PaperPlayer biorxiv bioinformatics

Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.11.11.378091v1?rss=1

Authors: Freiberger, M. I., Wolynes, P. G., Ferreiro, D. U., Fuxreiter, M.

Abstract:

Disordered proteins can fold into a well-defined structure upon binding but these complexes are often fuzzy: the originally disordered partner adopts different binding modes when bound to different partners. Here we perform a systematic analysis of 160 proteins that form fuzzy complexes and demonstrate that the disordered partner displays a high degree of frustration in both the free and bound states. Although the folding of disordered regions upon binding reduces frustration relative to that of the unbound state, the interactions at the binding interface do not become fully optimized. In addition, we show that sub-optimal interactions lead to alternative frustration patterns in the complexes with different partners. These results demonstrate that disordered proteins do not always achieve fully optimal interactions in their complexes and their residual frustration leads to interaction versatility with different partners.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.11.11.378133v1?rss=1

Authors: Ebler, J., Clarke, W. E., Rausch, T., Audano, P. A., Houwaart, T., Korbel, J., Eichler, E. E., Zody, M. C., Dilthey, A. T., Marschall, T.

Abstract:

Typical analysis workflows map reads to a reference genome in order to detect genetic variants. Generating such alignments introduces references biases, in particular against insertion alleles absent in the reference and comes with substantial computational burden. In contrast, recent k-mer-based genotyping methods are fast, but struggle in repetitive or duplicated regions of the genome. We propose a novel algorithm, called PanGenie, that leverages a pangenome reference built from haplotype-resolved genome assemblies in conjunction with k-mer count information from raw, short-read sequencing data to genotype a wide spectrum of genetic variation. The given haplotypes enable our method to take advantage of linkage information to aid genotyping in regions poorly covered by unique k-mers and provides access to regions otherwise inaccessible by short reads. Compared to classic mapping-based approaches, our approach is more than 4x faster at 30x coverage and at the same time, reached significantly better genotype concordances for almost all variant types and coverages tested. Improvements are especially pronounced for large insertions (> 50bp), where we are able to genotype > 99.9% of all tested variants with over 90% accuracy at 30x short-read coverage, where the best competing tools either typed less than 60% of variants or reached accuracies below 70%. PanGenie now enables the inclusion of this commonly neglected variant type in downstream analyses.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.11.12.379941v1?rss=1

Authors: Rowland, B., Huh, R., Hou, Z., Hu, M., Shen, Y., Li, Y.

Abstract:

Hi-C data provide population averaged estimates of three-dimensional chromatin contacts across cell types and states in bulk samples. To effectively leverage Hi-C data for biological insights, we need to control for the confounding factor of differential cell type proportions across heterogeneous bulk samples. We propose a novel unsupervised deconvolution method for inferring cell type composition from bulk Hi-C data, the Two-step Hi-c UNsupervised DEconvolution appRoach (THUNDER). We conducted extensive real data based simulations to test THUNDER constructed from published single-cell Hi-C (scHi-C) data. THUNDER more accurately estimates the underlying cell type proportions when compared to both supervised and unsupervised deconvolution methods including CIBERSORT, TOAST, and NMF. THUNDER will be a useful tool in adjusting for varying cell type composition in population samples, facilitating valid and more powerful downstream analysis such as differential chromatin organization studies. Additionally, THUNDER estimates cell-type-specific chromatin contact profiles for all cell types in bulk Hi-C mixtures. These estimated contact profiles provide a useful exploratory framework to investigate cell-type-specificity of the chromatin interactome while experimental data is still sparse.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.11.11.378083v1?rss=1

Authors: Ahmed, S. R., Banik, A., Anni, S. M., Chowdhury, M. M. H.

Abstract:

The ZIKA virus has caused a heavy concern everywhere the globe because of its high infectivity and mortality rate. Still, there's no specific drug or preventive medication to treat ZIKA infection despite comprehensive analysis by the researchers. This study was designed to demonstrate the efficacy of some plant derived bioactive compounds against ZIKV by using both structure and ligand based virtual screening methods. A number of 35 plant metabolites were screened against ZIKA NS2B-NS3 protease (5LC0), Envelop protein (5JHM), Capsid protein (5YGH) and NS5 RNA-dependent RNA polymerase protein (5U04) employing molecular docking approach. Results showed that there have been four metabolites, i.e. Chicoric acid, Luteone, Reserpine and Rosmarinic acid provide highest binding affinity to targeted ZIKV proteins. Crucial binding sites and drug surface hotspots are unraveled for every targeted viral protein. The ADME study showed that neither of the candidate compounds had side effects that would reduce their drug-like properties. As compared, the toxicity pattern analysis has unmasked the non-toxic essence of top drug candidates. The RMSD values of ligand-macromolecule complexes were 2 Angstrom apart from Envelop protein- Chicoric Acid, although the RMSF values showed normal atomic fluctuations within the molecular dynamics analysis, with the exception of Envelop protein- Chicoric Acid. The expected majority of the target class the highest drug candidates is enzyme classes (e.g. protease, hydrolase, phosphatase). In addition, the drug similarity prediction revealed several structural analogs from drugbank such as Isoformononetin (DB04202), Deserpidine (DB01089) and Rescinnamine (DB01180) etc. and these analogs could even be an option for the treatment of ZIKV infections. The study can pave the way for the creation of effective ZIKV medications and preventive measures. We highly recommend further in vivo trials for the experimental validation of our findings.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.11.11.376467v1?rss=1

Authors: Chattopadhyay, S., Karlsson, J., Valind, A., Andersson, N., Gisselsson, D.

Abstract:

To understand the evolutionary dynamics of cancer, clonal deconvolution of mutational landscapes across multiple biopsies from the same patient is crucial. However, the frequencies of mutated alleles are often distorted by variation in copy number of mutated loci as well as the purity across samples. We present a semi-supervised algorithm that normalizes for purity and incorporates allelic composition with bulk sequencing to reliably segregate clonal/subclonal variants even at low sequencing depth (~50x). In presence of at least one tumor sample with >70% purity, it deconvolves samples down to ~40% purity, allowing robust tracking of mutated cell populations through cancer evolution.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.11.11.377879v1?rss=1

Authors: Tesi, N., van der Lee, S. J., Hulsman, M., Holstege, H., Reinders, M.

Abstract:

Genetic association studies are largely used to study the genetic basis of numerous traits. However, the interpretation of genetic associations is often complex and requires the integration of multiple sources of annotation. We developed snpXplorer, a web-server application for exploring SNP-association statistics across human traits and functionally annotate sets of SNPs. snpXplorer allows superimposition of association statistics from multiple studies, and displays regional information including SNP-associations, structural variations, recombination rates, eQTL, linkage disequilibrium patterns, genes and gene-expressions per tissue. snpXplorer can be used to compare levels of association across different phenotypes, which may help the interpretation of variant consequences. Interestingly, given a list of SNPs, snpXplorer performs variant-to-gene mapping and gene-set overlap analysis to identify molecular pathways that are enriched in the genes likely associated with the variants of interest. Availability: snpXplorer is freely available at http://snpxplorer.eu.ngrok.io.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.11.12.379537v1?rss=1

Authors: Megahed, F. M., Jones-Farmer, L. A., Rigdon, S. E.

Abstract:

The COVID-19 pandemic in the U.S. has exhibited distinct waves, the first beginning in March 2020, the second beginning in early June, and additional waves currently emerging. Paradoxically, almost no county has exhibited this multi-wave pattern. We aim to answer three research questions: (1) How many distinct clusters of counties exhibit similar COVID-19 patterns in the time-series of daily confirmed cases?; (2) What is the geographic distribution of the counties within each cluster? and (3) Are county-level demographic, socioeconomic and political variables associated with the COVID-19 case patterns? We analyzed data from counties in the U.S. from March 1 to October 24, 2020. Time series clustering identified clusters in the daily confirmed cases of COVID-19. An explanatory model was used to identify demographic, socioeconomic and political variables associated the cluster patterns. Four patterns were identified from the timing of the outbreaks including counties experiencing a spring, an early summer, a late summer, and a fall outbreak. Several county-level demographic, socioeconomic, and political variables showed significant associations with the identified clusters. The timing of the outbreak is related both to the geographic location within the U.S. and several variables including age, poverty distribution, and political association. These results show that the reported pattern of cases in the U.S. is observed through aggregation of the COVID-19 cases, suggesting that local trends may be more informative. The timing of the outbreak varies by county, and is associated with important demographic, socioeconomic and geographic factors.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.11.12.380378v1?rss=1

Authors: Colquhoun, R. M., Hall, M. B., Lima, L., Roberts, L. W., Malone, K. M., Hunt, M., Letcher, B., Hawkey, J., George, S., Pankhurst, L., Iqbal, Z.

Abstract:

Bacterial genomes follow a U-shaped frequency distribution whereby most genomic loci are either rare (accessory) or common (core) - the alignable fraction of two genomes from a single species might be only 50%. Standard tools therefore analyse mutations only in the core genome, ignoring accessory mutations. We present a novel pan-genome graph structure and algorithms implemented in the software pandora, which approximates a sequenced genome as a recombinant of reference genomes, detects novel variation and then pan-genotypes multiple samples. Constructing a reference graph from 578 E. coli genomes, we analyse a diverse set of 20 E. coli isolates. We show, for rare variants, pandora recovers at least 13k more SNPs than single-reference based tools, achieving equal or better error rates with Nanopore as with Illumina data, and providing a stable framework for analysing diverse samples without reference bias. This is a significant step towards comprehensive analyses of bacterial genetic variation.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.11.12.344424v1?rss=1

Authors: An, H., Park, J.

Abstract:

Currently, more than 33 million peoples have been infected by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), and more than a million people died from coronavirus disease 2019 (COVID-19), a disease caused by the virus. There have been multiple reports of autoimmune and inflammatory diseases following SARS-CoV-2 infections. There are several suggested mechanisms involved in the development of autoimmune diseases, including cross-reactivity (molecular mimicry). A typical workflow for discovering cross-reactive epitopes (mimotopes) starts with a sequence similarity search between protein sequences of human and a pathogen. However, sequence similarity information alone is not enough to predict cross-reactivity between proteins since proteins can share highly similar conformational epitopes whose amino acid residues are situated far apart in the linear protein sequences. Therefore, we used a hidden Markov model-based tool to identify distant viral homologs of human proteins. Also, we utilized experimentally determined and modeled protein structures of SARS-CoV-2 and human proteins to find homologous protein structures between them. Next, we predicted binding affinity (IC50) of potentially cross-reactive T-cell epitopes to 34 MHC allelic variants that have been associated with autoimmune diseases using multiple prediction algorithms. Overall, from 8,138 SARS-CoV-2 genomes, we identified 3,238 potentially cross-reactive B-cell epitopes covering six human proteins and 1,224 potentially cross-reactive T-cell epitopes covering 285 human proteins. To visualize the predicted cross-reactive T-cell and B-cell epitopes, we developed a web-based application "Molecular Mimicry Map (3M) of SARS-CoV-2" (available at https://ahs2202.github.io/3M/). The web application enables researchers to explore potential cross-reactive SARS-CoV-2 epitopes alongside custom peptide vaccines, allowing researchers to identify potentially suboptimal peptide vaccine candidates or less ideal part of a whole virus vaccine to design a safer vaccine for people with genetic and environmental predispositions to autoimmune diseases. Together, the computational resources and the interactive web application provide a foundation for the investigation of molecular mimicry in the pathogenesis of autoimmune disease following COVID-19.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.11.11.377978v1?rss=1

Authors: Gursoy, G., Lu, N., Wagner, S., Gerstein, M.

Abstract:

With the recent increase in RNA sequencing efforts using large cohorts of individuals, studying allele-specific gene expression is becoming increasingly important. Here, we report that, despite not containing explicit variant information, a list of allele-specific gene names of an individual is enough to recover key variants and link the individual back to their genome or phenotype. This creates a privacy conundrum.

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