MATLAB: Bioinformatics Toolbox          Overview                  Pinky Sheetal V               M.Tech Bioinformatics
Contents•   Uses of bioinformatics toolbox•   Sequence utilities•   Microarray data analysis•   Phylogenetic analysis•   M...
Uses of Bioinformatics toolbox• Sequence Analysis• Microarray data analysis and visualization• Mass Spectrometry preproces...
Sequence Utilities• Both Nucleotide and Protein Sequences can be manipulated and  analyzed   – Sequence conversion   – Sta...
>> aacount(ND2AASeq, chart,bar)   Locally align the two amino acid                                      sequences         ...
Microarray data analysis• provides several methods for normalizing  microarray data-  – Lowess normalization  – Global mea...
>> clustergram   >> cluster
Phylogenetic Analysis•   Create and edit phylogenetic trees•   Calculate pairwise distances•   Prune distances of branch• ...
Mass Spectrometry Data Analysis• Designed for for preprocessing and classification of  raw data from SELDI-TOF and MALDI-T...
Extensions to MATLAB Bioinformatics              Toolbox
CGH-Plotter: MATLAB toolbox for CGH-data analysis• Graphical user interface for the analysis of comparative genomic  hybri...
MBEToolbox: a Matlab toolbox for sequence data analysis in molecular biology and evolution• Has the needed functions for m...
MatArray toolbox• Offers efficient implementations of the most needed  functions for microarray analysis• The functions in...
PrepMS: TOF MS data graphical preprocessing tool• A stand-alone application made freely• Its graphical user interface, def...
References• David Venet.,2002. MatArray: a Matlab toolbox for  microarray data. Vol. 19 no. 5 2003, pages 659–660.DOI:  10...
Thank you
MATLAB Bioinformatics tool box
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MATLAB Bioinformatics tool box

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Overview of the MATLAB Bioinformatics toolbox and its extensions

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MATLAB Bioinformatics tool box

  1. 1. MATLAB: Bioinformatics Toolbox Overview Pinky Sheetal V M.Tech Bioinformatics
  2. 2. Contents• Uses of bioinformatics toolbox• Sequence utilities• Microarray data analysis• Phylogenetic analysis• Mass Spectrometry data analysis• Extensions to MATLAB Bioinformatics toolbox
  3. 3. Uses of Bioinformatics toolbox• Sequence Analysis• Microarray data analysis and visualization• Mass Spectrometry preprocessing and visualization• Phylogenetic Analysis• Statistical Learning
  4. 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. 5. >> aacount(ND2AASeq, chart,bar) Locally align the two amino acid sequences using a Smith-Waterman algorithm >> [LocalScore, LocalAlignment] = swalign(humanProtein,mouseProtein) >> showalignment(LocalAlignment)
  6. 6. 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
  7. 7. >> clustergram >> cluster
  8. 8. Phylogenetic Analysis• Create and edit phylogenetic trees• Calculate pairwise distances• Prune distances of branch• Reorder the branches• Rename the branches• Explore distances
  9. 9. Mass Spectrometry Data Analysis• Designed for for preprocessing and classification of raw data from SELDI-TOF and MALDI-TOF spectrometers• Also involves spectrum analysis
  10. 10. Extensions to MATLAB Bioinformatics Toolbox
  11. 11. 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
  12. 12. 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
  13. 13. 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
  14. 14. 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
  15. 15. 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
  16. 16. Thank you

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