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C. Shou

Researcher at Yale University

Publications -  10
Citations -  4014

C. Shou is an academic researcher from Yale University. The author has contributed to research in topics: Gene & Genome. The author has an hindex of 8, co-authored 10 publications receiving 3781 citations.

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The Transcriptional Landscape of the Yeast Genome Defined by RNA Sequencing

TL;DR: A quantitative sequencing-based method is developed for mapping transcribed regions, in which complementary DNA fragments are subjected to high-throughput sequencing and mapped to the genome, and it is demonstrated that most (74.5%) of the nonrepetitive sequence of the yeast genome is transcribed.
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Integrative analysis of the Caenorhabditis elegans genome by the modENCODE project

Mark Gerstein, +130 more
- 24 Dec 2010 - 
TL;DR: These studies identified regions of the nematode and fly genomes that show highly occupied targets (or HOT) regions where DNA was bound by more than 15 of the transcription factors analyzed and the expression of related genes were characterized, providing insights into the organization, structure, and function of the two genomes.
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Zebrafish miR-1 and miR-133 shape muscle gene expression and regulate sarcomeric actin organization

TL;DR: Results suggest thatmiR-1 and miR-133 actively shape gene expression patterns in muscle tissue, where they regulate sarcomeric actin organization.
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A statistical framework for modeling gene expression using chromatin features and application to modENCODE datasets

TL;DR: A statistical framework is developed to study the relationship between chromatin features and gene expression that can be used to predict gene expression of protein coding genes, as well as microRNAs, including modENCODE worm datasets.
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Measuring the evolutionary rewiring of biological networks.

TL;DR: A formalism based on analogy to simple models of sequence evolution was developed and used to conduct a systematic study of network rewiring on all the currently available biological networks, and it was found that, similar to sequences, biological networks show a decreased rate of change at large time divergences.