Fast Structural Alignment of Biomolecules Using a Hash Table, N-Grams and String Descriptors

Bauer, Raphael André; Rother, Kristian; Moor, Peter; Reinert, Knut; Steinke, Thomas; Bujnicki, Janusz M.; Preißner, Robert

doi:10.3390/a2020692

Cited by 24 publications

(26 citation statements)

References 46 publications

(55 reference statements)

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“…Amongst the published methods, only LaJolla (Bauer et al, 2009) could be locally installed for comparison. An all against all comparison was attempted.…”

Section: Resultsmentioning

confidence: 99%

Fast alignment and comparison of RNA structures

2013

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Motivation: To recognize remote relationships between RNA molecules, one must be able to align structures without regard to sequence similarity. We have implemented a method, which is swift [O(n 2 )], sensitive and tolerant of large gaps and insertions. Molecules are broken into overlapping fragments, which are characterized by their memberships in a probabilistic classification based on local geometry and H-bonding descriptors. This leads to a probabilistic similarity measure that is used in a conventional dynamic programming method. Results: Examples are given of database searching, the detection of structural similarities, which would not be found using sequence based methods, and comparisons with a previously published approach. Availability and implementation: Source code (C and perl) and binaries for linux are freely available at www.zbh.uni-hamburg.de

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“…Amongst the published methods, only LaJolla (Bauer et al, 2009) could be locally installed for comparison. An all against all comparison was attempted.…”

Section: Resultsmentioning

confidence: 99%

Fast alignment and comparison of RNA structures

2013

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“…For every alphabet, the inner products of the normalized N-GRAM vectors were used to generate similarity measure matrices. This approach provided a parameter independent classification analysis based on a text modeling technique [1,35].…”

Section: N-grammentioning

confidence: 99%

“…secondary structure elements (SSEs) [28], or statistically derived geometrical local descriptions, such as overlapping fragment units of identical length [4,1,6,7,13,33,37,40,47,51,8]. Such fragment descriptions, or structural alphabets, offer an important technical advantage: they allow one to recode a protein 3D structure into a sequence of characters that can be compared through fast sequence methods.…”

Section: Introductionmentioning

confidence: 99%

Protein blocks versus hydrogen bonds based alphabets for protein structure classification

Franklin

2012

Proceedings of the ACM Conference on Bioinformatics, Computational Biology and Biomedicine

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Fragment-based descriptions of protein structures have been successfully used in fast comparison methods for protein structures mining and classification. These descriptions reduce the dimensionality problem and allow the application of sequence alignment techniques in structural comparison of proteins. Most of fragment-based alphabets were derived from secondary structure H-bond patterns or from local substructures clustering. Both approaches have shown promising results in protein mining and classification, though their accuracy is still lower than the accurate geometrical methods.In this paper, we describe two original H-bond based alphabets, HB A and HB B, obtained from clustering experimentally determined protein structures. We compare these two new H-bond based alphabets with two secondary-structure (DSSP and STR) based and two backbone-fragment (KL and Q16) based alphabets. Information theory analysis showed that the information content is proportional to the size and coverage of the structural alphabets and that the alphabets of the same classes are more similar between themselves. Amino acid sequences shared more information with the new H-bond based alphabets than with other alphabets, though they presented the lowest mutual information with the sequences of secondary-structure based alphabets. The comparison of alignments obtained using the Smith-Waterman algorithm showed that similar classes have similar alignments and that the most dissimilar alignments of alphabets of a same class were those from HB A and HB B. H-bond based alphabets presented the best performances for protein classification using First-Nearest Neighbor and three different similarity measures: the scores of alignments obtained from the Smith-Waterman algorithm; the inner products of the normalized vectors from N-GRAM (N=1, 2, 3 and 4) decomposition of the sequences; and the probabilities of belonging to the Hidden Markov Model of every training group of the dataset. In addition, we showed that using First-Nearest Neighbor and the Log-Likelihood Ratio index of Needleman-Wunsch algorithm scores, the H-bond based alphabets presented performances very close to DALI for structure based protein classification.

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“…SARA uses a set of unit vectors derived from consecutive nucleotides to represent each nucleotide, which can be compared with other nucleotide using unit-vector root mean square (URMS) as distance (1,18). LaJolla uses an n-gram model to analyze sequences derived from nucleotide torsion angles (4). Similarly, PRIMOS/AMIGOS (5) and DIAL (6) also represent nucleotides with torsion angles and align the sequences encoded by the torsion angle representation.…”

Section: Introductionmentioning

confidence: 99%

RASS: a web server for RNA alignment in the joint sequence-structure space

He¹,

Steppi²,

Laborde³

et al. 2014

Nucleic Acids Research

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Comparison of ribonucleic acid (RNA) molecules is important for revealing their evolutionary relationships, predicting their functions and predicting their structures. Many methods have been developed for comparing RNAs using either sequence or three-dimensional (3D) structure (backbone geometry) information. Sequences and 3D structures contain non-overlapping sets of information that both determine RNA functions. When comparing RNA 3D structures, both types of information need to be taken into account. However, few methods compare RNA structures using both sequence and 3D structure information. Recently, we have developed a new method based on elastic shape analysis (ESA) that compares RNA molecules by combining both sequence and 3D structure information. ESA treats RNA structures as 3D curves with sequence information encoded on additional coordinates so that the alignment can be performed in the joint sequence-structure space. The similarity between two RNA molecules is quantified by a formal distance, geodesic distance. In this study, we implement a web server for the method, called RASS, to make it publicly available to research community. The web server is located at http://cloud.stat.fsu.edu/RASS/.

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Fast Structural Alignment of Biomolecules Using a Hash Table, N-Grams and String Descriptors

Cited by 24 publications

References 46 publications

Fast alignment and comparison of RNA structures

Fast alignment and comparison of RNA structures

Protein blocks versus hydrogen bonds based alphabets for protein structure classification

RASS: a web server for RNA alignment in the joint sequence-structure space

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