Mapler: Assessing assembly quality in taxonomically rich metagenomes sequenced with HiFi reads
Maurice, N.; Lemaitre, C.; Vicedomini, R.; Frioux, C.
Show abstract
SummaryMetagenome 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 ImplementationMapler 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. Contactsnicolas.maurice@inria.fr, clemence.frioux@inria.fr. Supplementary informationAvailable online.
Matching journals
The top 5 journals account for 50% of the predicted probability mass.
Similar papers in this journal
Similar papers in this journal
- dadasnake, a Snakemake implementation of DADA2 to process amplicon sequencing data for microbial ecology 96%
- IDseq - An Open Source Cloud-based Pipeline and Analysis Service for Metagenomic Pathogen Detection and Monitoring 96%
- PathoGFAIR: a collection of FAIR and adaptable (meta)genomics workflows for (foodborne) pathogens detection and tracking 96%
Similar papers in this journal
Similar papers in this journal
Similar papers in this journal
- Addressing the dynamic nature of reference data: a new nt database for robust metagenomic classification 96%
- parafac4microbiome: Exploratory analysis of longitudinal microbiome data using Parallel Factor Analysis 94%
- MetaPhage: an automated pipeline for analyzing, annotating, and classifying bacteriophages in metagenomics sequencing data. 94%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.