|ZFIN ID: ZDB-PUB-170326-16|
Identification of long non-coding transcripts with feature selection: a comparative study
Ventola, G.M., Noviello, T.M., D'Aniello, S., Spagnuolo, A., Ceccarelli, M., Cerulo, L.
|Source:||BMC Bioinformatics 18: 187 (Journal)|
|Registered Authors:||D'Aniello, Salvatore|
|Keywords:||Classification, Feature selection, lncRNA|
|PubMed:||28335739 Full text @ BMC Bioinformatics|
Ventola, G.M., Noviello, T.M., D'Aniello, S., Spagnuolo, A., Ceccarelli, M., Cerulo, L. (2017) Identification of long non-coding transcripts with feature selection: a comparative study. BMC Bioinformatics. 18:187.
Background The unveiling of long non-coding RNAs as important gene regulators in many biological contexts has increased the demand for efficient and robust computational methods to identify novel long non-coding RNAs from transcripts assembled with high throughput RNA-seq data. Several classes of sequence-based features have been proposed to distinguish between coding and non-coding transcripts. Among them, open reading frame, conservation scores, nucleotide arrangements, and RNA secondary structure have been used with success in literature to recognize intergenic long non-coding RNAs, a particular subclass of non-coding RNAs.
Results In this paper we perform a systematic assessment of a wide collection of features extracted from sequence data. We use most of the features proposed in the literature, and we include, as a novel set of features, the occurrence of repeats contained in transposable elements. The aim is to detect signatures (groups of features) able to distinguish long non-coding transcripts from other classes, both protein-coding and non-coding. We evaluate different feature selection algorithms, test for signature stability, and evaluate the prediction ability of a signature with a machine learning algorithm. The study reveals different signatures in human, mouse, and zebrafish, highlighting that some features are shared among species, while others tend to be species-specific. Compared to coding potential tools and similar supervised approaches, including novel signatures, such as those identified here, in a machine learning algorithm improves the prediction performance, in terms of area under precision and recall curve, by 1 to 24%, depending on the species and on the signature.
Conclusions Understanding which features are best suited for the prediction of long non-coding RNAs allows for the development of more effective automatic annotation pipelines especially relevant for poorly annotated genomes, such as zebrafish. We provide a web tool that recognizes novel long non-coding RNAs with the obtained signatures from fasta and gtf formats. The tool is available at the following url: http://www.bioinformatics-sannio.org/software/ .
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