Bathycoccus prasinos sequencing for variant calling
Ontology highlight
ABSTRACT:
PROVIDER: PRJEB76454 | ENA |
REPOSITORIES: ENA
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| Other | ||||
| ERR13248619.fastq.gz | Fastqsanger.gz | |||
| ERR13248620.fastq.gz | Fastqsanger.gz | |||
| ERR13248621.fastq.gz | Fastqsanger.gz | |||
| ERR13248622.fastq.gz | Fastqsanger.gz |
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Project description:Next-generation sequencing technologies have enabled a dramatic expansion of clinical genetic testing both for inherited conditions and diseases such as cancer. Accurate variant calling in NGS data is a critical step upon which virtually all downstream analysis and interpretation processes rely. Just as NGS technologies have evolved considerably over the past 10 years, so too have the software tools and approaches for detecting sequence variants in clinical samples. In this review, I discuss the current best practices for variant calling in clinical sequencing studies, with a particular emphasis on trio sequencing for inherited disorders and somatic mutation detection in cancer patients. I describe the relative strengths and weaknesses of panel, exome, and whole-genome sequencing for variant detection. Recommended tools and strategies for calling variants of different classes are also provided, along with guidance on variant review, validation, and benchmarking to ensure optimal performance. Although NGS technologies are continually evolving, and new capabilities (such as long-read single-molecule sequencing) are emerging, the "best practice" principles in this review should be relevant to clinical variant calling in the long term.
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Project description:The sequential paradigm of data acquisition and analysis in next-generation sequencing leads to high turnaround times for the generation of interpretable results. We combined a novel real-time read mapping algorithm with fast variant calling to obtain reliable variant calls still during the sequencing process. Thereby, our new algorithm allows for accurate read mapping results for intermediate cycles and supports large reference genomes such as the complete human reference. This enables the combination of real-time read mapping results with complex follow-up analysis. In this study, we showed the accuracy and scalability of our approach by applying real-time read mapping and variant calling to seven publicly available human whole exome sequencing datasets. Thereby, up to 89% of all detected SNPs were already identified after 40 sequencing cycles while showing similar precision as at the end of sequencing. Final results showed similar accuracy to those of conventional post-hoc analysis methods. When compared to standard routines, our live approach enables considerably faster interventions in clinical applications and infectious disease outbreaks. Besides variant calling, our approach can be adapted for a plethora of other mapping-based analyses.
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Project description:MotivationAccurate detection, genotyping and downstream analysis of genomic variants from high-throughput sequencing data are fundamental features in modern production pipelines for genetic-based diagnosis in medicine or genomic selection in plant and animal breeding. Our research group maintains the Next-Generation Sequencing Experience Platform (NGSEP) as a precise, efficient and easy-to-use software solution for these features.ResultsUnderstanding that incorrect alignments around short tandem repeats are an important source of genotyping errors, we implemented in NGSEP new algorithms for realignment and haplotype clustering of reads spanning indels and short tandem repeats. We performed extensive benchmark experiments comparing NGSEP to state-of-the-art software using real data from three sequencing protocols and four species with different distributions of repetitive elements. NGSEP consistently shows comparative accuracy and better efficiency compared to the existing solutions. We expect that this work will contribute to the continuous improvement of quality in variant calling needed for modern applications in medicine and agriculture.Availability and implementationNGSEP is available as open source software at http://ngsep.sf.net.Supplementary informationSupplementary data are available at Bioinformatics online.
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