SUMMARY: Metagenome assembly seeks to reconstruct the most high-quality genomes from sequencing data of microbial ecosystems. Despite technological advancements that facilitate assembly, such as Hi-Fi long reads, the process remains challenging in complex environmental samples consisting of hundreds to thousands of populations. Mapler is a metagenome assembly and evaluation pipeline with a focus on evaluating the quality of Hi-Fi long read metagenome assemblies. It incorporates several state-of-the-art metrics, as well as novel metrics assessing the diversity that remains uncaptured by the assembly process. Mapler facilitates the comparison of assembly strategies and helps identify methodological bottlenecks that hinder genome reconstruction. AVAILABILITY AND IMPLEMENTATION: Mapler is open source and publicly available under the AGPL-3.0 licence at https://github.com/Nimauric/Mapler. Source code is implemented in Python and Bash as a Snakemake pipeline. A snapshot of the code is available on Software Heritage at swh:1:snp:df4f5f02e22ebbab285ec14af58d4d88436ee5d6. Raw data and results are available at https://entrepot.recherche.data.gouv.fr/dataset.xhtml?persistentId=doi:10.57745/2SA8AB.

SUMMARY: MUSET is a novel set of utilities designed to efficiently construct abundance unitig matrices from sequencing data. Unitig matrices extend the concept of k-mer matrices by merging overlapping k-mers that unambiguously belong to the same sequence. MUSET addresses the limitations of current software by integrating k-mer counting and unitig extraction to generate unitig matrices containing abundance values, as opposed to only presence-absence in previous tools. These matrices preserve variations between samples while reducing disk space and the number of rows compared to k-mer matrices. We evaluated MUSET's performance using datasets derived from a 618-GB collection of ancient oral sequencing samples, producing a filtered unitig matrix that records abundances in <10 h and 20 GB memory. AVAILABILITY AND IMPLEMENTATION: MUSET is open source and publicly available under the AGPL-3.0 licence in GitHub at https://github.com/CamilaDuitama/muset. Source code is implemented in C++ and provided with kmat_tools, a collection of tools for processing k-mer matrices. Version v0.5.1 is available on Zenodo with DOI 10.5281/zenodo.14164801.

MOTIVATION: The understanding of the ever-increasing number of metagenomic sequences accumulating in our databases demands for approaches that rapidly 'explore' the content of multiple and/or large metagenomic datasets with respect to specific domain targets, avoiding full domain annotation and full assembly. RESULTS: S3A is a fast and accurate domain-targeted assembler designed for a rapid functional profiling. It is based on a novel construction and a fast traversal of the Overlap-Layout-Consensus graph, designed to reconstruct coding regions from domain annotated metagenomic sequence reads. S3A relies on high-quality domain annotation to efficiently assemble metagenomic sequences and on the design of a new confidence measure for a fast evaluation of overlapping reads. Its implementation is highly generic and can be applied to any arbitrary type of annotation. On simulated data, S3A achieves a level of accuracy similar to that of classical metagenomics assembly tools while permitting to conduct a faster and sensitive profiling on domains of interest. When studying a few dozens of functional domains-a typical scenario-S3A is up to an order of magnitude faster than general purpose metagenomic assemblers, thus enabling the analysis of a larger number of datasets in the same amount of time. S3A opens new avenues to the fast exploration of the rapidly increasing number of metagenomic datasets displaying an ever-increasing size. AVAILABILITY AND IMPLEMENTATION: S3A is available at http://www.lcqb.upmc.fr/S3A_ASSEMBLER/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

BACKGROUND: In recent years more than 20 assemblers have been proposed to tackle the hard task of assembling NGS data. A common heuristic when assembling a genome is to use several assemblers and then select the best assembly according to some criteria. However, recent results clearly show that some assemblers lead to better statistics than others on specific regions but are outperformed on other regions or on different evaluation measures. To limit these problems we developed GAM-NGS (Genomic Assemblies Merger for Next Generation Sequencing), whose primary goal is to merge two or more assemblies in order to enhance contiguity and correctness of both. GAM-NGS does not rely on global alignment: regions of the two assemblies representing the same genomic locus (called blocks) are identified through reads' alignments and stored in a weighted graph. The merging phase is carried out with the help of this weighted graph that allows an optimal resolution of local problematic regions. RESULTS: GAM-NGS has been tested on six different datasets and compared to other assembly reconciliation tools. The availability of a reference sequence for three of them allowed us to show how GAM-NGS is a tool able to output an improved reliable set of sequences. GAM-NGS is also a very efficient tool able to merge assemblies using substantially less computational resources than comparable tools. In order to achieve such goals, GAM-NGS avoids global alignment between contigs, making its strategy unique among other assembly reconciliation tools. CONCLUSIONS: The difficulty to obtain correct and reliable assemblies using a single assembler is forcing the introduction of new algorithms able to enhance de novo assemblies. GAM-NGS is a tool able to merge two or more assemblies in order to improve contiguity and correctness. It can be used on all NGS-based assembly projects and it shows its full potential with multi-library Illumina-based projects. With more than 20 available assemblers it is hard to select the best tool. In this context we propose a tool that improves assemblies (and, as a by-product, perhaps even assemblers) by merging them and selecting the generating that is most likely to be correct.