Sentence examples for methods using sequence from inspiring English sources

Furthermore, methods using sequence coverage to identify repeats (e.g. Malde et al., 2006; Phillippy et al., 2008) should not be applied to pyrosequencing data without first filtering duplicates.

It is therefore superior to methods using sequence trees, which gave conflicting and debatable results because of tree reconstruction problems [ 1, 13, 116].

In addition to these comparisons with methods using sequence information only, we compared INTREPID to the machine learning algorithms reported by Petrova and Wu (2006) and by Youn et al. (2007) which make use of structural information.

In fact, several recent studies have successfully applied this approach by exploiting mass-spectrometry or high throughput enzymatic assay experiments and computational methods using sequence similarity networks, genomic contexts, structural modeling with metabolite docking and active site comparison [ 52, 61, 62].

Most methods used sequence alignments with an underlying hypothesis that sequence similarity is correlated with functional similarity.

Established methods use sequence or structure similarity to infer functions but those types of data do not suffice to determine the biological context in which proteins act.

These methods use sequence templates [ 7] and profiles of the sequences [ 8] as features.

Other methods use sequence quality information derived from the raw trace data.

These methods use sequence similarity to associate reads with taxonomic ranks.

Tree methods use sequence alignments and phylogenetic trees for predicting evolutionary relationships, while graph based methods use pairwise sequence similarity search methods (such as BLAST) to predict orthologs.

Some methods use sequence data from a control dataset and then use the control tag densities to assess the significance of peaks in the ChIP sample set.