Systems and methods for rational selection of context sequences and sequence templates

Abstract

Provided are systems and methods for rational selection of context sequences and sequence templates including a computer implemented method for obtaining a repository of attributes sets where the attributes sets are statistically associated with a sequence template representing two or more context sequences.

1 . A computer implemented method for obtaining a repository of attributes sets, wherein attributes sets are statistically associated with a sequence template representing two or more context sequences, comprising:
 (a) obtaining a dataset of context sequences;   (b) transforming each context sequence to a sequence template, thereby obtaining a dataset of sequence templates;   (c) clustering said dataset of sequence templates into a plurality of clusters according to a distance formula; wherein at least one cluster is statistically associated with at least one attributes set; and   (d) inserting into said repository each of said clusters and said attributes set which is statistically associated with said each of said clusters.   
   
   
       2 . The computer implemented of  claim 1 , wherein said dataset of context sequences of step (a) is further subjected to multiple sequence alignment. 
   
   
       3 . A repository obtained by the computer implemented method of  claim 1 . 
   
   
       4 . A computer implemented method for identifying a sequence template as statistically associated with an attributes set of interest, comprising:
 (a) providing a repository of attributes sets; wherein attributes sets are statistically associated with a sequence template representing two or more context sequences;   (b) selecting an attributes set; and   (c) retrieving at least one sequence template statistically associated with said attributes set.   
   
   
       5 . The computer implemented method of  claim 4 , further comprising the step of merging at least two of said retrieved sequence templates. 
   
   
       6 . The computer implemented method of  claim 4 , said attributes are selected from the group consisting of the Gene Ontology Project (GO), Interpro annotation (European Molecular Biology Laboratory, EMBL), SMART (a Simple Modular Architecture Research Tool, found at http://smart.embl.de/), UniProt Knowledgebase (SwissProt), OMIM (by NCBI) PROSITE (by the Swiss Institute of Bioinformatics), Protein Information Resource (PIR), GeneCards, and Kyoto Encyclopedia of Genes and Genomes (KEGG). 
   
   
       7 . A method of preparing a polynucleotide construct, comprising:
 (a) identifying a sequence template as statistically associated with an attributes set of interest according to the method of  claim 4 ; and   (b) preparing a polynucleotide construct having at least one portion operably linked to a context sequence; wherein said context sequence is characterized as having either 80%-85%, 85%-90%, or 90%-100% homology with said sequence template.   
   
   
       8 . The method of  claim 7 , wherein the preparing comprises synthesizing said context sequence. 
   
   
       9 . The method of  claim 7 , wherein the preparing comprises constructing an expression vector comprising said context sequence. 
   
   
       10 . The method of  claim 7 , wherein the preparing comprises constructing a probe comprising said context sequence. 
   
   
       11 . A computer memory system comprising a plurality of tree topologies representing plurality of (k) heaps, wherein the plurality of tree topologies is managed through a common interface; and (k≧1). 
   
   
       12 . The computer memory system of  claim 11 , wherein said heaps are min heaps. 
   
   
       13 . The computer memory system of  claim 11 , wherein said heaps are max heaps. 
   
   
       14 . The computer memory system of  claim 11 , wherein an active subset of heaps is held in Random Access Memory (RAM), while the rest of said heaps are maintained on a secondary storage. 
   
   
       15 . A computer implemented method for clustering a plurality of polynucleotide sequences, comprising:
 (a) determining an attributes set for the plurality of polynucleotide sequences; and   (b) clustering said polynucleotide sequences into a plurality of clusters according to values of said attributes set.   
   
   
       16 . A computerized system configured for identifying a sequence template as statistically associated with an attributes set of interest, the computerized system comprising: context sequence clustering module, configured to cluster said sequences into a plurality of clusters; and an enrichment analysis module, configured to provide enrichment appraisal, wherein context sequence clustering module being communicatively coupled to the enrichment analysis module.