Accurate genotyping is important for genetic testing. Sanger sequencing-based typing is the gold standard for genotyping, but it has been underused, due to its high cost and low throughput. In contrast, short-read sequencing provides inexpensive and high-throughput sequencing, holding great promise for reaching the goal of cost-effective and high-throughput genotyping. However, the short-read length and the paucity of appropriate genotyping methods, pose a major challenge. Here, we present RCHSBT-reliable, cost-effective and high-throughput sequence based typing pipeline-which takes short sequence reads as input, but uses a unique variant calling, haploid sequence assembling algorithm, can accurately genotype with greater effective length per amplicon than even Sanger sequencing reads. The RCHSBT method was tested for the human MHC loci HLA-A, HLA-B, HLA-C, HLA-DQB1, and HLA-DRB1, upon 96 samples using Illumina PE 150 reads. Amplicons as long as 950 bp were readily genotyped, achieving 100% typing concordance between RCHSBT-called genotypes and genotypes previously called by Sanger sequence. Genotyping throughput was increased over 10 times, and cost was reduced over five times, for RCHSBT as compared with Sanger sequence genotyping. We thus demonstrate RCHSBT to be a genotyping method comparable to Sanger sequencing-based typing in quality, while being more cost-effective, and higher throughput.
Copy-number variations (CNV), loss of heterozygosity (LOH), and uniparental disomy (UPD) are large genomic aberrations leading to many common inherited diseases, cancers, and other complex diseases. An integrated tool to identify these aberrations is essential in understanding diseases and in designing clinical interventions. Previous discovery methods based on whole-genome sequencing (WGS) require very high depth of coverage on the whole genome scale, and are cost-wise inefficient. Another approach, whole exome genome sequencing (WEGS), is limited to discovering variations within exons. Thus, we are lacking efficient methods to detect genomic aberrations on the whole genome scale using next-generation sequencing technology. Here we present a method to identify genome-wide CNV, LOH and UPD for the human genome via selectively sequencing a small portion of genome termed Selected Target Regions (SeTRs). In our experiments, the SeTRs are covered by 99.73%~99.95% with sufficient depth. Our developed bioinformatics pipeline calls genome-wide CNVs with high confidence, revealing 8 credible events of LOH and 3 UPD events larger than 5M from 15 individual samples. We demonstrate that genome-wide CNV, LOH and UPD can be detected using a cost-effective SeTRs sequencing approach, and that LOH and UPD can be identified using just a sample grouping technique, without using a matched sample or familial information.
W e sequence storage and retrieval jobs to minimize total travel time of a storage/retrieval S/R machine in a two-depot automated storage/retrieval system. These systems include storage systems with aislecaptive S/R machines and storage blocks with bridge cranes. The S/R machine must move retrieval unit loads from their current locations in the system to one of the two depots. In addition, it must move storage unit loads from given depots to given locations in the system. We model the problem as an asymmetric traveling salesman problem, which is in general -hard. We develop an algorithm to solve the problem in polynomial time, using the property that the system has two depots and the S/R machine always returns to one of the depots to pick up or deliver a load. Furthermore, we develop additional polynomial time algorithms for the following four special cases: (1) retrieval loads have to be delivered to given depots; (2) the system has one input depot and one output depot; (3) the system has a single depot; and (4) there are arbitrary S/R machine starting and ending locations. The computational results show the effectiveness of the proposed algorithms. Compared to first-come-first-served and nearest neighbor algorithms, commonly used in practice, the total travel time reduces on average by more than 30% and 15%, respectively.
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