Long intergenic non-coding RNAs (lincRNAs) are an abundant and functionally diverse class of eukaryotic transcripts. Reported lincRNA repertoires in mammals vary, but are commonly in the thousands to tens of thousands of transcripts, covering ~90% of the genome. In addition to elucidating function, there is particular interest in understanding the origin and evolution of lincRNAs. Aside from mammals, lincRNA populations have been sparsely sampled, precluding evolutionary analyses focused on lincRNA emergence and persistence.
Researchers from the University of Arizona have developed Evolinc, a two-module pipeline designed to facilitate lincRNA discovery and characterize aspects of lincRNA evolution. The first module (Evolinc-I) is a lincRNA identification workflow that also facilitates downstream differential expression analysis and genome browser visualization of identified lincRNAs. The second module (Evolinc-II) is a genomic and transcriptomic comparative analyses workflow that determines the phylogenetic depth to which a lincRNA locus is conserved within a user-defined group of related species. Evolinc-II builds families of homologous lincRNA loci, aligns constituent sequences, infers gene trees, and then uses gene tree / species tree reconciliation to reconstruct evolutionary processes such as gain, loss, or duplication of the locus. The researchers demonstrate that Evolinc-I is agnostic to target organism by validating against previously annotated Arabidopsis and human lincRNA data. Using Evolinc-II, we examine ways in which conservation can rapidly be used to winnow down large lincRNA datasets to a small set of candidates for functional analysis. Finally, they show how Evolinc-II can be used to recover the evolutionary history of a known lincRNA, the human telomerase RNA (TERC). The analyses revealed unexpected duplication events as well as the loss and subsequent acquisition of a novel TERC locus in the lineage leading to mice and rats. The Evolinc pipeline is currently integrated in CyVerse′s Discovery Environment and is free to use by researchers.
Schematic representation of the Evolinc-I workflow and validation
(A) Evolinc-I takes assembled transcripts as input and then filters over several steps (1-4). Evolinc generates several output files detailed in the materials and methods. (B) Evolinc validation on RNA-seq data from Liu et al. (2012). Four tissues were sequenced by Liu et al, as indicated by the red circles, including (from top to bottom) flowers, siliques, leaves, and roots. Assembled transcripts were fed through Evolinc-I to identify lincRNAs, Antisense Overlapping Transcripts (AOTs), and Sense Overlapping Transcripts (SOTs). A reconciliation was performed between the Evolinc-I identified lincRNAs and the Liu et al. dataset. Gene associated transcriptional unit (GATU) amd repeat containing transcriptional unit (RCTU) terminology comes from Liu et al. (2012). (C) Evolinc validation of Cabili et al. (2011) RNA-seq data. RNA-seq data was assembled and then filtered through additional Cabili-specific parameters (shown in box). Shown in the pie chart are the Evolinc-identified lincRNAs that correspond to Cabili et al. or are novel.
Availability -Evolinc is available as two apps (Evolinc-I and Evolinc-II) in CyVerse’s DE (https://de.cyverse.org/de/), for which a tutorial and sample data are available (https://wiki.cyverse.org/wiki/display/TUT/Evolinc+in+the+Discovery+Environment). Evolinc is also available as self-contained Docker images (https://hub.docker.com/r/cyverse/evolinc-i/ ; and https://hub.docker.com/r/cyverse/evolinc-ii/ ) for use in a Linux or Mac OSX command-line environment. The code for Evolinc is available as a github repository (https://github.com/Evolinc).