In particular, we tried to determine the application threshold for the global algorithm, i.e. the values of the above-mentioned parameters, which provided for the same or better quality of alignments by using the global algorithm in comparison to the quality of alignments by using the local algorithm (see the definition of the alignment quality in section 2.3).
After mapping the smRNAs in our datasets onto the selected introns using the BLAST algorithm (see the Methods section for detail), we found only a few perfectly matched smRNAs; therefore, only two mirtron precursor candidates could be identified (PAC: 19709968 and PAC: 19675165) (Additional file 5).
Quantitative analysis of the results, using the StructureSum algorithm (see Materials and Methods), demonstrated that five population clusters (K = 5) is the best fit for the data.
Seeds for the generation of the models were identified using the Autoseed algorithm (see 'Material and methods' and Figure 1 figure supplements 1 14 for details).
These gene family guide trees are used to identify diagnostic characters for each gene family member and then EST query sequences are screened for the presence of shared diagnostics using the CAOS algorithm (See [ 82] for details of the guide tree/CAOS approach).
The prior information model is extracted from Reactome and NCI PID using the PERA algorithm (see 'Materials and methods' for PERA algorithm and use of the prior model in inference).
These flattened representations of the pericentral cortex were found using the Isomap algorithm (see Methods).
A semi-local convergence analysis for the Gauss-Newton method (GNM) was presented using the popular algorithm (see, for example, [1, 7, 17]): (GNA): ((xi, Delta, x_{0})).
Here, we introduced stochasticity in the rates of protein production and degradation using the Gillespie algorithm (see Methods).
The Datamonkey analyses show that G2, G4 and G6 possess evidence for positive selection and note the type of selection for each site, using the SLAC algorithm (see Methods).
