Performance Assessment of Variant Calling Pipelines using Human Whole Exome Sequencing and Simulated data

Kumaran, Manojkumar; Umadevi, S.; Devarajan, Bharanidharan

doi:10.1101/359109

Cited by 13 publications

(16 citation statements)

References 27 publications

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“…Likewise, Stampy/SAMtools with 50 bp padding followed by BWA-MEM/GATK-UG with zero and 50 bp padding, BWA-MEM/SAMtools with 100 bp padding and BWA Enrichment application, were the top tier exonic SNV calling pipeline combinations. Our results, partly agree with previous data [ 3 , 4 ], supporting the finding that BWA-MEM/SAMtools pipeline showed the best performance for SNP calls. In contrast to what we present, Whang et al [ 3 ] showed that the variant caller has more influence than read aligner on SNP calling, whereas Kumaran et al [ 4 ] did not observe any significant changes in the top performing SNP calling pipelines.…”

Section: Discussionsupporting

confidence: 93%

Performance evaluation of pipelines for mapping, variant calling and interval padding, for the analysis of NGS germline panels

et al. 2021

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Background Next-generation sequencing (NGS) represents a significant advancement in clinical genetics. However, its use creates several technical, data interpretation and management challenges. It is essential to follow a consistent data analysis pipeline to achieve the highest possible accuracy and avoid false variant calls. Herein, we aimed to compare the performance of twenty-eight combinations of NGS data analysis pipeline compartments, including short-read mapping (BWA-MEM, Bowtie2, Stampy), variant calling (GATK-HaplotypeCaller, GATK-UnifiedGenotyper, SAMtools) and interval padding (null, 50 bp, 100 bp) methods, along with a commercially available pipeline (BWA Enrichment, Illumina®). Fourteen germline DNA samples from breast cancer patients were sequenced using a targeted NGS panel approach and subjected to data analysis. Results We highlight that interval padding is required for the accurate detection of intronic variants including spliceogenic pathogenic variants (PVs). In addition, using nearly default parameters, the BWA Enrichment algorithm, failed to detect these spliceogenic PVs and a missense PV in the TP53 gene. We also recommend the BWA-MEM algorithm for sequence alignment, whereas variant calling should be performed using a combination of variant calling algorithms; GATK-HaplotypeCaller and SAMtools for the accurate detection of insertions/deletions and GATK-UnifiedGenotyper for the efficient detection of single nucleotide variant calls. Conclusions These findings have important implications towards the identification of clinically actionable variants through panel testing in a clinical laboratory setting, when dedicated bioinformatics personnel might not always be available. The results also reveal the necessity of improving the existing tools and/or at the same time developing new pipelines to generate more reliable and more consistent data.

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Section: Discussionsupporting

confidence: 93%

Performance evaluation of pipelines for mapping, variant calling and interval padding, for the analysis of NGS germline panels

et al. 2021

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“…Using Illumina HiSeq X ten platform, 6800 clinically relevant genes were captured with the preconstructed library to generate 150 bp paired-end reads at 100X sequencing depth. Post-sequencing data processing and variants filtration was performed using inhouse UNIX scripts [19]. The quality of the raw data in FASTQ file was checked and the bad reads were removed using Fast QC (version 0.11.5).…”

Section: Targeted Exome Sequencing (Tes)mentioning

confidence: 99%

Genetic characterization of Stargardt clinical phenotype in South Indian patients using sanger and targeted sequencing

Raj¹,

Dhoble

Anjanamurthy

et al. 2020

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Background: Stargardt disease 1 (STGD1; MIM 248200) is a monogenic form of autosomal recessive genetic disease caused by mutation in ABCA4. This gene has a major role in hydrolyzing N-retinylidene-phosphatidylethanolamine to all-trans-retinal and phosphatidylethanolamine. The purpose of this study is to identify the frequency of putative disease-causing mutations associated with Stargardt disease in a South Indian population. Methods: A total of 28 clinically diagnosed Stargardt-like phenotype patients were recruited from south India. Ophthalmic examination of all patients was carefully carried out by a retina specialist based on the stages of fundus imaging and ERG grouping. Genetic analysis of ABCA4 was performed for all patients using Sanger sequencing and clinical exome sequencing. Results: This study identified disease-causing mutations in ABCA4 in 75% (21/28) of patients, 7% (2/28) exhibited benign variants and 18% (5/28) were negative for the disease-causing mutation. Conclusion: This is the first study describing the genetic association of ABCA4 disease-causing mutation in South Indian Stargardt 1 patients (STGD1). Our findings highlighted the presence of two novel missense mutations and an (in/del, single base pair deletion & splice variant) in ABCA4. However, genetic heterogeneity in ABCA4 mutants requires a larger sample size to establish a true correlation with clinical phenotype.

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“…: SRR098401). We built a pipeline similar to prior work Kumaran et al . (2019) to detect variants using GATK HaplotypeCaller (v4.1.0) (DePristo et al .…”

Section: Resultsmentioning

confidence: 99%

Accel-Align: A Fast Sequence Mapper and Aligner Based on the Seed–Embed–Extend Method

Yan

Chaturvedi²,

Appuswamy

2020

Preprint

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MotivationImprovements in sequencing technology continue to drive sequencing cost towards 100$ per genome. However, mapping sequenced data to a reference genome remains a computationally-intensive task due to the dependence on edit distance for dealing with indels and mismatches introduced by sequencing. All modern aligners use seed–filter–extend (SFE) methodology and rely on filtration heuristics to reduce the overhead of edit distance computation. However, filtering makes assumptions about error patterns, has inherent performance–accuracy trade-offs, and requires careful hand tuning to match the dataset.ResultsMotivated by algorithmic advances in randomized low-distortion embeddings, we introduce seed–embed–extend (SEE), a new design methodology for developing sequence mappers and aligners. While SFE focuses on eliminating sub-optimal candidates, SEE focuses instead on identifying optimal candidates. To do so, SEE transforms the read and reference strings from edit distance regime to the Hamming regime by embedding them using a randomized algorithm, and uses Hamming distance over the embedded set to identify optimal candidates. To show that SEE perfoms well in practice, we present Accel-Align, an SEE-based short-read sequence mapper and aligner that is 3-12× faster than state-of-the-art aligners on commodity CPUs, without any special-purpose hardware, while providing comparable accuracy.Availabilityhttps://github.com/raja-appuswamy/accel-align-releaseContactraja.appuswamy@eurecom.fr

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Performance Assessment of Variant Calling Pipelines using Human Whole Exome Sequencing and Simulated data

Cited by 13 publications

References 27 publications

Performance evaluation of pipelines for mapping, variant calling and interval padding, for the analysis of NGS germline panels

Performance evaluation of pipelines for mapping, variant calling and interval padding, for the analysis of NGS germline panels

Genetic characterization of Stargardt clinical phenotype in South Indian patients using sanger and targeted sequencing

Accel-Align: A Fast Sequence Mapper and Aligner Based on the Seed–Embed–Extend Method

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