The DOI matches its Crossref record; 12 of the 12 references it cites were found in a registry or an index.
A quantitative atlas of polyadenylation in five mammals
Open accessAbstract
We developed PolyA-seq, a strand-specific and quantitative method for high-throughput sequencing of 3′ ends of polyadenylated transcripts, and used it to globally map polyadenylation (polyA) sites in 24 matched tissues in human, rhesus, dog, mouse, and rat. We show that PolyA-seq is as accurate as existing RNA sequencing (RNA-seq) approaches for digital gene expression (DGE), enabling simultaneous mapping of polyA sites and quantitative measurement of their usage. In human, we confirmed 158,533 known sites and discovered 280,857 novel sites (FDR < 2.5%). On average 10% of novel human sites were also detected in matched tissues in other species. Most novel sites represent uncharacterized alternative polyA events and extensions of known transcripts in human and mouse, but primarily delineate novel transcripts in the other three species. A total of 69.1% of known human genes that we detected have multiple polyA sites in their 3′UTRs, with 49.3% having three or more. We also detected polyadenylation of noncoding and antisense transcripts, including constitutive and tissue-specific primary microRNAs. The canonical polyA signal was strongly enriched and positionally conserved in all species. In general, usage of polyA sites is more similar within the same tissues across different species than within a species. These quantitative maps of polyA usage in evolutionarily and functionally related samples constitute a resource for understanding the regulatory mechanisms underlying alternative polyadenylation.
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
Mapping and quantifying mammalian transcriptomes by RNA-Seq
Mortazavi, A., Williams, B. A., McCue, K., Schaeffer, L., & Wold, B. (2008) · Nature Methods · doi:10.1038/nmeth.1226 · Cited by 13,696
RNA-Seq: a revolutionary tool for transcriptomics
Wang, Z., Gerstein, M., & Snyder, M. (2009) · Nature Reviews Genetics · doi:10.1038/nrg2484 · Cited by 13,174
<tt>BLAT</tt> —The <tt>BLAST</tt> -Like Alignment Tool
Kent, W. J. (2002) · Genome Research · doi:10.1101/gr.229202 · Cited by 8,480
Alternative isoform regulation in human tissue transcriptomes
Wang, E. T., Sandberg, R., Luo, S., Khrebtukova, I., Zhang, L., Mayr, C., Kingsmore, S. F., Schroth, G. P., & Burge, C. B. (2008) · Nature · doi:10.1038/nature07509 · Cited by 5,181
MAQC Consortium, Shi, L., Shi, L., Reid, L. H., Jones, W. D., Shippy, R., Warrington, J. A., Baker, S. C., Collins, P. J., de Longueville, F., et al. (2006) · Nature Biotechnology · doi:10.1038/nbt1239 · Cited by 2,126
Bullard, J. H., Purdom, E., Hansen, K. D., & Dudoit, S. (2010) · BMC Bioinformatics · doi:10.1186/1471-2105-11-94 · Cited by 1,680
Mayr, C., & Bartel, D. P. (2009) · Cell · doi:10.1016/j.cell.2009.06.016 · Cited by 1,613
HITS-CLIP yields genome-wide insights into brain alternative RNA processing
Licatalosi, D. D., Mele, A., Fak, J. J., Ule, J., Kayikci, M., Chi, S. W., Clark, T. A., Schweitzer, A. C., Blume, J. E., Wang, X., et al. (2008) · Nature · doi:10.1038/nature07488 · Cited by 1,352
Understanding mechanisms underlying human gene expression variation with RNA sequencing
Pickrell, J. K., Marioni, J. C., Pai, A. A., Degner, J. F., Engelhardt, B. E., Nkadori, E., Veyrieras, J.-B., Stephens, M., Gilad, Y., & Pritchard, J. K. (2010) · Nature · doi:10.1038/nature08872 · Cited by 1,256
Transcriptome genetics using second generation sequencing in a Caucasian population
Montgomery, S. B., Sammeth, M., Gutierrez-Arcelus, M., Lach, R. P., Ingle, C., Nisbett, J., Guigo, R., & Dermitzakis, E. T. (2010) · Nature · doi:10.1038/nature08903 · Cited by 797
Cited by
O'Leary, N. A., Wright, M. W., Brister, J. R., Ciufo, S., Haddad, D., McVeigh, R., Rajput, B., Robbertse, B., Smith-White, B., Ako-Adjei, D., et al. (2016) · Nucleic Acids Research · doi:10.1093/nar/gkv1189 · Cited by 6,357
“RefSeq curators also evaluate polyAseq data to affirm 3′ completeness of transcripts lacking a polyA tail (19).”
BackgroundAlternative polyadenylation of mRNA precursors
Tian, B., & Manley, J. L. (2017) · Nature Reviews Molecular Cell Biology · doi:10.1038/nrm.2016.116 · Cited by 1,179
Regulation by 3′-Untranslated Regions
Mayr, C. (2017) · Annual Review of Genetics · doi:10.1146/annurev-genet-120116-024704 · Cited by 588
A joint NCBI and EMBL-EBI transcript set for clinical genomics and research
Morales, J., Pujar, S., Loveland, J. E., Astashyn, A., Bennett, R., Berry, A., Cox, E., Davidson, C., Ermolaeva, O., Farrell, C. M., et al. (2022) · Nature · doi:10.1038/s41586-022-04558-8 · Cited by 571
Mayr, C. (2019) · Cold Spring Harbor Perspectives in Biology · doi:10.1101/cshperspect.a034728 · Cited by 519
Metadata
- Authors
- Derti, A., Garrett-Engele, P., MacIsaac, K. D., Stevens, R. C., Sriram, S., Chen, R., Rohl, C. A., Johnson, J. M., & Babak, T.
- Venue
- Genome Research
- Publisher
- Cold Spring Harbor Laboratory
- Published
- 2012-03-27
- Volume
- 22
- Issue
- 6
- Pages
- 1173-1183
- ISSN
- 1088-9051 (ISSN-L)
- OpenAlex
- W2118834857
- Cited by
- 654
- References
- 12
- Type
- posted content
- 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
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: A quantitative atlas of polyadenylation in five mammals. https://app.cochat.ai/papers/10.1101/gr.132563.111
- The paper
- Derti, A., Garrett-Engele, P., MacIsaac, K. D., Stevens, R. C., Sriram, S., Chen, R., Rohl, C. A., Johnson, J. M., & Babak, T. (2012). A quantitative atlas of polyadenylation in five mammals. Genome Research. https://doi.org/10.1101/gr.132563.111
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.
