Review

Lee J-K, Choi Y-L, Kwon M, Park PJ. Mechanisms and Consequences of Cancer Genome Instability: Lessons from Genome Sequencing Studies. Annu Rev Pathol 2016;Abstract

During tumor evolution, cancer cells can accumulate numerous genetic alterations, ranging from single nucleotide mutations to whole-chromosomal changes. Although a great deal of progress has been made in the past decades in characterizing genomic alterations, recent cancer genome sequencing studies have provided a wealth of information on the detailed molecular profiles of such alterations in various types of cancers. Here, we review our current understanding of the mechanisms and consequences of cancer genome instability, focusing on the findings uncovered through analysis of exome and whole-genome sequencing data. These analyses have shown that most cancers have evidence of genome instability, and the degree of instability is variable within and between cancer types. Importantly, we describe some recent evidence supporting the idea that chromosomal instability could be a major driving force in tumorigenesis and cancer evolution, actively shaping the genomes of cancer cells to maximize their survival advantage. Expected final online publication date for the Annual Review of Pathology: Mechanisms of Disease Volume 11 is May 23, 2016. Please see http://www.annualreviews.org/catalog/pubdates.aspx for revised estimates.

Cortés-Ciriano I, Gulhan DC, Lee JJ-K, Melloni GEM, Park PJ*. Computational analysis of cancer genome sequencing data. Nature Reviews Genetics 2022;23(5):298-314.Abstract
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.
Jung YL, Kirli K, Alver BH, Park PJ. Resources and challenges for integrative analysis of nuclear architecture data. Curr Opin Genet Dev 2021;67:103-110.Abstract
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.
Chu C, Zhao B, Park PJ, Lee EA. Identification and Genotyping of Transposable Element Insertions From Genome Sequencing Data. Curr Protoc Hum Genet 2020;107(1):e102.Abstract
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.
Dou Y*, Gold HD*, Luquette LJ*, Park PJ. Detecting Somatic Mutations in Normal Cells. Trends in Genetics 2018;35(7):545-557.Abstract
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.
McConnell MJ, Moran JV, Abyzov A, Akbarian S, Bae T, Cortes-Ciriano I, Erwin JA, Fasching L, Flasch DA, Freed D, Ganz J, Jaffe AE, Kwan KY, Kwon M, Lodato MA, Mills RE, Paquola ACM, Rodin RE, Rosenbluh C, Sestan N, Sherman MA, Shin JH, Song S, Straub RE, Thorpe J, Weinberger DR, Urban AE, Zhou B, Gage FH, Lehner T, Senthil G, Walsh CA, Chess A, Courchesne E, Gleeson JG, Kidd JM, Park PJ, Pevsner J, Vaccarino FM, Brain Somatic Mosaicism Network BSM. Intersection of diverse neuronal genomes and neuropsychiatric disease: The Brain Somatic Mosaicism Network. Science 2017;356(6336)Abstract

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.

Kim T-M, Park PJ. A genome-wide view of microsatellite instability: old stories of cancer mutations revisited with new sequencing technologies. Cancer Res 2014;74(22):6377-82.Abstract

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.

Park PJ. Experimental design and data analysis for array comparative genomic hybridization. Cancer Invest 2008;26(9):923-8.Abstract

Array comparative genomic hybridization (aCGH) is a technique for measuring chromosomal aberrations in genomic DNA. With the availability of high-resolution microarrays, detailed characterization of the cancer genome has become possible. In this review, we discuss several issues in the generation and interpretation of aCGH data, including array platforms, experimental design, and data analysis. Due to the complexity of the data, application of appropriate statistical methods is crucial for avoiding false positive findings. We also describe integration of copy number data with other types of data to identify functional significance of observed aberrations.

Ferrari F, Alekseyenko AA, Park PJ, Kuroda MI. Transcriptional control of a whole chromosome: emerging models for dosage compensation. Nat Struct Mol Biol 2014;21(2):118-25.Abstract

Males and females of many animal species differ in their sex-chromosome karyotype, and this creates imbalances between X-chromosome and autosomal gene products that require compensation. Although distinct molecular mechanisms have evolved in three highly studied systems, they all achieve coordinate regulation of an entire chromosome by differential RNA-polymerase occupancy at X-linked genes. High-throughput genome-wide methods have been pivotal in driving the latest progress in the field. Here we review the emerging models for dosage compensation in mammals, flies and nematodes, with a focus on mechanisms affecting RNA polymerase II activity on the X chromosome.

Xi R, Kim T-M, Park PJ. Detecting structural variations in the human genome using next generation sequencing. Brief Funct Genomics 2010;9(5-6):405-15.Abstract

Structural variations are widespread in the human genome and can serve as genetic markers in clinical and evolutionary studies. With the advances in the next-generation sequencing technology, recent methods allow for identification of structural variations with unprecedented resolution and accuracy. They also provide opportunities to discover variants that could not be detected on conventional microarray-based platforms, such as dosage-invariant chromosomal translocations and inversions. In this review, we will describe some of the sequencing-based algorithms for detection of structural variations and discuss the key issues in future development.

Tolstorukov MY, Kharchenko PV, Park PJ. Analysis of primary structure of chromatin with next-generation sequencing. Epigenomics 2010;2(2):187-197.Abstract

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.

Park PJ. Epigenetics meets next-generation sequencing. Epigenetics 2008;3(6):318-21.Abstract

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.

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