Overview of the MATLAB Bioinformatics toolbox and its extensions

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

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

17. Thank you