Abstract: A computer system for executing k-mismatch searches, comprising at least one processor, configured to: receive a size of a search term; receive a similarity threshold: and generate a set of filters by: forming the set of filters based on a set of combinations of positions of matches, and gaps for the—size of the search term according to the similarity threshold, wherein each one of the combinations contains at least one filter from the set of filters.

Abstract: A computer system for executing k-mismatch searches, comprising at least one processor, configured to: receive a size of a search term; receive a similarity threshold: and generate a set of filters by: forming the set of filters based on a set of combinations of positions of matches, and gaps for the—size of the search term according to the similarity threshold, wherein each one—of the combinations contains at least one filter from the set of filters.

Abstract: A computer system for executing k-mismatch searches, comprising at least one processor, configured to: receive a size of a search term; receive a similarity threshold: and generate a set of filters by: forming the set of filters based on a set of combinations of positions of matches, and gaps for the—size of the search term according to the similarity threshold, wherein each one—of the combinations contains at least one filter from the set of filters.

Abstract: A method for annotating a protein sequence or a subsequence thereof includes the steps of providing an input protein sequence or a subsequence thereof. The subsequence is defined as a central node of a graph or protein network. A subgraph of the graph is calculated including the central node, according to a predefined radius and weights and/or resistances of edges of the subgraph are also calculated. Annotated nodes in the subgraph are identified. Resistance values between the central nodes and each of the annotated nodes in the subgraph are calculated and a list of annotated nodes is outputted. Each of the annotated nodes has a characteristic calculated resistance value to the central node of the input protein sequence.

Abstract: In a method of compressing a similarity network, the similarity network has nodes with a plurality of repetitions of characters sequences and a plurality of edges. Each edge connects a pair of the nodes based on a first similarity threshold. The method includes clustering of the nodes according to a second similarity threshold, where the second similarity threshold is higher than the first similarity threshold.

Abstract: Systems and methods are for generating a weighted relatedness protein network. The method includes steps of obtaining a protein network; generating training data; generating a weighting function derived from the training data values; and applying the weighting function to a protein network, thereby generating a weighted relatedness protein network. The protein network may be applied for prediction of protein properties by detection of relatedness with annotated sequences.