Ancestral gene synteny reconstruction improves extant species scaffolding

Abstract

We exploit the methodological similarity between ancestral genome reconstruction and extant genome scaffolding. We present a method, called ARt-DeCo that constructs neighborhood relationships between genes or contigs, in both ancestral and extant genomes, in a phylogenetic context. It is able to handle dozens of complete genomes, including genes with complex histories, by using gene phylogenies reconciled with a species tree, that is, annotated with speciation, duplication and loss events. Reconstructed ancestral or extant synteny comes with a support computed from an exhaustive exploration of the solution space. We compare our method with a previously published one that follows the same goal on a small number of genomes with universal unicopy genes. Then we test it on the whole Ensembl database, by proposing partial ancestral genome structures, as well as a more complete scaffolding for many partially assembled genomes on 69 eukaryote species. We carefully analyze a couple of extant adjacencies proposed by our method, and show that they are indeed real links in the extant genomes, that were missing in the current assembly. On a reduced data set of 39 eutherian mammals, we estimate the precision and sensitivity of ARt-DeCo by simulating a fragmentation in some well assembled genomes, and measure how many adjacencies are recovered. We find a very high precision, while the sensitivity depends on the quality of the data and on the proximity of closely related genomes.

Evolutionary superscaffolding and chromosome anchoring to improve Anopheles genome assemblies

“…The synteny-based adjacencies were used to define well-supported consensus sets, which were then validated with and complemented by physical mapping and/or RNAseq and/or re-sequencing data for 14 assemblies. This followed a reconciliation workflow to integrate the different sets of scaffold adjacencies from synteny, physical mapping, RNAseq, or alignment data for each assembly (see the “Methods” section; Additional file 1: Figure S1) [29–50]. Applying this integrative approach produced updated reference assemblies with increased scaffold N50 values (a median-like metric where half the genome is assembled into scaffolds of length N50 or longer) and reduced scaffold counts (Table 1).…”

Phylogenetic signal from rearrangements in 18 Anopheles species by joint scaffolding extant and ancestral genomes

“…ADSEQ builds upon a family of methods aimed at reconstructing the evolutionary history of gene adjacencies introduced with the DECO algorithm [ 50 ] and extended along several lines in [ 51 , 52 ]. It is implemented within the package DECOSTAR [ 53 ].…”

“…In the present work, the prior scores of extant adjacencies are either 1.0 for adjacencies that are observed in a contig or scaffold, or a scaffolding score obtained from sequencing data using the scaffolding software BESST . Pairs of genes located at the extremities of contigs and for which sequencing data do not provide any evidence for a scaffolding adjacency receive a small prior score as described in [ 52 ] (see also Additional file 1 ). The posterior scores are defined as the frequency out of a sample of 100 solutions with temperatures kT = kT 0 =0.1 that skews the Gibbs-Boltzmann distribution toward optimal solutions.…”

“…The key element is to provide to ADSEQ a genome whose assembly is more fragmented than the reference assembly, in order to verify that ADSEQ can retrieve the lost adjacencies. Moreover our simulation framework aims at generating a realistic fragmentation, as relying on a random fragmentation, as used in other validation protocols [ 16 , 52 ], generates data that are in general easy to scaffold using comparative methods.…”

CAMSA: A tool for comparative analysis and merging of scaffold assemblies

“…These methods are based on jumping libraries [2][3][4][5][6][7][8], long error-prone reads (such as PacBio or MinION reads) [9][10][11][12][13], homology relationship between multiple genomes [14][15][16], wet-lab experiments such as the fluorescence in situ hybridization (FISH) [17,18], genome maps [19][20][21], higher order chromatin interactions [22], and so on. Depending on the nature and accuracy of utilized information and techniques, assemblies produced by different methods may still be incomplete and contain errors, thus deviating from each other.…”