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Mark Gerstein

Researcher at Yale University

Publications -  802
Citations -  172183

Mark Gerstein is an academic researcher from Yale University. The author has contributed to research in topics: Genome & Gene. The author has an hindex of 168, co-authored 751 publications receiving 149578 citations. Previous affiliations of Mark Gerstein include Rutgers University & Structural Genomics Consortium.

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Comprehensive analysis of amino acid and nucleotide composition in eukaryotic genomes, comparing genes and pseudogenes

TL;DR: The results indicate that the composition of pseudogenes that are under no selective constraints progressively drifts from that of coding DNA towards non-coding DNA, and it is proposed that the degree to which pseudogene approach a random sequence composition may be useful in dating different sets of pseudogenees, as well as to assess the rate at which intergenic DNA accumulates mutations.
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MetaSV: an accurate and integrative structural-variant caller for next generation sequencing

TL;DR: MetaSV is proposed, an integrated SV caller which leverages multiple orthogonal SV signals for high accuracy and resolution and analyzes soft-clipped reads from alignment to detect insertions accurately since existing tools underestimate insertion SVs.
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Patterns of Protein-Fold Usage in Eight Microbial Genomes: A Comprehensive Structural Census

TL;DR: Eight microbial genomes are compared in terms of protein structure and patterns of fold usage—whether a given fold occurs in a particular organism and all the genomes appear to have similar usage patterns for these folds, according to a “Zipf‐like” law.
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Global changes in STAT target selection and transcription regulation upon interferon treatments

TL;DR: The results reveal a wealth of new information regarding IFN/STAT-binding targets and also fundamental insights into mechanisms of regulation of gene expression in different cell states.
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Detecting and annotating genetic variations using the HugeSeq pipeline

TL;DR: This research presents a meta-modelling architecture that automates the very labor-intensive and therefore time-heavy and expensive and therefore expensive and expensive process of designing and implementing nanofiltration systems.