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To make good use of sequence similarity information, it would be very useful to have a simple, user-friendly way to visualize relationships in their phylogenetic context, particularly the relationships among the proteins in the model organisms from which most of the functional annotations are derived.
The use of sequence similarity between proteins to infer a functional relationship helped to define the beginnings of bioinformatics.
We expect that the use of sequence similarity networks may soon become as common in laboratories as the use of multiple sequence alignments.
The inclusive nature of TribeMCL is attributable to its use of sequence similarity scores alone to assign groupings (i.e., this method is intended to identify homologs, in contrast to KOG and OrthoMCL, which purport to identify orthologs only).
Keeping in mind the quality of the underlying data and the caveats discussed here, we encourage the use of sequence similarity networks as a first step in analyzing diverse sequence data sets because of their potential to reveal new and unexpected relationships.
James McInerney (National University of Ireland, Ireland) presented the use of sequence similarity networks (SSN) to study protein evolution.
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Mapping of these SNPs was undertaken using 90% of sequence similarity and minimum coverage of four reads at each SNP position.
Tools like BWA [ 51], Bowtie [ 52], SOAP [ 53], GEM [ 54] and Masai [ 55] use this model of sequence similarity and employ index structures closely related to suffix arrays.
For 4,297 (51.0%) of the annotated CDS, a function could be automatically inferred using a number of sequence similarity based approaches implemented in the GenDB auto-annotator METANOR [ 16].
By using a combination of sequence similarity searches (i.e. BLASTx against NCBInr, NCBIest and Gene ontology databases) and structure analyses against Pfam, InterProScan and Conserved Domain Database, we have annotated several sequences from Ministeria and other Choanozoa as signaling and cell adhesion components (Table 1).
The method used for detection of sequence similarity has been explained by Okasaki et. al. [ 14].
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