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BLAST 8 Probability
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Classroom Contents
Computational Genomics
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- 1 About Dr. Rob Edwards
- 2 Learning Outcomes
- 3 Why we are going to use AWS in the course
- 4 Create Amazon Web Services Student Account
- 5 Start AWS Computational Genomics Image
- 6 SSH Keys
- 7 Central Dogma
- 8 What does it mean to sequence a genome?
- 9 What is genome sequencing part 1
- 10 What is genome sequencing part 2
- 11 Steps In Genome Sequencing
- 12 Sanger sequencing
- 13 454 pyrosequencing
- 14 Introduction to Illumina Sequencing
- 15 illumina paired end sequencing
- 16 Ion Torrent Sequencing
- 17 Pacific Biosciences Sequencing
- 18 Oxford Nanopore ONT Sequencing
- 19 Definitions
- 20 An overview of DNA sequence assembly
- 21 Naive DNA sequence assembly
- 22 Greedy Assembly of DNA Sequences
- 23 Overlap layout consensus sequence assembly approach
- 24 de Bruijn graph assembly for DNA sequences
- 25 Why does paired end sequencing help assembly?
- 26 Assembly problems 1 ... repeats
- 27 Assignment1
- 28 Assignment 1 solution
- 29 Assignment 3
- 30 BLAST 1
- 31 BLAST 2 seed and extend
- 32 BLAST 3 amino acid differences
- 33 BLAST 4 PAM vs BLOSUM
- 34 BLAST 5 BLOSUM62
- 35 BLAST 6 scoring alignments
- 36 BLAST 7 scoring gaps
- 37 BLAST 8 Probability
- 38 BLAST 9 E values
- 39 BLAST 10 Algorithms
- 40 BLAST 11 PSWM
- 41 ESKAPE Pathogens
- 42 Klebsiella Introduction
- 43 Antibiotics 1 mode of action
- 44 Antibiotics 2 mechanisms of resistance
- 45 Antibiotics 3 Role of HGT in antibiotic resistance
- 46 Antibiotics 4 Resistance Timeline
- 47 Antibiotics 5 resistance databases
- 48 Subsystems Introduction
- 49 Subsystem Classification
- 50 Subsystems How to Annotate Genomes and Metagenomes
- 51 Introduction to k-mers
- 52 Introduction on counting k-mers
- 53 k-mer algorithms: Compare and Swap
- 54 Bloom Filters
- 55 Counting k-mers with Bloom filters
- 56 Count Min Sketch
- 57 Counting Quotient Filters
- 58 Metagenome Binning
- 59 CheckM for Genome Consistency
- 60 Genome Peek
- 61 ABACBS Presentation 2020
