Evidence map›Paper›PMID 32019565›Full record

ArticleBMC medical genomics2020

AMLVaran: a software approach to implement variant analysis of targeted NGS sequencing data in an oncological care setting.

Christian Wünsch, Henrik Banck, Carsten Müller-Tidow, Martin Dugas

Abstract read
In one paragraph

Article in BMC medical genomics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
–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

4 citing papers in PubMed.

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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.

Christian WünschInstitute for Medical Informatics, University of Münster, Albert-Schweitzer-Campus 1, Building A11, Münster, Germany. c.wuensch@uni-muenster.de.ORCID 0000-0003-4367-3857
Henrik BanckInstitute for Medical Informatics, University of Münster, Albert-Schweitzer-Campus 1, Building A11, Münster, Germany.
Carsten Müller-TidowDepartment of Medicine V, Hematology, Oncology and Rheumatology, University of Heidelberg, Heidelberg, Germany.
Martin DugasInstitute for Medical Informatics, University of Münster, Albert-Schweitzer-Campus 1, Building A11, Münster, Germany.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundNext-Generation Sequencing (NGS) enables large-scale and cost-effective sequencing of genetic samples in order to detect genetic variants. After successful use in research-oriented projects, NGS is now entering clinical practice. Consequently, variant analysis is increasingly important to facilitate a better understanding of disease entities and prognoses. Furthermore, variant calling allows to adapt and optimize specific treatments of individual patients, and thus is an integral part of personalized medicine.However, the analysis of NGS data typically requires a number of complex bioinformatics processing steps. A flexible and reliable software that combines the variant analysis process with a simple, user-friendly interface is therefore highly desirable, but still lacking.

resultsWith AMLVaran (AML Variant Analyzer), we present a web-based software, that covers the complete variant analysis workflow of targeted NGS samples. The software provides a generic pipeline that allows free choice of variant calling tools and a flexible language (SSDL) for filtering variant lists. AMLVaran's interactive website presents comprehensive annotation data and includes curated information on relevant hotspot regions and driver mutations. A concise clinical report with rule-based diagnostic recommendations is generated.An AMLVaran configuration with eight variant calling tools and a complex scoring scheme, based on the somatic variant calling pipeline appreci8, was used to analyze three datasets from AML and MDS studies with 402 samples in total. Maximum sensitivity and positive predictive values were 1.0 and 0.96, respectively. The tool's usability was found to be satisfactory by medical professionals.

conclusionCoverage analysis, reproducible variant filtering and software usability are important for clinical assessment of variants. AMLVaran performs reliable NGS variant analyses and generates reports fulfilling the requirements of a clinical setting. Due to its generic design, the software can easily be adapted for use with different targeted panels for other tumor entities, or even for whole-exome data. AMLVaran has been deployed to a public web server and is distributed with Docker scripts for local use.

Indexed as

Genetic VariationHigh-Throughput Nucleotide SequencingSequence Analysis, DNASoftwareHumansNeoplasmsDiagnostic reportGenomicsMutation databaseNGS sequencingVariant annotationVariant callingVariant filteringVariant interpretation

Identifiers

PMID32019565
PMCPMC7001226

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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.