Once all spaced-mers are stored, for each spaced-mer in the table, all similar spaced-mers are computed and then all hits between the spaced-mer and similar ones are easily collected from the table and extended in search for similarities. We first process the similar subsequences we computed in the previous phase to remove those appearing too many times as they are believed to be just repeats that occur very often in the protein sequences without any relevance for the interaction process.
We explain the algorithm on the toy example below. Similarity processing. An example of similarities before a and after b post-processing. The counts in the bottom row indicate how many times each position occurs in all similarities found. In the figure above, this means the number of lines that cover that position.
All positions with a high count, above a threshold T hc , will be eliminated from all similarities, which will be modified accordingly. In our example, assuming the threshold is 5, positions 3, 4, 8, 9, and 10 have counts 5 or higher and are eliminated; see Fig. The new similarities are indicated by the lines above the sequence. The counterpart of each similarity is modified the same way. What we have computed so far are similarities, that is, pairs of similar subsequences of the same length.
We now show how to compute the scores. First, we extend the definition of the score from k -mers to arbitrary subsequences of equal length. For two subsequences X and Y of length n , the score is given by the sum of the scores of all corresponding k -mer pairs; using 3 :.
It is important to recall that any two similar sequences we find have the same length, therefore the above scoring function can be used. Finally, we describe how the scores for whole protein sequences are computed. Initially all scores are set to zero. Each pair of proteins P 1 , P 2 that are known to interact has its own contribution to the scores of other pairs. That means, the score of each corresponding k -mer pair between X 1 and Y 1 is multiplied by the number of k -mers in X 2 , that is, the number of times it is used to support the fact that Q 1 is interacting with Q 2.
Similarly, the score of each corresponding k -mer pair between X 2 and Y 2 is multiplied by the number of k -mers in X 1. The score obtained this way is then normalized by dividing it by the product of the lengths of the proteins involved. Once the score are computed, by considering all given interactions and similar subsequences and computing their impact on the other scores as above, predicting interactions is simply done according to the scores.
All protein pairs are sorted decreasingly by the scores; higher scores represent higher probability to interact. If a threshold is provided, then those pairs with scores above the threshold are reported as interacting. The input consists of the proteins sequences and PPIs.
The default set of seeds is given by SEED 4,5 above but any set can be used. All the other datasets have been used exclusively for testing.
The program is quite stable, the results being almost unaffected by small variations of these parameters. We have presented a new algorithm and software, SPRINT, for predicting PPIs that has higher performance than the current state-of-the-art programs while running orders of magnitude faster and using very little memory. It can be used on its own or in connection with other tools for PPI prediction. Plenty of room for improvement remains, especially for the C2 and C3 data.
Since they work directly with the sequence of amino acids, sequence-based methods often have an advantage in finding the actual positions where interaction occurs. Bonetta L.
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The Master Regulator Inference Algorithm identifies transcription factors TFs that control the transition between the two phenotypes, A and B, and the maintenance of the latter phenotype. An algorithm for the genome-wide discovery of modulators of transcriptional interactions , a new information theoretic method to identify multivariate statistical dependencies between a transcription factor and one or more of its targets, conditional on the presence or absence of a candidate modulator gene.
All interactions are derived from literature curation or direct user submissions and are freely available. The DIP database catalogs experimentally determined interactions between proteins.
It combines information from a variety of sources to create a single, consistent set of protein-protein interactions. The Molecular INTeraction database MINT focuses on experimentally verified protein-protein interactions mined from the scientific literature by expert curators. The curated data can be analyzed in the context of the high throughput data and viewed graphically with the 'MINT Viewer'.
To faciliate experimentation and integrated computational analysis with model organism PPI networks, we have integrated known, experimental and predicted PPIs for five model organisms and human in the I2D database. Homomint is a web available tool extending protein-protein interactions experimentally verified in models organisms, to the orthologous proteins in Homo sapiens.
The Unified Human Interactome is a comperehensive database of the computational and experimental based human protein interaction networks.
This database is aimed to integrate diverge maps, which offers the research a flexible and direct entry gate into the human interactome. APID Agile Protein Interaction DataAnalyzer is an interactive bioinformatic web-tool that has been developed to allow exploration and analysis of main currently known information about protein-protein interactions integrated and unified in a common and comparative platform. The Human Protein Reference Database represents a centralized platform to visually depict and integrate information pertaining to domain architecture, post-translational modifications, interaction networks and disease association for each protein in the human proteome.
We took great care to include only data from individually performed experiments since they usually provide the most reliable evidence for physical interactions. Multiple proteins. Multiple sequences. Protein families "COGs" Random entry. Protein Name: examples: 1 2 3. Advanced Settings Network Type:. Required score:. Size cutoff:. Single Protein by Sequence.
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