SUMMARY: Single-cell DNA template strand sequencing (Strand-seq) enables chromosome length haplotype phasing, construction of phased assemblies, mapping sister-chromatid exchange events and structural variant discovery. The initial quality control of potentially thousands of single-cell libraries is still done manually by domain experts. ASHLEYS automates this tedious task, delivers near-expert performance and labels even large datasets in seconds. AVAILABILITY AND IMPLEMENTATION: github.com/friendsofstrandseq/ashleys-qc, MIT license. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

SUMMARY: VISOR is a tool for haplotype-specific simulations of simple and complex structural variants (SVs). The method is applicable to haploid, diploid or higher ploidy simulations for bulk or single-cell sequencing data. SVs are implanted into FASTA haplotypes at single-basepair resolution, optionally with nearby single-nucleotide variants. Short or long reads are drawn at random from these haplotypes using standard error profiles. Double- or single-stranded data can be simulated and VISOR supports the generation of haplotype-tagged BAM files. The tool further includes methods to interactively visualize simulated variants in single-stranded data. The versatility of VISOR is unmet by comparable tools and it lays the foundation to simulate haplotype-resolved cancer heterogeneity data in bulk or at single-cell resolution. AVAILABILITY AND IMPLEMENTATION: VISOR is implemented in python 3.6, open-source and freely available at https://github.com/davidebolo1993/VISOR. Documentation is available at https://davidebolo1993.github.io/visordoc/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

SUMMARY: Harmonizing quality control (QC) of large-scale second and third-generation sequencing datasets is key for enabling downstream computational and biological analyses. We present Alfred, an efficient and versatile command-line application that computes multi-sample QC metrics in a read-group aware manner, across a wide variety of sequencing assays and technologies. In addition to standard QC metrics such as GC bias, base composition, insert size and sequencing coverage distributions it supports haplotype-aware and allele-specific feature counting and feature annotation. The versatility of Alfred allows for easy pipeline integration in high-throughput settings, including DNA sequencing facilities and large-scale research initiatives, enabling continuous monitoring of sequence data quality and characteristics across samples. Alfred supports haplo-tagging of BAM/CRAM files to conduct haplotype-resolved analyses in conjunction with a variety of next-generation sequencing based assays. Alfred's companion web application enables interactive exploration of results and comparison to public datasets. AVAILABILITY AND IMPLEMENTATION: Alfred is open-source and freely available at https://tobiasrausch.com/alfred/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

BACKGROUND: High-throughput technologies for analyzing chromosome conformation at a genome scale have revealed that chromatin is organized in topologically associated domains (TADs). While TADs are relatively stable across cell types, intra-TAD activities are cell type specific. Epigenetic profiling of different tissues and cell-types has identified a large number of non-coding epigenetic regulatory elements ('enhancers') that can be located far away from coding genes. Linear proximity is a commonly chosen criterion for associating enhancers with their potential target genes. While enhancers frequently regulate the closest gene, unambiguous identification of enhancer regulated genes remains to be a challenge in the absence of sample matched chromosome conformation data. RESULTS: To associate enhancers with their target genes, we have previously developed and applied a method that tests for significant correlations between enhancer and gene expressions across a cohort of samples. To limit the number of tests, we constrain this analysis to gene-enhancer pairs embedded in the same TAD, where information on TAD boundaries is borrowed from publicly available chromosome conformation capturing ('Hi-C') data. We have now implemented this method as an R Bioconductor package 'InTAD' and verified the software package by reanalyzing available enhancer and gene expression data derived from ependymoma brain tumors. CONCLUSION: The open-source package InTAD is an easy-to-use software tool for identifying proximal and distal enhancer target genes by leveraging information on correlated expression of enhancers and genes that are located in the same TAD. InTAD can be applied to any heterogeneous cohort of samples analyzed by a combination of gene expression and epigenetic profiling techniques and integrates either public or custom information of TAD boundaries.

Motivation: Current sequencing technologies are able to produce reads orders of magnitude longer than ever possible before. Such long reads have sparked a new interest in de novo genome assembly, which removes reference biases inherent to re-sequencing approaches and allows for a direct characterization of complex genomic variants. However, even with latest algorithmic advances, assembling a mammalian genome from long error-prone reads incurs a significant computational burden and does not preclude occasional misassemblies. Both problems could potentially be mitigated if assembly could commence for each chromosome separately. Results: To address this, we show how single-cell template strand sequencing (Strand-seq) data can be leveraged for this purpose. We introduce a novel latent variable model and a corresponding Expectation Maximization algorithm, termed SaaRclust, and demonstrates its ability to reliably cluster long reads by chromosome. For each long read, this approach produces a posterior probability distribution over all chromosomes of origin and read directionalities. In this way, it allows to assess the amount of uncertainty inherent to sparse Strand-seq data on the level of individual reads. Among the reads that our algorithm confidently assigns to a chromosome, we observed more than 99% correct assignments on a subset of Pacific Bioscience reads with 30.1× coverage. To our knowledge, SaaRclust is the first approach for the in silico separation of long reads by chromosome prior to assembly. Availability and implementation: https://github.com/daewoooo/SaaRclust.

MOTIVATION: The discovery of genomic structural variants (SVs) at high sensitivity and specificity is an essential requirement for characterizing naturally occurring variation and for understanding pathological somatic rearrangements in personal genome sequencing data. Of particular interest are integrated methods that accurately identify simple and complex rearrangements in heterogeneous sequencing datasets at single-nucleotide resolution, as an optimal basis for investigating the formation mechanisms and functional consequences of SVs. RESULTS: We have developed an SV discovery method, called DELLY, that integrates short insert paired-ends, long-range mate-pairs and split-read alignments to accurately delineate genomic rearrangements at single-nucleotide resolution. DELLY is suitable for detecting copy-number variable deletion and tandem duplication events as well as balanced rearrangements such as inversions or reciprocal translocations. DELLY, thus, enables to ascertain the full spectrum of genomic rearrangements, including complex events. On simulated data, DELLY compares favorably to other SV prediction methods across a wide range of sequencing parameters. On real data, DELLY reliably uncovers SVs from the 1000 Genomes Project and cancer genomes, and validation experiments of randomly selected deletion loci show a high specificity. AVAILABILITY: DELLY is available at www.korbel.embl.de/software.html CONTACT: [email protected].

Copy-number variations (CNVs) are widespread in the human genome, but comprehensive assignments of integer locus copy-numbers (i.e., copy-number genotypes) that, for example, enable discrimination of homozygous from heterozygous CNVs, have remained challenging. Here we present CopySeq, a novel computational approach with an underlying statistical framework that analyzes the depth-of-coverage of high-throughput DNA sequencing reads, and can incorporate paired-end and breakpoint junction analysis based CNV-analysis approaches, to infer locus copy-number genotypes. We benchmarked CopySeq by genotyping 500 chromosome 1 CNV regions in 150 personal genomes sequenced at low-coverage. The assessed copy-number genotypes were highly concordant with our performed qPCR experiments (Pearson correlation coefficient 0.94), and with the published results of two microarray platforms (95-99% concordance). We further demonstrated the utility of CopySeq for analyzing gene regions enriched for segmental duplications by comprehensively inferring copy-number genotypes in the CNV-enriched >800 olfactory receptor (OR) human gene and pseudogene loci. CopySeq revealed that OR loci display an extensive range of locus copy-numbers across individuals, with zero to two copies in some OR loci, and two to nine copies in others. Among genetic variants affecting OR loci we identified deleterious variants including CNVs and SNPs affecting ~15% and ~20% of the human OR gene repertoire, respectively, implying that genetic variants with a possible impact on smell perception are widespread. Finally, we found that for several OR loci the reference genome appears to represent a minor-frequency variant, implying a necessary revision of the OR repertoire for future functional studies. CopySeq can ascertain genomic structural variation in specific gene families as well as at a genome-wide scale, where it may enable the quantitative evaluation of CNVs in genome-wide association studies involving high-throughput sequencing.

MOTIVATION: Large-scale tiling array experiments are becoming increasingly common in genomics. In particular, the ENCODE project requires the consistent segmentation of many different tiling array datasets into 'active regions' (e.g. finding transfrags from transcriptional data and putative binding sites from ChIP-chip experiments). Previously, such segmentation was done in an unsupervised fashion mainly based on characteristics of the signal distribution in the tiling array data itself. Here we propose a supervised framework for doing this. It has the advantage of explicitly incorporating validated biological knowledge into the model and allowing for formal training and testing. METHODOLOGY: In particular, we use a hidden Markov model (HMM) framework, which is capable of explicitly modeling the dependency between neighboring probes and whose extended version (the generalized HMM) also allows explicit description of state duration density. We introduce a formal definition of the tiling-array analysis problem, and explain how we can use this to describe sampling small genomic regions for experimental validation to build up a gold-standard set for training and testing. We then describe various ideal and practical sampling strategies (e.g. maximizing signal entropy within a selected region versus using gene annotation or known promoters as positives for transcription or ChIP-chip data, respectively). RESULTS: For the practical sampling and training strategies, we show how the size and noise in the validated training data affects the performance of an HMM applied to the ENCODE transcriptional and ChIP-chip experiments. In particular, we show that the HMM framework is able to efficiently process tiling array data as well as or better than previous approaches. For the idealized sampling strategies, we show how we can assess their performance in a simulation framework and how a maximum entropy approach, which samples sub-regions with very different signal intensities, gives the maximally performing gold-standard. This latter result has strong implications for the optimum way medium-scale validation experiments should be carried out to verify the results of the genome-scale tiling array experiments.

SUMMARY: The Helmholtz Network for Bioinformatics (HNB) is a joint venture of eleven German bioinformatics research groups that offers convenient access to numerous bioinformatics resources through a single web portal. The 'Guided Solution Finder' which is available through the HNB portal helps users to locate the appropriate resources to answer their queries by employing a detailed, tree-like questionnaire. Furthermore, automated complex tool cascades ('tasks'), involving resources located on different servers, have been implemented, allowing users to perform comprehensive data analyses without the requirement of further manual intervention for data transfer and re-formatting. Currently, automated cascades for the analysis of regulatory DNA segments as well as for the prediction of protein functional properties are provided. AVAILABILITY: The HNB portal is available at http://www.hnbioinfo.de

MOTIVATION: Even though a number of genome projects have been finished on the sequence level, still only a small proportion of DNA regulatory elements have been identified. Growing amounts of gene expression data provide the possibility of finding coregulated genes by clustering methods. By analysis of the promoter regions of those genes, rather weak signals of transcription factor binding sites may be detected. RESULTS: We introduce the new algorithm ITB, an Integrated Tool for Box finding, which combines frequency and positional information to predict transcription factor binding sites in upstream regions of coregulated genes. Motifs are extracted by exhaustive analysis of regular expression-like patterns and by estimating probabilities of positional clusters of motifs. ITB detects consensus sequences of experimentally verified transcription factor binding sites of the yeast Saccharomyces cerevisiae. Moreover, a number of new binding site candidates with significant scores are predicted. Besides applying ITB on yeast upstream regions, the program is run on human promoter sequences. AVAILABILITY: ITB is available upon request.