Prediction of Rice Transcription Start Sites Using TransPrise: A Novel Machine Learning Approach
| Autor: | Stepan Pachganov, Tatiana V. Tatarinova, Duane R. Chartier, Alexei Zarubin, Tatiana Taran, Khalimat Murtazalieva |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
0106 biological sciences
0303 health sciences Source code Oryza sativa Computer science business.industry media_common.quotation_subject Deep learning Eukaryotic transcription Genome project Machine learning computer.software_genre 01 natural sciences Genome Set (abstract data type) 03 medical and health sciences Eukaryotic organism Transcription (biology) Code (cryptography) Artificial intelligence business computer 030304 developmental biology 010606 plant biology & botany media_common |
| Zdroj: | Methods in Molecular Biology ISBN: 9781071610671 |
| DOI: | 10.1007/978-1-0716-1068-8_17 |
| Popis: | As the interest in genetic resequencing increases, so does the need for effective mathematical, computational, and statistical approaches. One of the difficult problems in genome annotation is determination of precise positions of transcription start sites. In this paper, we present TransPrise-an efficient deep learning tool for predicting positions of eukaryotic transcription start sites. TransPrise offers significant improvement over existing promoter-prediction methods. To illustrate this, we compared predictions of TransPrise with the TSSPlant approach for well-annotated genome of Oryza sativa. Using a computer with a graphics processing unit, the run time of TransPrise is 250 min on a genome of 374 Mb long.We provide the full basis for the comparison and encourage users to freely access a set of our computational tools to facilitate and streamline their own analyses. The ready-to-use Docker image with all the necessary packages, models, and code as well as the source code of the TransPrise algorithm are available at http://compubioverne.group/ . The source code is ready to use and to be customized to predict TSS in any eukaryotic organism. |
| Databáze: | OpenAIRE |
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