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. 2009 Aug 4:10:239.
doi: 10.1186/1471-2105-10-239.

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

Roberto T Arrial  1 , Roberto C Togawa, Marcelo de M Brigido
Affiliations

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

Roberto T Arrial et al. BMC Bioinformatics. .

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 whole-genome 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.

Results: We propose an algorithm called PORTRAIT, which is suitable for ncRNA analysis of transcriptomes from poorly characterized species. Sequences are translated by software that is resistant to sequencing errors, and the predicted putative proteins, along with their source transcripts, are evaluated for coding potential by a support vector machine (SVM). Either of two SVM models may be employed: if a putative protein is found, a protein-dependent SVM model is used; if it is not found, a protein-independent SVM model is used instead. Only ab initio features are extracted, so that no homology information is needed. We illustrate the use of PORTRAIT by predicting ncRNAs from the transcriptome of the pathogenic fungus Paracoccidoides brasiliensis and five other related fungi.

Conclusion: PORTRAIT can be integrated into pipelines, and provides a low computational cost solution for ncRNA detection in transcriptome sequencing projects.

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Figures

Figure 1
Figure 1
Construction of the training database (dbTR). The dbTR comprises both negative and positive instances, and was subdivided as transcripts having identified ORFs (dbTR_OP) and transcripts lacking ORFs (dbTR_OA). Each of these subsets harbor their own negative and positive instances. dbTR_OP training subset was used to induce the protein-dependent SVM model, while dbTR_OA training subset generated the protein-independent SVM model.
Figure 2
Figure 2
ROC curves showing performance of classifiers on dbTS sets. Sensitivity is plotted against (1-specificity), allowing accuracy comparisons among classifiers. A perfect classifier would yield a curve with a point at (0,1) and the final point in (1,1), that is, top-leftmost curves have better classification performance. Classification threshold was set to 0.5 for all classifiers.
Figure 3
Figure 3
Distribution of P. brasiliensis transcript sequences classified as ncRNA by several classifiers as a function of specific annotations by Felipe et al. (2005). Annotations of the 6,022 transcripts [32] were considered only after classifier prediction, so even transcripts previously manually annotated as proteins were evaluated for coding potential. A "Confident annotation" refers to a transcript description which lacks the words: "putative", "probable" and "hypothetical". The numbers of transcripts classified as ncRNA are shown in the legend (except for dbPB, which shows the total of Pb transcripts).
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