Evidence map›Paper›PMID 33347457›Full record

ArticlePloS one2020

Assessment of transcriptional importance of cell line-specific features based on GTRD and FANTOM5 data.

Ruslan N Sharipov, Yury V Kondrakhin, Anna S Ryabova, Ivan S Yevshin, Fedor A Kolpakov

Abstract read
In one paragraph

Article in PloS one, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

5 authors.

Ruslan N SharipovLaboratory of Bioinformatics, Federal Research Center for Information and Computational Technologies, Novosibirsk, Russian Federation.ORCID 0000-0003-2182-5493
Yury V KondrakhinLaboratory of Bioinformatics, Federal Research Center for Information and Computational Technologies, Novosibirsk, Russian Federation.
Anna S RyabovaLaboratory of Bioinformatics, Federal Research Center for Information and Computational Technologies, Novosibirsk, Russian Federation.
Ivan S YevshinLaboratory of Bioinformatics, Federal Research Center for Information and Computational Technologies, Novosibirsk, Russian Federation.
Fedor A KolpakovLaboratory of Bioinformatics, Federal Research Center for Information and Computational Technologies, Novosibirsk, Russian Federation.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Creating a complete picture of the regulation of transcription seems to be an urgent task of modern biology. Regulation of transcription is a complex process carried out by transcription factors (TFs) and auxiliary proteins. Over the past decade, ChIP-Seq has become the most common experimental technology studying genome-wide interactions between TFs and DNA. We assessed the transcriptional significance of cell line-specific features using regression analysis of ChIP-Seq datasets from the GTRD database and transcriptional start site (TSS) activities from the FANTOM5 expression atlas. For this purpose, we initially generated a large number of features that were defined as the presence or absence of TFs in different promoter regions around TSSs. Using feature selection and regression analysis, we identified sets of the most important TFs that affect expression activity of TSSs in human cell lines such as HepG2, K562 and HEK293. We demonstrated that some TFs can be classified as repressors and activators depending on their location relative to TSS.

Indexed as

Databases, Nucleic AcidGene Expression ProfilingTranscription FactorsTranscriptomeHEK293 CellsHep G2 CellsHumansK562 CellsTranscription Factors

Identifiers

PMID33347457
PMCPMC7751965

What OpenQuestion holds

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Registered trials

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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.