Aligning biological sequences belongs to the most important problems in computational sequence analysis; it allows for detecting evolutionary relationships between sequences and for predicting biomolecular structure and function. Typically this is addressed through profile models, which capture position-specificities like conservation in sequences, but assume an independent evolution of different positions. RNA sequences are an exception where the coevolution of paired bases in the secondary structure is taken into account. Over the last years, it has been well established that coevolution is essential also in proteins for maintaining three-dimensional structure and function ; modeling approaches based on inverse statistical physics can catch the coevolution signal and are now widely used in predicting protein structure, protein-protein interactions, and mutational landscapes. Here, we present DCAlign, an efficient approach based on an approximate message-passing strategy, which is able to overcome the limitations of profile models, to include general second-order interactions among positions and to be therefore universally applicable to protein- and RNA-sequence alignment  . The potential of our algorithm is carefully explored using well-controlled simulated data, as well as real protein and RNA sequences.
Sequences of nucleotides (for DNA and RNA) or amino acids (for proteins) are central objects in biology. Among the most important computational problems is that of sequence alignment, i.e. arranging sequences from URLanisms in such a way to identify similar regions, to detect evolutionary relationships between sequences , and to predict biomolecular structure and function. This is typically addressed through profile models, which capture position-specificities like conservation in sequences, but assume an independent evolution of different positions. Over the last years, it has been well established that coevolution of different amino-acid positions is essential for maintaining three-dimensional structure and function . Modeling approaches based on inverse statistical physics can catch the coevolution signal in sequence ensembles; and they are now widely used in predicting protein structure, protein-protein interactions, and mutational landscapes. Here, we present DCAlign, an efficient alignment algorithm based on an approximate message-passing strategy, which is able to overcome the limitations of profile models, to include coevolution among positions in a general way, and to be therefore universally applicable to protein- and RNA-sequence alignment without the need of using complementary structural information . The potential of DCAlign is carefully explored using well-controlled simulated data, as well as real protein and RNA sequences.
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Aligning biological sequences belongs to
Sequences of nucleotides (for DNA and RNA) or amino acids (for proteins) are central objects in biology. Among
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problems in computational sequence analysis; it allows for detecting
computational problems is that of sequence alignment, i.e. arranging sequences from URLanisms in such a way to identify similar regions, to detect
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and for predicting
, and to predict
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Typically this is
This is typically
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RNA sequences are an exception where the coevolution of paired bases in the secondary structure is taken into account.
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is essential also in proteins
of different amino-acid positions is essential
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; modeling
. Modeling
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and
in sequence ensembles; and they
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approach
alignment algorithm
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general second-order interactions among positions
coevolution among positions in a general way,
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without the need of using complementary structural information
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our algorithm
DCAlign