Abstract
Data-driven machine learning is the method of choice for predicting molecular phenotypes from nucleotide sequence, modeling gene expression events including protein-DNA binding, chromatin states as well as mRNA and protein levels. Deep neural networks automatically learn informative sequence representations and interpreting them enables us to improve our understanding of the regulatory code governing gene expression. Here, we review the latest developments that apply shallow or deep learning to quantify molecular phenotypes and decode the cis-regulatory grammar from prokaryotic and eukaryotic sequencing data. Our approach is to build from the ground up, first focusing on the initiating protein-DNA interactions, then specific coding and non-coding regions, and finally on advances that combine multiple parts of the gene and mRNA regulatory structures, achieving unprecedented performance. We thus provide a quantitative view of gene expression regulation from nucleotide sequence, concluding with an information-centric overview of the central dogma of molecular biology.
Highlights
Genetic information is stored and encoded in genes that produce an organism’s phenotype by being expressed through multiple biochemical processes into a variety of functional molecules
Dependence on accurately labeled data: cannot achieve higher accuracy than that allowed by the noise inherent to the given experimental target labels (Li et al, 2019b; Barshai et al, 2020)
Multiple different methods exist for interpreting deep methods, many are a work in progress and no explicit solutions currently exist to benchmark these methods or to combine the findings into more complete and coherent interpretations (Azodi et al, 2020)
Summary
Genetic information is stored and encoded in genes that produce an organism’s phenotype by being expressed through multiple biochemical processes into a variety of functional molecules. We detail the current understanding of the regulatory grammar carried within the specific coding and non-coding regulatory regions, and its involvement in defining transcript and protein abundance Based on these principles, we review advanced modeling approaches that use multiple different parts of the gene regulatory structure or whole nucleotide sequences, demonstrating how this increases their predictive power.
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