Section: New Results

Axis 1: Genomics

Genome hybrid assembly

Long read sequencing technologies are considered to be the solution for handling genome repeats, allowing near reference-level reconstructions of large genomes. However, long read de novo assembly pipelines are computationally intense and require a considerable amount of coverage, thereby hindering their broad application to the assembly of large genomes. Alternatively, hybrid assembly methods that combine short and long read sequencing technologies can reduce the time and cost required to produce de novo assemblies of large genomes. In [10], we proposed a new method, called Fast-SG , that uses a new ultrafast alignment-free algorithm specifically designed for constructing a scaffolding graph using lightweight data structures. Fast-SG can construct the graph from either short or long reads. This allows the reuse of efficient algorithms designed for short read data and permits the definition of novel modular hybrid assembly pipelines. Using comprehensive standard datasets and benchmarks, we showed how Fast-SG outperforms the state-of-the-art short read aligners when building the scaffolding graph and can be used to extract linking information from either raw or error-corrected long reads. We also showed how a hybrid assembly approach using Fast-SG with shallow long-read coverage (5X) and moderate computational resources can produce long-range and accurate reconstructions of the genomes of Arabidopsis thaliana (Ler-0) and human (NA12878). We are currently working on the assembly process itself, using the scaffolding graphs obtained with Fast-SG . The results obtained so far are extremely promising and a paper is currently in preparation. This is part of the work done by Alex di Genova, postdoc in ERABLE.

Variant annotation

Genome-wide analyses estimate that more than 90% of multi exonic human genes produce at least two transcripts through a genomic variant called alternative splicing (AS). Various bioinformatics methods are available to analyse AS from RNAseq data. Most methods start by mapping the reads to an annotated reference genome, but some start by a de novo assembly of the reads. In [3], we presented a systematic comparison of a mapping-first approach (Farline ) and an assembly-first approach (scKisSplice). We applied these methods to two independent RNAseq datasets and found that the predictions of the two pipelines overlapped (70% of exon skipping events were common), but with noticeable differences. The assembly-first approach allowed to find more novel variants, including novel unannotated exons and splice sites. It also predicted AS in recently duplicated genes. The mapping-first approach allowed to find more lowly expressed splicing variants, and splice variants overlapping repeats. This work demonstrated that annotating AS with a single approach leads to missing out a large number of candidates, many of which are differentially regulated across conditions and can be validated experimentally. We therefore advocate for the combined use of both mapping-first and assembly-first approaches for the annotation and differential analysis of AS from RNAseq datasets. This was part of the work of Clara Benoît-Pilven, postdoc at Inserm and in ERABLE, to which also participated other current or ex-members of ERABLE, namely Camille Marchet (during her stay as ADT engineer with ERABLE), Emilie Chautard (when she was postdoc Inserm and in ERABLE), Gustavo Sacomoto (when he was PhD and then for one year postdoc in ERABLE), and Leandro Lima (current PhD student of ERABLE).

Another type of variant, namely SNPs was also considered in [51]. In this paper, mutations are detected by eBWT (extended Burrows-Wheeler Transform). Indeed, we notices that eBWT of a collection of DNA fragments tend to cluster together the copies of nucleotides sequenced from a genome. We showed that it is thus possible to accurately predict how many copies of any nucleotide are expected inside each such cluster, and that a precise LCP array based procedure can locate these clusters in the eBWT. These theoretical insights were validated in practice with SNPs being clustered in the eBWT of a reads collection. We developed a tool for finding SNPs with a simple scan of the eBWT and LCP arrays. Preliminary results show that our method requires much less coverage than the state-of-the-art tools while drastically improving precision and sensitivity.

Both types of variants correspond to special types of $st$-paths in graphs, a topic that was also explored from a more purely theoretical point of view in two papers, one already accepted [46] and on that is about to be submitted and extends the results obtained in 2017 on bubble (as $st$-paths are also called in bioinformatics) generators in directed graphs.

Full-length de novo viral quasispecies assembly through variation graph construction

Viruses populate their hosts as a viral quasispecies: a collection of genetically related mutant strains. Viral quasispecies assembly refers to reconstructing the strain-specific haplotypes from read data, and predicting their relative abundances within the mix of strains, an important step for various treatment-related reasons. Reference-genome-independent ("de novo") approaches have yielded benefits over reference-guided approaches, because reference-induced biases can become overwhelming when dealing with divergent strains. While being very accurate, extant de novo methods only yield rather short contigs. It remains to reconstruct full-length haplotypes together with their abundances from such contigs. In [34], we first constructed a variation graph, a recently popular, suitable structure for arranging and integrating several related genomes, from the short input contigs, without making use of a reference genome. To obtain paths through the variation graph that reflect the original haplotypes, we solved a minimisation problem that yields a selection of maximal-length paths that is optimal in terms of being compatible with the read coverages computed for the nodes of the variation graph. We output the resulting selection of maximal length paths as the haplotypes, together with their abundances. Benchmarking experiments on challenging simulated data sets showed significant improvements in assembly contiguity compared to the input contigs, while preserving low error rates. As a consequence, our method outperforms all state-of-the-art viral quasispecies assemblers that aim at the construction of full-length haplotypes, in terms of various relevant assembly measures. The tool, called Virus-VG , is availabe at https://bitbucket.org/jbaaijens/virus-vg.

A member of ERABLE was also involved in the Second Annual Meeting of the European Virus Bioinformatics Center (EVBC), held in Utrecht, Netherlands, and whose focus was on computational approaches in virology, with topics including (but not limited to) virus discovery, diagnostics, (meta-)genomics, modeling, epidemiology, molecular structure, evolution, and viral ecology. Approximately 120 researchers from around the world attended the meeting this year. An overview of new developments and novel research findings that emerged during the meeting was published in the journal Viruses [16].

Bacterial genome-wide association studies (GWAS)

Genome-wide association study (GWAS) methods applied to bacterial genomes have shown promising results for genetic marker discovery or detailed assessment of marker effect. Recently, alignment-free methods based on $k$-mer composition have proven their ability to explore the accessory genome. However, they lead to redundant descriptions and results which are sometimes hard to interpret. In [17], we introduced DBGWAS , an extended $k$-mer-based GWAS method producing interpretable genetic variants associated with distinct phenotypes. Relying on compacted de Bruijn graphs (cDBG), our method gathers cDBG nodes, identified by the association model, into subgraphs defined from their neighbourhood in the initial cDBG. DBGWAS is alignment-free and only requires a set of contigs and phenotypes. In particular, it does not require prior annotation or reference genomes. It produces subgraphs representing phenotype-associated genetic variants such as local polymorphisms and mobile genetic elements (MGE). It offers a graphical framework which helps interpret GWAS results. Importantly, it is also computationally efficient (the experiments took one hour and a half on average). We validated our method using antibiotic resistance phenotypes for three bacterial species. DBGWAS recovered known resistance determinants such as mutations in core genes in Mycobacterium tuberculosis, and genes acquired by horizontal transfer in Staphylococcus aureus and Pseudomonas aeruginosa along with their MGE context. It also enabled us to formulate new hypotheses involving genetic variants not yet described in the antibiotic resistance literature. This is part of the work of Magali Jaillard, PhD student of Laurent Jacob who is an external collaborator of ERABLE, and of Leandro I. S. de Lima, PhD student co-supervised by three members of ERABLE.