Éditeur(s) : HAL CCSD BioMed Central
Résumé : International audience
Background: Increasingly, similarity networks are being used for evolutionary analyses of molecular datasets. These networks are very useful, in particular for the analysis of gene sharing, lateral gene transfer and for the detection of distant homologs. Currently, such analyses require some computer programming skills due to the limited availability of user-friendly freely distributed software. Consequently, although appealing, the construction and analyses of these networks remain less familiar to biologists than do phylogenetic approaches. Results: In order to ease the use of similarity networks in the community of evolutionary biologists, we introduce a software program, EGN, that runs under Linux or MacOSX. EGN automates the reconstruction of gene and genome networks from nucleic and proteic sequences. EGN also implements statistics describing genetic diversity in these samples, for various user-defined thresholds of similarities. In the interest of studying the complexity of evolutionary processes affecting microbial evolution, we applied EGN to a dataset of 571,044 proteic sequences from the three domains of life and from mobile elements. We observed that, in Borrelia, plasmids play a different role than in most other eubacteria. Rather than being genetic couriers involved in lateral gene transfer, Borrelia's plasmids and their genes act as private genetic goods, that contribute to the creation of genetic diversity within their parasitic hosts. Conclusion: EGN can be used for constructing, analyzing, and mining molecular datasets in evolutionary studies. The program can help increase our knowledge of the processes through which genes from distinct sources and/or from multiple genomes co-evolve in lineages of cellular organisms.
ISSN: 1471-2148

hal-01544799
https://hal.archives-ouvertes.fr/hal-01544799
DOI : 10.1186/1471-2148-13-146

Éditeur(s) : HAL CCSD Oxford University Press (OUP)
Résumé : International audience
DNA sequencing technology is becoming more accessible to a variety of researchers as costs continue to decline. As researchers begin to sequence novel transcriptomes, most of these data sets lack a reference genome and will have to rely on de novo assemblers. Making comparisons across assemblies can be difficult: each program has its strengths and weaknesses, and no tool exists to comparatively evaluate these data sets. We developed software in R, called Sequence Comparative Analysis using Networks (SCAN), to perform statistical comparisons between distinct assemblies. SCAN uses a reference data set to identify the most accurate de novo assembly and the ``good'' transcripts in the user's data. We tested SCAN on three publicly available transcriptomes, each assembled using three assembly programs. Moreover, we sequenced the transcriptome of the oomycete Achlya hypogyna and compared de novo assemblies from Velvet, ABySS, and the CLC Genomics Workbench assembly algorithms. One thousand one hundred twenty-eight of the CLC transcripts were statistically similar to the reference, compared with 49 of the Velvet transcripts and 937 of the ABySS transcripts. SCAN's strength is providing statistical support for transcript assemblies in a biological context. However, SCAN is designed to compare distinct node sets in networks, therefore it can also easily be extended to perform statistical comparisons on any network graph regardless of what the nodes represent.
ISSN: 0737-4038

hal-01544800
https://hal.archives-ouvertes.fr/hal-01544800
DOI : 10.1093/molbev/mst087