Evidence map›Paper›PMID 38633426›Full record

ArticleNAR genomics and bioinformatics2024

A method to comprehensively identify germline SNVs, INDELs and CNVs from whole exome sequencing data of BRCA1/2 negative breast cancer patients.

Andrea Bianchi, Veronica Zelli, Andrea D'Angelo, Alessandro Di Matteo, Giulia Scoccia, Katia Cannita, Antigone S Dimas, Stavros Glentis, Francesca Zazzeroni, Edoardo Alesse and 2 more

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in NAR genomics and bioinformatics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
1.1field-weighted citation impact, top 21% 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

1 citing paper in PubMed, 2 citations in OpenAlex.

  1. Review
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

12 authors at 3 institutions in 2 countries.

Andrea BianchiDepartment of Information Engineering, Computer Science and Mathematics, University of L'Aquila, L'Aquila 67100, Italy.ORCID https://orcid.org/0000-0002-6046-4355
Veronica ZelliDepartment of Biotechnological and Applied Clinical Sciences, University of L'Aquila, L'Aquila 67100, Italy.ORCID https://orcid.org/0000-0002-0047-2919
Andrea D'AngeloDepartment of Information Engineering, Computer Science and Mathematics, University of L'Aquila, L'Aquila 67100, Italy.ORCID https://orcid.org/0000-0002-0577-2494
Alessandro Di MatteoDepartment of Information Engineering, Computer Science and Mathematics, University of L'Aquila, L'Aquila 67100, Italy.ORCID https://orcid.org/0009-0004-8163-5186
Giulia ScocciaDepartment of Information Engineering, Computer Science and Mathematics, University of L'Aquila, L'Aquila 67100, Italy.
Katia CannitaOncology Division, Mazzini Hospital, ASL Teramo, Teramo 64100, Italy.
Antigone S DimasInstitute for Bioinnovation, Biomedical Sciences Research Center, Alexander Fleming, Vari 16672, Greece.
Stavros GlentisInstitute for Bioinnovation, Biomedical Sciences Research Center, Alexander Fleming, Vari 16672, Greece.
Francesca ZazzeroniDepartment of Biotechnological and Applied Clinical Sciences, University of L'Aquila, L'Aquila 67100, Italy.
Edoardo AlesseDepartment of Biotechnological and Applied Clinical Sciences, University of L'Aquila, L'Aquila 67100, Italy.
Antinisca Di MarcoDepartment of Information Engineering, Computer Science and Mathematics, University of L'Aquila, L'Aquila 67100, Italy.ORCID https://orcid.org/0000-0001-7214-9945
Alessandra TessitoreDepartment of Biotechnological and Applied Clinical Sciences, University of L'Aquila, L'Aquila 67100, Italy.
University of L'Aquila · ITAlexander Fleming Biomedical Sciences Research Center · GRNational and Kapodistrian University of Athens · GR

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In the rapidly evolving field of genomics, understanding the genetic basis of complex diseases like breast cancer, particularly its familial/hereditary forms, is crucial. Current methods often examine genomic variants-such as Single Nucleotide Variants (SNVs), insertions/deletions (Indels), and Copy Number Variations (CNVs)-separately, lacking an integrated approach. Here, we introduced a robust, flexible methodology for a comprehensive variants' analysis using Whole Exome Sequencing (WES) data. Our approach uniquely combines meticulous validation with an effective variant filtering strategy. By reanalyzing two germline WES datasets from

Identifiers

PMID38633426
PMCPMC11023157
OpenAlexW4394869957

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
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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.