Biology subjects

Singh, U.

Publications and source records attributed to Singh, U..

44 records · Page 3Linked to original sources

bioRxiv2019

Towards a Digital Diatom: image processing and deep learning analysis of Bacillaria paradoxa dynamic morphology

Recent years have witnessed a convergence of data and methods that allow us to approximate the shape, size, and functional attributes of biological organisms. This is not only limited to traditional model species: given the ability to culture and visualize a specific organism, we can capture both its structural and functional attributes. We present a quantitative model for the colonial diatom Bacillaria paradoxa, an organism that presents a number of unique attributes in terms of form and function. To acquire a digital model of B. paradoxa, we extract a series of quantitative parameters from microscopy videos from both primary and secondary sources. These data are then analyzed using a variety of techniques, including two rival deep learning approaches. We provide an overview of neural networks for non-specialists as well as present a series of analysis on Bacillaria phenotype data. The application of deep learning networks allows for two analytical purposes. Application of the DeepLabv3 pre-trained model extracts phenotypic parameters describing the shape of cells constituting Bacillaria colonies. Application of a semantic model trained on nematode embryogenesis data (OpenDevoCell) provides a means to analyze masked images of potential intracellular features. We also advance the analysis of Bacillaria colony movement dynamics by using templating techniques and biomechanical analysis to better understand the movement of individual cells relative to an entire colony. The broader implications of these results are presented, with an eye towards future applications to both hypothesis-driven studies and theoretical advancements in understanding the dynamic morphology of Bacillaria.

systems biology↗

bioRxiv2019

Maximizing prediction of orphan genes in assembled genomes

Proteins encoded by newly-emerged genes ("orphan genes") share no sequence similarity with proteins in any other species. They provide organisms with a reservoir of genetic elements to quickly respond to changing selection pressures. Here, we systematically assess the ability of five gene annotation pipelines to accurately predict genes in genomes according to phylostratal origin. BRAKER and MAKER are existing, popular ab initio tools that infer gene structures by machine learning. Direct Inference is an evidence-based pipeline we developed to predict gene structures from alignments of RNA-Seq data. The BIND pipeline integrates ab initio predictions of BRAKER and Direct inference; MIND combines Direct Inference and MAKER predictions. We use highly-curated Arabidopsis and yeast annotations as gold-standard benchmarks, and cross-validate in rice. Each pipeline under-predicts orphan genes (as few as 11 percent, under one prediction scenario). Increasing RNA-Seq diversity greatly improves prediction efficacy. The combined methods (BIND and MIND) yield best predictions overall, BIND identifying 68% of annotated orphan genes and 99% of ancient genes in Arabidopsis. We provide a light weight, flexible, reproducible solution to improve gene prediction.

bioinformatics↗

bioRxiv2019

Contrasting inflammatory signatures in peripheral blood and bronchoalveolar cells reveal compartment-specific effects of HIV infection.

The mechanisms by which HIV increases susceptibility to tuberculosis and other respiratory infections are incompletely understood. We used transcriptomics of paired whole bronchoalveolar lavage (BAL) fluid and peripheral blood mononuclear cells to compare the effect of HIV at the lung mucosal surface and in the peripheral blood. The large majority of HIV-induced differentially expressed genes (DEGs) were specific to either the peripheral or lung mucosa compartments (1,307/1,404, 93%). Type I interferon signaling was the dominant signature of DEGs in HIV-positive blood with a less dominant and qualitatively distinct type I interferon gene set expression pattern in HIV-positive BAL. DEGs in the HIV-positive BAL were significantly enriched for infiltration with cytotoxic CD8+ T cells. Higher expression of representative transcripts and proteins in BAL CD8+ T cells during HIV infection, including IFNG (IFN-{gamma}), GZMB (Granzyme B) and PDCD1 (PD-1), was confirmed by cell-subset specific transcriptional analysis and flow cytometry. Thus, we report that a whole transcriptomic approach revealed qualitatively distinct effects of HIV in blood and bronchoalveolar compartments. Further work exploring the impact of distinct type I interferon programs and CD8+ T cells infiltration of the lung mucosa during HIV infection may provide novel insights into HIV-induced susceptibility to respiratory pathogens.

immunology↗

bioRxiv2019

MetaOmGraph: a workbench for interactive exploratory data analysis of large expression datasets

The diverse and growing omics data in public domains provide researchers with a tremendous opportunity to extract hidden knowledge. However, the challenge of providing domain experts with easy access to these big data has resulted in the vast majority of archived data remaining unused. Here, we present MetaOmGraph (MOG), a free, open-source, standalone software for exploratory data analysis of massive datasets by scientific researchers. Using MOG, a researcher can interactively visualize and statistically analyze the data, in the context of its metadata. Researchers can interactively hone-in on groups of experiments or genes based on attributes such as expression values, statistical results, metadata terms, and ontology annotations. MOGs statistical tools include coexpression, differential expression, and differential correlation analysis, with permutation test-based options for significance assessments. Multithreading and indexing enable efficient data analysis on a personal computer, with no need for writing code. Data can be visualized as line charts, box plots, scatter plots, and volcano plots. A researcher can create new MOG projects from any data or analyze an existing one. An R-wrapper lets a researcher select and send smaller data subsets to R for additional analyses. A researcher can save MOG projects with a history of the exploratory progress and later reopen or share them. We illustrate MOG by case studies of large curated datasets from human cancer RNA-Seq, in which we assembled a list of novel putative biomarker genes in different tumors, and microarray and metabolomics from A. thaliana.

bioinformatics↗

bioRxiv2019

Recycling RNA-Seq Data to Identify Candidate Orphan Genes for experimental analysis

The "dark transcriptome" can be considered the multitude of sequences that are transcribed but not annotated as genes. We evaluated expression of 6,692 annotated genes and 29,354 unannotated ORFs in the Saccharomyces cerevisiae genome across diverse environmental, genetic and developmental conditions (3,457 RNA-Seq samples). Over 48% of the transcribed ORFs have translation evidence. Phylostratigraphic analysis infers most of these transcribed ORFs would encode species-specific proteins ("orphan-ORFs"); hundreds have mean expression comparable to annotated genes. These data reveal unannotated ORFs most likely to be protein-coding genes. We partitioned a co-expression matrix by Markov Chain Clustering; the resultant clusters contain 2,468 orphan-ORFs. We provide the aggregated RNA-Seq yeast data with extensive metadata as a project in MetaOmGraph, a tool designed for interactive analysis and visualization. This approach enables reuse of public RNA-Seq data for exploratory discovery, providing a rich context for experimentalists to make novel, experimentally-testable hypotheses about candidate genes.

bioinformatics↗

bioRxiv2019

CGGBP1 regulates chromatin barrier activity and CTCF occupancy at repeats

CGGBP1 is a repeat-binding protein with diverse functions in regulation of gene expression, cytosine methylation, repeat silencing and genomic integrity. CGGBP1 has also been identified as a cooperator factor in histone modifying complexes and as a component of protein complexes that form the enhancer-promoter loops. Here we report that the occupancy of CTCF at repeats and the chromatin barrier function of these repeat sequences depends on CGGBP1. Using ChIP-sequencing for CTC we describe CTCF binding on repetitive DNA. Our results show that CGGBP1 determines the CTCF occupancy preference for repeats over canonical CTCF-motif. By combining CTCF ChIP-sequencing results with ChIP-sequencing for three different kinds of histone modifications (H3K4me3, H3K9me3 and H3K27me3) we uncover insulator-like chromatin barrier activities of the repeat-rich CTCF-binding sites. This work shows that CGGBP1 is a regulator of CTCF occupancy and posits it as a regulator of barrier functions of CTCF-binding sites.

genomics↗

bioRxiv2019

synder: inferring genomic orthologs from synteny maps

Ortholog inference is a key step in understanding the evolution and function of a gene or other genomic feature. Yet often no similar sequence can be identified, or the true ortholog is hidden among false positives. A solution is to consider the sequences genomic context. We present the generic program, synder, for tracing features of interest between genomes based on a synteny map. This approach narrows genomic search-space independently of the sequence of the feature of interest. We illustrate the utility of synder by finding orthologs for the Arabidopsis thaliana 13-member gene family of Nuclear Factor YC transcription factor across the Brassicaceae clade.

bioinformatics↗

bioRxiv2018

phylostratr: A framework for phylostratigraphy

MotivationThe goal of phylostratigraphy is to infer the evolutionary origin of each gene in an organism. Currently, there are no general pipelines for this task. We present an R package, phylostratr, to fill this gap, making high-quality phylostratigraphic analysis accessible to non-specialists.\n\nResultsPhylostratigraphic analysis entails searching for homologs within increasingly broad clades. The highest clade that contains all homologs of a gene is that genes phylostratum. We have created a general R-based framework, phylostratr, for estimating the phylostratum of every gene in a species. The program can fully automate an analysis: select species for a balanced representation of each strata, retrieve the sequences from UniProt, build BLAST databases, run BLAST, infer homologs for each gene against each subject species, determine phylostrata, and return summaries and diagnostics. phylostratr allows extensive customization. A user may: modify the automatically-generated clade tree or use their own tree; provide custom sequences in place of those automatically retrieved from UniProt; replace BLAST with an alternative algorithm; or tailor the method and sensitivity of the homology inference classifier. phylostratr also offers proteome quality assessments, false-positive diagnostics, and checks for missing organelle genomes. We show the utility of phylostratr through case studies in Arabidopsis thaliana and Saccharomyces cerevisiae.\n\nAvailabilityphylostratr source code and vignettes are available on GitHub at https://github.com/arendsee/phylostratr\n\nContactevewurtele@gmail.com

bioinformatics↗