Mining the NCBI Influenza Sequence Database: adaptive grouping of BLAST results using precalculated neighbor indexing.

The Influenza Virus Resource and other Virus Variation Resources at NCBI provide enhanced visualization web tools for exploratory analysis for influenza sequence data. Despite the improvements in data analysis, the initial data retrieval remains unsophisticated, frequently producing huge and imbalanced datasets due to the large number of identical and nearly-identical sequences in the database. We propose a data mining algorithm to organize reported sequences into groups based on their relatedness to the query sequence and to each other. The algorithm uses BLAST to find database sequences related to the query. Neighbor lists precalculated from pairwise BLAST alignments between database sequences are used to organize results in groups of nearly- identical and strongly related sequences. We propose to use a non-symmetric dissimilarity measure well crafted for dealing with sequences of different length (fragments). A balanced and representative data set produced by this tool can be used for further analysis, i.e. multiple sequence alignment and phylogenetic trees. The algorithm is implemented for protein coding sequences and is being integrated with the NCBI Influenza Virus Resource. In this paper, we describe an algorithm allowing to select the sequences with various degrees of similarity to the query and group the results based on their relatedness to each other. The algorithm uses BLAST scores to find and rate the database sequences related to the query. Pre-calculated neighborhood lists based on pair-wise BLAST hits between the database sequences allow to report the results in groups of identical and nearly identical sequences. The algorithm is implemented for protein coding sequences and is being integrated with the NCBI Influenza Virus Resource. Abstract The Influenza Virus Resource and other Virus Variation Resources at NCBI provide enhanced visualization web tools for exploratory analysis for influenza sequence data. Despite the improvements in data analysis, the initial data retrieval remains unsophisticated, frequently producing huge and imbalanced datasets due to the large number of identical and nearly-identical sequences in the database. We propose a data mining algorithm to organize reported sequences into groups based on their relatedness to the query sequence and to each other. The algorithm uses BLAST to find database sequences related to the query. Neighbor lists precalculated from pairwise BLAST alignments between database sequences are used to organize results in groups of nearly- identical and strongly related sequences. We propose to use a non-symmetric dissimilarity measure well crafted for dealing with sequences of different length (fragments). A balanced and representative data set produced by this tool can be used for further analysis, i.e. multiple sequence alignment and phylogenetic trees. The algorithm is implemented for protein coding sequences and is being integrated with the NCBI Influenza Virus Resource. In this paper, we describe an algorithm allowing to select the sequences with various degrees of similarity to the query and group the results based on their relatedness to each other. The algorithm uses BLAST scores to find and rate the database sequences related to the query. Pre-calculated neighborhood lists based on pair-wise BLAST hits between the database sequences allow to report the results in groups of identical and nearly identical sequences. The algorithm is implemented for protein coding sequences and is being integrated with the NCBI Influenza Virus Resource. Abstract The Influenza Virus Resource and other Virus Variation Resources at NCBI provide enhanced visualization web tools for exploratory analysis for influenza sequence data. Despite the improvements in data analysis, the initial data retrieval remains unsophisticated, frequently producing huge and imbalanced datasets due to the large number of identical and nearly-identical sequences in the database. We propose a data mining algorithm to organize reported sequences into groups based on their relatedness to the query sequence and to each other. The algorithm uses BLAST to find database sequences related to the query. Neighbor lists precalculated from pairwise BLAST alignments between database sequences are used to organize results in groups of nearly- identical and strongly related sequences. We propose to use a non-symmetric dissimilarity measure well crafted for dealing with sequences of different length (fragments). A balanced and representative data set produced by this tool can be used for further analysis, i.e. multiple sequence alignment and phylogenetic trees. The algorithm is implemented for protein coding sequences and is being integrated with the NCBI Influenza Virus Resource. In this paper, we describe an algorithm allowing to select the sequences with various degrees of similarity to the query and group the results based on their relatedness to each other. The algorithm uses BLAST scores to find and rate the database sequences related to the query. Pre-calculated neighborhood lists based on pair-wise BLAST hits between the database sequences allow to report the results in groups of identical and nearly identical sequences. The algorithm is implemented for protein coding sequences and is being integrated with the NCBI Influenza Virus Resource.