Bioinformatics homework
Jong Bhak's Bioinformatics homework (for any bioinforamtics students)
1. Learn Perl programming language and learn bioinformatics using it.
- Install and Learn BioPerl
- Make a Perl program translating 'all' combinations of triple bases into amino acids
- Pick 5 protein sequences and predict their secondary structures using available prediction programs
- Open and re-write one FASTA file containing one protein sequence of TERT
- Create a FASTA file with a sequence. Open it and reverse the sequence of it and print it out in another FASTA file
- Extract a sequence MKKTGIKG from ASMKATAHQMKKTGIKGMSTYALLRL and print it out in a file
- In a multi-sequence FASTA file, produce statistics such as sequence number, average seq length, GC content, AT content, etc
- Align two protein sequences using a dynamic programming method in Perl
- Randomly generate five 100 AA long protein sequences and store them in a FASTA file
- Create a flat text file database of protein sequences with hash function in Perl
2. Learn various bioinformatics program and tasks
- Access http://koreanreference.org/home/index.html and search for TERT_HUMAN DNA region
- Install a sequence alignment viewer program on your computer
- Install a protein 3D structure viewer program on your computer
- Create a MySQL database and add 5 protein sequences in your MySQL DB
- Download TERT_HUMAN sequence and search for 3D structure of it and build a 3D model using bat's TERT homolog sequence using the human 3D template
- Access http://string-db.org/ and find all the interaction partners of TERT_HUMAN protein
3. Watch the following YouTube lectures;
MIT 생정보연구자들의 시리즈
Chris Burge's local alignment lecture from MIT
Burge's alignment lecture from MIT
David Gifford MIT Library Complexity and Short Read Alignment
Chip-seq analyses + DNA protein interaction from MIT
RNA-seq analyses by MIT David Gifford
Modelling and discovery of sequence motifs by MIT
Markov and Hidden Markov models for genome and protein sequence features
RNA-sequence secondary structures by MIT
Intro to protein structure and comparisons
Predicting protein structure from MIT
Predicting protein-protein interactions
Gene regulartory networks by MIT
Protein interaction networks by MIT
Logic modeling of cell signalling networks by MIT
Analysis of chromatic structure by MIT
Discoverying quantitative trait loci by MIT
Human genetics SNPs, GWAS by MIT
Causality, Natural language and genome engineering
George Church's future of genomics and synthetic biology
Genome Engineering by Jennifer Doudnaby UC Berkeley 제니퍼 다우드나의 강의