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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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Integrated pseudogene annotation for human chromosome 22: evidence for transcription.

TL;DR: Analysis of corresponding syntenic regions in the mouse, rat and chimp genomes indicates, as previously suggested, that pseudogenes are less conserved than genes, but more preserved than the intergenic background.
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Robust-Linear-Model Normalization To Reduce Technical Variability in Functional Protein Microarrays

TL;DR: It is shown that RLM normalization is able to reduce both intra- and interarray technical variability while maintaining biological differences, and in titration experiments, RLMnormalization enhances the correlation of protein signals with serum concentration.
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VAT: A computational framework to functionally annotate variants in personal genomes within a cloud-computing environment

TL;DR: The Variant Annotation Tool (VAT) is developed to functionally annotate variants from multiple personal genomes at the transcript level as well as obtain summary statistics across genes and individuals.
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Global Identification and Characterization of Transcriptionally Active Regions in the Rice Genome

TL;DR: The identification of 25,352 and 27,744 TARs not encoded by annotated exons in the rice subspecies japonica and indica are reported, providing a systematic characterization of non-exonic transcripts in rice and expanding the current view of the complexity and dynamics of the rice transcriptome.
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Genomics and privacy: implications of the new reality of closed data for the field.

TL;DR: How the large scale of the data from personal genomic sequencing makes it especially hard to share data, exacerbating the privacy problem is described, as well as various computational strategies for dealing with the issue of genomic privacy.