CNV-guided multi-read allocation for ChIP-seq.

High-throughput sequencing (HTS) refers to the simultaneous sequencing of millions of fragments of DNA, which can be either assembled to reconstitute a genome, or aligned to an existing reference genome. The protocol can be extended to assay a wide variety of biological states of the cell, including DNA copy number, mRNA abundance and various properties of chromatin. HTS experiments allow for these biological states to be quantified as read counts at genome-wide scale with a single experiment. Though the experiments are expensive and often datasets are produced with limited sample size, information can be shared across thousands of genomic ranges in order to obtain robust models which control for technical biases. In this thesis, I present three statistical models for analyzing HTS read count data, aimed at answering concise biological questions. First, a hidden Markov model is developed for detecting copy number variants (CNVs) in individual samples while controlling for technical artifacts, such as variation in read counts due to local GC-content. Applied to a study of 248 male patients with X-linked intellectual disability, the model predicts 16 large CNVs, of which 10 candidate disease-causing CNVs were tested and all experimentally validated. The proposed software is then compared with state-of-the-art segmentation algorithms on normalized data, showing higher sensitivity while controlling the total rate of predicted CNVs. Second, improvements for parameter estimation are made for a statistical model of differential gene expression from RNA-Seq data. The improvements involve the use of empirical Bayes priors – priors estimated using the observations from all genes – in order to moderate otherwise noisy estimates of dispersion and fold changes for individual genes. The improved model shows increased sensitivity and more robust estimation of fold change in comparison with other differential expression software packages for RNA-Seq. Finally, a hierarchical Bayes model is used to associate transcription factor binding with chromatin and sequence features in regions of accessible chromatin. The hierarchical model incorporates three levels of parameters: one for individual experiments, one for experiments of the same cell type and one across all cell types. The model parameters are used to generate hypotheses regarding the DNA-binding behavior of a transcription factor, the glucocorticoid receptor. In summary, this thesis describes a set of statistical methods for HTS read count data which can be used across various biological domains. The methods form a framework for robust estimation of variables and hypothesis testing.%%%%Mit Hochdurchsatz-Sequenzierverfahren (HTS) bezeichnet man das gleichzeitige Sequenzieren von Millionen von DNA-Fragmenten, welche entweder zur Genomrekonstrution genutzt oder auf ein bestehendes Referenzgenom aligniert werden konnen. Das Protokoll kann erweitert werden, um verschiedene biologische Zustande der Zelle, wie z.B. die Anzahl an DNA-Kopien, mRNA-Abundanzen oder…

Next generation sequencing (NGS) technologies have profoundly impacted biological research and are becoming more and more popular due to cost effectiveness and their speed. NGS can be utilized to identify DNA structural variants, namely copy number variations (CNVs) which showed association with diseases like HIV, diabetes II, or cancer.There have been first approaches to detect CNVs in NGS data, where most of them detect a CNV by a significant difference of read counts within neighboring windows at the chromosome. However these methods suffer from systematical variations of the underlying read count distributions along the chromosome due to biological and technical noise. In contrast to these global methods, we locally model the read count distribution characteristics by a mixture of Poissons which allows to incorporate a linear dependence between copy numbers and read counts. Model selection is performed in a Bayesian framework by maximizing the posterior through an EM algorithm. We define a CNV call which indicates a deviation of the Poisson mixture parameters from the null hypothesis represented by the prior which is a model for constant copy number across the samples. A CNV call requires sufficient information in the data to push the model away from the null hypothesis given by the prior.We test our approach on the HapMap cohort where we rediscovered previously found CNVs which validates our approach. It is then tested on the tumor genome data set where we are able to considerably increase the detection while reducing the false discoveries.

Varying depth of high-throughput sequencing reads along a chromosome makes it possible to observe copy number variants (CNVs) in a sample relative to a reference. In exome and other targeted sequencing projects, technical factors increase variation in read depth while reducing the number of observed locations, adding difficulty to the problem of identifying CNVs. We present a hidden Markov model for detecting CNVs from raw read count data, using background read depth from a control set as well as other positional covariates such as GC-content. The model, exomeCopy, is applied to a large chromosome X exome sequencing project identifying a list of large unique CNVs. CNVs predicted by the model and experimentally validated are then recovered using a cross-platform control set from publicly available exome sequencing data. Simulations show high sensitivity for detecting heterozygous and homozygous CNVs, outperforming normalization and state-of-the-art segmentation methods.

Contigs assembled from the second generation sequencing short reads may contain misassemblies, and thus complicate downstream analysis or even lead to incorrect analysis results. Fortunately, with more and more sequenced species available, it becomes possible to use the reference genome of a closely related species to detect misassemblies. In addition, long reads of the third generation sequencing technology have been more and more widely used, and can also help detect misassemblies. Here, we introduce ReMILO, a reference assisted misassembly detection algorithm that uses both short reads and PacBio SMRT long reads. ReMILO aligns the initial short reads to both the contigs and reference genome, and then constructs a novel data structure called red-black multipositional de Bruijn graph to detect misassemblies. In addition, ReMILO also aligns the contigs to long reads and find their differences from the long reads to detect more misassemblies. In our performance test on short read assemblies of human chromosome 14 data, ReMILO can detect 41.8-77.9% extensive misassemblies and 33.6-54.5% local misassemblies. On hybrid short and long read assemblies of S.pastorianus data, ReMILO can also detect 60.6-70.9% extensive misassemblies and 28.6-54.0% local misassemblies. The ReMILO software can be downloaded for free under Artistic License 2.0 from this site: https://github.com/songc001/remilo. baoe@bjtu.edu.cn. Supplementary data are available at Bioinformatics online.

Copy number variations (CNVs) are increasingly recognized as an substantial source of individual genetic variation, and hence there is a growing interest in investigating the evolutionary history of CNVs as well as their impact on complex disease susceptibility. CNV/SNP haplotypes are critical for this research, but although many methods have been proposed for inferring integer copy number, few have been designed for inferring CNV haplotypic phase and none of these are applicable at genome-wide scale. Here, we present a method for inferring missing CNV genotypes, predicting CNV allelic configuration and for inferring CNV haplotypic phase from SNP/CNV genotype data. Our method, implemented in the software polyHap v2.0, is based on a hidden Markov model, which models the joint haplotype structure between CNVs and SNPs. Thus, haplotypic phase of CNVs and SNPs are inferred simultaneously. A sampling algorithm is employed to obtain a measure of confidence/credibility of each estimate. We generated diploid phase-known CNV-SNP genotype datasets by pairing male X chromosome CNV-SNP haplotypes. We show that polyHap provides accurate estimates of missing CNV genotypes, allelic configuration and CNV haplotypic phase on these datasets. We applied our method to a non-simulated dataset-a region on Chromosome 2 encompassing a short deletion. The results confirm that polyHap's accuracy extends to real-life datasets. Our method is implemented in version 2.0 of the polyHap software package and can be downloaded from http://www.imperial.ac.uk/medicine/people/l.coin.

Extensive Copy-Number Variation of the Human Olfactory Receptor Gene Family

Copy number variation (CNV) has been recently identified in human and other mammalian genomes and increasing awareness that CNV might be a major source for heritable variation in complex traits has emerged. Despite this, little has been published on CNVs in Holsteins. In order to fill this knowledge-gap, we proposed a genome-wide association study between quantitative trait loci (QTL) for economic traits and CNV in the Holstein cattle. The approved feasibility study was aimed at the genome-wide characterization of CNVs in Holstein cattle and at the demonstrating of their possible association with economic traits by performing the activities of preparation of DNA samples, Comparative Genomic Hybridization (CGH), initial association study between CNVs and production traits and characterization of CNVSNP associations. For both countries, 40 genomic DNA samples of bulls representing the extreme sub-populations for economically important traits were CGH analyzed using the same reference genome on a NimbleGen tiling array. We designed this array based on the latest build of the bovine genome (UMD3) with average probe spacing of 1150 bases (total number of probes was 2,166,672). Two CNV gene clusters, PLA2G2D on BTA2 and KIAA1683 on BTA7 revealed significant association with milk percentage and cow fertility, respectively, and were chosen for further characterization and verification in a larger sample using other methodologies including sequencing, tag SNPs and real time PCR (qPCR). Comparison between these four methods indicated that there is under estimation of the number of CNV loci in Holstein cattle and their complexity. The variation in sequence between different copies seemed to affect their functionality and thus the hybridization based methods were less informative than the methods that are based on sequencing. We thus conclude that large scale sequencing effort complemented by array CGH should be considered to better detect and characterize CNVs in order to effectively employ them in marker-assisted selection.

Copy number variation (CNV) serves as a crucial contributor to genetic diversity, exerting a profound influence on phenotypic diversity, traits of economic significance, and the evolutionary trajectory of livestock species. This study aimed to dissect the genome-wide CNV landscape of the Nanyang cattle line (Nanyang, Pinnan, and Xianan cattle) to identify functionally relevant CNVs associated with key economic traits and breed differentiation. In this study, 27 resequencing datasets were utilized to analyze the genome-wide distribution of CNVs in three breeds of Nanyang cattle (Nanyang cattle, Pinnan cattle, and Xianan cattle) based on the latest reference genome ARS-UCD2.0. This study identified a total of 97,564 CNVs, and after merging CNVs with overlapping genomic positions, we obtained 10,349 CNV regions (CNVRs), accounting for 1.48% of the reference genome. Functional enrichment analysis showed that CNVR genes were mainly involved in organ development, neural regulation, immune regulation, and metabolism. In addition, 131 CNVRs overlapped with 81 quantitative trait loci (QTLs), such as growth and carcass QTL, multiple birth QTL, tenderness score QTL, and antal follicle number QTL. Additionally, AOX1, KRT72, and ZBTB7C were found to overlap with body weight QTLs. Furthermore, a selective sweep analysis of CNVR revealed that numerous genes (KIF26A, SPINT4, OR5W1, etc.) exhibited divergent copy numbers between breeds. Conclusively, this study facilitates comprehension of the genetic characteristics of the Nanyang cattle line at the CNV level and furnishes valuable information for the advancement of the Nanyang cattle line breeding system.

In this article, we introduce a hierarchical clustering and Gaussian mixture model with expectation-maximization (EM) algorithm for detecting copy number variants (CNVs) using whole exome sequencing (WES) data. The R shiny package 'HCMMCNVs' is also developed for processing user-provided bam files, running CNVs detection algorithm and conducting visualization. Through applying our approach to 325 cancer cell lines in 22 tumor types from Cancer Cell Line Encyclopedia (CCLE), we show that our algorithm is competitive with other existing methods and feasible in using multiple cancer cell lines for CNVs estimation. In addition, by applying our approach to WES data of 120 oral squamous cell carcinoma (OSCC) samples, our algorithm, using the tumor sample only, exhibits more power in detecting CNVs as compared with the methods using both tumors and matched normal counterparts. HCMMCNVs R shiny software is freely available at github repository https://github.com/lunching/HCMM_CNVs.and Zenodo https://doi.org/10.5281/zenodo.4593371. Supplementary data are available at Bioinformatics online.

Copy number variations (CNVs) in the human genome provide exciting candidates for functional polymorphisms. Hence, we now assess association between CNV carrier status and diseases status by evaluating the signal intensity of SNP genotyping assays. Here, we present a novel statistical method designed to perform such inference and apply this method to a known CNV in a bipolar disorder linkage region. Using Bayesian computations we calculate the posterior probability for carrier status of a CNV in each individual of a sample by jointly analyzing genotype information and hybridization intensity. We model the signal intensity as a mixture of normal distributions, allowing for locus-specific and allele-specific distributions. Using an expectation maximization algorithm we estimate the parameters of these distributions and use these estimates for inferring carrier status of each individual and for the boundaries of the CNV. We applied the method to a sample of 3,512 individuals to a previously described common deletion on 8q24, a region consistently showing linkage to bipolar disorder, and unambiguously inferred 172 heterozygous and 1 homozygous deletion carrier. We observed no significant association between bipolar disorder and carrier status. We carefully assessed the validity of the inferred carrier status and observed no indication of errors. Furthermore, the algorithm precisely identifies the boundaries of the CNV. Finally, we assessed the power of this algorithm to detect shorter CNVs by sub-sampling from the SNPs covered by this deletion, demonstrating that our EM algorithm produces precise estimates of carrier status.

Only a few large systematic studies have evaluated the impact of copy number variants (CNVs) on common diseases. Several million individuals have been genotyped on single nucleotide variation arrays, which could be used for genome-wide CNVs association studies. However, CNV calls remain prone to false positives and only empirical filtering strategies exist in the literature. To overcome this issue, we defined a new quality score (QS) estimating the probability of a CNV called by PennCNV to be confirmed by other software. Out-of-sample comparison showed that the correlation between the consensus CNV status and the QS is twice as high as it is for any previously proposed CNV filters. ROC curves displayed an AUC higher than 0.8 and simulations showed an increase up to 20% in statistical power when using QS in comparison to other filtering strategies. Superior performance was confirmed also for alternative consensus CNV definition and through improving known CNV-trait associations. http://goo.gl/T6yuFM CONTACT: zoltan.kutalik@unil.ch or aurelien@mace@unil.chSupplementary information: Supplementary data are available at Bioinformatics online.

Deciphering new insights into copy number variations as drivers of genomic diversity and adaptation in farm animal species.

Next generation sequencing (NGS) technologies have enabled cheap, large-scale, and high-throughput production of short DNA sequence reads and thereby have promoted the explosive growth of data volume. Unfortunately, the produced reads are short and prone to contain errors that are incurred during sequencing cycles. Both large data volume and sequencing errors have complicated the mapping of NGS reads onto the reference genome and have motivated the development of various aligners for very short reads, typically less than 100 base pairs (bps) in length. As read length continues to increase, propelled by advances in NGS technologies, these longer reads tend to have higher sequencing error rates and more true mutations (including substitutions, insertions, or deletions) to the genome. Such new characteristics make inefficient the aligners, which are optimized for very short reads and support only ungapped alignments or gapped alignments with very limited number of gaps (typically one gap), and thereby call for new aligners with fully gapped alignment supported. In this chapter, we present the CUSHAW software suite for NGS read alignment, which is open-source and consists of three individual aligners: CUSHAW, CUSHAW2, and CUSHAW3. This suite offers parallel and efficient NGS read alignments to large genomes, such as the human genome, by harnessing multi-core CPUs or compute unified device architecture (CUDA)-enabled graphics processing units (GPUs). Moreover, it has the capability to align both base-space and color-space reads and is consistently shown to be one of the best alignment tools through our performance evaluations.

Single nucleotide polymorphism (SNP) array is the most widely used platform to assess somatic copy number variations (CNVs) in cancer studies. Many SNP data-based CNV callers are available, however, the false positive rates from automated calling are commonly high, and reported breakpoints can be inaccurate. Manual review for each reported CNV by visualizing the SNP data is important, but is challenging for users lacking computational experience. To address this, we present a Shiny/R application ShinyCNV, an interactive graphical user interface to view and annotate CNVs. With this application, normalized SNP data, which includes log R ratio (LRR) and B allele frequency, can be plotted against the reported CNVs, and users can visually check the reliability of CNVs per se or adjust the incorrectly assigned breakpoints. Further, the interactive LRR spectrum panel within ShinyCNV can facilitate the process to identify commonly affected CNV regions from a group of samples, and to visually check if important focal gains/losses are missing from reported CNVs. ShinyCNV is designed to be intuitive for cancer researchers and can be easily installed for either personal use or deployed on servers to provide online service. ShinyCNV and the tutorial are freely available from https://github.com/gzhmat/ShinyCNV. Supplementary data are available at Bioinformatics online.

Motivation and Objectives The output of a high throughput next generation sequencing (NGS) machine is a collection of short reads, which have to be properly assembled in order to reconstruct the original DNA sequence of the analyzed organism (Metzker, 2010; Earl, 2011). The DNA sequence assembly process is based on aligning and merging these reads for effectively reconstructing the real primary structure of the DNA sample sequence or reference genome. The use of NGS machines results in much larger sets of reads to be assembled, posing new problems for computer scientists and bioinformaticians. In particular, a relevant issue is related with the trade-off between precision of the assembly process and its computational time, stating the need for faster methods that can keep pace with the speed and volume of reads that are generated with NGS. An important step in DNA assembly is the identification of a subset of read pairs that have a high probability of being aligned sequentially in the reconstruction. Such step is often referred to as filtering, and amounts in selecting a significantly smaller subset of the initial set of read pairs (whose dimension is quadratic in the number of initial reads) that can be then processed by an alignment algorithm, usually quite time consuming. The desired effect of filtering is then to quickly filter out from the candidate set of read pairs those that would not provide a good alignment in the following phase. The computation cost of filtering should then be balanced by the speed-up obtained when a smaller set of read pairs is considered for alignment. In this work we propose and test the use of alignment free distances to evaluate the similarity between two short reads as a technique for filtering good read pairs to be assembled. The method operates in constant time in the string length and is tested in its ability to emulate, with a proper level of precision, much more time consuming methods to evaluate the similarity between short DNA sequences, such as the established Needleman-Wunsch edit distance (Needleman, Wunsch, and Christian, 1970), often used in the final step of the assembly procedure. These preliminary experiments show the efficacy of this approach for filtering the promising read pairs - eligible candidates to successfully assemble the entire genome of a given organism. Therefore, the alignment free reads filtering may significantly accelerate the assembly process without a substantial loss in accuracy of the DNA sample sequence reconstruction. Methods ODNA sequence assembly The DNA sequence assembly process is based on the alignment and merging of reads (stretch of sequences) in order to reconstruct the original primary structure of the DNA sample sequences. Given a set of sequences S = {s1, s2, …, sn}, where s ∈ S is a fragment of the primary structure of DNA (read) (e.g. s = {ATTCGA...CTGACT}), assembly is in charge of building the longest sequence from the set S where each pair of consequent reads obey certain similarity conditions. DNA read pairs filtering and Alignment Free Distance This step identifies the promising read pairs in order to reduce the amount of input data given to the real assembly algorithm. We adopted a very quick measure of the similarity between two reads, Alignment Free (AF) based distance (Vinga and Almeida, 2003). AF computes the similarity of two strings based only on the dictionary of their substrings, irrespective of their relative position. As a dictionary we considered the set of 4-mer (sequences composed of 4 different nucleotides) and then built a profile for each read composed by the relative frequencies of each 4-mer in the read. The Euclidean distance between the profiles of two reads was taken as an inverse measure of the similarity of the two reads and thus as an indication that the two reads formed a promising pair to be considered in the assembly phase. AF filtering was then used defining a proper threshold on the AF distance and discarding all the pairs that exhibited a AF distance above the threshold. Computational complexity of AF distance is a constant linearly bounded by the number of k-mers adopted and the length of the strings to be compared. Comparing with other distances: Needleman-Wunsch and “Bowtie” distance Along with AF we considered the well-established Needleman-Wunsch edit distance (NW) and compared them in their ability to identify significant pairs. This comparison was based on the computation of a sort of perfect distance computed after an alignment over an already known sequence has been performed. Such distance, referred to as Bowtie distance (BT), was obtained as follows: a. a large number of reads coming from a known sequence were considered; b. hese reads were aligned over the known sequence using the standard Bowtie algorithm (Langmead et al, 2009); c. any two reads received a maximum BT distance if their alignment did not intersect over the reference sequence, else they received a distance inversely proportional to their intersection over the sequence (e.g., they would have BT distance equal to 0 if they were aligned one on top (or inside) of the other by the Bowtie algorithm). By construction we assumed BT distance to be the reference distance, e.g., the distance that expressed the best possible alignments - being based on the knowledge of the reference sequence - and tested the correlation of AF and NW with BT; moreover, we verified the ability of AF and NW to predict that a given read pair had BT distance above or below a given threshold. Results and Discussion For our test we considered the E.Coli genome and a set of reads from this genome reads obtained by Roche 454 sequencing machine. Reads have average length of ~235 nucleotides and standard deviation of approx. 10 (the large majority of them having length in the interval 225-245). Reads were aligned with the reference sequence with Bowtie and then 100,000 were sampled at random according to their alignment along the sequence. Reads were considered both forward and reversed, giving rise to a total of 200,0002 read pairs. All 620,798 read pairs with BT distance < 1 were considered for the experiments; then, out of the remaining pairs, 233,099 were sampled at random. A total of 853,897 read pairs composed the working data set. For all these reads, NW distance and AF distance over the 4-mer were computed. AF, NW and BT distances were all normalized between 0 (maximal similarity) and 1 (maximal dissimilarity). The first interesting results was that the correlation between distances showed that AF approximates BT somehow better than NW: we obtained a correlation coefficient of 0.761 for AF and BT, compared with a smaller 0.706 when NW and BT were considered (coherently, correlation between AF and NW is 0,721). The second interesting results was obtained when we compared the ability of AF and NW to predict whether BT was above or below a given threshold. We defined a threshold predictor for a given function F2 based on function F1 and on a given pair a1, a2 as follows: if (F1 < a1) then predict (F2 < a2), else predict (F2 ≥ a2). To a given pair (a1, a2), we associated the measure of True Positive rate (TP) (percentage of cases where (F1 < a1) and (F2 < a2) and of True Negative rate (TN) (percentage of cases where (F1 ≥ a1) and (F2 ≥ a2); analogously we defined False Positive rate (FP) and False Negative rate (FN). For each (a1, a2) with both values ranging from 0 to 1, we then computed, with step 0.05, the positive and negative error rates taking AF as a predictor of BT and NW as a predictor of BT. Part of the results are summarized in the charts of Figure 1, that show for 3 different levels of a2 (0.1, 0.2, and 0.3) the precision of the predictors (y-axis) when the value of a1 is changed (x-axis), both when AF is used as a predictor of BT (charts in the first row) and when NW is used as a predictor of BT (charts in second row). Similar results are obtained also for other levels of a2, here omitted for brevity. The curves bring to light very clearly how AF is a very good threshold predictor for BT for the considered data; despite its light computational complexity, it appears to perform significantly better than the more complex NW edit distance when its ability to support a threshold predictor is considered. Acknowledgements The authors are partially supported by the FLAGSHIP ”InterOmics” project (PB.P05) funded by the Italian MIUR and CNR institutions, and by the cooperative programme 2010–2012 between the National Research Council of Italy (CNR) and the Polish Academy of Sciences (PAN). References Earl D et al (2011); Assemblathon 1: A competitive assessment of de novo short read assembly methods; Genome Research, 21 Langmead B, Trapnell C, et al (2009); Ultrafast and memory-efficient alignment of short DNA sequences to the human genome; Genome Biology, 10:R25 Metzker ML (2010); Sequencing technologies — the next generation; Nat Rev Genet., 11(1) Needleman SB, Wunsch CD. (1970). A general method applicable to the search for similarities in the amino acid sequence of two proteins. Journal of Molecular Biology 48(3): 443–53 Vinga S, Almeida J (2003); Alignment-free sequence comparison—a review, Bioinformatics 19 (4), 513-523 Note: Figures and tables are available in PDF version only.