Patchwork: allele-specific copy number analysis of whole-genome sequenced tumor tissue

Whole-genome sequencing of tumor tissue has the potential to provide comprehensive characterization of genomic alterations in tumor samples. We present Patchwork, a new bioinformatic tool for allele-specific copy number analysis using whole-genome sequencing data. Patchwork can be used to determine the copy number of homologous sequences throughout the genome, even in aneuploid samples with moderate sequence coverage and tumor cell content. No prior knowledge of average ploidy or tumor cell content is required. Patchwork is freely available as an R package, installable via R-Forge (http://patchwork.r-forge.r-project.org/).     

Long insert whole genome sequencing for copy number variant and translocation detection

As next-generation sequencing continues to have an expanding presence in the clinic, the identification of the most cost-effective and robust strategy for identifying copy number changes and translocations in tumor genomes is needed. We hypothesized that performing shallow whole genome sequencing (WGS) of 900–1000-bp inserts (long insert WGS, LI-WGS) improves our ability to detect these events, compared with shallow WGS of 300–400-bp inserts. A priori analyses show that LI-WGS requires less sequencing compared with short insert WGS to achieve a target physical coverage, and that LI-WGS requires less sequence coverage to detect a heterozygous event with a power of 0.99. We thus developed an LI-WGS library preparation protocol based off of Illumina’s WGS library preparation protocol and illustrate the feasibility of performing LI-WGS. We additionally applied LI-WGS to three separate tumor/normal DNA pairs collected from patients diagnosed with different cancers to demonstrate our application of LI-WGS on actual patient samples for identification of somatic copy number alterations and translocations. With the evolution of sequencing technologies and bioinformatics analyses, we show that modifications to current approaches may improve our ability to interrogate cancer genomes.     

seqCNA: an R package for DNA copy number analysis in cancer using high-throughput sequencing

Background

Deviations in the amount of genomic content that arise during tumorigenesis, called copy number alterations, are structural rearrangements that can critically affect gene expression patterns. Additionally, copy number alteration profiles allow insight into cancer discrimination, progression and complexity. On data obtained from high-throughput sequencing, improving quality through GC bias correction and keeping false positives to a minimum help build reliable copy number alteration profiles.

Results

We introduce seqCNA, a parallelized R package for an integral copy number analysis of high-throughput sequencing cancer data. The package includes novel methodology on (i) filtering, reducing false positives, and (ii) GC content correction, improving copy number profile quality, especially under great read coverage and high correlation between GC content and copy number. Adequate analysis steps are automatically chosen based on availability of paired-end mapping, matched normal samples and genome annotation.

Conclusions

seqCNA, available through Bioconductor, provides accurate copy number predictions in tumoural data, thanks to the extensive filtering and better GC bias correction, while providing an integrated and parallelized workflow.

Electronic supplementary material

The online version of this article (doi:10.1186/1471-2164-15-178) contains supplementary material, which is available to authorized users.     

Mutation Discovery in Regions of Segmental Cancer Genome Amplifications with CoNAn-SNV: A Mixture Model for Next Generation Sequencing of Tumors

Next generation sequencing has now enabled a cost-effective enumeration of the full mutational complement of a tumor genome-in particular single nucleotide variants (SNVs). Most current computational and statistical models for analyzing next generation sequencing data, however, do not account for cancer-specific biological properties, including somatic segmental copy number alterations (CNAs)-which require special treatment of the data. Here we present CoNAn-SNV (Copy Number Annotated SNV): a novel algorithm for the inference of single nucleotide variants (SNVs) that overlap copy number alterations. The method is based on modelling the notion that genomic regions of segmental duplication and amplification induce an extended genotype space where a subset of genotypes will exhibit heavily skewed allelic distributions in SNVs (and therefore render them undetectable by methods that assume diploidy). We introduce the concept of modelling allelic counts from sequencing data using a panel of Binomial mixture models where the number of mixtures for a given locus in the genome is informed by a discrete copy number state given as input. We applied CoNAn-SNV to a previously published whole genome shotgun data set obtained from a lobular breast cancer and show that it is able to discover 21 experimentally revalidated somatic non-synonymous mutations in a lobular breast cancer genome that were not detected using copy number insensitive SNV detection algorithms. Importantly, ROC analysis shows that the increased sensitivity of CoNAn-SNV does not result in disproportionate loss of specificity. This was also supported by analysis of a recently published lymphoma genome with a relatively quiescent karyotype, where CoNAn-SNV showed similar results to other callers except in regions of copy number gain where increased sensitivity was conferred. Our results indicate that in genomically unstable tumors, copy number annotation for SNV detection will be critical to fully characterize the mutational landscape of cancer genomes.     

The mutational burden of acral melanoma revealed by whole‐genome sequencing and comparative analysis

Acral melanoma is a subtype of melanoma with distinct epidemiological, clinical and mutational profiles. To define the genomic alterations in acral melanoma, we conducted whole‐genome sequencing and SNP array analysis of five metastatic tumours and their matched normal genomes. We identified the somatic mutations, copy number alterations and structural variants in these tumours and combined our data with published studies to identify recurrently mutated genes likely to be the drivers of acral melanomagenesis. We compared and contrasted the genomic landscapes of acral, mucosal, uveal and common cutaneous melanoma to reveal the distinctive mutational characteristics of each subtype.     

Inferring copy number and genotype in tumour exome data

Background

Using whole exome sequencing to predict aberrations in tumours is a cost effective alternative to whole genome sequencing, however is predominantly used for variant detection and infrequently utilised for detection of somatic copy number variation.

Results

We propose a new method to infer copy number and genotypes using whole exome data from paired tumour/normal samples. Our algorithm uses two Hidden Markov Models to predict copy number and genotypes and computationally resolves polyploidy/aneuploidy, normal cell contamination and signal baseline shift. Our method makes explicit detection on chromosome arm level events, which are commonly found in tumour samples. The methods are combined into a package named ADTEx (Aberration Detection in Tumour Exome). We applied our algorithm to a cohort of 17 in-house generated and 18 TCGA paired ovarian cancer/normal exomes and evaluated the performance by comparing against the copy number variations and genotypes predicted using Affymetrix SNP 6.0 data of the same samples. Further, we carried out a comparison study to show that ADTEx outperformed its competitors in terms of precision and F-measure.

Conclusions

Our proposed method, ADTEx, uses both depth of coverage ratios and B allele frequencies calculated from whole exome sequencing data, to predict copy number variations along with their genotypes. ADTEx is implemented as a user friendly software package using Python and R statistical language. Source code and sample data are freely available under GNU license (GPLv3) at http://adtex.sourceforge.net/.

Electronic supplementary material

The online version of this article (doi:10.1186/1471-2164-15-732) contains supplementary material, which is available to authorized users.     

Estimation of Copy Number Alterations from Exome Sequencing Data

Exome sequencing constitutes an important technology for the study of human hereditary diseases and cancer. However, the ability of this approach to identify copy number alterations in primary tumor samples has not been fully addressed. Here we show that somatic copy number alterations can be reliably estimated using exome sequencing data through a strategy that we have termed exome2cnv. Using data from 86 paired normal and primary tumor samples, we identified losses and gains of complete chromosomes or large genomic regions, as well as smaller regions affecting a minimum of one gene. Comparison with high-resolution comparative genomic hybridization (CGH) arrays revealed a high sensitivity and a low number of false positives in the copy number estimation between both approaches. We explore the main factors affecting sensitivity and false positives with real data, and provide a side by side comparison with CGH arrays. Together, these results underscore the utility of exome sequencing to study cancer samples by allowing not only the identification of substitutions and indels, but also the accurate estimation of copy number alterations.     

Methods, challenges, and promise of next-generation sequencing in cancer biology

It is generally accepted that cancers result from the aggregation of somatic mutations. The emergence of next-generation sequencing (NGS) technologies during the past half-decade has enabled studies of cancer genomes with high sensitivity and resolution through whole-genome and whole-exome sequencing approaches, among others. This saltatory advance introduces the possibility of assembling multiple cancer genomes for analysis in a cost-effective manner. Analytical approaches are now applied to the detection of a number of somatic genome alterations, including nucleotide substitutions, insertions/deletions, copy number variations, and chromosomal rearrangements. This review provides a thorough introduction to the cancer genomics pipeline as well as a case study of these methods put into practice.     

SAAS-CNV: A Joint Segmentation Approach on Aggregated and Allele Specific Signals for the Identification of Somatic Copy Number Alterations with Next-Generation Sequencing Data

Cancer genomes exhibit profound somatic copy number alterations (SCNAs). Studying tumor SCNAs using massively parallel sequencing provides unprecedented resolution and meanwhile gives rise to new challenges in data analysis, complicated by tumor aneuploidy and heterogeneity as well as normal cell contamination. While the majority of read depth based methods utilize total sequencing depth alone for SCNA inference, the allele specific signals are undervalued. We proposed a joint segmentation and inference approach using both signals to meet some of the challenges. Our method consists of four major steps: 1) extracting read depth supporting reference and alternative alleles at each SNP/Indel locus and comparing the total read depth and alternative allele proportion between tumor and matched normal sample; 2) performing joint segmentation on the two signal dimensions; 3) correcting the copy number baseline from which the SCNA state is determined; 4) calling SCNA state for each segment based on both signal dimensions. The method is applicable to whole exome/genome sequencing (WES/WGS) as well as SNP array data in a tumor-control study. We applied the method to a dataset containing no SCNAs to test the specificity, created by pairing sequencing replicates of a single HapMap sample as normal/tumor pairs, as well as a large-scale WGS dataset consisting of 88 liver tumors along with adjacent normal tissues. Compared with representative methods, our method demonstrated improved accuracy, scalability to large cancer studies, capability in handling both sequencing and SNP array data, and the potential to improve the estimation of tumor ploidy and purity.     

ITALICS: an algorithm for normalization and DNA copy number calling for Affymetrix SNP arrays

MOTIVATION: Affymetrix SNP arrays can be used to determine the DNA copy number measurement of 11 000-500 000 SNPs along the genome. Their high density facilitates the precise localization of genomic alterations and makes them a powerful tool for studies of cancers and copy number polymorphism. Like other microarray technologies it is influenced by non-relevant sources of variation, requiring correction. Moreover, the amplitude of variation induced by non-relevant effects is similar or greater than the biologically relevant effect (i.e. true copy number), making it difficult to estimate non-relevant effects accurately without including the biologically relevant effect. RESULTS: We addressed this problem by developing ITALICS, a normalization method that estimates both biological and non-relevant effects in an alternate, iterative manner, accurately eliminating irrelevant effects. We compared our normalization method with other existing and available methods, and found that ITALICS outperformed these methods for several in-house datasets and one public dataset. These results were validated biologically by quantitative PCR. AVAILABILITY: The R package ITALICS (ITerative and Alternative normaLIzation and Copy number calling for affymetrix Snp arrays) has been submitted to Bioconductor.     

Exome sequencing reveals comprehensive genomic alterations across eight cancer cell lines

It is well established that genomic alterations play an essential role in oncogenesis, disease progression, and response of tumors to therapeutic intervention. The advances of next-generation sequencing technologies (NGS) provide unprecedented capabilities to scan genomes for changes such as mutations, deletions, and alterations of chromosomal copy number. However, the cost of full-genome sequencing still prevents the routine application of NGS in many areas. Capturing and sequencing the coding exons of genes (the "exome") can be a cost-effective approach for identifying changes that result in alteration of protein sequences. We applied an exome-sequencing technology (Roche Nimblegen capture paired with 454 sequencing) to identify sequence variation and mutations in eight commonly used cancer cell lines from a variety of tissue origins (A2780, A549, Colo205, GTL16, NCI-H661, MDA-MB468, PC3, and RD). We showed that this technology can accurately identify sequence variation, providing ～95% concordance with Affymetrix SNP Array 6.0 performed on the same cell lines. Furthermore, we detected 19 of the 21 mutations reported in Sanger COSMIC database for these cell lines. We identified an average of 2,779 potential novel sequence variations/mutations per cell line, of which 1,904 were non-synonymous. Many non-synonymous changes were identified in kinases and known cancer-related genes. In addition we confirmed that the read-depth of exome sequence data can be used to estimate high-level gene amplifications and identify homologous deletions. In summary, we demonstrate that exome sequencing can be a reliable and cost-effective way for identifying alterations in cancer genomes, and we have generated a comprehensive catalogue of genomic alterations in coding regions of eight cancer cell lines. These findings could provide important insights into cancer pathways and mechanisms of resistance to anti-cancer therapies.     

T-lex: a program for fast and accurate assessment of transposable element presence using next-generation sequencing data

Transposable elements (TEs) are repetitive DNA sequences that are ubiquitous, extremely abundant and dynamic components of practically all genomes. Much effort has gone into annotation of TE copies in reference genomes. The sequencing cost reduction and the newly available next-generation sequencing (NGS) data from multiple strains within a species offer an unprecedented opportunity to study population genomics of TEs in a range of organisms. Here, we present a computational pipeline (T-lex) that uses NGS data to detect the presence/absence of annotated TE copies. T-lex can use data from a large number of strains and returns estimates of population frequencies of individual TE insertions in a reasonable time. We experimentally validated the accuracy of T-lex detecting presence or absence of 768 previously identified TE copies in two resequenced Drosophila melanogaster strains. Approximately 95% of the TE insertions were detected with 100% sensitivity and 97% specificity. We show that even at low levels of coverage T-lex produces accurate results for TE copies that it can identify reliably but that the rate of 'no data' calls increases as the coverage falls below 15×. T-lex is a broadly applicable and flexible tool that can be used in any genome provided the availability of the reference genome, individual TE copy annotation and NGS data.     

Using expression arrays for copy number detection: an example from E. coli

Background  

The sequencing of many genomes and tiling arrays consisting of millions of DNA segments spanning entire genomes have made high-resolution copy number analysis possible. Microarray-based comparative genomic hybridization (array CGH) has enabled the high-resolution detection of DNA copy number aberrations. While many of the methods and algorithms developed for the analysis microarrays have focused on expression analysis, the same technology can be used to detect genetic alterations, using for example standard commercial Affymetrix arrays. Due to the nature of the resultant data, standard techniques for processing GeneChip expression experiments are inapplicable.     

Allele-specific copy number profiling by next-generation DNA sequencing

The progression and clonal development of tumors often involve amplifications and deletions of genomic DNA. Estimation of allele-specific copy number, which quantifies the number of copies of each allele at each variant loci rather than the total number of chromosome copies, is an important step in the characterization of tumor genomes and the inference of their clonal history. We describe a new method, falcon, for finding somatic allele-specific copy number changes by next generation sequencing of tumors with matched normals. falcon is based on a change-point model on a bivariate mixed Binomial process, which explicitly models the copy numbers of the two chromosome haplotypes and corrects for local allele-specific coverage biases. By using the Binomial distribution rather than a normal approximation, falcon more effectively pools evidence from sites with low coverage. A modified Bayesian information criterion is used to guide model selection for determining the number of copy number events. Falcon is evaluated on in silico spike-in data and applied to the analysis of a pre-malignant colon tumor sample and late-stage colorectal adenocarcinoma from the same individual. The allele-specific copy number estimates obtained by falcon allows us to draw detailed conclusions regarding the clonal history of the individual''s colon cancer.     

CNAmet: an R package for integrating copy number, methylation and expression data

SUMMARY: Gene copy number and DNA methylation alterations are key regulators of gene expression in cancer. Accordingly, genes that show simultaneous methylation, copy number and expression alterations are likely to have a key role in tumor progression. We have implemented a novel software package (CNAmet) for integrative analysis of high-throughput copy number, DNA methylation and gene expression data. To demonstrate the utility of CNAmet, we use copy number, DNA methylation and gene expression data from 50 glioblastoma multiforme and 188 ovarian cancer primary tumor samples. Our results reveal a synergistic effect of DNA methylation and copy number alterations on gene expression for several known oncogenes as well as novel candidate oncogenes. AVAILABILITY: CNAmet R-package and user guide are freely available under GNU General Public License at http://csbi.ltdk.helsinki.fi/CNAmet.     

CNAReporter: a GenePattern pipeline for the generation of clinical reports of genomic alterations

Background

Genomic copy number alterations are widely associated with a broad range of human tumors and offer the potential to be used as a diagnostic tool. Especially in the emerging era of personalized medicine medical informatics tools that allow the fast visualization and analysis of genomic alterations of a patient's genomic profile for diagnostic and potential treatment purposes increasingly gain importance.

Results

We developed CNAReporter, a software tool that allows users to visualize SNP-specific data obtained from Affymetrix arrays and generate PDF-reports as output. We combined standard algorithms for the analysis of chromosomal alterations, utilizing the widely applied GenePattern framework. As an example, we show genome analyses of two patients with distinctly different CNA profiles using the tool.

Conclusions

Glioma subtypes, characterized by different genomic alterations, are often treated differently but can be difficult to differentiate pathologically. CNAReporter offers a user-friendly way to visualize and analyse genomic changes of any given tumor genomic profile, thereby leading to an accurate diagnosis and patient-specific treatment.     

Estimating the total genome length of a metagenomic sample using k-mers

Background

Metagenomic sequencing is a powerful technology for studying the mixture of microbes or the microbiomes on human and in the environment. One basic task of analyzing metagenomic data is to identify the component genomes in the community. This task is challenging due to the complexity of microbiome composition, limited availability of known reference genomes, and usually insufficient sequencing coverage.

Results

As an initial step toward understanding the complete composition of a metagenomic sample, we studied the problem of estimating the total length of all distinct component genomes in a metagenomic sample. We showed that this problem can be solved by estimating the total number of distinct k-mers in all the metagenomic sequencing data. We proposed a method for this estimation based on the sequencing coverage distribution of observed k-mers, and introduced a k-mer redundancy index (KRI) to fill in the gap between the count of distinct k-mers and the total genome length. We showed the effectiveness of the proposed method on a set of carefully designed simulation data corresponding to multiple situations of true metagenomic data. Results on real data indicate that the uncaptured genomic information can vary dramatically across metagenomic samples, with the potential to mislead downstream analyses.

Conclusions

We proposed the question of how long the total genome length of all different species in a microbial community is and introduced a method to answer it.

     

Using high-density DNA methylation arrays to profile copy number alterations

The integration of genomic and epigenomic data is an increasingly popular approach for studying the complex mechanisms driving cancer development. We have developed a method for evaluating both methylation and copy number from high-density DNA methylation arrays. Comparing copy number data from Infinium HumanMethylation450 BeadChips and SNP arrays, we demonstrate that Infinium arrays detect copy number alterations with the sensitivity of SNP platforms. These results show that high-density methylation arrays provide a robust and economic platform for detecting copy number and methylation changes in a single experiment. Our method is available in the ChAMP Bioconductor package: http://www.bioconductor.org/packages/2.13/bioc/html/ChAMP.html.