the budding yeastSaccharomyces cerevisiaehas many isolates currently being sequenced with long reads. However, analyzing long-read sequencing data to produce high-quality genome assembly
The PacBio庐 HiFi sequencing method yields highly accurate long-read sequencing datasets with read lengths averaging 10-25 kb and accuracies greater than 99.5%. These accurate long reads can be used to improve results for complex applications such as single nucleotide and structural variant ...
As long-read sequencing technologies are becoming increasingly popular, a number of methods have been developed for the discovery and analysis of structural variants (SVs) from long reads. Long reads enable detection of SVs that could not be previously d
In this article, we present a new method, called SVLR, to detect SVs based on long-read sequencing data. Comparing with existing methods, SVLR can detect three new kinds of SVs: block replacements, block interchanges, and translocations. Although these new SVs are structurally more complicated...
(Fig.1b). This identified trends in the evolution of research interests: likely due to the modest initial throughput of long-read sequencing technologies, the majority of tools were tested on non-human data; tools for de novo assembly, error correction, and polishing categories have received ...
Filtering and trimming of long read sequencing data. Please be aware that NanoFilt will no longer receive any updates, as (most of) its functionality is included in chopper (which should be lots faster, too). Filtering on quality and/or read length, and optional trimming after passing filters...
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 Licence. The source...
Wang, NextSV: a meta-caller for structural variants from low-coverage long-read sequencing data, BMC Bioinformatics 19 (2018) 180.Fang L, Hu J, Wang D, Wang K. NextSV: a meta-caller for structural variants from low- coverage long-read sequencing data. BMC Bioinformatics. 2018;19:180. ...
The Kraken2 mode can be used in real-time, allowing the workflow to run parallel with an ongoing sequencing run as read data is being produced by the Oxford Nanopore Technologies sequencing instrument. In this case,Kraken2is used with theKraken2-serverand the user can visualise the classificati...
cuteSV. It enables the thorough analysis of the complex signatures of SVs implied by read alignments. Benchmark results demonstrate that cuteSV achieves good yields and performance simultaneously. Especially, it has good sensitivity to detect SVs, even with low coverage sequencing data, and it also ...