A method of evaluating correlation between sequences by employing a hierarchy of evaluation algorithms. The evaluation algorithms may be arranged in order of computational efficiency as specified by a user or as determined by the system. The algorithms may range from a simple equality algorithm through to seeded alignment algorithms etc. Distributed and parallel processing systems may be employed in the method of the invention in graphical processing units may be employed. The method may be employed with a wide range of sequencers including sequencers produced by Illumina Inc Complete Genomics Inc. and Pacific Biosciences Inc.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A computer implemented method of evaluating the correlation between a plurality of sample sequences and a reference sequence using a plurality of evaluation algorithms, comprising applying the evaluation algorithms in an order designed to minimise the processing time for evaluating the correlation, wherein the plurality of evaluation algorithms comprises: a lower-bound algorithm which rejects alignments of the sample sequences in unmodified form to the reference sequence if an alignment quality threshold is not satisfied; and a modified sequence alignment algorithm in which the sample sequences are modified at potential alignment positions with the reference sequence, with the set of modifications of the sample sequence comprising an insertion. 2. A method as claimed in claim 1 wherein the plurality of evaluation algorithms further comprises at least one additional algorithm chosen from: an equality sequence aligner algorithm; a seeded alignment algorithm; and an algorithm that returns an optimal alignment. 3. A method as claimed in claim 2 wherein the first evaluation algorithm applied is an equality sequence aligner algorithm. 4. A method as claimed in claim 2 wherein the plurality of evaluation algorithms comprises at least one seeded alignment algorithm. 5. A method as claimed in claim 4 wherein the plurality of evaluation algorithms comprises a seeded alignment algorithm based on the Smith Waterman aligner. 6. A method as claimed in claim 1 wherein the algorithms are ordered according to the number or frequency of matches with respect to processing time. 7. A method as claimed in claim 1 wherein the algorithms are ordered according to the number and frequency of matches with respect to processing time. 8. A method as claimed in claim 1 wherein at least one of the evaluation algorithms includes an acceptance outcome which results in no further evaluation algorithms being applied. 9. A method as claimed in claim 1 wherein the order of application of algorithms is based on user input. 10. A method as claimed in claim 1 wherein the order of application of algorithms is set by an ordering algorithm. 11. A method as claimed in claim 10 wherein the ordering algorithm uses historical sequencing information to determine the order. 12. A method as claimed in claim 10 wherein the ordering algorithm uses known information on the efficiency of the evaluation algorithms to determine the order. 13. A method as claimed in claim 1 wherein an artificial intelligence engine determines the order of application of the evaluation algorithms. 14. A method as claimed in claim 12 wherein the artificial intelligence engine employs a neural network or genetic algorithm. 15. A method as claimed in claim 1 wherein the order of application of evaluation algorithms is set before the application of the evaluation algorithms. 16. A method as claimed in claim 1 wherein the order of application of evaluation algorithms is modified during the evaluation. 17. A method as claimed in claim 16 wherein the order is modified based on analysis of the previous or current evaluation algorithm results or performance. 18. A method as claimed in claim 1 wherein the sample sequence is a nucleotide sequence. 19. A method as claimed in claim 18 wherein the nucleotide sequence is a genomic sequence. 20. A method as claimed in claim 19 wherein the genomic sequence is a DNA sequence. 21. A method as claimed in claim 18 wherein the nucleotide sequence is a RNA sequence. 22. A method as claimed claim 1 wherein at least one evaluation algorithm includes a positioning algorithm which changes the relative positioning of sample and reference sequences and one or more evaluation algorithm which iteratively evaluates local or global alignment at the various relative positions of the sequences. 23. A method as claimed in claim 1 wherein at least one of the evaluation algorithms outputs a weighted probability. 24. A system configured to perform a computer implemented method of evaluating the correlation between a plurality of sample sequences and a reference sequence using a plurality of evaluation algorithms, comprising applying the evaluation algorithms in an order designed to minimise the processing time for evaluating the correlation, wherein the plurality of evaluation algorithms comprises: a lower-bound algorithm which rejects alignments of the sample sequences in unmodified form to the reference sequence if an alignment quality threshold is not satisfied; and a modified sequence alignment algorithm in which the sample sequences are modified at potential alignment positions with the reference sequence, with the set of modifications of the sample sequence comprising an insertion. 25. A system as claimed in claim 24 wherein the system comprises parallel processors and is configured to employ parallel processing to apply the plurality of evaluation algorithms. 26. A system as claimed in claim 25 wherein the evaluation algorithms are allocated to processors based upon performance characteristics of the processors. 27. A sequence analysis system as claimed in claim 25 wherein the parallel processors comprise graphics processors. 28. A system as claimed in claim 24, further comprising a sequencer for obtaining sample sequences for evaluation by the plurality of evaluation algorithms.