Brief Research Report ARTICLE
What do neighbors tell about you: the local context of cis-regulatory modules complicates prediction of regulatory variants
- 1Vavilov Institute of General Genetics, Russian Academy of Sciences, Russia
- 2Faculty of Bioengineering and Bioinformatics, Lomonosov Moscow State University, Russia
- 3Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins Medicine, United States
- 4Moscow Institute of Physics and Technology, Russia
- 5Engelhardt Institute of Molecular Biology (RAS), Russia
- 6Institute of Mathematical Problems of Biology (RAS), Russia
Many problems of modern genetics and functional genomics require the assessment of functional effects of sequence variants, including gene expression changes. Machine learning is considered to be a promising approach for solving this task, but its practical applications remain a challenge due to the insufficient volume and diversity of training data. A promising source of valuable data is a saturation mutagenesis massively parallel reporter assay, which quantitatively measures changes in transcription activity caused by sequence variants.
Here we explore the computational predictions of the effects of individual single-nucleotide variants on gene transcription measured in the massively parallel reporter assays, based on the data from the recent "Regulation Saturation" Critical Assessment of Genome Interpretation challenge. We show that the estimated prediction quality strongly depends on the structure of the training and validation data. Particularly, training on the sequence segments located next to the validation data results in the "information leakage" caused by the local context. This information leakage allows reproducing the prediction quality of the best CAGI challenge submissions with a fairly simple machine learning approach, and even obtaining notably better-than-random predictions using irrelevant genomic regions. Validation scenarios preventing such information leakage dramatically reduce the measured prediction quality. The performance at independent regulatory regions, entirely excluded from the training set, appears to be much lower than it is needed for practical applications, and even the performance estimation will become reliable only in the future with richer data from multiple reporters.
The source code and data are available at https://bitbucket.org/autosomeru_cagi2018/cagi2018_regsat and https://genomeinterpretation.org/content/expression-variants
Keywords: regulatory variants, rSNPs, machine learning, promoters, enhancers, saturation mutagenesis massively parallel reporter assay
Received: 15 Jul 2019;
Accepted: 09 Oct 2019.
Copyright: © 2019 Penzar, Zinkevich, Vorontsov, Sitnik, Favorov, Makeev and Kulakovskiy. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
Mr. Dmitry D. Penzar, Vavilov Institute of General Genetics, Russian Academy of Sciences, Moscow, 119333, Moscow Oblast, Russia, firstname.lastname@example.org
Prof. Vsevolod J. Makeev, Vavilov Institute of General Genetics, Russian Academy of Sciences, Moscow, 119333, Moscow Oblast, Russia, email@example.com
Dr. Ivan V. Kulakovskiy, Engelhardt Institute of Molecular Biology (RAS), Moscow, 119991, Moscow Oblast, Russia, firstname.lastname@example.org