Cortés-Ciriano I, Lee JJK, Xi R, Jain D, Jung YL, Yang L, Gordenin D, Klimczak LJ, Zhang CZ, Pellman DS, Group PCAWGSVW, Park PJ, Consortium PCAWG. Comprehensive analysis of chromothripsis in 2,658 human cancers using whole-genome sequencing [Internet]. Nature Genetics 2020;52(3):331-341. Publisher's VersionAbstract
Chromothripsis is a mutational phenomenon characterized by massive, clustered genomic rearrangements that occurs in cancer and other diseases. Recent studies in selected cancer types have suggested that chromothripsis may be more common than initially inferred from low-resolution copy-number data. Here, as part of the Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium of the International Cancer Genome Consortium (ICGC) and The Cancer Genome Atlas (TCGA), we analyze patterns of chromothripsis across 2,658 tumors from 38 cancer types using whole-genome sequencing data. We find that chromothripsis events are pervasive across cancers, with a frequency of more than 50% in several cancer types. Whereas canonical chromothripsis profiles display oscillations between two copy-number states, a considerable fraction of events involve multiple chromosomes and additional structural alterations. In addition to non-homologous end joining, we detect signatures of replication-associated processes and templated insertions. Chromothripsis contributes to oncogene amplification and to inactivation of genes such as mismatch-repair-related genes. These findings show that chromothripsis is a major process that drives genome evolution in human cancer.
Dou Y, Kwon M, Rodin RE, Cortés-Ciriano I, Doan R, J. Luquette L, Galor A, Bohrson C, Walsh CA, Park PJ. Accurate detection of mosaic variants in sequencing data without matched controls [Internet]. Nature Biotechnology 2020;38(3):314-319. Publisher's VersionAbstract

Detection of mosaic mutations that arise in normal development is challenging, as such mutations are typically present in only a minute fraction of cells and there is no clear matched control for removing germline variants and systematic artifacts. We present MosaicForecast, a machine-learning method that leverages read-based phasing and read-level features to accurately detect mosaic single-nucleotide variants and indels, achieving a multifold increase in specificity compared with existing algorithms. Using single-cell sequencing and targeted sequencing, we validated 80–90{\%} of the mosaic single-nucleotide variants and 60–80{\%} of indels detected in human brain whole-genome sequencing data. Our method should help elucidate the contribution of mosaic somatic mutations to the origin and development of disease.

Gulhan DC, Lee JJ-K, Melloni GEM, Cortés-Ciriano I, Park PJ. Detecting the mutational signature of homologous recombination deficiency in clinical samples. Nature Genetics 2019;51(5):912-919.Abstract
Mutations in BRCA1 and/or BRCA2 (BRCA1/2) are the most common indication of deficiency in the homologous recombination (HR) DNA repair pathway. However, recent genome-wide analyses have shown that the same pattern of mutations found in BRCA1/2-mutant tumors is also present in several other tumors. Here, we present a new computational tool called Signature Multivariate Analysis (SigMA), which can be used to accurately detect the mutational signature associated with HR deficiency from targeted gene panels. Whereas previous methods require whole-genome or whole-exome data, our method detects the HR-deficiency signature even from low mutation counts, by using a likelihood-based measure combined with machine-learning techniques. Cell lines that we identify as HR deficient show a significant response to poly (ADP-ribose) polymerase (PARP) inhibitors; patients with ovarian cancer whom we found to be HR deficient show a significantly longer overall survival with platinum regimens. By enabling panel-based identification of mutational signatures, our method substantially increases the number of patients that may be considered for treatments targeting HR deficiency.
Bohrson CL, Barton AR, Lodato MA, Rodin RE, Luquette LJ, Viswanadham VV, Gulhan DC, Cortés-Ciriano I, Sherman MA, Kwon M, Coulter ME, Galor A, Walsh CA, Park PJ. Linked-read analysis identifies mutations in single-cell DNA-sequencing data. Nature Genetics 2019;51:749-754.Abstract
Whole-genome sequencing of DNA from single cells has the potential to reshape our understanding of mutational heterogeneity in normal and diseased tissues. However, a major difficulty is distinguishing amplification artifacts from biologically derived somatic mutations. Here, we describe linked-read analysis (LiRA), a method that accurately identifies somatic singlenucleotide variants (sSNVs) by using read-level phasing with nearby germline heterozygous polymorphisms, thereby enabling the characterization of mutational signatures and estimation of somatic mutation rates in single cells.
Day DS*, Zhang B*, Stevens SM, Ferrari F, Larschan EN, Park PJ**, Pu WT**. Comprehensive analysis of promoter-proximal RNA polymerase II pausing across mammalian cell types. Genome Biol 2016;17(1):120.Abstract

BACKGROUND: For many genes, RNA polymerase II stably pauses before transitioning to productive elongation. Although polymerase II pausing has been shown to be a mechanism for regulating transcriptional activation, the extent to which it is involved in control of mammalian gene expression and its relationship to chromatin structure remain poorly understood. RESULTS: Here, we analyze 85 RNA polymerase II chromatin immunoprecipitation (ChIP)-sequencing experiments from 35 different murine and human samples, as well as related genome-wide datasets, to gain new insights into the relationship between polymerase II pausing and gene regulation. Across cell and tissue types, paused genes (pausing index > 2) comprise approximately 60 % of expressed genes and are repeatedly associated with specific biological functions. Paused genes also have lower cell-to-cell expression variability. Increased pausing has a non-linear effect on gene expression levels, with moderately paused genes being expressed more highly than other paused genes. The highest gene expression levels are often achieved through a novel pause-release mechanism driven by high polymerase II initiation. In three datasets examining the impact of extracellular signals, genes responsive to stimulus have slightly lower pausing index on average than non-responsive genes, and rapid gene activation is linked to conditional pause-release. Both chromatin structure and local sequence composition near the transcription start site influence pausing, with divergent features between mammals and Drosophila. Most notably, in mammals pausing is positively correlated with histone H2A.Z occupancy at promoters. CONCLUSIONS: Our results provide new insights into the contribution of RNA polymerase II pausing in mammalian gene regulation and chromatin structure.

Xi R, Lee S, Xia Y, Kim T-M, Park PJ. Copy number analysis of whole-genome data using BIC-seq2 and its application to detection of cancer susceptibility variants. Nucleic Acids Res 2016;Abstract

Whole-genome sequencing data allow detection of copy number variation (CNV) at high resolution. However, estimation based on read coverage along the genome suffers from bias due to GC content and other factors. Here, we develop an algorithm called BIC-seq2 that combines normalization of the data at the nucleotide level and Bayesian information criterion-based segmentation to detect both somatic and germline CNVs accurately. Analysis of simulation data showed that this method outperforms existing methods. We apply this algorithm to low coverage whole-genome sequencing data from peripheral blood of nearly a thousand patients across eleven cancer types in The Cancer Genome Atlas (TCGA) to identify cancer-predisposing CNV regions. We confirm known regions and discover new ones including those covering KMT2C, GOLPH3, ERBB2 and PLAG1 Analysis of colorectal cancer genomes in particular reveals novel recurrent CNVs including deletions at two chromatin-remodeling genes RERE and NPM2 This method will be useful to many researchers interested in profiling CNVs from whole-genome sequencing data.

Evrony GD*, Lee E*, Park PJ**, Walsh CA**. Resolving rates of mutation in the brain using single-neuron genomics. Elife 2016;5Abstract

Whether somatic mutations contribute functional diversity to brain cells is a long-standing question. Single-neuron genomics enables direct measurement of somatic mutation rates in human brain and promises to answer this question. A recent study (Upton et al., 2015) reported high rates of somatic LINE-1 element (L1) retrotransposition in the hippocampus and cerebral cortex that would have major implications for normal brain function, and further claimed these mutation events preferentially impact genes important for neuronal function. We identify errors in single-cell sequencing approach, bioinformatic analysis, and validation methods that led to thousands of false-positive artifacts being mistakenly interpreted as somatic mutation events. Our reanalysis of the data supports a corrected mutation frequency (0.2 per cell) more than fifty-fold lower than reported, inconsistent with the authors' conclusion of 'ubiquitous' L1 mosaicism, but consistent with L1 elements mobilizing occasionally. Through consideration of the challenges and pitfalls identified, we provide a foundation and framework for designing single-cell genomics studies.

Jin Z, Huang W, Shen N, Li J, Wang X, Dong J, Park PJ, Xi R. Single-cell gene fusion detection by scFusion. Nat Commun 2022;13(1):1084.Abstract
Gene fusions can play important roles in tumor initiation and progression. While fusion detection so far has been from bulk samples, full-length single-cell RNA sequencing (scRNA-seq) offers the possibility of detecting gene fusions at the single-cell level. However, scRNA-seq data have a high noise level and contain various technical artifacts that can lead to spurious fusion discoveries. Here, we present a computational tool, scFusion, for gene fusion detection based on scRNA-seq. We evaluate the performance of scFusion using simulated and five real scRNA-seq datasets and find that scFusion can efficiently and sensitively detect fusions with a low false discovery rate. In a T cell dataset, scFusion detects the invariant TCR gene recombinations in mucosal-associated invariant T cells that many methods developed for bulk data fail to detect; in a multiple myeloma dataset, scFusion detects the known recurrent fusion IgH-WHSC1, which is associated with overexpression of the WHSC1 oncogene. Our results demonstrate that scFusion can be used to investigate cellular heterogeneity of gene fusions and their transcriptional impact at the single-cell level.
Chu C, Borges-Monroy R, Viswanadham VV, Lee S, Li H, Lee EA**, Park PJ**. Comprehensive identification of transposable element insertions using multiple sequencing technologies. Nat Commun 2021;12(1):3836.Abstract
Transposable elements (TEs) help shape the structure and function of the human genome. When inserted into some locations, TEs may disrupt gene regulation and cause diseases. Here, we present xTea (x-Transposable element analyzer), a tool for identifying TE insertions in whole-genome sequencing data. Whereas existing methods are mostly designed for short-read data, xTea can be applied to both short-read and long-read data. Our analysis shows that xTea outperforms other short read-based methods for both germline and somatic TE insertion discovery. With long-read data, we created a catalogue of polymorphic insertions with full assembly and annotation of insertional sequences for various types of retroelements, including pseudogenes and endogenous retroviruses. Notably, we find that individual genomes have an average of nine groups of full-length L1s in centromeres, suggesting that centromeres and other highly repetitive regions such as telomeres are a significant yet unexplored source of active L1s. xTea is available at .
Jain D, Chu C, Alver BH, Lee S, Lee EA, Park PJ*. HiTea: a computational pipeline to identify non-reference transposable element insertions in Hi-C data. Bioinformatics 2021;37(8):1045-1051.Abstract
Hi-C is a common technique for assessing 3D chromatin conformation. Recent studies have shown that long-range interaction information in Hi-C data can be used to generate chromosome-length genome assemblies and identify large-scale structural variations. Here, we demonstrate the use of Hi-C data in detecting mobile transposable element (TE) insertions genome-wide. Our pipeline Hi-C-based TE analyzer (HiTea) capitalizes on clipped Hi-C reads and is aided by a high proportion of discordant read pairs in Hi-C data to detect insertions of three major families of active human TEs. Despite the uneven genome coverage in Hi-C data, HiTea is competitive with the existing callers based on whole-genome sequencing (WGS) data and can supplement the WGS-based characterization of the TE-insertion landscape. We employ the pipeline to identify TE-insertions from human cell-line Hi-C samples. AVAILABILITY AND IMPLEMENTATION: HiTea is available at and as a Docker image. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
Kwon M, Lee S, Berselli M, Chu C, Park PJ. BamSnap: a lightweight viewer for sequencing reads in BAM files. Bioinformatics 2021;37(2):263-4.Abstract
SUMMARY: Despite the improvement in variant detection algorithms, visual inspection of the read-level data remains an essential step for accurate identification of variants in genome analysis. We developed BamSnap, an efficient BAM file viewer utilizing a graphics library and BAM indexing. In contrast to existing viewers, BamSnap can generate high-quality snapshots rapidly, with customized tracks and layout. As an example, we produced read-level images at 1000 genomic loci for >2500 whole-genomes. AVAILABILITY: BamSnap is freely available at SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
Yun JW, Yang L, Park H-Y, Lee C-W, Cha H, Shin H-T, Noh K-W, Choi Y-L, Park W-Y**, Park PJ**. Dysregulation of cancer genes by recurrent intergenic fusions. Genome Biol 2020;21(1):166.Abstract
BACKGROUND: Gene fusions have been studied extensively, as frequent drivers of tumorigenesis as well as potential therapeutic targets. In many well-known cases, breakpoints occur at two intragenic positions, leading to in-frame gene-gene fusions that generate chimeric mRNAs. However, fusions often occur with intergenic breakpoints, and the role of such fusions has not been carefully examined. RESULTS: We analyze whole-genome sequencing data from 268 patients to catalog gene-intergenic and intergenic-intergenic fusions and characterize their impact. First, we discover that, in contrast to the common assumption, chimeric oncogenic transcripts-such as those involving ETV4, ERG, RSPO3, and PIK3CA-can be generated by gene-intergenic fusions through splicing of the intervening region. Second, we find that over-expression of an upstream or downstream gene by a fusion-mediated repositioning of a regulatory sequence is much more common than previously suspected, with enhancers sometimes located megabases away. We detect a number of recurrent fusions, such as those involving ANO3, RGS9, FUT5, CHI3L1, OR1D4, and LIPG in breast; IGF2 in colon; ETV1 in prostate; and IGF2BP3 and SIX2 in thyroid cancers. CONCLUSION: Our findings elucidate the potential oncogenic function of intergenic fusions and highlight the wide-ranging consequences of structural rearrangements in cancer genomes.
Goldman MJ*, Zhang J*, Fonseca NA*, Cortés-Ciriano I*, Xiang Q, Craft B, Piñeiro-Yáñez E, O'Connor BD, Bazant W, Barrera E, Muñoz-Pomer A, Petryszak R, Füllgrabe A, Al-Shahrour F, Keays M, Haussler D, Weinstein JN, Huber W, Valencia A, Park PJ, Papatheodorou I, Zhu J, Ferretti V, Vazquez M. A user guide for the online exploration and visualization of PCAWG data. Nat Commun 2020;11(1):3400.Abstract
The Pan-Cancer Analysis of Whole Genomes (PCAWG) project generated a vast amount of whole-genome cancer sequencing resource data. Here, as part of the ICGC/TCGA Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium, which aggregated whole genome sequencing data from 2658 cancers across 38 tumor types, we provide a user's guide to the five publicly available online data exploration and visualization tools introduced in the PCAWG marker paper. These tools are ICGC Data Portal, UCSC Xena, Chromothripsis Explorer, Expression Atlas, and PCAWG-Scout. We detail use cases and analyses for each tool, show how they incorporate outside resources from the larger genomics ecosystem, and demonstrate how the tools can be used together to understand the biology of cancers more deeply. Together, the tools enable researchers to query the complex genomic PCAWG data dynamically and integrate external information, enabling and enhancing interpretation.
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.
Wang S, Lee S, Chu C, Jain D, Kerpedjiev P, Nelson GM, Walsh JM, Alver BH, Park PJ. HiNT: a computational method for detecting copy number variations and translocations from Hi-C data [Internet]. Genome Biology 2020;21(1):73. Publisher's VersionAbstract
The three-dimensional conformation of a genome can be profiled using Hi-C, a technique that combines chromatin conformation capture with high-throughput sequencing. However, structural variations often yield features that can be mistaken for chromosomal interactions. Here, we describe a computational method HiNT (Hi-C for copy Number variation and Translocation detection), which detects copy number variations and interchromosomal translocations within Hi-C data with breakpoints at single base-pair resolution. We demonstrate that HiNT outperforms existing methods on both simulated and real data. We also show that Hi-C can supplement whole-genome sequencing in structure variant detection by locating breakpoints in repetitive regions.