PaperPlayer biorxiv bioinformatics

Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.09.09.289074v1?rss=1

Authors: Ortuso, F., Mercatelli, D., Guzzi, P. H., Giorgi, F. M.

Abstract:

SARS-CoV-2 entry in human cells is mediated by the interaction between the viral Spike protein and the human ACE2 receptor. This mechanism evolved from the ancestor bat coronavirus and is currently one of the main targets for antiviral strategies. However, there currently exist several Spike protein variants in the SARS-CoV-2 population as the result of mutations, and it is unclear if these variants may exert a specific effect on the affinity with ACE2 which, in turn, is also characterized by multiple alleles in the human population. In the current study, the GBPM analysis, originally developed for highlighting host-guest interaction features, has been applied to define the key amino acids responsible for the Spike/ACE2 molecular recognition, using four different crystallographic structures. Then, we intersected these results with the current mutational status of these amino acids in the SARS-CoV-2 population. We identified several Spike mutations interacting with ACE2 and mutated in at least 10 distinct patients: N439K, G476S, S477N and N501Y. We also identified five ACE2 rare variants that may affect interaction with Spike and susceptibility to infection: S19P, E37K, M82I, E329G and G352V.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.09.08.288811v1?rss=1

Authors: Pimentel, Z. T., Dufault-Thompson, K., Russo, K. T., Scro, A. K., Smolowitz, R., Gomez-Chiarri, M., Zhang, Y.

Abstract:

Marine invertebrate microbiomes play important roles in various host and ecological processes. However, a mechanistic understanding of host-microbe interactions is so far only available for a handful of model organisms. Here, an integrated taxonomic and functional analysis of the microbiome of the eastern oyster, Crassostrea virginica, was performed using 16S rRNA gene amplicon profiling, shotgun metagenomics, and genome-scale metabolic reconstruction. A relatively low number of amplicon sequence variants (ASVs) were observed in oyster tissues compared to water samples, while high variability was observed across individual oysters and among different tissue types. Targeted metagenomic sequencing of the gut microbiota led to further characterization of a dominant bacterial taxon, the class Mollicutes, which was captured by the reconstruction of a metagenome-assembled genome (MAG). Genome-scale metabolic reconstruction of the oyster Mollicutes MAG revealed a reduced set of metabolic functions and a high reliance on the uptake of host-derived nutrients. A chitin degradation and an arginine deiminase pathway were unique to the MAG as compared to other closely related Mycoplasma genomes, indicating a distinct mechanism of carbon and energy acquisition by the oyster-associated Mollicutes. A systematic reanalysis of public eastern oyster-derived microbiome data revealed the Mollicutes as a ubiquitous taxon among adult oysters despite their general absence in larvae and biodeposit samples, suggesting potential horizontal transmission via an unknown mechanism.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.09.08.287920v1?rss=1

Authors: Xiang, G., Giardine, B. M., Mahony, S., Zhang, Y., Hardison, R. C.

Abstract:

Summary: Epigenetic modifications reflect key aspects of transcriptional regulation, and many epigenomic data sets have been generated under many biological contexts to provide insights into regulatory processes. However, the technical noise in epigenomic data sets and the many dimensions (features) examined make it challenging to effectively extract biologically meaningful inferences from these data sets. We developed a package that reduces noise while normalizing the epigenomic data by a novel normalization method, followed by integrative dimensional reduction by learning and assigning epigenetic states. This package, called S3V2-IDEAS, can be used to identify epigenetic states for multiple features, or identify signal intensity states and a master peak list across different cell types for a single feature. We illustrate the outputs and performance of S3V2-IDEAS using 137 epigenomics data sets from the VISION project that provides ValIdated Systematic IntegratiON of epigenomic data in hematopoiesis. Availability and implementation: S3V2-IDEAS pipeline is freely available as open source software released under an MIT license at: https://github.com/guanjue/S3V2_IDEAS_ESMP Contact: [email protected], [email protected] Supplementary information: S3V2-IDEAS-bioinfo-supplementary-materials.pdf

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.09.08.287557v1?rss=1

Authors: Schmidt, T., Samaras, P., Dorfer, V., Panse, C., Kockmann, T., Bichmann, L., van Puyvelde, B., Perez-Riverol, Y., Deutsch, E. W., Kuster, B., Wilhelm, M.

Abstract:

Here we present the Universal Spectrum Explorer (USE), a web-based tool based on IPSA for cross-resource (peptide) spectrum visualization and comparison (https://www.proteomicsdb.org/use/). Mass spectra under investigation can either be provided manually by the user (table format), or automatically retrieved from online repositories supporting access to spectral data via the universal spectrum identifier (USI), or requested from other resources and services implementing a newly designed REST interface. As a proof of principle we implemented such an interface in ProteomicsDB thereby allowing the retrieval of spectra acquired within the ProteomeTools project. In addition, USE can retrieve real-time prediction of tandem mass spectra from the deep learning framework Prosit. Comparison results like annotated mirror spectrum plots can be exported from USE as editable scalable high quality vector graphics. The USE was designed and implemented with minimal external dependencies allowing local usage and seamless integration into websites (https://github.com/kusterlab/universal_spectrum_explorer).

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.09.08.287516v1?rss=1

Authors: Eling, N., Damond, N., Hoch, T., Bodenmiller, B.

Abstract:

Highly multiplexed imaging technologies enable spatial profiling of dozens of biomarkers in situ. Standard data processing pipelines quantify cell-specific features and generate object segmentation masks as well as multi-channel images. Therefore, multiplexed imaging data can be visualised across two layers of information: pixel intensities represent the spatial expression of biomarkers across an image while segmented objects visualise cellular morphology, interactions and cell phenotypes in their microenvironment. Here we describe cytomapper, a computational tool that enables visualisation of pixel- and cell-level information obtained by multiplexed imaging. The package is written in the statistical programming language R, integrates with the image and single-cell analysis infrastructure of the Bioconductor project, and allows visualisation of single to hundreds of images in parallel. Using cytomapper, expression of multiple markers is displayed as composite images, segmentation masks are coloured based on cellular features, and selected cells can be outlined in images based on their cell type, among other functions. We illustrate the utility of cytomapper by analysing 100 images obtained by imaging mass cytometry from a cohort of type 1 diabetes patients and healthy individuals. In addition, cytomapper includes a Shiny application that allows hierarchical gating of cells based on marker expression and visualisation of selected cells in corresponding images. Together, cytomapper offers tools for diverse image and single-cell visualisation approaches and supports robust cell phenotyping via gating.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.09.08.288662v1?rss=1

Authors: Croydon Veleslavov, I. A., Stumpf, M. P. H.

Abstract:

Here we introduce repeated decision stumping, to distill simple models from single cell data. We develop decision trees of depth one - hence 'stumps' - to identify in an inductive manner, gene products involved in driving cell fate transitions, and in applications to published data we are able to discover the key-players involved in these processes in an unbiased manner without prior knowledge. The approach is computationally efficient, has remarkable predictive power, and yields robust and statistically stable predictors: the same set of candidates is generated by applying the algorithm to different subsamples of the data.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.09.08.287573v1?rss=1

Authors: Ortmann, J., Rampasek, L., Tai, E., Mer, A. S., Shi, R., Mascaux, C., Fares, A., Pham, N.-A., Beri, G., Eeles, C., Tkachuk, D., Ho, C., Sakashita, S., Weiss, J., Jiang, X., Liu, G., Cescon, D. W., O'Brien, C., Guo, S., Tsao, M.-S., Haibe-Kains, B., Goldenberg, A.

Abstract:

Quantifying response to drug treatment in mouse models of human cancer is important for treatment development and assignment, and yet remains a challenging task. A preferred measure to quantify this response should take into account as much of the experimental data as possible, i.e. both tumor size over time and the variation among replicates. We propose a theoretically grounded measure, KuLGaP, to compute the difference between the treatment and control arms. KuLGaP is more selective than currently existing measures, reduces the risk of false positive calls and improves translation of the lab results to clinical practice.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.09.08.281220v1?rss=1

Authors: Kim, Y., Naghavi, M., Zhao, J. Y.

Abstract:

The human genome contains more than 4000 genes that are longer than 100 kb. These long genes require more time and resources to make a transcript than shorter genes do. Long genes have also been linked to various human diseases. Specific mechanisms are utilized by long genes to facilitate their transcription and co-transcriptional processes. This results in unique features in their multi-omics profiles. Although these unique profiles are important to understand long genes, a database that provides an integrated view and easy access to the multi-omics profiles of long genes does not exist. We leveraged the publicly accessible multi-omics data and systematically analyzed the genomic conservation, histone modifications, chromatin organization, tissue-specific transcriptome, and single cell transcriptome of 992 protein-coding genes that are longer than 200 kb in the mouse genome. We also examined the evolution history of their gene lengths in 15 species that belong to six Classes and 11 Orders. To share the multi-omics profiles of long genes, we developed a user-friendly and easy-to- use database, LongGeneDB (https://longgenedb.com), for users to search, browse, and download these profiles. LongGeneDB will be a useful data hub for the biomedical research community to understand long genes.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.09.09.289108v1?rss=1

Authors: Spence, M. A., Mortimer, M., Buckle, A. M., Jackson, C. J. A.

Abstract:

Serine protease inhibitors (serpins) are found in all kingdoms of life and play essential roles in multiple physiological processes. Owing to the diversity of the superfamily, phylogenetic analysis is challenging and prokaryotic serpins have been speculated to have been acquired from Metazoa through horizontal gene transfer (HGT) due to their unexpectedly high homology. Here we have leveraged a structural alignment of diverse serpins to generate a comprehensive 6000-sequence phylogeny that encompasses serpins from all kingdoms of life. We show that in addition to a central hub of highly conserved serpins, there has been extensive diversification of the superfamily into many novel functional clades. Our analysis indicates that the hub proteins are ancient and are similar because of convergent evolution, rather than the alternative hypothesis of HGT. This work clarifies longstanding questions in the evolution of serpins and provides new directions for research in the field of serpin biology.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.09.09.289355v1?rss=1

Authors: Meysman, P., Postovskaya, A., De Neuter, N., Ogunjimi, B., Laukens, K.

Abstract:

Much is still not understood about the human adaptive immune response to SARS-CoV-2, the causative agent of COVID-19. In this paper, we demonstrate the use of machine learning to classify SARS-CoV-2 epitope specific T-cell clonotypes in T-cell receptor (TCR) sequencing data. We apply these models to public TCR data and show how they can be used to study T-cell longitudinal profiles in COVID-19 patients to characterize how the adaptive immune system reacts to the SARS-CoV-2 virus. Our findings confirm prior knowledge that SARS-CoV-2 reactive T-cell diversity increases over the course of disease progression. However our results show a difference between those T cells that react to epitope unique to SARS-CoV-2, which show a more prominent increase, and those T cells that react to epitopes common to other coronaviruses, which begin at a higher baseline.

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