PaperPlayer biorxiv bioinformatics

Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.09.24.312355v1?rss=1

Authors: Marchan, J. G.

Abstract:

Coronavirus Disease 2019 (COVID-19) represents a new global threat demanding a multidisciplinary effort to fight its etiological agent, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). In this regard, immunoinformatics may aid to predict prominent immunogenic regions from critical SARS-CoV-2 structural proteins, such as the spike (S) glycoprotein, for their use in prophylactic or therapeutic interventions against this rapidly emerging coronavirus. Accordingly, in this study, an integrated immunoinformatics approach was applied to identify cytotoxic T cell (CTC), T helper cell (THC), and Linear B cell (BC) epitopes from the S glycoprotein in an attempt to design a high-quality multi-epitope vaccine. The best CTC, THC, and BC epitopes showed high viral antigenicity, lack of allergenic or toxic residues, and suitable HLA-viral peptide interactions. Remarkably, SARS-CoV-2 receptor-binding domain (RBD) and its receptor-binding motif (RBM) harbour several potential epitopes. The structure prediction, refinement, and validation data indicate that the multi-epitope vaccine has an appropriate conformation and stability. Three conformational epitopes and an efficient binding between Toll-like receptor 4 (TLR4) and the vaccine model were observed. Importantly, the population coverage analysis showed that the multi-epitope vaccine could be used globally. Notably, computer-based simulations suggest that the vaccine model has a robust potential to evoke and maximize both immune effector responses and immunological memory to SARS-CoV-2. Further research is needed to accomplish with the mandatory international guidelines for human vaccine formulations.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.09.23.310599v1?rss=1

Authors: Quispe, D. J., Medina-Feliciano, J. G., Cruz-Gonzalez, S., Ortiz-Zuazaga, H., Garcia-Arraras, J. E.

Abstract:

Echinoderms comprise a group of animals with impressive regenerative capabilities. They can replace complex internal organs following injury or autotomy. In holothurians or sea cucumbers, cellular processes of intestinal regeneration have been extensively studied. The molecular machinery behind this faculty, however, remains to be understood. Here we assembled and annotated a de novo transcriptome using RNA-seq data consisting of regenerating and non-regenerating intestinal tissues from the sea cucumber Holothuria glaberrima. Comparisons of differential expression were made using the mesentery as a reference against 24 hour and 3 days regenerating intestine, revealing a large number of differentially expressed transcripts. Gene ontology and pathway enrichment analysis showed evidence of increasing transcriptional and translational activity. Further analysis of transcripts associated with transcription factors revealed diverse expression patterns with mechanisms involving developmental and cancer-related activity that could be related to the regenerative process. Our study demonstrates the broad and diversified gene expression profile during the early stages of the process using the mesentery as the focal point of intestinal regeneration. It also establishes the genes that are the most important candidates in the cellular processes that underlie regenerative responses.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.09.23.309864v1?rss=1

Authors: Welzel, M., Lange, A., Heider, D., Schwarz, P. M., Freisleben, B., Jensen, M., Boenigk, J., Beisser, D.

Abstract:

Sequencing of marker genes amplified from environmental samples, known as amplicon sequencing, allows us to resolve some of the hidden diversity and elucidate evolutionary relationships and ecological processes among complex microbial communities. The analysis of large numbers of samples at high sequencing depths generated by high throughput sequencing technologies requires efficient, flexible, and reproducible bioinformatics pipelines. Only a few existing workflows can be run in a user-friendly, scalable, and reproducible manner on different computing devices using an efficient workflow management system. We present Natrix, an open-source bioinformatics workflow for preprocessing raw amplicon sequencing data. The workflow contains all analysis steps from quality assessment, read assembly, dereplication, chimera detection, split-sample merging, sequence representative assignment (OTUs or ASVs) to the taxonomic assignment of sequence representatives. The workflow is written using Snakemake, a workflow management engine for developing data analysis workflows. In addition, Conda is used for version control. Thus, Snakemake ensures reproducibility and Conda offers version control of the utilized programs. The encapsulation of rules and their dependencies support hassle-free sharing of rules between workflows and easy adaptation and extension of existing workflows. Natrix is freely available on GitHub (https://github.com/MW55/Natrix)

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.09.23.309633v1?rss=1

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

Abstract:

The histone group added to a gene sequence must be released during mitosis to halt transcription during the DNA replication stage of the cell cycle. However, the detailed mechanism of this transcription regulation remains unclear. In particular, it is not realistic to reconstruct all appropriate histone modifications throughout the genome from scratch after mitosis. Thus, it is reasonable to assume that there might be a type of ''bookmark'' that retains the positions of histone modifications, which can be readily restored after mitosis. We developed a novel computational approach comprising tensor decomposition (TD)-based unsupervised feature extraction (FE) to identify transcription factors (TFs) that bind to genes associated with reactivated histone modifications as candidate histone bookmarks. To the best of our knowledge, this is the first application of TD-based unsupervised FE to the cell division context and phases pertaining to the cell cycle in general. The candidate TFs identified with this approach were functionally related to cell division, suggesting the suitability of this method and the potential of the identified TFs as bookmarks for histone modification during mitosis.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.09.23.308239v1?rss=1

Authors: Schultz, B. T., Zaliani, A., Ebeling, C., Reinshagen, J., Bojkova, D., Lage-Rupprecht, V., Karki, R., Lukassen, S., Gadiya, Y., Ravindra, N. G., Das, S., Baksi, S., Domingo-Fernandez, D., Lentzen, M., Strivens, M., Raschka, T., Cinatl, J., DeLong, L., Gribbon, P., Geisslinger, G., Ciesek, S., van Dijk, D., Gardner, S., Tom Kodamullil, A., Froehlich, H., Peitsch, M., Jacobs, M., Hoeng, J., Eils, R., Claussen, C., Hofmann-Apitius, M.

Abstract:

The SARS-CoV-2 pandemic has challenged researchers at a global scale. The scientific community's massive response has resulted in a flood of experiments, analyses, hypotheses, and publications, especially in the field drug repurposing. However, many of the proposed therapeutic compounds obtained from SARS-CoV-2 specific assays are not in agreement and thus demonstrate the need for a singular source of COVID-19 related information from which a rational selection of drug repurposing candidates can be made. In this paper, we present the COVID-19 PHARMACOME, a comprehensive drug-target-mechanism graph generated from a compilation of several disease maps and experimental data focused on SARS-CoV-2 / COVID-19 pathophysiology. By applying a systematic approach, we were able to predict the effect of drug pairs on SARS-CoV-2 infection. Experimental validation of our results demonstrate that our graph can be used to not only explore the involved mechanistic pathways, but also to identify novel combinations of drug repurposing candidates.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.09.22.309278v1?rss=1

Authors: Lu, X., Tu, S.-W., Chang, W., Wan, C., Wang, J., Zang, Y., Ramdas, B., Kapur, R., Lu, X., Cao, S., Zhang, C.

Abstract:

Deconvolution of mouse transcriptomic data is challenged by the fact that mouse models carry various genetic and physiological perturbations, making it questionable to assume fixed cell types and cell type marker genes for different dataset scenarios. We developed a Semi-Supervised Mouse data Deconvolution (SSMD) method to study the mouse tissue microenvironment (TME). SSMD is featured by (i) a novel non-parametric method to discover data set specific cell type signature genes; (ii) a community detection approach for fixing cell types and their marker genes; (iii) a constrained matrix decomposition method to solve cell type relative proportions that is robust to diverse experimental platforms. In summary, SSMD addressed several key challenges in the deconvolution of mouse tissue data, including: (1) varied cell types and marker genes caused by highly divergent genotypic and phenotypic conditions of mouse experiment, (2) diverse experimental platforms of mouse transcriptomics data, (3) small sample size and limited training data source, and (4) capable to estimate the proportion of 35 cell types in blood, inflammatory, central nervous or hematopoietic systems. In silico and experimental validation of SSMD demonstrated its high sensitivity and accuracy in identifying (sub) cell types and predicting cell proportions comparing to state-of-the-arts methods. A user-friendly R package and a web server of SSMD are released via https://github.com/xiaoyulu95/SSMD.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.09.23.244095v1?rss=1

Authors: Tischer, C., Ravindran, A., Reither, S., Pepperkok, R., Norlin, N.

Abstract:

Modern bioimaging and related areas such as sensor technology has seen tremendous development the last years allowing several contemporary imaging techniques, electron microscopy (EM) and light sheet microscopy in particular, to generate datasets frequently reaching the size of several terabytes (TB). As a consequence, even seemingly simple data operations such as cropping, chromatic- and drift-corrections and even visualisation, poses challenges when applied to thousands of time points or tiles. To address this we developed BigDataProcessor2 - a Fiji plugin facilitating processing workflows for TB sized image datasets. BigDataProcessor2 is available as a Fiji plugin via the BigDataProcessor update site. The application is implemented in Java and the code is publicly available on GitHub (https://github.com/bigdataprocessor/bigdataprocessor2).

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.09.23.309625v1?rss=1

Authors: Devailly, G., Joshi, A.

Abstract:

Advances in sequencing technologies have enabled exploration of epigenetic and transcription profiles at a genome-wide level. The epigenetic and transcriptional landscape is now available in hundreds of mammalian cell and tissue contexts. Many studies have performed multi-omics analyses using these datasets to enhance our understanding of relationships between epigenetic modifications and transcription regulation. Nevertheless, most studies so far have focused on the promoters/enhancers and transcription start sites, and other features of transcription control including exons, introns and transcription termination remain under explored. We investigated interplay between epigenetic modifications and diverse transcription features using the data generated by the Roadmap Epigenomics project. A comprehensive analysis of histone modifications, DNA methylation, and RNA-seq data of about thirty human cell lines and tissue types, allowed us to confirm the generality of previously described relations, as well as to generate new hypotheses about the interplay between epigenetic modifications and transcript features. Importantly, our analysis included previously under-explored features of transcription control namely, transcription termination sites, exon-intron boundaries, middle exons and exon inclusion ratio. We have made the analyses freely available to the scientific community at joshiapps.cbu.uib.no/perepigenomics_app/ for easy exploration, validation and hypotheses generation.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.09.21.303800v1?rss=1

Authors: Jiang, Q., Sudalagunta, P., Meads, M. B., Ahmed, K. T., Rutkowski, T., Shain, K., Silva, A. S., Zhang, W.

Abstract:

Time-lapse microscopy is a powerful technique that generates large volumes of image-based information to quantify the behaviors of cell populations. This method has been applied to cancer studies to estimate the drug response for precision medicine and has great potential to address inter-patient (or intertumoral) heterogeneity. A couple of algorithms exist to analyze time-lapse microscopy images; however, most deal with very high-resolution images involving few cells (typically cell lines). There are currently no advanced and efficient computational frameworks available to process large-scale time-lapse microscopy imaging data to estimate patient-specific response to therapy based on a large population of primary cells. In this paper, we propose a robust and user-friendly pipeline to preprocess the images and track the behaviors of thousands of cancer cells simultaneously for a better drug response prediction of cancer patients. Availability and Implementation: Source code is available at: https://github.com/compbiolabucf/CellTrack

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.09.22.307512v1?rss=1

Authors: Ray, S., Schonhuth, A.

Abstract:

Many single cell typing methods require manual annotation which casts problems with respect to resolution of (sub-)types, manpower resources and bias towards existing human knowledge. The integration of heterogeneous data and biologically meaningful interpretation of results are further current key challenges. We introduce MarkerCapsule, which leverages the landmark advantages of capsule networks achieved in their original applications in single cell typing. Thereby, the small amount of labeled data required and the naturally arising, biologically meaningful interpretation of cell types in terms of characteristic gene activity patterns are exemplary strengths, beyond outperforming the state of the art in terms of basic typing accuracy. MarkerCapsule is available at: https://github.com/sumantaray/MarkerCapsule.

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