BACKGROUND: Long reads have gained popularity in the analysis of metagenomics data. Therefore, we comprehensively assessed metagenomics classification tools on the species taxonomic level. We analysed kmer-based tools, mapping-based tools and two general-purpose long reads mappers. We evaluated more than 20 pipelines which use either nucleotide or protein databases and selected 13 for an extensive benchmark. We prepared seven synthetic datasets to test various scenarios, including the presence of a host, unknown species and related species. Moreover, we used available sequencing data from three well-defined mock communities, including a dataset with abundance varying from 0.0001 to 20% and six real gut microbiomes. RESULTS: General-purpose mappers Minimap2 and Ram achieved similar or better accuracy on most testing metrics than best-performing classification tools. They were up to ten times slower than the fastest kmer-based tools requiring up to four times less RAM. All tested tools were prone to report organisms not present in datasets, except CLARK-S, and they underperformed in the case of the high presence of the host's genetic material. Tools which use a protein database performed worse than those based on a nucleotide database. Longer read lengths made classification easier, but due to the difference in read length distributions among species, the usage of only the longest reads reduced the accuracy. The comparison of real gut microbiome datasets shows a similar abundance profiles for the same type of tools but discordance in the number of reported organisms and abundances between types. Most assessments showed the influence of database completeness on the reports. CONCLUSION: The findings indicate that kmer-based tools are well-suited for rapid analysis of long reads data. However, when heightened accuracy is essential, mappers demonstrate slightly superior performance, albeit at a considerably slower pace. Nevertheless, a combination of diverse categories of tools and databases will likely be necessary to analyse complex samples. Discrepancies observed among tools when applied to real gut datasets, as well as a reduced performance in cases where unknown species or a significant proportion of the host genome is present in the sample, highlight the need for continuous improvement of existing tools. Additionally, regular updates and curation of databases are important to ensure their effectiveness.

Motivation: High-throughput sequencing has transformed the study of gene expression levels through RNA-seq, a technique that is now routinely used by various fields, such as genetic research or diagnostics. The advent of third generation sequencing technologies providing significantly longer reads opens up new possibilities. However, the high error rates common to these technologies set new bioinformatics challenges for the gapped alignment of reads to their genomic origin. In this study, we have explored how currently available RNA-seq splice-aware alignment tools cope with increased read lengths and error rates. All tested tools were initially developed for short NGS reads, but some have claimed support for long Pacific Biosciences (PacBio) or even Oxford Nanopore Technologies (ONT) MinION reads. Results: The tools were tested on synthetic and real datasets from two technologies (PacBio and ONT MinION). Alignment quality and resource usage were compared across different aligners. The effect of error correction of long reads was explored, both using self-correction and correction with an external short reads dataset. A tool was developed for evaluating RNA-seq alignment results. This tool can be used to compare the alignment of simulated reads to their genomic origin, or to compare the alignment of real reads to a set of annotated transcripts. Our tests show that while some RNA-seq aligners were unable to cope with long error-prone reads, others produced overall good results. We further show that alignment accuracy can be improved using error-corrected reads. Availability and implementation: https://github.com/kkrizanovic/RNAseqEval, https://figshare.com/projects/RNAseq_benchmark/24391. Contact: [email protected]. Supplementary information: Supplementary data are available at Bioinformatics online.

MOTIVATION: Recent emergence of nanopore sequencing technology set a challenge for established assembly methods. In this work, we assessed how existing hybrid and non-hybrid de novo assembly methods perform on long and error prone nanopore reads. RESULTS: We benchmarked five 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 Escherichia coli K-12, using several sequencing coverages of nanopore data (20×, 30×, 40× and 50×). We attempted to assess the assembly quality at each of these coverages, in order to estimate the requirements for closed bacterial genome assembly. For the purpose of the benchmark, an extensible genome assembly benchmarking framework was developed. 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. All non-hybrid methods correctly assemble the E. coli genome when coverage is above 40×, even the non-hybrid method tailored for Pacific Biosciences reads. While it requires higher coverage compared to a method designed particularly for nanopore reads, its running time is significantly lower. AVAILABILITY AND IMPLEMENTATION: https://github.com/kkrizanovic/NanoMark CONTACT: [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

We present some new results on expectation and threshold problem for Q-grams. We also introduce new exhaustive search algorithm based on sparse matrix to matrix multiplication for determining the threshold.

While the field of spatio-temporal databases, moving objects databases and the field of data stream management systems have separately been rather extensively researched in the last decade, a combined field of spatio-temporal data stream management systems has only recently become a focus of serious research effort. A spatio-temporal data stream management system manages moving objects whose position and/or extent is provided by one or more data streams, and can be viewed as an extension of a moving objects database enabling it to manage data streams, or as an extension of a data stream management system enabling it to use spatial and spatio-temporal data. In this paper we present a data model, consisting of data types and operations on these data types, needed to support spatio-temporal data in a data stream management system. Presented data model is formally defined using many sorted algebra, and is illustrated through an SQL-like query language. It can be used as a basis either for implementation completely new spatio-temporal DSMS, or for extending available open source products and research prototypes, towards managing moving objects.