Identification of long non-coding transcripts with feature selection: a comparative study.
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/ .
Department of Science and Technology, University of Sannio, via Port'Arsa, 11, Benevento, 82100, Italy.|BioGeM, Institute of Genetic Research "Gaetano Salvatore", c.da Camporeale, Ariano Irpino (AV), 83031, Italy.
Giovanna M M
GM
Ventola
Department of Science and Technology, University of Sannio, via Port'Arsa, 11, Benevento, 82100, Italy.|BioGeM, Institute of Genetic Research "Gaetano Salvatore", c.da Camporeale, Ariano Irpino (AV), 83031, Italy.
Teresa M R
TM
Noviello
Biology and Evolution of Marine Organisms, Stazione Zoologica Anton Dohrn, Villa Comunale, Napoli, 80121, Italy.
Salvatore
S
D'Aniello
Biology and Evolution of Marine Organisms, Stazione Zoologica Anton Dohrn, Villa Comunale, Napoli, 80121, Italy.
Antonietta
A
Spagnuolo
Department of Science and Technology, University of Sannio, via Port'Arsa, 11, Benevento, 82100, Italy.
Michele
M
Ceccarelli
Department of Science and Technology, University of Sannio, via Port'Arsa, 11, Benevento, 82100, Italy. lcerulo@unisannio.it.|BioGeM, Institute of Genetic Research "Gaetano Salvatore", c.da Camporeale, Ariano Irpino (AV), 83031, Italy. lcerulo@unisannio.it.
Luigi
http://orcid.org/0000-0001-8342-3487
L
Cerulo
D006801:Humans; D011506:Proteins / Q000235:genetics*; D062085:RNA, Long Noncoding / Q000235:genetics*
D011506:Proteins; D062085:RNA, Long Noncoding
Classification; Feature selection; lncRNA