Variant Calling Pipeline Research Articles

A Bioinformatics pipeline for variant discovery from Targeted Next Generation Sequencing of the human mitochondrial genome

Sequence variants of human mitochondrial DNA (mt DNA) have been implicated in a variety of disorders and conditions. Massive parallel sequencing is becoming increasingly popular due to its efficiency and cost-effectiveness. In relation to acquiring significant sequence information like levels of heteroplasmy in mt DNA, it offers a marked improvement compared to previous methods used. Here we describe a variant calling pipeline for human mitochondrial DNA using Next Generation Sequencing (NGS) data obtained by enriching the sample only for mitochondria prior to sequencing.

FVC as an adaptive and accurate method for filtering variants from popular NGS analysis pipelines

The quality control of variants from whole-genome sequencing data is vital in clinical diagnosis and human genetics research. However, current filtering methods (Frequency, Hard-Filter, VQSR, GARFIELD, and VEF) were developed to be utilized on particular variant callers and have certain limitations. Especially, the number of eliminated true variants far exceeds the number of removed false variants using these methods. Here, we present an adaptive method for quality control on genetic variants from different analysis pipelines, and validate it on the variants generated from four popular variant callers (GATK HaplotypeCaller, Mutect2, Varscan2, and DeepVariant). FVC consistently exhibited the best performance. It removed far more false variants than the current state-of-the-art filtering methods and recalled ~51-99% true variants filtered out by the other methods. Once trained, FVC can be conveniently integrated into a user-specific variant calling pipeline.

Editome landscape of CCM-derived endothelial cells

ABSTRACT By regulating several phases of gene expression, RNA editing modifications contribute to maintaining physiological RNA expression levels. RNA editing dysregulation can affect RNA molecule half-life, coding/noncoding RNA interaction, alternative splicing, and circular RNA biogenesis. Impaired RNA editing has been observed in several pathological conditions, including cancer and Alzheimer’s disease. No data has been published yet on the editome profile of endothelial cells (ECs) isolated from human cerebral cavernous malformation (CCM) lesions. Here, we describe a landscape of editome modifications in sporadic CCM-derived ECs (CCM-ECs) by comparing editing events with those observed in human brain microvascular endothelial cells (HBMECs). With a whole transcriptome-based variant calling pipeline, we identified differential edited genes in CCM-ECs that were enriched in pathways related to angiogenesis, apoptosis and cell survival, inflammation and, in particular, to thrombin signalling mediated by protease-activated receptors and non-canonical Wnt signalling. These pathways, not yet associated to CCM development, could be a novel field for further investigations on CCM molecular mechanisms. Moreover, enrichment analysis of differentially edited miRNAs suggested additional small noncoding transcripts to consider for development of targeted therapies.

Abstract 437: Characterization of SARS-CoV-2 using the Ion AmpliSeq SARS-CoV-2 research panel

Abstract Background: The rapid spread of COVID-19 has resulted in an urgent need for effective diagnostic and therapeutic strategies against SARS-CoV-2. Next-generation sequencing (NGS) is a powerful tool in the identification and characterization of this pathogen and genomic information may aid in understanding the mechanisms of therapeutic resistance, vaccine escape, virulence, and pathogenicity. The Ion AmpliSeq SARS-CoV-2 Research Panel is a targeted NGS solution that facilitates sequence analysis of the SARS-CoV-2 genome. Paired with a bioinformatics assembly and variant calling pipelines, this assay allows for accurate characterization of the dominant SARS-CoV-2 variant. This assay’s performance was analytically validated for the detection of mutations (substitutions, insertions, and deletions) in RNA derived from nasopharyngeal (NP) swabs. Method: The Ion AmpliSeq SARS-CoV-2 Research panel consists of two primer pair pools generating 237 amplicons specific to the SARS-CoV-2 virus. Reverse transcription of the RNA was performed using the SuperScript VILO cDNA Synthesis kit. Library preparation was then completed using the Ion AmpliSeq Library Kit Plus kit. The final library was quantified, normalized, pooled, and sequenced. Raw sequencing data was aligned to the AmpliSeq SARS-CoV-2 Research panel, using the MN908947.3 reference genome. Variants were called using the Torrent Variant Caller and annotated using the COVID19AnnotateSnpEff plugin. The reference-guided iterative assembler IRMA was used to produce a single consensus sequence consisting of the reference genome sequence modified to include sequence variations supported by the reads. The Pangolin COVID-19 lineage assigner software tool was used to assign SARS-CoV-2 lineage. Analytical validation was completed using controls (Twist Biosciences, BEI Resources, ATCC) and RNA derived from NP swabs. Accuracy and specificity were examined by evaluating the correctness of calling true negative variants compared to false positive and all other variant calls, respectively. Precision and limit of detection (LoD) were examined by evaluating the concordance of variants across replicate samples. Limit of Blank (LoB) was calculated as the 95th percentile of reads per amplicon in the negative samples. Results: Accuracy of base calling, specificity, and precision were 100% for SNVs, insertions, and deletions above 25% allele frequency. LoD was determined to be 576 viral copies/mL. LoB was determined to be 202 reads per amplicon. Pangolin lineage assignment was 100% for all samples. Conclusions: This panel accurately characterizes SARS-CoV-2 variants, allowing for accurate consensus sequence generation, mutation annotation, and lineage assignment. Citation Format: Desiree Unselt, Katherine Knudsen, Christopher Rounds, Janet Doolittle-Hall, Fernando Torres, Jennifer Sims, Jennifer Mason. Characterization of SARS-CoV-2 using the Ion AmpliSeq SARS-CoV-2 research panel [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 437.

Abstract 538: Cell-free whole genome sequencing for the detection of cancer in patients with germline BRCA1/2 mutations

Abstract Background: Hereditary breast and ovarian cancer syndrome (HBOC) is caused by a germline likely/pathogenic variant in BRCA1 or BRCA2, leading to increased risk of developing breast, ovarian, prostate, and pancreatic cancers. While HBOC patients often undergo regular surveillance including breast imaging, other cancers have no effective screening or biomarkers. One emerging technology is the analysis of circulating tumor DNA (ctDNA), fragments of DNA that are shed from the tumor into the bloodstream. In sporadic cancer, several studies have demonstrated the advantages and sensitivity of cell-free whole genome sequencing (cfWGS) compared to targeted panel sequencing. However, the use of cfWGS for cancer detection in HBOC has not been explored. Methods: Matched tumor, germline, and plasma were collected from 5 HBOC patients (3 breast, 2 ovarian) as part of the Canada-wide CHARM consortium (https://charmconsortium.ca). Tumor (40x), germline (20x), and plasma (20x) underwent whole genome sequencing (WGS) and subsequent variant calling pipeline using an ensemble of 6 variant callers. Insertions, deletions, and structural variants were not included in downstream analyses. Plasma tumor fraction prediction and copy number alterations were performed using ichorCNA. Detection of tumor associated mutations in plasma was compared to MRDetect software. Results: Variant calling identified an average of 11,170 tumor (7,649-16,706) and 1,215 (544-3,205) plasma SNVs per patient. Somatic BRCA1/2 mutations were detected in 0/5; whereas deletion of the intact BRCA1/2 allele was identified in 5/5 patients. An average of 24.6% (8.3%-75.9%) of plasma mutations where shared with SNVs identified in matched tumor. Targeted panel deep-sequencing (20,000X) was only able to detect mutations in TP53 in 3/5 cases (0.8% - 8.6%). Plasma derived copy number alterations correlated well with tumor derived copy number alterations in 3/5 cases and ichorCNA predicted tumor fractions ranged from 0.038 to 0.196. Conclusions: Traditionally, targeted panel sequencing has been used to identify tumor associated mutations in plasma. While this technique is sensitive, major disadvantages include the narrow breadth of the captured regions and the inability to detect deletion events such as those that occur in BRCA1/2. Here we show that plasma WGS is effective for detecting tumor associated mutations in HBOC and is more sensitive than targeted panel sequencing. We are currently expanding our cohort to an additional 20 matched tumor pairs, including patients with pre-diagnosis plasma timepoints. Citation Format: Derek Wong, Adriana Aguilar-Mahecha, Josiane Lafleur, Clarissa Chan, Kirsten Farncombe, Maia Norman, Leslie Oldfield, Prabhjit Basra, Stephanie Pederson, Johanna Wellum, Stephenie Prokopec, Mark Basik, Raymond H. Kim, Trevor Pugh. Cell-free whole genome sequencing for the detection of cancer in patients with germline BRCA1/2 mutations [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 538.

Recommendations for IVDR compliant in-house software development in clinical practice: a how-to paper with three use cases.

The In Vitro Diagnostics Regulation (IVDR) will be effective in May 2022 by which in-house developed tests need to apply to the general safety and performance requirements defined in Annex I of the IVDR ruling. Yet, article 16 from Annex I about software can be hard to interpret and implement, particularly as laboratories are unfamiliar with quality standards for software development. In this paper we provide recommendations on organizational structure, standards to use, and documentation, for IVDR compliant in-house software development. A practical insight is offered into novel standard operating procedures using three examples: an Excel file with a formula to calculate the pharmacokinetics of tacrolimus and to calculate the new dose, a rule for automated diagnosis of acute kidney injury and a bioinformatics pipeline for DNA variant calling. We recommend multidisciplinary development teams supported by higher management, use of ISO-15189 in synergy with IEC-62304, and concise documentation that includes intended purpose, classification, requirement management, risk management, verification and validation, configuration management and references to clinical or performance evidence.

PrecisionFDA Truth Challenge V2: Calling variants from short and long reads in difficult-to-map regions

SUMMARYThe precisionFDA Truth Challenge V2 aimed to assess the state of the art of variant calling in challenging genomic regions. Starting with FASTQs, 20 challenge participants applied their variant-calling pipelines and submitted 64 variant call sets for one or more sequencing technologies (Illumina, PacBio HiFi, and Oxford Nanopore Technologies). Submissions were evaluated following best practices for benchmarking small variants with updated Genome in a Bottle benchmark sets and genome stratifications. Challenge submissions included numerous innovative methods, with graph-based and machine learning methods scoring best for short-read and long-read datasets, respectively. With machine learning approaches, combining multiple sequencing technologies performed particularly well. Recent developments in sequencing and variant calling have enabled benchmarking variants in challenging genomic regions, paving the way for the identification of previously unknown clinically relevant variants.

Development and evaluation of ActSeq: A targeted next-generation sequencing panel for clinical oncology use.

PurposeThe demand for high-throughput genetic profiling of somatic mutations in cancer tissues is growing. We sought to establish a targeted next generation sequencing (NGS) panel test for clinical oncology practice.MethodsCustomized probes were designed to capture exonic regions of 141 genes selected for the panel, which was aimed for the detection of clinically actionable genetic variations in cancer, including KRAS, NRAS, BRAF, ALK, ROS1, KIT and EGFR. The size of entire targeted regions is 0.8 Mb. Library preparation used NEBNext Ultra II FS kit coupled with target enrichment. Paired-end sequencing was run on Illumina NextSeq 500 at a read length of 150 nt. A bioinformatics workflow focusing on single nucleotide variant and short insertions and deletions (SNV/indel) discovery was established using open source, in-house and commercial software tools. Standard reference DNA samples were used in testing the sensitivity and precision and limit of detection in variant calling.ResultsThe general performance of the panel was observed in pilot runs. Average total reads per sample ranged from 30 million to 48 million, 73% ~82% unique reads. All runs had more than 99% average mapping rate. Mean target coverage ranged from 727x to 879x. Depth of coverage at 50x or more reached 87% of targeted region and 60% of targeted region received 500x or more coverage depth. Using OncoSpan HD827 DNA, which bears 144 variants (SNV/indel) from 80 genes that are within the targeted region on the panel, our somatic variant calling pipeline reached 97% sensitivity and 100% precision respectively, with near 48 million reads. High concordance with orthogonal approaches in variant detection was further verified with 7 cancer cell lines and 45 clinical specimens.ConclusionWe developed a NGS panel with a focus on clinically actionable gene mutations and validated the performance in library construction, sequencing and variant calling. High concordance with reference materials and orthogonal mutation detection was observed.

Systematic benchmark of state-of-the-art variant calling pipelines identifies major factors affecting accuracy of coding sequence variant discovery

BackgroundAccurate variant detection in the coding regions of the human genome is a key requirement for molecular diagnostics of Mendelian disorders. Efficiency of variant discovery from next-generation sequencing (NGS) data depends on multiple factors, including reproducible coverage biases of NGS methods and the performance of read alignment and variant calling software. Although variant caller benchmarks are published constantly, no previous publications have leveraged the full extent of available gold standard whole-genome (WGS) and whole-exome (WES) sequencing datasets.ResultsIn this work, we systematically evaluated the performance of 4 popular short read aligners (Bowtie2, BWA, Isaac, and Novoalign) and 9 novel and well-established variant calling and filtering methods (Clair3, DeepVariant, Octopus, GATK, FreeBayes, and Strelka2) using a set of 14 “gold standard” WES and WGS datasets available from Genome In A Bottle (GIAB) consortium. Additionally, we have indirectly evaluated each pipeline’s performance using a set of 6 non-GIAB samples of African and Russian ethnicity. In our benchmark, Bowtie2 performed significantly worse than other aligners, suggesting it should not be used for medical variant calling. When other aligners were considered, the accuracy of variant discovery mostly depended on the variant caller and not the read aligner. Among the tested variant callers, DeepVariant consistently showed the best performance and the highest robustness. Other actively developed tools, such as Clair3, Octopus, and Strelka2, also performed well, although their efficiency had greater dependence on the quality and type of the input data. We have also compared the consistency of variant calls in GIAB and non-GIAB samples. With few important caveats, best-performing tools have shown little evidence of overfitting.ConclusionsThe results show surprisingly large differences in the performance of cutting-edge tools even in high confidence regions of the coding genome. This highlights the importance of regular benchmarking of quickly evolving tools and pipelines. We also discuss the need for a more diverse set of gold standard genomes that would include samples of African, Hispanic, or mixed ancestry. Additionally, there is also a need for better variant caller assessment in the repetitive regions of the coding genome.

Comprehensive variant calling from whole-genome sequencing identifies a complex inversion that disrupts ZFPM2 in familial congenital diaphragmatic hernia.

BackgroundGenetic disorders contribute to significant morbidity and mortality in critically ill newborns. Despite advances in genome sequencing technologies, a majority of neonatal cases remain unsolved. Complex structural variants (SVs) often elude conventional genome sequencing variant calling pipelines and will explain a portion of these unsolved cases.MethodsAs part of the Utah NeoSeq project, we used a research‐based, rapid whole‐genome sequencing (WGS) protocol to investigate the genomic etiology for a newborn with a left‐sided congenital diaphragmatic hernia (CDH) and cardiac malformations, whose mother also had a history of CDH and atrial septal defect.ResultsUsing both a novel, alignment‐free and traditional alignment‐based variant callers, we identified a maternally inherited complex SV on chromosome 8, consisting of an inversion flanked by deletions. This complex inversion, further confirmed using orthogonal molecular techniques, disrupts the ZFPM2 gene, which is associated with both CDH and various congenital heart defects.ConclusionsOur results demonstrate that complex structural events, which often are unidentifiable or not reported by clinically validated testing procedures, can be discovered and accurately characterized with conventional, short‐read sequencing and underscore the utility of WGS as a first‐line diagnostic tool.

Global diversity and balancing selection of 23 leading Plasmodium falciparum candidate vaccine antigens.

Investigation of the diversity of malaria parasite antigens can help prioritize and validate them as vaccine candidates and identify the most common variants for inclusion in vaccine formulations. Studies of vaccine candidates of the most virulent human malaria parasite, Plasmodium falciparum, have focused on a handful of well-known antigens, while several others have never been studied. Here we examine the global diversity and population structure of leading vaccine candidate antigens of P. falciparum using the MalariaGEN Pf3K (version 5.1) resource, comprising more than 2600 genomes from 15 malaria endemic countries. A stringent variant calling pipeline was used to extract high quality antigen gene ‘haplotypes’ from the global dataset and a new R-package named VaxPack was used to streamline population genetic analyses. In addition, a newly developed algorithm that enables spatial averaging of selection pressure on 3D protein structures was applied to the dataset. We analysed the genes encoding 23 leading and novel candidate malaria vaccine antigens including csp, trap, eba175, ama1, rh5, and CelTOS. Our analysis shows that current malaria vaccine formulations are based on rare haplotypes and thus may have limited efficacy against natural parasite populations. High levels of diversity with evidence of balancing selection was detected for most of the erythrocytic and pre-erythrocytic antigens. Measures of natural selection were then mapped to 3D protein structures to predict targets of functional antibodies. For some antigens, geographical variation in the intensity and distribution of these signals on the 3D structure suggests adaptation to different human host or mosquito vector populations. This study provides an essential framework for the diversity of P. falciparum antigens to be considered in the design of the next generation of malaria vaccines.

Failure to Detect Mutations in U2AF1 due to Changes in the GRCh38 Reference Sequence

The U2AF1 gene is a core part of mRNA splicing machinery and frequently contains somatic mutations that contribute to oncogenesis in myelodysplastic syndrome, acute myeloid leukemia, and other cancers. A change introduced in the GRCh38 version of the human reference build prevents detection of mutations in this gene, and others, by variant calling pipelines. This study describes the problem in detail and shows that a modified GRCh38 reference build with unchanged coordinates can be used to ameliorate the issue.

Halvade somatic: Somatic variant calling with Apache Spark.

BackgroundThe accurate detection of somatic variants from sequencing data is of key importance for cancer treatment and research. Somatic variant calling requires a high sequencing depth of the tumor sample, especially when the detection of low-frequency variants is also desired. In turn, this leads to large volumes of raw sequencing data to process and hence, large computational requirements. For example, calling the somatic variants according to the GATK best practices guidelines requires days of computing time for a typical whole-genome sequencing sample.FindingsWe introduce Halvade Somatic, a framework for somatic variant calling from DNA sequencing data that takes advantage of multi-node and/or multi-core compute platforms to reduce runtime. It relies on Apache Spark to provide scalable I/O and to create and manage data streams that are processed on different CPU cores in parallel. Halvade Somatic contains all required steps to process the tumor and matched normal sample according to the GATK best practices recommendations: read alignment (BWA), sorting of reads, preprocessing steps such as marking duplicate reads and base quality score recalibration (GATK), and, finally, calling the somatic variants (Mutect2). Our approach reduces the runtime on a single 36-core node to 19.5 h compared to a runtime of 84.5 h for the original pipeline, a speedup of 4.3 times. Runtime can be further decreased by scaling to multiple nodes, e.g., we observe a runtime of 1.36 h using 16 nodes, an additional speedup of 14.4 times. Halvade Somatic supports variant calling from both whole-genome sequencing and whole-exome sequencing data and also supports Strelka2 as an alternative or complementary variant calling tool. We provide a Docker image to facilitate single-node deployment. Halvade Somatic can be executed on a variety of compute platforms, including Amazon EC2 and Google Cloud.ConclusionsTo our knowledge, Halvade Somatic is the first somatic variant calling pipeline that leverages Big Data processing platforms and provides reliable, scalable performance. Source code is freely available.

Data Processing and Germline Variant Calling with the Sentieon Pipeline.

Public and private genomic sequencing initiatives generate ever-increasing amounts of genomic data creating a need for improved solutions for genomics data processing (Stephens et al.PLoS Biol 13:e1002195, 2015). The Sentieon® Genomics software enables rapid and accurate analysis of next-generation sequence data. In this work, we present a typical use of the Sentieon Genomics software for germline variant calling. The Sentieon germline variant calling pipeline produces more accurate results than other tools on third-party benchmarks (Katherine et al. Front Genet 10:736, 2019; Shen et al. bioRxiv, 885517, 2019) in one tenth the time of comparable pipelines. Parts of this guide come from the official Sentieon Genomics software manual in https://support.sentieon.com/manual (Sentieon. Sentieon Genomics software manual, n.d.) and from the official Sentieon Genomics software application notes in https://support.sentieon.com/appnotes (Sentieon. Sentieon Genomics software application notes, n.d.) and are republished with permission. For additional details and advanced usage instructions of the Sentieon tools, refer to the software manual.

Development of an SNP Assay for Marker-Assisted Selection of Soil-Borne Rhizoctonia solani AG-2-2-IIIB Resistance in Sugar Beet.

Rhizoctonia solani, causing Rhizoctonia crown and root rot, is a major risk to sugar beet (Beta vulgaris L.) cultivation. The development of resistant varieties accelerated by marker-assisted selection is a priority of breeding programs. We report the identification of a single-nucleotide polymorphism (SNP) marker linked to Rhizoctonia resistance using restriction site-associated DNA (RAD) sequencing of two geographically discrete sets of plant materials with different degrees of resistance/susceptibility to enable a wider selection of superior genotypes. The variant calling pipeline utilized SAMtools for variant calling and the resulting raw SNPs from RAD sequencing (15,988 and 22,439 SNPs) were able to explain 13.40% and 25.45% of the phenotypic variation in the two sets of material from different sources of origin, respectively. An association analysis was carried out independently on both the datasets and mutually occurring significant SNPs were filtered depending on their contribution to the phenotype using principal component analysis (PCA) biplots. To provide a ready-to-use marker for the breeding community, a systematic molecular validation of significant SNPs distributed across the genome was undertaken to combine high-resolution melting, Sanger sequencing, and rhAmp SNP genotyping. We report that RsBv1 located on Chromosome 6 (9,000,093 bp) is significantly associated with Rhizoctonia resistance (p < 0.01) and able to explain 10% of the phenotypic disease variance. The related SNP assay is thus ready for marker-assisted selection in sugar beet breeding for Rhizoctonia resistance.

Performance and Agreement Between WGS Variant Calling Pipelines Used for Bovine Tuberculosis Control: Toward International Standardization.

Whole genome sequencing (WGS) and allied variant calling pipelines are a valuable tool for the control and eradication of infectious diseases, since they allow the assessment of the genetic relatedness of strains of animal pathogens. In the context of the control of tuberculosis (TB) in livestock, mainly caused by Mycobacterium bovis, these tools offer a high-resolution alternative to traditional molecular methods in the study of herd breakdown events. However, despite the increased use and efforts in the standardization of WGS methods in human tuberculosis around the world, the application of these WGS-enabled approaches to control TB in livestock is still in early development. Our study pursued an initial evaluation of the performance and agreement of four publicly available pipelines for the analysis of M. bovis WGS data (vSNP, SNiPgenie, BovTB, and MTBseq) on a set of simulated Illumina reads generated from a real-world setting with high TB prevalence in cattle and wildlife in the Republic of Ireland. The overall performance of the evaluated pipelines was high, with recall and precision rates above 99% once repeat-rich and problematic regions were removed from the analyses. In addition, when the same filters were applied, distances between inferred phylogenetic trees were similar and pairwise comparison revealed that most of the differences were due to the positioning of polytomies. Hence, under the studied conditions, all pipelines offer similar performance for variant calling to underpin real-world studies of M. bovis transmission dynamics.

A comparative study of pan-genome methods for microbial organisms: Acinetobacter baumannii pan-genome reveals structural variation in antimicrobial resistance-carrying plasmids.

Microbial organisms have diverse populations, where using a single linear reference sequence in comparative studies introduces reference-bias in downstream analyses, and leads to a failure to account for variability in the population. Recently, pan-genome graphs have emerged as an alternative to the traditional linear reference with many successful applications and a rapid increase in the number of methods available in the literature. Despite this enthusiasm, there has been no attempt at exploring these graph construction methods in depth, demonstrating their practical use. In this study, we aim to develop a general guide to help researchers who may want to incorporate pan-genomes in their analyses of microbial organisms. We evaluated the state-of-the art pan-genome construction tools to model a collection of 70 Acinetobacter baumannii strains. Our results suggest that all tools produced pan-genome graphs conforming to our expectations based on previous literature, and that their approach to homologue detection is likely to be the most influential in determining the final size and complexity of the pan-genome. The graphs overlapped most in the core pan-genome content while the cloud genes varied significantly among tools. We propose an alternative approach for pan-genome construction by combining two of the tools, Panaroo and Ptolemy, to further exploit them in downstream analyses, and demonstrate the effectiveness of our pipeline for structural variant calling in beta-lactam resistance genes in the same set of A. baumannii isolates, identifying various transposon structures for carbapenem resistance in chromosome, as well as plasmids. We identify a novel plasmid structure in two multidrug-resistant clinical isolates that had previously been studied, and which could be important for their resistance phenotypes.

HashSeq: a Simple, Scalable, and Conservative De Novo Variant Caller for 16S rRNA Gene Data Sets.

ABSTRACT16S rRNA gene sequencing is a common and cost-effective technique for characterization of microbial communities. Recent bioinformatics methods enable high-resolution detection of sequence variants of only one nucleotide difference. In this study, we utilized a very fast HashMap-based approach to detect sequence variants in six publicly available 16S rRNA gene data sets. We then use the normal distribution combined with locally estimated scatterplot smoothing (LOESS) regression to estimate background error rates as a function of sequencing depth for individual clusters of sequences. This method is computationally efficient and produces inference that yields sets of variants that are conservative and well supported by reference databases. We argue that this approach to inference is fast, simple, and scalable to large data sets and provides a high-resolution set of sequence variants which are less likely to be the result of sequencing error.IMPORTANCE Recent bioinformatics development has enabled the detection of sequence variants with a high resolution of only one single-nucleotide difference in 16S rRNA gene sequence data. Despite this progress, there are several limitations that can be associated with variant calling pipelines, such as producing a large number of low-abundance sequence variants which need to be filtered out with arbitrary thresholds in downstream analyses or having a slow runtime. In this report, we introduce a fast and scalable algorithm which infers sequence variants based on the estimation of a normally distributed background error as a function of sequencing depth. Our pipeline has attractive performance characteristics, can be used independently or in parallel with other variant callers, and provides explicit P values for each variant evaluating the hypothesis that a variant is caused by sequencing error.

From Forensics to Clinical Research: Expanding the Variant Calling Pipeline for the Precision ID mtDNA Whole Genome Panel.

Despite a multitude of methods for the sample preparation, sequencing, and data analysis of mitochondrial DNA (mtDNA), the demand for innovation remains, particularly in comparison with nuclear DNA (nDNA) research. The Applied Biosystems™ Precision ID mtDNA Whole Genome Panel (Thermo Fisher Scientific, USA) is an innovative library preparation kit suitable for degraded samples and low DNA input. However, its bioinformatic processing occurs in the enterprise Ion Torrent Suite™ Software (TSS), yielding BAM files aligned to an unorthodox version of the revised Cambridge Reference Sequence (rCRS), with a heteroplasmy threshold level of 10%. Here, we present an alternative customizable pipeline, the PrecisionCallerPipeline (PCP), for processing samples with the correct rCRS output after Ion Torrent sequencing with the Precision ID library kit. Using 18 samples (3 original samples and 15 mixtures) derived from the 1000 Genomes Project, we achieved overall improved performance metrics in comparison with the proprietary TSS, with optimal performance at a 2.5% heteroplasmy threshold. We further validated our findings with 50 samples from an ongoing independent cohort of stroke patients, with PCP finding 98.31% of TSS’s variants (TSS found 57.92% of PCP’s variants), with a significant correlation between the variant levels of variants found with both pipelines.

Design considerations for workflow management systems use in production genomics research and the clinic

The changing landscape of genomics research and clinical practice has created a need for computational pipelines capable of efficiently orchestrating complex analysis stages while handling large volumes of data across heterogeneous computational environments. Workflow Management Systems (WfMSs) are the software components employed to fill this gap. This work provides an approach and systematic evaluation of key features of popular bioinformatics WfMSs in use today: Nextflow, CWL, and WDL and some of their executors, along with Swift/T, a workflow manager commonly used in high-scale physics applications. We employed two use cases: a variant-calling genomic pipeline and a scalability-testing framework, where both were run locally, on an HPC cluster, and in the cloud. This allowed for evaluation of those four WfMSs in terms of language expressiveness, modularity, scalability, robustness, reproducibility, interoperability, ease of development, along with adoption and usage in research labs and healthcare settings. This article is trying to answer, which WfMS should be chosen for a given bioinformatics application regardless of analysis type?. The choice of a given WfMS is a function of both its intrinsic language and engine features. Within bioinformatics, where analysts are a mix of dry and wet lab scientists, the choice is also governed by collaborations and adoption within large consortia and technical support provided by the WfMS team/community. As the community and its needs continue to evolve along with computational infrastructure, WfMSs will also evolve, especially those with permissive licenses that allow commercial use. In much the same way as the dataflow paradigm and containerization are now well understood to be very useful in bioinformatics applications, we will continue to see innovations of tools and utilities for other purposes, like big data technologies, interoperability, and provenance.