Abstract
Currently, more than 40 sequence tandem repeat detectors are published, providing heterogeneous, partly complementary, partly conflicting results. We present TRAL, a tandem repeat annotation library that allows running and parsing of various detection outputs, clustering of redundant or overlapping annotations, several statistical frameworks for filtering false positive annotations, and importantly a tandem repeat annotation and refinement module based on circular profile hidden Markov models (cpHMMs). Using TRAL, we evaluated the performance of a multi-step tandem repeat annotation workflow on 547 085 sequences in UniProtKB/Swiss-Prot. The researcher can use these results to predict run-times for specific datasets, and to choose annotation complexity accordingly. TRAL is an open-source Python 3 library and is available, together with documentation and tutorials via http://www.vital-it.ch/software/tral. elke.schaper@isb-sib.ch.
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