A model-based approach to identify binding sites in CLIP-Seq data.
about
Assessing Computational Steps for CLIP-Seq Data Analysis.Bioinformatic tools for analysis of CLIP ribonucleoprotein dataComputational challenges, tools, and resources for analyzing co- and post-transcriptional events in high throughput.Intramolecular circularization increases efficiency of RNA sequencing and enables CLIP-Seq of nuclear RNA from human cells.BackCLIP: a tool to identify common background presence in PAR-CLIP datasets.Design and bioinformatics analysis of genome-wide CLIP experiments.From benchmarking HITS-CLIP peak detection programs to a new method for identification of miRNA-binding sites from Ago2-CLIP dataRecent computational developments on CLIP-seq data analysis and microRNA targeting implications.CLIPSeqTools--a novel bioinformatics CLIP-seq analysis suite.Revealing protein-lncRNA interaction
P2860
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P2860
A model-based approach to identify binding sites in CLIP-Seq data.
description
2014 nî lūn-bûn
@nan
2014 թուականի Ապրիլին հրատարակուած գիտական յօդուած
@hyw
2014 թվականի ապրիլին հրատարակված գիտական հոդված
@hy
2014年の論文
@ja
2014年論文
@yue
2014年論文
@zh-hant
2014年論文
@zh-hk
2014年論文
@zh-mo
2014年論文
@zh-tw
2014年论文
@wuu
name
A model-based approach to identify binding sites in CLIP-Seq data.
@ast
A model-based approach to identify binding sites in CLIP-Seq data.
@en
type
label
A model-based approach to identify binding sites in CLIP-Seq data.
@ast
A model-based approach to identify binding sites in CLIP-Seq data.
@en
prefLabel
A model-based approach to identify binding sites in CLIP-Seq data.
@ast
A model-based approach to identify binding sites in CLIP-Seq data.
@en
P2093
P2860
P1433
P1476
A model-based approach to identify binding sites in CLIP-Seq data.
@en
P2093
Beibei Chen
Guanghua Xiao
MinSoo Kim
P2860
P304
P356
10.1371/JOURNAL.PONE.0093248
P407
P577
2014-04-08T00:00:00Z