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Bioinformatics
Dr. Aladdin Hamwieh Khalid Al-shamaaAbdulqader Jighly
2010-2011
Lecture 1Introduction
Aleppo UniversityFaculty of technical engineeringDepartment of Biotechnology
Main Lines• Definition• Bioinformatics areas• Bioinformatics data– Data types– Applications for these data
• Next generation sequencing• Bioinformatics algorithms• Joint international programming
initiatives
Definition• Bioinformatics is the field of science in
which biology, computer science, and information technology merge into a single discipline.
• Bioinformatics is the science of managing and analyzing biological data using advanced computing techniques
• Bioinformatics applies principles of information science to make the vast, diverse, and complex life sciences data more understandable and useful.
Definition• There are two extremes in
bioinformatics work– Tool users (biologists): know how to
press the buttons and the biology but have no clue what happens inside the program
– Tool shapers (informaticians): know the algorithms and how the tool works but have no clue about the biology
Bioinformatics areas
• Molecular sequence analysis1. Sequence alignment2. Sequence database searching3. Motif discovery4. Gene and promoter finding5. Reconstruction of evolutionary
relationships6. Genome assembly and
comparison
Bioinformatics areas
• Molecular structural analysis1. Protein structure analysis2. Nucleic acid structure analysis3. Comparison4. Classification5. prediction
Bioinformatics areas
• Molecular functional analysis1. gene expression profiling2. Protein–protein interaction
prediction3. protein sub-cellular localization
prediction4. Metabolic pathway reconstruction5. simulation
Bioinformatics data
There is different data types usually used in
bioinformatics
The same data may be used in different
areas
Data types• DNA sequences• RNA sequences• Expression (microarray) profile• Proteome (x-ray, NMR) profile• Metabolome profile• Haplotype profile• Phenotype profile
1 -DNA Sequences• Simple sequence analysis– Database searching– Pairwise and multiple analysis
• Regulatory regions • Gene finding• Whole genome annotation• Comparative genomics
2 -RNAs• Splice variants• Tissue specific expression• 2D structure• 3D structure• Single gene analysis• Microarray
2D and 3D structure of tRNA
2D and 3D structure of rRNA
Microarray
• 20,000 to 60,000 short DNA probes of specified sequences are orderly tethered on a small slide. Each probe corresponds to a particular short section of a gene.
• DNA microarrays measure the RNA abundance with either 1 channel (one color) or 2 channels (two colors).
• Stanford microarrays measure by competitive hybridization the relative expression under a given condition (fluorescent red dye Cy5) compared to its control (labeled with a green fluorescent dye, Cy3) (Two channels)
• Affymetrix GeneChip has 1 channel and use either fluorescent red dye Cy5 or green fluorescent dye, Cy3
Microarray
3 -Proteins• Protein sequences analysis– Database searching– Pairwise and multiple analysis
• 2D structure• 3D structure• Classification of proteins families• Protein arrays
3D structure
Animation
4- Metabolome and molecular biology
• Metabolic pathways• Regulatory networks
Helps to understand systems biology
5- Haplotype• Molecular Markers– RFLP– RAPD– SSR– ISSR– AFLP– DArT
– SNP– ….
SNP
6 -Phenotype• Morphological data• Physiological data• Stresses tolerance• Pathogenic infections• Diseases resistance • Cancers types• …..
Haplotype & Phenotype
Next Generation Sequencing
SMRT Helicos AB SOLiD
IlluminaSolexa
RocheGSFLX
ABI 3730 Sequencing Machine
Target release 2010
2008 2007 2006 2004 2000 Launched
964 28 25-35 35-70 250-400 800-1100 Read lengthNA 85M 170M 120M 400K 96 Reads/runNA 2 GB 6 GB 6 GB 100 MB 0.1 MB Throughput
per runNA NA $5.81 k $5.97 k $84.39 High cost Cost/Mb
Short reads assembly problems
Short reads assembly problems
Short reads assembly problems
• String algorithms• Dynamic programming• Machine learning (NN, k-NN, SVM, GA, ..)• Markov chain models• Hidden Markov models• Markov Chain Monte Carlo (MCMC) algorithms• Stochastic context free grammars• EM algorithms• Gibbs sampling• Clustering• Tree algorithms (suffix trees)• Graph algorithms• Text analysis• Hybrid/combinatorial techniques• ….
Algorithms in bioinformatics
Joint international programming initiatives
• Bioperlhttp://www.bioperl.org/wiki/Main_Page
• Biopythonhttp://www.biopython.org/
• BioTclhttp://wiki.tcl.tk/12367
• BioJavawww.biojava.org/wiki/Main_Page
Thank You