Abstract: A computer implemented method for characterizing one or more sequences by generating index values representing portions of the sequences and finding characterizing index values based on a comparison of the index values. The index values may be obtained by applying one or more mask over each sequence. The modified masks may have associated weightings and index values obtained using modified masks may be retained in the index only if the weightings are above a threshold value. Characterising index values may also be assessed for for their degree of uniqueness. Characterizing indexes may be used for predicting correlation between a sample sequence and one or more reference sequences. Biological monitoring systems utilising the characterizing index values are also disclosed. A biological indicator may be generatgenerated using one or more characterizing index values obtained by the above method and be used to produce an indicator that undergoes a property change in the presence of the one or more sequence.

Abstract: Methods of calling genomic sequence values in complex calling regions are disclosed. Following a preliminary sequence alignment a complex calling region may be identified where no sequence values satisfy preliminary alignment criteria. Potential hypotheses may be formulated for the complex calling region and the probability of each hypothesis representing a correct alignment may be calculated by evaluating the probability of each hypothesis being correct for the reads and the probability of each hypothesis occurring. The hypothesis best satisfying hypothesis selection criteria may be selected. The method may include an evaluation of possible indels in the complex calling region.

Abstract: Methods of calling genomic sequence values in complex calling regions are disclosed. Following a preliminary sequence alignment a complex calling region may be identified where no sequence values satisfy preliminary alignment criteria. Potential hypotheses may be formulated for the complex calling region and the probability of each hypothesis representing a correct alignment may be calculated by evaluating the probability of each hypothesis being correct for the reads and the probability of each hypothesis occurring. The hypothesis best satisfying hypothesis selection criteria may be selected. The method may include an evaluation of possible indels in the complex calling region.

Abstract: A sequencing system and method of generating index keys for one or more data sequence based on masked values of reads from a sample data sequence and/or one or more template data sequence. Each index key value may be based upon a concatenated form of each extracted value, although other transformations may be employed. A number of different masks may be applied to the data sequence at a number of locations. At least some of the masks may include indels and/or substitutions. The masks may be manually or computer generated. The data sequence may be one or more reference templates and/or one or more sample sequences, such as DNA or RNA sequences. Sample data may be stored in the one or more index by correlating masked values of reads with index key values and storing an identifier for each read in association with a corresponding index key value.

Abstract: The present invention relates to a method of implementing, using and also testing a machine learning system. Preferably the system employs the Naïve Bayesian prediction algorithm in conjunction with a feature data structure to provide probability distributions for an input record belonging to one or more categories. Elements of the feature data structure may be prioritized and sorted with a view to selecting relevant elements only for use in the calculation of a probability indication or distribution. A method of testing is also described which allows the influence of one input learning data record to be removed from the system with the same record being used to subsequently test the accuracy of the system.