Sentence examples for many alignment methods from inspiring English sources

There are many alignment methods available that differ in the amount of a priori information (including structural) they employ, computational complexity, etc.: MUSCLE [35], MAFFT [36], HMMER [42], and T-Coffee [43] are four we have investigated.

Many alignment methods often produce reasonable alignments which score worse than alignments with alternative templates.

While many alignment methods focus on detecting remote homologs in order to expand coverage of functional inference, obtaining high-quality alignments remains difficult even for closely-related families.

For many alignment methods, rescoring of alignment-induced models using structural information can improve the separation of useful and less useful models as compared with the alignment score.

The 1000-taxon simulatedatasetsts were analyzed using SATé and RAxML v. 7.0.4 on many alignment methods, including the default and Quicktree versions of ClustalW (Thompson et al., 1994), the L-INS-i and PartTree versions of MAFFT (Katoh and Toh, 2007, 2008), Muscle (Edgar, 2004), Opal (Wheeler and Kececioglu, 2007), and Prank+GT (Liu et al., 2009; Loytynoja and Goldman, 2005).

Many structural alignment methods are based on this principle.

Up to now, many local alignment methods have been proposed to detect conserved protein complexes.

We tried many different alignment methods while developing this method and found that HMMER profile alignment was the best performer in terms of both speed and accuracy of results.

Although many protein alignment methods use only local information, a few protein threading methods (Akutsu and Miyano, 1999; Godzik et al., 1992; Jones et al., 1992; Xu et al., 2003) were developed to use global information, such as pairwise contact potential, which quantifies how well two sequence residues can be placed into two template positions in a contact.

While many existing 1D alignment methods have attempted to align each spectrum in a data set to a single consensus spectrum, the quality of these "star alignments" can suffer if there is wide chemical diversity between spectra, as any individual spectrum chosen as a star may not include the full complement of metabolites present in the other data sets.

Alternatively, many fiducial-less alignment methods have been developed (Liu et al. 1995; Brandt et al. 2001; Renken and McEwen 2003; Castaño-Díez et al. 2007) but usually require that the specimen itself exhibits high-contrast features.