PaperPlayer biorxiv bioinformatics

Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.10.30.362954v1?rss=1

Authors: Felix, P. T., Silva, E. D. A. B., Venancio, D. B. R., Ramos, R. d. S.

Abstract:

In this work, we evaluated the levels of genetic diversity in 18 genomes of SARS-CoV-2 carrying the D614G mutation, coming from Malaysia and Venezuela and publicly available at the National Center of Biotechnology and Information (NCBI). These haplotypes were previously used for phylogenetic analysis, following the LaBECom protocols. All gaps and unconserved sites were extracted for the construction of a phylogenetic tree. As specific methodologies for paired FST estimators, Molecular Variance (AMOVA), Genetic Distance, mismatch, demographic and spatial expansion analyses, molecular diversity and evolutionary divergence time analyses, 20,000 random permutations were always used. The results revealed the presence of only 57 sites of polymorphic and parsimonium-informative among the 29,827bp analyzed and the analyses based on FST values confirmed the presence of two distinct genetic entities with fixation index of 22% and with a higher component of population variation (78.14%). Tau variations revealed a significant time of divergence, supported by mismatch analysis of the observed distribution ({tau} = 42%). It is safe to say that the small number of existing polymorphisms should not reflect major changes in the protein products of viral populations in both countries and this consideration provides the safety that, although there are differences in the haplotypes studied, these differences are minimal for both regions analyzed geographically and, therefore, it seems safe to extrapolate the levels of polymorphism and molecular diversity found in the samples for other mutant genomes of SARS-CoV-2 in other countries. This reduces speculation about the possibility of large differences between mutant strains of SARS-CoV-2 (D614G) and wild strains, at least at the level of their protein products, although the mutant form has higher transmission speed and infection. The analyses suggest that possible variations in protein products, of the wild virus in relation to its mutant form, should be minimal, bringing peace of mind as to the increased risk of death from the new form of the virus, as well as possible problems of gradual adjustments in some molecular targets for vaccines.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.11.01.363101v1?rss=1

Authors: Kalxdorf, M., Mueller, T., Stegle, O., Krijgsveld, J.

Abstract:

Label-free proteomics by data-dependent acquisition (DDA) enables the unbiased quantification of thousands of proteins, however it notoriously suffers from high rates of missing values, thus prohibiting consistent protein quantification across large sample cohorts. To solve this, we here present IceR, an efficient and user-friendly quantification workflow that combines high identification rates of DDA with low missing value rates similar to DIA. Specifically, IceR uses ion current information in DDA data for a hybrid peptide identification propagation (PIP) approach with superior quantification precision, accuracy, reliability and data completeness compared to other quantitative workflows. We demonstrate greatly improved quantification sensitivity on published plasma and single-cell proteomics data, enhancing the number of reliably quantified proteins, improving discriminability between single-cell populations, and allowing reconstruction of a developmental trajectory. IceR will be useful to improve performance of large scale global as well as low-input proteomics applications, facilitated by its availability as an easy-to-use R-package.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.10.31.363580v1?rss=1

Authors: Weinstein, J. N., Rohrdanz, M. N., Stucky, M. T., Melott, J. M., Jun, M., Mohanty, V., Manyam, G. C., Wakefield, C., Chen, K., Navin, N., Ryan, M. C., Akbani, R., Broom, B. M.

Abstract:

OmicPioneer-sc is an open-source data visualization/analysis package that integrates dimensionality-reduction plots (DRPs) such as t-SNE and UMAP with Next-Generation Clustered Heat Maps (NGCHMs) and Pathway Visualization Modules (PVMs) in a seamless, highly interactive exploratory environment. It includes fluent zooming and navigation, a statistical toolkit, dozens of link-outs to external public bioinformatic resources, high-resolution graphics that meet the requirements of all major journals, and the ability to store all metadata needed to reproduce the visualizations at a later time. A user-friendly, multi-panel graphical interface enables non-informaticians to interact with the system without programming, asking and answering questions that require navigation among the three types of modules or extension from them to the Gene Ontology or information on therapies. The visual integration can be useful for detective work to identify and annotate cell-types for color-coding of the DRPs, and multiple NGCHMs can be layered on top of each other (with toggling among them) as an aid to multi-omic analysis. The tools are available in containerized form with APIs to facilitate incorporation as a plug-in to other bioinformatic environments. The capabilities of OmicPioneer-sc are illustrated here through application to a single-cell RNA-seq airway dataset pertinent to the biology of both cancer and COVID-19

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.10.31.363176v1?rss=1

Authors: Warwicker, J.

Abstract:

SARS-CoV-2, causative agent of the COVID-19 pandemic, is thought to release its RNA genome at either the cell surface or within endosomes, the balance being dependent on spike protein stability, and the complement of receptors, co-receptors and proteases. To investigate possible mediators of pH-dependence, pKa calculations have been made on a set of structures for spike protein ectodomain and fragments from SARS-CoV-2 and other coronaviruses. Dominating a heat map of the aggregated predictions, 3 histidine residues in S2 are consistently predicted as destabilising in pre-fusion (all 3) and post-fusion (2 of 3) structures. Other predicted features include the more moderate energetics of surface salt-bridge interactions, and sidechain-mainchain interactions. Two aspartic acid residues in partially buried salt-bridges have pKas that are calculated to be elevated and destabilising. Notably, the degree of destabilisation is predicted to vary between open and closed receptor binding domain conformations. It is therefore suggested that these groups contribute to a pH-dependence of the open/closed equilibrium. These observations are discussed in the context of SARS-CoV-2 infection, mutagenesis studies, and other human coronaviruses.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.10.30.363002v1?rss=1

Authors: Kumar, A., Kumar, P., Garg, N., Giri, R.

Abstract:

Intraviral protein-protein interactions are crucial for replication, pathogenicity, and viral assembly. Among these, virus assembly is a critical step as it regulates the arrangements of viral structural proteins and helps in the encapsulation of genomic material. SARS-CoV-2 structural proteins play an essential role in the self-rearrangement, RNA encapsulation, and mature virus particle formation. In SARS-CoV, the membrane protein interacts with the envelope and spike protein in Endoplasmic Reticulum Golgi Intermediate Complex (ERGIC) to form an assembly in the lipid bilayer, followed by membrane-ribonucleoprotein (nucleocapsid) interaction. In this study, using protein-protein docking, we tried to understand the interaction of membrane protein's interaction with envelope, spike and nucleocapsid proteins. Further, simulation studies performed up to 100ns agreed that protein complexes M-E, M-S, and M-N were stable. Moreover, the calculated free binding energy and dissociation constant values support the protein complex formation. The interaction identified in the study will be of great importance, as it provides valuable insight into the protein complex, which could be the potential drug targets for future studies.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.10.30.360610v1?rss=1

Authors: Ecovoiu, A. A., Ghita, I. C., Chifiriuc, D. I. M., Ghionoiu, I. C., Ciuca, A. M., Bologa, A. M., Ratiu, A. C.

Abstract:

Transposon annotation is a very dynamic field of genomics and various tools assigned to support this bioinformatics endeavor were reported. Genome ARTIST (GA) software was initially developed for mapping artificial transposons mobilized during insertional mutagenesis projects. Now, the new functions of GA_v2 qualify it as an effective companion for mapping and annotation of class II natural transposons in assembled genomes, contigs or sequencing reads. Tabular export of mapping and annotation data for subsequent high-throughput data analysis, the export of a list of flanking sequences around either the coordinates of insertion or around the target site duplications (TSDs) and generation of a consensus sequence for the respective flanking sequences are all key assets of GA_v2. Additionally, we developed two accompanying short scripts that enable the user to annotate transposons existent in assembled genomes and to use various annotation offered by FlyBase for Drosophila melanogaster genome. Herein, we present the applicability of GA_v2 for a preliminary annotation of the class II transposon P-element in the genome of D. melanogaster strain Horezu, Romania, which was sequenced with Nanopore technology in our laboratory. Our results point that GA_v2 is a reliable tool to be integrated in pipelines designed to perform transposon annotation in new sequenced genomes. GA_v2 is open source software compatible with Ubuntu, Mac OS and Windows and is available at https://github.com/genomeartist/genomeartist and at www.genomeartist.ro.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.10.31.361725v1?rss=1

Authors: Dietze, J., van Pijkeren, A., Ziegler, M., Kwiatkowski, M., Heiland, I.

Abstract:

Stable isotope labelling in combination with high resolution mass spectrometry approaches are increasingly used to analyse both metabolite and protein modification dynamics. To enable correct estimation of the resulting dynamics it is critical to correct the measured values for naturally occurring stable isotopes, a process commonly called isotopologue correction or deconvolution. While the importance of isotopologue correction is well recognized in metabolomics, it has received far less attention in proteomics approaches. Although several tools exist that enable isotopologue correction of mass spectrometry data, none of them is universally applicable for all potential experimental approaches. We here present PICor which has been streamlined for multiple isotope labelling isotopologue correction in proteomics or metabolomics approaches. We demonstrate the importance for accurate measurement of the dynamics of protein modifications, such as histone acetylation.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.11.01.361691v1?rss=1

Authors: Yu, M. K., Fogarty, E. C., Eren, A. M.

Abstract:

Plasmids play a critical role in rapid bacterial adaptation by encoding accessory functions that may increase the host's fitness. However, the diversity and ecology of plasmids is poorly understood due to computational and experimental challenges in plasmid identification. Here, we report the Plasmid Classification System (PCS), a machine learning classifier that recognizes plasmid sequences based on gene functions. To train PCS, we performed a large-scale discovery and comparison of gene functions in a reference set of >16,000 plasmids and >14,000 chromosomes. PCS accurately recognizes a diverse range of plasmid subtypes, and it outperforms the previous state-of-the-art approach based on k-mer decomposition of sequences. Armed with this model, we conducted, to our knowledge, the largest search for naturally occurring human gut plasmids in 406 publicly available metagenomes representing 5 countries. This search yielded 6,257 high-confidence predicted plasmids, of which 576 had evidence of a circular conformation based on pair-end mapping. These predicted plasmids were found to be highly prevalent across the metagenomes compared to the reference set of known plasmids, suggesting there is extensive and uncharacterized plasmid diversity in the human gut microbiome.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.10.30.362764v1?rss=1

Authors: Dasgupta, R., Pradhan, A. K., Ghosh, S.

Abstract:

Mycobacterium are a genus of Actinobacteria known to be responsible for several deadly diseases in both humans and animals, including tuberculosis. Luciferase is the primary protein in Mycobacteria that plays a role in bioluminescence. It also plays a role in some bacteria of being a source of energy transference, such as in the case of lumazine proteins. Although studies have been conducted in different luciferase in bacteria, there has been hardly any structural studies on luciferase expressed in Mycobacterium sp. EPa45. Therefore, in this paper we have studied luciferase expressed in Mycobacterium sp. EPa45 by insilico analysis of its structure from its protein sequence. We report the observed differences within luciferase reported from other strains of mycobacterium and pathogenic and non-pathogenic forms of bacteria in terms of their (i) physiochemical characteristics, (ii) protein structure, (iii) multiple sequence alignment and (iv) phylogenetic relationships. We report for the first time the relation of this specific strain of Luciferase in mycobacterium and bacterium at large. Highlights: 1. Mycobacterium sp. EPa45 shows similar characteristics to pathogenic mycobacterium 2. Analysis of Luciferase sequence and protein qualities provides insight to pathogenicity 3. The deadly nature of infectious mycobacterium, especially with luciferase sequences similar to Mycobacterium sp. EPa45, is analyzed

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.10.30.362715v1?rss=1

Authors: Andreoletti, G., Lanata, C. M., Paranjpe, I., Jain, T. S., Nititham, J., Taylor, K. E., Combes, A. J., Maliskova, L., Jimmie Ye, C., Katz, P., Dall Era, M., Yazdany, J., Criswell, L. A., Sirota, M.

Abstract:

Systemic lupus erythematosus (SLE) is a heterogeneous autoimmune disease in which outcomes vary among different racial groups. The aim of this study is to leverage large-scale transcriptomic data from diverse populations to better sub-classify SLE patients into more clinically actionable groups. We leverage cell sorted RNA-seq data (CD14+ monocytes, B cells, CD4+T cells, and NK cells) from 120 SLE patients (63 Asian and 57 White individuals) and apply a four tier analytical approach to identify SLE subgroups within this multiethnic cohort: unsupervised clustering, differential expression analyses, gene co-expression analyses, and machine learning. K-means clustering on the individual cell type data resulted in three clusters for CD4 and CD14, and two clusters for B cells and NK cells. Correlation analysis revealed significant positive associations between the transcriptomic clusters of each immune cell and clinical parameters including disease activity and ethnicity. We then explored differentially expressed genes between Asian and White groups for each cell-type. The shared differentially expressed genes across the four cell types were involved in SLE or other autoimmune related pathways. Co-expression analysis identified similarly regulated genes across samples and grouped these genes into modules. Samples were grouped into White-high, Asians-high (high disease activity defined by SLEDAI score >=6) and White-low, Asians-low (SLEDAI < 6). Random forest classification of disease activity in the White and Asian cohorts showed the best classification in CD4+ T cells in White. The results from these analyses will help stratify patients based on their gene expression signatures to enable precision medicine for SLE.

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