High-throughput Next-generation Sequencing Data Research Articles

Comparison of seven SNP calling pipelines for the next-generation sequencing data of chickens.

Single nucleotide polymorphisms (SNPs) are widely used in genome-wide association studies and population genetics analyses. Next-generation sequencing (NGS) has become convenient, and many SNP-calling pipelines have been developed for human NGS data. We took advantage of a gap knowledge in selecting the appropriated SNP calling pipeline to handle with high-throughput NGS data. To fill this gap, we studied and compared seven SNP calling pipelines, which include 16GT, genome analysis toolkit (GATK), Bcftools-single (Bcftools single sample mode), Bcftools-multiple (Bcftools multiple sample mode), VarScan2-single (VarScan2 single sample mode), VarScan2-multiple (VarScan2 multiple sample mode) and Freebayes pipelines, using 96 NGS data with the different depth gradients of approximately 5X, 10X, 20X, 30X, 40X, and 50X coverage from 16 Rhode Island Red chickens. The sixteen chickens were also genotyped with a 50K SNP array, and the sensitivity and specificity of each pipeline were assessed by comparison to the results of SNP arrays. For each pipeline, except Freebayes, the number of detected SNPs increased as the input read depth increased. In comparison with other pipelines, 16GT, followed by Bcftools-multiple, obtained the most SNPs when the input coverage exceeded 10X, and Bcftools-multiple obtained the most when the input was 5X and 10X. The sensitivity and specificity of each pipeline increased with increasing input. Bcftools-multiple had the highest sensitivity numerically when the input ranged from 5X to 30X, and 16GT showed the highest sensitivity when the input was 40X and 50X. Bcftools-multiple also had the highest specificity, followed by GATK, at almost all input levels. For most calling pipelines, there were no obvious changes in SNP numbers, sensitivities or specificities beyond 20X. In conclusion, (1) if only SNPs were detected, the sequencing depth did not need to exceed 20X; (2) the Bcftools-multiple may be the best choice for detecting SNPs from chicken NGS data, but for a single sample or sequencing depth greater than 20X, 16GT was recommended. Our findings provide a reference for researchers to select suitable pipelines to obtain SNPs from the NGS data of chickens or nonhuman animals.

Advancing clinical genomics and precision medicine with GVViZ: FAIR bioinformatics platform for variable gene-disease annotation, visualization, and expression analysis

BackgroundGenetic disposition is considered critical for identifying subjects at high risk for disease development. Investigating disease-causing and high and low expressed genes can support finding the root causes of uncertainties in patient care. However, independent and timely high-throughput next-generation sequencing data analysis is still a challenge for non-computational biologists and geneticists.ResultsIn this manuscript, we present a findable, accessible, interactive, and reusable (FAIR) bioinformatics platform, i.e., GVViZ (visualizing genes with disease-causing variants). GVViZ is a user-friendly, cross-platform, and database application for RNA-seq-driven variable and complex gene-disease data annotation and expression analysis with a dynamic heat map visualization. GVViZ has the potential to find patterns across millions of features and extract actionable information, which can support the early detection of complex disorders and the development of new therapies for personalized patient care. The execution of GVViZ is based on a set of simple instructions that users without a computational background can follow to design and perform customized data analysis. It can assimilate patients’ transcriptomics data with the public, proprietary, and our in-house developed gene-disease databases to query, easily explore, and access information on gene annotation and classified disease phenotypes with greater visibility and customization. To test its performance and understand the clinical and scientific impact of GVViZ, we present GVViZ analysis for different chronic diseases and conditions, including Alzheimer’s disease, arthritis, asthma, diabetes mellitus, heart failure, hypertension, obesity, osteoporosis, and multiple cancer disorders. The results are visualized using GVViZ and can be exported as image (PNF/TIFF) and text (CSV) files that include gene names, Ensembl (ENSG) IDs, quantified abundances, expressed transcript lengths, and annotated oncology and non-oncology diseases.ConclusionsWe emphasize that automated and interactive visualization should be an indispensable component of modern RNA-seq analysis, which is currently not the case. However, experts in clinics and researchers in life sciences can use GVViZ to visualize and interpret the transcriptomics data, making it a powerful tool to study the dynamics of gene expression and regulation. Furthermore, with successful deployment in clinical settings, GVViZ has the potential to enable high-throughput correlations between patient diagnoses based on clinical and transcriptomics data.

Systematic comparative study of computational methods for HLA typing from next-generation sequencing.

The human leukocyte antigen (HLA) system plays an important role in hematopoietic stem cell transplantation (HSCT) and organ transplantations, immune disorders as well as oncological immunotherapy. However, HLA typing remains a challenging task due to the high level of polymorphism and homology among HLA genes. Based on the high-throughput next-generation sequencing data, new HLA typing algorithms and software tools were developed. But there is still a deficit of systematic comparative studies to assist in the selection of the optimal analytical approaches under different conditions. Here, we present a detailed comparison of eight software tools for HLA typing on different real datasets (whole-genome sequencing, whole-exome sequencing and transcriptomic sequencing data) and in-silico samples with different sequencing lengths, depths, and error rates. We figure out the algorithms with the best efficiency in different scenarios, and demonstrate the effect of different raw reads on analytical performances. Our results provide a comprehensive picture of specifications and performances of the eight existing HLA genotyping algorithms, which could assist researchers in selecting the most appropriate tool for specific raw datasets.

The complete chloroplast genome of Paeonia cv. Hwang-Moran (Paeoniaceae)

The complete chloroplast genome of Paeonia cv. Hwang-Moran (PHM), a yellow flowering tree peony, was de novo assembled and characterized from high-throughput next-generation sequencing data. The total length of the circular PHM chloroplast genome was 152,519 bp, including a large single-copy (LSC) region of 84,214 bp, a small single-copy (SSC) region of 17,026 bp, and a pair of inverted repeat regions (IRs) of 25,640 bp. The entire chloroplast genome contained 111 genes, including 77 protein-coding genes, 30 tRNAs, and four rRNAs. A phylogenetic tree constructed using the PHM and related chloroplast genome sequences revealed its close taxonomic relationship with P. ludlowii within the genus Paeonia.

Machine Learning Approaches on High Throughput NGS Data to Unveil Mechanisms of Function in Biology and Disease.

In this review, the fundamental basis of machine learning (ML) and data mining (DM) are summarized together with the techniques for distilling knowledge from state-of-the-art omics experiments. This includes an introduction to the basic mathematical principles of unsupervised/supervised learning methods, dimensionality reduction techniques, deep neural networks architectures and the applications of these in bioinformatics. Several case studies under evaluation mainly involve next generation sequencing (NGS) experiments, like deciphering gene expression from total and single cell (scRNA-seq) analysis; for the latter, a description of all recent artificial intelligence (AI) methods for the investigation of cell sub-types, biomarkers and imputation techniques are described. Other areas of interest where various ML schemes have been investigated are for providing information regarding transcription factors (TF) binding sites, chromatin organization patterns and RNA binding proteins (RBPs), while analyses on RNA sequence and structure as well as 3D dimensional protein structure predictions with the use of ML are described. Furthermore, we summarize the recent methods of using ML in clinical oncology, when taking into consideration the current omics data with pharmacogenomics to determine personalized treatments. With this review we wish to provide the scientific community with a thorough investigation of main novel ML applications which take into consideration the latest achievements in genomics, thus, unraveling the fundamental mechanisms of biology towards the understanding and cure of diseases.

Copy Number Variation Detection Using Total Variation.

Next-generation sequencing (NGS) technologies offer new opportunities for precise and accurate identification of genomic aberrations, including copy number variations (CNVs). For high-throughput NGS data, using depth of coverage has become a major approach to identify CNVs, especially for whole exome sequencing (WES) data. Due to the high level of noise and biases of read-count data and complexity of the WES data, existing CNV detection tools identify many false CNV segments. Besides, NGS generates a huge amount of data, requiring to use effective and efficient methods. In this work, we propose a novel segmentation algorithm based on the total variation approach to detect CNVs more precisely and efficiently using WES data. The proposed method also filters out outlier read-counts and identifies significant change points to reduce false positives. We used real and simulated data to evaluate the performance of the proposed method and compare its performance with those of other commonly used CNV detection methods. Using simulated and real data, we show that the proposed method outperforms the existing CNV detection methods in terms of accuracy and false discovery rate and has a faster runtime compared to the circular binary segmentation method.

Novel Discovery of LINE-1 in a Korean Individual by a Target Enrichment Method.

Long interspersed element-1 (LINE-1 or L1) is an autonomous retrotransposon, which is capable of inserting into a new region of genome. Previous studies have reported that these elements lead to genomic variations and altered functions by affecting gene expression and genetic networks. Mounting evidence strongly indicates that genetic diseases or various cancers can occur as a result of retrotransposition events that involve L1s. Therefore, the development of methodologies to study the structural variations and interpersonal insertion polymorphisms by L1 element-associated changes in an individual genome is invaluable. In this study, we applied a systematic approach to identify human-specific L1s (i.e., L1Hs) through the bioinformatics analysis of high-throughput next-generation sequencing data. We identified 525 candidates that could be inferred to carry non-reference L1Hs in a Korean individual genome (KPGP9). Among them, we randomly selected 40 candidates and validated that approximately 92.5% of non-reference L1Hs were inserted into a KPGP9 genome. In addition, unlike conventional methods, our relatively simple and expedited approach was highly reproducible in confirming the L1 insertions. Taken together, our findings strongly support that the identification of non-reference L1Hs by our novel target enrichment method demonstrates its future application to genomic variation studies on the risk of cancer and genetic disorders.

Alternative Splicing Detection Tool-a novel PERL algorithm for sensitive detection of splicing events, based on next-generation sequencing data analysis.

Next-generation sequencing (NGS) can provide researchers with high impact information regarding alternative splice variants or transcript identifications. However, the enormous amount of data acquired from NGS platforms make the analysis of alternative splicing events hard to accomplish. For this reason, we designed the "Alternative Splicing Detection Tool" (ASDT), an algorithm that is capable of identifying alternative splicing events, including novel ones from high-throughput NGS data. ASDT is available as a PERL script at http://aias.biol.uoa.gr/~mtheo and can be executed on any system with PERL installed. In addition to the detection of annotated and novel alternative splicing events from high-throughput NGS data, ASDT can also analyze the intronic regions of genes, thus enabling the detection of novel cryptic exons residing in annotated introns, extensions of previously annotated exons, or even intron retentions. Consequently, ASDT demonstrates many innovative and unique features that can efficiently contribute to alternative splicing analysis of NGS data.

Big Data Analytics for Genomic Medicine.

Genomic medicine attempts to build individualized strategies for diagnostic or therapeutic decision-making by utilizing patients’ genomic information. Big Data analytics uncovers hidden patterns, unknown correlations, and other insights through examining large-scale various data sets. While integration and manipulation of diverse genomic data and comprehensive electronic health records (EHRs) on a Big Data infrastructure exhibit challenges, they also provide a feasible opportunity to develop an efficient and effective approach to identify clinically actionable genetic variants for individualized diagnosis and therapy. In this paper, we review the challenges of manipulating large-scale next-generation sequencing (NGS) data and diverse clinical data derived from the EHRs for genomic medicine. We introduce possible solutions for different challenges in manipulating, managing, and analyzing genomic and clinical data to implement genomic medicine. Additionally, we also present a practical Big Data toolset for identifying clinically actionable genetic variants using high-throughput NGS data and EHRs.

Comparative Analysis of Immune Repertoires between Bactrian Camel's Conventional and Heavy-Chain Antibodies.

Compared to classical antibodies, camel heavy chain antibodies (HCAbs) are smaller in size due to lack of the light chain and the first constant domain of the heavy chain (CH1 region). The variable regions of HCAbs (VHHs) are more soluble and stable than that of conventional antibodies (VHs). Even with such simple structure, they are still functional in antigen binding. Although HCAbs have been extensively investigated over the past two decades, most efforts have been based upon low throughput sequence analysis, and there are only limited reports trying to analyze and describe the complete immune repertoire (IR) of camel HCAbs. Here we leveraged the high-throughput data generated by Next Generation Sequencing (NGS) of the variable domains of the antibody heavy chains from three Bactrian camels to conduct in-depth comparative analyses of the immunoglobulin repertoire. These include analyses of the complementary determining region 3 (CDR3) length and distribution, mutation rate, antibody characteristic amino acids, the distribution of the cysteine (Cys) codons, and the non-classical VHHs. We found that there is higher diversity in the CDR2 than in the other sub-regions, and there is a higher mutation rate in the VHHs than in the VHs (P < 0.05). In addition to substitutions at amino acid (AA) residue positions NO.49/50/52 between VH and VHH clones, we also observed other substitutions at the positions NO.40/54/57/96/101 that could lead to additional structural alterations. We also found that VH-derived VHH clones, referred to as non-classical VHH clones in this study, accounted for about 8% of all clones. Further, only 5%-10% clones had the Trp > Arg AA substitution at the first position of framework 4 for all types of clones. We present, for the first time, a relatively complete picture of the Bactrian camel antibody immune repertoire, including conventional antibody (Ab) and HCAbs, using PCR and in silico analysis based on high-throughput NGS data.

P076 KIR allele level typing by a novel neXtype algorithm

KIR genes are recognized to play an important role for stem cell transplantation. The high polymorphism of these genes makes it difficult to achieve high resolution typing results.Based on paired-end short amplicon next generation sequencing (NGS) data from the Ilumina Miseq and HiSeq systems, neXtype was adapted to type KIR on allelic level. neXtype reports KIR allele typing results in the genotype list (GL) string format and implicitly provides gene copy numbers.neXtype copes with KIR typing challenges in several consecutive steps:In a first step based on the IPD-KIR library, each read is assigned the best matching known exon sequence using the tree-concept of neXtype. In a second step, the copy numbers of the identified allelic variants per exon are determined for a given primer batch. This estimated exon copy number is subsequently required for the scoring system. As a third step, combination of exon-specific results determines KIR alleles present in the sample. The number of valid exon combinations per gene yields the gene copy number. Currently, neXtype uses exons 3, 4, 5, 7, 8, and 9. The assignment of reads to a specific gene is a priori not possible as some alleles of different genes share identical exon DNA sequences. Therefore, valid combinations for those gene-bridging exons must be analyzed simultaneously. neXtype uses a scoring system based on the difference between the copy number as given by valid exon combinations from step three and the estimated exon copy number from step two. The exon combination with the lowest score is taken as the typing result and presented as a GL string.With this algorithm, samples with pre-known typing results were confirmed. In summary, we show that cost efficient high-throughput NGS data can be used for allele level KIR typing. As a consequence, allele level KIR typing could be included into the standard typing profile of newly registered stem cell donors, thus allowing for refined donor selection algorithms.

A novice’s guide to analyzing NGS-derived organelle and metagenome data

Next generation sequencing (NGS) technologies have revolutionized many areas of biological research due to the sharp reduction in costs that has led to the generation of massive amounts of sequence information. Analysis of large genome data sets is however still a challenging task because it often requires significant computer resources and knowledge of bioinformatics. Here, we provide a guide for an uninitiated who wish to analyze high-throughput NGS data. We focus specifically on the analysis of organelle genome and metagenome data and describe the current bioinformatic pipelines suited for this purpose. Key words: bioinformatic; NGS data analysis; organelle genome; metagenome

Constructing an integrated genetic and epigenetic cellular network for whole cellular mechanism using high-throughput next-generation sequencing data

BackgroundEpigenetics has been investigated in cancer initiation, and development, especially, since the appearance of epigenomics. Epigenetics may be defined as the mechanisms that lead to heritable changes in gene function and without affecting the sequence of genome. These mechanisms explain how individuals with the same genotype produce phenotypic differences in response to environmental stimuli. Recently, with the accumulation of high-throughput next-generation sequencing (NGS) data, a key goal of systems biology is to construct networks for different cellular levels to explore whole cellular mechanisms. At present, there is no satisfactory method to construct an integrated genetic and epigenetic cellular network (IGECN), which combines NGS omics data with gene regulatory networks (GRNs), microRNAs (miRNAs) regulatory networks, protein-protein interaction networks (PPINs), and epigenetic regulatory networks of methylation using high-throughput NGS data.ResultsWe investigated different kinds of NGS omics data to develop a systems biology method to construct an integrated cellular network based on three coupling models that describe genetic regulatory networks, protein–protein interaction networks, microRNA (miRNA) regulatory networks, and methylation regulation. The proposed method was applied to construct IGECNs of gastric cancer and the human immune response to human immunodeficiency virus (HIV) infection, to elucidate human defense response mechanisms. We successfully constructed an IGECN and validated it by using evidence from literature search. The integration of NGS omics data related to transcription regulation, protein-protein interactions, and miRNA and methylation regulation has more predictive power than independent datasets. We found that dysregulation of MIR7 contributes to the initiation and progression of inflammation-induced gastric cancer; dysregulation of MIR9 contributes to HIV-1 infection to hijack CD4+ T cells through dysfunction of the immune and hormone pathways; dysregulation of MIR139-5p, MIRLET7i, and MIR10a contributes to the HIV-1 integration/replication stage; dysregulation of MIR101, MIR141, and MIR152 contributes to the HIV-1 virus assembly and budding mechanisms; dysregulation of MIR302a contributes to not only microvesicle-mediated transfer of miRNAs but also dysfunction of NF-κB signaling pathway in hepatocarcinogenesis.ConclusionThe coupling dynamic systems of the whole IGECN can allow us to investigate genetic and epigenetic cellular mechanisms via omics data and big database mining, and are useful for further experiments in the field of systems and synthetic biology.

Challenges of Identifying Clinically Actionable Genetic Variants for Precision Medicine.

Advances in genomic medicine have the potential to change the way we treat human disease, but translating these advances into reality for improving healthcare outcomes depends essentially on our ability to discover disease- and/or drug-associated clinically actionable genetic mutations. Integration and manipulation of diverse genomic data and comprehensive electronic health records (EHRs) on a big data infrastructure can provide an efficient and effective way to identify clinically actionable genetic variants for personalized treatments and reduce healthcare costs. We review bioinformatics processing of next-generation sequencing (NGS) data, bioinformatics infrastructures for implementing precision medicine, and bioinformatics approaches for identifying clinically actionable genetic variants using high-throughput NGS data and EHRs.

Abstract 4666: Revolutionizing clinical care using prospective, institution-wide tumor sequencing

Abstract Identification of predictive and prognostic biomarkers is central to clinical oncology. Use of targeted next generation sequencing (NGS) is increasing in molecular diagnostics labs, however the feasibility and impact of its routine application across all tumor types is largely untested. We launched an institution-wide effort to generate targeted NGS data (Oncopanel) for invasive tumors of all consenting patients. We hypothesized that this approach could replace traditional targeted testing, generate robust data on copy number alterations and structural variants, and provide novel patient-specific observations to facilitate clinical trial enrollment. Illumina NGS was performed on libraries prepared with Agilent SureSelect custom-designed hybrid capture of 4430 exons from 275 genes plus selected introns of 30 genes. Results were analyzed by an internally-developed computational pipeline for mutations, small insertions/deletions, copy number variation and rearrangements. Variants were characterized according to predictive, prognostic, or diagnostic actionability. Data is available for the first 4291 cases sequenced. Oncopanel succeeded in 96% of specimens with adequate DNA. In a subset analysis performed on the first 1000 cases, assay success ranged from 83-100% according to tumor type; breast carcinoma was most prone to failure (p&amp;lt;0.0001). Median number of mutations per case was 8 (range 0-205) and was lowest in endocrine malignancies and highest in skin malignancies. Three percent of tumors were hypermutated with mutational signatures revealing distinct pathogenic underpinnings, including prior temozolomide therapy, microsatellite instability, and UV exposure. Compared to clinical testing, Oncopanel showed 100% accuracy for detection of KRAS and BRAF point mutations in colon adenocarcinoma, EGFR exon 19 deletion mutations in lung adenocarcinoma, and for EGFR amplification in glioblastoma. Oncopanel was 97.5% sensitive and 87.5% specific for 1p19q deletion as compared to aCGH or FISH and was 80% sensitive and 100% specific as compared to ALK FISH and additionally detected 2 ALK rearranged tumors for which FISH failed. In the overall cohort, 26% of patients had an actionable variant (most commonly KRAS) and 39% had alterations with implications for clinical trial enrollment (most commonly in PI3K/PTEN/AKT pathway). TP53 was the most commonly altered gene overall and the most likely to be co-mutated with oncogenic drivers. High level amplifications were most common for EGFR, MDM2, CDK4, ERBB2, MYC, and CCND1. Two-copy deletions were most common for CDKN2A/B, followed by TP53 and PTEN. In several cases, Oncopanel data uncovered alterations that informed diagnosis and treatment of difficult-to-classify tumors. These efforts demonstrate that high quality, high throughput NGS data can be generated prospectively on an institutional level, thereby informing disease course and therapeutic options at an unprecedented scale. Citation Format: Lynette M. Sholl, Elizabeth Garcia, Yonghui Jia, Matthew Ducar, Bernard Fendler, Priyanka Shivdasani, Frank C. Kuo, Azra H. Ligon, Barrett J. Rollins, Neal I. Lindeman, Laura E. MacConaill. Revolutionizing clinical care using prospective, institution-wide tumor sequencing. [abstract]. In: Proceedings of the 106th Annual Meeting of the American Association for Cancer Research; 2015 Apr 18-22; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2015;75(15 Suppl):Abstract nr 4666. doi:10.1158/1538-7445.AM2015-4666

ACE: accurate correction of errors using K-mer tries.

The quality of high-throughput next-generation sequencing data significantly influences the performance and memory consumption of assembly and mapping algorithms. The most ubiquitous platform, Illumina, mainly suffers from substitution errors. We have developed a tool, ACE, based on K-mer tries to correct such errors. On real MiSeq and HiSeq Illumina archives, ACE yields higher gains in terms of coverage depth, outperforming state-of-the-art competitors in the majority of cases. ACE is licensed under the GPL license and can be freely obtained at https://github.com/sheikhizadeh/ACE/. The program is implemented in C++ and runs on most Unix-derived operating systems. siavash.sheikhizadehanari@wur.nl Supplementary data are available at Bioinformatics online.