PMID- 28254606
OWN - NLM
STAT- Publisher
DA  - 20170303
LR  - 20170303
IS  - 1095-9130 (Electronic)
IS  - 1046-2023 (Linking)
DP  - 2017 Feb 22
TI  - Computational Analysis of CLIP-seq Data.
LID - S1046-2023(17)30082-8 [pii]
LID - 10.1016/j.ymeth.2017.02.006 [doi]
AB  - CLIP-seq experiments are currently the most important means for determining the
      binding sites of RNA binding proteins on a genome-wide level. The computational
      analysis can be divided into three steps. In the first pre-processing stage, raw 
      reads have to be trimmed and mapped to the genome. This step has to be
      specifically adapted for each CLIP-seq protocol. The next step is peak calling,
      which is required to remove unspecific signals and to determine bona fide protein
      binding sites on target RNAs. Here, both protocol-specific approaches as well as 
      generic peak callers are available. Despite some peak callers being more widely
      used, each peak caller has its specific assets and drawbacks, and it might be
      advantageous to compare the results of several methods. Although peak calling is 
      often the final step in many CLIP-seq publications, an important follow-up task
      is the determination of binding models from CLIP-seq data. This is central
      because CLIP-seq experiments are highly dependent on the transcriptional state of
      the cell in which the experiment was performed. Thus, relying solely on binding
      sites determined by CLIP-seq from different cells or conditions can lead to a
      high false negative rate. This shortcoming can, however, be circumvented by
      applying models that predict additional putative binding sites.
CI  - Copyright (c) 2017. Published by Elsevier Inc.
FAU - Uhl, Michael
AU  - Uhl M
AD  - Bioinformatics Group, Department of Computer Science, University of Freiburg,
      Germany.
FAU - Houwaart, Torsten
AU  - Houwaart T
AD  - Bioinformatics Group, Department of Computer Science, University of Freiburg,
      Germany.
FAU - Corrado, Gianluca
AU  - Corrado G
AD  - Department of Information Engineering and Computer Science, University of Trento,
      Italy.
FAU - Wright, Patrick R
AU  - Wright PR
AD  - Bioinformatics Group, Department of Computer Science, University of Freiburg,
      Germany.
FAU - Backofen, Rolf
AU  - Backofen R
AD  - Bioinformatics Group, Department of Computer Science, University of Freiburg,
      Germany; Centre for Biological Signalling Studies (BIOSS), University of
      Freiburg, Freiburg, Germany.
LA  - eng
PT  - Journal Article
PT  - Review
DEP - 20170222
PL  - United States
TA  - Methods
JT  - Methods (San Diego, Calif.)
JID - 9426302
OTO - NOTNLM
OT  - CLIP-seq data analysis
OT  - Peak calling
OT  - RBP binding models
OT  - RBP binding site prediction
EDAT- 2017/03/04 06:00
MHDA- 2017/03/04 06:00
CRDT- 2017/03/04 06:00
PHST- 2016/10/04 [received]
PHST- 2017/02/17 [revised]
PHST- 2017/02/20 [accepted]
AID - S1046-2023(17)30082-8 [pii]
AID - 10.1016/j.ymeth.2017.02.006 [doi]
PST - aheadofprint
SO  - Methods. 2017 Feb 22. pii: S1046-2023(17)30082-8. doi:
      10.1016/j.ymeth.2017.02.006.