SMAC, a computational system to link literature, biomedical and expression data

Pirro, S.; Gadaleta, Emanuela; Galgani, Andrea; Colizzi, Vittorio; Chelala, Claude

doi:10.1038/s41598-019-47046-2

Cited by 3 publications

(2 citation statements)

References 27 publications

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“…MAP is powered by a custom (microRNA-focused) version of the Smart Automatic Classification system (SMAC) 6 . MAP has been designed for extrapolating, selecting and linking relevant articles from PubMed to biomedical and molecular data.…”

Section: Methodsmentioning

confidence: 99%

The microRNA analysis portal is a next-generation tool for exploring and analyzing miRNA-focused data in the literature

Pirro¹,

Matić

Colizzi

et al. 2021

Sci Rep

Self Cite

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MicroRNAs constitute a class of noncoding small RNAs involved in the posttranscriptional regulation of many biological pathways. In recent years, microRNAs have also been associated with regulation across kingdoms, demonstrating that exogenous miRNAs can function in mammals in a fashion similar to mammalian miRNAs. The growing interest in microRNAs and the increasing amount of literature and molecular and biomedical data available make it difficult to identify records of interest and keep up to date with novel findings. For these reasons, we developed the microRNA Analysis Portal (MAP). MAP selects relevant miRNA-focused articles from PubMed, links biomedical and molecular data and applies bioinformatics modules. At the time of this writing, MAP represents the richest, most complete and integrated database focused on microRNAs. MAP also integrates an updated version of MirCompare (2.0), a computational platform used for selecting plant microRNAs on the basis of their ability to regulate mammalian genes. Both MAP and MirCompare functionalities were used to predict that microRNAs from Moringa oleifera have putative roles across kingdoms by regulating human genes coding for proteins of the immune system. Starting from a selection of 94 human microRNAs, MirCompare selected 6 Moringa oleifera functional homologs. The subsequent prediction of human targets and areas of functional enrichment highlighted the central involvement of these genes in regulating immune system processes, particularly the host-virus interaction processes in hepatitis B, cytomegalovirus, papillomavirus and coronavirus. This case of use showed how MAP can help to perform complex queries without any computational background. MAP is available at http://stablab.uniroma2.it/MAP.

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Section: Methodsmentioning

confidence: 99%

The microRNA analysis portal is a next-generation tool for exploring and analyzing miRNA-focused data in the literature

Pirro¹,

Matić

Colizzi

et al. 2021

Sci Rep

Self Cite

View full text Add to dashboard Cite

show abstract

“…In addition, MeV and BRB-ArrayTools provide ways for researchers to import and export GEO datasets 37,38 . SMart Automatic Classification (SMAC) enables researchers to mine PubMed for datasets associated with user-defined queries, extract the data from GEO (using GEOquery), and then transform the data; it focuses primarily on cancer research, but some functionality can be applied more broadly 39 . TidyGEO provides diverse options for exporting data, thus providing flexibility for researchers who wish to analyze data with external tools.…”

Section: Making Geo Data Interoperablementioning

confidence: 99%

TidyGEO: Preparing analysis-ready datasets from Gene Expression Omnibus

Mecham¹,

Stephenson²,

Quinteros³

et al. 2023

Preprint

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TidyGEO is a Web-based tool for downloading, tidying, and reformatting data series from Gene Expression Omnibus (GEO). As a freely accessible repository with data from over 4 million biological samples across more than 4,000 organisms, GEO provides diverse opportunities for secondary research. Transcriptomic data are most common in GEO, but other measurement types are also prevalent, including DNA methylation levels, genotypes, and chromatin-accessibility profiles. GEO's diversity and expansiveness present opportunities and challenges. Although scientists may find assay data relevant to a given research question, most analyses require sample annotations, such as a sample's treatment group, disease subtype, or age. In GEO, such annotations are stored alongside assay data in delimited, text-based files. However, the structure and semantics of the annotations vary widely from one series to another, and many annotations are not useful for analysis purposes. Thus, every GEO series must be tidied before it is analyzed. Manual approaches may be used, but these are error prone and take time away from other research tasks. Custom computer scripts can be written, but many scientists lack the computational expertise to create such scripts. To address these challenges, we created TidyGEO, which supports essential data-cleaning tasks for sample-level annotations, such as selecting informative columns, renaming columns, splitting or merging columns, standardizing data values, and filtering samples. Additionally, users can integrate annotations with assay data, restructure assay data, and generate code that enables others to reproduce these steps. The source code for TidyGEO is at https://github.com/srp33/TidyGEO.

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TidyGEO: preparing analysis-ready datasets from Gene Expression Omnibus

Mecham,

Stephenson,

Quinteros

et al. 2023

Journal of Integrative Bioinformatics

View full text Add to dashboard Cite

TidyGEO is a Web-based tool for downloading, tidying, and reformatting data series from Gene Expression Omnibus (GEO). As a freely accessible repository with data from over 6 million biological samples across more than 4000 organisms, GEO provides diverse opportunities for secondary research. Although scientists may find assay data relevant to a given research question, most analyses require sample-level annotations. In GEO, such annotations are stored alongside assay data in delimited, text-based files. However, the structure and semantics of the annotations vary widely from one series to another, and many annotations are not useful for analysis purposes. Thus, every GEO series must be tidied before it is analyzed. Manual approaches may be used, but these are error prone and take time away from other research tasks. Custom computer scripts can be written, but many scientists lack the computational expertise to create such scripts. To address these challenges, we created TidyGEO, which supports essential data-cleaning tasks for sample-level annotations, such as selecting informative columns, renaming columns, splitting or merging columns, standardizing data values, and filtering samples. Additionally, users can integrate annotations with assay data, restructure assay data, and generate code that enables others to reproduce these steps.

show abstract

SMAC, a computational system to link literature, biomedical and expression data

Cited by 3 publications

References 27 publications

The microRNA analysis portal is a next-generation tool for exploring and analyzing miRNA-focused data in the literature

The microRNA analysis portal is a next-generation tool for exploring and analyzing miRNA-focused data in the literature

TidyGEO: Preparing analysis-ready datasets from Gene Expression Omnibus

TidyGEO: preparing analysis-ready datasets from Gene Expression Omnibus

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