Evidence map›Paper›PMID 37914828›Full record

ArticleScientific reports2023

Comparison of three bioinformatics tools in the detection of ASD candidate variants from whole exome sequencing data.

Apurba Shil, Liron Levin, Hava Golan, Gal Meiri, Analya Michaelovski, Yair Sadaka, Adi Aran, Ilan Dinstein, Idan Menashe

Open access · goldAbstract read
In one paragraph

Article in Scientific reports, 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
1.6field-weighted citation impact, top 14% 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, 5 citations in OpenAlex.

  1. Pathogenicity assessment of genetic variants in reproductive disorders.Journal of assisted reproduction and genetics · 2026
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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

9 authors at 3 institutions in 1 country.

Apurba ShilDepartment of Epidemiology, Biostatistics, and Health Community Sciences, Faculty of Health Sciences, Ben-Gurion University of the Negev, Beer-Sheva, Israel.
Liron LevinBioinformatics Core Facility, Ben-Gurion University of the Negev, Beer-Sheva, Israel.
Hava GolanAzrieli National Centre for Autism and Neurodevelopment Research, Ben-Gurion University of the Negev, Beer-Sheva, Israel.
Gal MeiriAzrieli National Centre for Autism and Neurodevelopment Research, Ben-Gurion University of the Negev, Beer-Sheva, Israel.
Analya MichaelovskiAzrieli National Centre for Autism and Neurodevelopment Research, Ben-Gurion University of the Negev, Beer-Sheva, Israel.
Yair SadakaAzrieli National Centre for Autism and Neurodevelopment Research, Ben-Gurion University of the Negev, Beer-Sheva, Israel.
Adi AranNeuropediatric Unit, Shaare Zedek Medical Center, Jerusalem, Israel.
Ilan DinsteinAzrieli National Centre for Autism and Neurodevelopment Research, Ben-Gurion University of the Negev, Beer-Sheva, Israel.
Idan MenasheDepartment of Epidemiology, Biostatistics, and Health Community Sciences, Faculty of Health Sciences, Ben-Gurion University of the Negev, Beer-Sheva, Israel. idanmen@bgu.ac.il.
Ben-Gurion University of the Negev · ILIsrael Ministry of Health · ILShaare Zedek Medical Center · IL

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Autism spectrum disorder (ASD) is a heterogenous multifactorial neurodevelopmental condition with a significant genetic susceptibility component. Thus, identifying genetic variations associated with ASD is a complex task. Whole-exome sequencing (WES) is an effective approach for detecting extremely rare protein-coding single-nucleotide variants (SNVs) and short insertions/deletions (INDELs). However, interpreting these variants' functional and clinical consequences requires integrating multifaceted genomic information. We compared the concordance and effectiveness of three bioinformatics tools in detecting ASD candidate variants (SNVs and short INDELs) from WES data of 220 ASD family trios registered in the National Autism Database of Israel. We studied only rare (< 1% population frequency) proband-specific variants. According to the American College of Medical Genetics (ACMG) guidelines, the pathogenicity of variants was evaluated by the InterVar and TAPES tools. In addition, likely gene-disrupting (LGD) variants were detected based on an in-house bioinformatics tool, Psi-Variant, that integrates results from seven in-silico prediction tools. Overall, 372 variants in 311 genes distributed in 168 probands were detected by these tools. The overlap between the tools was 64.1, 22.9, and 23.1% for InterVar-TAPES, InterVar-Psi-Variant, and TAPES-Psi-Variant, respectively. The intersection between InterVar and Psi-Variant (I ∩ P) was the most effective approach in detecting variants in known ASD genes (PPV = 0.274; OR = 7.09, 95% CI = 3.92-12.22), while the union of InterVar and Psi Variant (I U P) achieved the highest diagnostic yield (20.5%).Our results suggest that integrating different variant interpretation approaches in detecting ASD candidate variants from WES data is superior to each approach alone. The inclusion of additional criteria could further improve the detection of ASD candidate variants.

Indexed as

Autism Spectrum DisorderComputational BiologyExome SequencingGenetic Predisposition to DiseaseGenomicsHumans

Identifiers

PMID37914828
PMCPMC10620213
OpenAlexW4388126402

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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.