PaperPlayer biorxiv bioinformatics

Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.08.13.250167v1?rss=1

Authors: Halperin, R. F., Hedge, A., Lang, J. D., Raupach, E. A., C4RCD Research Group,, Legendre, C., Liang, W. S., LoRusso, P., Sekulic, A., Sosman, J. A., Trent, J. M., Rangasamy, S., Pirrotte, P., Schork, N. J.

Abstract:

Here we present a novel statistical approach to splicing outlier and differential splicing detection, implemented in a software package called Bisbee. We leverage Bisbee's prediction of protein level effects to benchmark using matched RNAseq and mass spectrometry data from normal tissues. Bisbee exhibits improved sensitivity and specificity over existing approaches. We applied Bisbee to confirm a pathogenic splicing event in a rare disease and to identify tumor-specific splice isoforms associated with an oncogenic splice factor mutation. We also identified common tumor associated splice isoforms replicated in an independent dataset, demonstrating the utility of Bisbee in discovering disease relevant splice variants.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.08.13.249649v1?rss=1

Authors: Braun, B. A., Schein, C. H., Braun, W.

Abstract:

Motivation: There is a need for rapid and easy to use, alignment free methods to cluster large groups of protein sequence data. Commonly used phylogenetic trees based on alignments can be used to visualize only a limited number of protein sequences. DGraph, introduced here, is a dynamic programming application developed to generate 2D-maps based on similarity scores for sequences. The program automatically calculates and graphically displays property distance (PD) scores based on physico-chemical property (PCP) similarities from an unaligned list of FASTA files. Such PD-graphs show the interrelatedness of the sequences, whereby clusters can reveal deeper connectivities. Results: PD-Graphs generated for flavivirus (FV), enterovirus (EV), and coronavirus (CoV) sequences from complete polyproteins or individual proteins are consistent with biological data on vector types, hosts, cellular receptors and disease phenotypes. PD-graphs separate the tick- from the mosquito-borne FV, clusters viruses that infect bats, camels, seabirds and humans separately and the clusters correlate with disease phenotype. The PD method segregates the {beta}-CoV spike proteins of SARS, SARS-CoV-2, and MERS sequences from other human pathogenic CoV, with clustering consistent with cellular receptor usage. The graphs also suggest evolutionary relationships that may be difficult to determine with conventional bootstrapping methods that require postulating an ancestral sequence.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.08.13.249839v1?rss=1

Authors: Lu, T.-Y. T., The Human Genome Structural Variation Consortium,, Chaisson, M. J.

Abstract:

Variable number tandem repeats (VNTR) are genetic loci composed of consecutive repeats of short segments of DNA, with hypervariable repeat count and composition across individuals. Many genes have coding VNTR sequences, and noncoding VNTR variants are associated with a wide spectrum of clinical disorders such as Alzheimer's disease, bipolar disorder and colorectal cancer. The identification of VNTR length and composition provides the basis for downstream analysis such as expression quantitative trait loci discovery and genome wide association studies. Disease studies that use high-throughput short read sequencing do not resolve the repeat structures of many VNTR loci because the VNTR sequence is missing from the reference or is too repetitive to map. We solve the VNTR mapping problem for short reads by representing a collection of genomes with a repeat-pangenome graph, a data structure that encodes both the population diversity and repeat structure of VNTR loci. We developed software to build a repeat-pangenome using haplotype-resolved single-molecule sequencing assemblies, and to estimate VNTR length and sequence composition based on the alignment of short read sequences to the graph. Using long-read assemblies as ground truth, we are able to determine which VNTR loci may be accurately profiled using repeat-pangenome graph analysis with short reads. This enabled measuring the global diversity of VNTR sequences in the 1000-Genomes Project, and the discovery of expression quantitative trait loci in the Genotype-Tissue Expression Project. This analysis reveals loci that have significant differences in length and repeat composition between continental populations. Furthermore, the repeat pangenome graph analysis establishes an association between previously inaccessible variation and gene expression. Taken together, these indicate that measuring VNTR sequence diversity with repeat-pangenome graphs will be a critical component of future studies on human diversity and disease.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.08.13.249623v1?rss=1

Authors: Ahlmann-Eltze, C., Huber, W.

Abstract:

Motivation: The Gamma-Poisson distribution is a theoretically and empirically motivated model for the sampling variability of single cell RNA-sequencing counts (Grün et al., 2014; Townes et al., 2019; Svensson, 2020; Silverman et al., 2018; Hafemeister and Satija, 2019) and an essential building block for analysis approaches including differential expression analysis (Robinson et al., 2010; McCarthy et al., 2012; Anders and Huber, 2010; Love et al., 2014), principal component analysis (Townes et al., 2019) and factor analysis(Risso et al., 2018). Existing implementations for inferring its parameters from data often struggle with the size of single cell datasets, which typically comprise thousands or millions of cells; at the same time, they do not take full advantage of the fact that zero and other small numbers are frequent in the data. These limitations have hampered uptake of the model, leaving room for statistically inferior approaches such as logarithm(-like) transformation. Results: We present a new R package for fitting the Gamma-Poisson distribution to data with the characteristics of modern single cell datasets more quickly and more accurately than existing methods. The software can work with data on disk without having to load them into RAM simultaneously.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.08.13.249532v1?rss=1

Authors: Pedersen, B. S., Brown, J. S., Dashnow, H., Wallace, A. D., Velinder, M., Tvrdik, T., Mao, R., Best, H. D., Bayrak-Toydemir, P., Quinlan, A. R.

Abstract:

In studies of families with rare disease, it is common to screen for de novo mutations, as well as recessive or dominant variants that explain the phenotype. However, the filtering strategies and software used to prioritize high-confidence variants vary from study to study. In an effort to establish recommendations for rare disease research, we derive effective guidelines for variant filtering and report the expected number of candidates for de novo dominant and recessive modes of inheritance. The filters are applied to common attributes, including genotype quality, sequencing depth, allele balance, and population allele frequency. The resulting guidelines yield approximately 10 candidate SNP and INDEL variants per exome, and 18 per genome. For whole genomes, this includes an average of three de novo, ten compound-heterozygotes, one autosomal recessive, four X-linked variants, and roughly 100 candidate variants following autosomal dominant inheritance. The slivar software we developed to establish and rapidly apply these filters to VCF files is available at https://github.com/brentp/slivar under an MIT license, and includes documentation and recommendations for best practices for rare disease analysis.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.08.13.249110v1?rss=1

Authors: Junier, I., Varoquaux, N.

Abstract:

Genome wide contact frequencies obtained using Hi-C-like experiments have raised novel challenges in terms of visualization and rationalization of chromosome structuring phenomena. In bacteria, display of Hi-C data should be congruent with the circularity of chromosomes. However, standard representations under the form of square matrices or horizontal bands are not adapted to periodic conditions as those imposed by (most) bacterial chromosomes. Here, we fill this gap and propose a Python library, built upon the widely used Matplotlib library, to display Hi-C data in circular strips, together with the possibility to overlay genomic data. The proposed tools are light and fast, aiming to facilitate the exploration and understanding of bacterial chromosome structuring data. The library further includes the possibility to handle linear chromosomes, providing a fresh way to display and explore eukaryotic data. Availability and implementation: The package runs under Python 3 and is freely available at https://github.com/TrEE-TIMC/circHiC. The documentation can be found at https://tree-timc.github.io/circhic/; images obtained in different organisms are provided in the gallery section and are accompanied with codes.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.08.14.251488v1?rss=1

Authors: Navidi Ghaziani, Z., Zhang, L., Wang, B.

Abstract:

Single-cell Assay for Transposase-Accessible Chromatin sequencing (scATAC-seq) identifies regulated chromatin accessibility modules at the single-cell resolution. Robust evaluation is critical to the development of scATAC-seq pipelines, which calls for reproducible datasets for benchmarking. We hereby present the simATAC framework, an R package that generates a scATAC-seq count matrix, highly resembling real scATAC-seq datasets in library size, sparsity, and averaged chromatin accessibility signals. simATAC deploys statistical functions derived from analyzing 90 real scATAC-seq cell groups to model read distributions. simATAC provides a robust and systematic approach to generate in silico scATAC-seq samples with cell labels for a comprehensive tool assessment.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.08.13.248575v1?rss=1

Authors: Park, J. J., Chen, S.

Abstract:

The COVID-19 pandemic caused by SARS-CoV-2 has become a major threat across the globe. Here, we developed machine learning approaches to identify key pathogenic regions in coronavirus genomes. We trained and evaluated 7,562,625 models on 3,665 genomes including SARS-CoV-2, MERS-CoV, SARS-CoV and other coronaviruses of human and animal origins to return quantitative and biologically interpretable signatures at nucleotide and amino acid resolutions. We identified hotspots across the SARS-CoV-2 genome including previously unappreciated features in spike, RdRp and other proteins. Finally, we integrated pathogenicity genomic profiles with B cell and T cell epitope predictions for enrichment of sequence targets to help guide vaccine development. These results provide a systematic map of predicted pathogenicity in SARS-CoV-2 that incorporates sequence, structural and immunological features, providing an unbiased collection of genetic elements for functional studies. This metavirome-based framework can also be applied for rapid characterization of new coronavirus strains or emerging pathogenic viruses.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.08.13.247007v1?rss=1

Authors: Rasmussen, A. W., Äijö, T., Gabitto, M. I., Carriero, N., Sanjana, N., Skok, J., Bonneau, R.

Abstract:

Clustered Regularly Interspace Short Palindromic Repeats (CRISPR)-Cas9 genome editing methods provide the tools necessary to examine phenotypic impacts of targeted perturbations in high-throughput screens. While these technologies have the potential to reveal functional elements with direct therapeutic applications, statistical techniques to analyze noncoding screen data remain limited. We present CRISPR-Decryptr, a computational tool for the analysis of CRISPR noncoding screens. Our method leverages experimental design: accounting for multiple conditions, controls, and replicates to infer the regulatory landscape of noncoding genomic regions. We validate our method on a variety of mutagenesis, CRISPR activation, and CRISPR interference screens, extracting new insights from previously published data.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.08.14.251835v1?rss=1

Authors: Cao, C., Greenberg, M., Long, Q.

Abstract:

Many tools can reconstruct viral sequences based on next generation sequencing reads. Although existing tools effectively recover local regions, their accuracy suffers when reconstructing the whole viral genomes (strains). Moreover, they consume significant memory when the sequencing coverage is high or when the genome size is large. We present WgLink to meet this challenge. WgLink takes local reconstructions produced by other tools as input and patches the resulting segments together into coherent whole-genome strains. We accomplish this using an L_0+L_1-regularized regression synthesizing variant allele frequency data with physical linkage between multiple variants spanning multiple regions simultaneously. WgLink achieves higher accuracy than existing tools both on simulated and real data sets while using significantly less memory (RAM) and fewer CPU hours. Source code and binaries are freely available at https://github.com/theLongLab/wglink.

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