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MATLAB: Bioinformatics Toolbox
          Overview


                  Pinky Sheetal V
               M.Tech Bioinformatics
Contents

•   Uses of bioinformatics toolbox
•   Sequence utilities
•   Microarray data analysis
•   Phylogenetic analysis
•   Mass Spectrometry data analysis
•   Extensions to MATLAB Bioinformatics toolbox
Uses of Bioinformatics toolbox

• Sequence Analysis

• Microarray data analysis and visualization

• Mass Spectrometry preprocessing and visualization

• Phylogenetic Analysis

• Statistical Learning
Sequence Utilities

• Both Nucleotide and Protein Sequences can be manipulated and
  analyzed

   – Sequence conversion
   – Statistical analysis
   – Search for specific patterns within a sequence
   – In-silico digestion of sequences
   – Identifying genes
   – Determining the similarity of two genes
   – Determining the protein coded by a gene
   – Determining the function of a gene by finding a similar gene in
     another organism with a known function
   – Searching for Words
   – Exploring Open Reading Frames
>> aacount(ND2AASeq, 'chart','bar')   Locally align the two amino acid
                                      sequences
                                      using a Smith-Waterman algorithm

                                      >> [LocalScore, LocalAlignment] =
                                      swalign(humanProtein,mouseProtein)

                                      >> showalignment(LocalAlignment)
Microarray data analysis
• provides several methods for normalizing
  microarray data-
  – Lowess normalization
  – Global mean normalization
  – Median absolute deviation (MAD) normalization
• Filtering functions let you clean raw data before
  running analysis and visualization routines
• Integrated set of visualization tools
>> clustergram   >> cluster
Phylogenetic Analysis

•   Create and edit phylogenetic trees
•   Calculate pairwise distances
•   Prune distances of branch
•   Reorder the branches
•   Rename the branches
•   Explore distances
Mass Spectrometry Data Analysis

• Designed for for preprocessing and classification of
  raw data from SELDI-TOF and MALDI-TOF
  spectrometers

• Also involves spectrum analysis
Extensions to MATLAB Bioinformatics
              Toolbox
CGH-Plotter: MATLAB toolbox for CGH-data analysis

• Graphical user interface for the analysis of comparative genomic
  hybridization (CGH) microarray data
• Provides a tool for rapid visualization of CGH-data according to
  the locations of the genes along the genome
• Identifies regions of amplification’s and deletions, using k -
  means clustering and dynamic programming
• The application can applied for the analysis of cDNA microarray
  expression data
• CGH-Plotter toolbox is platform independent and requires
  MATLAB 6.1 or higher to operate
MBEToolbox: a Matlab toolbox for sequence data
 analysis in molecular biology and evolution

• Has the needed functions for molecular biology and evolution
• Used to manipulate aligned sequences
• Calculate evolutionary distances
• Estimate synonymous and non-synonymous substitution rates
• Infer phylogenetic trees
• Provides an extensible, functional framework for users with
  more specialized requirements to explore and analyze aligned
  nucleotide or protein sequences from an evolutionary
  perspective
• The full functions in the toolbox are accessible through the
  command-line for seasoned MATLAB users
MatArray toolbox

• Offers efficient implementations of the most needed
  functions for microarray analysis

• The functions in the toolbox are command-line only, since
  it is geared toward seasoned Matlab users

• Availability:
http://www.ulb.ac.be/medecine/iribhm/microarray/toolbox
PrepMS: TOF MS data graphical preprocessing tool

• A stand-alone application made freely
• Its graphical user interface, default parameter settings, and
  display plots allow PrepMS to be used effectively for :
   – data preprocessing
   – peak detection
   – visual data quality assessment
• Availability:
   – Stand-alone executable files and Matlab toolbox are
      available for download at:
      http://sourceforge.net/projects/prepms
References
• David Venet.,2002. MatArray: a Matlab toolbox for
  microarray data. Vol. 19 no. 5 2003, pages 659–660.DOI:
  10.1093/bioinformatics/btg046

• James J Cai et al.,2005.MBEToolbox: a Matlab toolbox for
  sequence data analysis in molecular biology and
  evolution. BMC Bioinformatics 2005, 6:64
  doi:10.1186/1471-2105-6-64

• Reija Autio et al., CGH-Plotter: MATLAB toolbox for CGH-
  data analysis Vol. 19 no. 13 2003, pages 1714–1715 DOI:
  10.1093/bioinformatics/btg230

• Yuliya V. Karpievitch et al., PrepMS: TOF MS data
Thank you

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MATLAB Bioinformatics tool box

  • 1. MATLAB: Bioinformatics Toolbox Overview Pinky Sheetal V M.Tech Bioinformatics
  • 2. Contents • Uses of bioinformatics toolbox • Sequence utilities • Microarray data analysis • Phylogenetic analysis • Mass Spectrometry data analysis • Extensions to MATLAB Bioinformatics toolbox
  • 3. Uses of Bioinformatics toolbox • Sequence Analysis • Microarray data analysis and visualization • Mass Spectrometry preprocessing and visualization • Phylogenetic Analysis • Statistical Learning
  • 4. Sequence Utilities • Both Nucleotide and Protein Sequences can be manipulated and analyzed – Sequence conversion – Statistical analysis – Search for specific patterns within a sequence – In-silico digestion of sequences – Identifying genes – Determining the similarity of two genes – Determining the protein coded by a gene – Determining the function of a gene by finding a similar gene in another organism with a known function – Searching for Words – Exploring Open Reading Frames
  • 5.
  • 6. >> aacount(ND2AASeq, 'chart','bar') Locally align the two amino acid sequences using a Smith-Waterman algorithm >> [LocalScore, LocalAlignment] = swalign(humanProtein,mouseProtein) >> showalignment(LocalAlignment)
  • 7. Microarray data analysis • provides several methods for normalizing microarray data- – Lowess normalization – Global mean normalization – Median absolute deviation (MAD) normalization • Filtering functions let you clean raw data before running analysis and visualization routines • Integrated set of visualization tools
  • 8. >> clustergram >> cluster
  • 9. Phylogenetic Analysis • Create and edit phylogenetic trees • Calculate pairwise distances • Prune distances of branch • Reorder the branches • Rename the branches • Explore distances
  • 10. Mass Spectrometry Data Analysis • Designed for for preprocessing and classification of raw data from SELDI-TOF and MALDI-TOF spectrometers • Also involves spectrum analysis
  • 11. Extensions to MATLAB Bioinformatics Toolbox
  • 12. CGH-Plotter: MATLAB toolbox for CGH-data analysis • Graphical user interface for the analysis of comparative genomic hybridization (CGH) microarray data • Provides a tool for rapid visualization of CGH-data according to the locations of the genes along the genome • Identifies regions of amplification’s and deletions, using k - means clustering and dynamic programming • The application can applied for the analysis of cDNA microarray expression data • CGH-Plotter toolbox is platform independent and requires MATLAB 6.1 or higher to operate
  • 13. MBEToolbox: a Matlab toolbox for sequence data analysis in molecular biology and evolution • Has the needed functions for molecular biology and evolution • Used to manipulate aligned sequences • Calculate evolutionary distances • Estimate synonymous and non-synonymous substitution rates • Infer phylogenetic trees • Provides an extensible, functional framework for users with more specialized requirements to explore and analyze aligned nucleotide or protein sequences from an evolutionary perspective • The full functions in the toolbox are accessible through the command-line for seasoned MATLAB users
  • 14. MatArray toolbox • Offers efficient implementations of the most needed functions for microarray analysis • The functions in the toolbox are command-line only, since it is geared toward seasoned Matlab users • Availability: http://www.ulb.ac.be/medecine/iribhm/microarray/toolbox
  • 15. PrepMS: TOF MS data graphical preprocessing tool • A stand-alone application made freely • Its graphical user interface, default parameter settings, and display plots allow PrepMS to be used effectively for : – data preprocessing – peak detection – visual data quality assessment • Availability: – Stand-alone executable files and Matlab toolbox are available for download at: http://sourceforge.net/projects/prepms
  • 16. References • David Venet.,2002. MatArray: a Matlab toolbox for microarray data. Vol. 19 no. 5 2003, pages 659–660.DOI: 10.1093/bioinformatics/btg046 • James J Cai et al.,2005.MBEToolbox: a Matlab toolbox for sequence data analysis in molecular biology and evolution. BMC Bioinformatics 2005, 6:64 doi:10.1186/1471-2105-6-64 • Reija Autio et al., CGH-Plotter: MATLAB toolbox for CGH- data analysis Vol. 19 no. 13 2003, pages 1714–1715 DOI: 10.1093/bioinformatics/btg230 • Yuliya V. Karpievitch et al., PrepMS: TOF MS data