Massive mining of publicly available RNA-seq data from human and mouse
Open accessPublished version of a preprint
Abstract
RNA sequencing (RNA-seq) is the leading technology for genome-wide transcript quantification. However, publicly available RNA-seq data is currently provided mostly in raw form, a significant barrier for global and integrative retrospective analyses. ARCHS4 is a web resource that makes the majority of published RNA-seq data from human and mouse available at the gene and transcript levels. For developing ARCHS4, available FASTQ files from RNA-seq experiments from the Gene Expression Omnibus (GEO) were aligned using a cloud-based infrastructure. In total 187,946 samples are accessible through ARCHS4 with 103,083 mouse and 84,863 human. Additionally, the ARCHS4 web interface provides intuitive exploration of the processed data through querying tools, interactive visualization, and gene pages that provide average expression across cell lines and tissues, top co-expressed genes for each gene, and predicted biological functions and protein–protein interactions for each gene based on prior knowledge combined with co-expression.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
RNA sequencing is an effective method for serial analysis of gene expression, which aids in understanding regulation mechanisms.
“RNA sequencing (RNA-seq) is an effective method for serial analysis of gene expression, which aids in understanding regulation mechanisms (Lachmann et al., 2018).”
From Multimodal MRI combined with RNA sequencing reveals pathological signatures in the 9-month-old 3×Tg-AD mouse brain (2026) · cites this paper as Lachmann et al., 2018
The Introduction (page unavailable) describes RNA-seq as a common, sensitive method for quantifying gene expression. The Results section on predicting biological functions and protein interactions (page unavailable) uses expression data to predict upstream regulatory transcription factors, supporting a link to studying regulation. The paper does not describe serial or longitudinal analysis of gene expression. (LLM, conf=0.85)
Experimental — accuracy not yet measuredPrior research has demonstrated that gene expression distribution can be mapped onto functional MRI.
“Prior research has demonstrated that gene expression distribution can be mapped onto functional MRI (Lachmann et al., 2018).”
From Multimodal MRI combined with RNA sequencing reveals pathological signatures in the 9-month-old 3×Tg-AD mouse brain (2026) · cites this paper as Lachmann et al., 2018
The Discussion (page unavailable) says the resource calculates gene-expression distributions across tissues and cell lines. Our copy does not say those distributions were mapped onto functional MRI; searches for fMRI and functional magnetic resonance imaging found no passage addressing that link. (LLM, conf=0.76)
Experimental — accuracy not yet measuredAge-similar, non-diseased, normal lung tissues were procured from lungs not used by the Northern California Transplant Donor Network.
“Age-similar, non-diseased, normal lung tissues were procured from lungs not used by the Northern California Transplant Donor Network54.”
From Reference-based analysis of lung single-cell sequencing reveals a transitional profibrotic macrophage (2019) · cites this paper as [54]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Fast and accurate short read alignment with Burrows-Wheeler transform
Li, H., & Durbin, R. (2009) · Bioinformatics · doi:10.1093/bioinformatics/btp324 · Cited by 58,607 · see citing works
STAR: ultrafast universal RNA-seq aligner
Dobin, A., Davis, C. A., Schlesinger, F., Drenkow, J., Zaleski, C., Jha, S., Batut, P., Chaisson, M., & Gingeras, T. R. (2013) · Bioinformatics · doi:10.1093/bioinformatics/bts635 · Cited by 51,709 · see citing works
Gene Ontology: tool for the unification of biology
Ashburner, M., Ball, C. A., Blake, J. A., Botstein, D., Butler, H., Cherry, J. M., Davis, A. P., Dolinski, K., Dwight, S. S., Eppig, J. T., et al. (2000) · Nature Genetics · doi:10.1038/75556 · Cited by 43,206 · see citing works
KEGG: Kyoto Encyclopedia of Genes and Genomes
Kanehisa, M. (2000) · Nucleic Acids Research · doi:10.1093/nar/28.1.27 · Cited by 37,077 · see citing works
(2008) · Journal of Machine Learning Research · Cited by 33,051 · see citing works
KEGG: Kyoto Encyclopedia of Genes and Genomes
Ogata, H., Goto, S., Sato, K., Fujibuchi, W., Bono, H., & Kanehisa, M. (1999) · Nucleic Acids Research · doi:10.1093/nar/27.1.29 · Cited by 31,232 · see citing works
Ultrafast and memory-efficient alignment of short DNA sequences to the human genome.
Langmead, B., Trapnell, C., Pop, M., & Salzberg, S. L. (2009) · Genome Biology · doi:10.1186/gb-2009-10-3-r25 · Cited by 21,849 · see citing works
The sequence of the human genome
Venter, J. C., Adams, M. D., Myers, E. W., Li, P. W., Mural, R. J., Sutton, G. G., Smith, H. O., Yandell, M., Evans, C. A., Holt, R. A., et al. (2001) · Science · doi:10.1126/science.1058040 · Cited by 13,909 · see citing works
Gene Expression Omnibus: NCBI gene expression and hybridization array data repository
Edgar, R. (2002) · Nucleic Acids Research · doi:10.1093/nar/30.1.207 · Cited by 13,288 · see citing works
Kim, D., Pertea, G., Trapnell, C., Pimentel, H., Kelley, R., & Salzberg, S. L. (2013) · Genome Biology · doi:10.1186/gb-2013-14-4-r36 · Cited by 12,668 · see citing works
Enrichr: a comprehensive gene set enrichment analysis web server 2016 update
Kuleshov, M. V., Jones, M. R., Rouillard, A. D., Fernandez, N. F., Duan, Q., Wang, Z., Koplev, S., Jenkins, S. L., Jagodnik, K. M., Lachmann, A., et al. (2016) · Nucleic Acids Research · doi:10.1093/nar/gkw377 · Cited by 10,799 · see citing works
Self-citationNear-optimal probabilistic RNA-seq quantification
Bray, N. L., Pimentel, H., Melsted, P., & Pachter, L. (2016) · Nature Biotechnology · doi:10.1038/nbt.3519 · Cited by 9,723 · see citing works
Quantitative Monitoring of Gene Expression Patterns with a Complementary DNA Microarray
Schena, M., Shalon, D., Davis, R. W., & Brown, P. O. (1995) · Science · doi:10.1126/science.270.5235.467 · Cited by 9,609 · see citing works
The cancer genome atlas pan-cancer analysis project
The Cancer Genome Atlas Research Network, Weinstein, J. N., Collisson, E. A., Mills, G. B., Shaw, K. R. M., Ozenberger, B. A., Ellrott, K., Shmulevich, I., Sander, C., & Stuart, J. M. (2013) · Nature Genetics · doi:10.1038/ng.2764 · Cited by 9,092 · see citing works
The Cancer Cell Line Encyclopedia enables predictive modelling of anticancer drug sensitivity
Barretina, J., Caponigro, G., Stransky, N., Venkatesan, K., Margolin, A. A., Kim, S., Wilson, C. J., Lehár, J., Kryukov, G. V., Sonkin, D., et al. (2012) · Nature · doi:10.1038/nature11003 · Cited by 7,950 · see citing works
Self-citationEnrichr: interactive and collaborative HTML5 gene list enrichment analysis tool
Chen, E. Y., Tan, C. M., Kou, Y., Duan, Q., Wang, Z., Meirelles, G. V., Clark, N. R., & Ma’ayan, A. (2013) · BMC Bioinformatics · doi:10.1186/1471-2105-14-128 · Cited by 7,814 · see citing works
Self-citationThe Sequence of the Human Genome
Venter, J., Adams, M., Myers, E., Li, P. W., Mural, R., Sutton, G., Smith, H., Yandell, M., Evans, C., Holt, R., et al. (2001) · Science · doi:10.1373/clinchem.2014.237016 · Cited by 6,779 · see citing works
BioGRID: a general repository for interaction datasets
Stark, C. (2006) · Nucleic Acids Research · doi:10.1093/nar/gkj109 · Cited by 3,960 · see citing works
SOAP2: an improved ultrafast tool for short read alignment
Li, R., Yu, C., Li, Y., Lam, T., Yiu, S., Kristiansen, K., & Wang, J. (2009) · Bioinformatics · doi:10.1093/bioinformatics/btp336 · Cited by 3,763 · see citing works
GEOquery: a bridge between the Gene Expression Omnibus (GEO) and BioConductor
Davis, S. R., & Meltzer, P. S. (2007) · Bioinformatics · doi:10.1093/bioinformatics/btm254 · Cited by 3,289 · see citing works
Docker: lightweight Linux containers for consistent development and deployment
Merkel, D. (2014) · Linux journal · Cited by 3,277 · see citing works
Bostock, M., Ogievetsky, V., & Heer, J. (2011) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2011.185 · Cited by 3,165 · see citing works
RNA-seq: An assessment of technical reproducibility and comparison with gene expression arrays
Marioni, J. C., Mason, C. E., Mane, S. M., Stephens, M., & Gilad, Y. (2008) · Genome Research · doi:10.1101/gr.079558.108 · Cited by 2,771 · see citing works
The ENCODE (ENCyclopedia Of DNA Elements) Project
(2004) · Science · doi:10.1126/science.1105136 · Cited by 2,390 · see citing works
Entrez Gene: gene-centered information at NCBI
Maglott, D. R., Ostell, J., Pruitt, K. D., & Tatusova, T. A. (2010) · Nucleic Acids Research · doi:10.1093/nar/gkq1237 · Cited by 2,117 · see citing works
GeneCards Version 3: the human gene integrator
Safran, M., Dalah, I., Alexander, J., Rosen, N., Stein, T. I., Shmoish, M., Nativ, N., Bahir, I., Doniger, T., Krug, H., et al. (2010) · Database · doi:10.1093/database/baq020 · Cited by 2,105 · see citing works
Rouillard, A. D., Gundersen, G. W., Fernandez, N. F., Wang, Z., Monteiro, C. D., McDermott, M. G., & Ma’ayan, A. (2016) · Database · doi:10.1093/database/baw100 · Cited by 1,515 · see citing works
Self-citationThe BioPlex network: a systematic exploration of the human interactome
Huttlin, E. L., Ting, L., Bruckner, R. J., Gebreab, F., Gygi, M. P., Szpyt, J., Tam, S., Zarraga, G., Colby, G., Baltier, K., et al. (2015) · Cell · doi:10.1016/j.cell.2015.06.043 · Cited by 1,425 · cited 9× · see citing works
“This is likely because a part of BioPLEX is contained within hu.”
MethodInfluentialToil enables reproducible, open source, big biomedical data analyses
Vivian, J., Rao, A. A., Nothaft, F. A., Ketchum, C., Armstrong, J., Novak, A., Pfeil, J., Narkizian, J., Deran, A. D., Musselman-Brown, A., et al. (2017) · Nature Biotechnology · doi:10.1038/nbt.3772 · Cited by 1,365 · see citing works
ArrayExpress--a public repository for microarray gene expression data at the EBI
Parkinson, H. (2004) · Nucleic Acids Research · doi:10.1093/nar/gki056 · Cited by 1,075 · see citing works
The human phenotype ontology: a tool for annotating and analyzing human hereditary disease
Robinson, P. N., Köhler, S., Bauer, S., Seelow, D., Horn, D., & Mundlos, S. (2008) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2008.09.017 · Cited by 1,034 · see citing works
ChEA: transcription factor regulation inferred from integrating genome-wide ChIP-X experiments
Lachmann, A., Xu, H., Krishnan, J., Berger, S., Mazloom, A. R., & Ma’ayan, A. (2010) · Bioinformatics · doi:10.1093/bioinformatics/btq466 · Cited by 997 · see citing works
Self-citationAn elementary proof of a theorem of Johnson and Lindenstrauss
Dasgupta, S., & Gupta, A. (2002) · Random Structures and Algorithms · doi:10.1002/rsa.10073 · Cited by 927 · see citing works
ArrayExpress--a public repository for microarray gene expression data at the EBI
Brāzma, A. (2003) · Nucleic Acids Research · doi:10.1093/nar/gkg091 · Cited by 776 · see citing works
Entrez Gene: gene-centered information at NCBI
Maglott, D. R. (2004) · Nucleic Acids Research · doi:10.1093/nar/gki031 · Cited by 748 · see citing works
Petryszak, R., Keays, M., Tang, A., Fonseca, N. A., Barrera, E., Burdett, T., Füllgrabe, A., Fuentes, A. M.-P., Jupp, S., Koskinen, S., et al. (2015) · Nucleic Acids Research · doi:10.1093/nar/gkv1045 · Cited by 530 · see citing works
Reproducible RNA-seq analysis using recount2
Collado‐Torres, L., Nellore, A., Kammers, K., Ellis, S., Taub, M. A., Hansen, K. D., Jaffe, A. E., Langmead, B., & Leek, J. T. (2017) · Nature Biotechnology · doi:10.1038/nbt.3838 · Cited by 439 · see citing works
Smith, C. L., Goldsmith, C.-A. W., & Eppig, J. T. (2004) · Genome Biology · doi:10.1186/gb-2004-6-1-r7 · Cited by 417 · see citing works
BioGPS and MyGene.info: organizing online, gene-centric information
Wu, C., MacLeod, I., & Su, A. I. (2012) · Nucleic Acids Research · doi:10.1093/nar/gks1114 · Cited by 355 · see citing works
Costa, F. F. (2014) · Drug Discovery Today · doi:10.1016/j.drudis.2013.10.012 · Cited by 302 · see citing works
The Genotype-Tissue Expression (GTEx) project
Lonsdale, J., Thomas, J., Salvatore, M., Phillips, R., Lo, E., Shad, S., Hasz, R., Walters, G., Garcia, F., Young, N., et al. (2013) · Nature Genetics · doi:10.1038/ng.2653 · Cited by 263 · see citing works
SOAP3: ultra-fast GPU-based parallel alignment tool for short reads
Liu, C.-M., Wong, T. K. F., Wu, E., Luo, R., Yiu, S., Li, Y., Wang, B., Yu, C., Chu, X., Zhao, K., et al. (2012) · Bioinformatics · doi:10.1093/bioinformatics/bts061 · Cited by 248 · see citing works
Fernandez, N., Gundersen, G. W., Rahman, A. H., Grimes, M. L., Rikova, K., Hornbeck, P. V., & Ma’ayan, A. (2017) · Scientific Data · doi:10.1038/sdata.2017.151 · Cited by 238 · see citing works
Self-citationKEA: kinase enrichment analysis
Lachmann, A., & Ma’ayan, A. (2009) · Bioinformatics · doi:10.1093/bioinformatics/btp026 · Cited by 214 · see citing works
Self-citationDrew, K., Lee, C., Huizar, R. L., Tu, F., Borgeson, B., McWhite, C. D., Ma, Y., Wallingford, J. B., & Marcotte, E. M. (2017) · Molecular Systems Biology · doi:10.15252/msb.20167490 · Cited by 191 · see citing works
Near-optimal RNA-Seq quantificationPreprint
Bray, N., Pimentel, H., Melsted, P., & Pachter, L. S. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1505.02710 · Cited by 115 · see citing works
Lean Big Data integration in systems biology and systems pharmacology
Ma’ayan, A., Rouillard, A. D., Clark, N. R., Wang, Z., Duan, Q., & Kou, Y. (2014) · Trends in Pharmacological Sciences · doi:10.1016/j.tips.2014.07.001 · Cited by 88 · see citing works
Self-citationiRAP - an integrated RNA-seq Analysis PipelinePreprint
Fonseca, N. A., Petryszak, R., Marioni, J. C., & Brāzma, A. (2014) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/005991 · Cited by 33 · see citing works
Enabling cross-study analysis of RNA-Sequencing dataPreprint
Wang, Q., Armenia, J., Zhang, C., et al. (2017) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/110734 · Cited by 6 · see citing works
Ignazio, R. (2016) · Cited by 5 · see citing works
Flagged first, then by citations
Research works printed but not linked: 1
- [42]Folk, M., Heber, G., Koziol, Q., Pourmal, E. & Robinson, D. Proc. EDBT/ICDT 2011 Workshop on Array Databases (ACM, New York, NY, 2011).
Cited by
Sorted by citations
Szklarczyk, D., Gable, A. L., Lyon, D., Junge, A., Wyder, S., Huerta-Cepas, J., Simonovic, M., Doncheva, N. T., Morris, J. H., Bork, P., et al. (2019) · Nucleic Acids Research · doi:10.1093/nar/gky1131 · Cited by 17,749 · see citing works
Aran, D., Looney, A. P., Liu, L., Wu, E., Fong, V., Hsu, A., Chak, S., Naikawadi, R. P., Wolters, P. J., Abate, A. R., et al. (2019) · Nature Immunology · doi:10.1038/s41590-018-0276-y · Cited by 4,984 · see citing works
Alzheimer Disease Genetics Consortium (ADGC), Kunkle, B. W., The European Alzheimer’s Disease Initiative (EADI), Cohorts for Heart and Aging Research in Genomic Epidemiology Consortium (CHARGE), Genetic and Environmental Risk in AD/Defining Genetic, P. A. E. R. F. A. D. C. (., Grenier-Boley, B., Sims, R., Bis, J. C., Damotte, V., Naj, A. C., et al. (2019) · Nature Genetics · doi:10.1038/s41588-019-0358-2 · Cited by 2,946 · see citing works
Self-citationVõsa, U., Claringbould, A., Westra, H.-J., Bonder, M. J., Deelen, P., Zeng, B., Kirsten, H., Saha, A., Kreuzhuber, R., Yazar, S., et al. (2021) · Nature Genetics · doi:10.1038/s41588-021-00913-z · Cited by 2,607 · see citing works
ChEA3: transcription factor enrichment analysis by orthogonal omics integration
Keenan, A. B., Torre, D., Lachmann, A., Leong, A. K., Wojciechowicz, M. L., Utti, V., Jagodnik, K. M., Kropiwnicki, E., Wang, Z., & Ma’ayan, A. (2019) · Nucleic Acids Research · doi:10.1093/nar/gkz446 · Cited by 1,136 · see citing works
Self-citationReadhead, B., Haure-Mirande, J.-V., Funk, C. C., Richards, M. A., Shannon, P., Haroutunian, V., Sano, M., Liang, W. S., Beckmann, N. D., Price, N. D., et al. (2018) · Neuron · doi:10.1016/j.neuron.2018.05.023 · Cited by 708 · see citing works
Unraveling the polygenic architecture of complex traits using blood eQTL metaanalysis
Võsa, U., Claringbould, A., Westra, H.-J., Bonder, M. J., Deelen, P., Zeng, B., Kirsten, H., Saha, A., Kreuzhuber, R., Kasela, S., et al. (2018) · doi:10.1101/447367 · Cited by 2,607 · see citing works
Exploring genetic interaction manifolds constructed from rich single-cell phenotypes
Norman, T. M., Horlbeck, M. A., Replogle, J. M., Ge, A. Y., Xu, A., Jost, M., Gilbert, L. A., & Weissman, J. S. (2019) · Science · doi:10.1126/science.aax4438 · Cited by 392 · see citing works
Index and biological spectrum of human DNase I hypersensitive sites
Meuleman, W., Muratov, A., Rynes, E., Halow, J., Lee, K., Bates, D., Diegel, M., Dunn, D., Neri, F., Teodosiadis, A., et al. (2020) · Nature · doi:10.1038/s41586-020-2559-3 · Cited by 331 · see citing works
Holland, C. H., Tanevski, J., Perales-Patón, J., Gleixner, J., Kumar, M. P., Mereu, E., Joughin, B. A., Stegle, O., Lauffenburger, D. A., Heyn, H., et al. (2020) · Genome Biology · doi:10.1186/s13059-020-1949-z · Cited by 323 · see citing works
Activation of PDGF pathway links LMNA mutation to dilated cardiomyopathy
Lee, J., Termglinchan, V., Diecke, S., Itzhaki, I., Lam, C. K., Garg, P., Lau, E., Greenhaw, M., Seeger, T., Wu, H., et al. (2019) · Nature · doi:10.1038/s41586-019-1406-x · Cited by 222 · see citing works
Genetics of the human microglia regulome refines Alzheimer’s disease risk loci
Kosoy, R., Fullard, J. F., Zeng, B., Bendl, J., Dong, P., Rahman, S., Kleopoulos, S. P., Shao, Z., Girdhar, K., Humphrey, J., et al. (2022) · Nature Genetics · doi:10.1038/s41588-022-01149-1 · Cited by 168 · see citing works
Jamialahmadi, O., Mancina, R. M., Ciociola, E., Tavaglione, F., Luukkonen, P. K., Baselli, G., Malvestiti, F., Thuillier, D., Raverdy, V., Männistö, V., et al. (2021) · Gastroenterology · doi:10.1053/j.gastro.2020.12.023 · Cited by 148 · see citing works
Zeng, B., Glicksberg, B. S., Newbury, P., Chekalin, E., Xing, J., Liu, K., Wen, A., Chow, C., & Chen, B. (2021) · Nature Protocols · doi:10.1038/s41596-020-00430-z · Cited by 48 · see citing works
Lymphoid gene expression supports neuroprotective microglia function
Ayata, P., Crowley, J. M., Challman, M. F., Sahasrabuddhe, V., Gratuze, M., Werneburg, S., Ribeiro, D., Hays, E. C., Durán-Laforet, V., Faust, T. E., et al. (2025) · Nature · doi:10.1038/s41586-025-09662-z · Cited by 32 · see citing works
Russkikh, N., Antonets, D., Shtokalo, D., Makarov, A., Vyatkin, Y., Zakharov, A., & Terentyev, E. (2020) · Bioinformatics · doi:10.1093/bioinformatics/btaa624 · Cited by 21 · see citing works
SEQUIN is an R/Shiny framework for rapid and reproducible analysis of RNA-seq data.
Weber, C., Hirst, M. B., Ernest, B., Schaub, N. J., Wilson, K. M., Wang, K., Baskir, H. M., Chu, P.-H., Tristan, C. A., & Singeç, I. (2023) · Cell Reports Methods · doi:10.1016/j.crmeth.2023.100420 · Cited by 11 · see citing works
Computational challenges of cell cycle analysis using single cell transcriptomics
Chervov, A., & Zinovyev, A. (2022) · arXiv · doi:10.48550/arxiv.2208.05229 · Cited by 4 · see citing works
Li, Y., Tang, Z., Yao, M., Ran, Y., & Qiu, Z. (2026) · Neural Regeneration Research · doi:10.4103/nrr.nrr-d-25-00006
Mei, Y., Vad, O. B., Beyer, A. F., Ghouse, J., & Lundegaard, P. R. (2026) · Molecular & Cellular Proteomics · doi:10.1016/j.mcpro.2026.101692
- Authors
- Lachmann, A., Torre, D., Keenan, A. B., Jagodnik, K. M., Lee, H. J., Wang, L., Silverstein, M. C., & Ma’ayan, A.
- Affiliations
- Icahn School of Medicine at Mount Sinai (US)
- Corresponding author
- Ma’ayan, Avi
- ORCID
- Venue
- Nature Communications
- Published
- 2018-04-10
- Volume
- 9
- Issue
- 1
- Pages
- 1366-1366
- ISSN
- 2041-1723 (ISSN-L)
- PMC
- PMC5893633
- OpenAlex
- W2953067954
- Cited by
- 701
- References
- 50
- Citations by year
- 2026: 57 · 2025: 84 · 2024: 102 · 2023: 103 · 2022: 93 · 2021: 97 · 2020: 70 · 2019: 59 · 2018: 34 · 2017: 1
- FWCI
- 23.11
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Nature Communications (publishedVersion, cc-by)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion)
- Europe PMC (PubMed Central) (publishedVersion, other-oa)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- National Institutes of Health (UL1TR001433, OT3OD025467)
- National Heart, Lung, and Blood Institute (U54HL127624, T32HL007824)
- National Cancer Institute (U24CA224260, U54CA189201)
- National Institute of General Medical Sciences (T32GM062754)
- National Center for Advancing Translational Sciences
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
- Keywords
- Gene Expression Omnibus, human transcriptome, mouse transcriptome, gene co-expression, protein-protein interaction, gene function prediction, online resources
- MeSH terms
- Animals, Humans, Software, Genome, Internet, Protein Interaction Mapping, Databases, Genetic, Mice, Gene Regulatory Networks, Data Mining, Molecular Sequence Annotation, High-Throughput Nucleotide Sequencing, Transcriptome, Gene Ontology
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
