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MATLAB Bioinformatics Tools

MATLAB Bioinformatics Tools. Rob Henson The MathWorks, Inc. Who Am I?. Development manager for Bioinformatics group at The MathWorks Natick, MA Software developer Background in algorithm design and software engineering. What do I do?. Write software for bioinformatics Sequence analysis

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MATLAB Bioinformatics Tools

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  1. MATLAB Bioinformatics Tools Rob Henson The MathWorks, Inc.

  2. Who Am I? • Development manager for Bioinformatics group at The MathWorks • Natick, MA • Software developer • Background in algorithm design and software engineering

  3. What do I do? • Write software for bioinformatics • Sequence analysis • Microarray data analysis • Some consulting • Bioinformatics algorithm design • Machine learning tools • E.g. Neural networks, HMMs etc.

  4. My solution to dotplot >> map = eye(128); >> spy(map(seq1,seq2)) Why does this work? How could we make this better?

  5. Enhancements to dotplot • Does map need to be 128? • What is the right value? • Can we use less memory? • How do we deal with bad inputs? • Can we extend this to look for longer patterns?

  6. Some useful tools • edit • dbstop • profiler • Getting help • Documentation • Technical Support Knowledge Base • Newsgroup

  7. A full implementation of dotplot function matches = dotplot(seq1,seq2,window,stringency) % DOTPLOT Visualize sequence matches. % DOTPLOT(S,T) plots the sequence matches of sequences S and T. % % DOTPLOT(S,T,WINDOW,NUM) plots sequence matches when there % are at least NUM matches in a window of size WINDOW. For nucleotide % sequences a WINDOW of 11 and NUM of 7 is recommended in the % literature. % % MATCHES = DOTPLOT(...) returns the number of dots in the dotplot % matrix. % % Example: % moufflon = getgenbank('AB060288','sequence',true); % takin = getgenbank('AB060290','sequence',true); % dotplot(moufflon,takin,11,7) % % This shows the similarities between prion protein (PrP) nucleotide % sequences of two ruminants, the moufflon and the golden takin. % % See also NWALIGN, SWALIGN.

  8. Sequence properties • Amino acid composition • histc function • Molecular weight • Indexing and sum function • Hydrophobicity

  9. Molecular weights A: 89.000 R: 174.000 N: 132.000 D: 133.000 D: 121.000 Q: 146.000 E: 147.000 G: 75.000 H: 155.000 I: 131.000 L: 131.000 K: 146.000 M: 149.000 F: 165.000 P: 115.000 S: 105.000 T: 119.000 W: 204.000 Y: 181.000 V: 117.000 http://cn.expasy.org/tools/pscale/Molecularweight.html

  10. mw = [89.0900 0 121.1500 133.1000 147.1300 165.1900 75.0700 155.1600 131.1700 0 146.1900 131.1700 149.2100 132.1200 0 115.1300 146.1500 174.2000 105.0900 119.1200 0 117.1500 204.2300 0 181.1900]; seq = ‘MATLAPEEPQSDPSVEPPLSQETFSDLWKLLPENNVLSP’; seqmw = mw(seq-’A’+1); plot(seqmw)

  11. proteinplot

  12. Assignments 1. Create a hydrophobicity plot You can get the amino acid values from http://cn.expasy.org/cgi-bin/protscale.pl Use Kyte & Doolittle’s values. Create a function that has two inputs, the sequence and the window size. The function will create a hydrophobicity plot. The help for the function is on the next slide…

  13. function hydrophobic(sequence, window_length) % HYDROPHOBIC plots the hydrophobicity of an amino acid sequence % HYDROPHOBIC(SEQUENCE,WINDOW_LENGTH) creates a hydrophobicity plot of % SEQUENCE using a smoothing window of length, WINDOW_LENGTH. % % SEQUENCE must be a valid amino acid sequence. If SEQUENCE contain any % symbols other than the standard 20 amino acid letters, the function % will give an error message. SEQUENCE can be either upper or lower case. % % WINDOW_LENGTH must be an odd positive integer. %

  14. Assignments 2. Modify the function to return the maximum and minimum hydrophobicity values in the plot. Make appropriate changes to the help for the function.

  15. Advanced example • Alignment significance • Alignment algorithms such as Smith-Waterman and Needleman-Wunsch always find some alignment. How do we know if what they find is significant or simply random?

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