BIOINFORMATICS: An Introduction

Transcription

1 BIOINFORMATICS: An Introduction

2 What is Bioinformatics? The term was first coined in 1988 by Dr. Hwa Lim The original definition was : a collective term for data compilation, organisation, analysis and dissemination

3 Simple Concept The application of computer science and engineering techniques to biological analysis. The creation of repeatable, reusable, intelligent software

4 That means. Using information technology to help solve biological problems by designing novel and incisive algorithms and methods of analyses

5 And It also serves to establish innovative software and create new/maintain existing databases of information, allowing open access to the records held within them

6 It s a huge subject Bioinformatics - the new buzz word in the scientific community It is an umbrella term for genomics, proteomics and evolution, and computer science It is now necessary for scientists to be interdisciplinary

7 Why? The data is collected from a variety of sources The terminology is specific to its particular branch of science To make the data easily and universally interpretable by scientists

8 What data? Biologists have been classifying data on species of plants and animals since the 17th century The knowledge acquired has escalated in harmony with the evolution of technology

9 A brief history of progress. Genetics began when Mendel proved his laws of hereditary with varieties of peas and flowers in 1865 The invention of the compound microscope in the 19th century

10 A brief history of progress. The first protein to be sequenced insulin The first complete sequencing of an enzyme, ribonuclease in 1960 To the sequencing of the first complete genome (Haemophilus influenzae) published in 1995

11 A brief history of progress. We have since moved on to technologies permitting the sequencing, recombination and cloning of DNA The Human Genome Project

12 The Human Genome Project In 1990 the unveiling of the Human Genome Project (HGP) by the United States Department of Energy and the National Institutes of Health Goals:

13 HGP To identify all chemical base pairs and all genes that make up the 23 chromosome pairs found in human DNA To develop the next generation of methods for simulating cellular behaviour and pathways

14 HGP Ultimately to devise means to apply IT to the modelling of cellular functions as specified by the enormous datasets

15 The omic revolution Bioinformatics has been split into various subjects: Genomics the sequencing and annotation of genomes Proteomics the description of the complete set of proteins a particular genome codes for

16 Sequence Databases Since it became possible to elucidate protein & nucleic acid sequences, they have been determined at an ever increasing rate. These sequences were printed in research journals

17 Sequence Databases Their enormous numbers and lengths (particularly for genome sequences) make it no longer practical to do so It is far more useful to have sequences in computer-accessible form

18 Sequence Databases As an example of a sequence database, let us describe the annotated protein sequence database named SWISS-PROT A sequence record in SWISS-PROT begins with the proteins ID code of the form X_Y

19 Sequence Databases X is up-to-four-character mnemonic indicating the protein name e.g., CYC for cytochrome c e.g., HBA for hemoglobin α chain

20 Sequence Databases Y is up-to-five-character identification code indicating the proteins biological source that usually of the first three letters of the genus and the first two letters of the species e.g., CANFA for Canis familiaris (dog)

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24 Sequence alignment One can quantitate the sequence similarly of two polypeptides or two DNA's by determining their number of aligned residues that are identical

25 Sequence alignment For Example; human & dog cytochrome c, which differ in 11 of their 104 residues are 89% identical [ ( ) / 104 ] X 100 = 89%

26 The Homology of Distantly Related Proteins May Be Difficult to Recognized Mutation is a stochastic (Probabilistic or random) process At every stage of evolution each residue has an equal chance of mutation

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28 The Homology of distantly related proteins may be difficult to recognized The relative evolutionary distances between neighboring branch points are expressed as the number of amino acid differences per 100 residue of the protein or PAM units Percentage of Accepted Point Mutations

29 The Homology of distantly related proteins may be difficult to recognized Assume that we have a 100-residue protein in which all point mutations have an equal probability of being accepted and occur at a constant rate, thus at an evolutionary distance of one PAM units, the original and evolved proteins are 99% identical

30 The Homology of distantly related proteins may be difficult to recognized At an evolutionary distance of two PAM units they are 98% identical Whereas at 50 PAM units they are 61% identical (0.99) 1 X 100 = 99% (0.99) 2 X 100 = 98% (0.99) 50 X 100 = 61%

31 The Homology of distantly related proteins may be difficult to recognized Mutational events may result in the insertion or deletion of one or more residues within a chain SQMCILFKAQMNYGH MFYACRLPMGAHYWL

32 The Homology of distantly related proteins may be difficult to recognized If we allowed unlimited gapping: SQMCILFKAQMNYGH - - M - - F Y - - ACRLPMGAHYWL Thus we cannot allow unlimited gapping to maximize the match between two peptides, but neither can we forbid all gapping because it really do occur

33 The Homology of distantly related proteins may be difficult to recognized Consequently, for each allowed gap we must impose some sort of penalty in our alignment algorithm that strike a balance between finding the best alignment between: - distantly related peptides - rejecting improper alignment

34 The Homology of distantly related proteins may be difficult to recognized Unrelated protein will exhibit sequence identities in the range 15% to 25% Yet distantly related proteins may have similar levels of sequence identity This the origin of the twilight zone

35 The Homology of distantly related proteins may be difficult to recognized A plot of percent identify vs. evolutionary distance is an exponential curve that approaches but never equal zero

36 The Homology of distantly related proteins may be difficult to recognized To differentiate homologous proteins in the twilight zone from those that are unrelated, it requires sophisticated alignment algorithm Sequencing Alignment Using Dot Matrices

37 The Homology of distantly related proteins may be difficult to recognized

38 The Homology of distantly related proteins may be difficult to recognized The following sections in this introduction of Bioinformatics will be demonstrated from: the soft copy of the text book: BIOCHEMISTRY by: D. Voet & J. Voet, 3 rd Edition,

39 Biochemical Interactions Software Learning Objectives: 1. To understand the alignment process 2. To understand how natural selection affects the likelihood of an amino acid substitution being accepted 3. To understand the basis of sophisticated alignment programs such as BLAST