Evidence map›Paper›PMID 36707993›Full record

ArticleBioinformatics (Oxford, England)2023

TIVAN-indel: a computational framework for annotating and predicting non-coding regulatory small insertions and deletions.

Aman Agarwal, Fengdi Zhao, Yuchao Jiang, Li Chen

Open access · goldAbstract read
In one paragraph

Article in Bioinformatics (Oxford, England), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
1.8field-weighted citation impact, top 15% 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

5 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 1 country.

Aman AgarwalDepartment of Computer Science, Indiana University, Bloomington, IN 47405, USA.
Fengdi ZhaoDepartment of Biostatistics, University of Florida, Gainesville, FL 32603, USA.
Yuchao JiangDepartment of Biostatistics, University of North Carolina, Chapel Hill, NC 27516, USA.ORCID 0000-0001-6905-6377
Li ChenDepartment of Biostatistics, University of Florida, Gainesville, FL 32603, USA.ORCID 0000-0001-9372-5606
University of Florida · USIndiana University Bloomington · USUniversity of North Carolina at Chapel Hill · US

Funding

Statistical Methods for Bulk-Tissue and Single-Cell Multi-Omics IntegrationR35GM138342 · NIGMS · UNIV OF NORTH CAROLINA CHAPEL HILL · PI Yuchao Jiang · 2020 to 2026
$2.3M
Computational modeling of genetic variations by multi-omics integration todecipher personal genomeR35GM142701 · NIGMS · UNIVERSITY OF FLORIDA · PI CHEN, LI · 2021 to 2025
$1.8M
NIGMS NIH HHS R35 GM138342NIGMS NIH HHS R35 GM142701
6 · The paper itself

Abstract

motivationSmall insertion and deletion (sindel) of human genome has an important implication for human disease. One important mechanism for non-coding sindel (nc-sindel) to have an impact on human diseases and phenotypes is through the regulation of gene expression. Nevertheless, current sequencing experiments may lack statistical power and resolution to pinpoint the functional sindel due to lower minor allele frequency or small effect size. As an alternative strategy, a supervised machine learning method can identify the otherwise masked functional sindels by predicting their regulatory potential directly. However, computational methods for annotating and predicting the regulatory sindels, especially in the non-coding regions, are underdeveloped.

resultsBy leveraging labeled nc-sindels identified by cis-expression quantitative trait loci analyses across 44 tissues in Genotype-Tissue Expression (GTEx), and a compilation of both generic functional annotations and large-scale epigenomic profiles, we develop TIssue-specific Variant Annotation for Non-coding indel (TIVAN-indel), which is a supervised computational framework for predicting non-coding regulatory sindels. As a result, we demonstrate that TIVAN-indel achieves the best prediction performance in both with-tissue prediction and cross-tissue prediction. As an independent evaluation, we train TIVAN-indel from the 'Whole Blood' tissue in GTEx and test the model using 15 immune cell types from an independent study named Database of Immune Cell Expression. Lastly, we perform an enrichment analysis for both true and predicted sindels in key regulatory regions such as chromatin interactions, open chromatin regions and histone modification sites, and find biologically meaningful enrichment patterns. AVAILABILITY AND IMPLEMENTATION: https://github.com/lichen-lab/TIVAN-indel. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Indexed as

EpigenomicsQuantitative Trait LociChromatinHumansINDEL MutationRegulatory Sequences, Nucleic AcidChromatin

Identifiers

PMID36707993
PMCPMC9900211
OpenAlexW4318323842

What OpenQuestion holds

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LicenceCC BY
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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.