M
Michael Q. Zhang
Researcher at Tsinghua University
Publications - 396
Citations - 46412
Michael Q. Zhang is an academic researcher from Tsinghua University. The author has contributed to research in topics: Gene & Chromatin. The author has an hindex of 93, co-authored 378 publications receiving 42008 citations. Previous affiliations of Michael Q. Zhang include Chinese Academy of Sciences & Peking Union Medical College Hospital.
Papers
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Journal ArticleDOI
Computer-assisted identification of cell cycle-related genes: New targets for E2F transcription factors
Alexander E. Kel,Olga V. Kel-Margoulis,Peggy J. Farnham,Stephanie M. Bartley,Edgar Wingender,Michael Q. Zhang +5 more
TL;DR: This work developed a new method for identifying composite substructures in regulatory regions of genes consisting of a binding site for a key transcription factor and additional contextual motifs: potential targets for other transcription factors that may synergistically regulate gene transcription.
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Updates to the RMAP short-read mapping software
Andrew D. Smith,Wen-Yu Chung,Emily Hodges,Jude Kendall,Greg Hannon,James W. Hicks,Zhenyu Xuan,Michael Q. Zhang +7 more
TL;DR: A major new version of the RMAP software for mapping reads from short-read sequencing technology is reported, along with novel functionality for mapping paired-end reads, making more sophisticated use of quality scores, collecting mapping locations for ambiguously mapping reads and mapping bisulfite-treated reads.
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Computational prediction of methylation status in human genomic sequences
Rajdeep Das,Nevenka Dimitrova,Zhenyu Xuan,Robert A. Rollins,Fatemah Haghighi,John R. Edwards,Jingyue Ju,Timothy H. Bestor,Michael Q. Zhang +8 more
TL;DR: A computational pattern recognition method that is used to predict the methylation landscape of human brain DNA and can be applied both to CpG islands and to non-CpG island regions is described.
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An alternative-exon database and its statistical analysis.
TL;DR: Data indicate a combinatorial effect of weak splice sites, atypical nucleotide usage at certain positions, and functional enhancers as an important contribution to alternative-exon regulation.
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CART Classification of Human 5′ UTR Sequences
TL;DR: The present classification and characterization of the 5' UTRs provide precious information for better understanding the translational regulation of human mRNAs and can help people build better computational models for predicting the 5'-terminal exon and separating the 5- UTR from the coding region.