TransPrise: a novel machine learning approach for eukaryotic promoter prediction

Stepan Pachganov; Khalimat Murtazalieva; Aleksei Zarubin; Dmitry Sokolov; Duane R Chartier; Tatiana V Tatarinova

doi:10.7717/peerj.7990

TransPrise: a novel machine learning approach for eukaryotic promoter prediction

PeerJ. 2019 Nov 1:7:e7990. doi: 10.7717/peerj.7990. eCollection 2019.

Authors

Stepan Pachganov^#¹, Khalimat Murtazalieva^#^{2

3}, Aleksei Zarubin⁴, Dmitry Sokolov⁵, Duane R Chartier⁶, Tatiana V Tatarinova^{2

7

8

9}

Affiliations

¹ Ugra Research Institute of Information Technologies, Khanty-Mansiysk, Russia.
² Vavilov Institute for General Genetics, Moscow, Russia.
³ Institute of Bioinformatics, Moscow, Russia.
⁴ Tomsk National Research Medical Center of the Russian Academy of Sciences, Research Institute of Medical Genetics, Tomsk, Russia.
⁵ Neirika Solutions, Sarov, Russia.
⁶ International Center for Art Intelligence, Inc., Los Angeles, CA, United States of America.
⁷ Department of Biology, University of La Verne, La Verne, CA, United States of America.
⁸ A.A. Kharkevich Institute for Information Transmission Problems, Russian Academy of Sciences, Moscow, Russia.
⁹ Siberian Federal University, Krasnoyarsk, Russia.

^# Contributed equally.

Abstract

As 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 prediction of positions of eukaryotic transcription start sites. Our pipeline consists of two parts: the binary classifier operates the first, and if a sequence is classified as TSS-containing the regression step follows, where the precise location of TSS is being identified. TransPrise offers significant improvement over existing promoter-prediction methods. To illustrate this, we compared predictions of TransPrise classification and regression models with the TSSPlant approach for the well annotated genome of Oryza sativa. Using a computer equipped with a graphics processing unit, the run time of TransPrise is 250 minutes on a genome of 374 Mb long. The Matthews correlation coefficient value for TransPrise is 0.79, more than two times larger than the 0.31 for TSSPlant classification models. This represents a high level of prediction accuracy. Additionally, the mean absolute error for the regression model is 29.19 nt, allowing for accurate prediction of TSS location. TransPrise was also tested in Homo sapiens, where mean absolute error of the regression model was 47.986 nt. 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 necessary packages, models, 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 customizable to predict TSS in any eukaryotic organism.

Keywords: Deep learning; Genomics; Machine learning; Promoter; Rice; Transcription start site.

Grants and funding

The authors received no funding for this work.