ERaBLE: Evolutionary Rates and Branch Lengths Estimation




ERaBLE is a phylogenomic distance-based method to simultaneously estimate the branch lengths of a given reference topology, and the relative evolutionary rates of the genes employed in the analysis, using a collection of distance matrices (one distance matrix per gene). These distance matrices can either be directly estimated from pairwise alignments of the gene sequences, or they can be calculated from gene trees inferred for each gene.

Downloads

The ERABLE source code, binaries, and datasets are available in the ATGC GitLab repository. Download the software archive for your operating system (Linux or Windows), along with any datasets you need.

Run ERABLE online

ERABLE will also be available to run online through the ATGC platform, allowing you to submit analyses and retrieve results directly from your web browser, without installing the software locally.


PEWO: a collection of workflows to benchmark phylogenetic placement

PEWO: a collection of workflows…

Introduction and context In the Bioinformatics team of the LIRMM (CNRS & Univ. Montpellier), we develop a series of tools for metagenomics / metabarcoding analysis. Our tools exploit phylo-k-mers (which are k-mers combined with phylogenetic information) computed for an input set of reference sequences and their phylogeny. The phylo-k-mers are computed and indexed with IPK,…

Biodiversity Bioinformatics Evolutionary biology Molecular evolution Taxonomic classification Genome accession RNA sequence FASTA FASTQ newick
PhyML

PhyML

Overview: new algorithms, methods and utilities PhyML is a software package that uses modern statistical approaches to build phylogenetic trees from the analysis of alignments of nucleotide or amino acid sequences. The main tool in this package builds phylogenies under the maximum likelihood criterion. It implements a large number of substitution models coupled to efficient…

Phylogenetics Phylogenomics Phylogenetic inference (AI methods) FASTA PHYLIP format
TFscope

TFscope

Characterizing the binding preferences of transcription factors (TFs) in different cell types and conditions is key to understand how they orchestrate gene expression. TFscope is a machine learning approach that identifies sequence features explaining the binding differences observed between two ChIP-seq experiments targeting either the same TF in two conditions or two TFs with similar…

DNA binding sites Machine learning Transcription factors and regulatory sites Transcriptional regulatory element prediction JASPAR profile ID BED FASTA meme-motif
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