WES of Homo sapiens female breast
The original fluorescence image files obtained from Hiseq platform are transformed to short reads (Raw data) by base calling and these short reads are recorded in FASTQ format, which contains sequence information and corresponding sequencing quality information. Sequence artifacts, including reads containing adapter contamination, low-quality nucleotides and unrecognizable nucleotide (N), undoubtedly set the barrier for the subsequent reliable bioinformatics analysis. Hence quality control is an essential step and applied to guarantee the meaningful downstream analysis.Valid sequencing data was mapped to the reference human genome (UCSC hg19) by Burrows-Wheeler Aligner (BWA) software (Li H et al. 2009-1) to get the original mapping results stored in BAM format. If one or one paired read(s) were mapped to multiple positions, the strategy adopted by BWA was to choose the most likely placement. If two or more most likely placements presented, BWA picked one randomly. Then, SAMtools (Li H et al. 2009-2) and Picard (http://broadinstitute.github.io/picard/)were used to sort BAM files and do duplicate marking, local realignment, and base quality recalibration to generate final BAM file for computation of the sequence coverage and depth. Mapping step was very difficult due to mismatches, including true mutation and sequencing error, and duplicates resulted from PCR amplification. These duplicate reads were uninformative and shouldnt be considered as evidence for variants. These duplicate reads were uninformative and shouldnt be considered as evidence for variants. We used Picard to mark these duplicates for follow up analysis.Samtools (Wysoker A, et al. 2009) mpileup and bcftools were used to do variant calling and identify SNP, InDels.Functional annotation was very important because the link between genetic variations and diseases would be clarified in this step. ANNOVAR (Wang K et al.2010) was performed to do annotation for VCF (Variant Call Format) obtained in the previous effort. dbSNP, 1000 Genome and other related existing databases were applied to characterize the detected variants. Given to the significance of exonic variants, gene transcript annotation databases, such as Consensus CDS, RefSeq, Ensembl and UCSC, were also included to determine amino acid alternation. Annotation content contained the variant position, variant type, conservative prediction, etc. These annotation results would help to locate disease causal mutant. The details of annotation were exhibited in supplemented material.The somatic SNV was detected by muTect (Cibulskis, Lawrence et al. 2013), the somatic InDel by Strelka (Saunders, Wong et al. 2037).