LRRfinder identifies highly conserved regions of leucine rich repeats in a given sequence.

The position-specific scoring matrix (PSSM) used was generated from 184 carefully selected Toll-like receptor (TLR) template sequences from 53 species. A single sequence for each TLR was selected per species to reduce bias from identical sequences. Unique, highly conserved regions from these templates are used in conjunction with the PSSM to identify known and unknown LRRs.

TLRs can also contain several regions in addition to the LRR domain such as the signal peptide, LRR N-terminus, LRR C-terminus, transmembrane domain, and TIR domain. These can also be identifed by LRRfinder using alignments to template regions stored in the LRRdb.

It is possible to obtain and submit translated nucleotide or protein sequences using GenBank, RefSeq, SwissProt or EMBL acessions (not versions), however if your sequence is not present in any of these databases it is possible to input your own protein sequence below. LRRfinder will generate BLAST similarity results from the PDB database, align your sequence to those already present in the tLRRdb, identify potential LRRs and relevant TLR domains and provide a graphical display of your sequence with predicted LRRs.

Upper and lower boundaries are used to define stringent and less-stringent significance parameters. LRRs which have a non-random chance of occuring at a greater percentage thean the upper boundary will be significant and those which have a probability of non-random occurence falling between the upper and lower boundaires will be insignificant (or rather significant but at a lower stringency) Recommended values for upper and lower boundaries are 95% and 80% respectively.

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PSSM Selection

General

Taxon Specific

TLR Specific

DDBJ/EMBL/GenBank Accession Prefix Format

An accession number is a unique identifier given to a sequence when it is submitted to one of the DNA repositories (GenBank, EMBL, DDBJ). The initial deposition of a sequence record is referred to as version 1. If the sequence is updated, the version number is incremented, but the accession number will remain constant. Versions are not supported so please remove any ".integer" which may trail your accession.

For more information please visit the NCBI webpage.

Upper Boundary

The e-value is a parameter that describes the number of LRRs one can "expect" to see by chance when trawling a sequence. It decreases exponentially with the bit-score (S) that is assigned to a predicted LRR. The higher the e-value the greater the significance of the prediction.

The upper boundary denotes the higher cutoff level at which predicted LRRs will be defined as "significant". It must always be an integer between 0 and 100 and refers to the percentage likelihood that the particular LRR frame would occur naturally outside of an LRR-HS. An e-value of 0.05 would therefore have a significance value of 95% (upper boundary default).

Lower Boundary

The e-value is a parameter that describes the number of LRRs one can "expect" to see by chance when trawling a sequence. It decreases exponentially with the bit-score (S) that is assigned to a predicted LRR. The higher the e-value the greater the significance of the prediction.

The lower boundary denotes the lower cutoff level at which predicted LRRs will be defined as "insignificant". It must always be an integer between 0 and 100 and refers to the percentage likelihood that the particular LRR frame would occur naturally outside of an LRR-HS. THe lower boundary defines the base value above which a prediction will be displayed. It must be less than the upper boundary. An e-value of 0.20 would therefore have a significance value of 80% ( upper boundary default).