A hybrid-hierarchical genome assembly strategy to sequence the invasive golden mussel Limnoperna fortunei

Instituto de Biofísica Carlos Chagas Filho, Universidade Federal do Rio de Janeiro, Rio de Janeiro, Rio de Janeiro, Brazil
Università del Piemonte, Alessandria, Italy
Wellcome Genome Campus, Wellcome Trust Sanger Institute, Cambridge, United Kingdom
Instituto de Bioquímica Médica Leopoldo de Meis, Universidade Federal do Rio de Janeiro, Rio de Janeiro, Rio de Janeiro, Brazil
Bioinformatics Laboratory (LabInfo), National Laboratory for Scientific Computing (LNCC), Petrópolis, Rio de Janeiro, Rio de Janeiro
Department of Evolutionary Genetics and Berlin Center for Genomics in Biodiversity Research (BeGenDiv), Leibniz Institute for Zoo and Wildlife Research (IZW), Berlin, Germany
Berlin Center for Genomics in Biodiversity Research, Berlin, Germany
DOI
10.7287/peerj.preprints.2995v1
Subject Areas
Bioinformatics, Computational Biology, Conservation Biology, Genetics, Genomics
Keywords
invasive, bivalve genomics, golden mussel, amazon, river basin
Copyright
© 2017 Uliano da Silva et al.
Licence
This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ Preprints) and either DOI or URL of the article must be cited.
Cite this article
Uliano da Silva M, Dondero F, Otto T, Costa I, Lima NC, Americo JA, Mazzoni C, Prosdocimi F, Rebelo MF. 2017. A hybrid-hierarchical genome assembly strategy to sequence the invasive golden mussel Limnoperna fortunei. PeerJ Preprints 5:e2995v1

Abstract

Background: For more than 25 years the golden mussel Limnoperna fortunei has been an aggressive invader in South America freshwaters, having travelled more than 5,000 km upstream across 5 countries. The mussel has outcompeted native species in the invaded environments and since chemicals in the water have been unable to control infestation in closed environments, it has economically harmed aquaculture, hydroelectric generation and ship transit. We sequenced the complete genome of the golden mussel to try to understand the molecular basis of its invasiveness and search for ways to control it. Findings: We have assembled the 1.6 Gb genome into 20548 scaffolds with a N50 length of 312 Kb using a hybrid and hierarchical assembly strategy from short and long DNA reads and transcriptomes. A total of 60717 coding genes were inferred from a customized transcriptome-trained AUGUSTUS run. We also compared predicted protein sets with those of complete molluscan genomes, revealing an exacerbation of protein-binding domains in L. fortunei. Conclusions: We assembled one of the best bivalve genomes available using a cost-effective approach using Illumina pair-end, mate pair and PacBio long reads. We expect the continuous and careful annotation of L. fortunei’s genome to aid to the investigation of bivalve genetics, evolution and invasiveness, as well as the development of biotechnological tools for aquatic pest control.

Author Comment

This is a preprint submission to PeerJ Preprints.

Supplemental Information

Steps performed for the prediction and annotation of L. fortunei genome

DOI: 10.7287/peerj.preprints.2995v1/supp-1

Phylogenetic tree of Toll-like (TLRs) receptors found in L. fortunei

DOI: 10.7287/peerj.preprints.2995v1/supp-2

RNA raw reads sequenced for 3 L. fortunei specimens, 4 tissues each

DOI: 10.7287/peerj.preprints.2995v1/supp-3

RepeatMasker classification of repeats predicted in L. fortunei genome

DOI: 10.7287/peerj.preprints.2995v1/supp-4

Details of the online availability of the data used for ortholog assignment and protein domain expansion analysis

DOI: 10.7287/peerj.preprints.2995v1/supp-5

Fantasy names given to L. fortunei genes and proteins from the backers that have supported us through crowdfunding (www.catarse.me/genoma)

DOI: 10.7287/peerj.preprints.2995v1/supp-6