BinCons and SCONE use parsimony-based methods to infer the ancestral sequences and to identify sequences containing fewer inferred mutational events than neutral sequence (Asthana et al., 2007; Margulies et al., 2003).
We design and compare rule- and learning-based methods to infer the triggering relations on network data.
These methods represent orthogonal analyses and can be used alongside SNP-based methods to infer deletions.
This enables the use of knowledge-based methods to infer, structure, classify and query information about disease and phenotype, thereby facilitating translational research and medicine.
We employed two alternative event-based methods to infer ancestral areas: (1) a Bayesian Binary MCMC analysis as implemented in the computer software Reconstruct Ancestral States in Phylogenies (RASP) [ 68], and (2) a maximum likelihood-based method implemented in the computer program LAGRANGE [ 69].
The equilibrium assumption appears to be a critical limitation in the methods employed here and Yang and Roberts [82] and Galtier and Guoy [83] have incorporated parameters for fluctuating base composition in methods to infer ancestral GC content (these implementations do not calculate posterior probabilities of ancestral states).
Sequence similarity between subsets of functionally equivalent tRNAs can be very high, and therefore, methods to infer homology based on the sequences of tRNAs themselves often cannot resolve orthologs from paralogs (Withers et al. 2006).
Posterior mapping is an increasingly popular hierarchical Bayesian based method used to infer character histories and reconstruct ancestral states at nodes of molecular phylogenies, notably of morphological characters.
Recently, some computational prediction methods to infer host-pathogen interactions have been developed based on interologs [ 20, 21] or gene expression profiles [ 22, 23].
The statistical inference methods are discussed in the section of "Methods to infer demographic parameters".
