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A deep understanding of how regulation of the multiple levels of gene expression in mammalian tissues give rise to complex phenotypes has been impeded by cellular diversity. A handful of techniques were developed to tag-select nucleic acids of interest in specific cell types, thereby enabling their capture. We expanded this strategy by developing the Tagger knock-in mouse line bearing a quad-cistronic transgene combining enrichment tools for nuclei, nascent RNA, translating mRNA, and mature microRNA (miRNA). We demonstrate that Tagger can capture the desired nucleic acids, enabling multiple omics approaches to be applied to specific cell types in vivo using a single transgenic mouse line. [ABSTRACT FROM AUTHOR]

Motivation: The short read lengths of current high-throughput sequencing technologies limit the ability to recover long-range haplotype information. Dilution pool methods for preparing DNA sequencing libraries from high molecular weight DNA fragments enable the recovery of long DNA fragments from short sequence reads. These approaches require computational methods for identifying the DNA fragments using aligned sequence reads and assembling the fragments into long haplotypes. Although a number of computational methods have been developed for haplotype assembly, the problem of identifying DNA fragments from dilution pool sequence data has not received much attention. Results: We formulate the problem of detecting DNA fragments from dilution pool sequencing experiments as a genome segmentation problem and develop an algorithm that uses dynamic programming to optimize a likelihood function derived from a generative model for the sequence reads. This algorithm uses an iterative approach to automatically infer the mean background read depth and the number of fragments in each pool. Using simulated data, we demonstrate that our method, FragmentCut, has 25-30% greater sensitivity compared with an HMM based method for fragment detection and can also detect overlapping fragments. On a whole-genome human fosmid pool dataset, the haplotypes assembled using the fragments identified by FragmentCut had greater N50 length, 16.2% lower switch error rate and 35.8% lower mismatch error rate compared with two existing methods. We further demonstrate the greater accuracy of our method using two additional dilution pool datasets. [ABSTRACT FROM AUTHOR]

Background Estimation of individual ancestry from genetic data is useful for the analysis of disease association studies, understanding human population history and interpreting personal genomic variation. New, computationally efficient methods are needed for ancestry inference that can effectively utilize existing information about allele frequencies associated with different human populations and can work directly with DNA sequence reads. Results We describe a fast method for estimating the relative contribution of known reference populations to an individual's genetic ancestry. Our method utilizes allele frequencies from the reference populations and individual genotype or sequence data to obtain a maximum likelihood estimate of the global admixture proportions using the BFGS optimization algorithm. It accounts for the uncertainty in genotypes present in sequence data by using genotype likelihoods and does not require individual genotype data from external reference panels. Simulation studies and application of the method to real datasets demonstrate that our method is significantly times faster than previous methods and has comparable accuracy. Using data from the 1000 Genomes project, we show that estimates of the genome-wide average ancestry for admixed individuals are consistent between exome sequence data and whole-genome low-coverage sequence data. Finally, we demonstrate that our method can be used to estimate admixture proportions using pooled sequence data making it a valuable tool for controlling for population stratification in sequencing based association studies that utilize DNA pooling. Conclusions Our method is an efficient and versatile tool for estimating ancestry from DNA sequence data and is available from https://sites.google.com/site/vibansal/software/iAdmix [ABSTRACT FROM AUTHOR]

Rapid advances in high-throughput sequencing facilitate variant discovery and genotyping, but linking variants into a single haplotype remains challenging. Here we demonstrate HaploSeq, an approach for assembling chromosome-scale haplotypes by exploiting the existence of 'chromosome territories'. We use proximity ligation and sequencing to show that alleles on homologous chromosomes occupy distinct territories, and therefore this experimental protocol preferentially recovers physically linked DNA variants on a homolog. Computational analysis of such data sets allows for accurate (∼99.5%) reconstruction of chromosome-spanning haplotypes for ∼95% of alleles in hybrid mouse cells with 30× sequencing coverage. To resolve haplotypes for a human genome, which has a low density of variants, we coupled HaploSeq with local conditional phasing to obtain haplotypes for ∼81% of alleles with ∼98% accuracy from just 17× sequencing. Whereas methods based on proximity ligation were originally designed to investigate spatial organization of genomes, our results lend support for their use as a general tool for haplotyping. [ABSTRACT FROM AUTHOR]

Motivation: High-throughput sequencing technologies have made population-scale studies of human genetic variation possible. Accurate and comprehensive detection of DNA sequence variants is crucial for the success of these studies. Small insertions and deletions represent the second most frequent class of variation in the human genome after single nucleotide polymorphisms (SNPs). Although several alignment tools for the gapped alignment of sequence reads to a reference genome are available, computational methods for discriminating indels from sequencing errors and genotyping indels directly from sequence reads are needed.Results: We describe a probabilistic method for the accurate detection and genotyping of short indels from population-scale sequence data. In this approach, aligned sequence reads from a population of individuals are used to automatically account for context-specific sequencing errors associated with indels. We applied this approach to population sequence datasets from the 1000 Genomes exon pilot project generated using the Roche 454 and Illumina sequencing platforms, and were able to detect a significantly greater number of indels than reported previously. Comparison to indels identified in the 1000 Genomes pilot project demonstrated the sensitivity of our method. The consistency in the number of indels and the fraction of indels whose length is a multiple of three across different human populations and two different sequencing platforms indicated that our method has a low false discovery rate. Finally, the method represents a general approach for the detection and genotyping of small-scale DNA sequence variants for population-scale sequencing projects.Availability: A program implementing this method is available at http://polymorphism.scripps.edu/~vbansal/software/piCALL/Contact: vbansal@scripps.eduSupplementary information: Supplementary data are available at Bioinformatics online. [ABSTRACT FROM PUBLISHER]

Contemporary sequencing studies often ignore the diploid nature of the human genome because they do not routinely separate or 'phase' maternally and paternally derived sequence information. However, many findings - both from recent studies and in the more established medical genetics literature - indicate that relationships between human DNA sequence and phenotype, including disease, can be more fully understood with phase information. Thus, the existing technological impediments to obtaining phase information must be overcome if human genomics is to reach its full potential. [ABSTRACT FROM AUTHOR]

Background: Massively parallel DNA sequencing technologies have enabled the sequencing of several individual human genomes. These technologies are also being used in novel ways for mRNA expression profiling, genome-wide discovery of transcription-factor binding sites, small RNA discovery, etc. The multitude of sequencing platforms, each with their unique characteristics, pose a number of design challenges, regarding the technology to be used and the depth of sequencing required for a particular sequencing application. Here we describe a number of analytical and empirical results to address design questions for two applications: detection of structural variations from paired-end sequencing and estimating mRNA transcript abundance. Results: For structural variation, our results provide explicit trade-offs between the detection and resolution of rearrangement breakpoints, and the optimal mix of paired-read insert lengths. Specifically, we prove that optimal detection and resolution of breakpoints is achieved using a mix of exactly two insert library lengths. Furthermore, we derive explicit formulae to determine these insert length combinations, enabling a 15% improvement in breakpoint detection at the same experimental cost. On empirical short read data, these predictions show good concordance with Illumina 200 bp and 2 Kbp insert length libraries. For transcriptome sequencing, we determine the sequencing depth needed to detect rare transcripts from a small pilot study. With only 1 Million reads, we derive corrections that enable almost perfect prediction of the underlying expression probability distribution, and use this to predict the sequencing depth required to detect low expressed genes with greater than 95% probability. Conclusions: Together, our results form a generic framework for many design considerations related to highthroughput sequencing. We provide software tools http://bix.ucsd.edu/projects/NGS-DesignTools to derive platform independent guidelines for designing sequencing experiments (amount of sequencing, choice of insert length, mix of libraries) for novel applications of next generation sequencing. [ABSTRACT FROM AUTHOR]

Comparison of the DNA sequence of an individual human from the reference sequence reveals a surprising amount of difference. [ABSTRACT FROM AUTHOR]

Motivation: Next-generation sequencing technologies have enabled the sequencing of several human genomes in their entirety. However, the routine resequencing of complete genomes remains infeasible. The massive capacity of next-generation sequencers can be harnessed for sequencing specific genomic regions in hundreds to thousands of individuals. Sequencing-based association studies are currently limited by the low level of multiplexing offered by sequencing platforms. Pooled sequencing represents a cost-effective approach for studying rare variants in large populations. To utilize the power of DNA pooling, it is important to accurately identify sequence variants from pooled sequencing data. Detection of rare variants from pooled sequencing represents a different challenge than detection of variants from individual sequencing. [ABSTRACT FROM PUBLISHER]

In comparison to genotypes, knowledge about haplotypes (the combination of alleles present on a single chromosome) is much more useful for whole-genome association studies and for making inferences about human evolutionary history. Haplotypes are typically inferred from population genotype data using computational methods. Whole-genome sequence data represent a promising resource for constructing haplotypes spanning hundreds of kilobases for an individual. In this article, we propose a Markov chain Monte Carlo (MCMC) algorithm, HASH (haplotype assembly for single human), for assembling haplotypes from sequenced DNA fragments that have been mapped to a reference genome assembly. The transitions of the Markov chain are generated using min-cut computations on graphs derived from the sequenced fragments. We have applied our method to infer haplotypes using whole-genome shotgun sequence data from a recently sequenced human individual. The high sequence coverage and presence of mate pairs result in fairly long haplotypes (N50 length ~350 kb). Based on comparison of the sequenced fragments against the individual haplotypes, we demonstrate that the haplotypes for this individual inferred using HASH are significantly more accurate than the haplotypes estimated using a previously proposed greedy heuristic and a simple MCMC method. Using haplotypes from the HapMap project, we estimate the switch error rate of the haplotypes inferred using HASH to be quite low, ~1.1%. Our Markov chain Monte Carlo algorithm represents a general framework for haplotype assembly that can be applied to sequence data generated by other sequencing technologies. The code implementing the methods and the phased individual haplotypes can be downloaded from http://www.cse.ucsd.edu/users/vibansal/HASH/. [ABSTRACT FROM AUTHOR]

Knowledge about structural variation in the human genome has grown tremendously in the past few years. However, inversions represent a class of structural variation that remains difficult to detect. We present a statistical method to identify large inversion polymorphisms using unusual Linkage Disequilibrium (LD) patterns from high-density SNP data. The method is designed to detect chromosomal segments that are inverted (in a majority of the chromosomes) in a population with respect to the reference human genome sequence. We demonstrate the power of this method to detect such inversion polymorphisms through simulations done using the HapMap data. Application of this method to the data from the first phase of the International HapMap project resulted in 176 candidate inversions ranging from 200 kb to several megabases in length. Our predicted inversions include an 800-kb polymorphic inversion at 7p22, a 1.1-Mb inversion at 16p12, and a novel 1.2-Mb inversion on chromosome 10 that is supported by the presence of two discordant fosmids. Analysis of the genomic sequence around inversion breakpoints showed that 11 predicted inversions are flanked by pairs of highly homologous repeats in the inverted orientation. In addition, for three candidate inversions, the inverted orientation is represented in the Celera genome assembly. Although the power of our method to detect inversions is restricted because of inherently noisy LD patterns in population data, inversions predicted by our method represent strong candidates for experimental validation and analysis. [ABSTRACT FROM AUTHOR]