Supatcha Lertampaiporn1, Chinae Thammarongtham2, Chakarida Nukoolkit3, Boonserm Kaewkamnerdpong1, Marasri Ruengjitchatchawalya4. 1. Biological Engineering Program, Faculty of Engineering, King Mongkut's University of Technology Thonburi, 126 Pracha Uthit Rd, Bangmod, Thung Khru, Bangkok 10140, Thailand. 2. Biochemical Engineering and Pilot Plant Research and Development Unit, National Center for Genetic Engineering and Biotechnology at King Mongkut's University of Technology Thonburi (Bang Khun Thian Campus), 49 Soi Thian Thale 25, Bang Khun Thian Chai Thale Rd, Tha Kham, Bangkok 10150, Thailand. 3. School of Information Technology, King Mongkut's University of Technology Thonburi, 126 Pracha Uthit Rd, Bangmod, Thung Khru, Bangkok 10140, Thailand. 4. Biotechnology Program, School of Bioresources and Technology, King Mongkut's University of Technology Thonburi (Bang Khun Thian Campus), 49 Soi Thian Thale 25, Bang Khun Thian Chai Thale Rd, Tha Kham, Bangkok 10150, Thailand Bioinformatics and Systems Biology Program, King Mongkut's University of Technology Thonburi (Bang Khun Thian Campus), 49 Soi Thian Thale 25, Bang Khun Thian Chai Thale Rd, Tha Kham, Bangkok 10150, Thailand marasri.rue@kmutt.ac.th.
Abstract
To identify non-coding RNA (ncRNA) signals within genomic regions, a classification tool was developed based on a hybrid random forest (RF) with a logistic regression model to efficiently discriminate short ncRNA sequences as well as long complex ncRNA sequences. This RF-based classifier was trained on a well-balanced dataset with a discriminative set of features and achieved an accuracy, sensitivity and specificity of 92.11%, 90.7% and 93.5%, respectively. The selected feature set includes a new proposed feature, SCORE. This feature is generated based on a logistic regression function that combines five significant features-structure, sequence, modularity, structural robustness and coding potential-to enable improved characterization of long ncRNA (lncRNA) elements. The use of SCORE improved the performance of the RF-based classifier in the identification of Rfam lncRNA families. A genome-wide ncRNA classification framework was applied to a wide variety of organisms, with an emphasis on those of economic, social, public health, environmental and agricultural significance, such as various bacteria genomes, the Arthrospira (Spirulina) genome, and rice and human genomic regions. Our framework was able to identify known ncRNAs with sensitivities of greater than 90% and 77.7% for prokaryotic and eukaryotic sequences, respectively. Our classifier is available at http://ncrna-pred.com/HLRF.htm.
To identify non-coding RNA (ncRNA) signals within genomic regions, a classification tool was developed based on a hybrid random forest (RF) with a logistic regression model to efficiently discriminate short ncRNA sequences as well as long complex ncRNA sequences. This RF-based classifier was trained on a well-balanced dataset with a discriminative set of features and achieved an accuracy, sensitivity and specificity of 92.11%, 90.7% and 93.5%, respectively. The selected feature set includes a new proposed feature, SCORE. This feature is generated based on a logistic regression function that combines five significant features-structure, sequence, modularity, structural robustness and coding potential-to enable improved characterization of long ncRNA (lncRNA) elements. The use of SCORE improved the performance of the RF-based classifier in the identification of Rfam lncRNA families. A genome-wide ncRNA classification framework was applied to a wide variety of organisms, with an emphasis on those of economic, social, public health, environmental and agricultural significance, such as various bacteria genomes, the Arthrospira (Spirulina) genome, and rice and human genomic regions. Our framework was able to identify known ncRNAs with sensitivities of greater than 90% and 77.7% for prokaryotic and eukaryotic sequences, respectively. Our classifier is available at http://ncrna-pred.com/HLRF.htm.
Authors: Stefan Washietl; Sven Findeiss; Stephan A Müller; Stefan Kalkhof; Martin von Bergen; Ivo L Hofacker; Peter F Stadler; Nick Goldman Journal: RNA Date: 2011-02-28 Impact factor: 4.942
Authors: Pontus Larsson; Andrea Hinas; David H Ardell; Leif A Kirsebom; Anders Virtanen; Fredrik Söderbom Journal: Genome Res Date: 2008-03-17 Impact factor: 9.043
Authors: Louise Y Sun; Harindra C Wijeysundera; Douglas S Lee; Sean van Diepen; Marc Ruel; Anan Bader Eddeen; Thierry G Mesana Journal: CMAJ Open Date: 2022-03-08