The DOI matches its Crossref record; 6 of the 6 references it cites were found in a registry or an index.
Genome-wide computational identification and manual annotation of human long noncoding RNA genes
Open accessAbstract
Experimental evidence suggests that half or more of the mammalian transcriptome consists of noncoding RNA. Noncoding RNAs are divided into short noncoding RNAs (including microRNAs) and long noncoding RNAs (lncRNAs). We defined complementary DNAs (cDNAs) lacking any positive-strand open reading frames (ORFs) longer than 30 amino acids, as well as cDNAs lacking any evidence of interspecies conservation of their longer-than-30-amino acid ORFs, as noncoding. We have identified 5446 lncRNA genes in the human genome from ∼24,000 full-length cDNAs, using our new ORF-prediction pipeline. We combined them nonredundantly with lncRNAs from four published sources to derive 6736 lncRNA genes. In an effort to distinguish standalone and antisense lncRNA genes from database artifacts, we stratified our catalog of lncRNAs according to the distance between each lncRNA gene candidate and its nearest known protein-coding gene. We concurrently examined the protein-coding capacity of known genes overlapping with lncRNAs. Remarkably, 62% of known genes with “hypothetical protein” names actually lacked protein-coding capacity. This study has greatly expanded the known human lncRNA catalog, increased its accuracy through manual annotation of cDNA-to-genome alignments, and revealed that a large set of hypothetical-protein genes in GenBank lacks protein-coding capacity. In addition, we have developed, independently of existing NCBI tools, command-line programs with high-throughput ORF-finding and BLASTP-parsing functionality, suitable for future automated assessments of protein-coding capacity of novel transcripts.
Overview
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
Verification
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.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Chromatin signature reveals over a thousand highly conserved large non-coding RNAs in mammals
Guttman, M., Amit, I., Garber, M., French, C., Lin, M. F., Feldser, D., Huarte, M., Zuk, O., Carey, B. W., Cassady, J. P., et al. (2009) · Nature · doi:10.1038/nature07672 · Cited by 4,098
Khalil, A. M., Guttman, M., Huarte, M., Garber, M., Raj, A., Rivea Morales, D., Thomas, K., Presser, A., Bernstein, B. E., van Oudenaarden, A., et al. (2009) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0904715106 · Cited by 2,872
Faghihi, M. A., Modarresi, F., Khalil, A. M., Wood, D. E., Sahagan, B. G., Morgan, T. E., Finch, C. E., St. Laurent III, G., Kenny, P. J., & Wahlestedt, C. (2008) · Nature Medicine · doi:10.1038/nm1784 · Cited by 1,416
Specific expression of long noncoding RNAs in the mouse brain
Mercer, T. R., Dinger, M. E., Sunkin, S. M., Mehler, M. F., & Mattick, J. S. (2008) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0706729105 · Cited by 1,103
An RNA gene expressed during cortical development evolved rapidly in humans
Pollard, K. S., Salama, S. R., Lambert, N., Lambot, M.-A., Coppens, S., Pedersen, J. S., Katzman, S., King, B., Onodera, C., Siepel, A., et al. (2006) · Nature · doi:10.1038/nature05113 · Cited by 1,005
Distinguishing protein-coding and noncoding genes in the human genome
Clamp, M., Fry, B., Kamal, M., Xie, X., Cuff, J., Lin, M. F., Kellis, M., Lindblad-Toh, K., & Lander, E. S. (2007) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0709013104 · Cited by 624
Cited by
GENCODE: The reference human genome annotation for The ENCODE Project
Harrow, J., Frankish, A., Gonzalez, J. M., Tapanari, E., Diekhans, M., Kokocinski, F., Aken, B. L., Barrell, D., Zadissa, A., Searle, S., et al. (2012) · Genome Research · doi:10.1101/gr.135350.111 · Cited by 4,585
Kapusta, A., Kronenberg, Z., Lynch, V. J., Zhuo, X., Ramsay, L., Bourque, G., Yandell, M., & Feschotte, C. (2013) · PLoS Genetics · doi:10.1371/journal.pgen.1003470 · Cited by 687
Ng, S., Johnson, R., & Stanton, L. W. (2012) · The EMBO Journal · doi:10.1038/emboj.2011.459 · Cited by 514
Self-citationRNA-Seq Reveals Novel Transcriptional Reorganization in Human Alcoholic Brain
Farris, S. P., & Dayne Mayfield, R. (2014) · International Review of Neurobiology · doi:10.1016/b978-0-12-801105-8.00011-4 · Cited by 49
Emerging roles for ncRNAs in alcohol use disorders
Mayfield, R. D. (2017) · Alcohol · doi:10.1016/j.alcohol.2017.01.004 · Cited by 43
Metadata
- Authors
- Jia, H., Osak, M., Bogu, G. K., Stanton, L. W., Johnson, R., & Lipovich, L.
- Venue
- RNA
- Publisher
- Cold Spring Harbor Laboratory
- Published
- 2010-06-29
- Volume
- 16
- Issue
- 8
- Pages
- 1478-1487
- ISSN
- 1355-8382 (ISSN-L)
- 1469-9001
- OpenAlex
- W2110356447
- Cited by
- 381
- References
- 6
- FWCI
- 6.41
- Type
- journal article
- Language
- en
- Open access
- bronze
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
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.