Review

Distilling biologically meaningful information from cancer genome sequencing data requires comprehensive identification of somatic alterations using rigorous computational methods. As the amount and complexity of sequencing data have increased, so has the number of tools for analysing them. Here, we describe the main steps involved in the bioinformatic analysis of cancer genomes, review key algorithmic developments and highlight popular tools and emerging technologies. These tools include those that identify point mutations, copy number alterations, structural variations and mutational signatures in cancer genomes. We also discuss issues in experimental design, the strengths and limitations of sequencing modalities and methodological challenges for the future.

A large amount of genomic data for profiling three-dimensional genome architecture have accumulated from large-scale consortium projects as well as from individual laboratories. In this review, we summarize recent landmark datasets and collections in the field. We describe the challenges in collection, annotation, and analysis of these data, particularly for integration of sequencing and microscopy data. We introduce efforts from consortia and independent groups to harmonize diverse datasets. As the resolution and throughput of sequencing and imaging technologies continue to increase, more efficient utilization and integration of collected data will be critical for a better understanding of nuclear architecture.

Transposable element (TE) mobilization is a significant source of genomic variation and has been associated with various human diseases. The exponential growth of population-scale whole-genome sequencing and rapid innovations in long-read sequencing technologies provide unprecedented opportunities to study TE insertions and their functional impact in human health and disease. Identifying TE insertions, however, is challenging due to the repetitive nature of the TE sequences. Here, we review computational approaches to detecting and genotyping TE insertions using short- and long-read sequencing and discuss the strengths and weaknesses of different approaches. © 2020 Wiley Periodicals LLC.

Somatic mutations have been studied extensively in the context of cancer. Recent studies have demonstrated that high-throughput sequencing data can be used to detect somatic mutations in non-tumor cells. Analysis of such mutations allows us to better understand the mutational processes in normal cells, explore cell lineages in development, and examine potential associations with age-related disease. We describe here approaches for characterizing somatic mutations in normal and non-tumor disease tissues. We discuss several experimental designs and common pitfalls in somatic mutation detection, as well as more recent developments such as phasing and linked-read technology. With the dramatically increasing numbers of samples undergoing genome sequencing, bioinformatic analysis will enable the characterization of somatic mutations and their impact on non-cancer tissues.

Neuropsychiatric disorders have a complex genetic architecture. Human genetic population-based studies have identified numerous heritable sequence and structural genomic variants associated with susceptibility to neuropsychiatric disease. However, these germline variants do not fully account for disease risk. During brain development, progenitor cells undergo billions of cell divisions to generate the ~80 billion neurons in the brain. The failure to accurately repair DNA damage arising during replication, transcription, and cellular metabolism amid this dramatic cellular expansion can lead to somatic mutations. Somatic mutations that alter subsets of neuronal transcriptomes and proteomes can, in turn, affect cell proliferation and survival and lead to neurodevelopmental disorders. The long life span of individual neurons and the direct relationship between neural circuits and behavior suggest that somatic mutations in small populations of neurons can significantly affect individual neurodevelopment. The Brain Somatic Mosaicism Network has been founded to study somatic mosaicism both in neurotypical human brains and in the context of complex neuropsychiatric disorders.

Microsatellites are simple tandem repeats that are present at millions of loci in the human genome. Microsatellite instability (MSI) refers to DNA slippage events on microsatellites that occur frequently in cancer genomes when there is a defect in the DNA-mismatch repair system. These somatic mutations can result in inactivation of tumor-suppressor genes or disrupt other noncoding regulatory sequences, thereby playing a role in carcinogenesis. Here, we will discuss the ways in which high-throughput sequencing data can facilitate genome- or exome-wide discovery and more detailed investigation of MSI events in microsatellite-unstable cancer genomes. We will address the methodologic aspects of this approach and highlight insights from recent analyses of colorectal and endometrial cancer genomes from The Cancer Genome Atlas project. These include identification of novel MSI targets within and across tumor types and the relationship between the likelihood of MSI events to chromatin structure. Given the increasing popularity of exome and genome sequencing of cancer genomes, a comprehensive characterization of MSI may serve as a valuable marker of cancer evolution and aid in a search for therapeutic targets.

The recent development of next-generation sequencing technology has enabled significant progress in chromatin structure analysis. Here, we review the experimental and bioinformatic approaches to studying nucleosome positioning and histone modification profiles on a genome scale using this technology. These studies advanced our knowledge of the nucleosome positioning patterns of both epigenetically modified and bulk nucleosomes and elucidated the role of such patterns in regulation of gene expression. The identification and analysis of large sets of nucleosome-bound DNA sequences allowed better understanding of the rules that govern nucleosome positioning in organisms of various complexity. We also discuss the existing challenges and prospects of using next-generation sequencing for nucleosome positioning analysis and outline the importance of such studies for the entire chromatin structure field.

Next-generation sequencing is poised to unleash dramatic changes in every area of molecular biology. In the past few years, chromatin immunoprecipitation (ChIP) on tiled microarrays (ChIP-chip) has been an important tool for genome-wide mapping of DNA-binding proteins or histone modifications. Now, ChIP followed by direct sequencing of DNA fragments (ChIP-seq) offers superior data with less noise and higher resolution and is likely to replace ChIP-chip in the near future. We will describe advantages of this new technology and outline some of the issues in dealing with the data. ChIP-seq generates considerably larger quantities of data and the most challenging aspect for investigators will be computational and statistical analysis necessary to uncover biological insights hidden in the data.

Copy-number variation (CNV) is a major class of genomic variation with potentially important functional consequences in both normal and diseased populations. Remarkable advances in development of next-generation sequencing (NGS) platforms provide an unprecedented opportunity for accurate, high-resolution characterization of CNVs. In this unit, we give an overview of available computational tools for detection of CNVs and discuss comparative advantages and disadvantages of different approaches.

A transcriptional regulatory network represents a molecular framework in which developmental or environmental cues are transformed into differential expression of genes. Transcriptional regulation is mediated by the combinatorial interplay between cis-regulatory DNA elements and trans-acting transcription factors, and is perhaps the most important mechanism for controlling gene expression. Recent innovations, most notably the method for detecting protein-DNA interactions genome-wide, can help provide a comprehensive catalog of cis-regulatory elements and their interaction with given trans-acting factors in a given condition. A transcriptional regulatory network that integrates such information can lead to a systems-level understanding of regulatory mechanisms. In this review, we will highlight the key aspects of current knowledge on eukaryotic transcriptional regulation, especially on known transcription factors and their interacting regulatory elements. Then we will review some recent technical advances for genome-wide mapping of DNA-protein interactions based on high-throughput sequencing. Finally, we will discuss the types of biological insights that can be obtained from a network-level understanding of transcription regulation as well as future challenges in the field.

Chromatin immunoprecipitation followed by sequencing (ChIP-seq) is a technique for genome-wide profiling of DNA-binding proteins, histone modifications or nucleosomes. Owing to the tremendous progress in next-generation sequencing technology, ChIP-seq offers higher resolution, less noise and greater coverage than its array-based predecessor ChIP-chip. With the decreasing cost of sequencing, ChIP-seq has become an indispensable tool for studying gene regulation and epigenetic mechanisms. In this Review, I describe the benefits and challenges in harnessing this technique with an emphasis on issues related to experimental design and data analysis. ChIP-seq experiments generate large quantities of data, and effective computational analysis will be crucial for uncovering biological mechanisms.