D
David R. Kelley
Researcher at Harvard University
Publications - 61
Citations - 19557
David R. Kelley is an academic researcher from Harvard University. The author has contributed to research in topics: Genome & Gene. The author has an hindex of 28, co-authored 57 publications receiving 16654 citations. Previous affiliations of David R. Kelley include Johns Hopkins University School of Medicine & University of Maryland, College Park.
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Differential gene and transcript expression analysis of RNA-seq experiments with TopHat and Cufflinks
Cole Trapnell,Adam Roberts,Loyal A. Goff,Loyal A. Goff,Loyal A. Goff,Geo Pertea,Daehwan Kim,Daehwan Kim,David R. Kelley,David R. Kelley,Harold Pimentel,Steven L. Salzberg,John L. Rinn,John L. Rinn,Lior Pachter +14 more
TL;DR: This protocol begins with raw sequencing reads and produces a transcriptome assembly, lists of differentially expressed and regulated genes and transcripts, and publication-quality visualizations of analysis results, which takes less than 1 d of computer time for typical experiments and ∼1 h of hands-on time.
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A whole-genome assembly of the domestic cow, Bos taurus
Aleksey V. Zimin,Arthur L. Delcher,Liliana Florea,David R. Kelley,Michael C. Schatz,Daniela Puiu,Finnian Hanrahan,Geo Pertea,Curtis P. Van Tassell,Tad S. Sonstegard,Guillaume Marçais,Michael Roberts,Poorani Subramanian,James A. Yorke,Steven L. Salzberg +14 more
TL;DR: By using independent mapping data and conserved synteny between the cow and human genomes, this work was able to construct an assembly with excellent large-scale contiguity in which a large majority (approximately 91%) of the genome has been placed onto the 30 B. taurus chromosomes.
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Basset: learning the regulatory code of the accessible genome with deep convolutional neural networks
TL;DR: Basset offers a powerful computational approach to annotate and interpret the noncoding genome using a recent machine learning advance-deep convolutional neural networks to learn the functional activity of DNA sequences from genomics data.
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Quake: quality-aware detection and correction of sequencing errors
TL;DR: This work introduces Quake, a program to detect and correct errors in DNA sequencing reads using a maximum likelihood approach incorporating quality values and nucleotide specific miscall rates, which achieves the highest accuracy on realistically simulated reads.
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Topological organization of multichromosomal regions by the long intergenic noncoding RNA Firre
Ezgi Hacisuleyman,Loyal A. Goff,Cole Trapnell,Adam Williams,Jorge Henao-Mejia,Lei Sun,Patrick D. McClanahan,David G. Hendrickson,Martin Sauvageau,David R. Kelley,Michael A. Morse,Jesse M. Engreitz,Eric S. Lander,Mitch Guttman,Harvey F. Lodish,Richard A. Flavell,Arjun Raj,John L. Rinn +17 more
TL;DR: One lncRNA, Firre, is described that interacts with the nuclear-matrix factor hnRNPU through a 156-bp repeating sequence and localizes across an ~5-Mb domain on the X chromosome, suggesting a model in which lncRNAs such as Firre can interface with and modulate nuclear architecture across chromosomes.