Compare nucleotide or amino acid sequences using pairwise or multiple
sequence alignment functions. Standard algorithms for pairwise
alignments include Needleman-Wunsch (
swalign) algorithms. You can also
perform multiple sequence alignment using various functions, such as
and visualize the alignment results in the Sequence Alignment
app. In addition, you can use a hidden Markov model (HMM) to align a
query sequence to an HMM profile. Perform BLAST searches against known
sequences in online databases using various BLAST programs.
|Sequence Alignment||Visualize and edit multiple sequence alignments|
|Return local optimal and suboptimal alignments between two sequences|
|Globally align two sequences using Needleman-Wunsch algorithm|
|Locally align two sequences using Smith-Waterman algorithm|
|Create dot plot of two sequences|
|Calculate pairwise distance between sequences|
|Visualize and edit multiple sequence alignment|
|Align multiple sequences using progressive method|
|Align two profiles using Needleman-Wunsch global alignment|
|Calculate consensus sequence|
|Calculate sequence profile from set of multiply aligned sequences|
|Display sequence logo for nucleotide or amino acid sequences|
|Align query sequence to profile using hidden Markov model alignment|
|Estimate profile hidden Markov model (HMM) parameters using pseudocounts|
|Generate random sequence drawn from profile hidden Markov model (HMM)|
|Displays a set of HMM profile alignments|
|Create or edit hidden Markov model (HMM) profile structure|
|Plot hidden Markov model (HMM) profile|
|Create remote NCBI BLAST report request ID or link to NCBI BLAST report|
Determining the similarity between two sequences is a common task in computational biology.
Use the Sequence Alignment app to visually inspect a multiple alignment and make manual adjustments.
You can select from a list of analysis methods to compare nucleotide or amino acid sequences using pairwise or multiple sequence alignment functions.
You can manipulate and analyze your sequences to gain a deeper understanding of the physical, chemical, and biological characteristics of your data.