A memory-efficient algorithm for multiple sequence alignment with constraints

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چکیده

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A memory-efficient algorithm for multiple sequence alignment with constraints

MOTIVATION Recently, the concept of the constrained sequence alignment was proposed to incorporate the knowledge of biologists about structures/functionalities/consensuses of their datasets into sequence alignment such that the user-specified residues/nucleotides are aligned together in the computed alignment. The currently developed programs use the so-called progressive approach to efficientl...

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An Efficient Progressive Alignment Algorithm for Multiple Sequence Alignment

Analyzing and comparing the string representations of sequences reveals a lot of useful information about the sequences. As new biological sequences are being generated at exponential rates, sequence comparison is becoming increasingly important to draw functional and evolutionary inference of proteins. This paper presents a partitioning approach for biomolecular sequence alignment that signifi...

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Multiple sequence alignment with user-defined constraints

In many situations, automated multi-alignment programs are not able to correctly align families of nucleic acid or protein sequences. Difficult cases comprise not only distantly related sequences but also tandem duplications independent of their evolutionary age. Frequently, additional biological information is available that establishes homologies at least in parts of the sequences based on st...

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MuSiC: a tool for multiple sequence alignment with constraints

SUMMARY MuSiC is a web server to perform the constrained alignment of a set of sequences, such that the user-specified residues/nucleotides are aligned with each other. The input of the MuSiC system consists of a set of protein/DNA/RNA sequences and a set of user-specified constraints, each with a fragment of residue/nucleotide that (approximately) appears in all input sequences. The output of ...

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ژورنال

عنوان ژورنال: Bioinformatics

سال: 2004

ISSN: 1367-4803,1460-2059

DOI: 10.1093/bioinformatics/bth468