GLASSgo – Automated and Reliable Detection of sRNA Homologs From a Single Input Sequence

Abstract

Bacterial small RNAs (sRNAs) are important post-transcriptional regulators of gene expression. The functional and evolutionary characterization of sRNAs requires the identification of homologs, which is frequently challenging due to their heterogeneity, short length and partly, little sequence conservation. We developed the GLobal Automatic Small RNA Search go (GLASSgo) algorithm to identify sRNA homologs in complex genomic databases starting from a single sequence. GLASSgo combines an iterative BLAST strategy with pairwise identity filtering and a graph-based clustering method that utilizes RNA secondary structure information. We tested the specificity, sensitivity and runtime of GLASSgo, BLAST and the combination RNAlien/cmsearch in a typical use case scenario on 40 bacterial sRNA families. The sensitivity of the tested methods was similar, while the specificity of GLASSgo and RNAlien/cmsearch was significantly higher than that of BLAST. GLASSgo was on average ∼87 times faster than RNAlien/cmsearch, and only ∼7.5 times slower than BLAST, which shows that GLASSgo optimizes the trade-off between speed and accuracy in the task of finding sRNA homologs. GLASSgo is fully automated, whereas BLAST often recovers only parts of homologs and RNAlien/cmsearch requires extensive additional bioinformatic work to get a comprehensive set of homologs. GLASSgo is available as an easy-to-use web server to find homologous sRNAs in large databases.

Freiburg RNA tools: a central online resource for RNA-focused research and teaching

“…(GLobal Automatic Small RNA Search go) finds homologs of sRNAs ( 20 ). It combines an iterative BLASTn ( 21 ) search with an auto-adaptive, graph-based clustering algorithm.…”

A framework for the computational prediction and analysis of non-coding RNAs in microbial environmental populations and their experimental validation

“…Next, the contigs were mapped against the well-annotated genomes of two Prochlorococcus model strains, MED4 (accession number: BX548174) and NATL2A (accession number: CP000095), using segemehl [22,23]. Only contigs that did not overlap at all with a protein-coding gene were kept, and these contigs were used as the input to search for homologs throughout the entire cyanobacterial phylum using the GLASSgo v1.220 algorithm [24]. The putative sRNA homologs predicted by GLASSgo were aligned with Clustal Omega v1.2.3 and scored with RNAz v2.1, which computes a z-score based on secondary structure conservation and thermodynamic stability [25,26], using the default settings for both the Clustal Omega and RNAz.…”

Structure and Interaction Prediction in Prokaryotic RNA Biology

“…In fact, it has been stressed that RNA target predictions should be carried out in a comparative manner if possible [59]. A selection of homologous input sequences for comparative predictions can be obtained by using tools such as GLASSgo [92], RNAlien [93] or GotohScan [94]. There are two major approaches for comparative RNA-RNA interaction prediction.…”