PaperPlayer biorxiv bioinformatics

Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.10.12.333153v1?rss=1

Authors: Fong, J., Gardner, J. R., Andrews, J. M., Cashen, A. F., Payton, J. E., Weinberger, K. Q., Edwards, J. R.

Abstract:

Epigenetic changes, such as aberrant DNA methylation, contribute to cancer clonal expansion and disease progression. However, identifying subpopulation-level changes in a heterogeneous sample remains challenging. Thus, we have developed a computational approach, DXM, to deconvolve the methylation profiles of major allelic subpopulations from the bisulfite sequencing data of a heterogeneous sample. DXM does not require prior knowledge of the number of subpopulations or types of cells to expect. We benchmark DXM's performance and demonstrate improvement over existing methods. We further experimentally validate DXM predicted allelic subpopulation-methylation profiles in four Diffuse Large B-Cell Lymphomas (DLBCLs). Lastly, as proof-of-concept, we apply DXM to a cohort of 31 DLBCLs and relate allelic subpopulation methylation profiles to relapse. We thus demonstrate that DXM can robustly find allelic subpopulation methylation profiles that may contribute to disease progression using bisulfite sequencing data of any heterogeneous sample.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.10.09.333195v1?rss=1

Authors: Taguchi, Y.-h., Turki, T.

Abstract:

In this work, we extended the recently developed tensor decomposition (TD) based unsupervised feature extraction (FE) to a kernel based method, through a mathematical formulation. Subsequently, the kernel TD (KTD) based unsupervised FE was applied to two synthetic examples as well as real data sets, and the relevant findings were compared with those obtained previously using the TD based unsupervised FE approaches. The KTD based unsupervised FE demonstrated the most competitive performance against the TD based unsupervised FE in large p small n situations, involving a limited number of samples with many variables (observations). Nevertheless, the KTD based unsupervised FE outperformed the TD based unsupervised FE in non large p small situations. In general, although the use of the kernel trick can help the TD based unsupervised FE gain more variations, a wider range of problems may also be encountered. Considering the comparable performance of the KTD based unsupervised FE and TD based unsupervised FE when applied to large p small n problems, it is expected that the KTD based unsupervised FE can be applied in the genomic science domain, which involves many large p small n problems, and in which, the TD based unsupervised FE approach has been effectively applied.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.10.11.334847v1?rss=1

Authors: Mendieta-Esteban, J., Di Stefano, M., Castillo, D., Farabella, I., Marti-Renom, M. A.

Abstract:

Chromosome Conformation Capture (3C) technologies measure the interaction frequency between pairs of chromatin regions within the nucleus in a cell or a population of cells. Some of these 3C technologies retrieve interactions involving non-contiguous sets of loci, resulting in sparse interaction matrices. One of such 3C technologies is Promoter Capture Hi-C (pcHi-C) that is tailored to probe only interactions involving gene promoters. As such, pcHi-C provides sparse interaction matrices that are suitable to characterise short- and long-range enhancer-promoter interactions. Here, we introduce a new method to reconstruct the chromatin structural (3D) organisation from sparse 3C-based datasets such as pcHi-C. Our method allows for data normalisation, detection of significant interactions, and reconstruction of the full 3D organisation of the genomic region despite of the data sparseness. Specifically, it produces reliable reconstructions, in line with the ones obtained from dense interaction matrices, with as low as the 2-3% of the data from the matrix. Furthermore, the method is sensitive enough to detect cell-type-specific 3D organisational features such as the formation of different networks of active gene communities.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.10.11.335273v1?rss=1

Authors: Sahin, M., Wong, W., Zhan, Y., Van Deynze, K., Koche, R., Leslie, C. S.

Abstract:

We present HiC-DC+, a software tool for Hi-C/Hi-ChIP interaction calling and differential analysis using an efficient implementation of the HiC-DC statistical framework. HiC-DC+ integrates with popular preprocessing and visualization tools, includes TAD and A/B compartment callers, and outperformed existing tools in H3K27ac HiChIP benchmarking as validated by CRISPRi-FlowFISH. Differential HiC-DC+ analysis recovered global principles of 3D organization during cohesin perturbation and differentiation, including TAD aggregation, enhancer hubs, and promoter-enhancer loop dynamics.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.10.11.335323v1?rss=1

Authors: Choi, K., He, H., Gatti, D. M., Philip, V. M., Raghupathy, N., Munger, S. C., Chesler, E. J., Churchill, G. A.

Abstract:

Transcriptome profiling by RNA sequencing (RNA-Seq) of genetically segregating populations is widely used to investigate the regulatory programme of gene expression and its downstream phenotypic consequences. We address analytical challenges of RNA-Seq experiments in multi-parent populations (MPPs) that are derived from two or more inbred founder strains. Genotyping arrays or low-coverage DNA sequencing are commonly used to detect founder strain variants and to reconstruct the founder haplotype mosaic of MPP individuals. We propose and evaluate a new method, Genome reconstruction by RNA-Seq (GBRS), that simultaneously reconstructs individual diploid genomes and quantifies total and allele-specific expression directly from RNA-Seq data. We demonstrate that there is sufficient information in the genetic variants revealed in RNA-Seq data to reconstruct MPP haplotypes. Unlike existing RNA-seq-based genotyping methods, GBRS exploits multi-way allele-specific expression and jointly use closely neighboring variants. GBRS haplotype reconstructions have accuracy comparable to array-based reconstructions. GBRS provides RNA-Seq quantification that is tailored to individual genomes and does not suffer from biases that can arise when using reference genome alignment. GBRS also provides a quality control for detecting sample mix-ups and can improve power to detect expression quantitative trait loci. GBRS software is freely available at https://github.com/churchill-lab/gbrs.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.10.11.335034v1?rss=1

Authors: Mehta, D., Ramesh, A.

Abstract:

Computational approaches are often used to predict regulatory RNAs in bacteria, but their success is limited to RNAs that are highly conserved across phyla, in sequence and structure. The ANTAR regulatory system consists of a family of RNAs (the ANTAR-target RNAs) that selectively recruit ANTAR proteins. This protein-RNA complex together regulates genes at the level of translation or transcriptional elongation. Despite the widespread distribution of ANTAR proteins in bacteria, their targets RNAs haven't been identified in certain bacterial phyla such as actinobacteria. Here, by using a computational search model that is tuned to actinobacterial genomes, we comprehensively identify ANTAR-target RNAs in actinobacteria. These RNA motifs lie in select transcripts, often overlapping with the ribosome binding site or start codon, to regulate translation. Transcripts harboring ANTAR-target RNAs majorly encode proteins involved in the transport and metabolism of cellular metabolites like sugars, amino acids and ions; or encode transcription factors that in turn regulate diverse genes. In this report, we substantially diversify and expand the family of ANTAR RNAs across bacteria.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.10.10.334391v1?rss=1

Authors: Srivastava, J., Sunthar, P., Balaji, P. V.

Abstract:

A distinctive feature of glycans vis-a-vis proteins and nucleic acids is its structural complexity which arises from the huge repertoire of monosaccharides, isomeric linkages and branching. Around 70 monosaccharides have so far been discovered in natural glycans and the pathway for the biosynthesis of 57 of these have been experimentally characterized. However, there is no common platform with a comprehensive information of monosaccharide pathways and enzymes. We have gathered 542 experimentally characterized enzymes of these pathways from literature and set up a first of its kind database called GlycoPathDB (http://www.bio.iitb.ac.in/glycopathdb/). Annotations such as the reaction catalysed, substrate specificity, biosynthesis pathway and PubMed IDs are provided for all the enzymes in the database. Sequence homologs of the experimentally characterized enzymes found in nearly 13,000 completely sequenced genomes from Bacteria and Archaea have also been included in the database. This platform will help in deduction of evolutionary relationships among enzymes such as aminotransferases, nucleotidyltransferases, acetyltransferases and SDR family enzymes. It can also facilitate experimental studies such as direct enzyme assays to validate putative annotations, establish structure-function relationship, expression profiling to determine the function, determine the phenotypic consequences of gene knock-out/knock-in and complementation studies.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.10.09.333286v1?rss=1

Authors: Fukunaga, T., Iwasaki, W.

Abstract:

Reconstruction of gene-content evolutionary history is an essential approach for understanding how complex biological systems have been organized. However, the existing gene-content evolutionary models cannot formulate complex and heterogeneous gene gain/loss processes, which reflect diverse evolutionary events and greatly depend on gene families. In this study, we developed Mirage (MIxture model with a Realistic evolutionary rate model for Ancestral Genome Estimation), which allows different gene families to have flexible gene gain/loss rates, but reasonably limits the number of parameters to be estimated by the expectation-maximization algorithm. Simulation analysis showed that Mirage can accurately estimate complex and heterogeneous gene gain/loss rates and reconstruct gene-content evolutionary history. Application to empirical datasets demonstrated that our evolutionary model better fits genome data from various taxonomic groups than other models. Using Mirage, we revealed that gene families of metabolic function-related gene families displayed frequent gene gains and losses in all taxa investigated. The source code of Mirage is freely available at https://github.com/fukunagatsu/Mirage.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.10.09.334219v1?rss=1

Authors: Zhang, W., Wu, S., Zhang, H., Guan, W., Zeng, B., Wei, Y., Chan, G. C.-F., Li, W., Zhang, W.

Abstract:

Many studies have uncovered that piRNAs (PIWI-interacting RNA) are associated with a broad range of diseases and might be a novel type of biomarkers and targets for precision medicine. However, public resource of high-quality curated human disease-associated piRNAs remains unavailable. Therefore, we developed the piRPheno (http://www.biomedical-web.com/pirpheno) database to provide an up-to-date, interactive and extensible data reference for human piRNA-disease associations. piRPheno includes 9057 experimentally supported associations between 474 piRNAs and 204 diseases through a manual curation of publications. To prioritize the piRNA-disease associations, each association in piRPheno is assigned with a confidence score and clinical correlations based on the experimentally supported evidences. piRPheno is freely available with user-friendly interface and novel applications to enable easy exploration and analysis of the human disease related piRNAs.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.10.09.333716v1?rss=1

Authors: Hossain, S. M. M., Khatun, L., Ray, S., Mukhopadhyay, A.

Abstract:

In the last few decades, application of DNA microarray technology has sprung up as a powerful technique for discovering stage specific changes in expression pattern of a disease progression. Human Immunodeficiency Virus (HIV) infection causes Acquired Immunodeficiency Syndrome (AIDS) which is one of the most devastating diseases affecting humankind. Here, we have proposed a framework to examine the difference among microarray gene expression data of uninfected and three different HIV--1 infection stages using module preservation statistics. Initially, we detected differentially expressed genes among all the stages and identified coexpression modules by using topological overlap as a dissimilarity measure. To examine relationship among co-expression modules, we have compiled a module eigenegene network for each sample category which models similarity among all coexpression modules. To further examine the network, we have found clusters in it which are termed as `meta-modules'. Different module preservation statistics with two composite statistics: "Zsummary" and "MedianRank" are utilized to examine changes in structure of coexpression modules. We have applied our proposed methodology to discover modular changes between uninfected and acute samples, acute and chronic samples, chronic and AIDS samples. We have found several interesting results on preservation characteristics of gene modules across different stages. Some genes are identified to be preserved in a pair of stages while alter their characteristics across other stages. We further validated the obtained results using permutation test and classification techniques. Biological significance of the obtained modules have been examined using gene ontology and pathway based analysis. Additionally, we have detected key immune regulatory hub genes in the associated protein-protein interaction networks (PPINs) of the differentially expressed genes (DEGs) using twelve topological and centrality analysis methods. Moreover, we have analyzed the key immune regulatory genes which interacts with HIV-1 proteins inside the preserved and perturbed meta-modules across different HIV-1 stages and thus likely to act as potential biomarkers in HIV-1 progression.

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