Identification and characterization of novel conserved RNA structures in Drosophila

Abstract

BackgroundComparative genomics approaches have facilitated the discovery of many novel non-coding and structured RNAs (ncRNAs). The increasing availability of related genomes now makes it possible to systematically search for compensatory base changes – and thus for conserved secondary structures – even in genomic regions that are poorly alignable in the primary sequence. The wealth of available transcriptome data can add valuable insight into expression and possible function for new ncRNA candidates. Earlier work identifying ncRNAs in Drosophila melanogaster made use of sequence-based alignments and employed a sliding window approach, inevitably biasing identification toward RNAs encoded in the more conserved parts of the genome.ResultsTo search for conserved RNA structures (CRSs) that may not be highly conserved in sequence and to assess the expression of CRSs, we conducted a genome-wide structural alignment screen of 27 insect genomes including D. melanogaster and integrated this with an extensive set of tiling array data. The structural alignment screen revealed ∼30,000 novel candidate CRSs at an estimated false discovery rate of less than 10%. With more than one quarter of all individual CRS motifs showing sequence identities below 60%, the predicted CRSs largely complement the findings of sliding window approaches applied previously. While a sixth of the CRSs were ubiquitously expressed, we found that most were expressed in specific developmental stages or cell lines. Notably, most statistically significant enrichment of CRSs were observed in pupae, mainly in exons of untranslated regions, promotors, enhancers, and long ncRNAs. Interestingly, cell lines were found to express a different set of CRSs than were found in vivo. Only a small fraction of intergenic CRSs were co-expressed with the adjacent protein coding genes, which suggests that most intergenic CRSs are independent genetic units.ConclusionsThis study provides a more comprehensive view of the ncRNA transcriptome in fly as well as evidence for differential expression of CRSs during development and in cell lines.Electronic supplementary materialThe online version of this article (10.1186/s12864-018-5234-4) contains supplementary material, which is available to authorized users.

Tailored machine learning models for functional RNA detection in genome-wide screens

“…H/ACA-box snoRNAs, in contrast, typically fold into double stem loop structures with the H-box in between and the ACA-box on the 3’end of the exterior loop. This difference in recall has also been observed in previous screens with RNAz ( 50 , 51 ). The detection of box C/D snoRNA genes thus cannot rely on secondary structure predictions alone, calling for specialized tools ( 52 ).…”

Transcriptomic analyses reveal groups of co-expressed, syntenic lncRNAs in four species of the genus Caenorhabditis

“…We also evaluated the presence of conserved structural motifs using CMfinder [46], a software that uses an expectation maximization algorithm that allows to compare secondary structures independently from sequence alignment. A recent study has shown that its sensitivity is optimal in the low conservation range, finding 1/ 3 of the conservation structures when identity is <60% [47].…”

“…We used this software to compare the secondary structures of lncRNAs, intergenic regions and rRNAs, the two later used as negative and positive controls respectively. We first computed the secondary structures individually for each sequence using the RNAfold software from the Vienna Package 2.0 with default parameters [47]. The secondary structures computed with RNAfold were used as input for beagle.…”

In silico methods for predicting functional synonymous variants

“…RNA populates a heterogeneous ensemble of conformations, and the goal of most prediction tools is to differentiate the native structure from its numerous subpopulations [ 66 ]. Second, across species, coding regions contain many structurally conserved elements [ 59 , 67 – 69 ], which can be used to infer both function and structure. Based on these assumptions, many tools have been established with algorithms designed to identify the minimum free energy (MFE) structure with consideration of conserved motifs, temperature, ion concentrations, and sequence-based properties.…”