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Session A: (July 22 and July 23)	Session B: (July 24 and July 25)
Presentation Schedule for July 22, 6:00 pm – 8:00 pm A) 3DSIG B) BioVis C) Function D) HitSeq E) RegSys F) SysMod G) TransMed H) Special Session (SST01): Text Mining for Biology and Healthcare Presentation Schedule for July 23, 6:00 pm – 8:00 pm I) Bio-Ontologies J) CompMS K) EvolCompGen L) Microbiome M) NetBio	Presentation Schedule for July 24, 6:00 pm – 8:00 pm P) BOSC Q) CAMDA R) Education S) MLCSB T) RNA U) VarI V) General Computational Biology
Session A Poster Set-up and Dismantle Session A Posters set up: Monday, July 22 between 7:30 am - 10:00 am Session A Posters should be removed at 8:00 pm, Tuesday, July 23.	Session B Poster Set-up and Dismantle Session B Posters set up: Wednesday, July 24 between 7:30 am - 10:00 am Session B Posters should be removed at 2:00 pm, Thursday, July 25.

Short Abstract: In order to understand age-related changes in an organ such as skin it is necessary first to establish the baseline young, healthy proteome. However, we have shown that there is little consensus between existing experimentally determined skin proteomes. Therefore, in order to define the healthy skin proteome (Manchester Skin Proteome: MSP) we developed a novel approach which combined text mining, web-scaping and systematic literature review approaches to screen peer-reviewed publications in the ‘Web of Science’ and ’Pubmed’ databases. The resultant, consensus skin proteome is hosted on http://www.manchesterproteome.manchester.ac.uk/. We have subsequently used this proteome to identify potential protein biomarkers of ageing. Proteins were stratified according to known degradative mechanisms: UV, ROS, glycation, protease-mediated proteolysis and DNA damage. This analysis predicts that, in general, collagens will be degraded by extracellular proteases whilst elastic fibre associated proteins will be susceptible to UVR and ROS. In order to validate these predictions mass-spectrometry proteomics methods have been employed to distinguish proteomic differences between photoaged and intrinsically aged skin. In order to further refine this analysis we have developed a new bioinformatic approached to predict regional susceptibility within protein structure and to validate these predictions by mapping peptide fingerprints (differential peptide yield between experimental conditions).

Short Abstract: One of the most important steps in peptide identification is to estimate the false discovery rate (FDR). The most commonly used method for estimating FDR is the target-decoy search strategy (TDS). While this method is simple and effective, it is time/space-inefficient because it searches a database that is twice as large as the original protein database. This inefficiency problem becomes more evident as protein databases get bigger and bigger. We propose a target-small decoy search strategy and present a rigorous verification that it reduces the database size and search time while retaining the accuracy of target-decoy search strategy (TDS). We show that peptide spectrum matches (PSMs) obtained at 1% FDR in TDS overlap ~99% with those in our method. (Considering that 1% FDR is used, 99% overlap means our method is very accurate.) Moreover, our method is more time/space-efficient than TDS. The search time of our method is reduced to only 1/4 of that of TDS when UniProt and its 1/8 decoy database are used.

Short Abstract: Isoelectric point, the pH at which a particular molecule carries no net electrical charge, is an critical parameter for many analytical biochemistry and proteomics techniques, especially for 2D gel electrophoresis (2D-PAGE), capillary isoelectric focusing (cIEF), X-ray crystallography and high-throughput liquid chromatography–mass spectrometry (LC-MS). Here, I present Isoelectric Point Calculator (IPC), a web service and a standalone program for the accurate estimation of protein and peptide pI using different sets of dissociation constant (pKa) values, including two computationally optimized pKa sets. Initial version of IPC used basinhopping optimization with truncated Newton algorithm, but new version of the tool will use deep learning with multiple new features for boosting the program accuracy. Additionally, I present Proteome-pI, an online database containing information about predicted isoelectric points for 5029 proteomes calculated using 18 methods. Moreover, the data sets corresponding to major protein databases such as UniProtKB/TrEMBL and the NCBI non-redundant (nr) database are also available. An updated version of Proteome-pI, apart from containing predictions for over 13,000 UniProt reference proteomes, will also contain multiple features such as new improved algorithms for pI prediction and usage of structural information. http://isoelectric.org IPC – Isoelectric Point Calculator http://isoelectricpointdb.org Proteome-pI - Proteome Isoelectric Point Database

Short Abstract: Mass spectrometry coupled to liquid chromatography (LC-MS) is routinely used for metabolomics studies. While steps in data acquisition are fairly standardised and automated, structural metabolite identification still depends on manual curation and expert knowledge, forming a major bottleneck in LC-MS based pipelines. Here we present a novel data pre-processing strategy, which aids metabolite identification through deliberate use of data acquisition order, chromatographic profile and spectral features correlation structure. This strategy aligns features originating from the same chemical entity across all samples as a group, ensuring that chemically-related features are accurately aligned despite fluctuations in the chromatographic and mass spectrometric measurements occurring during the experimental run time. Spectral features aligned in this way can consequently be matched to in-house chemical standards databases more efficiently and accurately, on account of the retained spectral information. This pipeline has been developed and is presented as an open-source R package - massFlowR. We demonstrate the utility of massFlowR with simulated data and open-source urine metabolomics experiments, where the application of massFlowR is compared with the widely-used package XCMS.

Short Abstract: Sentinel animals are widely used for monitoring the quality of our environment. We explored the response of fresh-water gammarids by RNA-seq informed proteomics. To take into account the biodiversity of these gammarids, we measured the protein abundances by shotgun label-free proteomics of 164 individuals belonging to 7 species, leading to an amazing proteomic dataset. We also sequenced by RNAseq a male and a female of each of these 7 species. We explored different strategies to improve the assembly of the RNAseq data considering the number of MS/MS spectra assigned as a key parameter, but also to optimize the construction of the RNA-seq derived protein sequence database. Once the proteomics data interpreted, we specifically analysed two regional Gammarus pulex populations to characterize the potential proteome divergence induced in one site by natural bioavailable mono-metallic contamination, i.e. cadmium, compared to a non-contaminated site. We observed that the intra-population proteome variability of long-term exposed G. pulex was inflated relatively to the non-contaminated population. These bioinformatics results show that, while remaining a challenge for such organisms with not yet sequenced genomes, taking into account intra-population variability is important to better define the molecular players induced by toxic stress in a comparative field proteomics approach.

Short Abstract: The identification of molecules remains a central question in analytical chemistry, in particular for natural products research, untargeted metabolomics, etc. Mass spectrometry is a predominant experimental technique in these fields, but metabolite structural elucidation remains highly challenging. We report SIRIUS 4, a specialized tool that addresses two fundamental questions: What is the molecular formula of the query compound among all molecular formulas, both previously observed and unobserved? For both questions, SIRIUS 4 offers best-of-class performance; for searching in molecular structure databases, SIRIUS 4 integrates CSI:FingerID as a web service. In evaluation, the number of wrongly assigned molecular formulas decreased by 31.7% compared to SIRIUS 3.0. For structure elucidation, the CSI:FingerID web service achieved identification rates of 74% on challenging independent metabolomics datasets, searching in a biocompound structure database with 0.5 million structures. Finally, running times improved substantially (231- to 332-fold) to the previous version. To this end, Users can now analyze full full liquid chromatography-mass spectrometry (LC-MS) datasets, rather than just one spectrum at a time; MS-driven annotations can be obtained for all detected features, not just those passing a preliminary statistical test, say, on fold change.

Short Abstract: Protein-protein interactions are difficult to characterize, especially in complex samples. Here, we present TX-MS, a targeted approach that combines the sensitivity of XL-MS with the power of computational modeling to rapidly determine the quaternary structures of protein complexes using data collected on unfractionated and biologically relevant samples. By using artificial intelligence, TX-MS combines different MS acquisition methods (hrMS1, DDA, and DIA) and computational modeling. RosettaDock was used to produce a compendium of quaternary protein structure models which then ranked using the hrMS1 data. The best models were iteratively improved by analyzing DDA data to find fragments of XLs within cross-linkable distance. High-resolution modeling adjusted the final model which then was validated using DIA data. Here, we demonstrated the applicability of TX-MS by studying two macromolecular assemblies in single experiment by cross-linking intact bacteria in human plasma with disuccinimidyl suberate. First, we elucidated the M1 protein interactome on the surface of the Streptococcus pyogenes with 15 plasma proteins, determining a 1.8MDa large complex supported by 200 XLs. Secondly, we modelled the human complement system membrane attack complex on the surface of the bacteria in the same sample, and proposed a near to atomic resolution model supported by more than 170 XLs.

Short Abstract: With advances of genomics and proteomics technologies, we can better identify sample-specific and/or novel peptides. However, it is still a bottleneck to identify combinatorially mutated peptides using tandem mass spectrometry. We propose a new peptide identification software tool, ntMODa, which identifies peptides allowing combinatorial mutations derived from a large amount of potential mutation sites given in public databases, possibly in conjunction with sample-specific variant calls. ntMODa matches a spectrum to a sequence database in a variant graph form, which represents nucleotide sequences of a given transcriptome model as a directed acyclic graph, while variant calls can be augmented to the graph. We used a proteogenomic dataset for stomach cancer patients that includes tandem mass spectra, RNA expression and sample-specific variant calls from a previous study, together with 83,873 nonsynonymous disease-specific mutations in COSMIC database. Allowing up to three mutations per peptide, ntMODa identified 151,970 PSMs with estimated false discovery rate of 1%. Among them were 603 genes encoding 778 mutated peptides. Seven genes encoding stomach cancer-related mutations were additionally identified, when compared with the previous cohort studies, suggesting that the proposed method can expand our horizon in mutation analyses.

Short Abstract: Motivation: Peptidic Natural Products (PNPs) are considered a promising compound class that has many applications in medicine. Recently developed mass spectrometry-based pipelines are transforming PNP discovery into a high-throughput technology. However, the current computational methods for PNP identification via database search of mass spectra are still in their infancy and could be substantially improved. Results: Here we present NPS, a statistical learning-based approach for scoring PNP–spectrum matches. We incorporated NPS into two leading PNP discovery tools and benchmarked them on millions of natural product mass spectra. The results demonstrate more than 45% increase in the number of identified spectra and 20% more found PNPs at a false discovery rate of 1%. Availability: NPS is available as a command line tool and as a web application at http://cab.spbu.ru/software/NPS

Short Abstract: The MetaboLights database is an international metabolomics repository recommended by many leading journals including Nature, PLOS and Metabolomics. It hosts a wealth of cross-species, cross-technique, open access experimental research. As a part of our ongoing efforts to encourage both submission and re-usability, the MetaboLights team at EMBL-EBI has re-developed the website and introduced a new tool to edit and submit studies online. This provides MetaboLights users and curators with an intuitive and easy to use interface to create, edit, annotate and interrogate studies online. The aim is to provide a rich description of the experimental metadata including study characteristics, protocols, technology and related factors. Metadata descriptions are enhanced by mapping this information to controlled ontologies repositories. Capturing such a complete data set benefits the community by making results findable, accessible, interoperable and reusable. We are now developing a workbench for researchers to do metabolomics analysis in standardized workflows, both on their own studies and as community annotation on legacy studies. We would welcome the opportunity to interact with the computational mass spectrometry community to present what we have and to develop collaborations going forward.

Short Abstract: Relevant advances have been done on the data analysis pipeline of Liquid Chromatography Mass Spectrometry (LC-MS) based metabolomics experiments. Despite all published efforts, uncontrolled sources of variance may introduce fluctuations and biases along the main elements of the experiment. Although identification and control of those sources is essential to ensure high data quality, there is a lack on harmonized tools to characterize the experiment quality. We present a residuals analysis approach based on Evolving Window Principal Component Analysis (EWPCA) to detect unknown sources of variance. Variances captured on a window of Total Ion Chromatograms (TIC) along the experiment are used to project the immediate incoming sample. Multivariate residuals of the projection serve to monitor dynamic deviations from the regular behavior. The latter is characterized using the samples that conform the evolving window and thought as a null distribution. Correlation test of the residuals with the experimental design and its divergence from the null are proposed as metrics to quantify the quality of the experiment. The strategy has been validated on a urine-based LC-MS dataset with real and simulated events. For example, method identifies batch effect (p.val = 7.17e-6) after correcting through SVA (COMBAT) suggesting the presence of a coupled variance.

Short Abstract: Identification of the RNA-bound proteome has been greatly facilitated by protocols linking RNA interactome capture with mass spectrometry. There, RBPs are covalently coupled to RNA by UV crosslinking. Following RNA pulldown with oligodT beads, RNA-bound proteins are identified by mass spectrometry. Traditionally, protein intensities from cross-linked samples are compared against protein intensities from non-crosslinked samples. Proteins that have a significant, positive fold-change are considered RBP candidates. This analysis however poses some issues. First, strong RNA-binders are likely to also be detected in the non-crosslinked samples, which creates the risk of omitting some of the best candidates. Second, a statistical analyses for the proteins that are not measured in non-crosslinked samples is not possible, although those are highly promising RBP candidates. Lastly, the need for generating non-crosslinked samples is labor- and cost-intensive. Here, we propose an alternative approach using total proteome measurements as controls. Our approach is based on comparing protein ranks between pulldown samples and input lysates (total proteome), which eliminates the need to create non-crosslinked samples. Based on two independent datasets, we show that using the total proteome as a control increases the fraction of known RBPs that are successfully identified compared to the classical approach.

Short Abstract: Gene expression differs among individuals and populations, and is thought to be a major determinant of phenotypic variation. Although variation and genetic loci responsible for RNA expression levels have been analyzed extensively in human populations, our knowledge is limited regarding the differences in human protein abundance and the genetic basis for this difference. Here we have used quantitative mass spectrometry to determine protein abundance in human tissues. We describe the variation in proteins between individuals. Integrating genomic and transcriptomic data, we discuss genetic regulation of protein abundance, and consider approaches for integrating protein abundance in studies of complex traits and diseases.

Short Abstract: For many years it has been known that bodily odors and breath confer valuable information about human health and underlying mechanisms to doctors. With the advent of Multi-Capillary-Column Ion-Mobility-Spectrometry a valuable diagnostic tool was introduced to measure the composition of these gases. Most crucially, the goal of breath analysis is the discovery of metabolite patterns in disease or phenotype. Even though MCC-IMS has seen increased rates in adoption, until now the practice of breath analysis requires tedious manual annotation and customized statistical analysis for interpretation. We present BALSAM - a web-platform to greatly facilitate and automate this process, providing a tool for pre-processing, feature extraction, visualization and pattern discovery for clinicians. We hope to encourage further development in the field and provide medical practitioners with a valuable tool to guide further research.

Short Abstract: Antimicrobial resistance (AMR), especially multidrug resistance, is one of the most serious global threats facing public health. We performed a proof of concept study assessing the suitability of shotgun proteomics as an alternative or supplementary approach to whole-genome sequencing (WGS) for predicting AMR. We used previously published shotgun proteomics and WGS data on four isolates of Campylobacter jejuni to perform AMR prediction by searching the Comprehensive Antibiotic Resistance Database, and assessed their predictive ability relative to traditional phenotypic testing measured by minimum inhibitory concentration. Both genomic and proteomic approaches identified the wild type and variant molecular determinants responsible for resistance to tetracycline and ciprofloxacin, in agreement with phenotypic testing. In contrast, the genomic method identified the presence of the β-lactamase gene, blaOXA-61, in three isolates. However, its corresponding protein product was detected in only a single isolate, consistent with results obtained from phenotypic testing. In our proof-of-concept study, proteomic methods are comparable to, and in certain cases, better than genomics methods for AMR prediction, as judged by phenotypic testing. The result underscores the value of a large-scale evaluation of proteomic testing as a potential alternative or supplementary approach to genomic AMR prediction.

Short Abstract: Proteotypes, like genotypes, have been found to vary between individuals in several studies, but consistent molecular functional traits across studies remain to be quantified. In a meta-analysis of 11 proteomics datasets from human and mice, we use co-variation of proteins in known functional modules across datasets and individuals to obtain a consensus landscape of proteotype variation. We find that individuals differ considerably in both protein complex abundances and stoichiometry. We disentangle genetic and environmental factors impacting these metrics, with genetic sex and specific diets together explaining 12.5% and 11.6% of the observed variation of complex abundance and stoichiometry, respectively. Sex-specific differences, for example, include various proteins and complexes, where the respective genes are not located on sex-specific chromosomes. Diet-specific differences, added to the individual genetic backgrounds, might become a starting point for personalized proteotype modulation towards desired features.