Alignment-Free Sequence Comparison Based on Next Generation Sequencing Reads: Extended Abstract

Next-generation sequencing (NGS) technologies have generated enormous amounts 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, D(2), D(*)(2) and D(s)(2), 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 D(*)(2) and D(s)(2), outperform D(2) 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 D(*)(2) and D(s)(2). Finally, variations of these statistics, d(2), d(*)(2) and d(s)(2), respectively, are used to first cluster five 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 d(s)(2) are consistent with biological knowledge for the 5 mammalian and 13 tree species, respectively. Thus, the statistic d(s)(2) provides a powerful alignment-free comparison tool to study the relationships among different organisms based on NGS read data without assembly.

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

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.

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.

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

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

e23528 Background: The rarity and heterogeneity of sarcoma has been complicating the diagnosis of sarcoma for years. Even expert pathologists of sarcoma could make mistakes in the diagnosis of this disease. The availability of Next Generation Sequencing (NGS) data enabled more accurate diagnosis of sarcoma. In this study, we systematically described the application of NGS on the diagnosis of sarcoma and the contribution of NGS to the diagnostic accuracy of sarcoma. Methods: A multi-center, retrospective study included 235 sarcoma patients’ tumor and paired normal samples that were sent from 56 hospitals to a College of American Pathologists (CAP) accredited and Clinical Laboratory Improvement Amendments (CLIA) certified laboratory, at Shanghai, China for Next Generation Sequencing (NGS) was performed. Using next generation sequencing based YS panel consisting 450 genes, these 235 sarcoma patients’ sample were sequenced and the NGS data was analyzed. The initial diagnosis without NGS information was reconsidered by expert pathologists. Results: Taking into consideration both the initial diagnosis and the NGS results, the final diagnosis of these 235 sarcoma cases included 8 low grade malignant fibromyxoid tumors, 11 dermatofibrosarcoma protuberans (DFSP), 38 myxoliposarcomas, 22 alveolar rhabdomyosarcomas, 11 alveolar soft tissue sarcoma, 2 desmoplastic small round cell tumors, 37 NTRK rearrangement spindle cell tumors, 40 Ewing’s sarcoma and 66 synoviosarcomas. In total, 29% initial diagnoses were changed according to NGS identified fusions, including 13% low grade malignant fibromyxoid tumors (1 FUS- CREB3L2 fusion), 27% DFSPs (3 COL1A1- PDGFB fusions), 11% myxoliposarcomas (3 FUS- DDIT3 fusions and 1 EWSR1- DDIT3 fusion), 14% alveolar rhabdomyosarcomas (2 PAX7- FOXO1 fusions and 1 FOXO1- LINC00598 fusion), 18% alveolar soft tissue sarcomas (2 ASPSCR1- TFE3 fusions), 50% desmoplastic small round cell tumor (1 EWSR1- WT1 fusion), 95% NTRK rearrangement spindle cell tumors, 13% Ewing’s sarcomas (3 EWSR1- FLI1 fusions and 2 EWSR1- ERG fusions) and 21% synoviosarcomas (9 SS18- SSX1 fusions and 5 SS18- SSX2 fusions). Conclusions: NGS would be highly recommended for accurate diagnosis of sarcoma, especially for NTRK rearrangement spindle cell tumor, the majority of which were confirmed according to NGS identified fusions.

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.

Recent advances in High-Throughput Sequencing (HTS) technology have greatly facilitated the researches in bioinformatics field. With the ultra-high sequencing speed and improved base-calling accuracy, Illumina Genome Analyzer is currently the most widely used platform in the field. To use the raw reads generated from the sequencing machine, the 3’ adapter sequence attached to the real read in the process of ligation needs to be correctly trimmed. This is often done by some inhouse scripts or different packages with various parameters. They either use the Smith-Waterman algorithm or search for an exact match of the 3’ adapter sequence. In this report, I investigated methodologies as well as the strengths and weaknesses of five representative mainstream adapter trimming tools in order to suggest a direction for other researchers. Furthermore, four sets of detailed analysis were performed to evaluate the performances of these tools. I demonstrated that my adapter trimming method is flexible, accurate and efficient for Next Generation Sequencing (NGS) analysis. Type of Report: MS Project Report Department of Computer Science & Engineering Washington University in St. Louis Campus Box 1045 St. Louis, MO 63130 ph: (314) 935-6160 Master’s Project Report LCScanner: An Efficient and Accurate Trimming Tool for Illumina Next Generation Sequencing Reads by Xiang Zhou Master of Engineering in Computer Science Washington University in St. Louis, 2012 Research Advisor: Professor Weixiong Zhang

Next Generation Sequencing (NGS) and Cancer: New Steps Towards Personalized MedicineIn the era of precision oncology, the scenario of cancer diagnostics, prognostics, and therapeutics has been revolutionized by the advent of next-generation sequencing (NGS). The ability to investigate cancer genomes and transcriptomes at remarkable depth has enabled the identification of novel mutations, fusion events, gene expression profiles, and non-coding RNA (ncRNA) signatures that contribute to tumorigenesis and therapeutic resistance. This Research Topic, Next Generation Sequencing (NGS) and Cancer: New Steps Towards Personalized Medicine, has been conceived to explore how NGS technologies continue to drive forward the era of personalized oncology. This collection, including original research and case reports, reflects the diversity and translational potential of NGS across tumor types and clinical contexts. The contributions cover key areas of precision oncology, ranging from biomarker discovery and drug sensitivity profiling to clinical case studies, that highlight the practical utility of NGS in guiding targeted therapies. Several studies emphasize the growing role of integrative bioinformatics and machine learning in finding out actionable insights from complex datasets. For example, Cui et al. applied topological data analysis and deep learning to uncover molecular drivers and predict therapeutic targets in melanoma, exploiting immune gene regulatory networks constructed from NGS data. Their computational approach exemplifies how artificial intelligence can bridge network-based analysis with precision oncology. Prognostic stratification is another critical application of NGS. Xu et al. performed a comprehensive analysis of programmed cell death (PCD) modalities in cholangiocarcinoma, integrating immune microenvironment features and drug sensitivity profiles. Their multidimensional approach stratified patients into prognostic subgroups and highlighted pathways that could inform personalized treatment strategies for this rare and aggressive cancer. Collectively, the articles in this Research Topic exemplify how NGS is reshaping oncology across research and clinical practice, from molecular discovery to clinical decision-making. Several contributions explore the identification of novel fusion genes, regulatory RNAs, and resistance mechanisms, while others focus on integrating multi-omic data, refining prognostic models, or demonstrating NGS-guided therapeutic interventions. These studies highlight the critical need to integrate high-throughput sequencing data into clinical workflows, in order to optimize treatment strategies and improve patient outcomes. They also reveal the ongoing need for reliable bioinformatics pipelines, functional validation, and close interdisciplinary collaboration among researchers, oncologists, pathologists, and bioinformaticians. As NGS technologies continue to evolve, becoming faster, more affordable, and more accessible, they are increasingly positioned to serve as core components of routine oncology care. To fully realize their potential, this technological progress must be accompanied by collaborative efforts to standardize data interpretation, address ethical considerations around genomic information, and ensure equitable access to testing across all patient populations. This Research Topic reflects the rapid advancement in the integration of genomics into clinical practice, offering novel insights into the molecular drivers of cancer and illustrating how precision oncology is already transforming patient care.

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.

Intellectual disability (ID) is a group of neurodevelopmental disorders with high heterogeneous in both genotypes and phenotypes and its definitive diagnosis is increasingly dependent ongenome-wide molecular diagnostics.Based on next generation sequencing(NGS), panel sequencing, whole exome sequencing (WES) and even whole genome sequencing are well applied to the molecular diagnosis of ID. Based on these, we recommend WES, especially trio-WES as the preferred detection method. NGS data analysis and reanalysis for ID have clinical significance for diagnosis, and can detect small scale variation and copy number variation in the genome reliably. Therefore, it has the potential to become the next recommended molecular diagnostic toolfor ID. Key words: Intellectual disability; Next generation sequencing; Whole exome sequencing; Mutation

External quality assessment (EQA) is a keystone element in the validation and implementation of next generation sequencing (NGS)-based HIV drug resistance testing (DRT). Software validation and evaluation is a critical element in NGS EQA programs. While the development, sharing, and adoption of wet lab protocols is coupled with the increasing access to NGS technology worldwide, rendering it easy to produce NGS data for HIV-DRT, bioinformatic data analysis remains a bottleneck for most of the diagnostic laboratories. Several computational tools have been made available, via free or commercial sources, to automate the conversion of raw NGS data into an actionable clinical report. Although different software platforms yield equivalent results when identical raw NGS datasets are analyzed for variations at higher abundance, discrepancies arise when variations at lower frequencies are considered. This implies that validation and performance assessment of the bioinformatics tools applied in NGS HIV-DRT is critical, and the origins of the observed discrepancies should be determined. Well-characterized reference NGS datasets with ground truth on the genotype composition at all examined loci and the exact frequencies of HIV variations they may harbor, so-called dry panels, would be essential in such cases. The strategic design and construction of such panels are challenging but imperative tasks in support of EQA programs for NGS-based HIV-DRT and the validation of relevant bioinformatics tools. Here, we present criteria that can guide the design of such dry panels, which were discussed in the Second International Winnipeg Symposium themed for EQA strategies for NGS HIVDR assays.

P090 Development of a next generation sequencing based ABO blood group assay and typing software