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Marchet, C.

Publications and source records attributed to Marchet, C..

2 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considered three holobionts models, of which two transcriptomes were previously assembled and published, and a yet unpublished transcriptome, to analyze their raw reads and assign them to the host and/or to the symbiont(s) using Short Read Connector, a k-mer based similarity method.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

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