Dataset Information

Computational Methods and Software Tools for Functional Analysis of miRNA Data.

ABSTRACT: miRNAs are important regulators of gene expression that play a key role in many biological processes. High-throughput techniques allow researchers to discover and characterize large sets of miRNAs, and enrichment analysis tools are becoming increasingly important in decoding which miRNAs are implicated in biological processes. Enrichment analysis of miRNA targets is the standard technique for functional analysis, but this approach carries limitations and bias; alternatives are currently being proposed, based on direct and curated annotations. In this review, we describe the two workflows of miRNAs enrichment analysis, based on target gene or miRNA annotations, highlighting statistical tests, software tools, up-to-date databases, and functional annotations resources in the study of metazoan miRNAs.

SUBMITTER: Garcia-Moreno A

PROVIDER: S-EPMC7563698 | biostudies-literature | 2020 Aug

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Computational Methods and Software Tools for Functional Analysis of miRNA Data.

Garcia-Moreno Adrian A Carmona-Saez Pedro P

Biomolecules 20200828 9

miRNAs are important regulators of gene expression that play a key role in many biological processes. High-throughput techniques allow researchers to discover and characterize large sets of miRNAs, and enrichment analysis tools are becoming increasingly important in decoding which miRNAs are implicated in biological processes. Enrichment analysis of miRNA targets is the standard technique for functional analysis, but this approach carries limitations and bias; alternatives are currently being pr ...[more]

PMID: 32872205

Similar Datasets

Project description:To gain a thorough appreciation of microbiome dynamics, researchers characterize the functional relevance of expressed microbial genes or proteins. This can be accomplished through metaproteomics, which characterizes the protein expression of microbiomes. Several software tools exist for analyzing microbiomes at the functional level by measuring their combined proteome-level response to environmental perturbations. In this survey, we explore the performance of six available tools, to enable researchers to make informed decisions regarding software choice based on their research goals. Tandem mass spectrometry-based proteomic data obtained from dental caries plaque samples grown with and without sucrose in paired biofilm reactors were used as representative data for this evaluation. Microbial peptides from one sample pair were identified by the X! tandem search algorithm via SearchGUI and subjected to functional analysis using software tools including eggNOG-mapper, MEGAN5, MetaGOmics, MetaProteomeAnalyzer (MPA), ProPHAnE, and Unipept to generate functional annotation through Gene Ontology (GO) terms. Among these software tools, notable differences in functional annotation were detected after comparing differentially expressed protein functional groups. Based on the generated GO terms of these tools we performed a peptide-level comparison to evaluate the quality of their functional annotations. A BLAST analysis against the NCBI non-redundant database revealed that the sensitivity and specificity of functional annotation varied between tools. For example, eggNOG-mapper mapped to the most number of GO terms, while Unipept generated more accurate GO terms. Based on our evaluation, metaproteomics researchers can choose the software according to their analytical needs and developers can use the resulting feedback to further optimize their algorithms. To make more of these tools accessible via scalable metaproteomics workflows, eggNOG-mapper and Unipept 4.0 were incorporated into the Galaxy platform.

Project description:High-throughput assays from genomics, proteomics, metabolomics, and next generation sequencing produce massive omics datasets that are challenging to analyze in biological or clinical contexts. Thus far, there is no publicly available program for converting quantitative omics data into input formats to be used in off-the-shelf robust phylogenetic programs. To the best of our knowledge, this is the first report on creation of two Windows-based programs, OmicsTract and SynpExtractor, to address this gap. We note, as a way of introduction and development of these programs, that one particularly useful bioinformatics inferential modeling is the phylogenetic cladogram. Cladograms are multidimensional tools that show the relatedness between subgroups of healthy and diseased individuals and the latter's shared aberrations; they also reveal some characteristics of a disease that would not otherwise be apparent by other analytical methods. The OmicsTract and SynpExtractor were written for the respective tasks of (1) accommodating advanced phylogenetic parsimony analysis (through standard programs of MIX [from PHYLIP] and TNT), and (2) extracting shared aberrations at the cladogram nodes. OmicsTract converts comma-delimited data tables through assigning each data point into a binary value ("0" for normal states and "1" for abnormal states) then outputs the converted data tables into the proper input file formats for MIX or with embedded commands for TNT. SynapExtractor uses outfiles from MIX and TNT to extract the shared aberrations of each node of the cladogram, matching them with identifying labels from the dataset and exporting them into a comma-delimited file. Labels may be gene identifiers in gene-expression datasets or m/z values in mass spectrometry datasets. By automating these steps, OmicsTract and SynpExtractor offer a veritable opportunity for rapid and standardized phylogenetic analyses of omics data; their model can also be extended to next generation sequencing (NGS) data. We make OmicsTract and SynpExtractor publicly and freely available for non-commercial use in order to strengthen and build capacity for the phylogenetic paradigm of omics analysis.

Project description:BackgroundThe study and analysis of gene expression measurements is the primary focus of functional genomics. Once expression data is available, biologists are faced with the task of extracting (new) knowledge associated to the underlying biological phenomenon. Most often, in order to perform this task, biologists execute a number of analysis activities on the available gene expression dataset rather than a single analysis activity. The integration of heterogeneous tools and data sources to create an integrated analysis environment represents a challenging and error-prone task. Semantic integration enables the assignment of unambiguous meanings to data shared among different applications in an integrated environment, allowing the exchange of data in a semantically consistent and meaningful way. This work aims at developing an ontology-based methodology for the semantic integration of gene expression analysis tools and data sources. The proposed methodology relies on software connectors to support not only the access to heterogeneous data sources but also the definition of transformation rules on exchanged data.ResultsWe have studied the different challenges involved in the integration of computer systems and the role software connectors play in this task. We have also studied a number of gene expression technologies, analysis tools and related ontologies in order to devise basic integration scenarios and propose a reference ontology for the gene expression domain. Then, we have defined a number of activities and associated guidelines to prescribe how the development of connectors should be carried out. Finally, we have applied the proposed methodology in the construction of three different integration scenarios involving the use of different tools for the analysis of different types of gene expression data.ConclusionsThe proposed methodology facilitates the development of connectors capable of semantically integrating different gene expression analysis tools and data sources. The methodology can be used in the development of connectors supporting both simple and nontrivial processing requirements, thus assuring accurate data exchange and information interpretation from exchanged data.

Dataset Information

Computational Methods and Software Tools for Functional Analysis of miRNA Data.

Publications

Computational Methods and Software Tools for Functional Analysis of miRNA Data.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets