scispace - formally typeset
Search or ask a question
Author

Yin Yao

Bio: Yin Yao is an academic researcher from National Institutes of Health. The author has contributed to research in topics: Expression quantitative trait loci. The author has an hindex of 2, co-authored 2 publications receiving 4932 citations.

Papers
More filters
Journal ArticleDOI
John T. Lonsdale, Jeffrey Thomas, Mike Salvatore, Rebecca Phillips, Edmund Lo, Saboor Shad, Richard Hasz, Gary Walters, Fernando U. Garcia1, Nancy Young2, Barbara A. Foster3, Mike Moser3, Ellen Karasik3, Bryan Gillard3, Kimberley Ramsey3, Susan L. Sullivan, Jason Bridge, Harold Magazine, John Syron, Johnelle Fleming, Laura A. Siminoff4, Heather M. Traino4, Maghboeba Mosavel4, Laura Barker4, Scott D. Jewell5, Daniel C. Rohrer5, Dan Maxim5, Dana Filkins5, Philip Harbach5, Eddie Cortadillo5, Bree Berghuis5, Lisa Turner5, Eric Hudson5, Kristin Feenstra5, Leslie H. Sobin6, James A. Robb6, Phillip Branton, Greg E. Korzeniewski6, Charles Shive6, David Tabor6, Liqun Qi6, Kevin Groch6, Sreenath Nampally6, Steve Buia6, Angela Zimmerman6, Anna M. Smith6, Robin Burges6, Karna Robinson6, Kim Valentino6, Deborah Bradbury6, Mark Cosentino6, Norma Diaz-Mayoral6, Mary Kennedy6, Theresa Engel6, Penelope Williams6, Kenyon Erickson, Kristin G. Ardlie7, Wendy Winckler7, Gad Getz7, Gad Getz8, David S. DeLuca7, MacArthur Daniel MacArthur7, MacArthur Daniel MacArthur8, Manolis Kellis7, Alexander Thomson7, Taylor Young7, Ellen Gelfand7, Molly Donovan7, Yan Meng7, George B. Grant7, Deborah C. Mash9, Yvonne Marcus9, Margaret J. Basile9, Jun Liu8, Jun Zhu10, Zhidong Tu10, Nancy J. Cox11, Dan L. Nicolae11, Eric R. Gamazon11, Hae Kyung Im11, Anuar Konkashbaev11, Jonathan K. Pritchard12, Jonathan K. Pritchard11, Matthew Stevens11, Timothée Flutre11, Xiaoquan Wen11, Emmanouil T. Dermitzakis13, Tuuli Lappalainen13, Roderic Guigó, Jean Monlong, Michael Sammeth, Daphne Koller14, Alexis Battle14, Sara Mostafavi14, Mark I. McCarthy15, Manual Rivas15, Julian Maller15, Ivan Rusyn16, Andrew B. Nobel16, Fred A. Wright16, Andrey A. Shabalin16, Mike Feolo17, Nataliya Sharopova17, Anne Sturcke17, Justin Paschal17, James M. Anderson17, Elizabeth L. Wilder17, Leslie Derr17, Eric D. Green17, Jeffery P. Struewing17, Gary F. Temple17, Simona Volpi17, Joy T. Boyer17, Elizabeth J. Thomson17, Mark S. Guyer17, Cathy Ng17, Assya Abdallah17, Deborah Colantuoni17, Thomas R. Insel17, Susan E. Koester17, Roger Little17, Patrick Bender17, Thomas Lehner17, Yin Yao17, Carolyn C. Compton17, Jimmie B. Vaught17, Sherilyn Sawyer17, Nicole C. Lockhart17, Joanne P. Demchok17, Helen F. Moore17 
TL;DR: The Genotype-Tissue Expression (GTEx) project is described, which will establish a resource database and associated tissue bank for the scientific community to study the relationship between genetic variation and gene expression in human tissues.
Abstract: Genome-wide association studies have identified thousands of loci for common diseases, but, for the majority of these, the mechanisms underlying disease susceptibility remain unknown. Most associated variants are not correlated with protein-coding changes, suggesting that polymorphisms in regulatory regions probably contribute to many disease phenotypes. Here we describe the Genotype-Tissue Expression (GTEx) project, which will establish a resource database and associated tissue bank for the scientific community to study the relationship between genetic variation and gene expression in human tissues.

6,545 citations

John T. Lonsdale, Jeffrey Thomas, Mike Salvatore, Rebecca Phillips, Edmund Lo, Saboor Shad, Richard Hasz, Gary Walters, Fernando U. Garcia, Nancy Young, Barbara A. Foster, Mike Moser, Ellen Karasik, Bryan Gillard, Kimberley Ramsey, Susan L. Sullivan, Jason Bridge, Harold Magazine, John Syron, Johnelle Fleming, Laura A. Siminoff, Heather M. Traino, Maghboeba Mosavel, Laura Barker, Scott D. Jewell, Daniel C. Rohrer, Dan Maxim, Dana Filkins, Philip Harbach, Eddie Cortadillo, Bree Berghuis, Lisa Turner, Eric Hudson, Kristin Feenstra, Leslie H. Sobin, James A. Robb, Phillip Branton, Greg E. Korzeniewski, Charles Shive, David Tabor, Liqun Qi, Kevin Groch, Sreenath Nampally, Steve Buia, Angela Zimmerman, Anna M. Smith, Robin Burges, Karna Robinson, Kim Valentino, Deborah Bradbury, Mark Cosentino, Norma Diaz-Mayoral, Mary Kennedy, Theresa Engel, Penelope Williams, Kenyon Erickson, Kristin G. Ardlie, Wendy Winckler, Gad Getz, David S. DeLuca, Daniel G. MacArthur, Manolis Kellis, Alexander Thomson, Taylor Young, Ellen Gelfand, Molly Donovan, Yan Meng, George B. Grant, Deborah C. Mash, Yvonne Marcus, Margaret J. Basile, Jun Liu, Jun Zhu, Zhidong Tu, Nancy J. Cox, Dan L. Nicolae, Eric R. Gamazon, Hae Kyung Im, Anuar Konkashbaev, Jonathan K. Pritchard, Matthew Stevens, Timothée Flutre, Xiaoquan Wen, Emmanouil T. Dermitzakis, Tuuli Lappalainen, Roderic Guigó, Jean Monlong, Michael Sammeth, Daphne Koller, Alexis Battle, Sara Mostafavi, Mark I. McCarthy, Manual Rivas, Julian Maller, Ivan Rusyn, Andrew B. Nobel, Fred A. Wright, Andrey A. Shabalin, Mike Feolo, Nataliya Sharopova, Anne Sturcke, Justin Paschal, James M. Anderson, Elizabeth L. Wilder, Leslie Derr, Eric D. Green, Jeffery P. Struewing, Gary F. Temple, Simona Volpi, Joy T. Boyer, Elizabeth J. Thomson, Mark S. Guyer, Cathy Ng, Assya Abdallah, Deborah Colantuoni, Thomas R. Insel, Susan E. Koester, A. Roger Little, Patrick Bender, Thomas Lehner, Yin Yao, Carolyn C. Compton, Jimmie B. Vaught, Sherilyn Sawyer, Nicole C. Lockhart, Joanne P. Demchok, Helen F. Moore 
01 May 2013
TL;DR: In this article, the authors proposed a new method for the detection of cancer using a set of genes extracted from the human brain, which they called LSTM-CRF.
Abstract: National Institutes of Health (U.S.) (US NIH to the Broad Institute of Harvard and MIT, R01 DA006227-17)

2 citations


Cited by
More filters
Journal ArticleDOI
Zefang Tang1, Chenwei Li1, Boxi Kang1, Ge Gao1, Cheng Li1, Zemin Zhang 
TL;DR: GEPIA (Gene Expression Profiling Interactive Analysis) fills in the gap between cancer genomics big data and the delivery of integrated information to end users, thus helping unleash the value of the current data resources.
Abstract: Tremendous amount of RNA sequencing data have been produced by large consortium projects such as TCGA and GTEx, creating new opportunities for data mining and deeper understanding of gene functions. While certain existing web servers are valuable and widely used, many expression analysis functions needed by experimental biologists are still not adequately addressed by these tools. We introduce GEPIA (Gene Expression Profiling Interactive Analysis), a web-based tool to deliver fast and customizable functionalities based on TCGA and GTEx data. GEPIA provides key interactive and customizable functions including differential expression analysis, profiling plotting, correlation analysis, patient survival analysis, similar gene detection and dimensionality reduction analysis. The comprehensive expression analyses with simple clicking through GEPIA greatly facilitate data mining in wide research areas, scientific discussion and the therapeutic discovery process. GEPIA fills in the gap between cancer genomics big data and the delivery of integrated information to end users, thus helping unleash the value of the current data resources. GEPIA is available at http://gepia.cancer-pku.cn/.

5,980 citations

Journal ArticleDOI
TL;DR: This work presents a method named HISAT2 (hierarchical indexing for spliced alignment of transcripts 2) that can align both DNA and RNA sequences using a graph Ferragina Manzini index, and uses it to represent and search an expanded model of the human reference genome.
Abstract: The human reference genome represents only a small number of individuals, which limits its usefulness for genotyping. We present a method named HISAT2 (hierarchical indexing for spliced alignment of transcripts 2) that can align both DNA and RNA sequences using a graph Ferragina Manzini index. We use HISAT2 to represent and search an expanded model of the human reference genome in which over 14.5 million genomic variants in combination with haplotypes are incorporated into the data structure used for searching and alignment. We benchmark HISAT2 using simulated and real datasets to demonstrate that our strategy of representing a population of genomes, together with a fast, memory-efficient search algorithm, provides more detailed and accurate variant analyses than other methods. We apply HISAT2 for HLA typing and DNA fingerprinting; both applications form part of the HISAT-genotype software that enables analysis of haplotype-resolved genes or genomic regions. HISAT-genotype outperforms other computational methods and matches or exceeds the performance of laboratory-based assays. A graph-based genome indexing scheme enables variant-aware alignment of sequences with very low memory requirements.

4,855 citations

Journal ArticleDOI
Kristin G. Ardlie, David S. DeLuca, Ayellet V. Segrè, Timothy J. Sullivan, Taylor Young, Ellen Gelfand, Casandra A. Trowbridge, Julian Maller, Taru Tukiainen, Monkol Lek, Lucas D. Ward, Pouya Kheradpour, Benjamin Iriarte, Yan Meng, Cameron D. Palmer, Tõnu Esko, Wendy Winckler, Joel N. Hirschhorn, Manolis Kellis, Daniel G. MacArthur, Gad Getz, Andrey A. Shabalin, Gen Li, Yi-Hui Zhou, Andrew B. Nobel, Ivan Rusyn, Fred A. Wright, Tuuli Lappalainen, Pedro G. Ferreira, Halit Ongen, Manuel A. Rivas, Alexis Battle, Sara Mostafavi, Jean Monlong, Michael Sammeth, Marta Melé, Ferran Reverter, Jakob M. Goldmann, Daphne Koller, Roderic Guigó, Mark I. McCarthy, Emmanouil T. Dermitzakis, Eric R. Gamazon, Hae Kyung Im, Anuar Konkashbaev, Dan L. Nicolae, Nancy J. Cox, Timothée Flutre, Xiaoquan Wen, Matthew Stephens, Jonathan K. Pritchard, Zhidong Tu, Bin Zhang, Tao Huang, Quan Long, Luan Lin, Jialiang Yang, Jun Zhu, Jun Liu, Amanda Brown, Bernadette Mestichelli, Denee Tidwell, Edmund Lo, Mike Salvatore, Saboor Shad, Jeffrey A. Thomas, John T. Lonsdale, Michael T. Moser, Bryan Gillard, Ellen Karasik, Kimberly Ramsey, Christopher Choi, Barbara A. Foster, John Syron, Johnell Fleming, Harold Magazine, Rick Hasz, Gary Walters, Jason Bridge, Mark Miklos, Susan L. Sullivan, Laura Barker, Heather M. Traino, Maghboeba Mosavel, Laura A. Siminoff, Dana R. Valley, Daniel C. Rohrer, Scott D. Jewell, Philip A. Branton, Leslie H. Sobin, Mary Barcus, Liqun Qi, Jeffrey McLean, Pushpa Hariharan, Ki Sung Um, Shenpei Wu, David Tabor, Charles Shive, Anna M. Smith, Stephen A. Buia, Anita H. Undale, Karna Robinson, Nancy Roche, Kimberly M. Valentino, Angela Britton, Robin Burges, Debra Bradbury, Kenneth W. Hambright, John Seleski, Greg E. Korzeniewski, Kenyon Erickson, Yvonne Marcus, Jorge Tejada, Mehran Taherian, Chunrong Lu, Margaret J. Basile, Deborah C. Mash, Simona Volpi, Jeffery P. Struewing, Gary F. Temple, Joy T. Boyer, Deborah Colantuoni, Roger Little, Susan E. Koester, Latarsha J. Carithers, Helen M. Moore, Ping Guan, Carolyn C. Compton, Sherilyn Sawyer, Joanne P. Demchok, Jimmie B. Vaught, Chana A. Rabiner, Nicole C. Lockhart 
08 May 2015-Science
TL;DR: The landscape of gene expression across tissues is described, thousands of tissue-specific and shared regulatory expression quantitative trait loci (eQTL) variants are cataloged, complex network relationships are described, and signals from genome-wide association studies explained by eQTLs are identified.
Abstract: Understanding the functional consequences of genetic variation, and how it affects complex human disease and quantitative traits, remains a critical challenge for biomedicine. We present an analysi...

4,418 citations

Journal ArticleDOI
20 Jun 2014-Science
TL;DR: The genome sequence of single cells isolated from brain glioblastomas was examined, which revealed shared chromosomal changes but also extensive transcription variation, including genes related to signaling, which represent potential therapeutic targets.
Abstract: Human cancers are complex ecosystems composed of cells with distinct phenotypes, genotypes, and epigenetic states, but current models do not adequately reflect tumor composition in patients. We used single-cell RNA sequencing (RNA-seq) to profile 430 cells from five primary glioblastomas, which we found to be inherently variable in their expression of diverse transcriptional programs related to oncogenic signaling, proliferation, complement/immune response, and hypoxia. We also observed a continuum of stemness-related expression states that enabled us to identify putative regulators of stemness in vivo. Finally, we show that established glioblastoma subtype classifiers are variably expressed across individual cells within a tumor and demonstrate the potential prognostic implications of such intratumoral heterogeneity. Thus, we reveal previously unappreciated heterogeneity in diverse regulatory programs central to glioblastoma biology, prognosis, and therapy.

3,475 citations

Journal ArticleDOI
TL;DR: The remarkable range of discoveriesGWASs has facilitated in population and complex-trait genetics, the biology of diseases, and translation toward new therapeutics are reviewed.
Abstract: Application of the experimental design of genome-wide association studies (GWASs) is now 10 years old (young), and here we review the remarkable range of discoveries it has facilitated in population and complex-trait genetics, the biology of diseases, and translation toward new therapeutics. We predict the likely discoveries in the next 10 years, when GWASs will be based on millions of samples with array data imputed to a large fully sequenced reference panel and on hundreds of thousands of samples with whole-genome sequencing data.

2,669 citations