnosis-specific genotype-phenotype associations via joint multi-task sparse canonical correlation analysis and classification
Date: July 15

  • Lei Du, Northwestern Polytechnical University, China
  • Fang Liu, Northwestern Polytechnical University, China
  • Kefei Liu, University of Pennsylvania, United States
  • Xiaohui Yao, University of Pennsylvania, United States
  • Shannon Leigh Risacher, Indiana University School of Medicine, United States
  • Junwei Han, Northwestern Polytechnical University, China
  • Lei Guo, Northwestern Polytechnical University, China
  • Andrew Saykin, Indiana University School of Medicine, United States
  • Li Shen, University of Pennsylvania, United States

Presentation Overview: Show

Brain imaging genetics provides us a new opportunity to understand the pathophysiology of brain disorders. It studies the complex association between genotypic data such as single nucleotide polymorphisms (SNPs) and imaging quantitative traits (QTs). The neurodegenerative disorders usually exhibit the diversity and heterogeneity, originating from which different diagnostic groups might carry distinct imaging QTs, SNPs and their interactions. Sparse canonical correlation analysis (SCCA) is widely used to identify bi-multivariate genotype-phenotype associations. However, most existing SCCA methods are unsupervised, leading to an inability to identify diagnosis-specific genotype-phenotype associations.
In this paper, we propose a new joint multi-task learning method, named MT-SCCALR, which absorbs the merits of both SCCA and logistic regression. MT-SCCALR learns genotype-phenotype associations of multiple tasks jointly, with each task focusing on identifying the task-specific genotype-phenotype pattern. To ensure the interpretation and stability, we endow the proposed model with the selection of SNPs and imaging QTs for each diagnostic group alone, while allowing the selection of them shared by multiple diagnostic groups. We derive an efficient optimization algorithm whose convergence to a local optimum is guaranteed. Compared with two state-of-the-art methods, the results show that MT-SCCALR yields better or similar canonical correlation coefficients (CCCs) and classification performances. In addition, it owns much better discriminative canonical weight patterns of great interest than competitors. This demonstrates the power and capability of MTSCCAR in identifying diagnostically heterogeneous genotype-phenotype patterns, which would be helpful to understand the pathophysiology of brain disorders.

Robust and accurate deconvolution of tumor populations uncovers evolutionary mechanisms of breast cancer metastasis
Date: July 15

  • Yifeng Tao, Carnegie Mellon University, United States
  • Haoyun Lei, Carnegie Mellon University, United States
  • Xuecong Fu, Carnegie Mellon University, United States
  • Adrian Lee, University of Pittsburgh, United States
  • Jian Ma, Carnegie Mellon University, United States
  • Russell Schwartz, Carnegie Mellon University, United States

Presentation Overview: Show

Motivation: Cancer develops and progresses through a clonal evolutionary process. Understanding progression to metastasis is of particular clinical importance, but is not easily analyzed by recent methods because it generally requires studying samples gathered years apart, for which modern single-cell genomics is rarely an option. Understanding clonal evolution in the metastatic transition thus still depends on unmixing tumor subpopulations from bulk genomic data.
Methods: We develop a method for progression inference from bulk transcriptomic data of paired primary and metastatic samples. We develop a novel toolkit, the Robust and Accurate Deconvolution (RAD) method, to deconvolve biologically meaningful tumor populations from multiple transcriptomic samples spanning distinct progression states. RAD employs a hybrid optimizer to achieve an accurate solution, and a gene module representation to mitigate considerable noise in RNA data. Finally, we apply phylogenetic methods to infer how associated cell populations adapt across the metastatic transition via changes in expression programs and cell-type composition.
Results: We validated the superior robustness and accuracy of RAD over other algorithms on a real dataset, and validated the effectiveness of gene module compression on both simulated and real bulk RNA data. We further applied the methods to a breast cancer metastasis dataset, and discovered common early events that promote tumor progression and migration to different metastatic sites, such as dysregulation of ECM-receptor, focal adhesion, and PI3k-Akt pathways.

Privacy-preserving Construction of Generalized Linear Mixed Model for Biomedical Computation
Date: July 15

  • Rui Zhu, Indiana University, United States
  • Chao Jiang, Auburn University, United States
  • Xiaofeng Wang, Indiana University, United States
  • Shuang Wang, Indiana University, United States
  • Hao Zheng, Hangzhou Nuowei Information Technology, China
  • Haixu Tang, Indiana University, United States

Presentation Overview: Show

The Generalized Linear Mixed Model (GLMM) is an extension of the generalized linear model (GLM) in which the linear predictor takes into account random effects.Given its power of precisely modeling the mixed effects from multiple sources of random variations, the method has been widely used in biomedical computation, for instance in the genome-wide association studies (GWAS) that aim to detect genetic variance significantly associated with phenotypes such as human diseases. Collaborative GWAS on large cohorts of patients across multiple institutions is often impeded by the privacy concerns of sharing personal genomic and other health data. To address such concerns, we present in this paper a privacy-preserving Expectation-Maximization (EM) algorithm to build GLMM collaboratively when input data are distributed to multiple participating parties and cannot be transferred to a central server. We assume that the data are horizontally partitioned among participating parties: i.e., each party holds a subset of records (including observational values of fixed effect variables and their corresponding outcome), and for all records, the outcome is regulated by the same set of known fixed effects and random effects. Our collaborative EM algorithm is mathematically equivalent to the original EM algorithm commonly used in GLMM construction. The algorithm also runs efficiently when tested on simulated and real human genomic data, and thus can be practically used for privacy-preserving GLMM construction.

VarI: Variant Interpretation

VarI: Variant Interpretation

Combinatorial and statistical prediction of gene expression from haplotype sequence
Date: July 15

  • Berk Alpay, University of Connecticut, United States
  • Pinar Demetci, Brown University, United States
  • Sorin Istrail, Department of Computer Science and Center for Computational Molecular Biology, Brown University, United States
  • Derek Aguiar, University of Connecticut, United States

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Genome-wide association studies have discovered thousands of significant genetic effects on disease phenotypes. By considering gene expression as the intermediary between genotype and disease phenotype, eQTL studies have interpreted these variants by their regulatory effects on gene expression. However, there remains a considerable gap between genotype-to-gene expression association and genotype-to-gene expression prediction. Accurate prediction of gene expression enables gene-based association studies to be performed post-hoc for existing GWAS, reduces multiple testing burden, and can prioritize genes for subsequent experiments.
In this work, we develop gene expression prediction methods that relax the independence and additivity assumptions between genetic markers. First, we consider gene expression prediction from a conventional regression perspective and develop the HAPLEXR algorithm which combines haplotype clusterings with allelic dosages. Second, we introduce the new gene expression classification problem, which focuses on identifying expression groups rather than continuous measurements; we formalize the selection of an appropriate number of expression groups using the principle of maximum entropy. Third, we develop the HAPLEXD algorithm that incorporates suffix tree based haplotype sharing with spectral clustering to identify expression classes from haplotype sequences. In both models, we penalize model complexity by prioritizing genetic clusters that indicate significant effects on expression. We compare HAPLEXR and HAPLEXD on five GTEx v8 tissues with three state-of-the-art expression prediction methods.
HAPLEXD exhibits significantly higher classification accuracy overall and HAPLEXR shows higher prediction accuracy on a significant subset of genes. These results demonstrate the importance of explicitly modelling non-dosage dependent and intragenic epistatic effects when predicting expression.

BIRD: Identifying Cell Doublets via Biallelic Expression from Single cells
Date: July 15

  • Kerem Wainer-Katsir, The Hebrew University of Jerusalem, Israel
  • Michal Linial, The Hebrew University of Jerusalem, Israel

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Current technologies for single-cell transcriptomics allow thousands of cells to be analyzed in a single experiment. The increased scale of these methods raises the risk of cell doublets contamination. Available tools and algorithms for identifying doublets and estimating their occurrence in single-cell experimental data focus on doublets of different species, cell types or individuals. In this study, we analyze transcriptomic data from single cells having an identical genetic background. We claim that the ratio of monoallelic to biallelic expression provides a discriminating power towards doublets’ identification. We present a pipeline called BIRD (BIallelic Ratio for Doublets) that relies on heterologous genetic variations, from single-cell RNA-Seq (scRNA-seq). For each dataset, doublets were artificially created from the actual data and used to train a predictive model. BIRD was applied on Smart-Seq data from 163 primary fibroblast single cells. The model achieved 100% accuracy in annotating the randomly simulated doublets. Bonafide doublets were verified based on a biallelic expression signal amongst X-chromosome of female fibroblasts. Data from 10X Genomics microfluidics of human peripheral blood cells achieved in average 83% (± 3.7%) accuracy, and an area under the curve of 0.88 (± 0.04) for a collection of ~13,300 single cells. BIRD addresses instances of doublets which were formed from cell mixtures of identical genetic background and cell identity. Maximal performance is achieved for high coverage data from Smart-Seq. Success in identifying doublets is data specific which varies according to the experimental methodology, genomic diversity between haplotypes, sequence coverage, and depth.

General Computational Biology

CRISPRLand: Interpretable Large-Scale Inference of DNA Repair Landscape Based on a Spectral Approach
Date: July 16

  • Amirali Aghazadeh, University of California, Berkeley, United States
  • Orhan Ocal, University of California, Berkeley, United States
  • Kannan Ramchandran, University of California, Berkeley, United States

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We propose a new spectral framework for reliable training, scalable inference, and interpretable explanation of the DNA repair outcome following a Cas9 cutting. Our framework, dubbed CRISPRLand, relies on an unexploited observation about the nature of the repair process: the landscape of the DNA repair is highly sparse in the (Walsh-Hadamard) spectral domain. This observation enables our framework to address key shortcomings that limit the interpretability and scaling of current deep-learning-based DNA repair models. In particular, CRISPRLand reduces the time to compute the full DNA repair landscape from a striking 5230 years to one week and the sampling complexity from 10^12 to 3 million guide RNAs with only a small loss in accuracy (R^2 ~ 0.9). Our proposed framework is based on a divide-and-conquer strategy that uses a fast peeling algorithm to learn the DNA repair models. CRISPRLand captures lower-degree features around the cut site which enrich for short insertions and deletions as well as higher-degree microhomology patterns that enrich for longer deletions.
The CRISPRLand software is publicly available at https://github.com/UCBASiCS/CRISPRLand.

Inference Attacks Against Differentially-Private Query Results from Genomic Datasets Including Dependent Tuples
Date: July 16

  • Nour Almadhoun, Bilkent University, Turkey
  • Erman Ayday, Case Western Reserve University, United States
  • Ozgur Ulusoy, Bilkent University, Turkey

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Motivation: The rapid decrease in the sequencing technology costs leads to a revolution in medical research and clinical care. Today, researchers have access to large genomic datasets to study associations between variants and complex traits. However, availability of such genomic datasets also results in new privacy concerns about personal information of the participants in genomic studies. Differential privacy (DP) is one of the rigorous privacy concepts, which received widespread adoption for sharing summary statistics from genomic datasets while protecting the privacy of participants against inference attacks. However, DP has a known drawback as it does not take into account the correlation between dataset tuples. Therefore, privacy guarantees of DP-based mechanisms may degrade if the dataset includes dependent tuples, which is a common situation for genomic datasets due to the inherent correlations between genomes of family members.

Results: In this paper, using two real-life genomic datasets, we show that exploiting the correlation between the dataset participants results in significant information leak from differentially-private results of complex queries. We formulate this as an attribute inference attack and show the privacy loss in minor allele frequency (MAF) and chi-square queries. Our results show that using the results of differentially-private MAF queries and utilizing he dependency between tuples, an adversary can reveal up to 50% more sensitive information about the genome of a target (compared to original privacy guarantees of standard DP-based mechanisms), while differentially-privacy chi-square queries can reveal up to 40% more sensitive information. Furthermore, we show that the adversary can use the inferred genomic data obtained from the attribute inference attack to infer the membership of a target in another genomic dataset (e.g., associated with a sensitive trait). Using a log-likelihood-ratio (LLR) test, our results also show that the inference power of the adversary can be significantly high in such an attack even by using inferred (and hence partially incorrect) genomes.

Efficient Exact Inference for Dynamical Systems with Noisy Measurements using Sequential Approximate Bayesian Computation
Date: July 16

  • Yannik Schälte, Helmholtz Zentrum München, Germany
  • Jan Hasenauer, Universaty of Bonn, Germany

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Approximate Bayesian Computation (ABC) is an increasingly popular method for likelihood-free parameter inference in systems biology and other fields of research, since it allows analysing complex stochastic models. However, the introduced approximation error is often not clear. It has been shown that ABC actually gives exact inference under the implicit assumption of a measurement noise model. Noise being common in biological systems, it is intriguing to exploit this insight. But this is difficult in practice, since ABC is in general highly computationally demanding. Thus, the question we want to answer here is how to efficiently account for measurement noise in ABC.

We illustrate exemplarily how ABC yields erroneous parameter estimates when neglecting measurement noise. Then, we discuss practical ways of correctly including the measurement noise in the analysis. We present an efficient adaptive sequential importance sampling based algorithm applicable to various model types and noise models. We test and compare it on several models, including ordinary and stochastic differential equations, Markov jump processes, and stochastically interacting agents, and noise models including normal, Laplace, and Poisson noise. We conclude that the proposed algorithm could improve the accuracy of parameter estimates for a broad spectrum of applications.