Posterpalooza Virtual Viewing Hall

Keywords: Topological Data Analysis, Euler characteristic transform, mathematical biology, data science, morphology

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Short Abstract: Shape is data and data is shape. Biologists are accustomed to thinking about how the shape of biomolecules, cells, tissues, and organisms arise from the effects of genetics, development, and the environment. Less often do we consider that data itself has shape and structure, or that it is possible to measure the shape of data and analyze it. Topological Data Analysis is a novel mathematical discipline that uses principles from algebraic topology to comprehensively measure shape in datasets. Using a function that relates the similarity of data points to each other, we can monitor the evolution of topological features—connected components, loops, and voids. This evolution, a topological signature, concisely summarizes large, complex datasets. Here, we focus on quantifying the morphology of barley spikes and seeds using topological descriptors based on the Euler characteristic and relate the output back to genetic information. The vision of TDA, that data is shape and shape is data, will be relevant as biology transitions into a data-driven era where meaningful interpretation of large datasets is a limiting factor.

Keywords: programming languages, reproducibility, workflows, compilers

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Short Abstract: The functional core of a bioinformatics pipeline is usually an elegant series of data transformations. But there is a flaw in the system that complicates both pipeline and tool development: the passing of untyped textual data between subcomponents. Under the textual paradigm, all tool must parse incoming text, untangle the metadata and data (for example, FASTA headers and sequences), operate on them, then re-entangle the results in the output. The complexity of propagating metadata through the pipeline leads to error-prone and unmaintainable workarounds. Furthermore, lack of agreement on data formats requires tools to either support many formats and variations or to rely on their users to provide correct input. Pipelines written within the context of a single programming language and bioinformatics library (e.g., Bioperl, Biopython, or Bioconductor) are an improvement, but limit the userbase to one language. As a tentative solution, we offer morloc, a compiler that performs function composition between languages through type-directed generation of interoperability code. In morloc subcomponents are source functions and their input/outputs are typed data structures in memory. Every function is described by a pair of parallel type signatures: one specifies the function's language-specific data structures in memory and the other specifies the general form of the data. This allows functions from across languages to be indexed, tested, benchmarked, and packaged within one ecosystem. The compiler is open source and publicly available at https://github.com/morloc-project/morloc.

Keywords: Exhaustive docking, ensemble docking, protein-ligand interaction profile

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Short Abstract: Molecular docking is a computational method that predicts the preferred binding orientation of a ligand into a receptor, that has become an essential method in rational drug design. For binding prediction, docking algorithms determine the "best docking solution" using a scoring function.1 However, the best solution is not always detected as the best ranked, and this issue raises the question how to obtain a solution that best describes how a ligand interacts with a specific target, avoiding the scoring function problem? In this sense, it is important to be able to discern between the best energy solution, and the correct orientation. Thus, we have implemented an ensemble docking protocol, where though Molecular Dynamics simulations (MDs) the different protein-apo conformations are explored, thus considering the binding site flexibility. Then, ligands are docked into multiple structures, and the resulting poses are subsequently merged and clustered to find the most representative(s) conformation(s) that best described how a ligand interacts with a target. In this work we present the exhaustive docking protocol using multiple human Acetylcholinesterase crystallographic structures as the selected target. The exhaustive docking protocol presented here will be freely available to the community, to be implemented using free software such as Autodock Vina, Python and KNIME. Our goal is to provide the community with alternatives to potentially improve the way protein-ligand interaction issue is addressed.

Keywords: differential expression analysis, survival analysis, shiny, gene expression omnibus

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Short Abstract: We describe an R/Shiny web application for analyzing gene expression data from the Gene Expression Omnibus, a public repository of gene expression datasets. The web application allows users to download gene expression data sets directly from GEO, select a gene of interest, and carry out differential expression and survival analyses with customizable graphics. Importantly, shinyGEO can generate R code for all analyses to help ensure any results obtained are reproducible. The availability of shinyGEO makes GEO datasets more accessible to non-bioinformaticians, promising to lead to better understanding of biological processes and genetic diseases such as cancer.

Keywords: TCR-Seq, RNA-Seq, CDR3, T-cell receptors, Tumor-infiltrating lymphocytes (TILs)

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Short Abstract: The tumour microenvironment consists of a heterogeneous population of cancer cells and a range of resident and infiltrating host cells, in addition to secreted factors and extracellular matrix proteins. Tumour progression is influenced by interactions of cancer cells, proteins and other factors within the microenvironment, which determines how or whether the primary tumour progresses, metastasises, or gets eradicated. Identification of antigen specific, tumour-infiltrating T lymphocytes (TIL) has demonstrated applications in cancer immunotherapy, such as checkpoint blockade and adoptive cell therapies. Useful in diagnostic applications of early cancer detection, in addition to prognostic or predictive purposes of personalizing cancer therapy. Antigen-binding CDR3 regions of T-cell receptors are extremely diverse and their targets are usually unknown. Recent efforts by researchers have demonstrated the feasibility of clustering TIL repertoire into groups linking to antigen specificity. In this project, we demonstrate the identification of T-Cell receptor repertoire sequences from the 5’-RACE TCR-Seq and the whole RNA-Seq datasets. We have carefully reviewed and selected a wide range of public domain datasets, that were originally intended to study gene expression profiles or immunogenicity, for the purpose of identifying novel T- Cell clusters and screen them in-silico against a range of tumour-associated antigens (TAA). In this project we screen and compare TIL CDR3 clusters of unknown TAAs against CDR3 sequences of known antigens and study the differential aspects of TIL clusters between cancer types, subtypes and normal tissue types. We develop novel algorithms as well as utilize existing software for the data analyses.

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Keywords: Markov modelling, Cancer cell plasticity, single cell phenotype, Intra-tumour heterogeneity

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Short Abstract: The phenotypic composition of many heterogeneous cellular populations (e.g., tumors) evolve over time. The dynamics of such populations are governed by many factors, key among which are stochastic phenotype transitions, and phenotype-specific differences in growth kinetics. Single-cell stochastic phenotype transitions have been implicated in re-equilibration phenomena, where a heterogeneous population gradually moves toward an equilibrium phenotype composition, irrespective of initial conditions. Estimating the rates of such stochastic transitions can provide insight into long-term evolution of heterogenous populations. However, experimental quantification of stochastic transition rates is non-trivial, can be resource-intensive and confounded by simultaneous bidirectional transitions and asymmetric growth kinetics. We have developed TRANSCOMPP – a Markov modeling-based tool that solves for the combination of stochastic transition rates and phenotype-specific growth rates that best fit experimentally observed population dynamics. TRANSCOMPP also includes a resampling module that allows the computation of a bootstrap confidence interval for estimated transition rates. We applied TRANSCOMPP to time-series datasets that follow phenotype compositions of stem-like or non-stem cancer cells in different cancer cell lines (MCF10CA1a for breast cancer, and patient-derived head and neck cancer). Our results show that cell population equilibria can be affected by the environment. We show that commonly-used cell culture reagents such as hydrocortisone (HC), insulin and EGF shifted the cell population equilibrium toward stem-like states in both cancer contexts. In addition, we show that transition rates computed from short-term experiments could predict long-term trajectories and equilibrium convergence of the cultured cell population.

Keywords: Deep Learning, Phenotype prediction

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Short Abstract: Phenotype prediction from genomic data has important applications in crop breeding and deep learning can be applied to use features from genomic data to predict phenotypes. The challenge is the scale and quality of paired genotype and phenotype data required to train an accurate model. The work done on this project builds on pervious work done in lab and aims to apply transfer learning to increase predictive power of model when small number of data samples used.

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Short Abstract: Humoral immune responses to infection and/or vaccination result in the generation of populations of antibodies with reactivities to various regions (epitopes) of the encountered exogenous agent (antigens of pathogen or vaccine). Epitope microarrays, in which short peptides represent sequences contained within antigens, are an effective method for high-throughput measurement of antibody reactivity towards a collection of antigens and their epitopes. Such arrays are widely applied for epitope mapping, biomarker discovery, and rationale selection of vaccine candidates. However, high-throughput experiments require specialized tools for data analysis, and standards for epitope microarray analysis have not yet been well-established. Several R packages exist for comparing epitope array data from different cohorts, but there are no online services that offer a web-based interface. Here we present EPIphany, the first publicly available web-server devoted to the analysis of epitope array data for immunological biomarker discovery. EPIphany requires no registration, provides a simple user interface with access to important analysis parameters, data normalization options, and produces unique data visualizations that provide researchers the greatest opportunities to extract biologically meaningful information from the epitope array data.

Keywords: Ensemble docking, Nitrate/peptide transporter family (NPF), Glucosinolate transporters (GTRs), transport mechanisms, molecular determinats

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Short Abstract: Nitrate/peptide transporter family (NPF) is one of the largest transporter families in the plant kingdom, with members capable to recognize and transport nitrate, peptides, phytohormones and glucosinolate defense compounds. However only a few NPF transporters have been studied so far, and little is known about their interaction with substrates and physiological functions. For instance, the glucosinolate transporters (GTRs) have shown to be essential for glucosinolate uptake in Arabinosis thaliana. However, the structural determinants for substrate specify and transport mechanisms remain unknown. Aiming to understand the transport mechanism of GTRs, we selected GTR1 and GTR3 members of NPF since while GTR1 shares a 60% of identity with GTR3, GTR1 can transport 4MTB (4-methylthiobutyl) and I3M (indol-3-ylmethyl) glucosinolates, meanwhile GTR3 only transports I3M. GTR1 and GTR3 were modeled in inward and outward conformations, subjected to molecular dynamics simulations and multiples GTRs conformations were extracted from each trajectory, then both 4MTB and I3M were docked into the multiple GTRs structures and the resulted poses were merged and subsequently clustered to study the most relevant substrate-GTR interactions. Our ensemble docking protocol is being fully automated in Python and KNIME, will be made freely available to the entire community through GitHub and can be implemented on any membrane transporter system to find substrate specify and transport mechanism key residues that can then be experimentally tested, thus reducing time and resources in the process of identifying molecular determinants.

Keywords: epithelial polarity, protein sorting, apical membrane, basolateral membrane, sorting signals, linear motifs

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Short Abstract: Specialized subcellular compartments are an essential feature of life and transmembrane proteins are no exception. In the epithelial cells of multicellular organisms the apical (or lumenal) and the baso-lateral domains differ greatly in their protein composition. Despite the enormous number of previous publications, no concise database is available, where one could find details of polarized protein distribution in the apical - basolateral membranes. To remedy the situation, we present PolarProtDB as a freely accessible, online resource, where one can find all details of mammalian transmembrane proteins by cells, tissues and experiments and even potential traffic-regulating motifs. We found the data stored in our database has a strong predictive power and can be utilized for in silico prediction of protein localization in polarized cells. Thanks to its easy-to-use interfaces, we expect PolarProtDB to become a useful resouce for cell biologists. The database is available at http://polarprotdb.enzim.hu.

Short Abstract: Uno de los problemas generales de la biología es vincular genotipo con fenotipo, es decir, entender con exactitud cómo ciertos pedazos de ADN eventualmente determinan cierto rasgo o propiedad física. La forma física de los organismos es quizá el fenotipo más estudiado a lo largo de la historia, siendo fundamental para entender la evolución de las especies. La forma no se limita a la altura de un organismo: podemos también hablar de la morfología de biomoléculas, células, y tejidos. Existe una diversidad increíble de forma, y necesitamos de un método suficientemente general para medir y comparar tal diversidad. El Análisis Topológico de Datos (ATD) ofrece precisamente herramientas versátiles para cuantificar y comparar la morfología de múltiples individuos a la vez. En este caso, tenemos interés de entender mejor la cebada. Usando curvas de Característica de Euler, podemos clasificar con precisión más de 28 variedades distintas de cebada basados únicamente en escanes 3D de sus granos. Esta caracterización de forma de granos luego nos permitirá esclarecer cuáles genes contribuyen a su forma, y como estos evolucionaron a lo largo del tiempo.

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