Dataset Information

Performance comparison and evaluation of software tools for microRNA deep-sequencing data analysis.

ABSTRACT: With the development of next-generation sequencing (NGS) techniques, many software tools have emerged for the discovery of novel microRNAs (miRNAs) and for analyzing the miRNAs expression profiles. An overall evaluation of these diverse software tools is lacking. In this study, we evaluated eight software tools based on their common feature and key algorithms. Three deep-sequencing data sets were collected from different species and used to assess the computational time, sensitivity and accuracy of detecting known miRNAs as well as their capacity for predicting novel miRNAs. Our results provide useful information for researchers to facilitate their selection of the optimal software tools for miRNA analysis depending on their specific requirements, i.e. novel miRNAs discovery or miRNA expression profile analysis of sequencing data sets.

SUBMITTER: Li Y

PROVIDER: S-EPMC3378883 | biostudies-literature | 2012 May

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Performance comparison and evaluation of software tools for microRNA deep-sequencing data analysis.

Li Yue Y Zhang Zhuo Z Liu Feng F Vongsangnak Wanwipa W Jing Qing Q Shen Bairong B

Nucleic acids research 20120128 10

With the development of next-generation sequencing (NGS) techniques, many software tools have emerged for the discovery of novel microRNAs (miRNAs) and for analyzing the miRNAs expression profiles. An overall evaluation of these diverse software tools is lacking. In this study, we evaluated eight software tools based on their common feature and key algorithms. Three deep-sequencing data sets were collected from different species and used to assess the computational time, sensitivity and accuracy ...[more]

PMID: 22287634

Similar Datasets

Project description:Background:For evaluating the fate of xenobiotics in the environment, a variety of degradation or environmental metabolism experiments are routinely conducted. The data generated in such experiments are evaluated by optimizing the parameters of kinetic models in a way that the model simulation fits the data. No comparison of the main software tools currently in use has been published to date. This article shows a comparison of numerical results as well as an overall, somewhat subjective comparison based on a scoring system using a set of criteria. The scoring was separately performed for two types of uses. Uses of type I are routine evaluations involving standard kinetic models and up to three metabolites in a single compartment. Evaluations involving non-standard model components, more than three metabolites or more than a single compartment belong to use type II. For use type I, usability is most important, while the flexibility of the model definition is most important for use type II. Results:Test datasets were assembled that can be used to compare the numerical results for different software tools. These datasets can also be used to ensure that no unintended or erroneous behaviour is introduced in newer versions. In the comparison of numerical results, good agreement between the parameter estimates was observed for datasets with up to three metabolites. For the now unmaintained reference software DegKinManager/ModelMaker, and for OpenModel which is still under development, user options were identified that should be taken care of in order to obtain results that are as reliable as possible. Based on the scoring system mentioned above, the software tools gmkin, KinGUII and CAKE received the best scores for use type I. Out of the 15 software packages compared with respect to use type II, again gmkin and KinGUII were the first two, followed by the script based tool mkin, which is the technical basis for gmkin, and by OpenModel. Conclusions:Based on the evaluation using the system of criteria mentioned above and the comparison of numerical results for the suite of test datasets, the software tools gmkin, KinGUII and CAKE are recommended for use type I, and gmkin and KinGUII for use type II. For users that prefer to work with scripts instead of graphical user interfaces, mkin is recommended. For future software evaluations, it is recommended to include a measure for the total time that a typical user needs for a kinetic evaluation into the scoring scheme. It is the hope of the authors that the publication of test data, source code and overall rankings foster the evolution of useful and reliable software in the field.

Project description:MicroRNAs (miRNAs) regulate many genes critical for tumorigenesis. We profiled miRNAs from 11 normal breast tissues, 17 non-invasive, 151 invasive breast carcinomas, and 6 cell lines by in-house-developed barcoded Solexa sequencing. miRNAs were organized in genomic clusters representing promoter-controlled miRNA expression and sequence families representing seed-sequence-dependent miRNA-target regulation. Unsupervised clustering of samples by miRNA sequence families best reflected the clustering based on mRNA expression available for this sample set. Clustering and comparative analysis of miRNA read frequencies showed that normal breast samples were separated from most non-invasive ductal carcinoma in situ and invasive carcinomas by increased miR-21 (the most abundant miRNA in carcinomas) and multiple decreased miRNA families (including mir-98/let-7), with most miRNA changes apparent already in the non-invasive carcinomas. In addition, patients that went on to develop metastasis demonstrated increased expression of mir-423, and triple negative breast carcinomas were most distinct from other tumor subtypes due to up-regulation of the mir-17~92 cluster. However, absolute miRNA levels between normal breast and carcinomas did not reveal any significant differences. We also discovered two polymorphic nucleotide variations among the more abundant miRNAs miR-181a (T19G) and miR-185 (T16G), but we did not identify nucleotide variations expected for classical tumor suppressor function associated with miRNAs. The differentiation of tumor subtypes and prediction of metastasis based on miRNA levels is statistically possible, but is not driven by deregulation of abundant miRNAs, implicating far fewer miRNAs in tumorigenic processes than previously suggested. 21 barcoded sequencing runs, including 185 unique samples and 54 samples in replicate (6 in triplicate and the remaining in duplicate). The details of each sample can be found in Supplementary Tables S1 and S2.

Project description:BackgroundBioinformatic tools for the enrichment of 'omics' datasets facilitate interpretation and understanding of data. To date few are suitable for metabolomics datasets. The main objective of this work is to give a critical overview, for the first time, of the performance of these tools. To that aim, datasets from metabolomic repositories were selected and enriched data were created. Both types of data were analysed with these tools and outputs were thoroughly examined.ResultsAn exploratory multivariate analysis of the most used tools for the enrichment of metabolite sets, based on a non-metric multidimensional scaling (NMDS) of Jaccard's distances, was performed and mirrored their diversity. Codes (identifiers) of the metabolites of the datasets were searched in different metabolite databases (HMDB, KEGG, PubChem, ChEBI, BioCyc/HumanCyc, LipidMAPS, ChemSpider, METLIN and Recon2). The databases that presented more identifiers of the metabolites of the dataset were PubChem, followed by METLIN and ChEBI. However, these databases had duplicated entries and might present false positives. The performance of over-representation analysis (ORA) tools, including BioCyc/HumanCyc, ConsensusPathDB, IMPaLA, MBRole, MetaboAnalyst, Metabox, MetExplore, MPEA, PathVisio and Reactome and the mapping tool KEGGREST, was examined. Results were mostly consistent among tools and between real and enriched data despite the variability of the tools. Nevertheless, a few controversial results such as differences in the total number of metabolites were also found. Disease-based enrichment analyses were also assessed, but they were not found to be accurate probably due to the fact that metabolite disease sets are not up-to-date and the difficulty of predicting diseases from a list of metabolites.ConclusionsWe have extensively reviewed the state-of-the-art of the available range of tools for metabolomic datasets, the completeness of metabolite databases, the performance of ORA methods and disease-based analyses. Despite the variability of the tools, they provided consistent results independent of their analytic approach. However, more work on the completeness of metabolite and pathway databases is required, which strongly affects the accuracy of enrichment analyses. Improvements will be translated into more accurate and global insights of the metabolome.

Dataset Information

Performance comparison and evaluation of software tools for microRNA deep-sequencing data analysis.

Publications

Performance comparison and evaluation of software tools for microRNA deep-sequencing data analysis.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets