Abstract: Methods and systems for simultaneously evaluating genomic or biological sequences across multiple population members, and methods and systems for simultaneously calling normal and cancerous genomic or biological sequences from a mixed sample containing normal and cancerous material are disclosed. This may be achieved by evaluating the probability of one or more hypothesis being correct for a plurality of population members based on genomic or biological sequence information for the population. For related family members, Mendelian inheritance may be integrated into the method. For populations, information from members under evaluation may be used to refine priors to more accurately call population members. Copy number variation, de novo mutations, and phenotypic traits and their genetic explanations may also be accommodated in the methods. Specific systems for implementing the methods are also disclosed.

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 and systems for evaluating genomic sequences are described. The methods include approaches for evaluating the prevalence of genomes in a source including changes over time based on the prevalence of segments in samples obtained from the source, and may additionally rely on the prevalence of segments in reference genomes and an estimated genome population distribution of the sample.

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 and systems for simultaneously evaluating biological sequences across multiple population members, and methods and systems for simultaneously calling normal and cancerous biological sequences from a mixed sample containing normal and cancerous material are disclosed. This may be achieved by evaluating the probability of one or more hypothesis being correct for a plurality of population members based on biological sequence information for the population. For related family members, Mendelian inheritance may be integrated into the method. For populations, information from members under evaluation may be used to refine priors to more accurately call population members. Copy number variation, de novo mutations, and phenotypic traits and their genetic explanations may also be accommodated in the methods. Specific systems for implementing the methods are also disclosed.

Abstract: Methods and systems for simultaneously evaluating genomic sequences across multiple population members, and methods and systems for simultaneously calling normal and cancerous genomic sequences from a mixed sample containing normal and cancerous material are disclosed. This may be achieved by evaluating the probability of one or more hypothesis being correct for a plurality of population members based on genomic sequence information for the population. For related family members, Mendelian inheritance may be integrated into the method. For populations, information from members under evaluation may be used to refine priors to more accurately call population members. Copy number variation and de novo mutations may also be accommodated in the methods. Specific systems for implementing the methods are also disclosed.

Abstract: Methods and systems for evaluating genomic sequences are described. The methods include approaches for evaluating the prevalence of genomes in a sample based on the prevalence of segments in the sample, and may additionally rely on the prevalence of segments in reference genomes and an estimated genome population distribution of the sample.

Abstract: A method and system are provided for evaluating the correlation between sequences by entering segments of one sequence in a database and comparing segments of the other sequence with the index values to find correlated segments. The correlated segments are analysed to determine whether the spacing is within a defined range indicating that a correlation threshold has been met. A processing methodology may be employed whereby a coarse potential alignment algorithm is first applied to determine potential alignment at a plurality of potential alignment positions, which are filtered based on alignment scores, and a fine alignment algorithm is then applied.

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.