iRSpot-SF: Prediction of recombination hotspots by incorporating sequence based features into Chou's Pseudo components.
Identifieur interne : 000319 ( Main/Exploration ); précédent : 000318; suivant : 000320iRSpot-SF: Prediction of recombination hotspots by incorporating sequence based features into Chou's Pseudo components.
Auteurs : Md Abdullah Al Maruf [Bangladesh] ; Swakkhar Shatabda [Bangladesh]Source :
- Genomics [ 1089-8646 ] ; 2019.
Descripteurs français
- KwdFr :
- MESH :
English descriptors
- KwdEn :
- MESH :
- methods : Sequence Analysis, DNA.
- Animals, Humans, Nucleotide Motifs, Recombination, Genetic, Software.
Abstract
Recombination hotspots in a genome are unevenly distributed. Hotspots are regions in a genome that show higher rates of meiotic recombinations. Computational methods for recombination hotspot prediction often use sophisticated features that are derived from physico-chemical or structure based properties of nucleotides. In this paper, we propose iRSpot-SF that uses sequence based features which are computationally cheap to generate. Four feature groups are used in our method: k-mer composition, gapped k-mer composition, TF-IDF of k-mers and reverse complement k-mer composition. We have used recursive feature elimination to select 17 top features for hotspot prediction. Our analysis shows the superiority of gapped k-mer composition and reverse complement k-mer composition features over others. We have used SVM with RBF kernel as a classification algorithm. We have tested our algorithm on standard benchmark datasets. Compared to other methods iRSpot-SF is able to produce significantly better results in terms of accuracy, Mathew's Correlation Coefficient and sensitivity which are 84.58%, 0.6941 and 84.57%. We have made our method readily available to use as a python based tool and made the datasets and source codes available at: https://github.com/abdlmaruf/iRSpot-SF. An web application is developed based on iRSpot-SF and freely available to use at: http://irspot.pythonanywhere.com/server.html.
DOI: 10.1016/j.ygeno.2018.06.003
PubMed: 29935224
Affiliations:
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Hotspots are regions in a genome that show higher rates of meiotic recombinations. Computational methods for recombination hotspot prediction often use sophisticated features that are derived from physico-chemical or structure based properties of nucleotides. In this paper, we propose iRSpot-SF that uses sequence based features which are computationally cheap to generate. Four feature groups are used in our method: k-mer composition, gapped k-mer composition, TF-IDF of k-mers and reverse complement k-mer composition. We have used recursive feature elimination to select 17 top features for hotspot prediction. Our analysis shows the superiority of gapped k-mer composition and reverse complement k-mer composition features over others. We have used SVM with RBF kernel as a classification algorithm. We have tested our algorithm on standard benchmark datasets. Compared to other methods iRSpot-SF is able to produce significantly better results in terms of accuracy, Mathew's Correlation Coefficient and sensitivity which are 84.58%, 0.6941 and 84.57%. We have made our method readily available to use as a python based tool and made the datasets and source codes available at: https://github.com/abdlmaruf/iRSpot-SF. An web application is developed based on iRSpot-SF and freely available to use at: http://irspot.pythonanywhere.com/server.html.</div></front></TEI><affiliations><list><country><li>Bangladesh</li></country></list><tree><country name="Bangladesh"><noRegion><name sortKey="Al Maruf, Md Abdullah" sort="Al Maruf, Md Abdullah" uniqKey="Al Maruf M" first="Md Abdullah" last="Al Maruf">Md Abdullah Al Maruf</name></noRegion><name sortKey="Shatabda, Swakkhar" sort="Shatabda, Swakkhar" uniqKey="Shatabda S" first="Swakkhar" last="Shatabda">Swakkhar Shatabda</name></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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