Supplementary data are available at Bioinformatics online.

Recent emergence of nanopore sequencing technology set a challenge for the established assembly methods not optimized for the combination of read lengths and high error rates of nanopore reads. In this work we assessed how existing de novo assembly methods perform on these reads. We benchmarked three non-hybrid (in terms of both error correction and scaffolding) assembly pipelines as well as two hybrid assemblers which use third generation sequencing data to scaffold Illumina assemblies. Tests were performed on several publicly available MinION and Illumina datasets of E. coli K-12, using several sequencing coverages of nanopore data (20x, 30x, 40x and 50x). We attempted to assess the quality of assembly at each of these coverages, to estimate the requirements for closed bacterial genome assembly. Results show that hybrid methods are highly dependent on the quality of NGS data, but much less on the quality and coverage of nanopore data and perform relatively well on lower nanopore coverages. Furthermore, when coverage is above 40x, all nonhybrid methods correctly assemble the E. coli genome, even a non-hybrid method tailored for Pacific Bioscience reads. While it requires higher coverage compared to a method designed particularly for nanopore reads, its running time is significantly lower.

Supplementary data are available at Bioinformatics online.

In this paper we present Graphmap2, a splice-aware mapper built on our previously developed DNA mapper Graphmap. Graphmap2 is tailored for long reads produced by Pacific Biosciences and Oxford Nanopore devices. It uses several newly developed algorithms which enable higher precision and recall of correctly detected transcripts and exon boundaries. We compared its performance with the state-of-the-art tools Minimap2 and Gmap. On both simulated and real datasets Graphmap2 achieves higher mappability and more correctly recognized exons and their ends. In addition we present an analysis of potential of splice aware mappers and long reads for the identification of previously unknown isoforms and even genes. The Graphmap2 tool is publicly available at https://github.com/lbcb-sci/graphmap2 .

In recent years, both long-read sequencing and metagenomic analysis have been significantly advanced. Although long-read sequencing technologies have been primarily used for de novo genome assembly, they are rapidly maturing for widespread use in other applications. In particular, long reads could potentially lead to more precise taxonomic identification, which has sparked an interest in using them for metagenomic analysis.Here we present a benchmark of several state-of-the-art tools for metagenomic taxonomic classification, tested on in-silico datasets constructed using real long reads from isolate sequencing. We compare tools that were either newly developed or modified to work with long reads, including k-mer based tools Kraken2, Centrifuge and CLARK, and mapping-based tools MetaMaps and MEGAN-LR. The test datasets were constructed with varying numbers of bacterial and eukaryotic genomes to simulate different real-life metagenomic applications. The tools were tested to detect species accurately and precisely estimate species abundances in the samples.Our analysis shows that all tested classifiers provide useful results, and the composition of the used database strongly influences the performance. Using the same database, tested tools achieve comparable results except for MetaMaps, which slightly outperform others in most metrics, but it is significantly slower than k-mer based tools.We deem there is significant room for improvement for all tested tools, especially in lowering the number of false-positive detections.