@article{Mueller-LocalAlign-2020,
author = {Müller, Teresa and Miladi, Milad and Hutter, Frank and Hofacker, Ivo and Will, Sebastian and Backofen, Rolf},
title = {The locality dilemma of {Sankoff-like} {RNA} alignments},
journal = {Bioinformatics},
year = {2020},
doi = {10.1093/bioinformatics/btaa431},
volume = {},
user = {miladim},
pages = {},
number = {},
note = {(accepted for publication)},
month = {},
issn = {1367-4803},
abstract = {Motivation: Elucidating the functions of non-coding RNAs by homology has been strongly limited due
            to fundamental computational and modeling issues. While existing simultaneous alignment and folding
            (SAF) algorithms successfully align homologous RNAs with precisely known boundaries (global SAF),
            the more pressing problem of identifying new classes of homologous RNAs in the genome (local SAF)
            is intrinsically more difficult and much less understood. Typically, the length of local alignments is strongly
            overestimated and alignment boundaries are dramatically mispredicted. We hypothesize that local SAF
            approaches are compromised this way due to a score bias, which is caused by the contribution of RNA
            structure similarity to their overall alignment score.
            Results: In the light of this hypothesis, we study pairwise local SAF for the first time systematically—
            based on a novel local RNA alignment benchmark set and quality measure. First, we vary the relative
            influence of structure similarity compared to sequence similarity. Putting more emphasis on the structure
            component leads to overestimating the length of local alignments. This clearly shows the bias of current
            scores and strongly hints at the structure component as its origin. Second, we study the interplay of several
            important scoring parameters by learning parameters for local and global SAF. The divergence of these
            optimized parameter sets underlines the fundamental obstacles for local SAF. Thirdly, by introducing a
            position-wise correction term in local SAF, we constructively solve its principal issues.}
}