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Peterlongo, P.

Publications and source records attributed to Peterlongo, P..

5 recordsLinked to original sources

A de novo approach to disentangle partner identity and function in holobiont systems

BackgroundStudy of meta-transcriptomic datasets involving non-model organisms represents bioinformatic challenges. The production of chimeric sequences and our inability to distinguish the taxonomic origins of the sequences produced are inherent and recurrent difficulties in de novo assembly analyses. The study of holobiont transcriptomes shares similarities with meta-transcriptomic, and hence, is also affected by challenges invoked above. Here we propose an innovative approach to tackle such difficulties which was applied to the study of marine holobiont models as a proof of concept.\n\nResultsWe considered three holobionts models, of which two transcriptomes were previously assembled and published, and a yet unpublished transcriptome, to analyze their raw reads and assign them to the host and/or to the symbiont(s) using Short Read Connector, a k-mer based similarity method. We were able to define four distinct categories of reads for each holobiont transcriptome: host reads, symbiont reads, shared reads and unassigned reads. The result of the independent assemblies for each category within a transcriptome led to a significant diminution of de novo assembled chimeras compared to classical assembly methods. Combining independent functional and taxonomic annotations of each partners transcriptome is particularly convenient to explore the functional diversity of an holobiont. Finally, our strategy allowed to propose new functional annotations for two well-studied holobionts and a first transcriptome from a planktonic Radiolaria-Dinophyta system forming widespread symbiotic association for which our knowledge is limited. Conclusions\n\nIn contrast to classical assembly approaches, our bioinformatic strategy not only allows biologists to studying separately host and symbiont data from a holobiont mixture, but also generates improved transcriptome assemblies. The use of Short Read Connector has proven to be an effective way to tackle meta-transcriptomic challenges to study holobiont systems composed of either well-studied or poorly characterized symbiotic lineages such as the newly sequenced marine plankton Radiolaria-Dinophyta symbiosis and ultimately expand our knowledge about these marine symbiotic associations.

genomics

DiscoSnp-RAD: de novo detection of small variants for population genomics

We present an original method to de novo call variants for Restriction site associated DNA Sequencing (RAD-Seq). RAD-Seq is a technique characterized by the sequencing of specific loci along the genome, that is widely employed in the field of evolutionary biology since it allows to exploit variants (mainly SNPs) information from entire populations at a reduced cost. Common RAD dedicated tools, as STACKS or IPyRAD, are based on all-versus-all read comparisons, which require consequent time and computing resources. Based on the variant caller DiscoSnp, initially designed for shotgun sequencing, DiscoSnp-RAD avoids this pitfall as variants are detected by exploring the De Bruijn Graph built from all the read datasets. We tested the implementation on RAD data from 259 specimens of Chiastocheta flies, morphologically assigned to 7 species. All individuals were successfully assigned to their species using both STRUCTURE and Maximum Likelihood phylogenetic reconstruction. Moreover, identified variants succeeded to reveal a within species structuration and the existence of two populations linked to their geographic distributions. Furthermore, our results show that DiscoSnp-RAD is at least one order of magnitude faster than state-of-the-art tools. The overall results show that DiscoSnp-RAD is suitable to identify variants from RAD data, and stands out from other tools due to his completely different principle, making it significantly faster, in particular on large datasets.\n\nLicenseGNU Affero general public license\n\nAvailabilityhttps://github.com/GATB/DiscoSnp\n\nContactjeremy.gauthier@inria.fr

bioinformatics

DiscoSnp++: de novo detection of small variants from raw unassembled read set(s)

MotivationNext Generation Sequencing (NGS) data provide an unprecedented access to life mechanisms. In particular, these data enable to detect polymorphisms such as SNPs and indels. As these polymorphisms represent a fundamental source of information in agronomy, environment or medicine, their detection in NGS data is now a routine task. The main methods for their prediction usually need a reference genome. However, non-model organisms and highly divergent genomes such as in cancer studies are extensively investigated.\n\nResultsWe propose DiscoSnp++, in which we revisit the DiscoSnp algorithm. DiscoSnp++ is designed for detecting and ranking all kinds of SNPs and small indels from raw read set(s). It outputs files in fasta and VCF formats. In particular, predicted variants can be automatically localized afterwards on a reference genome if available. Its usage is extremely simple and its low resource requirements make it usable on common desktop computers. Results show that DiscoSnp++ performs better than state-of-the-art methods in terms of computational resources and in terms of results quality. An important novelty is the de novo detection of indels, for which we obtained 99% precision when calling indels on simulated human datasets and 90% recall on high confident indels from the Platinum dataset.\n\nLicenseGNU Affero general public license\n\nAvailabilityhttps://github.com/GATB/DiscoSnp\n\nContactpierre.peterlongo@inria.fr

bioinformatics

De novo Clustering Nanopore Long Reads of Transcriptomics Data by Gene

Long-read sequencing currently provides sequences of several thousand base pairs. This allows to obtain complete transcripts, which offers an un-precedented vision of the cellular transcriptome.\n\nHowever the literature is lacking tools to cluster such data de novo, in particular for Oxford Nanopore Technologies reads, because of the inherent high error rate compared to short reads.\n\nOur goal is to process reads from whole transcriptome sequencing data accurately and without a reference genome in order to reliably group reads coming from the same gene. This de novo approach is therefore particularly suitable for non-model species, but can also serve as a useful pre-processing step to improve read mapping. Our contribution is both to propose a new algorithm adapted to clustering of reads by gene and a practical and free access tool that permits to scale the complete processing of eukaryotic transcriptomes.\n\nWe sequenced a mouse RNA sample using the MinION device, this dataset is used to compare our solution to other algorithms used in the context of biological clustering. We demonstrate its is better-suited for transcriptomics long reads. When a reference is available thus mapping possible, we show that it stands as an alternative method that predicts complementary clusters.

bioinformatics

Critical Assessment of Metagenome Interpretation - a benchmark of computational metagenomics software

In metagenome analysis, computational methods for assembly, taxonomic profiling and binning are key components facilitating downstream biological data interpretation. However, a lack of consensus about benchmarking datasets and evaluation metrics complicates proper performance assessment. The Critical Assessment of Metagenome Interpretation (CAMI) challenge has engaged the global developer community to benchmark their programs on datasets of unprecedented complexity and realism. Benchmark metagenomes were generated from ~700 newly sequenced microorganisms and ~600 novel viruses and plasmids, including genomes with varying degrees of relatedness to each other and to publicly available ones and representing common experimental setups. Across all datasets, assembly and genome binning programs performed well for species represented by individual genomes, while performance was substantially affected by the presence of related strains. Taxonomic profiling and binning programs were proficient at high taxonomic ranks, with a notable performance decrease below the family level. Parameter settings substantially impacted performances, underscoring the importance of program reproducibility. While highlighting current challenges in computational metagenomics, the CAMI results provide a roadmap for software selection to answer specific research questions.

bioinformatics