Bioinformatický seminár

Tue 15 Nov. 2011, 17:20

Title: Schoenhuth et al. Pair HMM Based Gap Statistics for Re-evaluation of Indels in Alignments with Affine Gap Penalties
Speaker: Peter Kováč

Although computationally aligning sequence is a crucial step
in the vast majority of comparative genomics studies our
understanding of alignment biases still needs to be improved.
To infer true structural or homologous regions computational
alignments need further evaluation. It has been shown that
the accuracy of aligned positions can drop substantially
in particular around gaps. Here we focus on re-evaluation
of score-based alignments with affine gap penalty costs.
We exploit their relationships with pair hidden Markov models
and develop efficient algorithms by which to identify gaps
which are significant in terms of length and multiplicity.
We evaluate our statistics with respect to the well-established
structural alignments from SABmark and find that indel
reliability substantially increases with their significance
in particular in worst-case twilight zone alignments.
This points out that our statistics can reliably complement
other methods which mostly focus
on the reliability of match positions.