Droplet single-cell RNA-sequencing (dscRNA-seq) has enabled rapid, massively parallel profiling of transcriptomes. However, assessing differential expression across multiple individuals has been hampered by inefficient sample processing and technical batch effects. Here we describe a computational tool, demuxlet, that harnesses natural genetic variation to determine the sample identity of each cell and detect droplets containing two cells. These capabilities enable multiplexed dscRNA-seq experiments in which cells from unrelated individuals are pooled and captured at higher throughput than in standard workflows. Using simulated data, we show that 50 SNPs per cell are sufficient to assign 97% of singlets and identify 92% of doublets in pools of up to 64 individuals. Given genotyping data for each of 8 pooled samples, demuxlet correctly recovers the sample identity of >99% of singlets and identifies doublets at rates consistent with previous estimates. We apply demuxlet to assess cell type-specific changes in gene expression in 8 pooled lupus patient samples treated with IFN-β and perform eQTL analysis on 23 pooled samples.

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The success of genome-wide association studies has paralleled the development of efficient genotyping technologies. We describe the development of a next-generation microarray based on the new highly-efficient Affymetrix Axiom genotyping technology that we are using to genotype individuals of European ancestry from the Kaiser Permanente Research Program on Genes, Environment and Health (RPGEH). The array contains 674,517 SNPs, and provides excellent genome-wide as well as gene-based and candidate-SNP coverage. Coverage was calculated using an approach based on imputation and cross validation. Preliminary results for the first 80,301 saliva-derived DNA samples from the RPGEH demonstrate very high quality genotypes, with sample success rates above 94% and over 98% of successful samples having SNP call rates exceeding 98%. At steady state, we have produced 462 million genotypes per week for each Axiom system. The new array provides a valuable addition to the repertoire of tools for large scale genome-wide association studies.

The Kaiser Permanente (KP) Research Program on Genes, Environment and Health (RPGEH), in collaboration with the University of California-San Francisco, undertook genome-wide genotyping of .100,000 subjects that constitute the Genetic Epidemiology Research on Adult Health and Aging (GERA) cohort. The project, which generated .70 billion genotypes, represents the first large-scale use of the Affymetrix Axiom Genotyping Solution. Because genotyping took place over a short 14-month period, creating a near-real-time analysis pipeline for experimental assay quality control and final optimized analyses was critical. Because of the multi-ethnic nature of the cohort, four different ethnic-specific arrays were employed to enhance genome-wide coverage. All assays were performed on DNA extracted from saliva samples. To improve sample call rates and significantly increase genotype concordance, we partitioned the cohort into disjoint packages of plates with similar assay contexts. Using strict QC criteria, the overall genotyping success rate was 103,067 of 109,837 samples assayed (93.8%), with a range of 92.1-95.4% for the four different arrays. Similarly, the SNP genotyping success rate ranged from 98.1 to 99.4% across the four arrays, the variation depending mostly on how many SNPs were included as single copy vs. double copy on a particular array. The high quality and large scale of genotype data created on this cohort, in conjunction with comprehensive longitudinal data from the KP electronic health records of participants, will enable a broad range of highly powered genome-wide association studies on a diversity of traits and conditions. KEYWORDS genome-wide genotyping; GERA cohort; Affymetrix Axiom; saliva DNA; quality control T HE Genetic Epidemiology Research on Adult Health and Aging (GERA) resource is a cohort of .100,000 subjects who are participants in the Kaiser Permanente Medical Care Plan, Northern California Region (KPNC), Research Program on Genes, Environment and Health (RPGEH) (detailed description of the cohort and study design can be found in dbGaP, Study Accession: phs000674.v1.p1). Genome-wide genotyping was targeted for this cohort to enable large-scale genome-wide association studies by linkage to comprehensive longitudinal clinical data derived from extensive KPNC electronic health record databases. The cohort is multi-ethnic, with 20% minority representation (African American, East Asian, and Latino or mixed), and the remaining 80% nonHispanic white. For this project, four ethnic-specific arrays were designed based on the Affymetrix Axiom Genotyping System (Hoffmann et al. 2011a,b). The genotyping assay experiment took place over a 14-month period and to our knowledge, is the single largest genotyping experiment to date, producing .70 billion genotypes. The magnitude of the experiment, in conjunction with the long duration and simultaneous high throughput, required new protocols for assuring quality control (QC) during the assays and new genotyping strategies in postassay data analysis.Samp...

The advancement of sequencing technologies has made it feasible for researchers to consider many highthroughput biological applications. A core step of these applications is to align an enormous amount of short reads to a reference genome. For example, to resequence a human genome, billions of reads of 35 bp are produced in 1-2 weeks, putting a lot of pressure of faster software for alignment. Based on existing indexing and pattern matching technologies, several short read alignment software have been developed recently. Yet this is still strong need to further improve the speed. In this paper, we show a new indexing data structure called bi-directional BWT, which allows us to build the fastest software for aligning short reads. When compared with existing software (Bowtie is the best), our software is at least 3 times faster for finding unique best alignments, and 25 times faster for finding all possible alignments. We believe that bi-directional BWT is an interesting data structure on its own and could be applied to other pattern matching problems. Availability: http://www.bio5.cs.hku.hk:8080/P2BWT, where two human genomes are indexed for alignment.