PaperPlayer biorxiv bioinformatics

Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.08.07.240689v1?rss=1

Authors: Moraga, C., Sanchez, E., Galvao Ferrarini, M., Gutierrez, R. A., Vidal, E. A., Sagot, M.-F.

Abstract:

MicroRNAs (miRNAs) are small non-coding RNAs that are key players in the regulation of gene expression. In the last decade, with the increasing accessibility of high-throughput sequencing technologies, different methods have been developed to identify miRNAs, most of which rely on pre-existing reference genomes. However, when a reference genome is absent or is not of high quality, such identification becomes more difficult. In this context, we developed BrumiR, an algorithm that is able to discover miRNAs directly and exclusively from sRNA-seq data. We benchmarked BrumiR with datasets encompassing animal and plant species using real and simulated sRNA-seq experiments. The results demonstrate that BrumiR reaches the highest recall for miRNA discovery, while at the same time being much faster and more efficient than the state-of-the-art tools evaluated. The latter allows BrumiR to analyze a large number of sRNA-seq experiments, from plants or animals species. Moreover, BrumiR detects additional information regarding other expressed sequences (sRNAs, isomiRs, etc.), thus maximizing the biological insight gained from sRNA-seq experiments. Finally, when a reference genome is available, BrumiR provides a new mapping tool (BrumiR2ref) that performs an a posteriori exhaustive search to identify the precursor sequences. The code of BrumiR is freely available at https://github.com/camoragaq/BrumiR.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.08.07.240473v1?rss=1

Authors: Smith, M. S., Devarajan, K.

Abstract:

A problem that arises frequently in high-throughput biological studies is the assessment of technical reproducibility of data obtained under homogeneous experimental conditions. This is an important problem considering the significant growth in the number of high-throughput technologies that have become available to the researcher in the past decade. Although certain ad hoc and graphical methods for determining data quality have been in existence, these methods lack statistical rigor and are not broadly applicable across different technologies. There is an inherent need for the quantitative evaluation of the reproducibility of technical replicates from high-throughput compound and siRNA screening, next-generation sequencing and other modern "omics" studies. To this end, we have developed an approach that accounts for technical variability and potential asymmetry that arise naturally in the distribution of replicate data, and aids in the identification of outliers. Our methods for outlier detection rely on flexible statistical models, employ maximum likelihood methods for estimation and are broadly applicable to a variety of high-throughput biological studies. We discuss an adaptation of these methods when there are multiple replicates as well as current limitations to these techniques. We illustrate our methods using experimental data from high-throughput compound screening and protein expression studies as well as simulated data. Our methods are implemented in the R package replicateOutliers and are currently available at github.com/matthew-seth-smith/replicateOutliers.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.08.07.238279v1?rss=1

Authors: Zeller, M. A., Gauger, P. C., Arendsee, Z. W., Souza, C. K., Vincent, A. L., Anderson, T. K.

Abstract:

The antigenic diversity of influenza A virus (IAV) circulating in swine challenges the development of effective vaccines, increasing zoonotic threat and pandemic potential. High throughput sequencing technologies are able to quantify IAV genetic diversity, but there are no accurate approaches to adequately describe antigenic phenotypes. This study evaluated an ensemble of non-linear regression models to estimate virus phenotype from genotype. Regression models were trained with a phenotypic dataset of pairwise hemagglutination inhibition (HI) assays, using genetic sequence identity and pairwise amino acid mutations as predictor features. The model identified amino acid identity, ranked the relative importance of mutations in the hemagglutinin (HA) protein, and demonstrated good prediction accuracy. Four previously untested IAV strains were selected to experimentally validate model predictions by HI assays. Error between predicted and measured distances of uncharacterized strains were 0.34, 0.70, 2.19, and 0.17 antigenic units. These empirically trained regression models can be used to estimate antigenic distances between different strains of IAV in swine using sequence data. By ranking the importance of mutations in the HA, we provide criteria for identifying antigenically advanced IAV strains that may not be controlled by existing vaccines and can inform strain updates to vaccines to better control this pathogen.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.08.05.237206v1?rss=1

Authors: Anaya, J., Sidhom, J.-W., Cummings, C. A., Baras, A. S., The AACR Project GENIE Consortium

Abstract:

Deep learning has the ability to extract meaningful features from data given enough training examples. Large scale genomic data are well suited for this class of machine learning algorithms; however, for many of these data the labels are at the level of the sample instead of at the level of the individual genomic measures. To leverage the power of deep learning for these types of data we turn to a multiple instance learning framework, and present an easily extensible tool built with TensorFlow and Keras. We show how this tool can be applied to somatic variants (featurizing genomic position and sequence context), and accurately classify samples according to whether they contain a specific variant (hotspot or tumor suppressor) or whether they contain a type of variant (microsatellite instability). We then apply our model to the calibration of tumor mutational burden (TMB), an increasingly important metric in the field of immunotherapy, across a variety of commonly used gene panels. Regardless of the panel, we observed improvements in regression to the gold standard whole exome derived value for this metric, with additional performance benefits as more data were provided to the model (such as noncoding variants from panel assays). Our results suggest this framework could lead to improvements in a range of tasks where the sample level metric is determined by the aggregation of a set of genomic measures, such as somatic mutations that we focused on in this study.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.08.05.237412v1?rss=1

Authors: Taechawattananant, P., Yoshii, K., Ishihama, Y.

Abstract:

Recent advances in liquid chromatography/mass spectrometry (LC/MS) technology have notably improved the sensitivity, resolution, and speed of proteome analysis, resulting in increasing demand for more sophisticated algorithms to interpret highly complex mass spectrograms. We propose a novel application of mass spectrogram decomposition with a group sparsity constraint for joint identification and quantification of peptides and proteins. By incorporating protein-peptide hierarchical relationship knowledge, the isotopic distribution profiles of peptide ions, the learned noise subspace, and predicted retention time initialization into standard non-regularized approach, we have significantly improved the accuracy of analysis. In benchmarking studies, our proteomic mass spectrogram decomposition (protMSD) showed excellent agreement [3277 peptide ions (94.79%) and 493 proteins (98.21%)] with the results of conventional identification and quantification based on Mascot and Skyline of E. coli cell lysate. This is the first application of proteomic mass spectrogram decomposition as a tool for LC/MS-based identification and quantification. Since pre-processing, such as thresholding, is not required, protMSD can maximize the efficiency of both protein and peptide identification and quantification.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.08.05.236711v1?rss=1

Authors: Tran, V. N.-N., Shams, A., Ascioglu, S., Martinecz, A., Liang, J., Clarelli, F., Mostowy, R., Cohen, T., Abel zur Wiesch, P.

Abstract:

Motivation: As antibiotic resistance creates a significant global health threat, we need not only to accelerate the development of novel antibiotics but also to develop better treatment strategies using existing drugs to improve their efficacy and prevent the selection of further resistance. We require new tools to rationally design dosing regimens to from data collected in early phases of antibiotic and dosing development. Mathematical models such as mechanistic pharmacodynamic drug-target binding explain mechanistic details of how the given drug concentration affects its targeted bacteria. However, there are no available tools in the literature that allows non-quantitative scientists to develop computational models to simulate antibiotic-target binding and its effects on bacteria. Results: In this work, we have devised an extension of a mechanistic binding-kinetic model to incorporate clinical drug concentration data. Based on the extended model, we develop a novel and interactive web-based tool that allows non-quantitative scientists to create and visualize their own computational models of bacterial antibiotic target-binding based on their considered drugs and bacteria. We also demonstrate how Rifampicin affects bacterial populations of Tuberculosis (TB) bacteria using our vCOMBAT tool. Availability: vCOMBAT online tool is publicly available at https://combat-bacteria.org/.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.08.06.240101v1?rss=1

Authors: St Clair, R., Teti, M., Pavlovic, M., Hahn, W., Barenholtz, E.

Abstract:

Computer-aided rational vaccine design (RVD) and synthetic pharmacology are rapidly developing fields that leverage existing datasets for developing compounds of interest. Computational proteomics utilizes algorithms and models to probe proteins for functional prediction. A potentially strong target for such a computational approach is autoimmune antibodies which are the result of broken tolerance in the immune system where it cannot distinguish self from non-self resulting in attack of its own structures (proteins and DNA, mainly). The information on structure, function and pathogenicity of autoantibodies may assist in engineering RVD against autoimmune diseases. Current computational approaches exploit large datasets curated with extensive domain knowledge, most of which include the need for many computational resources and have been applied indirectly to problems of interest for DNA, RNA, and monomer protein binding. Here, we present a novel method for discovering potential binding sites. We employed long short-term memory (LSTM) models trained on FASTA primary sequences directly to predict protein binding in DNA-binding hydrolytic antibodies (abzymes). We also employed CNN models applied to the same dataset. While the CNN model outperformed the LSTM on the primary task of binding prediction, analysis of internal model representations of both models showed that the LSTM models highlighted sub-sequences that were more strongly correlated with sites known to be involved in binding. These results demonstrate that analysis of internal processes of recurrent neural network models may serve as a powerful tool for primary sequence analysis.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.08.06.240044v1?rss=1

Authors: Jiang, J., Wang, C., Qi, R., Fu, H., Ma, Q.

Abstract:

Summary: Alzheimer's disease (AD) is a progressive neurodegenerative disorder of the brain and the most common form of dementia among the elderly. The single-cell RNA-sequencing (scRNA-Seq) and single-nucleus RNA-sequencing (snRNA-Seq) techniques are extremely useful for dissecting the function/dysfunction of highly heterogeneous cells in the brain at the single-cell level, and the corresponding data analyses can significantly improve our understanding of why particular cells are vulnerable in AD. We developed an integrated database named scREAD (single-cell RNA-Seq database for Alzheimer's Disease), which is the first database dedicated to the management of all the existing scRNA-Seq and snRNA-Seq datasets from human postmortem brain tissue with AD and mouse models with AD pathology. scREAD provides comprehensive analysis results for 55 datasets from eight brain regions, including control atlas construction, cell type prediction, identification of differentially expressed genes, and identification of cell-type-specific regulons. Availability and Implementation: scREAD is a one-stop and user-friendly interface and freely available at https://bmbls.bmi.osumc.edu/scread/. The backend workflow can be downloaded from https://github.com/OSU-BMBL/scread/tree/master/script, to enable more discovery-driven analyses. Contact: [email protected] or [email protected]

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.08.06.239566v1?rss=1

Authors: Smail, M. A., Wu, X., Henkel, N. D., Eby, H. M., Herman, J. P., McCullumsmith, R. E., Shukla, R.

Abstract:

The common molecular mechanisms underlying psychiatric disorders are not well understood. Prior attempts to assess the pathological mechanisms responsible for psychiatric disorders have been limited by biased selection of comparable disorders, datasets as well as challenges associated with data normalization. However, publicly available databases offer a unique opportunity to expand such investigations both in terms of the number and types of diseases. Here, we used DisGeNET, a database of over 24,000 gene-disease associations to investigate the similarities and dissimilarities associated with enrichment of pathways, cell-types, drug targets, and human chromosomes within an unbiased cluster of psychiatric disorders. We show that cognition and neurotransmission related pathways are involved across all disorders, whereas those associated with immune system and signal-response coupling (cell-surface receptors, signal-transduction, gene-expression, and metabolic process) are associated with few disorders of the cluster. The drug-target based enrichment confirms the involvement of neurotransmission related changes across these disorders. At cell-type level, dendrite targeting interneurons, across all layers, are most involved across all disorders. Finally, using a clustering-based similarity index, we showed that the similarity between the disorders are influenced most at the chromosomal level and to some extent at the cellular level. Collectively, the results provide a comprehensive comparison of many psychiatric diseases in an unbiased manner and expand our understanding of the cellular and molecular pathologies associated with similar and comorbid psychiatric disorders.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.08.06.240390v1?rss=1

Authors: Ranallo-Benavidez, T. R., Lemmon, Z. H., Soyk, S., Aganezov, S., Salerno, W. J., McCoy, R. C., Lippman, Z. B., Schatz, M. C., Sedlazeck, F. J.

Abstract:

An increasingly important scenario in population genetics is when a large cohort has been genotyped using a low-resolution approach (e.g. microarrays, exome capture, short-read WGS), from which a few individuals are selected for resequencing using a more comprehensive approach, especially long-read sequencing. The subset of individuals selected should ensure that the captured genetic diversity is fully representative and includes variants across all subpopulations. For example, human variation has historically been focused on individuals with European ancestry, but this represents a small fraction of the overall diversity. To address this goal, SVCollector identifies the optimal subset of individuals for resequencing. SVCollector analyzes a population-level VCF file from a low resolution genotyping study. It then computes a ranked list of samples that maximizes the total number of variants present from a subset of a given size. To solve this optimization problem, SVCollector implements a fast greedy heuristic and an exact algorithm using integer linear programming. We apply SVCollector on simulated data, 2504 human genomes from the 1000 Genomes Project, and 3024 genomes from the 3K Rice Genomes Project and show the rankings it computes are more representative than widely used naive strategies. Notably, we show that when selecting an optimal subset of 100 samples in these two cohorts, SVCollector identifies individuals from every subpopulation while naive methods yield an unbalanced selection. Finally, we show the number of variants present in cohorts of different sizes selected using this approach follows a power-law distribution that is naturally related to the population genetic concept of the allele frequency spectrum, allowing us to estimate the diversity present with increasing numbers of samples.

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