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Identification of long non-coding transcripts with feature selection: a comparative study

Overview of attention for article published in BMC Bioinformatics, March 2017
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About this Attention Score

  • Above-average Attention Score compared to outputs of the same age (61st percentile)
  • Above-average Attention Score compared to outputs of the same age and source (53rd percentile)

Mentioned by

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6 tweeters

Citations

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9 Dimensions

Readers on

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54 Mendeley
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Title
Identification of long non-coding transcripts with feature selection: a comparative study
Published in
BMC Bioinformatics, March 2017
DOI 10.1186/s12859-017-1594-z
Pubmed ID
Authors

Giovanna M. M. Ventola, Teresa M. R. Noviello, Salvatore D’Aniello, Antonietta Spagnuolo, Michele Ceccarelli, Luigi Cerulo

Abstract

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. 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. 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/ .

Twitter Demographics

The data shown below were collected from the profiles of 6 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 54 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Sweden 1 2%
Switzerland 1 2%
Unknown 52 96%

Demographic breakdown

Readers by professional status Count As %
Researcher 15 28%
Student > Ph. D. Student 10 19%
Student > Bachelor 8 15%
Student > Master 6 11%
Other 3 6%
Other 5 9%
Unknown 7 13%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 17 31%
Agricultural and Biological Sciences 14 26%
Computer Science 4 7%
Engineering 4 7%
Medicine and Dentistry 3 6%
Other 3 6%
Unknown 9 17%

Attention Score in Context

This research output has an Altmetric Attention Score of 3. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 31 March 2017.
All research outputs
#3,778,153
of 9,272,034 outputs
Outputs from BMC Bioinformatics
#1,774
of 3,932 outputs
Outputs of similar age
#98,138
of 259,830 outputs
Outputs of similar age from BMC Bioinformatics
#45
of 98 outputs
Altmetric has tracked 9,272,034 research outputs across all sources so far. This one has received more attention than most of these and is in the 58th percentile.
So far Altmetric has tracked 3,932 research outputs from this source. They receive a mean Attention Score of 5.0. This one has gotten more attention than average, scoring higher than 53% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 259,830 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 61% of its contemporaries.
We're also able to compare this research output to 98 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 53% of its contemporaries.