PaperPlayer biorxiv bioinformatics

Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.08.26.268268v1?rss=1

Authors: Drivas, T. G., Lucas, A., Ritchie, M. D.

Abstract:

Summary: Genomic studies increasingly integrate expression quantitative trait loci (eQTL) information into their analysis pipelines, but few tools exist for the visualization of colocalization between eQTL and GWAS results. To address this issue, we developed the intuitive R package eQTpLot, which takes as input GWAS and eQTL summary statistics to generate a series of plots visualizing colocalization, correlation, and enrichment between eQTL and GWAS signals for a given gene-trait pair. We believe eQTpLot will prove a useful tool for investigators seeking a convenient and customizable visualization of genomic data colocalization. Availability and Implementation: The eQTpLot R package and tutorial are available at https://github.com/RitchieLab/eQTpLot Contact: [email protected]

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.08.25.267021v1?rss=1

Authors: Kurtenbach, S., Dollar, J. J., Durante, M. A., Harbour, J. W.

Abstract:

Single cell RNA sequencing (scRNA-seq) has been a transformative technology in many research fields. Dimensional reduction techniques such as UMAP and tSNE are used to visualize scRNA-seq data in two or three dimensions in order for cells to be clustered in biologically meaningful ways. Subsequently, gene expression is frequently mapped onto these plots to show the distribution of expression across cell types. However, plotting each cell with only one color leads to repetitive and unintuitive representations. Here, we present Pie Party, which allows scRNA-seq data to be plotted such that every cell is represented as a pie chart, where every slice in the pie charts correspond to the gene expression of individual genes, which allows for the simultaneous visualization of the expression of multiple genes and gene networks. The resulting figures are information dense, space efficient and highly intuitive. PieParty is publicly available on GitHub at https://github.com/harbourlab/PieParty.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.08.25.266072v1?rss=1

Authors: Watson, J., Schwartz, J.-M., Francavilla, C.

Abstract:

Mass spectrometry-based quantitative phosphoproteomics has become an essential approach in the study of cellular processes such as cell signaling. Commonly used methods to analyze phosphoproteomics datasets depend on generic, gene-centric annotations such as Gene Ontology terms and do not account for the function of a protein in a given phosphorylation state. Thus, analysis of phosphoproteomics data is hampered by a lack of phosphorylated site-specific annotations. Here, we propose a method that combines shotgun phosphoproteomics data, protein-protein interactions and functional annotations from ontologies or pathway databases into a heterogeneous multilayer network. Phosphorylation sites are then associated to potential functions using a random walk on heterogeneous network (RWHN) algorithm. We validated our approach using a dataset modelling the MAPK/ERK pathway and were able to associate differentially regulated sites on the same protein to their previously described functions. Random permutation analysis proved that these associations were not random and were determined by the network topology. We then applied the RWHN algorithm to two previously published datasets; the algorithm was able to reproduce the experimentally validated conclusions from the publications, and associate phosphorylation sites with both new and known functions based on their regulatory patterns. The approach described here provides a robust, phosphorylation site-centric method to analyzing phosphoproteomics data and identifying potential context-specific functions for sites with similar phosphorylation profiles.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.08.24.265488v1?rss=1

Authors: Hudson, M. L., Samudrala, R.

Abstract:

Drug repurposing, the practice of utilizing existing drugs for novel clinical indications, has tremendous potential for improving human health outcomes and increasing therapeutic development efficiency. The goal of multidisease multitarget drug repurposing, also known as shotgun drug repurposing, is to develop platforms that assess the therapeutic potential of each existing drug for every clinical indication. Our Computational Analysis of Novel Drug Opportunities (CANDO) platform for shotgun multitarget repurposing implements several pipelines via large scale modelling and simulation of interactions between comprehensive libraries of drugs/compounds and protein structures. In these pipelines, each drug is described by an interaction signature that is then compared to all other signatures that are then sorted and ranked based on similarity. Pipelines within the platform are benchmarked based on their ability to recover known drugs for all indications in our library, and predictions are generated based on the hypothesis that (novel) drugs with similar signatures may be repurposed for the same indication(s). The drug-protein interactions in the platform used to create the drug-proteome signatures may be determined by any screening or docking method but the primary approach used thus far has been an in house similarity docking protocol. In this study, we calculated drug-proteome interaction signatures using the publicly available molecular docking method Autodock Vina and created hybrid decision tree pipelines that combined our original bio- and cheminformatic approach with the goal of assessing and benchmarking their drug repurposing capabilities and performance. The hybrid decision tree pipeline outperformed the corresponding two docking-based pipelines it was synthesized from, yielding an average indication accuracy of 13.3% at the top10 cutoff (the most stringent), relative to 10.9% and 7.1% for its constituent pipelines, and a random control accuracy of 2.2%. We demonstrate that docking based virtual screening pipelines have unique performance characteristics and that the CANDO shotgun repurposing paradigm is not dependent on a specific docking method. Our results also provide further evidence that multiple CANDO pipelines can be synthesized to enhance drug repurposing predictive capability relative to their constituent pipelines. Overall, this study indicates that pipelines consisting of varied docking based signature generation methods can capture unique and useful signal for accurate comparison of drug-proteome interaction signatures, leading to improvements in the benchmarking and predictive performance of the CANDO shotgun drug repurposing platform.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.08.24.265298v1?rss=1

Authors: Sokolowski, D. J., Faykoo-Martinez, M., Erdman, L., Hou, H., Chan, C., Zhu, H., Holmes, M. M., Goldenberg, A., Wilson, M. D.

Abstract:

RNA sequencing (RNA-seq) is widely used to identify differentially expressed genes (DEGs) and reveal biological mechanisms underlying complex biological processes. RNA-seq is often performed on heterogeneous samples and the resulting DEGs do not necessarily indicate the cell types where the differential expression occurred. While single-cell RNA-seq (scRNA-seq) methods solve this problem, technical and cost constraints currently limit its widespread use. Here we present single cell Mapper (scMappR), a method that assigns cell-type specificity scores to DEGs obtained from bulk RNA-seq by integrating cell-type expression data generated by scRNA-seq and existing deconvolution methods. After benchmarking scMappR using RNA-seq data obtained from sorted blood cells, we asked if scMappR could reveal known cell-type specific changes that occur during kidney regeneration. We found that scMappR appropriately assigned DEGs to cell-types involved in kidney regeneration, including a relatively small proportion of immune cells. While scMappR can work with any user supplied scRNA-seq data, we curated scRNA-seq expression matrices for ~100 human and mouse tissues to facilitate its use with bulk RNA-seq data alone. Overall, scMappR is a user-friendly R package that complements traditional differential expression analysis available at CRAN.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.08.24.265702v1?rss=1

Authors: Maksimovic, J., Oshlack, A., Phipson, B.

Abstract:

DNA methylation is one of the most commonly studied epigenetic marks, due to its role in disease and development. Illumina methylation arrays have been extensively used to measure methylation across the human genome. Methylation array analysis has primarily focused on preprocessing, normalisation and identification of differentially methylated CpGs and regions. GOmeth and GOregion are new methods for performing unbiased gene set testing following differential methylation analysis. Benchmarking analyses demonstrate GOmeth outperforms other approaches and GOregion is the first method for gene set testing of differentially methylated regions. Both methods are publicly available in the missMethyl Bioconductor R package.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.08.24.264713v1?rss=1

Authors: Elmes, K., Schmich, F., Szczurek, E., Jenkins, J., Beerenwinkel, N., Gavryushkin, A.

Abstract:

The treatment of complex diseases often relies on combinatorial therapy, a strategy where drugs are used to target multiple genes simultaneously. Promising candidate genes for combinatorial perturbation often constitute epistatic genes, i.e., genes which contribute to a phenotype in a non-linear fashion. Experimental identification of the full landscape of genetic interactions by perturbing all gene combinations is prohibitive due to the exponential growth of testable hypotheses. Here we present a model for the inference of pairwise epistatic, including synthetic lethal, gene interactions from siRNA-based perturbation screens. The model exploits the combinatorial nature of siRNA-based screens resulting from the high numbers of sequence-dependent off-target effects, where each siRNA apart from its intended target knocks down hundreds of additional genes. We show that conditional and marginal epistasis can be estimated as interaction coefficients of regression models on perturbation data. We compare two methods, namely glinternet and xyz, for selecting non-zero effects in high dimensions as components of the model, and make recommendations for the appropriate use of each. For data simulated from real RNAi screening libraries, we show that glinternet successfully identifies epistatic gene pairs with high accuracy across a wide range of relevant parameters for the signal-to-noise ratio of observed phenotypes, the effect size of epistasis and the number of observations per double knockdown. xyz is also able to identify interactions from lower dimensional data sets (fewer genes), but is less accurate for many dimensions. Higher accuracy of glinternet, however, comes at the cost of longer running time compared to xyz. The general model is widely applicable and allows mining the wealth of publicly available RNAi screening data for the estimation of epistatic interactions between genes. As a proof of concept, we apply the model to search for interactions, and potential targets for treatment, among previously published sets of siRNA perturbation screens on various pathogens. The identified interactions include both known epistatic interactions as well as novel findings.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.08.25.266049v1?rss=1

Authors: Fleck, J., Pontes de Miranda, S., Baiao, F., Macaira, P., Piccolo, S. R.

Abstract:

Cancer cell lines, which are cell cultures developed from tumor samples, represent one of the least expensive and most studied preclinical models for drug development. Accurately predicting drug response for a given cell line based on molecular features may help to optimize drug-development pipelines and explain mechanisms behind treatment responses. In this study, we focus on DNA methylation profiles as one type of molecular feature that is known to drive tumorigenesis and modulate treatment responses. Using genome-wide, DNA methylation profiles from 987 cell lines from the Genomics of Drug Sensitivity in Cancer database, we applied machine-learning algorithms to evaluate the potential to predict cytotoxic responses for eight anti-cancer drugs. We compared the performance of five classification algorithms and four regression algorithms that use diverse methodologies, including tree-, probability-, kernel-, ensemble-, and distance-based approaches. For both types of algorithm, we artificially subsampled the data to varying degrees, aiming to understand whether training models based on relatively extreme outcomes would yield improved performance. We also performed an information-gain analysis to examine which genes were most predictive of drug responses. Finally, we used tumor data from The Cancer Genome Atlas to evaluate the feasibility of predicting clinical responses in humans based on models derived from cell lines. When using classification or regression algorithms to predict discrete or continuous responses, respectively, we consistently observed excellent predictive performance when the training and test sets both consisted of cell-line data. However, classification models derived from cell-line data failed to generalize effectively for tumors.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.08.25.265983v1?rss=1

Authors: Sciabola, S., Xi, H., Cruz, D., Cao, Q., Lawrence, C., Zhang, T., Rotstein, S., Hughes, J. D., Caffrey, D. R., Stanton, R. V.

Abstract:

PFRED a software application for the design, analysis, and visualization of antisense oligonucleotides and siRNA is described. The software provides an intuitive user-interface for scientists to design a library of siRNA or antisense oligonucleotides that target a specific gene of interest. Moreover, the tool facilitates the incorporation of various design criteria that have been shown to be important for stability and potency. PFRED has been made available as an open-source project so the code can be easily modified to address the future needs of the oligonucleotide research community. A compiled version is available for downloading at https://github.com/pfred/pfred-gui/releases as a java Jar file. The source code and the links for downloading the precompiled version can be found at https://github.com/pfred .

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.08.24.265827v1?rss=1

Authors: Prasad, P., Prakash, S., Sahu, K., Singh, B., Shukla, S., Mishra, H., Khan, D. N., Prakash, O., Bhatt, M., Barik, S., Asif, M. H., Sawant, S. V., Jain, A., Bag, S. K.

Abstract:

COVID-19 leads to a global emergency that causes more than 7 million causalities until mid-August throughout the world. In India alone, 2 million confirmed cases were reported that increased abruptly day by day with the lowest fatality rate. The availability of a large number of Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV2) genome in the public domain provides a great opportunity to study mutational changes in COVID-19 genomes in Indian populations. In this study, we sequenced the genomes of SARS-CoV2 viruses isolated from 47 individuals from 13 districts of Uttar Pradesh (UP), the largest state of India using Third Generation Sequencing Technology. We further did the phylogenetic clustering of UP state of Indian SARS-CoV2 genomes revealed a perceivable point that no UP samples were aligned on the USA defined clade where the fatality rate is high. We also identified 56 distinctive Single Nucleotide Polymorphism variations in UP state that majorly clustered into two groups which shows the deleterious effects on the genome. Additionally, we conducted the mutation analysis of the 2323 SARS-CoV2 genome of different states of India from the Global Initiative on Sharing All Influenza Data (GISAID) where we find ~80% unique mutations rate in each sample of the Indian population. Thus, this is the first extensive mutational study of the largest state of Indian populations in which we report the novel deleterious SNPs in virus genome along with the other states which access the less infectious form of SARS-CoV2 genome through synonymous to nonsynonymous mutation variation.

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