TAM: A method for enrichment and depletion analysis of a microRNA category in a list of microRNAs

Abstract

BackgroundMicroRNAs (miRNAs) are a class of important gene regulators. The number of identified miRNAs has been increasing dramatically in recent years. An emerging major challenge is the interpretation of the genome-scale miRNA datasets, including those derived from microarray and deep-sequencing. It is interesting and important to know the common rules or patterns behind a list of miRNAs, (i.e. the deregulated miRNAs resulted from an experiment of miRNA microarray or deep-sequencing).ResultsFor the above purpose, this study presents a method and develops a tool (TAM) for annotations of meaningful human miRNAs categories. We first integrated miRNAs into various meaningful categories according to prior knowledge, such as miRNA family, miRNA cluster, miRNA function, miRNA associated diseases, and tissue specificity. Using TAM, given lists of miRNAs can be rapidly annotated and summarized according to the integrated miRNA categorical data. Moreover, given a list of miRNAs, TAM can be used to predict novel related miRNAs. Finally, we confirmed the usefulness and reliability of TAM by applying it to deregulated miRNAs in acute myocardial infarction (AMI) from two independent experiments.ConclusionTAM can efficiently identify meaningful categories for given miRNAs. In addition, TAM can be used to identify novel miRNA biomarkers. TAM tool, source codes, and miRNA category data are freely available at http://cmbi.bjmu.edu.cn/tam.

MicroRNA expression changes after lung cancer resection

“…We performed a miRNA over-representation analysis to identify a common biological function of the 27 different miRNAs that showed a correlation with the time distance from tumor resection for at least a single patient. Using the TAM database (Tool for annotations of microRNAs, http://202.38.126.151/hmdd/ tools/tam.html), 5 we compared the 27 miRNAs with the 1,205 miRNAs of the microarray. As most striking biological categorie we found six miRNAs (22%) namely hsa-miR-15a, hsa-miR-20a, hsa-miR-16, hsa-miR-494, hsa-miR-144 and hsa-miR-92a, that have previously been shown to influence apoptosis with a significance value of 0.000053 that remained significant after correction miRNAs for patient C, 5 miRNAs for patient D and 8 miRNAs for patient E. Out of the 33 miRNAs six showed a positive or negative correlation with at least 2 patients, i.e., the 33 correlated miRNAs represent 27 different miRNAs.…”

Differentially regulated miRNAs as prognostic biomarkers in the blood of primary CNS lymphoma patients

“…For miRNA enrichment analysis, the tool for annotations of microRNAs (TAM) was applied (19). Specifically, all miRNAs that were influenced by short-or long-term survival were compared to the background set of all miRNAs included in the NGS study.…”

“…Within the group of abundantly expressed miRNAs, we observed several molecules that are known to be involved in various physiological and pathological processes. We thus carried out a downstream miRNA enrichment analysis using the tool for annotations of microRNAs (TAM) that includes different miRNA families, data from the human miRNA and disease database (HMDD) (19) and information on various functional classifications of miRNAs. As input for TAM, we used all miRNAs with significance values below 0.1 to increase the sensitivity of our downstream analysis.…”

Analysis of Human miRNAs, Gene Targets and Diseases Network

“…First, we extract all pre-miRNAs that are connected to at least two disease nodes in the disease cluster. The list of pre-miRNAs is then inputted to a web-accessible program called TAM [10] to find out the enriched biological functions.…”