Next-generation Sequencing Pipeline Research Articles

Performance Comparison and Tuning of Virtual Machines For Sequence Alignment Software

We explore the performance cost of virtualisation for the fast growing application domain of genomics. Traditionally, scientific applications have been considered too high-performance to pay the performance cost of virtualisation. However, as the demand for computing power for genomics is ever-increasing, the cloud can become an attractive way to meet the scaling challenge presented by Next-Generation Sequencing (NGS). We seek to explore the feasibility of running an NGS pipeline in a cloud, and in doing so consider two prevalent short-read sequence alignment programs, BWA and Novoalign. We executed those applications in three separate open-source system virtualisation solutions: the KVM hypervisor, the Xen para-virtualised hypervisor, and Linux Containers. We compare the runtime in each environment against the runtime of the same system without virtualisation and measure the relative performance of each hypervisor. We investigate and reduce as much as possible any overhead, presenting tuning suggestions for cloud implementers and users. Overall, we find that the overhead introduced by virtualisation can be reduced to low single-digit percentages, a cost we believe to be more than acceptable, especially given that two of the three solutions, Xen and Containers, exhibit near-zero overhead.

Choice of next-generation sequencing pipelines.

The next-generation sequencing (NGS) technologies are revolutionary tools which have made possible achieving remarkable advances in genetics since the beginning of the twenty-first century. Thanks to the possibility to produce large amount of sequence data, these tools are going to completely substitute other high-throughput technologies. Moreover, the large applications of NGS protocols are increasing the genetic decoding of biological systems through studies of genome anatomy and gene mapping, coupled to the transcriptome pictures. The application of NGS pipelines such as (1) de-novo genomic sequencing by mate-paired and whole-genome shotgun strategies; (2) specific gene sequencing on large bacterial communities; and (3) RNA-seq methods including whole transcriptome sequencing and Serial Analysis of Gene Expression (Sage-analysis) are fundamental in the genome-wide fields like metagenomics. Recently, the availability of these advanced protocols has allowed to overcome the usual sequencing technical issues related to the mapping specificity over standard shotgun library sequencing, the detection of large structural genomes variations and bridging sequencing gaps, as well as more precise gene annotation. In this chapter we will discuss how to manage a successful NGS pipeline from the planning of sequencing projects through the choice of the platforms up to the data analysis management.

CRCDA--Comprehensive resources for cancer NGS data analysis.

Next generation sequencing (NGS) innovations put a compelling landmark in life science and changed the direction of research in clinical oncology with its productivity to diagnose and treat cancer. The aim of our portal comprehensive resources for cancer NGS data analysis (CRCDA) is to provide a collection of different NGS tools and pipelines under diverse classes with cancer pathways and databases and furthermore, literature information from PubMed. The literature data was constrained to 18 most common cancer types such as breast cancer, colon cancer and other cancers that exhibit in worldwide population. NGS-cancer tools for the convenience have been categorized into cancer genomics, cancer transcriptomics, cancer epigenomics, quality control and visualization. Pipelines for variant detection, quality control and data analysis were listed to provide out-of-the box solution for NGS data analysis, which may help researchers to overcome challenges in selecting and configuring individual tools for analysing exome, whole genome and transcriptome data. An extensive search page was developed that can be queried by using (i) type of data [literature, gene data and sequence read archive (SRA) data] and (ii) type of cancer (selected based on global incidence and accessibility of data). For each category of analysis, variety of tools are available and the biggest challenge is in searching and using the right tool for the right application. The objective of the work is collecting tools in each category available at various places and arranging the tools and other data in a simple and user-friendly manner for biologists and oncologists to find information easier. To the best of our knowledge, we have collected and presented a comprehensive package of most of the resources available in cancer for NGS data analysis. Given these factors, we believe that this website will be an useful resource to the NGS research community working on cancer.Database URL: http://bioinfo.au-kbc.org.in/ngs/ngshome.html.

Unipro UGENE NGS pipelines and components for variant calling, RNA-seq and ChIP-seq data analyses.

The advent of Next Generation Sequencing (NGS) technologies has opened new possibilities for researchers. However, the more biology becomes a data-intensive field, the more biologists have to learn how to process and analyze NGS data with complex computational tools. Even with the availability of common pipeline specifications, it is often a time-consuming and cumbersome task for a bench scientist to install and configure the pipeline tools. We believe that a unified, desktop and biologist-friendly front end to NGS data analysis tools will substantially improve productivity in this field. Here we present NGS pipelines “Variant Calling with SAMtools”, “Tuxedo Pipeline for RNA-seq Data Analysis” and “Cistrome Pipeline for ChIP-seq Data Analysis” integrated into the Unipro UGENE desktop toolkit. We describe the available UGENE infrastructure that helps researchers run these pipelines on different datasets, store and investigate the results and re-run the pipelines with the same parameters. These pipeline tools are included in the UGENE NGS package. Individual blocks of these pipelines are also available for expert users to create their own advanced workflows.

COSMOS: Python library for massively parallel workflows.

Summary: Efficient workflows to shepherd clinically generated genomic data through the multiple stages of a next-generation sequencing pipeline are of critical importance in translational biomedical science. Here we present COSMOS, a Python library for workflow management that allows formal description of pipelines and partitioning of jobs. In addition, it includes a user interface for tracking the progress of jobs, abstraction of the queuing system and fine-grained control over the workflow. Workflows can be created on traditional computing clusters as well as cloud-based services.Availability and implementation: Source code is available for academic non-commercial research purposes. Links to code and documentation are provided at http://lpm.hms.harvard.edu and http://wall-lab.stanford.edu.Contact: dpwall@stanford.edu or peter_tonellato@hms.harvard.edu.Supplementary information: Supplementary data are available at Bioinformatics online.

Pipeliner: software to evaluate the performance of bioinformatics pipelines for next-generation resequencing.

The choice of technology and bioinformatics approach is critical in obtaining accurate and reliable information from next-generation sequencing (NGS) experiments. An increasing number of software and methodological guidelines are being published, but deciding upon which approach and experimental design to use can depend on the particularities of the species and on the aims of the study. This leaves researchers unable to produce informed decisions on these central questions. To address these issues, we developed pipeliner - a tool to evaluate, by simulation, the performance of NGS pipelines in resequencing studies. Pipeliner provides a graphical interface allowing the users to write and test their own bioinformatics pipelines with publicly available or custom software. It computes a number of statistics summarizing the performance in SNP calling, including the recovery, sensitivity and false discovery rate for heterozygous and homozygous SNP genotypes. Pipeliner can be used to answer many practical questions, for example, for a limited amount of NGS effort, how many more reliable SNPs can be detected by doubling coverage and halving sample size or what is the false discovery rate provided by different SNP calling algorithms and options. Pipeliner thus allows researchers to carefully plan their study's sampling design and compare the suitability of alternative bioinformatics approaches for their specific study systems. Pipeliner is written in C++ and is freely available from http://github.com/brunonevado/Pipeliner.

ESTIMATING BACTERIAL DIVERSITY IN SCIRTOTHRIPS DORSALIS (THYSANOPTERA: THRIPIDAE) VIA NEXT GENERATION SEQUENCING.

The last 2 decades have produced a better understanding of insect-microbial associations and yielded some important opportunities for insect control. However, most of our knowledge comes from model systems. Thrips (Thysanoptera: Thripidae) have been understudied despite their global importance as invasive species, plant pests and disease vectors. Using a culture and primer independent next-generation sequencing and metagenomics pipeline, we surveyed the bacteria of the globally important pest, Scirtothrips dorsalis Hood. The most abundant bacterial phyla identified were Actinobacteria and Proteobacteria and the most abundant genera were Propionibacterium, Stenotrophomonas, and Pseudomonas. A total of 189 genera of bacteria were identified. The absence of any vertically transferred symbiont taxa commonly found in insects is consistent with other studies suggesting that thrips primarilly acquire resident microbes from their environment. This does not preclude a possible beneficial/intimate association between S. dorsalis and the dominant taxa identified and future work should determine the nature of these associations.

Rich annotation of DNA sequencing variants by leveraging the Ensembl Variant Effect Predictor with plugins

High-throughput DNA sequencing has become a mainstay for the discovery of genomic variants that may cause disease or affect phenotype. A next-generation sequencing pipeline typically identifies thousands of variants in each sample. A particular challenge is the annotation of each variant in a way that is useful to downstream consumers of the data, such as clinical sequencing centers or researchers. These users may require that all data storage and analysis remain on secure local servers to protect patient confidentiality or intellectual property, may have unique and changing needs to draw on a variety of annotation data sets and may prefer not to rely on closed-source applications beyond their control. Here we describe scalable methods for using the plugin capability of the Ensembl Variant Effect Predictor to enrich its basic set of variant annotations with additional data on genes, function, conservation, expression, diseases, pathways and protein structure, and describe an extensible framework for easily adding additional custom data sets.

Targeted NGS gene panel identifies mutations in RSPH1 causing primary ciliary dyskinesia and a common mechanism for ciliary central pair agenesis due to radial spoke defects

Primary ciliary dyskinesia (PCD) is an inherited chronic respiratory obstructive disease with randomized body laterality and infertility, resulting from cilia and sperm dysmotility. PCD is characterized by clinical variability and extensive genetic heterogeneity, associated with different cilia ultrastructural defects and mutations identified in >20 genes. Next generation sequencing (NGS) technologies therefore present a promising approach for genetic diagnosis which is not yet in routine use. We developed a targeted panel-based NGS pipeline to identify mutations by sequencing of selected candidate genes in 70 genetically undefined PCD patients. This detected loss-of-function RSPH1 mutations in four individuals with isolated central pair (CP) agenesis and normal body laterality, from two unrelated families. Ultrastructural analysis in RSPH1-mutated cilia revealed transposition of peripheral outer microtubules into the ‘empty’ CP space, accompanied by a distinctive intermittent loss of the central pair microtubules. We find that mutations in RSPH1, RSPH4A and RSPH9, which all encode homologs of components of the ‘head’ structure of ciliary radial spoke complexes identified in Chlamydomonas, cause clinical phenotypes that appear to be indistinguishable except at the gene level. By high-resolution immunofluorescence we identified a loss of RSPH4A and RSPH9 along with RSPH1 from RSPH1-mutated cilia, suggesting RSPH1 mutations may result in loss of the entire spoke head structure. CP loss is seen in up to 28% of PCD cases, in whom laterality determination specified by CP-less embryonic node cilia remains undisturbed. We propose this defect could arise from instability or agenesis of the ciliary central microtubules due to loss of their normal radial spoke head tethering.

Copy number variants are a common cause of non-syndromic hearing loss.

BackgroundCopy number variants (CNVs) are a well-recognized cause of genetic disease; however, methods for their identification are often gene-specific, excluded as ‘routine’ in screens of genetically heterogeneous disorders, and not implemented in most next-generation sequencing pipelines. For this reason, the contribution of CNVs to non-syndromic hearing loss (NSHL) is most likely under-recognized. We aimed to incorporate a method for CNV identification as part of our standard analysis pipeline and to determine the contribution of CNVs to genetic hearing loss.MethodsWe used targeted genomic enrichment and massively parallel sequencing to isolate and sequence all exons of all genes known to cause NSHL. We completed testing on 686 patients with hearing loss with no exclusions based on type of hearing loss or any other clinical features. For analysis we used an integrated method for detection of single nucleotide changes, indels and CNVs. CNVs were identified using a previously published method that utilizes median read-depth ratios and a sliding-window approach.ResultsOf 686 patients tested, 15.2% (104) carried at least one CNV within a known deafness gene. Of the 38.9% (267) of individuals for whom we were able to determine a genetic cause of hearing loss, a CNV was implicated in 18.7% (50). We identified CNVs in 16 different genes including 7 genes for which no CNVs have been previously reported. CNVs of STRC were most common (73% of CNVs identified) followed by CNVs of OTOA (13% of CNVs identified).ConclusionCNVs are an important cause of NSHL and their detection must be included in comprehensive genetic testing for hearing loss.

Challenges using diagnostic next-generation sequencing in the clinical environment for inherited retinal disorders.

Visual impairment, and in particular the inherited retinopathies, is a significant problem worldwide. Many disorders are progressive so their early and accurate detection is crucial to the development and application of appropriate disease management and treatment strategies, some of which are currently being tested in clinical trials. Over the past few decades, the identification of genetic causes that mediate many inherited diseases has largely been based on traditional 'Sanger sequencing' and microchip approaches that are expensive and time consuming. However, with the advent of next-generation sequencing it is now possible to apply high-throughput technologies to the clinical arena and sequence the entire exome or genome of an affected individual. Despite the potential for a paradigm shift in the clinical diagnosis of retinal disease, it may prove difficult to interpret and confirm the pathogenicity of any variants discovered by next-generation sequencing pipelines. In this review, I examine the application of next-generation sequencing to inherited retinal disorders and discuss current limitations and future perspectives.

A framework for organizing cancer-related variations from existing databases, publications and NGS data using a High-performance Integrated Virtual Environment (HIVE)

Years of sequence feature curation by UniProtKB/Swiss-Prot, PIR-PSD, NCBI-CDD, RefSeq and other database biocurators has led to a rich repository of information on functional sites of genes and proteins. This information along with variation-related annotation can be used to scan human short sequence reads from next-generation sequencing (NGS) pipelines for presence of non-synonymous single-nucleotide variations (nsSNVs) that affect functional sites. This and similar workflows are becoming more important because thousands of NGS data sets are being made available through projects such as The Cancer Genome Atlas (TCGA), and researchers want to evaluate their biomarkers in genomic data. BioMuta, an integrated sequence feature database, provides a framework for automated and manual curation and integration of cancer-related sequence features so that they can be used in NGS analysis pipelines. Sequence feature information in BioMuta is collected from the Catalogue of Somatic Mutations in Cancer (COSMIC), ClinVar, UniProtKB and through biocuration of information available from publications. Additionally, nsSNVs identified through automated analysis of NGS data from TCGA are also included in the database. Because of the petabytes of data and information present in NGS primary repositories, a platform HIVE (High-performance Integrated Virtual Environment) for storing, analyzing, computing and curating NGS data and associated metadata has been developed. Using HIVE, 31 979 nsSNVs were identified in TCGA-derived NGS data from breast cancer patients. All variations identified through this process are stored in a Curated Short Read archive, and the nsSNVs from the tumor samples are included in BioMuta. Currently, BioMuta has 26 cancer types with 13 896 small-scale and 308 986 large-scale study-derived variations. Integration of variation data allows identifications of novel or common nsSNVs that can be prioritized in validation studies.Database URL: BioMuta: http://hive.biochemistry.gwu.edu/tools/biomuta/index.php; CSR: http://hive.biochemistry.gwu.edu/dna.cgi?cmd=csr; HIVE: http://hive.biochemistry.gwu.edu

Node-Oriented Workflow (NOW): A Command Template Workflow Management Tool for High Throughput Data Analysis Pipelines

Next generation sequencing (NGS) systems produce vast quantities of data that require substantial computational resources for typical analysis tasks. In addition, data that are generated by different NGS systems are not homogeneous. Moreover, there are an overwhelming number of tools available for performing typical tasks. Managing NGS workflows involves writing custom scripts that quickly grow in complexity, often resulting in unwieldy workflows that underutilize typical high performance compute resources, and increase the demands of the staff managing these workflows. We present Node-Oriented Workflow (NOW), a dynamic command template workflow engine for high performance distributed computing (HPC) systems. Our system provides a simple-to-use browserbased front end for designing and managing complex workflows. Workflows are configured using a simple browser interface, and are managed by the integrated job engine, which initializes nodes, monitors node status, and processes results of individual jobs across nodes in an HPC configuration. We reduce excessive messaging across nodes by placing the burden on nodes to start tasks in a workflow when dependencies are met, i.e., node oriented workflow. Our system was designed for NGS processing in the clinical research setting, emphasizing user simplicity, tool scalability, minimization of redundancy in workflows, while maximizing throughput in an HPC environment. Furthermore, NOW is not restricted to NGS pipeline management, but can used to manage any computational pipeline.

Evaluation of variant identification methods for whole genome sequencing data in dairy cattle.

BackgroundAdvances in human genomics have allowed unprecedented productivity in terms of algorithms, software, and literature available for translating raw next-generation sequence data into high-quality information. The challenges of variant identification in organisms with lower quality reference genomes are less well documented. We explored the consequences of commonly recommended preparatory steps and the effects of single and multi sample variant identification methods using four publicly available software applications (Platypus, HaplotypeCaller, Samtools and UnifiedGenotyper) on whole genome sequence data of 65 key ancestors of Swiss dairy cattle populations. Accuracy of calling next-generation sequence variants was assessed by comparison to the same loci from medium and high-density single nucleotide variant (SNV) arrays.ResultsThe total number of SNVs identified varied by software and method, with single (multi) sample results ranging from 17.7 to 22.0 (16.9 to 22.0) million variants. Computing time varied considerably between software. Preparatory realignment of insertions and deletions and subsequent base quality score recalibration had only minor effects on the number and quality of SNVs identified by different software, but increased computing time considerably. Average concordance for single (multi) sample results with high-density chip data was 58.3% (87.0%) and average genotype concordance in correctly identified SNVs was 99.2% (99.2%) across software. The average quality of SNVs identified, measured as the ratio of transitions to transversions, was higher using single sample methods than multi sample methods. A consensus approach using results of different software generally provided the highest variant quality in terms of transition/transversion ratio.ConclusionsOur findings serve as a reference for variant identification pipeline development in non-human organisms and help assess the implication of preparatory steps in next-generation sequencing pipelines for organisms with incomplete reference genomes (pipeline code is included). Benchmarking this information should prove particularly useful in processing next-generation sequencing data for use in genome-wide association studies and genomic selection.Electronic supplementary materialThe online version of this article (doi:10.1186/1471-2164-15-948) contains supplementary material, which is available to authorized users.

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

Remarkable advances in our ability to study the molecular profiles of human cancers by Next Generation Sequencing (NGS) methods are in rapid progress. The promise of scientific discovery that is offered by NGS also includes the challenge concerning the interpretation of large and non-trivial experimental results. This task is potentially further complicated when a multitude of molecular profiling modalities are available, with the goal of a more integrative type analysis of the cancer biology.As technology advances, it is critical to leverage what has been gained from historic approaches (e.g., microarrays) to new approaches (NGS). Microarray transcriptome analyses have resulted in important advances in biomedicine. For instance in multiple myeloma (MM), microarray approaches led to the development of a prognostic 70 gene risk score as well as an effective disease subtyping via cluster assignment. Both enabled an improved molecular understanding of MM, and have provided translational information for the purposes of more advanced patient management. Many researchers are now transitioning to NGS DNA approaches for the detection of sequence variants, as well as RNA-seq, also due to its discovery-based nature, improved sensitivity, and dynamic range. Additionally, RNA-seq allows for the analysis of gene isoforms, splice variants, novel gene fusions, etc. There is now a pressing need to associate and integrate microarray and NGS data via advanced bioinformatic approaches.Utilizing a model-view-controller (MVC) approach, a suite of custom software tools have been developed to integrate NGS experimental data along with microarray and Affymetix probe set-IDs, and gene annotation information from a variety of public sources, e.g., Affy HG-U133 Plus 2 release 33, Ensembl gene annotations and cross references for GRCh37, and NCBI gene annotations. The approach employs a variety of strategies to integrate, annotate, and associate microarray and NGS datasets. Output from the Tuxedo suite and GATK can be directly integrated, and/or associated with microarray and Affymetrix probe set data, as well as necessary gene identifiers and/or symbols from a variety of sources. Strategies are employed to maximize the integration and annotation processes across molecular profiling modalities. To further enhance a cancer biology focus, novel gene sets (e.g., MM 70 risk score) can be specified. Finally, the tool can be directly interfaced into a NGS pipeline. As a proof of concept, a custom MM gene list composed of CCND1, CCND3, DKK1, FGFR3, MAF, MAFB, NFKB, and WHSC1 were applied to NGS data sets derived from the MM cell lines RMPI-8226 and H929, which were obtained from ATCC. All NGS experiments were run on an Illumina HiSeq-2000 using 101 bp PE sequencing. Agilent SureSelect was used for whole exome sample prep and Illumina TrueSeq mRNA kit v2 with poly-A selection for RNA-seq. Integrative visualizations and renderings of MM molecular biology will be illustrated.A novel bioinformatic approach to aid in the facilitation of both annotation and association of historic microarray data in conjunction with richer NGS data is now assisting with the study of MM cancer biology. Disclosures:No relevant conflicts of interest to declare.

Panel-based next generation sequencing as a reliable and efficient technique to detect mutations in unselected patients with retinal dystrophies

Hereditary retinal dystrophies (RD) constitute a group of blinding diseases that are characterized by clinical variability and pronounced genetic heterogeneity. The different forms of RD can be caused by mutations in >100 genes, including >1600 exons. Consequently, next generation sequencing (NGS) technologies are among the most promising approaches to identify mutations in RD. So far, NGS is not routinely used in gene diagnostics. We developed a diagnostic NGS pipeline to identify mutations in 170 genetically and clinically unselected RD patients. NGS was applied to 105 RD-associated genes. Underrepresented regions were examined by Sanger sequencing. The NGS approach was successfully established using cases with known sequence alterations. Depending on the initial clinical diagnosis, we identified likely causative mutations in 55% of retinitis pigmentosa and 80% of Bardet-Biedl or Usher syndrome cases. Seventy-one novel mutations in 40 genes were newly associated with RD. The genes USH2A, EYS, ABCA4, and RHO were more frequently affected than others. Occasionally, cases carried mutations in more than one RD-associated gene. In addition, we found possible dominant de-novo mutations in cases with sporadic RD, which implies consequences for counseling of patients and families. NGS-based mutation analyses are reliable and cost-efficient approaches in gene diagnostics of genetically heterogeneous diseases like RD.

Next-generation sequencing in the clinic: Promises and challenges

The advent of next generation sequencing (NGS) technologies has revolutionized the field of genomics, enabling fast and cost-effective generation of genome-scale sequence data with exquisite resolution and accuracy. Over the past years, rapid technological advances led by academic institutions and companies have continued to broaden NGS applications from research to the clinic. A recent crop of discoveries have highlighted the medical impact of NGS technologies on Mendelian and complex diseases, particularly cancer. However, the ever-increasing pace of NGS adoption presents enormous challenges in terms of data processing, storage, management and interpretation as well as sequencing quality control, which hinder the translation from sequence data into clinical practice. In this review, we first summarize the technical characteristics and performance of current NGS platforms. We further highlight advances in the applications of NGS technologies towards the development of clinical diagnostics and therapeutics. Common issues in NGS workflows are also discussed to guide the selection of NGS platforms and pipelines for specific research purposes.

Generation of Artificial FASTQ Files to Evaluate the Performance of Next-Generation Sequencing Pipelines

Pipelines for the analysis of Next-Generation Sequencing (NGS) data are generally composed of a set of different publicly available software, configured together in order to map short reads of a genome and call variants. The fidelity of pipelines is variable. We have developed ArtificialFastqGenerator, which takes a reference genome sequence as input and outputs artificial paired-end FASTQ files containing Phred quality scores. Since these artificial FASTQs are derived from the reference genome, it provides a gold-standard for read-alignment and variant-calling, thereby enabling the performance of any NGS pipeline to be evaluated. The user can customise DNA template/read length, the modelling of coverage based on GC content, whether to use real Phred base quality scores taken from existing FASTQ files, and whether to simulate sequencing errors. Detailed coverage and error summary statistics are outputted. Here we describe ArtificialFastqGenerator and illustrate its implementation in evaluating a typical bespoke NGS analysis pipeline under different experimental conditions. ArtificialFastqGenerator was released in January 2012. Source code, example files and binaries are freely available under the terms of the GNU General Public License v3.0. from https://sourceforge.net/projects/artfastqgen/.

Exploring functional variant discovery in non-coding regions with SInBaD

The thousand genomes project and many similar ongoing large-scale sequencing efforts require new methods to predict functional variants in both coding and non-coding regions in order to understand phenotype and genotype relationships. We report the design of a new model SInBaD (Sequence-Information-Based-Decision-model) which relies on nucleotide conservation information to evaluate any annotated human variant in all known exons, introns, splice junctions and promoter regions. SInBaD builds separate mathematical models for promoters, exons and introns, using the human disease mutations annotated in human gene mutation database as the training dataset for functional variants. The ten-fold cross validation shows high prediction accuracy. Validations on test datasets, demonstrate that variants predicted as functional have a significantly higher occurrence in cancer patients. We also applied our model to variants found in four different individual human genomes to identify a set of functional variants, which might be of interest for further studies. Scores for any possible variants for all annotated genes are available under http://tingchenlab.cmb.usc.edu/sinbad/. SInBaD supports the current standard format of genotyping, the variant call files (VCF 4.0), making it easy to integrate it into any existing next-generation sequencing pipeline. The accuracy of SNP detection poses the only limitation to the use of SInBaD.