Visualization and analysis of medically relevant tandem repeats in nanopore sequencing of control cohorts with pathSTR.

The lack of population-scale databases hampers research and diagnostics for medically relevant tandem repeats and repeat expansions. We attempt to fill this gap using our pathSTR web tool, which leverages long-read sequencing of large cohorts to determine repeat length and sequence composition in a healthy population. The current version includes 1040 individuals of the 1000 Genomes Project cohort sequenced on the Oxford Nanopore Technologies PromethION. A comprehensive set of medically relevant tandem repeats was genotyped using STRdust and LongTR to determine the tandem repeat length and sequence composition. PathSTR provides rich visualizations of this dataset and the feature to upload one's data for comparison along the control cohort. We demonstrate the implementation of this application using data from targeted nanopore sequencing of a patient with Myotonic Dystrophy type 1. This resource will empower the genetics community to get a more complete overview of normal variation in tandem repeat length and sequence composition and, as such, enable a better assessment of rare tandem repeat alleles observed in patients.

NanoSatellite: accurate characterization of expanded tandem repeat length and sequence through whole genome long-read sequencing on PromethION.

Technological limitations have hindered the large-scale genetic investigation of tandem repeats in disease. We show that long-read sequencing with a single Oxford Nanopore Technologies PromethION flow cell per individual achieves 30× human genome coverage and enables accurate assessment of tandem repeats including the 10,000-bp Alzheimer's disease-associated ABCA7 VNTR. The Guppy "flip-flop" base caller and tandem-genotypes tandem repeat caller are efficient for large-scale tandem repeat assessment, but base calling and alignment challenges persist. We present NanoSatellite, which analyzes tandem repeats directly on electric current data and improves calling of GC-rich tandem repeats, expanded alleles, and motif interruptions.

Structural variants identified by Oxford Nanopore PromethION sequencing of the human genome.

We sequenced the genome of the Yoruban reference individual NA19240 on the long-read sequencing platform Oxford Nanopore PromethION for evaluation and benchmarking of recently published aligners and germline structural variant calling tools, as well as a comparison with the performance of structural variant calling from short-read sequencing data. The structural variant caller Sniffles after NGMLR or minimap2 alignment provides the most accurate results, but additional confidence or sensitivity can be obtained by a combination of multiple variant callers. Sensitive and fast results can be obtained by minimap2 for alignment and a combination of Sniffles and SVIM for variant identification. We describe a scalable workflow for identification, annotation, and characterization of tens of thousands of structural variants from long-read genome sequencing of an individual or population. By discussing the results of this well-characterized reference individual, we provide an approximation of what can be expected in future long-read sequencing studies aiming for structural variant identification.

NanoPack: visualizing and processing long-read sequencing data.

Summary: Here we describe NanoPack, a set of tools developed for visualization and processing of long-read sequencing data from Oxford Nanopore Technologies and Pacific Biosciences. Availability and implementation: The NanoPack tools are written in Python3 and released under the GNU GPL3.0 License. The source code can be found at https://github.com/wdecoster/nanopack, together with links to separate scripts and their documentation. The scripts are compatible with Linux, Mac OS and the MS Windows 10 subsystem for Linux and are available as a graphical user interface, a web service at http://nanoplot.bioinf.be and command line tools. Supplementary information: Supplementary data are available at Bioinformatics online.