Тип публикации: статья из журнала
Год издания: 2021
Идентификатор DOI: 10.1007/978-1-0716-1068-8_17
Ключевые слова: machine learning, rice, Transcription start site, TransPrise
Аннотация: 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.
Издание
Журнал: Methods in molecular biology (Clifton, N.J.)
Выпуск журнала: Т. 2238
Номера страниц: 261-274
ISSN журнала: 10643745
Издатель: Humana Press, Inc.
Персоны
- Pachganov S. (Ugra Research Institute of Information Technologies)
- Murtazalieva K. (Vavilov Institute of General Genetics)
- Tatarinova T.V. (Siberian Federal University)
- Zarubin A. (Tomsk National Research Medical Center of the Russian Academy of Sciences,Research Institute of Medical Genetics)
- Taran T. (Odessa National University)
- Chartier D. (International Center for Art Intelligence, Inc, Los Angeles, CA, USA)
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