ReformAlign: improved multiple sequence alignments using a profile-based meta-alignment approach.

BACKGROUND: Obtaining an accurate sequence alignment is fundamental for consistently analyzing biological data. Although this problem may be efficiently solved when only two sequences are considered, the exact inference of the optimal alignment easily gets computationally intractable for the multiple sequence alignment case. To cope with the high computational expenses, approximate heuristic methods have been proposed that address the problem indirectly by progressively aligning the sequences in pairs according to their relatedness. These methods however are not flexible to change the alignment of an already aligned group of sequences in the view of new data, resulting thus in compromises on the quality of the deriving alignment. In this paper we present ReformAlign, a novel meta-alignment approach that may significantly improve on the quality of the deriving alignments from popular aligners. We call ReformAlign a meta-aligner as it requires an initial alignment, for which a variety of alignment programs can be used. The main idea behind ReformAlign is quite straightforward: at first, an existing alignment is used to construct a standard profile which summarizes the initial alignment and then all sequences are individually re-aligned against the formed profile. From each sequence-profile comparison, the alignment of each sequence against the profile is recorded and the final alignment is indirectly inferred by merging all the individual sub-alignments into a unified set. The employment of ReformAlign may often result in alignments which are significantly more accurate than the starting alignments. RESULTS: We evaluated the effect of ReformAlign on the generated alignments from ten leading alignment methods using real data of variable size and sequence identity. The experimental results suggest that the proposed meta-aligner approach may often lead to statistically significant more accurate alignments. Furthermore, we show that ReformAlign results in more substantial improvement in cases where the starting alignment is of relatively inferior quality or when the input sequences are harder to align. CONCLUSIONS: The proposed profile-based meta-alignment approach seems to be a promising and computationally efficient method that can be combined with practically all popular alignment methods and may lead to significant improvements in the generated alignments.

Leveraging dynamic user profiles for the assistance of anxiety disorders' treatment using inferential rules.

While anxiety disorders exhibit an impressive spread especially in western societies, context-awareness seems a promising technology to provide assistance to physicians in psychotherapy sessions. In the present paper an approach addressing the assistance of the anxiety disorders' treatment is proposed. The suggested method employs the a priori association rule mining algorithm in order to achieve dynamic update of patient profiles according to generated rules describing the underlying relations between patients' main context conditions and their stress level. This method was evaluated by therapists specializing in the mental health domain and the feedback received was very encouraging with respect to the assistance dynamic patient profiles offer, during CBT sessions.