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
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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- EMBL- DDBJ- 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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