GPU-Acceleration of Sequence Homology Searches with Database Subsequence Clustering

Fast RNA-RNA Interaction Prediction Methods for Interaction Analysis of Transcriptome-Scale Large Datasets

The computational prediction of RNA-RNA interactions has long been studied in RNA informatics. Most of the existing approaches focused on the interaction prediction of short RNAs in small datasets. However, in recent years, two fast prediction methods, RIsearch2 and RIblast, have been developed to predict transcriptome-scale interactions or long RNA interactions. The key idea of the software acceleration of these tools was the integration of a seed-and-extend method, which is used in fast sequence alignment tools, into RNA-RNA interaction prediction. As a result, the two software programs were ten to a thousand times faster than the existing tools; because of this acceleration, detection of genome-wide microRNA target sites or interaction partners of function-unknown long noncoding RNAs has become possible. In this review, we describe the basic concept of the algorithm, its applications, and the future perspectives of the fast RNA-RNA interaction prediction tools.

Taxonomic and Gene Category Analyses of Subgingival Plaques from a Group of Japanese Individuals with and without Periodontitis

Periodontitis is an inflammation of tooth-supporting tissues, which is caused by bacteria in the subgingival plaque (biofilm) and the host immune response. Traditionally, subgingival pathogens have been investigated using methods such as culturing, DNA probes, or PCR. The development of next-generation sequencing made it possible to investigate the whole microbiome in the subgingival plaque. Previous studies have implicated dysbiosis of the subgingival microbiome in the etiology of periodontitis. However, details are still lacking. In this study, we conducted a metagenomic analysis of subgingival plaque samples from a group of Japanese individuals with and without periodontitis. In the taxonomic composition analysis, genus Bacteroides and Mycobacterium demonstrated significantly different compositions between healthy sites and sites with periodontal pockets. The results from the relative abundance of functional gene categories, carbohydrate metabolism, glycan biosynthesis and metabolism, amino acid metabolism, replication and repair showed significant differences between healthy sites and sites with periodontal pockets. These results provide important insights into the shift in the taxonomic and functional gene category abundance caused by dysbiosis, which occurs during the progression of periodontal disease.

RIblast: an ultrafast RNA-RNA interaction prediction system based on a seed-and-extension approach

Motivation

LncRNAs play important roles in various biological processes. Although more than 58 000 human lncRNA genes have been discovered, most known lncRNAs are still poorly characterized. One approach to understanding the functions of lncRNAs is the detection of the interacting RNA target of each lncRNA. Because experimental detections of comprehensive lncRNA–RNA interactions are difficult, computational prediction of lncRNA–RNA interactions is an indispensable technique. However, the high computational costs of existing RNA–RNA interaction prediction tools prevent their application to large-scale lncRNA datasets.

Results

Here, we present ‘RIblast’, an ultrafast RNA–RNA interaction prediction method based on the seed-and-extension approach. RIblast discovers seed regions using suffix arrays and subsequently extends seed regions based on an RNA secondary structure energy model. Computational experiments indicate that RIblast achieves a level of prediction accuracy similar to those of existing programs, but at speeds over 64 times faster than existing programs.

Availability and implementation

The source code of RIblast is freely available at https://github.com/fukunagatsu/RIblast.

Supplementary information

Supplementary data are available at Bioinformatics online.