Evidence map›Paper›PMID 31077313›Full record

ArticleGigaScience2019

Gigwa v2-Extended and improved genotype investigator.

Guilhem Sempéré, Adrien Pétel, Mathieu Rouard, Julien Frouin, Yann Hueber, Fabien De Bellis, Pierre Larmande

Abstract read
In one paragraph

Article in GigaScience, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.

0numbers the graph read from it
0cells of the map it votes in
18citing 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

18 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

7 authors.

Guilhem SempéréCentre de coopération Internationale en Recherche Agronomique pour le Développement (CIRAD), UMR INTERTRYP, F-34398 Montpellier, France.
Adrien PételSouth Green Bioinformatics Platform, Bioversity, CIRAD, Institut National de la Recherche Agronomique (INRA), IRD, Montpellier, France.
Mathieu RouardSouth Green Bioinformatics Platform, Bioversity, CIRAD, Institut National de la Recherche Agronomique (INRA), IRD, Montpellier, France.
Julien FrouinCIRAD, UMR AGAP, F-34398 Montpellier, France.
Yann HueberSouth Green Bioinformatics Platform, Bioversity, CIRAD, Institut National de la Recherche Agronomique (INRA), IRD, Montpellier, France.
Fabien De BellisCIRAD, UMR AGAP, F-34398 Montpellier, France.
Pierre LarmandeSouth Green Bioinformatics Platform, Bioversity, CIRAD, Institut National de la Recherche Agronomique (INRA), IRD, Montpellier, France.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe study of genetic variations is the basis of many research domains in biology. From genome structure to population dynamics, many applications involve the use of genetic variants. The advent of next-generation sequencing technologies led to such a flood of data that the daily work of scientists is often more focused on data management than data analysis. This mass of genotyping data poses several computational challenges in terms of storage, search, sharing, analysis, and visualization. While existing tools try to solve these challenges, few of them offer a comprehensive and scalable solution.

resultsGigwa v2 is an easy-to-use, species-agnostic web application for managing and exploring high-density genotyping data. It can handle multiple databases and may be installed on a local computer or deployed as an online data portal. It supports various standard import and export formats, provides advanced filtering options, and offers means to visualize density charts or push selected data into various stand-alone or online tools. It implements 2 standard RESTful application programming interfaces, GA4GH, which is health-oriented, and BrAPI, which is breeding-oriented, thus offering wide possibilities of interaction with third-party applications. The project home page provides a list of live instances allowing users to test the system on public data (or reasonably sized user-provided data).

conclusionsThis new version of Gigwa provides a more intuitive and more powerful way to explore large amounts of genotyping data by offering a scalable solution to search for genotype patterns, functional annotations, or more complex filtering. Furthermore, its user-friendliness and interoperability make it widely accessible to the life science community.

Indexed as

Computational BiologyGenomicsGenotypeSoftwareDatabases, GeneticGenetic VariationHigh-Throughput Nucleotide SequencingHumansInternetPolymorphism, Single NucleotideUser-Computer InterfaceBrAPIGA4GHgenomic variationsHapMapindelinteroperabilityMongoDBNoSQLPLINKRESTSNPVCFweb

Identifiers

PMID31077313
PMCPMC6511067

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.