Motivation: Tandem Repeats (TRs) are abundant in proteins, having a variety of fundamental functions. In many cases, evolution has blurred their repetitive patterns. This leads to the problem of distinguishing between sequences that contain highly imperfect TRs, and the sequences without TRs. The 3D structure of proteins can be used as a benchmarking criterion for TR detection in sequences, because the vast majority of proteins having TRs in sequences are built of repetitive 3D structural blocks. According to our benchmark, none of the existing scoring methods are able to clearly distinguish, based on the sequence analysis, between structures with and without 3D TRs.
Results: We developed a scoring tool called Tally, which is based on a machine learning approach. Tally is able to achieve a better separation between sequences with structural TRs and sequences of aperiodic structures, than existing scoring procedures. It performs at a level of 81% sensitivity, while achieving a high specificity of 74% and an Area Under the Receiver Operating Characteristic Curve of 86%. Tally can be used to select a set of structurally and functionally meaningful TRs from all TRs detected in proteomes. The generated dataset is available for benchmarking purposes.
Availability and implementation: Source code is available upon request. Tool and dataset can be accessed through our website: http://bioinfo.montp.cnrs.fr/?r=Tally
Contact: andrey.kajava@crbm.cnrs.fr
Supplementary information: Supplementary data are available at Bioinformatics online.
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