Literature DB >> 23357921

AbCD: arbitrary coverage design for sequencing-based genetic studies.

Jian Kang1, Kuan-Chieh Huang, Zheng Xu, Yunfei Wang, Gonçalo R Abecasis, Yun Li.   

Abstract

Recent advances in sequencing technologies have revolutionized genetic studies. Although high-coverage sequencing can uncover most variants present in the sequenced sample, low-coverage sequencing is appealing for its cost effectiveness. Here, we present AbCD (arbitrary coverage design) to aid the design of sequencing-based studies. AbCD is a user-friendly interface providing pre-estimated effective sample sizes, specific to each minor allele frequency category, for designs with arbitrary coverage (0.5-30×) and sample size (20-10 000), and for four major ethnic groups (Europeans, Africans, Asians and African Americans). In addition, we also present two software tools: ShotGun and DesignPlanner, which were used to generate the estimates behind AbCD. ShotGun is a flexible short-read simulator for arbitrary user-specified read length and average depth, allowing cycle-specific sequencing error rates and realistic read depth distributions. DesignPlanner is a full pipeline that uses ShotGun to generate sequence data and performs initial SNP discovery, uses our previously presented linkage disequilibrium-aware method to call genotypes, and, finally, provides minor allele frequency-specific effective sample sizes. ShotGun plus DesignPlanner can accommodate effective sample size estimate for any combination of high-depth and low-depth data (for example, whole-genome low-depth plus exonic high-depth) or combination of sequence and genotype data [for example, whole-exome sequencing plus genotyping from existing Genomewide Association Study (GWAS)].

Mesh:

Year:  2013        PMID: 23357921      PMCID: PMC3597143          DOI: 10.1093/bioinformatics/btt041

Source DB:  PubMed          Journal:  Bioinformatics        ISSN: 1367-4803            Impact factor:   6.937


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