bioinformatics dr. aladdin hamwiehkhalid al-shamaa abdulqader jighly 2010-2011 lecture 1...
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Bioinformatics
Dr. Aladdin Hamwieh Khalid Al-shamaaAbdulqader Jighly
2010-2011
Lecture 1Introduction
Aleppo UniversityFaculty of technical engineeringDepartment of Biotechnology
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Main Lines• Definition• Bioinformatics areas• Bioinformatics data– Data types– Applications for these data
• Next generation sequencing• Bioinformatics algorithms• Joint international programming
initiatives
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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.
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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
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Bioinformatics areas
• Molecular sequence analysis1. Sequence alignment2. Sequence database searching3. Motif discovery4. Gene and promoter finding5. Reconstruction of evolutionary
relationships6. Genome assembly and
comparison
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Bioinformatics areas
• Molecular structural analysis1. Protein structure analysis2. Nucleic acid structure analysis3. Comparison4. Classification5. prediction
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Bioinformatics areas
• Molecular functional analysis1. gene expression profiling2. Protein–protein interaction
prediction3. protein sub-cellular localization
prediction4. Metabolic pathway reconstruction5. simulation
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Bioinformatics data
There is different data types usually used in
bioinformatics
The same data may be used in different
areas
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Data types• DNA sequences• RNA sequences• Expression (microarray) profile• Proteome (x-ray, NMR) profile• Metabolome profile• Haplotype profile• Phenotype profile
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1 -DNA Sequences• Simple sequence analysis– Database searching– Pairwise and multiple analysis
• Regulatory regions • Gene finding• Whole genome annotation• Comparative genomics
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2 -RNAs• Splice variants• Tissue specific expression• 2D structure• 3D structure• Single gene analysis• Microarray
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2D and 3D structure of tRNA
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2D and 3D structure of rRNA
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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.
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• 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
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3 -Proteins• Protein sequences analysis– Database searching– Pairwise and multiple analysis
• 2D structure• 3D structure• Classification of proteins families• Protein arrays
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3D structure
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Animation
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4- Metabolome and molecular biology
• Metabolic pathways• Regulatory networks
Helps to understand systems biology
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5- Haplotype• Molecular Markers– RFLP– RAPD– SSR– ISSR– AFLP– DArT
– SNP– ….
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SNP
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6 -Phenotype• Morphological data• Physiological data• Stresses tolerance• Pathogenic infections• Diseases resistance • Cancers types• …..
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Haplotype & Phenotype
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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
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Short reads assembly problems
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Short reads assembly problems
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Short reads assembly problems
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• 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
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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
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Thank You