Screening non-coding RNAs in transcriptomes from neglected species using PORTRAIT: case study of the pathogenic fungus Paracoccidioides brasiliensis

Abstract

Background: Transcriptome sequences provide a complement to structural genomic information and provide snapshots of an organism's transcriptional profile. Such sequences also represent an alternative method for characterizing neglected species that are not expected to undergo wholegenome sequencing. One difficulty for transcriptome sequencing of these organisms is the low quality of reads and incomplete coverage of transcripts, both of which compromise further bioinformatics analyses. Another complicating factor is the lack of known protein homologs, which frustrates searches against established protein databases. This lack of homologs may be caused by divergence from well-characterized and over-represented model organisms. Another explanation is that non-coding RNAs (ncRNAs) may be caught during sequencing. NcRNAs are RNA sequences that, unlike messenger RNAs, do not code for protein products and instead perform unique functions by folding into higher order structural conformations. There is ncRNA screening software available that is specific for transcriptome sequences, but their analyses are optimized for those transcriptomes that are well represented in protein databases, and also assume that input ESTs are full-length and high quality.

Novel Long Noncoding RNAs (lncRNAs) in Myogenesis: a miR-31 Overlapping lncRNA Transcript Controls Myoblast Differentiation

“…To further analyze the noncoding features of these transcripts, we used a combination of three in silico approaches, Coding Potential Calculator (CPC) (31), PORTRAIT (32), and CPAT (33) (see Fig. S1E in the supplemental material).…”

ncRNA-Agents: A Multiagent System for Non-coding RNA Annotation

“…In the supervised learning paradigm, we take a set of known ncRNAs and a set of known proteins, computing ab initio characteristics for these sequences, in order to create a model to predict ncRNAs. Examples are PORTRAIT [2] and DARIO [7]; and (iii) De novo model: in this group, ncRNAs are predicted using models distinct from homology and class prediction, e.g., the Vienna [13] thermodynamic model.…”

“…The execution of these three programs results is a tRNA identifier presenting high sensibility (99 − 100%) and specificity (with a false positive rate less than 0.00007 by Mb) in a reasonable velocity. Portrait [2] identifies ncRNAs of not complete transcriptomes of yet not entirely characterized species, based on Support Vector Machine (SVM). The result of Portrait is a probability indicating the likelihood of a transcript to be non protein coding.…”

“…Manager agents are responsible for filtering the suggestions sent by the analyst agents. We defined four types of manager agents, three of them following the classification of the computational methods to annotate ncRNAs proposed in Section 2.2: (i) homology manager agent: coordinates agents working on tools based on homology, e.g., BLAST [1] and Infernal [6]; (ii) class prediction manager: coordinates agents working on tools based on machine learning, e.g., Portrait [2]; (iii) de novo manager: coordinates agents working on tools that do not use reference organisms, e.g., RNAfold [14] from Vienna package; and (iv) seeker manager: coordinates agents created to refine analyses of the other manager agents (mainly removing false positives), simulating the reasoning of a biologist when analyzing the results of the required programs and data bases. It also gives flexibility to the architecture, since it can be adapted according to the purposes of each project.…”

LncRNAs in vertebrates: Advances and challenges

“…In the first attempts to computationally identify lncRNAs, algorithms that calculated the presence of a long ORF and its conservation through evolution such as CPC, CPAT, PORTRAIT, PhyloCSF and RNACode [26][27][28][29][30] were used to differentiate coding RNAs from lncRNAs. Recently, a previously annotated lncRNA was shown to harbor a 138nt fragment encoding a conserved 46 amino acid small peptide in the 3 rd and last exon of both the mouse and human genes [31].…”