FastML: a web server for probabilistic reconstruction of ancestral sequences

By Haim Ashkenazy, Osnat Penn, Adi Doron-Faigenboim, Ofir Cohen, Gina Cannarozzi, Oren Zomer, Tal Pupko. Nucleic Acids Res., 2012

327 citations recorded by the source corpus.

Ancestral sequence reconstruction is essential to a variety of evolutionary studies. Here, we present the FastML web server, a user-friendly tool for the reconstruction of ancestral sequences. FastML implements various novel features that differentiate it from existing tools: (i) FastML uses an indel-coding method, in which each gap, possibly spanning multiples sites, is coded as binary data. FastML then reconstructs ancestral indel states assuming a continuous time Markov process. FastML provides the most likely ancestral sequences, integrating both indels and characters; (ii) FastML accounts for uncertainty in ancestral states: it provides not only the posterior probabilities for each character and indel at each sequence position, but also a sample of ancestral sequences from this posterior distribution, and a list of the k-most likely ancestral sequences; (iii) FastML implements a large array of evolutionary models, which makes it generic and applicable for nucleotide, protein and codon sequences; and (iv) a graphical representation of the results is provided, including, for example, a graphical logo of the inferred ancestral sequences. The utility of FastML is demonstrated by reconstructing ancestral sequences of the Env protein from various HIV-1 subtypes. FastML is freely available for all academic users and is available online at http://fastml.tau.ac.il/.

Subfields: Molecular Biology, Plant Science

View Paper: https://www.semanticscholar.org/paper/21a494f9c9819fc266be0a2423fb6cb14b107b67

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Citations in the corpus

Combining Natural Sequence Variation with High Throughput Mutational Data to Reveal Protein Interaction Sites (2015, methodology): “Probabilities for ancestral states were calculated using the JTT model of substitution by the FastML tool [48].”
Rapid phylogenetic analysis of large samples of recombinant bacterial whole genome sequences using Gubbins (2014, methodology): “Following the masking of putative recombination events by replacing such sequence with missing data, a second maximum likelihood phylogeny and sequence reconstruction are produced using the specified phylogenetic software and FastML, respectively.”
Low-N protein engineering with data-efficient deep learning (2020, methodology): “Importantly, eUniRep designs were diverse and occupied a unique region of sequence space, different from evotuning, ASR, and consensus sequences (median minimum number of mutations = 5, Fig.”