ncRNA orthologies in the vertebrate lineage

Abstract

Annotation of orthologous and paralogous genes is necessary for many aspects of evolutionary analysis. Methods to infer these homology relationships have traditionally focused on protein-coding genes and evolutionary models used by these methods normally assume the positions in the protein evolve independently. However, as our appreciation for the roles of non-coding RNA genes has increased, consistently annotated sets of orthologous and paralogous ncRNA genes are increasingly needed. At the same time, methods such as PHASE or RAxML have implemented substitution models that consider pairs of sites to enable proper modelling of the loops and other features of RNA secondary structure. Here, we present a comprehensive analysis pipeline for the automatic detection of orthologues and paralogues for ncRNA genes. We focus on gene families represented in Rfam and for which a specific covariance model is provided. For each family ncRNA genes found in all Ensembl species are aligned using Infernal, and several trees are built using different substitution models. In parallel, a genomic alignment that includes the ncRNA genes and their flanking sequence regions is built with PRANK. This alignment is used to create two additional phylogenetic trees using the neighbour-joining (NJ) and maximum-likelihood (ML) methods. The trees arising from both the ncRNA and genomic alignments are merged using TreeBeST, which reconciles them with the species tree in order to identify speciation and duplication events. The final tree is used to infer the orthologues and paralogues following Fitch's definition. We also determine gene gain and loss events for each family using CAFE. All data are accessible through the Ensembl Comparative Genomics (‘Compara’) API, on our FTP site and are fully integrated in the Ensembl genome browser, where they can be accessed in a user-friendly manner.Database URL: http://www.ensembl.org

Exploring the RNA landscape of endothelial exosomes

“…For the analysis of long RNAs, sequences from the total RNA libraries were mapped against the coding (Aken et al 2017) or the noncoding genome (Pignatelli et al 2016). The noncoding genes were classified following the Ensembl guidelines into long noncoding RNAs (lncRNAs), short noncoding RNAs (ncRNAs), or processed transcripts (genes without an open reading frame that cannot be included in any of the two previous groups).…”

“…Total (long) RNA libraries were used to identify the number of sequences mapping against noncoding genes too. The reads were mapped against the ncRNA genome available from Ensembl (Pignatelli et al 2016) using Bowtie2 (Langmead and Salzberg 2012). The mapping was performed in "local" mode and all valid alignments were reported.…”

Strategies to avoid drowning in the deep sequencing data flood

“…To avoid this loss of information the putative human ortholog information can be used. One resource for ortholog information is for example EnsemblCompara (Vilella et al, 2009;Pignatelli et al, 2016). In order to combine information derived from different databases provided by the NCBI and Ensembl we are developing a Mammalian Ortholog and Annotation database (MOA-Db) integrated in the Galaxy platform in our group (Jochen Bick, 2016;ETH Zurich;unpublished results).…”

Empowering the annotation and discovery of structured RNAs with scalable and accessible integrative clustering

“…for discovering homologs of known families [21] or comparing two families [22]. Profile-based methods [20,23] such as Nofold generally rely on a CM database of known families to annotate and cluster sequences by comparing against the profiles, therefore their applicability for de novo family or motif discovery is affected by the characteristics of the already known families and the provided models.…”