Cell type specific TFBS prediction

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Method, code, references Input data Algorithm Comment
TEPIC [1]

http://www.github.de/schulzlab/TEPIC.

Input:

  • open chromatin data (DNaseI-seq, NOMe-seq)
  • PWM (Jaspar, HOCOMOCO, Uniprobe)

Output:

  • TFBS (TF affinities)
  • TF-gene scores

1) TEPIC segments the genome into user specified regions and annotates those with TF binding using TRAP [2].

2) These predictions are aggregated to gene scores. Within this aggregation TEPIC offers exponential decay [3] and scaling of TF region scores using the signal of an open chromatin assay.

These can be used in downstream applications, e.g. to determine the influence of chromatin accessiblity on gene expression, without considering detailed information on TF binding.

BinDNase [4]

http://research.ics.aalto.fi/csb/software/bindnase/

No permission to access software on they site.

Ask the access.


References

  1. Schmidt F, Gasparoni N, Gasparoni G, Gianmoena K, Cadenas C, Polansky JK, Ebert P, Nordström K, Barann M, Sinha A, Fröhler S, Xiong J, Dehghani Amirabad A, Behjati Ardakani F, Hutter B, Zipprich G, Felder B, Eils J, Brors B, Chen W, Hengstler JG, Hamann A, Lengauer T, Rosenstiel P, Walter J, and Schulz MH. Combining transcription factor binding affinities with open-chromatin data for accurate gene expression prediction. Nucleic Acids Res. 2017 Jan 9;45(1):54-66. DOI:10.1093/nar/gkw1061 | PubMed ID:27899623 | HubMed [Schmidt217]
  2. Roider HG, Kanhere A, Manke T, and Vingron M. Predicting transcription factor affinities to DNA from a biophysical model. Bioinformatics. 2007 Jan 15;23(2):134-41. DOI:10.1093/bioinformatics/btl565 | PubMed ID:17098775 | HubMed [Roider2007]
  3. Ouyang Z, Zhou Q, and Wong WH. ChIP-Seq of transcription factors predicts absolute and differential gene expression in embryonic stem cells. Proc Natl Acad Sci U S A. 2009 Dec 22;106(51):21521-6. DOI:10.1073/pnas.0904863106 | PubMed ID:19995984 | HubMed [Ouyang2007]
  4. Kähärä J and Lähdesmäki H. BinDNase: a discriminatory approach for transcription factor binding prediction using DNase I hypersensitivity data. Bioinformatics. 2015 Sep 1;31(17):2852-9. DOI:10.1093/bioinformatics/btv294 | PubMed ID:25957350 | HubMed [Kahara2015]
All Medline abstracts: PubMed | HubMed
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