차세대 시퀀싱 데이터에 대한 SNV/InDel 호출 및 하플로타이핑의 새로운 접근 방법

학위논문 (박사) -- 서울대학교 대학원 : 공과대학 전기·컴퓨터공학부, 2021. 2. 박근수.차세대 시퀀싱 데이터에 대한 수많은 변이 호출 알고리즘이 개발되어 왔다. 대다수 변이 호출 알고리즘은 신뢰할 수 있지만 추가적인 성능 향상을 위한 여지는 남아있다. 특히, 낮은 리드 깊이를 가지는 데이터에 대한 변이 호출과 체세포 변이 호출은 개선될 여지가 많다. 첫 번째로, 본 논문에서는 위 양성 변이를 제거하여 변이 호출의 정밀도를 개선하는 새로운 알고리즘인 RDscan을 제안한다. RDscan은 잘못 정렬된 리드를 제거하고 리드를 재배치 한 후, 리드 깊이 분포에 기반한 변이의 신뢰도 점수를 계산하여 위 양성 변이를 제거한다. 우리는 최신 변이 호출 알고리즘들을 사용하여 RDscan의 성능을 평가하였다. 1000 게놈 프로젝트와 일루미나의 데이터세트에 대하여 RDscan을 통한 추가적인 변이 필터링은 테스트에 사용된 대부분의 변이 호출 알고리즘의 정확성을 개선시켰다. 생식세포 변이에 대한 호출은 12건의 테스트 중 11건, 체세포 변이 대한 호출은 24건의 테스트 중 21건에서 정확성이 증가되었다. 알려진 골드 스탠다드 변이 세트를 사용하여 각 알고리즘의 파라미터 최적화를 통해 생성된 최적의 변이 세트에 대해서도, RDscan 은 생식세포 변이에 대한 12건 중 5건, 체세포 변이에 대한 24 건 중 21건에서 변이 호출 정확성을 개선하였다. 임상 및 연구에서는 단일 게놈 가닥에 존재하는 변이의 세트 정보 (하플로타이핑)를 필요로 하는 경우가 있다. 특히 인간 백혈구 항원 유전자들에 대한 하플로타이핑은 실제 임상에서 다루는 중요한 문제이다. 기존 연구들은 차세대 시퀀싱 기반 알고리즘이 인간 백혈구 항원 유전자에 대한 하플로타이핑을 수행하기에 적합함을 보여주었다. 하지만, 하플로타이핑의 정확성을 저하시키는 대립 유전자의 상 조정 문제를 해결한 알고리즘은 없다. 두 번째로, 본 논문에서는 차세대 시퀀싱 데이터로부터 인간 백혈구 항원 유전자들에 대한 하플로타이핑을 수행하는 새로운 알고리즘인 HLAscan을 소개한다. HLAscan은 ImMunoGeneTics 프로젝트에서 제공하는 IMGT/HLA 데이터베이스의 인간 백혈구 항원 유전자 서열들에 대해 개인의 유전체 리드를 정렬한다. 그 후, 정렬된 리드의 분포에 기반한 점수를 사용하여 올바른 대립유전자의 상을 결정할 수 있다. HLAscan을 통한 하플로타이핑은1000 게놈 프로젝트와 HapMap 프로젝트의 공식 데이터세트에 대해서 기존의 차세대 시퀀싱 기반 알고리즘들보다 높은 정확성을 보여주었다. 또한 HiSeq X-TEN으로 생성한 아홉 가족의 데이터세트에 대해서, HLAscan을 사용한 하플로타이핑 결과는 96.9%의 정확성을 보였고, 그 중 90× 이상의 높은 리드 깊이를 가지는 데이터세트에 대해서는 100% 정확성을 보였다.Several tools have been developed for calling variants from next-generation sequencing data. Although they are generally accurate and reliable, most of them have room for improvement, especially in regard to calling variants in datasets with low read depth coverage. In addition, the somatic variants predicted by several somatic variant callers tend to have very low concordance rates. First, we propose a new tool (RDscan) for improving germline and somatic variant calling in next-generation sequencing data. RDscan removes misaligned reads, repositions reads, and calculates RDscore based on the read depth distribution. With RDscore, RDscan improves the precision of variant callers by removing false variants. When we tested our new tool using the latest variant calling algorithms, accuracy was improved for most of the algorithms. After screening variants with RDscan, calling accuracies increased for germline variants in 11 out of 12 cases and for somatic variants in 21 out of 24 cases. For the best set of variants produced by optimizing the parameters of each algorithm using the known truth sets, RDscan increased the calling accuracies for germline variants in 5 out of 12 cases and for somatic variants in 21 out of 24 cases. Some applications require information on multiple variants in a single genome strand (haplotyping). In particular, precise haplotyping for human leukocyte antigen genes is of great clinical importance. Several recent studies showed that next-generation sequencing based method is a feasible and promising technique for haplotyping of human leukocyte antigen genes. To date, however, no method with sufficient read depth has completely solved the allele phasing issue. Second, we developed a new method (HLAscan) for HLA haplotyping using NGS data. HLAscan performs alignment of reads to HLA sequences from human leukocyte antigen (IMGT/HLA) database in the international ImMunoGeneTics project. The distribution of aligned reads was used to calculate a score function to determine correctly phased alleles by progressively removing false-positive alleles. Comparative HLA typing tests using public datasets from the 1000 Genomes Project and the International HapMap Project demonstrated that HLAscan could perform HLA typing more accurately than previously reported NGS-based methods. We also applied HLAscan to a family dataset with various coverage depths generated on the Illumina HiSeq X-TEN platform. HLAscan identified allele types of HLA-A, -B, -C, -DQB1, and -DRB1 with 100% accuracy for sequences at ≥ 90× depth, and the overall accuracy was 96.9%.Abstract i Contents iii List of Figures iv List of Tables vii Chapter 1 Introduction 1 1.1 Background 1 1.2 Problem Statement 7 1.3 Previous Works and New Results 8 1.4 Organization 10 Chapter 2 SNV and InDel Calling 11 2.1 Preliminaries 11 2.2 Germline Variant Calling Algorithm 16 2.3 Somatic Variant Calling Algorithm 21 2.4 Results 22 2.5 Discussions 46 Chapter 3 Haplotyping for MHC region 48 3.1 Preliminaries 48 3.2 Haplotyping Algorithm 52 3.3 Results 58 3.4 Discussions 72 Chapter 4 Conclusion 74 4.1 Summary 74 4.2 Future Directions 76 Bibliography 78Docto

Introducing deep learning -based methods into the variant calling analysis pipeline

Biological interpretation of the genetic variation enhances our understanding of normal and pathological phenotypes, and may lead to the development of new therapeutics. However, it is heavily dependent on the genomic data analysis, which might be inaccurate due to the various sequencing errors and inconsistencies caused by these errors. Modern analysis pipelines already utilize heuristic and statistical techniques, but the rate of falsely identified mutations remains high and variable, particular sequencing technology, settings and variant type. Recently, several tools based on deep neural networks have been published. The neural networks are supposed to find motifs in the data that were not previously seen. The performance of these novel tools is assessed in terms of precision and recall, as well as computational efficiency. Following the established best practices in both variant detection and benchmarking, the discussed tools demonstrate accuracy metrics and computational efficiency that spur further discussion

Parsimony-based genetic algorithm for haplotype resolution and block partitioning

This dissertation proposes a new algorithm for performing simultaneous haplotype resolution and block partitioning. The algorithm is based on genetic algorithm approach and the parsimonious principle. The multiloculs LD measure (Normalized Entropy Difference) is used as a block identification criterion. The proposed algorithm incorporates missing data is a part of the model and allows blocks of arbitrary length. In addition, the algorithm provides scores for the block boundaries which represent measures of strength of the boundaries at specific positions. The performance of the proposed algorithm was validated by running it on several publicly available data sets including the HapMap data and comparing results to those of the existing state-of-the-art algorithms. The results show that the proposed genetic algorithm provides the accuracy of haplotype decomposition within the range of the same indicators shown by the other algorithms. The block structure output by our algorithm in general agrees with the block structure for the same data provided by the other algorithms. Thus, the proposed algorithm can be successfully used for block partitioning and haplotype phasing while providing some new valuable features like scores for block boundaries and fully incorporated treatment of missing data. In addition, the proposed algorithm for haplotyping and block partitioning is used in development of the new clustering algorithm for two-population mixed genotype samples. The proposed clustering algorithm extracts from the given genotype sample two clusters with substantially different block structures and finds haplotype resolution and block partitioning for each cluster

The genomic diversification of grapevine clones.

BACKGROUND:Vegetatively propagated clones accumulate somatic mutations. The purpose of this study was to better appreciate clone diversity and involved defining the nature of somatic mutations throughout the genome. Fifteen Zinfandel winegrape clone genomes were sequenced and compared to one another using a highly contiguous genome reference produced from one of the clones, Zinfandel 03. RESULTS:Though most heterozygous variants were shared, somatic mutations accumulated in individual and subsets of clones. Overall, heterozygous mutations were most frequent in intergenic space and more frequent in introns than exons. A significantly larger percentage of CpG, CHG, and CHH sites in repetitive intergenic space experienced transition mutations than in genic and non-repetitive intergenic spaces, likely because of higher levels of methylation in the region and because methylated cytosines often spontaneously deaminate. Of the minority of mutations that occurred in exons, larger proportions of these were putatively deleterious when they occurred in relatively few clones. CONCLUSIONS:These data support three major conclusions. First, repetitive intergenic space is a major driver of clone genome diversification. Second, clones accumulate putatively deleterious mutations. Third, the data suggest selection against deleterious variants in coding regions or some mechanism by which mutations are less frequent in coding than noncoding regions of the genome

A machine learning pipeline for quantitative phenotype prediction from genotype data

<p>Abstract</p> <p>Background</p> <p>Quantitative phenotypes emerge everywhere in systems biology and biomedicine due to a direct interest for quantitative traits, or to high individual variability that makes hard or impossible to classify samples into distinct categories, often the case with complex common diseases. Machine learning approaches to genotype-phenotype mapping may significantly improve Genome-Wide Association Studies (GWAS) results by explicitly focusing on predictivity and optimal feature selection in a multivariate setting. It is however essential that stringent and well documented Data Analysis Protocols (DAP) are used to control sources of variability and ensure reproducibility of results. We present a genome-to-phenotype pipeline of machine learning modules for quantitative phenotype prediction. The pipeline can be applied for the direct use of whole-genome information in functional studies. As a realistic example, the problem of fitting complex phenotypic traits in heterogeneous stock mice from single nucleotide polymorphims (SNPs) is here considered.</p> <p>Methods</p> <p>The core element in the pipeline is the L1L2 regularization method based on the naïve elastic net. The method gives at the same time a regression model and a dimensionality reduction procedure suitable for correlated features. Model and SNP markers are selected through a DAP originally developed in the MAQC-II collaborative initiative of the U.S. FDA for the identification of clinical biomarkers from microarray data. The L1L2 approach is compared with standard Support Vector Regression (SVR) and with Recursive Jump Monte Carlo Markov Chain (MCMC). Algebraic indicators of stability of partial lists are used for model selection; the final panel of markers is obtained by a procedure at the chromosome scale, termed ’saturation’, to recover SNPs in Linkage Disequilibrium with those selected.</p> <p>Results</p> <p>With respect to both MCMC and SVR, comparable accuracies are obtained by the L1L2 pipeline. Good agreement is also found between SNPs selected by the L1L2 algorithms and candidate loci previously identified by a standard GWAS. The combination of L1L2-based feature selection with a saturation procedure tackles the issue of neglecting highly correlated features that affects many feature selection algorithms.</p> <p>Conclusions</p> <p>The L1L2 pipeline has proven effective in terms of marker selection and prediction accuracy. This study indicates that machine learning techniques may support quantitative phenotype prediction, provided that adequate DAPs are employed to control bias in model selection.</p

Analysis of NGS Data from Immune Response and Viral Samples

This thesis is devoted to designing and applying advanced algorithmical and statistical tools for analysis of NGS data related to cancer and infection diseases. NGS data under investigation are obtained either from host samples or viral variants. Recently, random peptide phage display libraries (RPPDL) were applied to studies of host\u27s antibody response to different diseases. We study human antibody response to breast cancer and mouse antibody response to Lyme disease by sequencing of the whole antibody repertoire profiles which are represented by RPPDL. Alternatively, instead of sequencing immune response NGS can be applied directly to a viral population within an infected host. Specifically, we analyze the following RNA viruses: the human immunodeficiency virus (HIV) and the infectious bronchitis virus (IBV). Sequencing of RNA viruses is challenging because there are many variants inside population due to high mutation rate. Our results show that NGS helps to understand RNA viruses and explore their interaction with infected hosts. NGS also helps to analyze immune response to different diseases, trace changing of immune response at different disease stages