Bio-Informatics Lectures. A Short Introduction

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1 Bio-Informatics Lectures A Short Introduction

2 The History of Bioinformatics

3 Sanger Sequencing PCR in presence of fluorescent, chain-terminating dideoxynucleotides

4 Massively Parallel Sequencing

5 Massively Parallel Sequencing Illumina/Solexa

6 Roche/454, Emulsion PCR Metzker, Nature Review: Genetics (11):31-46

7 Illumina/Solexa: Solid-Phase Amplification

8

9

10

11

12

13

14

15

16

17

18

19

20 Growth of GenBank and WGS 1000 billion bases ~200 million sequences

21 Growth of UniProtKB/TrEMBL

22 How Does the Sequence Information Tell Us?

23 How Does the Sequence Information Tell Us? Bio-Informatics

24 Scope of this lab 1. Be familiar with sequence databases and some online bioinformatics tools DATABASES: GenBank-http://www.ncbi.nlm.nih.gov EMBL-http://www.ebi.ac.uk DDBJ-http://www.ddbj.nig.ac.jp Sequence Search and Retrieval: BLAST Sequence Alignement: ClustalW2, MAFFT Sequences Analysis and Domain Search: Pfam and SMART Protein Structure and Prediction: Pymol Molecular Evolution: MEGA More Tools to Discover on Your Own

25 Online Tools

26 Scope of this lab 2. Touch Some Simple Programming (Stand-alone) Basic UNIX Commands: cd, mkdir, mv. cp, rm, cat, ls, pwd, gunzip, unzip, tar Perl: String, Array, Hash R: Read a file, column, row, plot, hist, heat map

27 Beginning with a DNA Sequence

28 Proteins N-termnus MQIFVKTLTGKTITLEVESSDTIDNVKAKIQDKEGIPPDQQ RLIFAGKQLEDGRTLADYNIQKESTLHLVLRLRGG C-termnus The primary sequence, structure, and function of a protein are inter-related

29 Database Sequence Similarity Searching Definition: Applies computation, mathematical algorithms, statistical inference to rapidly find similar sequences (hits) to a target (query) sequence from a database. All similarity searching methods rely on the concepts of alignment between sequences. A similarity score is calculated from a distance: the number of DNA bases or amino acids that are different between two sequences.

30 Edit Distance

31 Edit Distance

32 Sequence Alignement and Dynamic Programming

33 Sequence Alignement Comparison and Substitution Matrix Some popular scoring matrices are: PAM (Point Accepted Mutation): for evolutionary studies. For example in PAM1, 1 accepted point mutation per 100 amino acids is required. BLOSUM (BLOcks amino acid Substitution Matrix): for finding common motifs. For example in BLOSUM62, the alignment is created using sequences sharing no more than 62% identity. Experimentation has shown that the BLOSUM-62 matrix is among the best for detecting most weak protein similarities.

34 Sequence Alignement Comparison and Substitution Matrix Some popular scoring matrices are: PAM (Point Accepted Mutation): for evolutionary studies. For example in PAM1, 1 accepted point mutation per 100 amino acids is required. BLOSUM (BLOcks amino acid Substitution Matrix): for finding common motifs. For example in BLOSUM62, the alignment is created using sequences sharing no more than 62% identity. Experimentation has shown that the BLOSUM-62 matrix is among the best for detecting most weak protein similarities.

35 Sequence Alignement Comparison and Substitution Matrix Some popular scoring matrices are: PAM (Point Accepted Mutation): for evolutionary studies. For example in PAM1, 1 accepted point mutation per 100 amino acids is required. BLOSUM (BLOcks amino acid Substitution Matrix): for finding common motifs. For example in BLOSUM62, the alignment is created using sequences sharing no more than 62% identity. Experimentation has shown that the BLOSUM-62 matrix is among the best for detecting most weak protein similarities.

36 Sequence Alignement Comparison and Substitution Matrix Some popular scoring matrices are: PAM (Point Accepted Mutation): for evolutionary studies. For example in PAM1, 1 accepted point mutation per 100 amino acids is required. BLOSUM (BLOcks amino acid Substitution Matrix): for finding common motifs. For example in BLOSUM62, the alignment is created using sequences sharing no more than 62% identity. Experimentation has shown that the BLOSUM-62 matrix is among the best for detecting most weak protein similarities.

37 Sequence Alignement Comparison and Substitution Matrix

38 Sequence Alignement Comparison and Substitution Matrix Log-odds matrices

39 Local and Global Alignements Needleman-Wunsch Smith-Waterman

40 BLAST/FASTA Search and k-tuple Method

41 Use proteins for database similarity searches when possible

42

43 Lab 1 Sequence Search and Retrieval: BLAST Sequence Alignement: ClustalW2, MAFFT Sequences Analysis and Domain Search: Pfam and SMART Protein Structure and Prediction: Pymol Molecular Evolution: MEGA Sequence Format - Fasta >AT4G05320 ATGCAGATCTTTGTTAAGACTCTCACCGGAAAGACAATCACCCTCGAGGTGGAAAGCTCCGACACCATCGACAACGTTAAGGC CAAGATCCAGGATAAGGAGGGCATTCCTCCGGATCAGCAGAGGCTTATTTTCGCCGGCAAGCAGCTAGAGGATGGCCGTACG TTGGCTGATTACAATATCCAGAAGGAATCCACCCTCCACTTGGTCCTCAGGCTCCGTGGTGGTATGCAGATTTTCGTTAAAACC CTAACGGGAAAGACGATTACTCTTGAGGTGGAGAGTTCTGACACCATCGACAACGTCAAGGCCAAGATCCAAGACAAAGAGG GTATTCCTCCGGACCAGCAGAGGCTGATCTTCGCCGGAAAGCAGTTGGAGGATGGCAGAACTCTTGCTGACTACAATATCCA GAAGGAGTCCACCCTTCATCTTGTTCTCAGGCTCCGTGGTGGTATGCAGATTTTCGTTAAGACGTTGACTGGGAAAACTATCAC TTTGGAGGTGGAGAGTTCTGACACCATTGATAACGTGAAAGCCAAGATCCAAGACAAAGAGGGTATTCCTCCGGACCAGCAG AGATTGATCTTCGCCGGAAAACAACTTGAAGATGGCAGAACTTTGGCCGACTACAACATTCAGAAGGAGTCCACACTCCACTT GGTCTTGCGTCTGCGTGGAGGTATGCAGATCTTCGTGAAGACTCTCACCGGAAAGACCATCACTTTGGAGGTGGAGAGTTCT GACACCATTGATAACGTGAAAGCCAAGATCCAGGACAAAGAGGGTATCCCACCGGACCAGCAGAGATTGATCTTCGCCGGAA AGCAACTTGAAGATGGAAGAACTTTGGCTGACTACAACATTCAGAAGGAGTCCACACTTCACTTGGTCTTGCGTCTGCGTGGA GGTATGCAGATCTTCGTGAAGACTCTCACCGGAAAGACTATCACTTTGGAGGTAGAGAGCTCTGACACCATTGACAACGTGAA GGCCAAGATCCAGGATAAGGAAGGAATCCCTCCGGACCAGCAGAGGTTGATCTTTGCCGGAAAACAATTGGAGGATGGTCGT ACTTTGGCGGATTACAACATCCAGAAGGAGTCGACCCTTCACTTGGTGTTGCGTCTGCGTGGAGGTATGCAGATCTTCGTCAA GACTTTGACCGGAAAGACCATCACCCTTGAAGTGGAAAGCTCCGACACCATTGACAACGTCAAGGCCAAGATCCAGGACAA GGAAGGTATTCCTCCGGACCAGCAGCGTCTCATCTTCGCTGGAAAGCAGCTTGAGGATGGACGTACTTTGGCCGACTACAAC ATCCAGAAGGAGTCTACTCTTCACTTGGTCCTGCGTCTTCGTGGTGGTTTCTAA

44 Lab 1 - BLAST

45 Lab 1 - BLAST

46 Lab 1 - BLAST

47 Lab 1 - BLAST E value: is the expectation value or probability to find by chance hits similar to your sequence. The lower the E, the more significant the score.

48 Lab 1 - BLAST

49 Lab 1 - BLAST

50 Lab 1 - BLAST

51 Lab 1 - BLAST

52 Lab 1 - BLAST

53 Lab 1 - Domain Search

54 Lab 1 - Domain Search

55 Lab 1 - Domain Search

56 Lab 1 - Structure Visualization Pymol

57 Lab 1 - Phylogenetics

58 Lab 1 - Phylogenetics UPGMA (Unweighted Pair Group Method with Arithmetic Mean) Maximum likelihood Maximum parsimony Neighbor joining MrBayes: Bayesian Inference of Phylogeny

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