Detecting and annotating genetic variations using the HugeSeq pipeline - PubMed (original) (raw)

Detecting and annotating genetic variations using the HugeSeq pipeline

Hugo Y K Lam et al. Nat Biotechnol. 2012.

No abstract available

Conflict of interest statement

COMPETING FINANCIAL INTERESTS

The authors declare competing financial interests: details accompany the full-text HTML version of the paper at http://www.nature.com/naturebiotechnology.

Figures

Figure 1

A MapReduce approach for detecting genetic variants from high-throughput genome sequencing. Phase 1 is the mapping phase including sequence alignment. Phase 2 is the sorting phase including sorting alignments by mapped chromosomes. Phase 3 is the reduction phase including variant detection. chr1, chromosome one; chrM, chromosome M; SV, structural variation; SR, split-read analysis; RD, read-depth analysis; RP, read-pair mapping; JM, junction mapping; VCF, variant call format; GFF, general feature format.

Figure 2

Accuracy and sensitivity of variant detection. (a) Concordant and specific calls in SNP detection by GATK and SAMtools. Original calls, total number of calls from methods; specific calls, total number of method-specific calls. Validated calls, number of calls validated by the array. Ti/Tv, transition-to-transversion rate. (b) Overall sensitivity of SNP and structural variation or CNV detection over different sequencing coverage. X is the average number of reads representing a given nucleotide in a haploid human genome. 50% indicates that detected structural variation calls overlap ≥50% the array calls.

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Detecting and annotating genetic variations using the HugeSeq pipeline - PubMed (original) (raw)