Evidence map›Paper›PMID 23282203›Full record

ArticleBMC genomics2012

CTF: a CRF-based transcription factor binding sites finding system.

Yupeng He, Yizhe Zhang, Guangyong Zheng, Chaochun Wei

Open access · goldAbstract read
In one paragraph

Article in BMC genomics, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
0.8field-weighted citation impact, top 28% of its field
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

4 citing papers in PubMed, 12 citations in OpenAlex.

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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

4 authors at 3 institutions in 2 countries.

Yupeng HeSchool of Life Sciences and Biotechnology, Shanghai Jiao Tong University, 800 Dongchuan Road, Shanghai 200240, China.
Yizhe Zhang
Guangyong Zheng
Chaochun Wei
Shanghai Jiao Tong University · CNChinese Academy of Sciences · CNUniversity of California, San Diego · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundIdentifying the location of transcription factor bindings is crucial to understand transcriptional regulation. Currently, Chromatin Immunoprecipitation followed with high-throughput Sequencing (ChIP-seq) is able to locate the transcription factor binding sites (TFBSs) accurately in high throughput and it has become the gold-standard method for TFBS finding experimentally. However, due to its high cost, it is impractical to apply the method in a very large scale. Considering the large number of transcription factors, numerous cell types and various conditions, computational methods are still very valuable to accurate TFBS identification.

resultsIn this paper, we proposed a novel integrated TFBS prediction system, CTF, based on Conditional Random Fields (CRFs). Integrating information from different sources, CTF was able to capture patterns of TFBSs contained in different features (sequence, chromatin and etc) and predicted the TFBS locations with a high accuracy. We compared CTF with several existing tools as well as the PWM baseline method on a dataset generated by ChIP-seq experiments (TFBSs of 13 transcription factors in mouse genome). Results showed that CTF performed significantly better than existing methods tested.

conclusionsCTF is a powerful tool to predict TFBSs by integrating high throughput data and different features. It can be a useful complement to ChIP-seq and other experimental methods for TFBS identification and thus improve our ability to investigate functional elements in post-genomic era. AVAILABILITY: CTF is freely available to academic users at: http://cbb.sjtu.edu.cn/~ccwei/pub/software/CTF/CTF.php.

Indexed as

SoftwareAlgorithmsAnimalsArea Under CurveBinding SitesDatabases, FactualGenomeInternetMiceProtein BindingROC CurveTranscription FactorsUser-Computer InterfaceTranscription Factors

Identifiers

PMID23282203
PMCPMC3535700
OpenAlexW2101459357

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.