NGDC Cloud

Data

Input data text:

Upload a file

Example file

Input data text:

Upload a file

Example file

Parameters

Gap open

Gap extend

End gap penalty

End gap open

End gap extend

Matrix

Result format

References

Needleman, S. B. and Wunsch, C. D. (1970) J. Mol. Biol. 48, 443-453.
Kruskal, J. B. (1983) An overview of squence comparison In D. Sankoff and J. B. Kruskal, (ed.), Time warps, string edits and macromolecules: the theory and practice of sequence comparison, pp. 1-44 Addison Wesley.

Instructions

Needleman-Wunsch global alignment of two sequences

needle reads two input sequences and writes their optimal global sequence alignment to file. It uses the Needleman-Wunsch alignment algorithm to find the optimum alignment (including gaps) of two sequences along their entire length. The algorithm uses a dynamic programming method to ensure the alignment is optimum, by exploring all possible alignments and choosing the best. A scoring matrix is read that contains values for every possible residue or nucleotide match. Needle finds the alignment with the maximum possible score where the score of an alignment is equal to the sum of the matches taken from the scoring matrix, minus penalties arising from opening and extending gaps in the aligned sequences. The substitution matrix and gap opening and extension penalties are user-specified.

Contributor(s)

Yingke Ma

bit@big.ac.cn

Data

Input data text:

Upload a file

Example file

Input data text:

Upload a file

Example file

Parameters

Gap open

Gap extend

End gap penalty

End gap open

End gap extend

Matrix

Result format

References

Needleman, S. B. and Wunsch, C. D. (1970) J. Mol. Biol. 48, 443-453.
Kruskal, J. B. (1983) An overview of squence comparison In D. Sankoff and J. B. Kruskal, (ed.), Time warps, string edits and macromolecules: the theory and practice of sequence comparison, pp. 1-44 Addison Wesley.

Instructions

Needleman-Wunsch global alignment of two sequences

needle reads two input sequences and writes their optimal global sequence alignment to file. It uses the Needleman-Wunsch alignment algorithm to find the optimum alignment (including gaps) of two sequences along their entire length. The algorithm uses a dynamic programming method to ensure the alignment is optimum, by exploring all possible alignments and choosing the best. A scoring matrix is read that contains values for every possible residue or nucleotide match. Needle finds the alignment with the maximum possible score where the score of an alignment is equal to the sum of the matches taken from the scoring matrix, minus penalties arising from opening and extending gaps in the aligned sequences. The substitution matrix and gap opening and extension penalties are user-specified.

Contributor(s)

Yingke Ma

bit@big.ac.cn