The DOI matches its Crossref record; 7 of the 7 references it cites were found in a registry or an index.
PeakSeq enables systematic scoring of ChIP-seq experiments relative to controls
Open accessAbstract
Chromatin immunoprecipitation (ChIP) followed by tag sequencing (ChIP-seq) using high-throughput next-generation instrumentation is fast, replacing chromatin immunoprecipitation followed by genome tiling array analysis (ChIP-chip) as the preferred approach for mapping of sites of transcription-factor binding and chromatin modification. Using two deeply sequenced data sets for human RNA polymerase II and STAT1, each with matching input-DNA controls, we describe a general scoring approach to address unique challenges in ChIP-seq data analysis. Our approach is based on the observation that sites of potential binding are strongly correlated with signal peaks in the control, likely revealing features of open chromatin. We develop a two-pass strategy called PeakSeq to compensate for this. A two-pass strategy compensates for signal caused by open chromatin, as revealed by inclusion of the controls. The first pass identifies putative binding sites and compensates for genomic variation in the 'mappability' of sequences. The second pass filters out sites not significantly enriched compared to the normalized control, computing precise enrichments and significances. Our scoring procedure enables us to optimize experimental design by estimating the depth of sequencing required for a desired level of coverage and demonstrating that more than two replicates provides only a marginal gain in information.
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
Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing
Benjamini, Y., & Hochberg, Y. (1995) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1995.tb02031.x · Cited by 109,482
The ENCODE Project Consortium (2007) · Nature · doi:10.1038/nature05874 · Cited by 5,223
SOAP: Short oligonucleotide alignment program
Li, R., Li, Y., Kristiansen, K., & Wang, J. (2008) · Bioinformatics · doi:10.1093/bioinformatics/btn025 · Cited by 3,597
Robertson, G., Hirst, M., Bainbridge, M., Bilenky, M., Zhao, Y., Zeng, T., Euskirchen, G., Bernier, B., Varhol, R., Delaney, A., et al. (2007) · Nature Methods · doi:10.1038/nmeth1068 · Cited by 1,454
Self-citationA Global Map of p53 Transcription-Factor Binding Sites in the Human Genome
Wei, C.-L., Wu, Q., Vega, V. B., Chiu, K. P., Ng, P., Zhang, T., Shahab, A., Yong, H. C., Fu, Y., Weng, Z., et al. (2006) · Cell · doi:10.1016/j.cell.2005.10.043 · Cited by 1,141
Mapping and sequencing of structural variation from eight human genomes
Kidd, J. M., Cooper, G. M., Donahue, W. F., Hayden, H. S., Sampas, N., Graves, T., Hansen, N., Teague, B., Alkan, C., Antonacci, F., et al. (2008) · Nature · doi:10.1038/nature06862 · Cited by 1,119
Cawley, S., Bekiranov, S., Ng, H. H., Kapranov, P., Sekinger, E. A., Kampa, D., Piccolboni, A., Sementchenko, V., Cheng, J., Williams, A. J., et al. (2004) · Cell · doi:10.1016/s0092-8674(04)00127-8 · Cited by 1,095
Cited by
A scaling normalization method for differential expression analysis of RNA-seq data
Robinson, M. D., & Oshlack, A. (2010) · Genome Biology · doi:10.1186/gb-2010-11-3-r25 · Cited by 7,876
GREAT improves functional interpretation of cis-regulatory regions
McLean, C. Y., Bristor, D., Hiller, M., Clarke, S. L., Schaar, B. T., Lowe, C. B., Wenger, A. M., & Bejerano, G. (2010) · Nature Biotechnology · doi:10.1038/nbt.1630 · Cited by 4,502
Widespread transcription at neuronal activity-regulated enhancers
Kim, T.-K., Hemberg, M., Gray, J. M., Costa, A. M., Bear, D. M., Wu, J., Harmin, D. A., Laptewicz, M., Barbara-Haley, K., Kuersten, S., et al. (2010) · Nature · doi:10.1038/nature09033 · Cited by 2,450
ChIP-seq guidelines and practices of the ENCODE and modENCODE consortia
Landt, S. G., Marinov, G. K., Kundaje, A., Kheradpour, P., Pauli, F., Batzoglou, S., Bernstein, B. E., Bickel, P., Brown, J. B., Cayting, P., et al. (2012) · Genome Research · doi:10.1101/gr.136184.111 · Cited by 2,033
Self-citationChIP–seq: advantages and challenges of a maturing technology
Park, P. J. (2009) · Nature Reviews Genetics · doi:10.1038/nrg2641 · Cited by 2,217
Abyzov, A., Urban, A. E., Snyder, M., & Gerstein, M. (2011) · Genome Research · doi:10.1101/gr.114876.110 · Cited by 1,658
Self-citationRobust transcriptome-wide discovery of RNA-binding protein binding sites with enhanced CLIP (eCLIP)
Van Nostrand, E. L., Pratt, G. A., Shishkin, A. A., Gelboin-Burkhart, C., Fang, M. Y., Sundararaman, B., Blue, S. M., Nguyen, T. B., Surka, C., Elkins, K., et al. (2016) · Nature Methods · doi:10.1038/nmeth.3810 · Cited by 1,397
Base-resolution models of transcription-factor binding reveal soft motif syntax
Avsec, Ž., Weilert, M., Shrikumar, A., Krueger, S., Alexandari, A., Dalal, K., Fropf, R., McAnany, C., Gagneur, J., Kundaje, A., et al. (2021) · Nature Genetics · doi:10.1038/s41588-021-00782-6 · Cited by 610
Insights from genomic profiling of transcription factors
Farnham, P. J. (2009) · Nature Reviews Genetics · doi:10.1038/nrg2636 · Cited by 519
Annotating non-coding regions of the genome.
Alexander, R. P., Fang, G., Rozowsky, J., Snyder, M., & Gerstein, M. B. (2010) · Nature Reviews Genetics · doi:10.1038/nrg2814 · Cited by 464
Self-citationAlleleSeq: analysis of allele-specific expression and binding in a network framework.
Rozowsky, J., Abyzov, A., Wang, J., Alves, P., Raha, D., Harmanci, A., Leng, J., Bjornson, R., Kong, Y., Kitabayashi, N., et al. (2011) · Molecular Systems Biology · doi:10.1038/msb.2011.54 · Cited by 316
Self-citationGenomic views of STAT function in CD4+ T helper cell differentiation
O'Shea, J. J., Lahesmaa, R., Vahedi, G., Laurence, A., & Kanno, Y. (2011) · Nature Reviews Immunology · doi:10.1038/nri2958 · Cited by 295
Transposable Elements in TDP-43-Mediated Neurodegenerative Disorders
Li, W., Jin, Y., Prazak, L., Hammell, M., & Dubnau, J. (2012) · PLoS ONE · doi:10.1371/journal.pone.0044099 · Cited by 195
Systematic bias in high-throughput sequencing data and its correction by BEADS
Cheung, M.-S., Down, T. A., Latorre, I., & Ahringer, J. (2011) · Nucleic Acids Research · doi:10.1093/nar/gkr425 · Cited by 160
Control of embryonic stem cell lineage commitment by core promoter factor, TAF3
Liu, Z., Scannell, D. R., Eisen, M. B., & Tjian, R. (2011) · Cell · doi:10.1016/j.cell.2011.08.005 · Cited by 167
Girdhar, K., Hoffman, G. E., Jiang, Y., Brown, L., Kundakovic, M., Hauberg, M. E., Francoeur, N. J., Wang, Y.-C., Shah, H., Kavanagh, D. H., et al. (2018) · Nature Neuroscience · doi:10.1038/s41593-018-0187-0 · Cited by 139
Ligand-dependent dynamics of retinoic acid receptor binding during early neurogenesis
Mahony, S., Mazzoni, E. O., McCuine, S., Young, R. A., Wichterle, H., & Gifford, D. K. (2011) · Genome Biology · doi:10.1186/gb-2011-12-1-r2 · Cited by 142
Features that define the best ChIP-seq peak calling algorithms
Thomas, R., Thomas, S., Holloway, A. K., & Pollard, K. S. (2016) · Briefings in Bioinformatics · doi:10.1093/bib/bbw035 · Cited by 112
Transcriptome assembly and isoform expression level estimation from biased RNA-Seq reads
Li, W., & Jiang, T. (2012) · Bioinformatics · doi:10.1093/bioinformatics/bts559 · Cited by 97
Changes in nucleosome occupancy associated with metabolic alterations in aged mammalian liver
Bochkis, I. M., Przybylski, D., Chen, J., & Regev, A. (2014) · Cell Reports · doi:10.1016/j.celrep.2014.09.048 · Cited by 78
Carlyle, B. C., Duque, A., Kitchen, R. R., Bordner, K. A., Coman, D., Doolittle, E., Papademetris, X., Hyder, F., Taylor, J. R., & Simen, A. A. (2012) · Development and Psychopathology · doi:10.1017/s095457941200079x · Cited by 80
Non-targeted transcription factors motifs are a systemic component of ChIP-seq datasets
Worsley Hunt, R., & Wasserman, W. W. (2014) · Genome Biology · doi:10.1186/s13059-014-0412-4 · Cited by 73
Transcription factor binding variation in the evolution of gene regulation
Dowell, R. D. (2010) · Trends in Genetics · doi:10.1016/j.tig.2010.08.005 · Cited by 61
Metadata
- Authors
- Rozowsky, J., Euskirchen, G., Auerbach, R. K., Zhang, Z. D., Gibson, T., Bjornson, R., Carriero, N., Snyder, M., & Gerstein, M. B.
- Venue
- Nature Biotechnology
- Published
- 2009-01
- Volume
- 27
- Issue
- 1
- Pages
- 66-75
- ISSN
- 0733-222X (ISSN-L)
- 1087-0156
- 1546-1696
- OpenAlex
- W1996212302
- Cited by
- 561
- References
- 7
- FWCI
- 30.97
- Type
- journal article
- Language
- en
- Open access
- green
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.