M
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.
Papers
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TIP: A probabilistic method for identifying transcription factor target genes from ChIP-seq binding profiles
TL;DR: A probabilistic model called target identification from profiles (TIP) is proposed that quantitatively measures the regulatory relationships between TFs and target genes and shows the advantages of TIP by comparing it to the 'simple' approach on several representative datasets.
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Systematic analysis of transcribed loci in ENCODE regions using RACE sequencing reveals extensive transcription in the human genome
Jia Qian Wu,Jiang Du,Joel Rozowsky,Zhengdong D. Zhang,Alexander E. Urban,Ghia Euskirchen,Sherman M. Weissman,Mark Gerstein,Michael Snyder +8 more
TL;DR: RACE sequencing is an efficient, sensitive, and highly accurate method for characterization of the transcriptome of specific cell/tissue types, and it appears that much of the genome is represented in polyA+ RNA.
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Supervised enhancer prediction with epigenetic pattern recognition and targeted validation
Anurag Sethi,Mengting Gu,Emrah Gumusgoz,Landon L Chan,Koon-Kiu Yan,Joel Rozowsky,Iros Barozzi,Veena Afzal,Jennifer A. Akiyama,Ingrid Plajzer-Frick,Chengfei Yan,Catherine S. Novak,Momoe Kato,Tyler H. Garvin,Quan Pham,Anne N. Harrington,Brandon J. Mannion,Elizabeth Lee,Yoko Fukuda-Yuzawa,Axel Visel,Diane E. Dickel,Kevin Y. Yip,Richard E. Sutton,Len A. Pennacchio,Mark Gerstein +24 more
TL;DR: A framework using Drosophila STARR-seq to create shape-matching filters based on meta-profiles of epigenetic features that can be transferred to predict mouse and human enhancers and examined the transcription factor binding patterns at predicted enhancers versus promoters.
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Measurement of the effectiveness of transitive sequence comparison, through a third 'intermediate' sequence.
TL;DR: This study examines what fraction of the known structural relationships transitive sequence matching can uncover beyond that found by normal pairwise comparison, using a well-characterized test set taken from the scop classification of protein structure.
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Comparing classical pathways and modern networks: towards the development of an edge ontology
Long J. Lu,Long J. Lu,Andrea Sboner,Yuanpeng J. Huang,Hao Xin Lu,Tara A. Gianoulis,Kevin Y. Yip,Philip M. Kim,Gaetano T. Montelione,Gaetano T. Montelione,Mark Gerstein +10 more
TL;DR: In this paper, a standardized and well-defined edge ontology is proposed to represent pathways in large-scale networks, and a prototype is proposed as a starting point for reaching this goal.