Downloaded 10 times
More Related Content
Similar to Introduction to Bioinformatics 2025.....pdf
Introduction to Bioinformatics 2025.....pdf
- 1.
- 2. Course contents • 12weeks •Lectures, exercise, discussion. • Materials (presentations, links, books, etc). Class Structure 2hours lecture 1hour tutorial
- 3. Grading • Final exam60% • Practical exam 20% • Quizzes (Homework assignments), midterm 10% • Oral 10%
- 4. Course description An introductionto theory and practice of Bioinformatics and computational biology Goals for the course: The course will familiarize the students with the tools and principles of contemporary bioinformatics. By the end of the course, students will have a working knowledge of a variety of publicly available data and computational tools important in bioinformatics and a grasp of the underlying principles that is adequate for them to evaluate and use novel techniques as they arise the future.
- 5. What is Bioinformatics •Bioinformatics: Collection and storage of biological information. • It is The field of science in which biology, computer science, and information technology merge to form a single discipline Computational Biology: Development of statistical models to analyze biological data. • Ultimate goal: to enable the discovery of new biological insights as well as to create a global perspective from which unifying principles in biology can be discerned.
- 6. • Bioinformatics: anyuse of computers to handle biological information. • Bioinformatics (Oxford English Dictionary): The branch of science concerned with information and information flow in biological systems, esp. the use of computational methods in genetics and genomics. • Molecular Bioinformatics: involves the use of computational tools to discover new information in complex data sets (from the one-dimensional information of DNA through the two-dimensional information of RNA and the three-dimensional information of proteins, to the four-dimensional information of evolving living systems ) .
- 7. The field ofscience in which biology, computer science and information technology merge into a single line Biologists collect molecular data: DNA& Protein sequences, gene expression, etc. Bioinformaticians Study biological questions by analyzing molecular data Computer scientists (+Mathematicians, Statisticians, etc.) Develop tools, softwares, algorithms to store and analyze the data.
- 8. • Store data 01001011 01100101 01010101010 010101010101 011 010 010 010 000 •Create Interfaces to the data • Build tools to analyze data The objective of biological experimentation is not just to generate biological data, but also to analyze the data and extract information and knowledge from it. The high complexities and volumes of these data require the use of computers for the storage and analysis of the data, and makes bioinformatics an complete part of modern molecular science.
- 9. • Bioinformatics startedin the appears in the 1989 . • Bioinformatics was created to Studying life at molecular level. 1960s, Term ‘Bioinformatics’ serve molecular biology or • Development of fast computers, good algorithmic techniques. • Bioinformatics mathematics, Biochemistry. is the application of many sciences (Applied Statistics, Physics, Biology, Genetics and Origin of Bioinformatics
- 10. 1953 :DNAstructure discovered. 1956:First protein sequenced (insulin) 1960 :Assembly of protein sequence databases. 1972: Protein Data Bank (PDB). 1977 :Sanger sequencing technique developed . :1979 First DNAData Bank (GenBank). 1987 :Multiple sequence alignment. 1981: National Centre for Biotechnology information (NCBI) is released by Larry Wall.
- 11. 1990 : Human GenomeProject started. 1990 : BLAST program introduced by Kartin and Altshul. 1993: The first genome database (C.elegans). 1995 :Influenza genome sequences (5Mb). 2000: Drosophila genome sequences (180Mb.) 2001 :The human genome (3.000 Mbp) is published.
- 12. The evolution ofbioinformatics as seen in the 90’s
- 13. The requirement ofbioinformatics • Data collection techniques sequencing, microarrays) • Theoretical concept (concepts structure, evolution) (DNA sequencing, protein of DNA structure, protein • Programs (BLAST, FASTA) • Databases • Institutions • Complex genomic and high throughput data
- 14. The important ofBioinformatics • Applications areas include • Medicine • Pharmaceutical drug design • Toxicology • Molecular evolution • Biosensors • Biomaterials • Biological computing models • DNAcomputing
- 15. 15 What could Bioinformaticsoffer? Analyze and interpret biological data: Genomic Sequences, RNA structure & Transcriptomic Sequences & Protein sequences and Structures - RNAstructure (RNA) Develop new algorithms and tools to: Assess the biological information, handle large datasets, find relationships between data sources etc… Basic Science: - Understand the living cell - Find the function of a new protein - Find the genes/proteins that are unique to human Medical applications: identify the mutations (SNPs) that cause genetic diseases, disease diagnosis & find and develop new and better drugs Agriculture applications: higher yield crop, increase shelf life, etc.
- 16. • Structural genomicsis a field of genomics that involves the characterization of genome structures. • This knowledge can be useful in the practice of manipulating the genes and DNAsegments of a species. • Functional genomics is a field of molecular biology that attempts to describe gene functions and interactions. • Functional genomics make use of the large data generated by genomic and transcriptomic projects.
- 17. Bioinformtics could beused to: • Sequence complete genome • Identify protein coding regions • Identify unique genes • Gene knockout • Functional analysis (phenotype, detailed functional characterization).. • Structural studies, drug development
- 18. • The mostlogical way to look at how bioinformatics assists molecular biology, is to look at it from the central dogma. • Bioinformatics plays a role at each stage of the central dogma of molecular biology. First, there is DNA • DNA is the most basic data gathered from molecular experiments, and data types associated with DNA are genomes, genes, and gene features. 18
- 19. Central Paradigm inMolecular Biology mRNA Gene (DNA) Protein 21ST centaury Genome Transcriptome Proteome
- 20. The second partin the dogma is mRNA. • Data is generated in many areas of experimentation involving mRNA, and this includes the levels of expression of the mRNA. • Typically these would be microarray experiments. • Data associated with the structure of RNA, and then data associated with other RNA. • This include ribosomal RNA, transfer RNA and studies involving RNAi 20
- 21. Next are dataassociated with proteins. • Especially in modern molecular biology and biochemistry, proteomics is a growing field with many resources being allocated to the molecular study of proteins. • The data types most associated with proteins are sequence data, structural data and phylogenetics data. 21
- 22. • Today, thefield of Systems Biology is very high on the list of important research topics. It is a field that studies the complex interactions of genes proteins and other cellular elements and is very important for the advancement of knowledge regarding the function of the life. Metabolic pathway determination and modeling is a very important aspect of systems biology. • The highest level at which molecular biologists are working, is at the phenotypic level, trying to explain the reasons for phenotypes given genetic and proteomic makeup of cells. • Human disease manifestation as a result of genetic flaws is a big topic of study, and bioinformatics is playing a major role in determining the causes of genetic disease, as well as helping with the search for cures for these conditions. • Genome browsers, is one that exists only in silico, inside computers, these resources provide an integrated look at entire organisms, and how everything known about its biology is interrelated. 22
- 23. Central Dogma andGenome Browsers DNA Genomes Genes Features RNA Expression Structure Protein Sequence Structure Phylogenetics Systems Phenotype Metabolic Pathways Disease Integrated Resources ( Other RNA tRNA,rRNA&iRNA)
- 24. Central Dogma andGenome Browsers DNA RNA Protein Systems Phenotype Integrated Resources
- 25. DNA Genomes • Automated sequencing •200million by 1998 • 1.5 billion by 2003 (Human Genome complete) • Roche 454: >1Gb/day • Cost drops too.... • ATCGATCGATCATGCTAGCTAGCTAGCTAGCTAGCG CTATGCTAGCTCGTGCTAGCATGATCGATCATG.......
- 26. DNA ACCTGTGTTCATCGGTCATGC TCATCGGTCA TCATCGGTCATGCACGGTTA TCATCGGTCATGC •Huge number ofsequences from library (1000 000 000 clones) •Sequence must be determined •Must be assembled •Must be stored TCATCGGTCA TCA TCGGTCA TGC TCATCGGTCATGCATGC TCATCGGTCATGCAATCGA
- 28. Genome Storage –whatwe need? Computational tools are needed to distill pathways of interest from large molecular interaction databases • Need modern computer systems • Files • Specific formats • Databases • Manage data • Searchable • Fast, reliable, available
- 29.
- 30. DNA • Genome annotation •Finding genes and the features associated with the genes • Must be done on computers TTTATATAGGAGGAGATTCTCGCGATATCGGATCATCGAGCGCGCGCTATATATAGCTAGCTAGCTAGCTACTAGATCATCGATCGATCGCGCGATTACGATGCTAGCTACTACGGAAT TACGATCGATCGATGCATCGATGCATGCATGCATGCATGCTAGCTAGCTAGCTAGCTAGCATGCTAGCTAGCTGATCGATCGGCGATCGATGCATCGATGCATCGATCGTAGCTAGCT AAAGAGAGAGAGATCTCTCTTATAATTATAGCGCGATATATATGCGCATATATATATGCGATCATCGACTGCGCTATATACGATCGATCTAGCATCTAGCGCTATATACGATCATCTAA TATGCTAGCTACTACTATCATCGATGCTATCAGCTCGGCGCAATTATATATATTTTCTCTTATATAACTCGATAGCTACTACTACCTACTAGTAGCTAGCTAGCTAGCTAGCATCGTACG TTTATATAGGAGGAGATTCTCGCGATATCGGATCATCGAGCGCGCGCTATATATAGCTAGCTAGCTAGCTACTAGATCATCGATCGATCGCGCGATTACGATGCTAGCTACTACGGAAT TACGATCGATCGATGCATCGATGCATGCATGCATGCATGCTAGCTAGCTAGCTAGCTAGCATGCTAGCTAGCTGATCGATCGGCGATCGATGCATCGATGCATCGATCGTAGCTAGCT AAAGAGAGAGAGATCTCTCTTATAATTATAGCGCGATATATATGCGCATATATATATGCGATCATCGACTGCGCTATATACGATCGATCTAGCATCTAGCGCTATATACGATCATCTAA TATGCTAGCTACTACTATCATCGATGCTATCAGCTCGGCGCGCATTATATATATTTTCTCTTCTCTCTCTCGATAGCTACTACTAGCTACTAGTAGCTAGCTAGCTAGCTAGCATCGTACG TTTATATAGGAGGAGATTCTCGCGATATCGGATCATCGAGCGCGCGCTATATATAGCTAGCTAGCTAGCTACTAGATCATCGATCGATCGCGCGATTACGATGCTAGCTACTACGGAAT TACGATCGATCGATGCATCGATGCATGCATGCATGCATGCTAGCTAGCTAGCTAGCTAGCATGCTAGCTAGCTGATCGATCGGCGATCGATGCATCGATGCATCGATCGTAGCTAGCT AAAGAGAGAGAGATCTCTCTTATAATTATAGCGCGATATATATGCGCATATATATATGCGATCATCGACTGCGCTATATACGATCGATCTAGCATCTAGCGCTATATACGATCATCTAA TATGCTAGCTACTACTATCATCGATGCTATCAGCTCGGCGCGCATTATATATATTTTCTCTTCTCTCTCTCGATAGCTACTACTAGCTACTAGTAGCTAGCTAGCTAGCTAGCATCGTACG TTTATATAGGAGGAGATTCTCGCGATATCGGATCATCGAGCGCGCGCTATATATAGCTAGCTAGCTAGCTACTAGATCATCGATCGATCGCGCGATTACGATGCTAGCTACTACGGAAT TACGATCGATCGATGCATCGATGCATGCATGCATGCATGCTAGCTAGCTAGCTAGCTAGCATGCTAGCTAGCTGATCGATCGGCGATCGATGCATCGATGCATCGATCGTAGCTAGCT AAAGAGAGAGAGATCTCTCTTATAATTATAGCGCGATATATATGCGCATATATATATGCGATCATCGACTGCGCTATATACGATCGATCTAGCATCTAGCGCTATATACGATCATCTAA TATGCTAGCTACTACTATCATCGATGCTATCAGCTCGGCGCGCATTATATATATTTTCTCTTCTCTCTCTCGATAGCTACTACTAGCTACTAGTAGCTAGCTAGCTAGCTAGCATCGTACG TTTATATAGGAGGAGATTCTCGCGATATCGGATCATCGAGCGCGCGCTATATATAGCTAGCTAGCTAGCTACTAGATCATCGATCGATCGCGCGATTACGATGCTAGCTACTACGGAAT TACGATCGATCGATGCATCGATGCATGCATGCATGCATGCTAGCTAGCTAGCTAGCTAGCATGCTAGCTAGCTGATCGATCGGCGATCGATGCATCGATGCATCGATCGTAGCTAGCT AAAGAGAGAGAGATCTCTCTTATAATTATAGCGCGATATATATGCGCATATATATATGCGATCATCGACTGCGCTATATACGATCGATCTAGCATCTAGCGCTATATACGATCATCTAA TATGCTAGCTACTACTATCATCGATGCTATCAGCTCGGCGCGCATTATATATATTTTCTCTTCTCTCTCTCGATAGCTACTACTAGCTACTAGTAGCTAGCTAGCTAGCTAGCATCGTACG
- 31. 31 CCTGACAAATTCGACGTGCGGCATTGCATGCAGACGTGCATG CGTGCAAATAATCAATGTGGACTTTTCTGCGATTATGGAAGAA CTTTGTTACGCGTTTTTGTCATGGCTTTGGTCCCGCTTTGTTC AGAATGCTTTTAATAAGCGGGGTTACCGGTTTGGTTAGCGAGA AGAGCCAGTAAAAGACGCAGTGACGGAGATGTCTGATG CAA TAT GGACAA TTG GTT TCT TCT CTG AAT ................................. .............. TGAAAAACGTA TF binding site promoter Ribosome binding Site ORF=Open Reading Frame CDS=Coding Sequence Transcription Start Site
- 32. Central Dogma andGenome Browsers DNA RNA Protein Systems Phenotype Integrated Resources
- 33. RNA • Poly-nucleotide molecules •mRNAmost well known • Carries gene information for translation • Expression analysis for genes • Comparison between different environmental stimuli or different cellular states •Level of protein production proportional to RNA level Gives a good indication of gene expression
- 34. RNA Structure – Functionrelationship important Primary Secondary Tertiary 5 ’ AACUCGAGC UACUAGCUAG GCGCGUUAAU UAUCGUACUA UAGCUACUAC UUCGCGUAAU UAUUACGAUG UUCGGCUAGA UUAGCGAUAU UAUUACGAUA UAUAUGCGCA UAUCAGAUU 3 ’
- 35. Central Dogma andGenome Browsers DNA RNA Protein Systems Phenotype Integrated Resources
- 36. Protein • Relatively easyto find DNAsequence • More than 200 organisms sequenced • Putative proteomes are abundant • Bioinformatics: Computational tools to determine protein structure and function from sequence • Proteomics analyze protein sequences
- 37. • Translation ofRNA into proteins. The process of genome sequencing is relatively simple, and to data several organisms had their entire genome sequenced. • Through various annotation processes using computers, it is possible to predict the genes, and the proteins they will encode. • This creates the need for bioinformatics tools to computationally determine protein structure and function from sequence, with the end goal being that the accuracy of predicting the structure and function of proteins would be extremely high.
- 38. • Classify proteins(Database of protein motifs) • Choose and express representative proteins from all families • Determine structure by X-ray • Predict the rest by homology modeling Protein function
- 39. • When comparingprotein sequences, it can be assumed that for proteins with similar sequences, the proteins should have similar functions. • This fact is also true for subsections of a particular sequence, and not only for the entire sequences. • In Proteomics, many discoveries as to the specific functionality of a particular stretch of sequence (motif) have been made, and these discoveries were stored, and are being used today in sequence function assignment.
- 40. • Protein structureshave been available in the public domain for longer than any other type of biological data, with the first public repository being created in the early 70’s. • More than 33000 structures are available for download via the internet. • These structures can be downloaded for the purpose of homology modeling. • If 2 sequences are homologues, and functionally similar as well, then their 3-dimensional structures should be similar as well. This approach can be useful during drug discovery, or just studies on the functioning of a particular protein.
- 41. Protein • Similar sequence Similar function • Smaller stretches of sequence carries similar function • Motifs or signature sequences • DNAbinding motifs SequenceA Sequence B
- 42. Protein • Motifs andsignatures Identify unknown proteins • Search protein database for proteins with probable functionality • Databases of protein signatures and motifs • Pfam, Prosite, Prints, BLOCKS • Various methods of representation • HMMs – Pfam • Regular expressions - Prosite • PSSMs - BLOCKS
- 43. Central Dogma andGenome Browsers DNA RNA Protein Systems Phenotype Integrated Resources
- 44. • Knowing thefunction of one protein is not enough. If we are to understand the way life function, we must know and understand the way in which all proteins in the cell function together. • We must understand the complex relationships there are between the molecules in the cell that makes it function. • The way in which this is done, is first to understand the metabolic pathways. • These are networks into which functional groups of proteins are divided.
- 45. • To dothat, we must add all the different pathways and their effects together. • The field of study concerned with doing this is systems biology, in which the biological system, as a whole, is studied with the end goal of studying the effects of all the pathways eventually put together. • Bioinformatics is an essential component of systems biology.
- 46. • Many softwarepackages exist for the study of metabolic pathways and the greater system as a whole. • One such packages is called CellDesigner (http://www.celldesigner.org.)/ • It is a structured diagram editor for drawing gene-regulatory and biochemical networks. • It is a package used to create in silico metabolic systems. • These models can then be assigned metabolic characteristics, for example the effect of the up regulation of one component on another. This can eventually lead to the modeling of entire cascading effects in the system.
- 47.
- 48. Central Dogma andGenome Browsers DNA RNA Protein Systems Phenotype Integrated Resources
- 49. Phenotype • All individualsystems together phenotype • Phenotype studies big money required • Human Health Phenotype studies • Agriculture Phenotype studies