PaperPlayer biorxiv bioinformatics

Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.10.26.355073v1?rss=1

Authors: Quignot, C., Granger, P., Chacon, P., Guerois, R., Andreani, J.

Abstract:

The crucial role of protein interactions and the difficulty in characterising them experimentally strongly motivates the development of computational approaches for structural prediction. Even when protein-protein docking samples correct models, current scoring functions struggle to discriminate them from incorrect decoys. The previous incorporation of conservation and coevolution information has shown promise for improving protein-protein scoring. Here, we present a novel strategy to integrate atomic-level evolutionary information into different types of scoring functions to improve their docking discrimination. We applied this general strategy to our residue-level statistical potential from InterEvScore and to two atomic-level scores, SOAP-PP and Rosetta interface score (ISC). Including evolutionary information from as few as ten homologous sequences improves the top 10 success rates of these individual scores by respectively 6.5, 6 and 13.5 percentage points, on a large benchmark of 752 docking cases. The best individual homology-enriched score reaches a top 10 success rate of 34.4%. A consensus approach based on the complementarity between different homology-enriched scores further increases the top 10 success rate to 40%. All data used for benchmarking and scoring results, as well as pipelining scripts, are available at http://biodev.cea.fr/interevol/interevdata/ .

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.10.25.352625v1?rss=1

Authors: Gemayel, K., Lomsadze, A., Borodovsky, M.

Abstract:

Algorithms of ab initio gene finding were shown to make sufficiently accurate predictions in prokaryotic genomes. Nonetheless, for up to 15-25% of genes per genome the gene start predictions might differ even when made by the supposedly most accurate tools. To address this discrepancy, we have introduced StartLink+, an approach combining ab initio and multiple sequence alignment based methods. StartLink+ makes predictions for a majority of genes per genome (73% on average); in tests on sets of genes with experimentally verified starts the StartLink+ accuracy was shown to be 98-99%. When StartLink+ predictions made for a large set of prokaryotic genomes were compared with the database annotations we observed that on average the gene start annotations deviated from the predictions for ~5% of genes in AT-rich genomes and for 10-15% of genes in GC-rich genomes.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.10.25.354456v1?rss=1

Authors: Choi, M.-H., Sohn, J.-i., Yi, D., Menon, A. V., Kim, Y. J., Kyoung, S., Shin, S.-H., Na, B., Joung, J.-G., Yoon, S., Koh, Y., Baek, D., Kim, T.-M., Nam, J.-W.

Abstract:

Genome rearrangements often result in copy number alterations of cancer-related genes and cause the formation of cancer-related fusion genes. Current structural variation (SV) callers, however, still produce massive numbers of false positives (FPs) and require high computational costs. Here, we introduce an ultra-fast and high-performing somatic SV detector, called ETCHING, that significantly reduces the mapping cost by filtering reads matched to pan-genome and normal k-mer sets. To reduce the number of FPs, ETCHING takes advantage of a Random Forest classifier that utilizes six breakend-related features. We systematically benchmarked ETCHING with other SV callers on reference SV materials, validated SV biomarkers, tumor and matched-normal whole genomes, and tumor-only targeted sequencing datasets. For all datasets, our SV caller was much faster ([≥]15X) than other tools without compromising performance or memory use. Our approach would provide not only the fastest method for largescale genome projects but also an accurate clinically practical means for real-time precision medicine.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.10.25.354100v1?rss=1

Authors: Ouyang, J. F., Kamaraj, U. S., Cao, E. Y., Rackham, O. J. L.

Abstract:

Motivation: As the generation of complex single-cell RNA sequencing datasets becomes more commonplace it is the responsibility of researchers to provide access to these data in a way that can be easily explored and shared. Whilst it is often the case that data is deposited for future bioinformatic analysis many studies do not release their data in a way that is easy to explore by non-computational researchers. Results: In order to help address this we have developed ShinyCell, an R package that converts single-cell RNA sequencing datasets into explorable and shareable interactive interfaces. These interfaces can be easily customised in order to maximise their usability and can be easily uploaded to online platforms to facilitate wider access to published data.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.10.26.354787v1?rss=1

Authors: Luo, R., Wong, Y.-S., Lam, T.-W.

Abstract:

Motivation: Danchin et al. have pointed out that cytosine drives the evolution of SARS-CoV-2. A depletion of cytosine might lead to the attenuation of SARS-CoV-2. Results: We built a website to track the composition change of mono-, di-, and tri-nucleotide of SARS- CoV-2 over time. The website downloads new strains available from GISAID and updates its results daily. Our analysis suggests that the composition of cytosine in coronaviruses is related to their reported mortality. Using 137,315 SARS-CoV-2 strains collected in ten months, we observed cytosine depletion at a rate of about one cytosine loss per month from the whole genome. Availability: The website is available at http://www.bio8.cs.hku.hk/sarscov2/.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.10.25.353946v1?rss=1

Authors: Gelman, S., Romero, P. A., Gitter, A.

Abstract:

The mapping from protein sequence to function is highly complex, making it challenging to predict how sequence changes will affect a protein's behavior and properties. We present a supervised deep learning framework to learn the sequence-function mapping from deep mutational scanning data and make predictions for new, uncharacterized sequence variants. We test multiple neural network architectures, including a graph convolutional network that incorporates protein structure, to explore how a network's internal representation affects its ability to learn the sequence-function mapping. Our supervised learning approach displays superior performance over physics-based and unsupervised prediction methods. We find networks that capture nonlinear interactions and share parameters across sequence positions are important for learning the relationship between sequence and function. Further analysis of the trained models reveals the networks' ability to learn biologically meaningful information about protein structure and mechanism. Our software is available from https://github.com/gitter-lab/nn4dms.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.10.24.353599v1?rss=1

Authors: McGregor, K., Labbe, A., Greenwood, C. M., Parsons, T., Quince, C.

Abstract:

Background: The human microbiome comprises the microorganisms that inhabit the various locales of the human body and plays a vital role in human health. The composition of a microbial population is often quantified through measures of species diversity, which summarize the number of species along with their relative abundances into a single value. In a microbiome sample there will certainly be species missing from the target population which will affect the diversity estimates. Methods: We employ a model based on the hierarchical Pitman-Yor (HPY) process to model the species abundance distributions over multiple microbiome populations. The model parameters are estimated using a Gibbs sampler. We also derive estimates of species diversity as a function of the HPY parameters. Results: We show that the Gibbs sampler for the HPY model performs well in the simulation study. We also show that the estimates of diversity from the HPY model improve over naive estimates when species are missing. Similarly the HPY estimates tend to perform better than the naive estimates when the number of individuals sampled from a population is small.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.10.25.353821v1?rss=1

Authors: Ashraf, M. A., Feng, X., Hu, X., Ashraf, F., Shen, L., Zhang, S.

Abstract:

Sugarcane Bacilliform Virus (SCBV) is considered an economically the most damaging pathogen for sugarcane production worldwide. Three ORFs are characterized in a single molecule of circular, ds-DNA genome of the SCBV, encoding for hypothetical protein (ORF1), DNA binding protein (ORF2) and Polyprotein (ORF3). The study was aimed to predict and comprehensively evaluate sugarcane miRNAs for the silencing of SCBV genome using in-silico algorithms. Computational methods were used for prediction of candidate miRNAs from sugarcane (S. officinarum L.) to silence the expression of SCBV genes through translational inhibition by mRNA cleavage. Mature sugarcane miRNAs were retrieved and were assessed to hybridization with the SCBV genome. A total of fourteen potential candidate miRNAs from sugarcane were computed by all the algorithms used for the silencing of SCBV. A consensus of three algorithms predicts hybridization sites of sof-miR159e at common locus 5534. The miRNA-mRNA interaction was estimated by computing free-energy of miRNA-mRNA duplex using RNAcofold algorithm. Regulatory network of predicted candidate miRNAs of sugarcane with SCBV ORFs, generated using Circos, identify novel targets. Consequently, detecting and discarding inefficient amiRNAs prior to cloning would help suppressed mutants faster. The efficacy of predicted candidate miRNAs was evaluated to test the survival rate of the in vitro amiRNA-mediated effective badnaviral silencing and resistance in sugarcane cultivars.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.10.24.352591v1?rss=1

Authors: Busa, V. F., Favorov, A. V., Fertig, E. J., Leung, A.

Abstract:

The etiology of diseases driven by dysregulated mRNA metabolism can be elucidated by characterizing the responsible RNA-binding proteins (RBPs). Although characterizations of RBPs have been mainly focused on their binding sequences, not much has been investigated about their preferences for RNA structures. We present nearBynding, an R/Bioconductor pipeline that incorporates RBP binding sites and RNA structure information to discern structural binding preferences for an RBP. nearBynding visualizes RNA structure at and proximal to sites of RBP binding transcriptome-wide, analyzes CLIP-seq data without peak-calling, and provides a flexible scaffold to study RBP binding preferences relative to diverse RNA structure data types.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.10.23.349407v1?rss=1

Authors: Wu, C.-Y., Lau, B. T., Kim, H., Sathe, A., Grimes, S. M., Ji, H. P., Zhang, N. R.

Abstract:

Cancer progression is driven by both somatic copy number aberrations (CNAs) and chromatin remodeling, yet little is known about the interplay between these two classes of events in shaping the clonal diversity of cancers. We present Alleloscope, a method for allele-specific copy number estimation that can be applied to single cell DNA and ATAC sequencing data, either separately or in combination. This approach allows for integrative multi-omic analysis of allele-specific copy number and chromatin accessibility on the same cell. On scDNA-seq data from gastric, colorectal, and breast cancer samples, with extensive validation using matched linked-read sequencing, Alleloscope finds pervasive occurrence of highly complex, multi-allelic copy number aberrations, where cells that carry varying allelic configurations adding to the same total copy number co-evolve within a tumor. The contributions of such allele-specific events to intratumor heterogeneity have been under-reported and under-studied due to the lack of methods for their detection. On scATAC-seq from two basal cell carcinoma samples and a gastric cancer cell line, Alleloscope detects multi-allelic copy number events and copy neutral loss-of-heterozygosity, enabling the dissection of the contributions of chromosomal instability and chromatin remodeling in tumor evolution.

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