Dataset Information

Computational identification of condition-specific miRNA targets based on gene expression profiles and sequence information.

ABSTRACT:

Background

MicroRNAs (miRNAs) are small and noncoding RNAs that play important roles in various biological processes. They regulate target mRNAs post-transcriptionally through complementary base pairing. Since the changes of miRNAs affect the expression of target genes, the expression levels of target genes in specific biological processes could be different from those of non-target genes. Here we demonstrate that gene expression profiles contain useful information in separating miRNA targets from non-targets.

Results

The gene expression profiles related to various developmental processes and stresses, as well as the sequences of miRNAs and mRNAs in Arabidopsis, were used to determine whether a given gene is a miRNA target. It is based on the model combining the support vector machine (SVM) classifier and the scoring method based on complementary base pairing between miRNAs and mRNAs. The proposed model yielded low false positive rate and retrieved condition-specific candidate targets through a genome-wide screening.

Conclusion

Our approach provides a novel framework into screening target genes by considering the gene regulation of miRNAs. It can be broadly applied to identify condition-specific targets computationally by embedding information of gene expression profiles.

SUBMITTER: Joung JG

PROVIDER: S-EPMC2648752 | biostudies-literature | 2009 Jan

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Computational identification of condition-specific miRNA targets based on gene expression profiles and sequence information.

Joung Je-Gun JG Fei Zhangjun Z

BMC bioinformatics 20090130

<h4>Background</h4>MicroRNAs (miRNAs) are small and noncoding RNAs that play important roles in various biological processes. They regulate target mRNAs post-transcriptionally through complementary base pairing. Since the changes of miRNAs affect the expression of target genes, the expression levels of target genes in specific biological processes could be different from those of non-target genes. Here we demonstrate that gene expression profiles contain useful information in separating miRNA ta ...[more]

PMID: 19208135

Similar Datasets

Project description:miRNAs are small non-coding RNAs that regulate gene expression by binding to the 3'-UTR of genes. Many recent studies have reported that miRNAs play important biological roles by regulating specific mRNAs or genes. Many sequence-based target prediction algorithms have been developed to predict miRNA targets. However, these methods are not designed for condition-specific target predictions and produce many false positives; thus, expression-based target prediction algorithms have been developed for condition-specific target predictions. A typical strategy to utilize expression data is to leverage the negative control roles of miRNAs on genes. To control false positives, a stringent cutoff value is typically set, but in this case, these methods tend to reject many true target relationships, i.e., false negatives. To overcome these limitations, additional information should be utilized. The literature is probably the best resource that we can utilize. Recent literature mining systems compile millions of articles with experiments designed for specific biological questions, and the systems provide a function to search for specific information. To utilize the literature information, we used a literature mining system, BEST, that automatically extracts information from the literature in PubMed and that allows the user to perform searches of the literature with any English words. By integrating omics data analysis methods and BEST, we developed Context-MMIA, a miRNA-mRNA target prediction method that combines expression data analysis results and the literature information extracted based on the user-specified context. In the pathway enrichment analysis using genes included in the top 200 miRNA-targets, Context-MMIA outperformed the four existing target prediction methods that we tested. In another test on whether prediction methods can re-produce experimentally validated target relationships, Context-MMIA outperformed the four existing target prediction methods. In summary, Context-MMIA allows the user to specify a context of the experimental data to predict miRNA targets, and we believe that Context-MMIA is very useful for predicting condition-specific miRNA targets.

Project description:BACKGROUND: MicroRNAs (miRNAs) regulate the expression of target genes by mediating gene silencing in both plants and animals. The miRNA targets have been extensively investigated in Arabidopsis and rice using computational prediction, experimental validation by overexpression in transgenic plants, and by degradome or PARE (parallel analysis of RNA ends) sequencing. However, miRNA targets mostly remain unknown in soybean (Glycine max). More specifically miRNA mediated gene regulation at different seed developmental stages in soybean is largely unexplored. In order to dissect miRNA guided gene regulation in soybean developing seeds, we performed a transcriptome-wide experimental method using degradome sequencing to directly detect cleaved miRNA targets. RESULTS: In this study, degradome libraries were separately prepared from immature soybean cotyledons representing three stages of development and from seed coats of two stages. Sequencing and analysis of 10 to 40 million reads from each library resulted in identification of 183 different targets for 53 known soybean miRNAs. Among these, some were found only in the cotyledons representing cleavage by 25 miRNAs and others were found only in the seed coats reflecting cleavage by 12 miRNAs. A large number of targets for 16 miRNAs families were identified in both tissues irrespective of the stage. Interestingly, we identified more miRNA targets in the desiccating cotyledons of late seed maturation than in immature seed. We validated four different auxin response factor genes as targets for gma-miR160 via RNA ligase mediated 5' rapid amplification of cDNA ends (RLM-5'RACE). Gene Ontology (GO) analysis indicated the involvement of miRNA target genes in various cellular processes during seed development. CONCLUSIONS: The miRNA targets in both the cotyledons and seed coats of several stages of soybean seed development have been elucidated by experimental evidence from comprehensive, high throughput sequencing of the enriched fragments resulting from miRNA-guided cleavage of messenger RNAs. Nearly 50% of the miRNA targets were transcription factors in pathways that are likely important in setting or maintaining the developmental program leading to high quality soybean seeds that are one of the dominant sources of protein and oil in world markets.

Dataset Information

Computational identification of condition-specific miRNA targets based on gene expression profiles and sequence information.

Background

Results

Conclusion

Publications

Computational identification of condition-specific miRNA targets based on gene expression profiles and sequence information.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets