In biological sequences, tandem repeats consist of tens to hundreds of residues of a repeated pattern, such as atgatgatgatgatg ('atg' repeated), often the result of replication slippage. Over time, these repeats decay so that the original sharp pattern of repetition is somewhat obscured, but even degenerate repeats pose a problem for sequence annotation: when two sequences both contain shared patterns of similar repetition, the result can be a false signal of sequence homology. We describe an implementation of a new hidden Markov model for detecting tandem repeats that shows substantially improved sensitivity to labeling decayed repetitive regions, presents low and reliable false annotation rates across a wide range of sequence composition, and produces scores that follow a stable distribution. On typical genomic sequence, the time and memory requirements of the resulting tool (ULTRA) are competitive with the most heavily used tool for repeat masking (TRF). ULTRA is released under an open source license and lays the groundwork for inclusion of the model in sequence alignment tools and annotation pipelines.
Keywords: Annotation Error; Sequence Alignment; Tandem Repeats.