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Cited by 579

The DOI matches its Crossref record; 8 of the 8 references it cites were found in a registry or an index.

RBPmap: a web server for mapping binding sites of RNA-binding proteins

Paz, Inbal, Kosti, Idit, Ares, Manuel, Cline, Melissa, Mandel-Gutfreund, Yael·2014·Nucleic Acids Research
DOI 10.1093/nar/gku406PMC PMC4086114
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Figure 1. A pipeline summarizing RBPmap algorithm. (A) The mandatory input parameters for RBPmap run; a query sequence and a motif of interest to be mapped to the sequence. (B) A match score for the motif is calculated for each site in the query sequence, in overlapping windows of the motif size. (C) The match scores are compared to the average match score that is calculated for each motif in a background of randomly chosen regulatory regions. This step uses two different thresholds; a significant threshold for the anchor site (default P-value<0.005) and a suboptimal threshold for the secondary sites (default P-value<0.01) used to evaluate the clustering propensity. (D) A WR score is calculated for a window of 50 nts around each significant site. This score reflects the propensity of suboptimal sites to cluster around the significant site, weighted by their match score to the motif of interest. (E) To reduce false-positive predictions, the WR scores are compared to a region-specific background model that is generated independently per each motif for different genomic regions, removing non-significant results (P-value≥0.05). The figure exemplifies the procedure conducted for a query sequence spanning three different genomic regions (mid-intron, intronic region flanking a splice site and an internal exon). (F) Finally, a conservation-based filtering step is applied only to sites mapped to mid-intron/intergenic regions, filtering out sites which fall in non-conserved regions (below the average conservation level calculated for intronic regulatory regions).
Figure 1. A pipeline summarizing RBPmap algorithm. (A) The mandatory input parameters for RBPmap run; a query sequence and a motif of interest to be mapped to the sequence. (B) A match score for the motif is calculated for each site in the query sequence, in overlapping windows of the motif size. (C) The match scores are compared to the average match score that is calculated for each motif in a background of randomly chosen regulatory regions. This step uses two different thresholds; a significant threshold for the anchor site (default P-value<0.005) and a suboptimal threshold for the secondary sites (default P-value<0.01) used to evaluate the clustering propensity. (D) A WR score is calculated for a window of 50 nts around each significant site. This score reflects the propensity of suboptimal sites to cluster around the significant site, weighted by their match score to the motif of interest. (E) To reduce false-positive predictions, the WR scores are compared to a region-specific background model that is generated independently per each motif for different genomic regions, removing non-significant results (P-value≥0.05). The figure exemplifies the procedure conducted for a query sequence spanning three different genomic regions (mid-intron, intronic region flanking a splice site and an internal exon). (F) Finally, a conservation-based filtering step is applied only to sites mapped to mid-intron/intergenic regions, filtering out sites which fall in non-conserved regions (below the average conservation level calculated for intronic regulatory regions).CC BY · PubMed Central
Figure 2. A view of RBPmap input and output pages. (A) An example of RBPmap home page demonstrating the mandatory input parameters needed for RBPmap run. (B) By clicking the link ‘Click here to select motifs from RBPmap full list’, a sorted list of all motifs in the RBPmap database is opened and the user is prompted to select the proteins/motifs of interest. (C) An example of RBPmap output page. In the example presented the job includes more than one query sequence. The results per each sequence are shown followed by a link to a text file summarizes the binding sites predictions for all the input sequences. (D) An example of the output summary of all predicted binding sites within one query sequence in a web-based presentation. The results are provided for each of the proteins selected by the user, where all the occurrences of motifs belonging to the same protein are listed together. (E) A visualized presentation of the predicted binding sites as custom tracks in the UCSC Genome Browser.
Figure 2. A view of RBPmap input and output pages. (A) An example of RBPmap home page demonstrating the mandatory input parameters needed for RBPmap run. (B) By clicking the link ‘Click here to select motifs from RBPmap full list’, a sorted list of all motifs in the RBPmap database is opened and the user is prompted to select the proteins/motifs of interest. (C) An example of RBPmap output page. In the example presented the job includes more than one query sequence. The results per each sequence are shown followed by a link to a text file summarizes the binding sites predictions for all the input sequences. (D) An example of the output summary of all predicted binding sites within one query sequence in a web-based presentation. The results are provided for each of the proteins selected by the user, where all the occurrences of motifs belonging to the same protein are listed together. (E) A visualized presentation of the predicted binding sites as custom tracks in the UCSC Genome Browser.CC BY · PubMed Central

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Citations

Cites

Show all 8 references

Research works printed but not linked: 27

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  2. Ray, 2009, Rapid and systematic analysis of the RNA recognition specificities of RNA-binding proteins, Nat. Biotechnol., 27, 667, 10.1038/nbt.1550 · DOI
  3. Tenenbaum, 2000, Identifying mRNA subsets in messenger ribonucleoprotein complexes by using cDNA arrays, PNAS, 97, 14085, 10.1073/pnas.97.26.14085 · DOI
  4. Keene, 2006, RIP-Chip: the isolation and identification of mRNAs, microRNAs and protein components of ribonucleoprotein complexes from cell extracts, Nat. Protoc., 1, 302, 10.1038/nprot.2006.47 · DOI
  5. Hafner, 2010, Transcriptome-wide identification of RNA-binding protein and microRNA target sites by PAR-CLIP, Cell, 141, 129, 10.1016/j.cell.2010.03.009 · DOI · Paper page
  6. Khorshid, 2011, CLIPZ: a database and analysis environment for experimentally determined binding sites of RNA-binding proteins, Nucleic Acids Res., 39, D245, 10.1093/nar/gkq940 · DOI
  7. Anders, 2011, doRiNA: a database of RNA interactions in post-transcriptional regulation, Nucleic Acids Res., 40, D180, 10.1093/nar/gkr1007 · DOI
  8. Yao, 2006, CMfinder–a covariance model based RNA motif finding algorithm, Bioinformatics, 22, 445, 10.1093/bioinformatics/btk008 · DOI
  9. Hiller, 2006, Using RNA secondary structures to guide sequence motif finding towards single-stranded regions, Nucleic Acids Res., 34, e117, 10.1093/nar/gkl544 · DOI
  10. Bailey, 1994, Fitting a mixture model by expectation maximization to discover motifs in biopolymers, Proc. Int. Conf. Intell. Syst. Mol. Biol., 2, 28
  11. Linhart, 2008, Transcription factor and microRNA motif discovery: The Amadeus platform and a compendium of metazoan target sets, Genome Res., 18, 1180, 10.1101/gr.076117.108 · DOI
  12. Georgiev, 2010, Evidence-ranked motif identification, Genome Biol., 11, R19, 10.1186/gb-2010-11-2-r19 · DOI
  13. Leibovich, 2013, DRIMust: a web server for discovering rank imbalanced motifs using suffix trees, Nucleic Acids Res., 41, W174, 10.1093/nar/gkt407 · DOI
  14. Grillo, 2010, UTRdb and UTRsite (RELEASE 2010): a collection of sequences and regulatory motifs of the untranslated regions of eukaryotic mRNAs, Nucleic Acids Res., 38, D75, 10.1093/nar/gkp902 · DOI
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  16. Cook, 2011, RBPDB: a database of RNA-binding specificities, Nucleic Acids Res., 39, D301, 10.1093/nar/gkq1069 · DOI · Paper page
  17. Cartegni, 2003, ESEfinder: a web resource to identify exonic splicing enhancers, Nucl. Acids Res., 31, 3568, 10.1093/nar/gkg616 · DOI
  18. Huang, 2006, RegRNA: an integrated web server for identifying regulatory RNA motifs and elements, Nucleic Acids Res., 34, W429, 10.1093/nar/gkl333 · DOI
  19. Akerman, 2009, A computational approach for genome-wide mapping of splicing factor binding sites, Genome Biol., 10, R30, 10.1186/gb-2009-10-3-r30 · DOI
  20. Kosti, 2012, An Integrated Regulatory Network Reveals Pervasive Cross-Regulation among Transcription and Splicing Factors, PLoS Comput. Biol., 8, e1002603, 10.1371/journal.pcbi.1002603 · DOI
  21. Cereda, 2014, RNAmotifs: prediction of multivalent RNA motifs that control alternative splicing, Genome Biol., 15, R20, 10.1186/gb-2014-15-1-r20 · DOI
  22. Zhang, 2013, Prediction of clustered RNA-binding protein motif sites in the mammalian genome, Nucleic Acids Res., 41, 6793, 10.1093/nar/gkt421 · DOI
  23. Grillo, 2003, PatSearch: A program for the detection of patterns and structural motifs in nucleotide sequences, Nucleic Acids Res., 31, 3608, 10.1093/nar/gkg548 · DOI
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  26. Tollervey, 2011, Characterizing the RNA targets and position-dependent splicing regulation by TDP-43, Nat. Neurosci., 14, 452, 10.1038/nn.2778 · DOI · Paper page
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Cited by

Metadata

Authors
Paz, I., Kosti, I., Ares, M., Cline, M., & Mandel-Gutfreund, Y.
Venue
Nucleic Acids Research
Publisher
Oxford University Press (OUP)
Published
2014-05-14
Volume
42
Issue
W1
Pages
W361-W367
ISSN
  • 1362-4962
  • 0305-1048
DOI
10.1093/nar/gku406
PMC
PMC4086114
Cited by
579
References
8
Language
en
Licence
creativecommons.org/licenses/by/3.0/ (vor, 911 d)

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