Evidence map›Paper›PMID 31690036›Full record

ReviewCancers2019

Comprehensive Outline of Whole Exome Sequencing Data Analysis Tools Available in Clinical Oncology.

Áron Bartha, Balázs Győrffy

Abstract readReview
In one paragraph

Review in Cancers, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 38 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
38citing papers in PubMed, 1 pooled it
–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

38 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Trial
  3. Article
  4. Review
  5. Review
  6. Review
  7. Article
  8. Article
  9. Article
  10. Pan-cancer analysis and oncogenic implications ofJournal of cell communication and signaling · 2025
    Article
  11. Review
  12. Review
  13. Article
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  15. Review
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  18. [Molecular pathological analysis through the ages].Pathologie (Heidelberg, Germany) · 2024
    Review
  19. Article
  20. Article
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

2 authors.

Áron BarthaSemmelweis University, Department of Bioinformatics and 2nd Department of Pediatrics, H-1094 Budapest, Hungary. bartha.aron@med.semmelweis-univ.hu.
Balázs GyőrffySemmelweis University, Department of Bioinformatics and 2nd Department of Pediatrics, H-1094 Budapest, Hungary. gyorffy.balazs@med.semmelweis-univ.hu.

Funding

National Research, Development and Innovation Office of Hungary 2018-1.3.1-VKE-2018-00032National Research, Development and Innovation Office of Hungary KH-129581National Research, Development and Innovation Office of Hungary NVKP_16-1-2016-0037
6 · The paper itself

Abstract

Whole exome sequencing (WES) enables the analysis of all protein coding sequences in the human genome. This technology enables the investigation of cancer-related genetic aberrations that are predominantly located in the exonic regions. WES delivers high-throughput results at a reasonable price. Here, we review analysis tools enabling utilization of WES data in clinical and research settings. Technically, WES initially allows the detection of single nucleotide variants (SNVs) and copy number variations (CNVs), and data obtained through these methods can be combined and further utilized. Variant calling algorithms for SNVs range from standalone tools to machine learning-based combined pipelines. Tools for CNV detection compare the number of reads aligned to a dedicated segment. Both SNVs and CNVs help to identify mutations resulting in pharmacologically druggable alterations. The identification of homologous recombination deficiency enables the use of PARP inhibitors. Determining microsatellite instability and tumor mutation burden helps to select patients eligible for immunotherapy. To pave the way for clinical applications, we have to recognize some limitations of WES, including its restricted ability to detect CNVs, low coverage compared to targeted sequencing, and the missing consensus regarding references and minimal application requirements. Recently, Galaxy became the leading platform in non-command line-based WES data processing. The maturation of next-generation sequencing is reinforced by Food and Drug Administration (FDA)-approved methods for cancer screening, detection, and follow-up. WES is on the verge of becoming an affordable and sufficiently evolved technology for everyday clinical use.

Indexed as

bioinformaticscancerwhole exome sequencing

Identifiers

PMID31690036
PMCPMC6895801

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

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.