Abstract
Protein family databases are an important tool for biologists trying to dissect the function of proteins. Comparing potential new families to the thousands of existing entries is an important task when operating a protein family database. This comparison helps to understand whether a collection of protein regions forms a novel family or has overlaps with existing families of proteins. In this paper, we describe a method for performing this analysis with an adjustable level of accuracy, depending on the desired speed, enabling interactive comparisons. This method is based upon the MinHash algorithm, which we have further extended to calculate the Jaccard containment rather than the Jaccard index of the original MinHash technique. Testing this method with the Pfam protein family database, we are able to compare potential new families to the over 17,000 existing families in Pfam in less than a second, with little loss in accuracy.
Highlights
Protein family databases are an important resource for biologists seeking to characterise the function of proteins
We chose 50 random families from Pfam, and for each of these we timed the calculation of the Jaccard index between the family and every family in Pfam, and the MinHash estimate for the Jaccard index with n values of 25, 50, 100, and 200
For the family sizes tested, calculating the Jaccard containment was faster than the Jaccard index
Summary
Protein family databases are an important resource for biologists seeking to characterise the function of proteins. The domains, motifs and other features found in a protein form an important organisational structure that can be used to design and interpret experiments on the protein of interest. Protein family databases generally describe a particular family using a sequence profile, often in the form of a hidden Markov model (HMM)[1]. The profile HMM is a representation of the multiple sequence alignment of a number of representatives of a family. The likelihood that a given sequence is a member of a family (that is, it has homology with the other members of the family) is estimated by the probability of its alignment to this profile HMM
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