Dataset Information

Comparative analysis of secondary structure of insect mitochondrial small subunit ribosomal RNA using maximum weighted matching.

ABSTRACT: Comparative analysis is the preferred method of inferring RNA secondary structure, but its use requires considerable expertise and manual effort. As the importance of secondary structure for accurate sequence alignment and phylogenetic analysis becomes increasingly realised, the need for secondary structure models for diverse taxonomic groups becomes more pressing. The number of available structures bears little relation to the relative diversity or importance of the different taxonomic groups. Insects, for example, comprise the largest group of animals and yet are very poorly represented in secondary structure databases. This paper explores the utility of maximum weighted matching (MWM) to help automate the process of comparative analysis by inferring secondary structure for insect mitochondrial small subunit (12S) rRNA sequences. By combining information on correlated changes in substitutions and helix dot plots, MWM can rapidly generate plausible models of secondary structure. These models can be further refined using standard comparative techniques. This paper presents a secondary structure model for insect 12S rRNA based on an alignment of 225 insect sequences and an alignment for 16 exemplar insect sequences. This alignment is used as a template for a web server that automatically generates secondary structures for insect sequences.

SUBMITTER: Page RD

PROVIDER: S-EPMC110784 | biostudies-other | 2000 Oct

REPOSITORIES: biostudies-other

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Comparative analysis of secondary structure of insect mitochondrial small subunit ribosomal RNA using maximum weighted matching.

Page R D RD

Nucleic acids research 20001001 20

Comparative analysis is the preferred method of inferring RNA secondary structure, but its use requires considerable expertise and manual effort. As the importance of secondary structure for accurate sequence alignment and phylogenetic analysis becomes increasingly realised, the need for secondary structure models for diverse taxonomic groups becomes more pressing. The number of available structures bears little relation to the relative diversity or importance of the different taxonomic groups. ...[more]

PMID: 11024161

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Project description:BACKGROUND:microRNA (miRNA) is a short RNA (~?22?nt) that regulates gene expression at the posttranscriptional level. Aberration of miRNA expressions could affect their targeting mRNAs involved in cancer-related signaling pathways. We conduct clustering analysis of miRNA and mRNA using expression data from the Cancer Genome Atlas (TCGA). We combine the Hungarian algorithm and blossom algorithm in graph theory. Data analysis is done using programming language R and Python. METHODS:We first quantify edge-weights of the miRNA-mRNA pairs by combining their expression correlation coefficient in tumor (T_CC) and correlation coefficient in normal (N_CC). We thereby introduce a bipartite graph partition procedure to identify cluster candidates. Specifically, we propose six weight formulas to quantify the change of miRNA-mRNA expression T_CC relative to N_CC, and apply the traditional hierarchical clustering to subjectively evaluate the different weight formulas of miRNA-mRNA pairs. Among these six different weight formulas, we choose the optimal one, which we define as the integrated mean value weights, to represent the connections between miRNA and mRNAs. Then the Hungarian algorithm and the blossom algorithm are employed on the miRNA-mRNA bipartite graph to passively determine the clusters. The combination of Hungarian and the blossom algorithms is dubbed maximum weighted merger method (MWMM). RESULTS:MWMM identifies clusters of different sizes that meet the mathematical criterion that internal connections inside a cluster are relatively denser than external connections outside the cluster and biological criterion that the intra-cluster Gene Ontology (GO) term similarities are larger than the inter-cluster GO term similarities. MWMM is developed using breast invasive carcinoma (BRCA) as training data set, but can also applies to other cancer type data sets. MWMM shows advantage in GO term similarity in most cancer types, when compared to other algorithms. CONCLUSIONS:miRNAs and mRNAs that are likely to be affected by common underlying causal factors in cancer can be clustered by MWMM approach and potentially be used as candidate biomarkers for different cancer types and provide clues for targets of precision medicine in cancer treatment.

Dataset Information

Comparative analysis of secondary structure of insect mitochondrial small subunit ribosomal RNA using maximum weighted matching.

Publications

Comparative analysis of secondary structure of insect mitochondrial small subunit ribosomal RNA using maximum weighted matching.

Similar Datasets

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