Provided by: fastml_3.1-4_amd64

**NAME**

fastml - maximum likelihood ancestral amino-acid sequence reconstruction

**SYNOPSIS**

fastml[options]

**DESCRIPTION**

FastML is a bioinformatics tool for the reconstruction of ancestral sequences based on the phylogenetic relations between homologous sequences. FastML runs several algorithms that reconstruct the ancestral sequences with emphasis on an accurate reconstruction of both indels and characters. For character reconstruction the previously described FastML algorithms are used to efficiently infer the most likely ancestral sequences for each internal node of the tree. Both joint and the marginal reconstructions are provided. For indels reconstruction the sequences are first coded according to the indel events detected within the multiple sequence alignment (MSA) and then a state-of-the-art likelihood model is used to reconstruct ancestral indels states. The results are the most probable sequences, together with posterior probabilities for each character and indel at each sequence position for each internal node of the tree. FastML is generic and is applicable for any type of molecular sequences (nucleotide, protein, or codon sequences).

**OPTIONS**

-hhelp-ssequence input file (for example use-semySequences/eseq.txt)-ttree input file (if tree is not given, a neighbor joining tree is computed).-gAssume among site rate variation model (Gamma) [By default the program will assume an homogeneous model. very fast, but less accurate!]-mmodel name-mj[JTT]-mlLG-mrmtREV (for mitochondrial genomes)-mdDAY-mwWAG-mccpREV (for chloroplasts genomes)-maJukes and Cantor (JC) for amino acids-mnJukes and Cantor (JC) for nucleotides-mhHKY Model for nucleotides-mgnucgtr Model for nucleotides-mttamura92 Model for nucleotides-myyang M5 codons model-meempirical codon matrix Controling the output options:-xtree file output in Newick format [tree.newick.txt]-ytree file output in ANCESTOR format [tree.ancestor.txt]-jjoint sequences output file [seq.joint.txt]-kmarginal sequences output file [seq.marginal.txt]-djoint probabilities output file [prob.joint.txt]-emarginal probabilities output file [prob.marginal.txt]-qancestral sequences output format. (-qc= [CLUSTAL],-qf= FASTA,-qm= MOLPHY,-qs= MASE,-qp= PHLIYP,-qn= Nexus) Advanced options:-aThreshold for computing again marginal probabilities [0.9]-bDo not optimize branch lengths on starting tree [by default branches and alpha are ML optimized from the data]-cnumber of discrete Gamma categories for the gamma distribution [8]-fdon't compute Joint reconstruction (good if the branch and bound algorithm takes too much time, and the goal is to compute the marginal reconstruction with Gamma).-zThe bound used.-zs- bound based on sum.-zmbased on max.-zb[both]-puser alpha parameter of the gamma distribution [if alpha is not given, alpha and branches will be evaluated from the data (override-b)