Section: New Results

Algorithms & Methods

Genome assembly of targeted organisms in metagenomic data

Participants : Wesley Delage, Cervin Guyomar, Fabrice Legeai, Claire Lemaitre.

In this work, we propose a two-step reference-guided assembly method tailored for metagenomic data. First, a subset of the reads belonging to the species of interest are recruited by mapping and assembled de novo into backbone contigs using a classical assembler. Then an all-versus-all contig gap-filling is performed using a modified version of MindTheGap with the whole metagenomic dataset. The originality and success of the approach lie in this second step, that enables to assemble the missing regions between the backbone contigs, which may be regions absent or too divergent from the reference genome. The result of the method is a genome assembly graph in gfa format, accounting for the potential structural variations identified within the sample. We showed that this method is able to assemble the Buchnera aphidicola genome in a single contig in pea aphid metagenomic samples, even when using a divergent reference genome, it runs at least 5 times faster than classical de novo metagenomics assemblers and it is able to recover large structural variations co-existing in a sample. The modified version of MindTheGap is freely available at http://github.com/GATB/MindTheGap (version $>$ 2.1.0) [31].

De Novo Clustering of Long Reads by Gene from Transcriptomics Data

Participants : Camille Marchet, Lolita Lecompte, Jacques Nicolas, Pierre Peterlongo.

Long-read sequencing currently provides sequences of a few thousand base pairs. It is therefore possible to obtain complete transcripts, offering an unprecedented vision of the cellular transcriptome. However the literature lacks tools for de novo clustering of such data, in particular for Oxford Nanopore Technologies reads, because of the inherent high error rate compared to short reads. Our 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 both proposes a new algorithm adapted to clustering of reads by gene and a practical and free access tool that allows to scale the complete processing of eukaryotic transcriptomes. We 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 that it is the best approach for transcriptomics long reads. When a reference is available to enable mapping, we show that it stands as an alternative method that predicts complementary clusters.

The tool, called CARNAC-LR, is freely available at https://github.com/kamimrcht/CARNAC-LR. This work has been published in Nucleic Acids Research journal [16] and presented in several conferences [33], [28].

Comparison of approaches for finding alternative splicing events in RNA-seq

Participant : Camille Marchet.

In this work we compared an assembly-first and a mapping-first approach to analyze RNA-seq data and find alternative splicing (AS) events. Assembly-first approach enables to identify novel AS events and to detect events in paralog genes that are hard to find using mapping because of multiple equivalent matches. On the other hand, the mapping-first approach is more sensitive and detects AS events in lowly expressed genes, and is also able to find AS events with exons containing transposable elements. In addition we support these results with experimental validation. We showed that in order to extensively study the alternative splicing via RNA-seq data and retrieve the most candidates, both approaches should be led. We provide a pipeline consituted of parallel local de novo assembly executed by KisSplice and mapping using a novel mapping workflow called FaRLine [11].

Short read correction

Participant : Pierre Peterlongo.

We proposed a new method to correct short reads using de Bruijn graphs, and we implemented it as a tool called Bcool. As a first step, Bcool constructs a corrected compacted de Bruijn graph from the reads. This graph is then used as a reference and the reads are corrected according to their mapping on the graph. We showed that this approach yields a better correction than kmer-spectrum techniques, while being scalable, making it possible to apply it to human-size genomic datasets and beyond [27].

Long read splitting of heterozygous genomes

Participants : Dominique Lavenier, Maxime Bridoux.

This study aims to directly split long reads of highly heterozygous genomes to help assembly. Long read technologies provide very noisy sequences with many short indel errors. Standard assembly software do not really make difference between heterozygosity and sequencing errors. For highly heterozygous genomes this confusion may lead to misassembly. To separate long reads accordingly to their haplotype, we developed a new k-mer based method. After an alignment step to group similar reads, we build slices of 1 kbp along the multiple alignment containing a representative number of reads. The splitting is done by focusing on k-mers that are absent in one group and not in another one. This in an ongoing work started by the internship of M. Bridoux [29] in the framework of the France Genomique ALPAGA project.