Results 1 to 10 of about 7,703,039 (197)

DAVID: a web server for functional enrichment analysis and functional annotation of gene lists (2021 update)

Nucleic Acids Res., 2022
DAVID is a popular bioinformatics resource system including a web server and web service for functional annotation and enrichment analyses of gene lists. It consists of a comprehensive knowledgebase and a set of functional analysis tools. Here, we report
Brad T. Sherman, Ming Hao, J. Qiu, Xiaoli Jiao, M. Baseler, H. C. Lane, T. Imamichi, Weizhong Chang   +7 more
semanticscholar   +1 more source

SCANPY: large-scale single-cell gene expression data analysis

Genome Biology, 2018
Scanpy is a scalable toolkit for analyzing single-cell gene expression data. It includes methods for preprocessing, visualization, clustering, pseudotime and trajectory inference, differential expression testing, and simulation of gene regulatory ...
F. A. Wolf, Philipp Angerer, Fabian J Theis   +2 more
semanticscholar   +1 more source

Enrichr: a comprehensive gene set enrichment analysis web server 2016 update

Nucleic Acids Res., 2016
Enrichment analysis is a popular method for analyzing gene sets generated by genome-wide experiments. Here we present a significant update to one of the tools in this domain called Enrichr.
Maxim V. Kuleshov, Matthew R. Jones, Andrew D. Rouillard, Nicolas F. Fernandez, Qiaonan Duan, Zichen Wang, Simon Koplev, Sherry L. Jenkins, Kathleen M. Jagodnik, Alexander Lachmann, Michael G. McDermott, Caroline D. Monteiro, Gregory W. Gundersen, Avi Ma’ayan   +13 more
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GEPIA: a web server for cancer and normal gene expression profiling and interactive analyses

Nucleic Acids Res., 2017
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.
Zefang Tang, Chenwei Li, Boxi Kang, Ge Gao, Cheng Li, Zemin Zhang   +5 more
semanticscholar   +1 more source

Spatial reconstruction of single-cell gene expression

Nature Biotechnology, 2015
Spatial localization is a key determinant of cellular fate and behavior, but methods for spatially resolved, transcriptome-wide gene expression profiling across complex tissues are lacking.
R. Satija, J. A. Farrell, David G Gennert, A. Schier, A. Regev   +4 more
semanticscholar   +1 more source

Gene Set Knowledge Discovery with Enrichr

Current Protocols, 2021
Profiling samples from patients, tissues, and cells with genomics, transcriptomics, epigenomics, proteomics, and metabolomics ultimately produces lists of genes and proteins that need to be further analyzed and integrated in the context of known biology.
Zhuorui Xie, Allison Bailey, Maxim V. Kuleshov, Daniel J. B. Clarke, J. E. Evangelista, Sherry L. Jenkins, Alexander Lachmann, Megan L. Wojciechowicz, Eryk Kropiwnicki, Kathleen M. Jagodnik, Minji Jeon, Avi Ma’ayan   +11 more
semanticscholar   +1 more source

edgeR: a Bioconductor package for differential expression analysis of digital gene expression data

Bioinform., 2009
Summary: It is expected that emerging digital gene expression (DGE) technologies will overtake microarray technologies in the near future for many functional genomics applications.
M. Robinson, Davis J. McCarthy, G. Smyth
semanticscholar   +1 more source

The Gene Ontology knowledgebase in 2023

Genetics, 2023
The Gene Ontology (GO) knowledgebase (http://geneontology.org) is a comprehensive resource concerning the functions of genes and gene products (proteins and noncoding RNAs).
Suzi A Aleksander, J. Balhoff, S. Carbon, J. Cherry, H. Drabkin, Dustin Ebert, M. Feuermann, P. Gaudet, N. Harris, D. Hill, Raymond Lee, H. Mi, S. Moxon, C. Mungall, Anushya Muruganugan, Tremayne Mushayahama, P. Sternberg, P. Thomas, K. V. Van Auken, Jolene Ramsey, D. Siegele, R. Chisholm, P. Fey, M. C. Aspromonte, María Victoria Nugnes, F. Quaglia, Silvio C. E. Tosatto, M. Giglio, Suvarna Nadendla, Giulia Antonazzo, H. Attrill, Gilberto dos Santos, Steven J. Marygold, V. Strelets, C. Tabone, Jim Thurmond, Pinglei Zhou, Saadullah H. Ahmed, Praoparn Asanitthong, Diana Luna Buitrago, Meltem N Erdol, M. Gage, Mohamed Ali Kadhum, Kan Yan Chloe Li, Miao Long, Aleksandra Michalak, Angeline Pesala, Armalya Pritazahra, Shirin C. C. Saverimuttu, Renzhi Su, Kate E. Thurlow, R. Lovering, C. Logie, S. Oliferenko, J. Blake, K. Christie, L. Corbani, M. E. Dolan, H. Drabkin, D. Hill, L. Ni, D. Sitnikov, Cynthia L. Smith, A. Cuzick, James Seager, L. Cooper, J. Elser, P. Jaiswal, Parul Gupta, P. Jaiswal, Sushma Naithani, Manuel Lera-Ramírez, Kim M Rutherford, V. Wood, J. D. De Pons, M. Dwinell, G. Hayman, M. Kaldunski, A. Kwitek, S. Laulederkind, M. Tutaj, M. Vedi, Shur-Jen Wang, P. D’Eustachio, L. Aimo, K. Axelsen, A. Bridge, Nevila Hyka-Nouspikel, A. Morgat, Suzi A Aleksander, J. Cherry, S. Engel, K. Karra, S. Miyasato, R. Nash, M. Skrzypek, S. Weng, E. Wong, E. Bakker, T. Berardini, L. Reiser, A. Auchincloss, K. Axelsen, Ghislaine Argoud-Puy, M. Blatter, E. Boutet, L. Breuza, A. Bridge, Cristina Casals-Casas, E. Coudert, A. Estreicher, M. Famiglietti, M. Feuermann, A. Gos, N. Gruaz-Gumowski, C. Hulo, Nevila Hyka-Nouspikel, F. Jungo, Philippe le Mercier, D. Lieberherr, P. Masson, A. Morgat, I. Pedruzzi, L. Pourcel, S. Poux, C. Rivoire, S. Sundaram, A. Bateman, E. Bowler-Barnett, Hema Bye-A-Jee, Paul Denny, A. Ignatchenko, Rizwan Ishtiaq, A. Lock, Yvonne Lussi, M. Magrane, M. Martin, S. Orchard, Pedro Raposo, Elena Speretta, N. Tyagi, Kate Warner, Rossana Zaru, A. Diehl, Raymond Lee, J. Chan, Stavros Diamantakis, Daniela Raciti, M. Zarowiecki, M. Fisher, Christina James-Zorn, V. Ponferrada, A. Zorn, S. Ramachandran, Leyla Ruzicka, M. Westerfield   +155 more
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KEGG as a reference resource for gene and protein annotation

Nucleic Acids Res., 2015
KEGG (http://www.kegg.jp/ or http://www.genome.jp/kegg/) is an integrated database resource for biological interpretation of genome sequences and other high-throughput data.
M. Kanehisa, Yoko Sato, Masayuki Kawashima, Miho Furumichi, M. Tanabe   +4 more
semanticscholar   +1 more source

From the Cover: Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles

, 2005
Although genomewide RNA expression analysis has become a routine tool in biomedical research, extracting biological insight from such information remains a major challenge.
A. Subramanian, P. Tamayo, V. Mootha, Sayan Mukherjee, B. Ebert, Michael A. Gillette, A. Paulovich, S. Pomeroy, T. Golub, E. Lander, J. Mesirov   +10 more
semanticscholar   +1 more source