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Ayal B. Gussow

Researcher at National Institutes of Health

Publications -  20
Citations -  1258

Ayal B. Gussow is an academic researcher from National Institutes of Health. The author has contributed to research in topics: Genome & Human virome. The author has an hindex of 11, co-authored 20 publications receiving 824 citations. Previous affiliations of Ayal B. Gussow include Duke University & Columbia University.

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Discovery of an expansive bacteriophage family that includes the most abundant viruses from the human gut

TL;DR: A comprehensive search of genomic and metagenomic databases with sensitive methods for protein sequence analysis identifies an expansive, diverse group of bacteriophages related to crAssphage and predicts the functions of the majority of phage proteins, in particular those that comprise the structural, replication and expression modules.
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Genomic determinants of pathogenicity in SARS-CoV-2 and other human coronaviruses.

TL;DR: An in-depth molecular analysis is reported to reconstruct the evolutionary origins of the enhanced pathogenicity of SARS-CoV-2 and other coronaviruses that are severe human pathogens.
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Orion: Detecting regions of the human non-coding genome that are intolerant to variation using population genetics.

TL;DR: This work presents Orion, an approach that detects regions of the non-coding genome that are depleted of variation, suggesting that the regions are intolerant of mutations and subject to purifying selection in the human lineage.
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The intolerance of regulatory sequence to genetic variation predicts gene dosage sensitivity

TL;DR: The results highlight that the intolerance of noncoding sequence stretches in the human genome can provide a critical complementary tool to other genome annotation approaches to help identify the parts of thehuman genome increasingly likely to harbor mutations that influence risk of disease.
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The intolerance to functional genetic variation of protein domains predicts the localization of pathogenic mutations within genes

TL;DR: It is shown that the intolerance scores of these sub-regions significantly correlate with reported pathogenic mutations, which extends the utility of intolerance scores to indicating where pathogen mutations are mostly likely to fall within genes.