GANDAFL: Dataflow Acceleration for Short Read Alignment on NGS Data

Sensitive detection of internal tandem duplication (ITD) mutation of FLT3 is very important in acute myeloid leukemia. To increase detection sensitivity of FLT3-ITD, we developed new detection algorithm using next generation sequencing (NGS) data. We validated results using nested polymerase chain reaction (PCR) methods. We compared results of NGS data, nested PCR and conventional PCR methods. First, using whole exome sequencing data of 83 AML patients, we applied calling algorithm for FLT3-ITD. Briefly, to detect ITDs with NGS data, the reads are aligned to a reference sequence (UCSC hg19), with BWA which is a read aligner allowing soft-clipping. Some reads can be an indication of the occurrence of ITD and BWA aligns those reads as soft-clipped. Second, we deigned two types of primer for Nested PCR. The first primer was targeted wildly for between exon14 and exon15 of FLT3 gene. Nested PCR primer was deigned to target previously reported regions which are frequently occurred ITD mutation. PCR reactions of two steps were performed using the PCR primers sequentially. In these 83 patients, FLT3-ITD was positive only in 7 patients when tested by conventional PCR methods. When NGS detection method was applied, this resulted in positive FLT3-ITD in 11 patients (11/83, 13%). When validation was performed using nested PCR, FLT3-ITD was confirmed in all of 11 patients. Nested PCR detected additional 4 patient positive for FLT3-ITD in this population. For 68 patients, FLT3-ITD was negative by both NGS and nested PCR method. Overall, NGS method improved sensitivity of FLT3-ITD detection by 57% in this population. And the concordance rate of NGS method and nested PCR was 95.2% (79/83). Then we investigated clinical significance of sensitive FLT3-ITD detection. For this, we performed nested PCR and conventional PCR at the same time in 238 AML patients to detect FLT3-ITD. Positive rate for FLT3-ITD was 20% (48/238) and 10% (24/238) by nested PCR and conventional PCR respectively. When survival analysis was performed, among patients with negative FLT3-ITD result by conventional PCR, patients who showed positive for FLT3-ITD by nested PCR had shorter overall survival compared to those who showed negative for FLT3-ITD by nested PCR. (p = 0.03). This implies that sensitive FLT3-ITD detection using nested PCR is clinically meaningful. Diagnosis of FLT3-ITD is very important genetic factor, leading a therapeutic direction for AML patient. Here we report that we have developed alternative more sensitive detection methods for FLT3-ITD based on nested PCR and NGS. Sensitive detection of FLT3-ITD was clinically meaningful, suggesting that these methods should be incorporated in a future clinical practice. Also, we want to note that, NGS method is capable of quantifying FLT3-ITD size and amount in AML patients. Citation Format: Daeyoon Kim, Yoojin Hong, Youngil Koh, Sung-Soo Yoon, Choong-Hyun Sun, Kwang-Sung Ahn, Seungmook Lee, Hongseok Yun, Suyeon Lee. Improved sensitive detection method of FLT3 (FMS-like tyrosine kinase) internal tandem duplication (ITD) mutation using next-generation sequencing technology and nested PCR. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr 3186.

The next-generation sequencing (NGS) technology has revolutionized research in genetics and genomics, resulting in massive NGS data and opening more fronts to answer unresolved issues in genetics. NGS data are usually stored at three levels: image files, sequence tags, and alignment reads. The sizes of these types of data usually range from several hundreds of gigabytes to several terabytes. Biostatisticians and bioinformaticians are typically working with the aligned NGS read count data (hence the last level of NGS data) for data modeling and interpretation.To horn in on the use of NGS technology, researchers utilize it to profile the whole genome to study DNA copy number variations (CNVs) for an individual subject (or patient) as well as groups of subjects (or patients). The resulting aligned NGS read count data are then modeled by proper mathematical and statistical approaches so that the loci of CNVs can be accurately detected. In this book chapter, a summary of most popularly used statistical methods for detecting CNVs using NGS data is given. The goal is to provide readers with a comprehensive resource of available statistical approaches for inferring DNA copy number variations using NGS data.

Computational analysis of next generation sequencing data and its applications in clinical oncology

In medical and genomic research, the Next Generation Sequencing (NGS) has a major role. Presently, NGS data are produced at the rate of 10 TB a day and challenge the storage and data processing capacities. These huge datasets are being used by a wide sort of applications such as customized cancer healing and precision medicine. NGS technologies offer prospects for understanding unidentified species and complex syndrome. To utilize genomic data for such applications, the genomic data in the form of short reads produced by NGS initially has to be assembled into whole genome sequence. And then, the sequences have to be compared for similarity and variation discovery which will be useful for analyzing and arriving at health-related solutions. In this paper, the fundamental methods for short read alignment such as assembly-based and alignment-based methods are discussed. Followed by which, the different ways to compare the sequences to check the alignment for similarity/dissimilarity discovery are discussed. This comparative analysis report can be utilized for health-related medical decisions.

Motivation. The third generation sequencing (3GS) technology generates long sequences of thousands of bases. However, its current error rates are estimated in the range of 15–40%, significantly higher than those of the prevalent next generation sequencing (NGS) technologies (less than 1%). Fundamental bioinformatics tasks such as de novo genome assembly and variant calling require high-quality sequences that need to be extracted from these long but erroneous 3GS sequences.Results. We describe a versatile and efficient linear complexity consensus algorithm Sparc to facilitate de novo genome assembly. Sparc builds a sparse k-mer graph using a collection of sequences from a targeted genomic region. The heaviest path which approximates the most likely genome sequence is searched through a sparsity-induced reweighted graph as the consensus sequence. Sparc supports using NGS and 3GS data together, which leads to significant improvements in both cost efficiency and computational efficiency. Experiments with Sparc show that our algorithm can efficiently provide high-quality consensus sequences using both PacBio and Oxford Nanopore sequencing technologies. With only 30× PacBio data, Sparc can reach a consensus with error rate <0.5%. With the more challenging Oxford Nanopore data, Sparc can also achieve similar error rate when combined with NGS data. Compared with the existing approaches, Sparc calculates the consensus with higher accuracy, and uses approximately 80% less memory and time.Availability. The source code is available for download at https://github.com/yechengxi/Sparc.

P842: Unified comprehensive analysis of NGS and optical genome mapping data for constitutional applications using Bionano VIA software

Schizophrenia is thought to be the most prevalent chronic psychiatric disorder. Researchers have identified numerous proteins associated with the occurrence and development of schizophrenia. This study aimed to identify potential core genes and pathways involved in schizophrenia through exhaustive bioinformatics and next generation sequencing (NGS) data analyses using GSE106589 NGS data of neural progenitor cells and neurons obtained from healthy controls and patients with schizophrenia. The NGS data were downloaded from the Gene Expression Omnibus database. NGS data was processed by the DESeq2 package in R software, and the differentially expressed genes (DEGs) were identified. Gene ontology (GO) enrichment analysis and REACTOME pathway enrichment analysis were carried out to identify potential biological functions and pathways of the DEGs. Protein-protein interaction network, module, micro-RNA (miRNA)-hub gene regulatory network, transcription factor (TF)-hub gene regulatory network, and drug-hub gene interaction network analysis were performed to identify the hub genes, miRNA, TFs, and drug molecules. Potential hub genes were analyzed using receiver operating characteristic curves in the R package. In this investigation, an overall 955 DEGs were identified: 478 genes were remarkably upregulated and 477 genes were distinctly downregulated. These genes were enriched for GO terms and pathways mainly involved in the multicellular organismal process, G protein-coupled receptor ligand binding, regulation of cellular processes, and amine ligand-binding receptors. MYC, FN1, CDKN2A, EEF1G, CAV1, ONECUT1, SYK, MAPK13, TFAP2A, and BTK were considered the potential hub genes. The MiRNA-hub gene regulatory network, TF-hub gene regulatory network, and drug-hub gene interaction network were constructed successfully and predicted key miRNAs, TFs, and drug molecules for schizophrenia diagnosis and treatment. On the whole, the findings of this investigation enhance our understanding of the potential molecular mechanisms of schizophrenia and provide potential targets for further investigation.

Collection and storage of HLA NGS genotyping data for the 17th International HLA and Immunogenetics Workshop

Gene fusions are important genomic events in human cancer because their fusion gene products can drive the development of cancer and thus are potential prognostic tools or therapeutic targets in anti-cancer treatment. Major advancements have been made in computational approaches for fusion gene discovery over the past 3 years due to improvements and widespread applications of high-throughput next generation sequencing (NGS) technologies. To identify fusions from NGS data, existing methods typically leverage the strengths of both sequencing technologies and computational strategies. In this article, we review the NGS and computational features of existing methods for fusion gene detection and suggest directions for future development.

Next generation sequencing as a suitable analysis for authentication of degraded and ancient DNA

Next generation sequencing (NGS) technologies have generated enormous amount of shotgun read data and assembly of the reads can be challenging, especially for organisms without template sequences. We study the power of genome comparison based on shotgun read data without assembly using three alignment-free sequence comparison statistics, D2, D2*, and D2S, both theoretically and by simulations. Theoretical formulas for the power of detecting the relationship between two sequences related through a common motif model are derived. It is shown that both D2* and D2S outperform D2 for detecting the relationship between two sequences based on NGS data. We then study the effects of length of the tuple, read length, coverage, and sequencing error on the power of D2* and D2S. Finally, variations of these statistics, d2, d2* and d2S, respectively, are used to first cluster 5 mammalian species with known phylogenetic relationships and then cluster 13 tree species whose complete genome sequences are not available using NGS shotgun reads. The clustering results using d2S are consistent with biological knowledge for the 5 mammalian and 13 tree species, respectively. Thus, the statistic d2S provides a powerful alignment-free comparison tool to study the relationships among different organisms based on NGS read data without assembly.

Leveraging The Old With The New: Exploring and Integrating Historic Microarray Studies With Next Generation Sequencing For Multiple Myeloma

Genetic heterogeneity of rare mutations with severe effects is more commonly being viewed as a major component of disease[1]. Autism is an excellent example where research is active in identifying matches between the phenotypic and genomic heterogeneities. A substantial portion of autism appears to be correlated with copy number variation which is not directly probed by high-throughput next generation sequencing (NGS) or single nucleotide polymorphism (SNP) array technologies[2]. Furthermore, de novo and mapping based genome assembly methods produce phase ambiguous assemblies due to the limitations of current sequencing technologies. As a result, phase-dependent interactions between SNP variants may hide complex genetic heterogeneities associated with disease. Thus, identifying the genetic heterogeneity of complex disease remains a major unresolved computational problem due, in part, to the inability of algorithms to detect small deletions and phase single nucleotide polymorphism. In the first part of this talk, we will present an algorithmic framework, termed DELISHUS, that implements a highly efficient algorithm for inferring genomic deletions of all sizes and frequencies in SNP array data. The core of the algorithm is a de facto polynomial time backtracking algorithm - that finishes on a 1 billion entry genome-wide association study SNP matrix in a few minutes - to compute all potential inherited deletions in a dataset. With very few modifications, DELISHUS may also infer regions that contain de novo deletions. We will show that DELISHUS has significantly higher sensitivity and specificity than previously developed methods and present a genome-wide deletion map of autism. DELISHUS may be run with SNP array or NGS data. In the second part of the talk, we will present our recent work on haplotype assembly of NGS data using our HAPCOMPASS algorithm. We suggest two new metrics for evaluating the quality of a haplotype assembly that do not require knowledge of the true haplotypes. Finally, we will show that HAPCOMPASS performs significantly better than the Genome Analysis ToolKit[3] and HapCut[4] for 1000 genomes data as well as simulated data for a variety of metrics.

BackgroundRecent statistical methods for next generation sequencing (NGS) data have been successfully applied to identifying rare genetic variants associated with certain diseases. However, most commonly used methods (e.g., burden tests and variance-component tests) rely on large sample sizes. Notwithstanding, due to its-still high cost, NGS data is generally restricted to small sample sizes, that cannot be analyzed by most existing methods.MethodsIn this work, we propose a new exact association test for sequencing data that does not require a large sample approximation, which is applicable to both common and rare variants. Our method, based on the Generalized Cochran-Mantel-Haenszel (GCMH) statistic, was applied to NGS datasets from intraductal papillary mucinous neoplasm (IPMN) patients. IPMN is a unique pancreatic cancer subtype that can turn into an invasive and hard-to-treat metastatic disease.ResultsApplication of our method to IPMN data successfully identified susceptible genes associated with progression of IPMN to pancreatic cancer. ConclusionsOur method is expected to identify disease-associated genetic variants more successfully, and corresponding signal pathways, improving our understanding of specific disease’s etiology and prognosis.

Analysis of NGS (Next Generation Sequencing) data is a computationally demanding task requiring large amounts of CPU, memory, and disk space. There is also a requirement for high performance data storage systems, resilient to hardware failure, to be connected directly to the computing infrastructure (typically a multi-node cluster) to store large quantities of NGS data reliably. Traditional shared file systems such as NFS (Network File System) do not offer the performance, scalability or cache coherence required by modern NGS data analysis, so alternatives including GlusterFS, Ceph, and Lustre have been developed. However, there is a trade-off between data safety on replicated local storage and degradation of performance across distributed storage. Resilience to hardware failure is typically provided by RAID (Redundant Array of Independent Disks) and redundant storage nodes. Here we describe the evaluation of an alternative file system, RozoFS ( https://github.com/rozofs/rozofs ) for use with demanding NGS data analysis workloads. We used a synthetic data set (DREAM-TCGA data set 3) to run a complete tumor-normal analysis pipeline (“bcbio”, https://github.com/chapmanb/bcbio-nextgen ), including base quality recalibration, local indel realignment, somatic variant calling, and structural variants as a benchmark to compare RozoFS with a traditional shared file system (NFS) on two different HPC (High Performance Computing; Cloud4CaRE project) clusters. Our results show high reliability and good performance of RozoFS compared to NFS, in particular, during heavy I/O workloads. These findings indicate that the reliability and robustness of RozoFS's make it a good candidate for demanding NGS analysis workloads.