PaperPlayer biorxiv bioinformatics

Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.09.24.311720v1?rss=1

Authors: Stamboulian, M., Doak, T. G., Ye, Y.

Abstract:

Background: Recent advances in genome and metagenome sequencing have dramatically enriched the collection of genomes of bacterial species related to human health and diseases. In metagenomic studies phylogenetic trees are commonly used to depict, describe, and compare the bacterial members of the community under study. The most accurate tree-building algorithms now use large sets of marker genes taken from across genomes. However, many of the current bacterial genomes were assembled from metagenomic datasets (i.e., metagenome assembled genomes, MAGs), and often contain missing information. It is therefore important to study how well the phylogeny approach performs on such genomes. Further, phylogeny methods are not perfect and it is important to know how reliable an inferred tree is. Results: Here we examined the impact of incompleteness of the genomes on the tree reconstruction, and we showed that phylogeny approaches including RAxML (which handles missing data explicitly) and FastTree generally performed well on simulated collection of 400 genomes with missing information. As RAxML is computationally prohibitive for the much larger collections of gut genomes, we chose FastTree to build a unified tree of human-gut associated bacterial species (referred to as gut tree), including more than 3000 genomes, most of which are incomplete. We developed two downstream applications of the gut tree: peptide-centric analysis of metaproteomics datasets; and taxonomic characterization of metagenomic sequences. In both applications, the gut tree provided the basis for quantification of species composition at various taxonomic resolutions. Conclusions: The gut tree presented in this study provides a useful framework for taxonomic profiling of human gut microbiome. Including MAGs in the tree provides more comprehensive representation of microbial species diversity associated with human gut, important for studying the taxonomic composition of gut microbiome. Availability and Implementation: The tree construction pipeline and downstream applications of the gut tree are freely available at https://github.com/mgtools/guttree.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.09.23.310003v1?rss=1

Authors: Lakkis, J., Wang, D., Zhang, Y., Hu, G., Wang, K., Pan, H., Ungar, L., Reilly, M., Li, X., Li, M.

Abstract:

Recent development of single-cell RNA-seq (scRNA-seq) technologies has led to enormous biological discoveries. As the scale of scRNA-seq studies increases, a major challenge in analysis is batch effect, which is inevitable in studies involving human tissues. Most existing methods remove batch effect in a low-dimensional embedding space. Although useful for clustering, batch effect is still present in the gene expression space, leaving downstream gene-level analysis susceptible to batch effect. Recent studies have shown that batch effect correction in the gene expression space is much harder than in the embedding space. Popular methods such as Seurat3.0 rely on the mutual nearest neighbor (MNN) approach to remove batch effect in the gene expression space, but MNN can only analyze two batches at a time and it becomes computationally infeasible when the number of batches is large. Here we present CarDEC, a joint deep learning model that simultaneously clusters and denoises scRNA-seq data, while correcting batch effect both in the embedding and the gene expression space. Comprehensive evaluations spanning different species and tissues showed that CarDEC consistently outperforms scVI, DCA, and MNN. With CarDEC denoising, those non-highly variable genes offer as much signal for clustering as the highly variable genes, suggesting that CarDEC substantially boosted information content in scRNA-seq. We also showed that trajectory analysis using CarDEC's denoised and batch corrected expression as input revealed marker genes and transcription factors that are otherwise obscured in the presence of batch effect. CarDEC is computationally fast, making it a desirable tool for large-scale scRNA-seq studies.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.09.23.310276v1?rss=1

Authors: Greenfest-Allen, E., Klamann, C., Valladares, O., Kuzma, A., Gangadharan, P., Leung, Y. Y., Stoeckert, C. J., Wang, L.-S.

Abstract:

INTRODUCTION: The NIAGADS Alzheimer's Genomics Database is an interactive knowledgebase for AD genetics that provides access to GWAS summary statistics datasets deposited at NIAGADS, a national genetics data repository for AD and related dementia (ADRD). METHODS: The website makes available >70 genome-wide summary statistics datasets from GWAS and genome sequencing analysis for AD/ADRD. Variants identified from these datasets are mapped to up-to-date variant and gene annotations from a variety of resources and linked to functional genomics data. The database is powered by a big-data optimized relational database and uses ontologies to consistently annotate study designs and phenotypes, facilitating data harmonization and efficient real-time data analysis and variant or gene report generation. RESULTS: Detailed variant reports provide tabular and interactive graphical summaries of known ADRD associations, as well as highlight variants flagged by the Alzheimer's Disease Sequencing Project (ADSP). Gene reports provide summaries of co-located ADRD risk-associated variants and have been expanded to include meta-analysis results from aggregate association tests performed by the ADSP allowing us to flag genes with genetic-evidence for AD. DISCUSSION: The GenomicsDB makes available >100 million variant annotations, including ~30 million (5 million novel) variants identified as AD-relevant by ADSP, for browsing and real-time mining via the website or programmatically through a REST API. With a newly redesigned, efficient, search interface and comprehensive record pages linking summary statistics to variant and gene annotations, this resource makes these data both accessible and interpretable, establishing itself as valuable tool for AD research.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.09.24.298851v1?rss=1

Authors: Ayyappan, V., Wat, R., Barber, C., Vivelo, C., Gauch, K., Visanpattanasin, P., Cook, G., Sazeides, C., Leung, A. K. L.

Abstract:

ADP-ribosylation is a protein modification responsible for biological processes such as DNA repair, RNA regulation, cell cycle, and biomolecular condensate formation. Dysregulation of ADP-ribosylation is implicated in cancer, neurodegeneration, and viral infection. We developed ADPriboDB (adpribodb.leunglab.org) to facilitate studies in uncovering insights into the mechanisms and biological significance of ADP-ribosylation. ADPriboDB 2.0 serves as a one-stop repository comprising 48,346 entries and 9,097 ADP-ribosylated proteins, of which 6,708 were newly identified since the original database release. In this updated version, we provide information regarding the sites of ADP-ribosylation in 32,946 entries. The wealth of information allows us to interrogate existing databases or newly available data. For example, we found that ADP-ribosylated substrates are significantly associated with the recently identified human protein interaction networks associated with SARS-CoV-2, which encodes a conserved protein domain called macrodomain that binds and removes ADP-ribosylation. In addition, we create a new interactive tool to visualize the local context of ADP-ribosylation, such as structural and functional features as well as other post-translational modifications (e.g., phosphorylation, methylation and ubiquitination). This information provides opportunities to explore the biology of ADP-ribosylation and generate new hypotheses for experimental testing.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.09.23.310300v1?rss=1

Authors: Colavin, A., Atolia, E., Bitbol, A.-F., Huang, K. C.

Abstract:

Despite the structural and functional information contained in the statistical coupling between pairs of residues in a protein, coevolution associated with function is often obscured by artifactual signals such as genetic drift, which shapes a protein's phylogenetic history and gives rise to concurrent variation between protein sequences that is not driven by selection for function. Here, we introduce a method for explicitly defining a phylogenetic dimension of coevolution signal, and demonstrate that coevolution can occur on multiple phylogenetic timescales within a single protein. Our method, Nested Coevolution (NC), can be applied as an extension to any coevolution metric. We use NC to demonstrate that poorly conserved residues can nonetheless have important roles in protein function. Moreover, NC improved structural-contact prediction over gold-standard coevolution-based methods, particularly in subsampled alignments with fewer sequences. NC also lowered the noise in detecting functional sectors of collectively coevolving residues. Sectors of coevolving residues identified after NC correction were more spatially compact and phylogenetically distinct from the rest of the protein, and strongly enriched for mutations that disrupt protein activity. Our conceptualization of the phylogenetic separation of coevolution represents an advance from previous pragmatic attempts to reduce phylogenetic artifacts in measurements of coevolution. Application of NC broadens the application of protein coevolution measurements, particularly to eukaryotic proteins with fewer naturally available sequences, and further elucidates relationships among protein evolution and genetic diseases.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.09.24.311605v1?rss=1

Authors: The, M., Käll, L.

Abstract:

Protein quantification for shotgun proteomics is a complicated process where errors can be introduced in each of the steps. Triqler is a Python package that estimates and integrates errors of the different parts of the label-free protein quantification pipeline into a single Bayesian model. Specifically, it weighs the quantitative values by the confidence we have in the correctness of the corresponding PSM. Furthermore, it treats missing values in a way that reflects their uncertainty relative to observed values. Finally, it combines these error estimates in a single differential abundance FDR that not only reflects the errors and uncertainties in quantification but also in identification. In this tutorial, we show how to (1) generate input data for Triqler from quantification packages such as MaxQuant and Quandenser, (2) run Triqler and what the different options are, (3) interpret the results, (4) investigate the posterior distributions of a protein of interest in detail and (5) verify that the hyperparameter estimations are sensible.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.09.23.310045v1?rss=1

Authors: Gauthier, J., Derome, N.

Abstract:

Shannon's diversity index is a popular alpha diversity metric because it estimates both richness and evenness in a single equation. However, since its value is dependent of both those parameters, there is theoretically an infinite number of richness / evenness value combinations translating into the same index score. By decoupling both components measured by Shannon's index, two communities having identical indices can be differentiated by mapping richness and evenness coordinates on a scatter plot. In such graphs, confidence ellipses would allow testing significant differences between groups of samples. Multivariate statistical tests such as PERMANOVA can be performed on distance matrices calculated from richness and evenness coordinates and detect statistically significant differences that would have remained unforeseen otherwise.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.09.23.310441v1?rss=1

Authors: Bernstein, M. N., Ni, Z., Collins, M., Burkard, M. E., Kendziorski, C., Stewart, R.

Abstract:

Background: Single-cell RNA-seq (scRNA-seq) enables the profiling of genome-wide gene expression at the single-cell level and in so doing facilitates insight into and information about cellular heterogeneity within a tissue. Perhaps nowhere is this more important than in cancer, where tumor and tumor microenvironment heterogeneity directly impact development, maintenance, and progression of disease. While publicly available scRNA-seq cancer datasets offer unprecedented opportunity to better understand the mechanisms underlying tumor progression, metastasis, drug resistance, and immune evasion, much of the available information has been underutilized, in part, due to the lack of tools available for aggregating and analysing these data. Results: We present CHARacterizing Tumor Subpopulations (CHARTS), a computational pipeline and web application for analyzing, characterizing, and integrating publicly available scRNA-seq cancer datasets. CHARTS enables the exploration of individual gene expression, cell type, malignancy-status, differentially expressed genes, and gene set enrichment results in subpopulations of cells across multiple tumors and datasets. Conclusion: CHARTS is an easy to use, comprehensive platform for exploring single-cell subpopulations within tumors across the ever-growing collection of public scRNA-seq cancer datasets. CHARTS is freely available at charts.morgridge.org.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.09.25.285619v1?rss=1

Authors: Zhang, W., Claesen, M., Moerman, T., Groseclose, M. R., Waelkens, E., De Moor, B., Verbeeck, N.

Abstract:

Computational analysis is crucial to capitalize on the wealth of spatio-molecular information generated by mass spectrometry imaging (MSI) experiments. Currently, the spatial information available in MSI data is often under-utilized, due to the challenges of in-depth spatial pattern extraction. The advent of deep learning has greatly facilitated such complex spatial analysis. In this work, we use a pre-trained neural network to extract high-level features from ion images in MSI data, and test whether this improves downstream data analysis. The resulting neural network interpretation of ion images, coined neural ion images, are used to cluster ion images based on spatial expressions. We evaluate the impact of neural ion images on two ion image clustering pipelines, namely DBSCAN clustering, combined with UMAP-based dimensionality reduction, and k-means clustering. In both pipelines, we compare regular and neural ion images from two different MSI datasets. All tested pipelines could extract underlying spatial patterns, but the neural network-based pipelines provided better assignment of ion images, with more fine-grained clusters, and greater consistency in the spatial structures assigned to individual clusters. Additionally, we introduce the Relative Isotope Ratio metric to quantitatively evaluate clustering quality. The resulting scores show that isotopical m/z values are more often clustered together in the neural network-based pipeline, indicating improved clustering outcomes. The usefulness of neural ion images extends beyond clustering towards a generic framework to incorporate spatial information into any MSI-focused machine learning pipeline, both supervised and unsupervised.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.09.25.312959v1?rss=1

Authors: Wichmann, S., Scherer, S., Ardern, Z.

Abstract:

Overlapping genes (OLGs) with long protein-coding overlapping sequences are often excluded by genome annotation programs, with the exception of virus genomes. A recent study used a novel algorithm to construct OLGs from arbitrary protein domain pairs and concluded that virus genes are best suited for creating OLGs, a result which fitted with common assumptions. However, improving sequence evaluation using Hidden Markov Models shows that the previous result is an artifact originating from dataset-database biases. When parameters for OLG design and evaluation are optimized we find that 94.5% of the constructed OLG pairs score at least as highly as naturally occurring sequences, while 9.6% of the artificial OLGs cannot be distinguished from typical sequences in their protein family. Constructed OLG sequences are also indistinguishable from natural sequences in terms of amino acid identity and secondary structure, while the minimum nucleotide change required for overprinting an overlapping sequence can be as low as 1.8% of the sequence. Separate analysis of datasets containing only sequences from either archaea, bacteria, eukaryotes or viruses showed that, surprisingly, virus genes are much less suitable for designing OLGs than bacterial or eukaryotic genes. An important factor influencing OLG design is the structure of the standard genetic code. Success rates in different reading frames strongly correlate with their code-determined respective amino acid constraints. There is a tendency indicating that the structure of the standard genetic code could be optimized in its ability to create OLGs while conserving mutational robustness. The findings reported here add to the growing evidence that OLGs should no longer be excluded in prokaryotic genome annotations. Determining the factors facilitating the computational design of artificial overlapping genes may improve our understanding of the origin of these remarkable genetic constructs and may also open up exciting possibilities for synthetic biology.

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