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
• Make a Perl program to calculate the lengths of DNA and protein sequences
• Make a Perl program to calculate the length of a Mycoplasma genitalium bacterium
• 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
• How to use NCBI database to construct a protein sequence

 

3. Watch the following YouTube lectures;

   MIT 생정보연구자들의 시리즈

   MIT Bioinforamtics intro

   Chris Burge's local alignment lecture from MIT   

   Burge's alignment lecture from MIT

   Genome Assembly 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

   Synthetic biology 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

 

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UNIST Bioinformatics course

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