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Related Experiment Video

Updated: Aug 12, 2025

Targeted Next-generation Sequencing and Bioinformatics Pipeline to Evaluate Genetic Determinants of Constitutional Disease

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Published on: April 4, 2018

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

Aman Agarwal¹, Fengdi Zhao², Yuchao Jiang³

¹Department of Computer Science, Indiana University, Bloomington, IN 47405, USA.

Bioinformatics (Oxford, England)

|January 28, 2023

Summary

This summary is machine-generated.

This study introduces TIVAN-indel, a machine learning tool to identify functional non-coding small insertions and deletions (sindels) that impact gene expression and human diseases. The framework accurately predicts regulatory sindels across various tissues, improving disease association studies.

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Screening for Functional Non-coding Genetic Variants Using Electrophoretic Mobility Shift Assay EMSA and DNA-affinity Precipitation Assay DAPA

Screening for Functional Non-coding Genetic Variants Using Electrophoretic Mobility Shift Assay EMSA and DNA-affinity Precipitation Assay DAPA

Published on: August 21, 2016

Area of Science:

Genomics
Computational Biology
Human Genetics

Background:

Small insertions and deletions (sindels) in the human genome are implicated in disease, particularly non-coding sindels (nc-sindels) that regulate gene expression.
Current sequencing methods often lack the power to detect functional sindels due to low allele frequencies or small effect sizes.
Computational tools for predicting regulatory sindels, especially in non-coding regions, are underdeveloped.

Purpose of the Study:

To develop a supervised machine learning framework, TIssue-specific Variant Annotation for Non-coding indel (TIVAN-indel), for predicting the regulatory potential of nc-sindels.
To enhance the identification of functional sindels that may be missed by traditional sequencing approaches.
To improve understanding of the genetic basis of human diseases influenced by non-coding variants.

Main Methods:

Leveraged labeled nc-sindels from cis-expression quantitative trait loci (eQTL) analyses across 44 Genotype-Tissue Expression (GTEx) tissues.
Integrated generic functional annotations and large-scale epigenomic profiles.
Developed a supervised computational framework (TIVAN-indel) for predicting non-coding regulatory sindels.

Main Results:

TIVAN-indel demonstrated superior prediction performance in both within-tissue and cross-tissue predictions.
Independent evaluation using immune cell types from the Database of Immune Cell Expression confirmed TIVAN-indel's efficacy.
Enrichment analysis revealed biologically meaningful patterns for true and predicted sindels in key regulatory regions (e.g., chromatin interactions, open chromatin).

Conclusions:

TIVAN-indel is an effective computational framework for predicting non-coding regulatory sindels.
The tool can identify functional variants that regulate gene expression and contribute to human diseases.
The findings highlight the importance of nc-sindels in gene regulation and disease etiology.