Section: New Results

Algorithms & Methods

SV genotyping

Participants : Dominique Lavenier, Lolita Lecompte, Claire Lemaitre, Pierre Peterlongo.

Structural variations (SV) are genomic variants of at least 50 base pairs (bp) that can be rearranged within the genome and thus can have a major impact on biological processes. Sequencing data from third generation technologies have made it possible to better characterize SVs. Although many SV callers have been published recently, there is no published method to date dedicated to genotyping SVs with this type of data. Variant genotyping consists in estimating the presence and ploidy or absence of a set of known variants in a newly sequenced individual. Thus, in this paper, we present a new method and its implementation, SVJedi, to genotype SVs with long reads. From a set of known SVs and a reference genome, our approach first generates local sequences representing the two possible alleles for each SV. Long read data are then aligned to these generated sequences and a careful analysis of the alignments consists in identifying only the informative ones to estimate the genotype for each SV. SVJedi achieves high accuracy on simulated and real human data and we demonstrate its substancial benefits with respect to other existing approaches, namely SV discovery with long reads and SV genotyping with short reads [23], [24], [35]. SVJedi is implemented in Python and available at https://github.com/llecompte/SVJedi.

Genome assembly of targeted organisms in metagenomic data

Participants : Wesley Delage, Fabrice Legeai, Claire Lemaitre.

In this work, we propose a two-step targeted assembly method tailored for metagenomic data, called MinYS (for MineYourSymbiont). 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 novel 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 MinYS 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 10 times faster than classical de novo metagenomics assemblers and it is able to recover large structural variations co-existing in a sample. MinYS is a Python3 pipeline, distributed on github (https://github.com/cguyomar/MinYS) and as a conda package in the bio-conda repository [22].

SimkaMin: subsampling the kmer space for efficient comparative metagenomics

Participants : Claire Lemaitre, Pierre Peterlongo.

SimkaMin [12] is a quick comparative metagenomics tool with low disk and memory footprints, thanks to an efficient data subsampling scheme used to estimate Bray-Curtis and Jaccard dissimilarities. One billion metagenomic reads can be analyzed in less than 3 minutes, with tiny memory (1.09 GB) and disk ($\sim$0.3 GB) requirements and without altering the quality of the downstream comparative analyses, making of SimkaMin a tool perfectly tailored for very large-scale metagenomic projects.

Haplotype reconstruction: phasing co-localized variants

Participants : Mohammed Amin Madoui, Pierre Peterlongo.

In collaboration with Amin Madoui from the Genoscope (CEA), we develop a new methodology to reconstruct haplotypes or strain genomes directly from raw sequencing set of (metagenomic) reads. The goal is to propose long assembled sequences (i.e. complete genomes are not mandatory) such that each assembled sequence belongs to only one sequenced chromosome and is not a consensus of several similar sequences. Downstream, this enables to perform population genomics analyses.

The key idea is to use the DiscoSnp [10] output, detecting set of variant alleles that are co-localized on input reads or pairs of input reads. Then we finally reconstruct set of sequences that are as parsimonious as possible with those observations.

Finding all maximal perfect haplotype blocks in linear time

Participant : Pierre Peterlongo.

Recent large-scale community sequencing efforts allow at an unprecedented level of detail the identification of genomic regions that show signatures of natural selection. However, traditional methods for identifying such regions from individuals' haplotype data require excessive computing times and therefore are not applicable to current datasets. In 2019, Cunha et al. (Proceedings of BSB 2019) suggested the maximal perfect haplotype block as a very simple combinatorial pattern, forming the basis of a new method to perform rapid genome-wide selection scans. The algorithm they presented for identifying these blocks, however, had a worst-case running time quadratic in the genome length. It was posed as an open problem whether an optimal, linear-time algorithm exists. We gave two algorithms that achieve this time bound, one which is conceptually very simple and uses suffix trees and a second one using the positional Burrows-Wheeler Transform, that is very efficient also in practice [20].

Short read correction

Participant : Pierre Peterlongo.

We propose a new approach for the correction of NGS reads. This approach is based on the construction of a clean de Bruijn graph in which the correction is made at the contig level. In a second step, original reads are mapped on this graph, allowing to correct the original reads [16].

Large-scale kmer indexation

Participants : Téo Lemane, Pierre Peterlongo.

In the SeqDigger ANR project framework (see dedicated Section), we aim to index TB or PB of genomic sequences, assembled or not. The central idea is to assign any kmer (word of length $k$) to the set of indexed dataset it belongs to. For doing this we have proposed a method that improves one of the state of the art algorithm (HowDeSBT  [38]) by optimizing the way kmers are counted and represented [36].

Proteogenomics workflow for the expert annotation of eukaryotic genomes

Participant : Pierre Peterlongo.

Accurate structural annotation of genomes is still a challenge, despite the progress made over the past decade. The prediction of gene structure remains difficult, especially for eukaryotic species, and is often erroneous and incomplete. In [15], we proposed a proteogenomics strategy, taking advantage of the combination of proteomics datasets and bioinformatics tools, to identify novel protein coding-genes and splice isoforms, to assign correct start sites, and to validate predicted exons and genes.

Gap-filling with linked-reads data

Participants : Anne Guichard, Fabrice Legeai, Claire Lemaitre, Arthur Le Bars, Pierre Peterlongo.

We develop a novel approach for filling assembly gaps with linked reads data (typically 10X Genomics technology). The approach is based on local assembly using our tool MindTheGap [9], and takes advantage of barcode information to reduce the input read set in order to reduce the de Bruijn graph complexity. The approach is applied to recover the genomic structure of a 1.3 Mb locus of interest in a dozen of re-sequenced butterfly genomes (H. numata) in the Supergene ANR project context.

Statistically Significant Discriminative Patterns Searching

Participants : Gwendal Virlet, Dominique Lavenier.

We propose a novel algorithm, called SSDPS, to discover patterns in two-class datasets. The algorithm, developed in collaboration with the LACODAM Inria team, owes its efficiency to an original enumeration strategy of the patterns, which allows to exploit some degrees of anti-monotonicity on the measures of discriminance and statistical significance. Experimental results demonstrate that the performance of the algorithm is better than others. In addition, the number of generated patterns is much less than the number of the other algorithms. An experiment on real data also shows that SSDPS efficiently detects multiple SNPs combinations in genetic data [27].