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Combining next-generation sequencing technologies and high-throughput genotyping platforms has revolutionized the search for genetic variants, from single nucleotide polymorphisms to large-scale copy number variants. Understanding the human variome holds considerable promise for the discovery of new disease-causing genes or potential drug targets, as well as for the design of more nuanced, realistic and complex models of human history and evolution.
This collection by Nature Reviews Genetics and Nature Genetics highlights the breadth of impact that DNA sequence variation has on biological processes and disease development, as well as some of the tools and challenges related to the extraction, collection, curation, interpretation and sharing of human genetic variant data.
In this Review, the authors describe advances in deep learning approaches in genomics, whereby researchers are moving beyond the typical ‘black box’ nature of models to obtain biological insights through explainable artificial intelligence (xAI).
Machine learning is widely applied in various fields of genomics and systems biology. In this Review, the authors describe how responsible application of machine learning requires an understanding of several common pitfalls that users should be aware of (and mitigate) to avoid unreliable results.
This Review describes different deep learning techniques and how they can be applied to extract biologically relevant information from large, complex genomic data sets.
Machine learning methods are becoming increasingly important in the analysis of large-scale genomic, epigenomic, proteomic and metabolic data sets. In this Review, the authors consider the applications of supervised, semi-supervised and unsupervised machine learning methods to genetic and genomic studies. They provide general guidelines for the selection and application of algorithms that are best suited to particular study designs.
Applying deep learning to large-scale genomic data of species or populations is providing new opportunities to understand the evolutionary forces that drive genetic diversity. This Review introduces common deep learning architectures and provides comprehensive guidelines to implement deep learning models for population genetic inference. The authors also discuss current opportunities and challenges for deep learning in population genetics.
In this Review, the authors summarize recent progress in cell–cell interaction (CCI) research. They describe the recent evolution in computational tools that underpin CCI studies, discuss improvements in experimental methods enabling more high-throughput analyses of CCIs, and highlight future directions for the field.
In this Review, the authors discuss computational methods for interpreting the molecular and clinical effects of genetic variants. They focus on methods leveraging machine learning, including those that characterize the effects on wider molecular networks.
In this Review, Zhang et al. discuss how recent advances in computational methods are helping to reveal the multiscale features involved in genome folding within the nucleus and how the resulting 3D genome organization relates to genome function.
In this Review, the authors discuss the latest advances in profiling multiple molecular modalities from single cells, including genomic, transcriptomic, epigenomic and proteomic information. They describe the diverse strategies for separately analysing different modalities, how the data can be computationally integrated, and approaches for obtaining spatially resolved data.