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Although computationally aligning sequence is a crucial step in the vastmajority of comparative genomics studies our understanding of alignment biasesstill needs to be improved. To infer true structural or homologous regionscomputational alignments need further evaluation. It has been shown that theaccuracy of aligned positions can drop substantially in particular around gaps.Here we focus on re-evaluation of score-based alignments with affine gappenalty costs. We exploit their relationships with pair hidden Markov modelsand develop efficient algorithms by which to identify gaps which aresignificant in terms of length and multiplicity. We evaluate our statisticswith respect to the well-established structural alignments from SABmark andfind that indel reliability substantially increases with their significance inparticular in worst-case twilight zone alignments. This points out that ourstatistics can reliably complement other methods which mostly focus on thereliability of match positions.

Pair HMM based gap statistics for re-evaluation of indels in alignments with affine gap penalties: Extended Version