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AcCNET / Accessory Genome Constellation Network

Aims to compare accessory genomes of a large number of genomic units, both at qualitative and quantitative levels. The AcCNET workflow comprises three steps: (i) protein cluster construction, (ii) assignation of phylogenetic distances, and (iii) network set up. Using the proteomes extracted from the analysed genomes, AccNET creates a bipartite network of with two types of nodes (genomic units and homologous protein clusters) compatible with standard network analysis platforms. It also allows merging phylogenetic and functional information about the concerned genomes, thus improving the capability of current methods of network analysis. AccNET opens a new perspective to explore the pangenome of bacterial species, focusing on the accessory genome behind the idiosyncrasy of a particular strain and/or population.


A tool for automated alignment trimming, which is especially suited for large-scale phylogenetic analyses. trimAl can consider several parameters, alone or in multiple combinations, for selecting the most reliable positions in the alignment. These include the proportion of sequences with a gap, the level of amino acid similarity and, if several alignments for the same set of sequences are provided, the level of consistency across different alignments. Moreover, trimAl can automatically select the parameters to be used in each specific alignment so that the signal-to-noise ratio is optimized.


Constructs a consensus multiple sequence alignment from multiple independent alignments. Using dynamic programming MergeAlign efficiently combines individual multiple sequence alignments to generate a consensus that is maximally representative of all constituent alignments. Using MergeAlign to combine multiple sequence alignments generated using different matrices of amino acid substitution produces multiple sequence alignments that are more robust and more accurate than alignments generated using only a single matrix of amino acid substitution. Phylogenetic trees inferred from these MergeAlign alignments have better topological support values, are better resolved and show increased consistency. MergeAlign generates column support scores for each column in a multiple sequence alignment. When constituent alignments are generated using different models of amino acid substitution these support scores are related to alignment precision. MergeAlign can therefore be used to select accurately aligned data for downstream phylogenetic applications.