BACKGROUND: The yield obtained from next generation sequencers has increased almost exponentially in recent years, making sample multiplexing common practice. While barcodes (known sequences of fixed length) primarily encode the sample identity of sequenced DNA fragments, barcodes made of random sequences (Unique Molecular Identifier or UMIs) are often used to distinguish between PCR duplicates and transcript abundance in, for example, single-cell RNA sequencing (scRNA-seq). In paired-end sequencing, different barcodes can be inserted at each fragment end to either increase the number of multiplexed samples in the library or to use one of the barcodes as UMI. Alternatively, UMIs can be combined with the sample barcodes into composite barcodes, or with standard Illumina® indexing. Subsequent analysis must take read duplicates and sample identity into account, by identifying UMIs. RESULTS: Existing tools do not support these complex barcoding configurations and custom code development is frequently required. Here, we present Je, a suite of tools that accommodates complex barcoding strategies, extracts UMIs and filters read duplicates taking UMIs into account. Using Je on publicly available scRNA-seq and iCLIP data containing UMIs, the number of unique reads increased by up to 36 %, compared to when UMIs are ignored. CONCLUSIONS: Je is implemented in JAVA and uses the Picard API. Code, executables and documentation are freely available at http://gbcs.embl.de/Je . Je can also be easily installed in Galaxy through the Galaxy toolshed.

BACKGROUND: Identity by descent (IBD) has played a fundamental role in the discovery of genetic loci underlying human diseases. Both pedigree-based and population-based linkage analyses rely on estimating recent IBD, and evidence of ancient IBD can be used to detect population structure in genetic association studies. Various methods for detecting IBD, including those implemented in the soft- ware programs fastIBD and GERMLINE, have been developed in the past several years using population genotype data from microarray platforms. Now, next-generation DNA sequencing data is becoming increasingly available, enabling the comprehensive analysis of genomes, in- cluding identifying rare variants. These sequencing data may provide an opportunity to detect IBD with higher resolution than previously possible, potentially enabling the detection of disease causing loci that were previously undetectable with sparser genetic data. RESULTS: Here, we investigate how different levels of variant coverage in sequencing and microarray genotype data influences the resolution at which IBD can be detected. This includes microarray genotype data from the WTCCC study, denser genotype data from the HapMap Project, low coverage sequencing data from the 1000 Genomes Project, and deep coverage complete genome data from our own projects. With high power (78%), we can detect segments of length 0.4 cM or larger using fastIBD and GERMLINE in sequencing data. This compares to similar power to detect segments of length 1.0 cM or higher with microarray genotype data. We find that GERMLINE has slightly higher power than fastIBD for detecting IBD segments using sequencing data, but also has a much higher false positive rate. CONCLUSION: We further quantify the effect of variant density, conditional on genetic map length, on the power to resolve IBD segments. These investigations into IBD resolution may help guide the design of future next generation sequencing studies that utilize IBD, including family-based association studies, association studies in admixed populations, and homozygosity mapping studies.

MOTIVATION: Copy number variations (CNVs) are increasingly recognized as an substantial source of individual genetic variation, and hence there is a growing interest in investigating the evolutionary history of CNVs as well as their impact on complex disease susceptibility. CNV/SNP haplotypes are critical for this research, but although many methods have been proposed for inferring integer copy number, few have been designed for inferring CNV haplotypic phase and none of these are applicable at genome-wide scale. Here, we present a method for inferring missing CNV genotypes, predicting CNV allelic configuration and for inferring CNV haplotypic phase from SNP/CNV genotype data. Our method, implemented in the software polyHap v2.0, is based on a hidden Markov model, which models the joint haplotype structure between CNVs and SNPs. Thus, haplotypic phase of CNVs and SNPs are inferred simultaneously. A sampling algorithm is employed to obtain a measure of confidence/credibility of each estimate. RESULTS: We generated diploid phase-known CNV-SNP genotype datasets by pairing male X chromosome CNV-SNP haplotypes. We show that polyHap provides accurate estimates of missing CNV genotypes, allelic configuration and CNV haplotypic phase on these datasets. We applied our method to a non-simulated dataset-a region on Chromosome 2 encompassing a short deletion. The results confirm that polyHap's accuracy extends to real-life datasets. AVAILABILITY: Our method is implemented in version 2.0 of the polyHap software package and can be downloaded from http://www.imperial.ac.uk/medicine/people/l.coin.

MOTIVATION: Most genome-wide association studies rely on single nucleotide polymorphism (SNP) analyses to identify causal loci. The increased stringency required for genome-wide analyses (with per-SNP significance threshold typically approximately 10(-7)) means that many real signals will be missed. Thus it is still highly relevant to develop methods with improved power at low type I error. Haplotype-based methods provide a promising approach; however, they suffer from statistical problems such as abundance of rare haplotypes and ambiguity in defining haplotype block boundaries. RESULTS: We have developed an ancestral haplotype clustering (AncesHC) association method which addresses many of these problems. It can be applied to biallelic or multiallelic markers typed in haploid, diploid or multiploid organisms, and also handles missing genotypes. Our model is free from the assumption of a rigid block structure but recognizes a block-like structure if it exists in the data. We employ a Hidden Markov Model (HMM) to cluster the haplotypes into groups of predicted common ancestral origin. We then test each cluster for association with disease by comparing the numbers of cases and controls with 0, 1 and 2 chromosomes in the cluster. We demonstrate the power of this approach by simulation of case-control status under a range of disease models for 1500 outcrossed mice originating from eight inbred lines. Our results suggest that AncesHC has substantially more power than single-SNP analyses to detect disease association, and is also more powerful than the cladistic haplotype clustering method CLADHC. AVAILABILITY: The software can be downloaded from http://www.imperial.ac.uk/medicine/people/l.coin.

BACKGROUND: The power of haplotype-based methods for association studies, identification of regions under selection, and ancestral inference, is well-established for diploid organisms. For polyploids, however, the difficulty of determining phase has limited such approaches. Polyploidy is common in plants and is also observed in animals. Partial polyploidy is sometimes observed in humans (e.g. trisomy 21; Down's syndrome), and it arises more frequently in some human tissues. Local changes in ploidy, known as copy number variations (CNV), arise throughout the genome. Here we present a method, implemented in the software polyHap, for the inference of haplotype phase and missing observations from polyploid genotypes. PolyHap allows each individual to have a different ploidy, but ploidy cannot vary over the genomic region analysed. It employs a hidden Markov model (HMM) and a sampling algorithm to infer haplotypes jointly in multiple individuals and to obtain a measure of uncertainty in its inferences. RESULTS: In the simulation study, we combine real haplotype data to create artificial diploid, triploid, and tetraploid genotypes, and use these to demonstrate that polyHap performs well, in terms of both switch error rate in recovering phase and imputation error rate for missing genotypes. To our knowledge, there is no comparable software for phasing a large, densely genotyped region of chromosome from triploids and tetraploids, while for diploids we found polyHap to be more accurate than fastPhase. We also compare the results of polyHap to SATlotyper on an experimentally haplotyped tetraploid dataset of 12 SNPs, and show that polyHap is more accurate. CONCLUSION: With the availability of large SNP data in polyploids and CNV regions, we believe that polyHap, our proposed method for inferring haplotypic phase from genotype data, will be useful in enabling researchers analysing such data to exploit the power of haplotype-based analyses.