Distributed file systems for storage and analysis of Next-Generation Sequencing data

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

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

BackgroundHigh-throughput Next-Generation Sequencing (NGS) techniques are advancing genomics and molecular biology research. This technology generates substantially large data which puts up a major challenge to the scientists for an efficient, cost and time effective solution to analyse such data. Further, for the different types of NGS data, there are certain common challenging steps involved in analysing those data. Spliced alignment is one such fundamental step in NGS data analysis which is extremely computational intensive as well as time consuming. There exists serious problem even with the most widely used spliced alignment tools. TopHat is one such widely used spliced alignment tools which although supports multithreading, does not efficiently utilize computational resources in terms of CPU utilization and memory. Here we have introduced PVT (Pipelined Version of TopHat) where we take up a modular approach by breaking TopHat’s serial execution into a pipeline of multiple stages, thereby increasing the degree of parallelization and computational resource utilization. Thus we address the discrepancies in TopHat so as to analyze large NGS data efficiently.ResultsWe analysed the SRA dataset (SRX026839 and SRX026838) consisting of single end reads and SRA data SRR1027730 consisting of paired-end reads. We used TopHat v2.0.8 to analyse these datasets and noted the CPU usage, memory footprint and execution time during spliced alignment. With this basic information, we designed PVT, a pipelined version of TopHat that removes the redundant computational steps during ‘spliced alignment’ and breaks the job into a pipeline of multiple stages (each comprising of different step(s)) to improve its resource utilization, thus reducing the execution time.ConclusionsPVT provides an improvement over TopHat for spliced alignment of NGS data analysis. PVT thus resulted in the reduction of the execution time to ~23% for the single end read dataset. Further, PVT designed for paired end reads showed an improved performance of ~41% over TopHat (for the chosen data) with respect to execution time. Moreover we propose PVT-Cloud which implements PVT pipeline in cloud computing system.

Whole-genome sequencing has become an essential tool for real-time genomic surveillance of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) worldwide. The handling of raw next-generation sequencing (NGS) data is a major challenge for sequencing laboratories. We developed an easy-to-use web-based application (EPISEQ SARS-CoV-2) to analyse SARS-CoV-2 NGS data generated on common sequencing platforms using a variety of commercially available reagents. This application performs in one click a quality check, a reference-based genome assembly, and the analysis of the generated consensus sequence as to coverage of the reference genome, mutation screening and variant identification according to the up-to-date Nextstrain clade and Pango lineage. In this study, we validated the EPISEQ SARS-CoV-2 pipeline against a reference pipeline and compared the performance of NGS data generated by different sequencing protocols using EPISEQ SARS-CoV-2. We showed a strong agreement in SARS-CoV-2 clade and lineage identification (>99%) and in spike mutation detection (>99%) between EPISEQ SARS-CoV-2 and the reference pipeline. The comparison of several sequencing approaches using EPISEQ SARS-CoV-2 revealed 100% concordance in clade and lineage classification. It also uncovered reagent-related sequencing issues with a potential impact on SARS-CoV-2 mutation reporting. Altogether, EPISEQ SARS-CoV-2 allows an easy, rapid and reliable analysis of raw NGS data to support the sequencing efforts of laboratories with limited bioinformatics capacity and those willing to accelerate genomic surveillance of SARS-CoV-2.

Tyrosine sulfation plays a vital role in various biochemical reactions. Although sulfated tyrosine (sTyr) has a similar structure to phosphotyrosine (pTyr), the number of available sTyr sites is significantly less than that of pTyr sites, mainly because of the lack of effective sTyr probes. A few sTyr binders were identified on the basis of structural similarity by engineering the pTyr-binding pocket of an Src Homology 2 (SH2) domain through phage selections against sTyr peptides. Nevertheless, they still interact with pTyr peptides with comparable affinity. This study aims to identify sTyr superbinders using the SH2 domain as a template. We created a distinctive phage selection scheme that separately covered selections against sTyr and pTyr peptides, followed by next-generation sequencing (NGS). After selections, phage pools showed strong enzyme-linked immunosorbent assay (ELISA) signal intensities for both modified peptides, indicating that the variants evolved with a high affinity for these peptides, which causes difficulty in identifying sTyr-specific binders. In contrast, NGS data from selected pools showed significant differences, suggesting the enrichment of sTyr-specific variants during selections. Accordingly, we obtained the sTyr features based on NGS data analysis and prioritized a few potential sTyr binders. The variant SH2-4 showed a stronger affinity for sTyr than pTyr and was superior to previous sTyr binders as measured by the Biolayer Interferometry assay. In summary, we described the strategy of integrating NGS data mining with a novel selection scheme to identify sTyr superbinders.

In the present era, genomics represent a crucial role in the field of life sciences. Advancements in genomics and the development of high-throughput techniques facilitate the characterization of a wide range of genes according to their functions such as regulation of genes, metabolic pathways, and their reconstruction. In the era of genomics we face the challenge of storage and analysis of a huge amount of important data. Even in this early stage of the era there are many commercial techniques and tools/software available to analyze next-generation sequencing (NGS) data. All of these programs can be used for many uses such as sequence alignments, polymorphism detection, and functional and structural comparative genomics. In this chapter, we focus on advances in bioinformatics and also computational biology in genome sequencing and NGS data analysis and on the potential applications for the efficient collection, storage, and analysis of the huge amount of genomic data generated by researchers and the information retrieved from different sources and web browsers in relation to NGS analysis.

A new generation of non-Sanger-based sequencing technologies, so called “next-generation” sequencing (NGS), has been changing the landscape of genetics at unprecedented speed. In particular, our capacity in deciphering the genotypes underlying phenotypes, such as diseases, has never been greater. However, before fully applying NGS in medical genetics, researchers have to bridge the widening gap between the generation of massively parallel sequencing output and the capacity to analyze the resulting data. In addition, even a list of candidate genes with potential causal variants can be obtained from an effective NGS analysis, to pinpoint disease genes from the long list remains a challenge. The issue becomes especially difficult when the molecular basis of the disease is not fully elucidated.
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\nNew NGS users are always bewildered by a plethora of options in mapping, assembly, variant calling and filtering programs and may have no idea about how to compare these tools and choose the “right” ones. To get an overview of various bioinformatics attempts in mapping and assembly, a series of performance evaluation work was conducted by using both real and simulated NGS short reads. For NGS variant detection, the performances of two most widely used toolkits were assessed, namely, SAM tools and GATK. Based on the results of systematic evaluation, a NGS data processing and analysis pipeline was constructed. And this pipeline was proved a success with the identification of a mutation (a frameshift deletion on Hnrnpa1, p.Leu181Valfs*6) related to congenital heart defect (CHD) in procollagen type IIA deficient mice.
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\nIn order to prioritize risk genes for diseases, especially those with limited prior knowledge, a network-based gene prioritization model was constructed. It consists of two parts: network analysis on known disease genes (seed-based network strategy)and network analysis on differential expression (DE-based network strategy). Case studies of various complex diseases/traits demonstrated that the DE-based network strategy can greatly outperform traditional gene expression analysis in predicting disease-causing genes. A series of simulation work indicated that the DE-based strategy is especially meaningful to diseases with limited prior knowledge, and the model’s performance can be further advanced by integrating with seed-based network strategy. Moreover, a successful application of the network-based gene prioritization model in influenza host genetic study further demonstrated the capacity of the model in identifying promising candidates and mining of new risk genes and pathways not biased toward our current knowledge.
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\nIn conclusion, an efficient NGS analysis framework from the steps of quality control and variant detection, to those of result analysis and gene prioritization has been constructed for medical genetics. The novelty in this framework is an encouraging attempt to prioritize risk genes for not well-characterized diseases by network analysis on known disease genes and differential expression data. The successful applications in detecting genetic factors associated with CHD and influenza host resistance demonstrated the efficacy of this framework. And this may further stimulate more applications of high throughput genomic data in dissecting the genetic components of human disorders in the near future.

The availability of high-throughput sequencing has created enormous possibilities for scientific discovery. However, the massive amount of data being generated has resulted in a severe informatics bottleneck. A large number of tools exist for analyzing next-generation sequencing (NGS) data, yet often there remains a disconnect between these research tools and the ability of many researchers to use them. As a consequence, several online resources and communities have been developed to assist researchers with both the management and the analysis of sequencing data sets. Here we describe the use and applications of common file formats for coding and storing genomic data, consider several web-accessible open-source resources for the visualization and analysis of NGS data, and provide examples of typical analyses with links to further detailed exercises.

Study question To explore ploidy concordance between invasive and non-invasive PGTA (niPGT-A) at different embryo culture time. Summary answer High level (>84%) of concordance rate for ploidy and sex, sensitivity (>88%), and specificity (76%) were obtained for both day6/7 samples and day5 samples. What is known already The analysis of embryo cell free DNA (cfDNA) that are released into culture media during in vitro embryo development has the potential to evaluate embryo ploidy status. However, obtaining sufficient quality and quantity of cfDNA is essential to achieve interpretable results for niPGT-A. More culture time is expected to be directly proportional to the release of more cfDNA. But embryo culture time is limited due to in-vitro embryo survival potential. Therefore, it is important to estimate the duration of the culture that will provide the maximum cfDNA that can be obtained without adversely affecting the development of the embryo. Study design, size, duration A total of 105 spent culture media (SCM) from day5-day7 blastocyst stage embryos have been included in this cohort study. The cfDNA of SCM samples were amplified and analyzed for niPGT-A by NGS analysis. The SCM samples were divided into 2 subgroups according the embryo culture hours (Day5 and Day6/7 group). The DNA concentration, informativity and euploidy results have then been compared with their corresponding embryos after trophectoderm biopsy (TE) and PGT-A analysis by NGS Participants/materials, setting, methods Embryos cultured until Day3 washed and cultured again in 20µl fresh culture media until embryo biopsy on Day5, 6, or 7. After biopsy SCM samples were immediately collected in PCR tubes and conserved at –20 °C until whole genome amplification by MALBAC® (Yicon Genomics). The TE and SCM samples were analyzed by next-generation sequencing (NGS) using Illumina MiSeq® System. NGS data analysis has been done by Bluefuse Multi Software 4.5 (Illumina) for SCM and TE samples Main results and the role of chance Only the SCM samples which have an embryo with a conclusive result were included in this cohort (n = 105). Overall 97.1% (102/105) of SCM samples gave a successful DNA amplification with a concentration ranging 32.4–128.5ng/µl. Non-informative (NI) results including a chaotic profile (>5 chromosome aneuploidies) were observed in 17 samples, so 83.3%(85/102) of SCM samples were informative for NGS data analysis. Ploidy concordance rate with the corresponding TE biopsies (euploid vs euploid, aneuploid vs aneuploid) was 84.7% (72/85). Sensitivity and specificity were 92,8% and 76,7%, respectively with no significant difference for all parameters for day 6/7 samples compared with day 5 samples. The false-negative rate was 3.5% (3/85), and false-positive rate was 11.7% (10/85). Limitations, reasons for caution The sample size is relatively small. Larger prospective studies are needed. As this is a single-center study, the impact of the variations in embryo culture conditions can be underestimated. Maternal DNA contamination risk cannot be revealed in SCM, therefore the use of molecular markers would increase the reliability. Wider implications of the findings: Non-invasive analysis of embryo cfDNA analyzed in spent culture media demonstrates high concordance with TE biopsy results in both early and late culture time. A non-invasive approach for aneuploidy screening offers important advantages such as avoiding invasive embryo biopsy and decreased cost, potentially increasing accessibility for a wider patient population. Trial registration number Not applicable

Summary: Seq2pathway is an R/Python wrapper for pathway (or functional gene-set) analysis of genomic loci, adapted for advances in genome research. Seq2pathway associates the biological significance of genomic loci with their target transcripts and then summarizes the quantified values on the gene-level into pathway scores. It is designed to isolate systematic disturbances and common biological underpinnings from next-generation sequencing (NGS) data. Seq2pathway offers Bioconductor users enhanced capability in discovering collective pathway effects caused by both coding genes and cis-regulation of non-coding elements.Availability and implementation: The package is freely available at http://www.bioconductor.org/packages/release/bioc/html/seq2pathway.html.Contact: xyang2@uchicago.eduSupplementary information:Supplementary data are available at Bioinformatics online.

Objectives: The aim of this study was to assess the taxonomic diversity of the microbiota associated with periapical lesions of endodontic origin and to determine whether microbial profiles vary across different populations and clinical characteristics using a unified in silico analysis of next-generation sequencing (NGS) data. Methods: Raw 16S rRNA sequencing data from three published studies were retrieved from the NCBI Sequence Read Archive and reprocessed using a standardized bioinformatics pipeline. Amplicon sequence variants were inferred using DADA2, and taxonomic assignments were performed using BLASTN against a curated 16S rRNA reference database. Alpha and beta diversity analyses were conducted using QIIME 2 and R, and differential abundance was assessed with ANCOM-BC2. Statistical comparisons were made based on population, sex, symptomatology, and other clinical metadata. Results: A total of 38 periapical lesion samples yielded 566,223 high-confidence reads assigned to 347 bacterial species. Significant differences in microbial composition were observed between geographic regions (China vs. Spain), sexes, and symptoms. Core species such as Fretibacterium sp. HMT 360 and Porphyromonas endodontalis were prevalent across datasets. Porphyromonas gingivalis and Fusobacterium nucleatum were found in abundance across all three studies. Beta diversity metrics revealed distinct clustering by study and country. Symptomatic lesions were associated with higher abundance of Alloprevotella tannerae and Prevotella oris. Conclusions: The periapical lesion microbiota is taxonomically diverse and varies significantly by geographic and clinical features.

Redundant array of independent disks (RAID) has been widely used to address the reliability and performance issues of storage systems. As the scale of modern storage systems continues growing, disk failure becomes the norm. With the ever-increasing disk capacity, RAID recovery based on disk rebuild becomes more and more costly, which causes significant performance degradation and even unavailability of storage systems. Declustered data layout enables parallel RAID reconstruction by shuffling data and parity blocks among all drives (including spares) in a RAID group. However, the reliability and performance of declustered RAID in real-world storage environments have not been thoroughly studied. With the popularity of ZFS file system and software RAID used in production data centers, in this paper, we extensively evaluate declustered RAID with regard to the RAID recovery time and I/O performance on a high-performance storage platform at Los Alamos National Laboratory. Our empirical study reveals the advantages and disadvantages of declustered RAID technology. We qualitatively characterize the recovery performance of declustered RAID and compare with that of ZFS RAIDZ under various I/O workloads and access patterns. The experimental results show that the speedup of declustered RAID over traditional RAID is sub-linear to the parallelism of recovery I/O. Furthermore, we formally model and analyze the reliability of declustered RAID in terms of the mean-time-to-data-loss (MTTDL) and discover that the improved recovery performance leads to higher storage reliability compared with the traditional RAID.

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.

Somatic variant analysis of a tumour sample and its matched normal has been widely used in cancer research to distinguish germline polymorphisms from somatic mutations. However, due to the extensive intratumour heterogeneity of cancer, sequencing data from a single tumour sample may greatly underestimate the overall mutational landscape. In recent studies, multiple spatially or temporally separated tumour samples from the same patient were sequenced to identify the regional distribution of somatic mutations and study intratumour heterogeneity. There are a number of tools to perform somatic variant calling from matched tumour-normal next-generation sequencing (NGS) data; however none of these allow joint analysis of multiple same-patient samples. We discuss the benefits and challenges of multisample somatic variant calling and present multiSNV, a software package for calling single nucleotide variants (SNVs) using NGS data from multiple same-patient samples. Instead of performing multiple pairwise analyses of a single tumour sample and a matched normal, multiSNV jointly considers all available samples under a Bayesian framework to increase sensitivity of calling shared SNVs. By leveraging information from all available samples, multiSNV is able to detect rare mutations with variant allele frequencies down to 3% from whole-exome sequencing experiments.

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.