Evidence map›Paper›PMID 30691411›Full record

ArticleBMC bioinformatics2019

First step toward gene expression data integration: transcriptomic data acquisition with COMMAND>_.

Marco Moretto, Paolo Sonego, Ana B Villaseñor-Altamirano, Kristof Engelen

Abstract read
In one paragraph

Article in BMC bioinformatics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing 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

6 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Open Data for Differential Network Analysis in Glioma.International journal of molecular sciences · 2020
    Article
  6. HpeNet: Co-expression Network Database forFrontiers in genetics · 2020
    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

4 authors.

Marco MorettoUnit of Computational Biology, Research and Innovation Centre, Fondazione Edmund Mach, via E. Mach 1, 38010, San Michele all'Adige, Italy. marco.moretto@fmach.it.ORCID http://orcid.org/0000-0003-4555-7243
Paolo SonegoUnit of Computational Biology, Research and Innovation Centre, Fondazione Edmund Mach, via E. Mach 1, 38010, San Michele all'Adige, Italy.
Ana B Villaseñor-AltamiranoLaboratorio Internacional de Investigación Sobre el Genoma Humano, Universidad Nacional Autónoma De México, 76230, Juriquilla, Querétaro, Mexico.
Kristof EngelenUnit of Computational Biology, Research and Innovation Centre, Fondazione Edmund Mach, via E. Mach 1, 38010, San Michele all'Adige, Italy. engelen.kristof@gmail.com.

Funding

Provincia Autonoma di Trento Accordo di Programma P1611051I
6 · The paper itself

Abstract

backgroundExploring cellular responses to stimuli using extensive gene expression profiles has become a routine procedure performed on a daily basis. Raw and processed data from these studies are available on public databases but the opportunity to fully exploit such rich datasets is limited due to the large heterogeneity of data formats. In recent years, several approaches have been proposed to effectively integrate gene expression data for analysis and exploration at a broader level. Despite the different goals and approaches towards gene expression data integration, the first step is common to any proposed method: data acquisition. Although it is seemingly straightforward to extract valuable information from a set of downloaded files, things can rapidly get complicated, especially as the number of experiments grows. Transcriptomic datasets are deposited in public databases with little regard to data format and thus retrieving raw data might become a challenging task. While for RNA-seq experiments such problem is partially mitigated by the fact that raw reads are generally available on databases such as the NCBI SRA, for microarray experiments standards are not equally well established, or enforced during submission, and thus a multitude of data formats has emerged.

resultsCOMMAND>_ is a specialized tool meant to simplify gene expression data acquisition. It is a flexible multi-user web-application that allows users to search and download gene expression experiments, extract only the relevant information from experiment files, re-annotate microarray platforms, and present data in a simple and coherent data model for subsequent analysis.

conclusionsCOMMAND>_ facilitates the creation of local datasets of gene expression data coming from both microarray and RNA-seq experiments and may be a more efficient tool to build integrated gene expression compendia. COMMAND>_ is free and open-source software, including publicly available tutorials and documentation.

Indexed as

Gene Expression RegulationSoftwareDatabases, GeneticGene Expression ProfilingHumansTranscriptomeWorkflowCompendiaData integrationGene expressionMicroarrayRna-seqTranscriptomic

Identifiers

PMID30691411
PMCPMC6348648

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.