Control-free calling of copy number alterations in deep-sequencing data using GC-content normalization

Summary: We present a tool for control-free copy number alteration (CNA) detection using deep-sequencing data, particularly useful for cancer studies. The tool deals with two frequent problems in the analysis of cancer deep-sequencing data: absence of control sample and possible polyploidy of cancer cells. FREEC (control-FREE Copy number caller) automatically normalizes and segments copy number profiles (CNPs) and calls CNAs. If ploidy is known, FREEC assigns absolute copy number to each predicted CNA. To normalize raw CNPs, the user can provide a control dataset if available; otherwise GC content is used. We demonstrate that for Illumina single-end, mate-pair or paired-end sequencing, GC-contentr normalization provides smooth profiles that can be further segmented and analyzed in order to predict CNAs

WisecondorX : improved copy number detection for routine shallow whole-genome sequencing

Shallow whole-genome sequencing to infer copy number alterations (CNAs) in the human genome is rapidly becoming the method par excellence for routine diagnostic use. Numerous tools exist to deduce aberrations from massive parallel sequencing data, yet most are optimized for research and often fail to redeem paramount needs in a clinical setting. Optimally, a read depth-based analytical software should be able to deal with single-end and low-coverage datathis to make sequencing costs feasible. Other important factors include runtime, applicability to a variety of analyses and overall performance. We compared the most important aspect, being normalization, across six different CNA tools, selected for their assumed ability to satisfy the latter needs. In conclusion, WISECONDOR, which uses a within-sample normalization technique, undoubtedly produced the best results concerning variance, distributional assumptions and basic ability to detect true variations. Nonetheless, as is the case with every tool, WISECONDOR has limitations, which arise through its exclusiveness for non-invasive prenatal testing. Therefore, this work presents WisecondorX in addition, an improved WISECONDOR that enables its use for varying types of applications. WisecondorX is freely available at https://github.com/CenterForMedicalGeneticsGhent/WisecondorX

Statistical Methods For Genomic And Transcriptomic Sequencing

Part 1: High-throughput sequencing of DNA coding regions has become a common way of assaying genomic variation in the study of human diseases. Copy number variation (CNV) is an important type of genomic variation, but CNV profiling from whole-exome sequencing (WES) is challenging due to the high level of biases and artifacts. We propose CODEX, a normalization and CNV calling procedure for WES data. CODEX includes a Poisson latent factor model, which includes terms that specifically remove biases due to GC content, exon capture and amplification efficiency, and latent systemic artifacts. CODEX also includes a Poisson likelihood-based segmentation procedure that explicitly models the count-based WES data. CODEX is compared to existing methods on germline CNV detection in HapMap samples using microarray-based gold standard and is further evaluated on 222 neuroblastoma samples with matched normal, with focus on somatic CNVs within the ATRX gene. Part 2: Cancer is a disease driven by evolutionary selection on somatic genetic and epigenetic alterations. We propose Canopy, a method for inferring the evolutionary phylogeny of a tumor using both somatic copy number alterations and single nucleotide alterations from one or more samples derived from a single patient. Canopy is applied to bulk sequencing datasets of both longitudinal and spatial experimental designs and to a transplantable metastasis model derived from human cancer cell line MDA-MB-231. Canopy successfully identifies cell populations and infers phylogenies that are in concordance with existing knowledge and ground truth. Through simulations, we explore the effects of key parameters on deconvolution accuracy, and compare against existing methods. Part 3: Allele-specific expression is traditionally studied by bulk RNA sequencing, which measures average expression across cells. Single-cell RNA sequencing (scRNA-seq) allows the comparison of expression distribution between the two alleles of a diploid organism and thus the characterization of allele-specific bursting. We propose SCALE to analyze genome-wide allele-specific bursting, with adjustment of technical variability. SCALE detects genes exhibiting allelic differences in bursting parameters, and genes whose alleles burst non-independently. We apply SCALE to mouse blastocyst and human fibroblast cells and find that, globally, cis control in gene expression overwhelmingly manifests as differences in burst frequency

Change-point model on nonhomogeneous Poisson processes with application in copy number profiling by next-generation DNA sequencing

We propose a flexible change-point model for inhomogeneous Poisson Processes, which arise naturally from next-generation DNA sequencing, and derive score and generalized likelihood statistics for shifts in intensity functions. We construct a modified Bayesian information criterion (mBIC) to guide model selection, and point-wise approximate Bayesian confidence intervals for assessing the confidence in the segmentation. The model is applied to DNA Copy Number profiling with sequencing data and evaluated on simulated spike-in and real data sets.Comment: Published in at http://dx.doi.org/10.1214/11-AOAS517 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

cn.MOPS: mixture of Poissons for discovering copy number variations in next-generation sequencing data with a low false discovery rate

Quantitative analyses of next-generation sequencing (NGS) data, such as the detection of copy number variations (CNVs), remain challenging. Current methods detect CNVs as changes in the depth of coverage along chromosomes. Technological or genomic variations in the depth of coverage thus lead to a high false discovery rate (FDR), even upon correction for GC content. In the context of association studies between CNVs and disease, a high FDR means many false CNVs, thereby decreasing the discovery power of the study after correction for multiple testing. We propose ‘Copy Number estimation by a Mixture Of PoissonS’ (cn.MOPS), a data processing pipeline for CNV detection in NGS data. In contrast to previous approaches, cn.MOPS incorporates modeling of depths of coverage across samples at each genomic position. Therefore, cn.MOPS is not affected by read count variations along chromosomes. Using a Bayesian approach, cn.MOPS decomposes variations in the depth of coverage across samples into integer copy numbers and noise by means of its mixture components and Poisson distributions, respectively. The noise estimate allows for reducing the FDR by filtering out detections having high noise that are likely to be false detections. We compared cn.MOPS with the five most popular methods for CNV detection in NGS data using four benchmark datasets: (i) simulated data, (ii) NGS data from a male HapMap individual with implanted CNVs from the X chromosome, (iii) data from HapMap individuals with known CNVs, (iv) high coverage data from the 1000 Genomes Project. cn.MOPS outperformed its five competitors in terms of precision (1–FDR) and recall for both gains and losses in all benchmark data sets. The software cn.MOPS is publicly available as an R package at http://www.bioinf.jku.at/software/cnmops/ and at Bioconductor

Control-FREEC: a tool for assessing copy number and allelic content using next-generation sequencing data

Summary: More and more cancer studies use next-generation sequencing (NGS) data to detect various types of genomic variation. However, even when researchers have such data at hand, single-nucleotide polymorphism arrays have been considered necessary to assess copy number alterations and especially loss of heterozygosity (LOH). Here, we present the tool Control-FREEC that enables automatic calculation of copy number and allelic content profiles from NGS data, and consequently predicts regions of genomic alteration such as gains, losses and LOH. Taking as input aligned reads, Control-FREEC constructs copy number and B-allele frequency profiles. The profiles are then normalized, segmented and analyzed in order to assign genotype status (copy number and allelic content) to each genomic region. When a matched normal sample is provided, Control-FREEC discriminates somatic from germline events. Control-FREEC is able to analyze overdiploid tumor samples and samples contaminated by normal cells. Low mappability regions can be excluded from the analysis using provided mappability tracks

Transcriptome Sequencing for Precise and Accurate Measurement of Transcripts and Accessibility of TCGA for Cancer Datasets and Analysis

Next-generation sequencing (NGS) technologies are now well established and have become a routine analysis tool for its depth, coverage, and cost. RNA sequencing (RNA-Seq) has readily replaced the conventional array-based approaches and has become method of choice for qualitative and quantitative analysis of transcriptome, quantification of alternative spliced isoforms, identification of sequence variants, novel transcripts, and gene fusions, among many others. The current chapter discusses the multi-step transcriptome data analysis processes in detail, in the context of re-sequencing (where a reference genome is available). We have discussed the processes including quality control, read alignment, quantification of gene from read level, visualization of data at different levels, and the identification of differentially expressed genes and alternatively spliced transcripts. Considering the data that are freely available to the public, we also discuss The Cancer Genome Atlas (TCGA), as a resource of RNA-Seq data on cancer for selection and analysis in specific contexts of experimentation. This chapter provides insights into the applicability, data availability, tools, and statistics for a beginner to get familiar with RNA-Seq data analysis and TCGA

CONTRA: copy number analysis for targeted resequencing

Motivation: In light of the increasing adoption of targeted resequencing (TR) as a cost-effective strategy to identify disease-causing variants, a robust method for copy number variation (CNV) analysis is needed to maximize the value of this promising technology