Evidence map›Paper›PMID 27519564›Full record

ArticleBMC genomics2016

iTAR: a web server for identifying target genes of transcription factors using ChIP-seq or ChIP-chip data.

Chia-Chun Yang, Erik H Andrews, Min-Hsuan Chen, Wan-Yu Wang, Jeremy J W Chen, Mark Gerstein, Chun-Chi Liu, Chao Cheng

Abstract read
In one paragraph

Article in BMC genomics, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Target Gene Prediction of Transcription Factor Using a New Neighborhood-regularized Tri-factorization One-class Collaborative Filtering Algorithm.ACM-BCB ... ... : the ... ACM Conference on Bioinformatics, Computational Biology and Biomedicine. ACM Conference on Bioinformatics, Computational Biology and Biomedicine
    Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Chia-Chun YangInstitute of Molecular Biology, National Chung Hsing University, Taichung, 402, Taiwan.
Erik H AndrewsGeisel School of Medicine at Dartmouth, Institute for Quantitative Biomedical Sciences, Lebanon, NH, 03766, USA.
Min-Hsuan ChenInstitute of Genomics and Bioinformatics, National Chung Hsing University, Taichung, 402, Taiwan.
Wan-Yu WangInstitute of Genomics and Bioinformatics, National Chung Hsing University, Taichung, 402, Taiwan.
Jeremy J W ChenInstitute of Molecular Biology, National Chung Hsing University, Taichung, 402, Taiwan.
Mark GersteinProgram in Computational Biology and Bioinformatics, Yale University, 260 Whitney Avenue, New Haven, CT, 06520, USA.
Chun-Chi LiuInstitute of Genomics and Bioinformatics, National Chung Hsing University, Taichung, 402, Taiwan. jimliu@nchu.edu.tw.
Chao ChengDepartment of Genetics, Geisel School of Medicine at Dartmouth, Hanover, NH, 03755, USA. chao.cheng@dartmouth.edu.

Funding

Yale Clinical and Translational Science Award (U Component)UL1TR001863 · NCATS · YALE UNIVERSITY · PI John H. Krystal, LUCILA OHNO-MACHADO · 2016 to 2026
$102.9M
Quantitative Biology Research InstituteP20GM103534 · NIGMS · DARTMOUTH COLLEGE · PI AMOS, CHRISTOPHER I. · 2012 to 2016
$8.6M
NCATS NIH HHS UL1 TR001863NIGMS NIH HHS P20 GM103534
6 · The paper itself

Abstract

backgroundChromatin immunoprecipitation followed by massively parallel DNA sequencing (ChIP-seq) or microarray hybridization (ChIP-chip) has been widely used to determine the genomic occupation of transcription factors (TFs). We have previously developed a probabilistic method, called TIP (Target Identification from Profiles), to identify TF target genes using ChIP-seq/ChIP-chip data. To achieve high specificity, TIP applies a conservative method to estimate significance of target genes, with the trade-off being a relatively low sensitivity of target gene identification compared to other methods. Additionally, TIP's output does not render binding-peak locations or intensity, information highly useful for visualization and general experimental biological use, while the variability of ChIP-seq/ChIP-chip file formats has made input into TIP more difficult than desired. DESCRIPTION: To improve upon these facets, here we present are fined TIP with key extensions. First, it implements a Gaussian mixture model for p-value estimation, increasing target gene identification sensitivity and more accurately capturing the shape of TF binding profile distributions. Second, it enables the incorporation of TF binding-peak data by identifying their locations in significant target gene promoter regions and quantifies their strengths. Finally, for full ease of implementation we have incorporated it into a web server ( http://syslab3.nchu.edu.tw/iTAR/ ) that enables flexibility of input file format, can be used across multiple species and genome assembly versions, and is freely available for public use. The web server additionally performs GO enrichment analysis for the identified target genes to reveal the potential function of the corresponding TF.

conclusionsThe iTAR web server provides a user-friendly interface and supports target gene identification in seven species, ranging from yeast to human. To facilitate investigating the quality of ChIP-seq/ChIP-chip data, the web server generates the chart of the characteristic binding profiles and the density plot of normalized regulatory scores. The iTAR web server is a useful tool in identifying TF target genes from ChIP-seq/ChIP-chip data and discovering biological insights.

Indexed as

Chromatin ImmunoprecipitationUser-Computer InterfaceAlgorithmsHeLa CellsHigh-Throughput Nucleotide SequencingHumansInternetPromoter Regions, GeneticSequence Analysis, DNASTAT3 Transcription FactorSTAT3 Transcription FactorChIP-chipChIP-seqGaussian mixture modelGene ontology analysisTranscription factor

Identifiers

PMID27519564
PMCPMC4983039

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.