We report an efficient method for detecting functional RNAs. The approach, which combines comparative sequence analysis and structure prediction, already has yielded excellent results for a small number of aligned sequences and is suitable for large-scale genomic screens.
There are abundance of transcripts that code for no particular protein and that remain functionally uncharacterized. Some of these transcripts may have novel functions while others might be junk transcripts. Unfortunately, the experimental validation of such transcripts to find functional non-coding RNA candidates is very costly. Therefore, our primary interest is to computationally mine candidate functional transcripts from a pool of uncharacterized transcripts.
NONCODE is an integrated knowledge database dedicated to non-coding RNAs (ncRNAs), that is to say, RNAs that function without being translated into proteins. All ncRNAs in NONCODE were filtered automatically from literature and GenBank, and were later manually curated. The distinctive features of NONCODE are as follows: (i) the ncRNAs in NONCODE include almost all the types of ncRNAs, except transfer RNAs and ribosomal RNAs. (ii) All ncRNA sequences and their related information (e.g.
MicroRNAs (miRNAs) represent an important class of small non-coding RNAs (sRNAs) that regulate gene expression by targeting messenger RNAs. However, assigning miRNAs to their regulatory target genes remains technically challenging. Recently, high-throughput CLIP-Seq and degradome sequencing (Degradome-Seq) methods have been applied to identify the sites of Argonaute interaction and miRNA cleavage sites, respectively.
The recent discoveries of large numbers of non-coding RNAs and computational advances in genome-scale RNA search create a need for tools for automatic, high quality identification and characterization of conserved RNA motifs that can be readily used for database search. Previous tools fall short of this goal.
In recent years, there have been increasing numbers of transcripts identified that do not encode proteins, many of which are developmentally regulated and appear to have regulatory functions. Here, we describe the construction of a comprehensive mammalian noncoding RNA database (RNAdb) which contains over 800 unique experimentally studied non-coding RNAs (ncRNAs), including many associated with diseases and/or developmental processes.
Unlike tRNAs and microRNAs, both classes of snoRNAs, which direct two distinct types of chemical modifications of uracil residues, have proved to be surprisingly difficult to find in genomic sequences. Most computational approaches so far have explicitly used the fact that snoRNAs predominantly target ribosomal RNAs and spliceosomal RNAs. The target is specified by a short stretch of sequence complementarity between the snoRNA and its target.
miRNAs are small, non-coding RNA that negatively regulate gene expression at post-transcriptional level, which play crucial roles in various physiological and pathological processes, such as development and tumorigenesis. Although deep sequencing technologies have been applied to investigate various small RNA transcriptomes, their computational methods are far away from maturation as compared to microarray-based approaches. In this study, a comprehensive web server mirTools was developed to allow researchers to comprehensively characterize small RNA transcriptome.
Advances in high-throughput next-generation sequencing technology have reshaped the transcriptomic research landscape. However, exploration of these massive data remains a daunting challenge. In this study, we describe a novel database, deepBase, which we have developed to facilitate the comprehensive annotation and discovery of small RNAs from transcriptomic data.
The noncoding RNA database (ncRNAdb) was created as a source of information on RNA molecules, which do not possess protein-coding capacity. It is now widely accepted that, in addition to constitutively expressed, housekeeping or infrastructural RNAs, there is a wide variety of RNAs participating in mechanisms involved in regulation of gene expression at all levels of transmission of genetic information from DNA to proteins.