Biology subjects

Desvillechabrol, D.

Publications and source records attributed to Desvillechabrol, D..

4 recordsLinked to original sources

bioRxiv2026

LORA: a polymorphic multi-sample LOng Read Assembly pipeline.

Genome assembly from long-read sequencing data has become a standard approach for resolving complex genomic regions and producing high-contiguity assemblies. However, the diversity of available assemblers, their varying performance across species, and the need for reproducible workflows present ongoing challenges. We developed LORA, an easy-to-use and reproducible application for assembling genomes from long-read data. LORA integrates several well-established assemblers, including Canu, HiFiasm, Flye, and Unicycler, as well as more recent tools such as Necat and Pecat. It is implemented as a Snakemake pipeline to parallelize tasks and support seamless execution on both local machines and computing clusters. LORA includes multiple quality assessment steps, interactive HTML reports for interpretation, BLAST-based taxonomic identification, and completeness evaluation. Together, these features provide users with a comprehensive view of assembly quality and potential problematic. We illustrate the capabilities of LORA using datasets from bacterial genomes and unicellular eukaryotes, sequenced with both PacBio and Oxford Nanopore technologies, highlighting typical outcomes and common pitfalls encountered during long-read assemblies. LORA is distributed as part of the Sequana project, an open-source framework designed for reproducibility, maintainability, and straightforward deployment across computing environments.

bioinformatics↗

bioRxiv2023

BioConvert: a comprehensive format converter for life sciences

AO_SCPLOWBSTRACTC_SCPLOWBioinformatics is a field known for the numerous standards and formats that have been developed over the years. This plethora of formats, sometimes complementary, and often redundant, poses many challenges to bioinformatics data analysts. They constantly need to find the best tool to convert their data into the suitable format, which is often a complex, technical and time consuming task. Moreover, these small yet important tasks are often difficult to make reproducible. To over-come these difficulties, we initiated BioConvert, a collaborative project to facilitate the conversion of life science data from one format to another. BioConvert aggregates existing software within a single framework and complemented them with original code when needed. It provides a common interface to make the user experience more streamlined instead of having to learn tens of them. Currently, BioConvert supports about 50 formats and 100 direct conversions in areas such as alignment, sequencing, phylogeny, and variant calling. In addition to being useful for end-users, BioConvert can also be utilized by developers as a universal benchmarking framework for evaluating and comparing numerous conversion tools. Additionally, we provide a web server implementing an online user-friendly interface to BioConvert, hence allowing direct use for the community.

bioinformatics↗

bioRxiv2017

Sequanix: A Dynamic Graphical Interface for Snakemake Workflows

SummaryWe designed a PyQt graphical user interface - Sequanix - aiming at democratizing the use of Snakemake pipelines. Although the primary goal of Sequanix was to facilitate the execution of NGS Snakemake pipelines available in the Sequana project (http://sequana.readthedocs.io), it can also handle any Snakemake pipelines. Therefore, Sequanix should be useful to all Snakemake developers willing to expose their pipelines to a wider audience.\n\nAvailabilitySource code available on http://github.com/sequana/sequana and standalone on http://bioconda.github.io (sequana package).

bioinformatics↗

bioRxiv2016

Detection and characterization of low and high genome coverage regions using an efficient running median and a double threshold approach.

MotivationNext Generation Sequencing (NGS) provides researchers with powerful tools to investigate both prokaryotic and eukaryotic genetics. An accurate assessment of reads mapped to a specific genome consists of inspecting the genome coverage as number of reads mapped to a specific genome location. Most current methods use the average of the genome coverage (sequencing depth) to summarize the overall coverage. This metric quickly assess the sequencing quality but ignores valuable biological information like the presence of repetitive regions or deleted genes. The detection of such information may be challenging due to a wide spectrum of heterogeneous coverage regions, a mixture of underlying models or the presence of a non-constant trend along the genome. Using robust statistics to systematically identify genomic regions with unusual coverage is needed to characterize these regions more precisely.\n\nResultsWe implemented an efficient running median algorithm to estimate the genome coverage trend. The distribution of the normalized genome coverage is then estimated using a Gaussian mixture model. A z-score statistics is then assigned to each base position and used to separate the central distribution from the regions of interest (ROI) (i.e., under and over-covered regions). Finally, a double threshold mechanism is used to cluster the genomic ROIs. HTML reports provide a summary with interactive visual representations of the genomic ROIs.\n\nAvailabilityAn implementation of the genome coverage characterization is available within the Sequana project. The standalone application is called sequana_coverage. The source code is available on GitHub (http://github.com/sequana/sequana), and documentation on ReadTheDocs (http://sequana.readtheodcs.org). An example of HTML report is provided on http://sequana.github.io.\n\nContactdimitri.desvillechabrol@pasteur.fr, thomas.cokelaer@pasteur.fr

bioinformatics↗