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MetaSV: an accurate and integrative structural-variant caller for next generation sequencing.

Mohiyuddin M, Mu JC, Li J, Bani Asadi N, Gerstein MB, Abyzov A, Wong WH, Lam HY - Bioinformatics (2015)

Bottom Line: Structural variations (SVs) are large genomic rearrangements that vary significantly in size, making them challenging to detect with the relatively short reads from next-generation sequencing (NGS).Paired-end and coverage information is used to predict SV genotypes.Using simulation and experimental data, we demonstrate the effectiveness of MetaSV across various SV types and sizes.

View Article: PubMed Central - PubMed

Affiliation: Bina Technologies, Roche Sequencing, Redwood City, CA 94065, USA.

No MeSH data available.


Related in: MedlinePlus

Accuracy comparisons for deletions and insertions. Accuracy metrics are shown on a per size bin basis in the plots. The tables below the plots show the aggregate accuracy scores. If a tool does not support detecting the SV type, an NA is indicated in the table. Each tool name is color coded to match the color code in the plots. DELLY’s suboptimal deletion performance was due to its lower breakpoint resolution. For insertions, although Pindel’s sensitivity was close to MetaSV, it had a significantly lower precision and overall accuracy
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btv204-F2: Accuracy comparisons for deletions and insertions. Accuracy metrics are shown on a per size bin basis in the plots. The tables below the plots show the aggregate accuracy scores. If a tool does not support detecting the SV type, an NA is indicated in the table. Each tool name is color coded to match the color code in the plots. DELLY’s suboptimal deletion performance was due to its lower breakpoint resolution. For insertions, although Pindel’s sensitivity was close to MetaSV, it had a significantly lower precision and overall accuracy

Mentions: We demonstrate the effectiveness of MetaSV using the VarSim simulation and validation framework (Mu etal., 2014). Simulated 2 × 100 bp NGS reads were generated at 50× coverages for the NA12878 genome using published variant sets. The reads were aligned using BWA-MEM. For comparing reported SVs against the ground truth, we use a reciprocal overlap of 90% and a wiggle of 100 bp which captures accuracy at a high breakpoint resolution. The SV size cutoff was set to 100 bp since smaller variants are a target of indel callers such as GATK’s HaplotypeCaller. Our results show that each method has varying performance in different SV size ranges. By integrating multiple methods, MetaSV achieved a steady performance across all sizes (Fig. 2). We report accuracy as F1-score, which is the harmonic mean of sensitivity and precision. For this dataset, MetaSV achieved an F1-score of 96.2% (sensitivity and precision were 93.7 and 98.8%, respectively) for deletions, indicating high accuracy and resolution. For insertions, it achieved an F1-score of 84.7% (sensitivity and precision were 85.3 and 84.1%, respectively) comparing to less than 65% for all the individual tools analyzed. Insertion length was omitted from the accuracy analysis since long insertions cannot be assembled completely with NGS reads. Nevertheless, the significantly enhanced detection of insertion events can definitely improve interpretation largely as they may cause impactful disruption in the genome. Finally, genotyping accuracies of 95.2 and 95.5% were achieved for deletions and insertions, respectively.Fig. 2.


MetaSV: an accurate and integrative structural-variant caller for next generation sequencing.

Mohiyuddin M, Mu JC, Li J, Bani Asadi N, Gerstein MB, Abyzov A, Wong WH, Lam HY - Bioinformatics (2015)

Accuracy comparisons for deletions and insertions. Accuracy metrics are shown on a per size bin basis in the plots. The tables below the plots show the aggregate accuracy scores. If a tool does not support detecting the SV type, an NA is indicated in the table. Each tool name is color coded to match the color code in the plots. DELLY’s suboptimal deletion performance was due to its lower breakpoint resolution. For insertions, although Pindel’s sensitivity was close to MetaSV, it had a significantly lower precision and overall accuracy
© Copyright Policy - creative-commons
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4528635&req=5

btv204-F2: Accuracy comparisons for deletions and insertions. Accuracy metrics are shown on a per size bin basis in the plots. The tables below the plots show the aggregate accuracy scores. If a tool does not support detecting the SV type, an NA is indicated in the table. Each tool name is color coded to match the color code in the plots. DELLY’s suboptimal deletion performance was due to its lower breakpoint resolution. For insertions, although Pindel’s sensitivity was close to MetaSV, it had a significantly lower precision and overall accuracy
Mentions: We demonstrate the effectiveness of MetaSV using the VarSim simulation and validation framework (Mu etal., 2014). Simulated 2 × 100 bp NGS reads were generated at 50× coverages for the NA12878 genome using published variant sets. The reads were aligned using BWA-MEM. For comparing reported SVs against the ground truth, we use a reciprocal overlap of 90% and a wiggle of 100 bp which captures accuracy at a high breakpoint resolution. The SV size cutoff was set to 100 bp since smaller variants are a target of indel callers such as GATK’s HaplotypeCaller. Our results show that each method has varying performance in different SV size ranges. By integrating multiple methods, MetaSV achieved a steady performance across all sizes (Fig. 2). We report accuracy as F1-score, which is the harmonic mean of sensitivity and precision. For this dataset, MetaSV achieved an F1-score of 96.2% (sensitivity and precision were 93.7 and 98.8%, respectively) for deletions, indicating high accuracy and resolution. For insertions, it achieved an F1-score of 84.7% (sensitivity and precision were 85.3 and 84.1%, respectively) comparing to less than 65% for all the individual tools analyzed. Insertion length was omitted from the accuracy analysis since long insertions cannot be assembled completely with NGS reads. Nevertheless, the significantly enhanced detection of insertion events can definitely improve interpretation largely as they may cause impactful disruption in the genome. Finally, genotyping accuracies of 95.2 and 95.5% were achieved for deletions and insertions, respectively.Fig. 2.

Bottom Line: Structural variations (SVs) are large genomic rearrangements that vary significantly in size, making them challenging to detect with the relatively short reads from next-generation sequencing (NGS).Paired-end and coverage information is used to predict SV genotypes.Using simulation and experimental data, we demonstrate the effectiveness of MetaSV across various SV types and sizes.

View Article: PubMed Central - PubMed

Affiliation: Bina Technologies, Roche Sequencing, Redwood City, CA 94065, USA.

No MeSH data available.


Related in: MedlinePlus