Biol425 2014: Difference between revisions
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Computational Molecular Biology (BIOL 425/790.49, Spring 2014)
Instructors: Weigang Qiu (Associate Professor of Biology; weigang at genectr.hunter.cuny.edu, 212-772-5296) & Slav Kendall (Assistant; sviatoslav.kendall at gmail.com)
Room:1000G HN (10th Floor, North Building, Computer Science Department, Linux Lab FAQ)
Class Hours: Wednesdays 10:10 am-12:40 pm
Office Hours: Room 839 HN; Wednesdays 5-7pm or by appointment
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* In-Class Challenge 1. Compose a BioPerl-based script ("translation.pl") for translating a DNA sequence. Input: a DNA file in FASTA (e.g., "bio425/data/TenSeq.nuc") . Output: protein sequences in FASTA. Once the code is working, add a regular expression to skip and warn users for any translated sequence that doesn't start with with a start codon ("ATG" or "TTG"), or doesn't end with a stop codon ("TAG", "TAA", "TGA"), or contains internal stop codons. | * In-Class Challenge 1. Compose a BioPerl-based script ("translation.pl") for translating a DNA sequence. Input: a DNA file in FASTA (e.g., "bio425/data/TenSeq.nuc") . Output: protein sequences in FASTA. Once the code is working, add a regular expression to skip and warn users for any translated sequence that doesn't start with with a start codon ("ATG" or "TTG"), or doesn't end with a stop codon ("TAG", "TAA", "TGA"), or contains internal stop codons. | ||
* BLAST tutorials | * BLAST tutorials | ||
<syntaxhighlight lang=bash"> | <syntaxhighlight lang=bash" line enclose="div"> | ||
cp ../../bio425/data/GBB.pep ~/. # Copy the FASTA file containing all protein sequences coded by the B31 genome | cp ../../bio425/data/GBB.pep ~/. # Copy the FASTA file containing all protein sequences coded by the B31 genome to home directory | ||
makeblastdb -in GBB.pep -dbtype 'prot' -out ref # make a blast database | cp ../../bio425/data/ospA.pep ~/. # Copy the query file | ||
makeblastdb -in GBB.pep -dbtype 'prot' -out ref # make a blast database named "ref" | |||
blastp -query ospA.pep -db ref # Run simple protein blast | |||
blastp -query ospA.pep -db ref -evalue 1e-5 # filter by evalue | |||
blastp -query ospA.pep -db ref -evalue 1e-5 -outfmt 6 # tab-delimited concise output | |||
</syntaxhighlight> | </syntaxhighlight> | ||
{| class="wikitable sortable mw-collapsible" | {| class="wikitable sortable mw-collapsible" | ||
Revision as of 01:12, 24 February 2014
Course Schedule (All Wednesdays)
January 29. Course overview & Unix tools
- Course Overview: File:Session-1-small.pdfLecture Slides
- Learning Goals: (1) Understand the "Omics" files; (2) Review/Learn Unix tools
- In-Class Tutorial: Unix file filters
- Without changing directory, long-list genome, transcriptome, and proteome files using
ls - Without changing directory, view genome, transcriptome, and proteome files using
less -S - Using
grep, find out the size of Borrelia burgdorferi B31 genome in terms of the number of replicons ("GBB.1con" file) - Using
sort, sort the replicons by (a) contig id (3rd field, numerically); and (b) replicon type (4th field) - Using
cut, show only the (a) contig id (3rd field); (b) replicon type (4th field) - Using
tr, (a) replace "_" with "|"; (b) remove ">" - Using
sed, (a) replace "Borrelia" with abbreviation "B."; (b) remove "plasmid" from all lines - Using
paste -s, extract all contig ids and concatenate them with ";" - Using a combination of
cut and uniq, count how many circular plasmids ("cp") and how many linear plasmids ("lp")
- In-Class Challenges
- Using the "GBB.seq" file, find out the number of genes on each plasmid
grep ">" ../../bio425/data/GBB.seq | cut -c1-4| sort | uniq -c- Using the "ge.dat" file, find out (a) the number of genes; (b) the number of cell lines; (c) the expression values of three genes: ERBB2, ESR1, and PGR
grep -v "Description" ../../bio425/data/ge.dat | wc -l; or: grep -vc "Description" ../../bio425/data/ge.dat
grep "Description" ../../bio425/data/ge.dat | tr '\t' '\n'| grep -v "Desc" | wc -l
grep -Pw "ERBB2|PGR|ESR1" ../../bio425/data/ge.dat| Assignment #1 |
|---|
Unix Text Filters (5 pts) Show both commands and outputs for the following questions:
|
| Read & Respond (5 pts) Genome sequencing technologies: textbook, pg. 79-83
|
February 5. Genomics (1): Gene-Finding
- Lecture Slides: File:Session-2-small.pdfSession 2
- Learning goals: (1) Running UNIX programs; (2) Parse text with Perl anonymous hash
- In-Class Tutorials
- Identify ORFs in a prokaryote genome
- Go to NCBI ORF Finder page
- Paste in the GenBank Accession: AE000791.1 and click "orfFind"
- Change minimum length for ORFs to "300" and click "Redraw". How many genes are predicted? What is the reading frame for each ORF? Coordinates? Coding direction?
- Click "Six Frames" to show positions of stop codons (magenta) and start codons (cyan)
- Gene finder using GLIMMER
- Locate the GLIMMER executables:
ls /data/biocs/b/bio425/bin/ - Locate Borrelia genome files:
ls /data/biocs/b/bio425/data/GBB.1con-splitted/ - Predict ORFs:
../../bio425/bin/long-orfs ../../bio425/data/GBB.1con-splitted/Borrelia_burgdorferi_4041_cp9_plasmid_C.fas cp9.coord[Note the two arguments: one input file and the other output filename] - Open output file with
cat cp9.coord. Compare results with those from NCBI ORF Finder. - Extract sequences into a FASTA file:
../../bio425/bin/extract ../../bio425/data/GBB.1con-splitted/Borrelia_burgdorferi_4041_cp9_plasmid_C.fas cp9.coord > cp9.fas[Note two input files and standard output, which is then redirected (i.e., saved) into a new file]
- Locate the GLIMMER executables:
- Complex data structure with references
- Type the code from slides and save it as a file "read-coord.pl".
- Check syntax with
perl -c read-coord.pl - Make it executable:
chmod +x read-coord.pl - Run the code:
./read-coord.pl cp9.coord
- In-Class Challenge
- Use NCBI ORF Finder & GLIMMER to predict ORFs in
../../bio425/data/mystery_seq1.fas
| Assignment #2 (Finalized on: Sat 2/8 4pm) |
|---|
UNIX & Perl Exercise (5 pts)
Note: Show the code itself, the command to run the code, and the output. |
Sample code:#!/usr/bin/perl
use strict;
use warnings;
# ----------------------------------------
# File : extract.pl
# Author : WGQ
# Date : February 20, 2014
# Description : Emmulate glimmer EXTRACT program
# Input : A FASTA file with 1 DNA seq and coord file from LONG-ORF
# Output : A FASTA file with extracted DNA sequences
# ----------------------------------------
die "Usage: $0 <FASTA_file> <coord_file>\n" unless @ARGV > 0;
my ($fasta_file, $coord_file) = @ARGV;
# Read DNA sequence file
open FASTA, "<" . $fasta_file;
my $seq_id;
my $dna_string = "";
my $count_seq = 0;
while (<FASTA>) {
my $line = $_;
chomp $line;
if ($line =~ /^>(.+)$/) {
$seq_id = $1;
$count_seq++;
next;
} else {
$dna_string .= $line
}
}
close FASTA;
die "More than one DNA sequence found. Quit.\n" if $count_seq > 1;
# Read COORD file & extract sequences
open COORD, "<" . $coord_file;
while (<COORD>) {
my $line = $_;
chomp $line;
next unless $line =~ /^s*(\d+)\s+(\d+)\s+(\S+)\s+(\S+)\s+\S+\s*/; # extract data using regex & skip all other lines
my ($id, $cor1, $cor2, $frame) = ($1, $2, $3, $4);
print ">$id\n";
print &orf_seq($cor1, $cor2, $frame), "\n";
}
close COORD;
exit;
###### Subroutines and Functions ####################
sub orf_seq {
my ($x, $y, $fr) = @_;
my $orf;
if ($x < $y) { # positive frame
$orf = substr($dna_string, $x - 1, $y - $x + 1); # substr uses zero-based coord system
} else { # negative frame
$orf = substr($dna_string, $y - 1, $x - $y + 1); # substr uses zero-based coord system
$orf = &revcom($orf);
}
return $orf;
}
sub revcom {
my $string = shift @_;
$string =~ tr/atcg/tagc/; # complement if lower cases
$string =~ tr/ATCG/TAGC/; # complement if upper cases
my $rev = reverse $string; # reverse
return $rev;
}
|
Read, Watch, & Respond (5 pts)
{{#ev:youtube|SS-9y0H3Si8|200|left|An introduction to Object-Oriented Programming}} |
February 12 (No Class)
- Lincoln's Birthday
February 19. Genomics (2): BioPerl
- Lecture Slides: File:Session-3-small.pdfSession 3
- Learning goal: (1) Object-Oriented Perl; (2) BioPerl
- In-Class Exercises
Construct and dump a Bio::Seq object
#!/usr/bin/perl -w
use strict;
use lib '/data/biocs/b/bio425/bioperl-live';
use Bio::Seq;
use Data::Dumper;
my $seq_obj = Bio::Seq->new( -id => "ospC", -seq =>"tgtaataattcaggaaaaga" );
print Dumper($seq_obj);
exit;
Apply Bio::Seq methods:
my $seq_rev=$seq_obj->revcom()->seq(); # reverse-complement & get sequence string
my $eq_length=$seq_obj->length();
my $seq_id=$seq_obj->display_id();
my $seq_string=$seq_obj->seq(); # get sequence string
my $seq_translate=$seq_obj->translate()->seq(); # translate & get sequence string
my $subseq1 = $seq_obj->subseq(1,10); # subseq() returns a string
my $subseq2= $seq_obj->trunc(1,10)->seq(); # trunc() returns a truncated Bio::Seq object
- Challenge 1: Write a BioPerl-based script called "bioperl-exercise.pl". Start by constructing a Bio::Seq object using the "mystery_seq1.fas" sequence. Apply the
trunc()method to obtain a coding segment from base #308 to #751. Reverse-complement and then translate the segment. Output the translated protein sequence.
#!/usr/bin/perl -w
use strict;
use lib '/data/biocs/b/bio425/bioperl-live';
use Bio::Seq;
my $seq_obj = Bio::Seq->new( -id => "mystery_seq", -seq =>"tgtaataattcaggaaaaga.............." );
print $seq_obj->trunc(308, 751)->revcom()->translate()->seq(), "\n";
exit;
- Challenge 2. Re-write the above code using Bio::SeqIO to read the "mystery_seq1.fas" sequence and output the protein sequence.
#!/usr/bin/perl -w
use strict;
use lib '/data/biocs/b/bio425/bioperl-live';
use Bio::SeqIO;
die "$0 <fasta_file>\n" unless @ARGV == 1;
my $file = shift @ARGV;
my $input = Bio::Seq->new( -file => $file, -format =>"fasta" );
my $seq_obj = $input->next_seq();
print $seq_obj->trunc(308, 751)->revcom()->translate()->seq(), "\n";
exit;
| Assignment #3 (Finalized 2/20, 9pm) |
|---|
BioPerl exercises
|
Read & Respond
|
February 26. Genomics (3). Homology searching with BLAST
- Learning goals: Homology, BLAST, & Alignments
- In-Class Challenge 1. Compose a BioPerl-based script ("translation.pl") for translating a DNA sequence. Input: a DNA file in FASTA (e.g., "bio425/data/TenSeq.nuc") . Output: protein sequences in FASTA. Once the code is working, add a regular expression to skip and warn users for any translated sequence that doesn't start with with a start codon ("ATG" or "TTG"), or doesn't end with a stop codon ("TAG", "TAA", "TGA"), or contains internal stop codons.
- BLAST tutorials
cp ../../bio425/data/GBB.pep ~/. # Copy the FASTA file containing all protein sequences coded by the B31 genome to home directory
cp ../../bio425/data/ospA.pep ~/. # Copy the query file
makeblastdb -in GBB.pep -dbtype 'prot' -out ref # make a blast database named "ref"
blastp -query ospA.pep -db ref # Run simple protein blast
blastp -query ospA.pep -db ref -evalue 1e-5 # filter by evalue
blastp -query ospA.pep -db ref -evalue 1e-5 -outfmt 6 # tab-delimited concise output| Assignment #3 (Tentative) |
|---|
Unix Text Filters
|
Read & Respond
|
March 5: Genomics (4). Molecular phylogenetics
- Learning goal: File I/O with BioPerl
- In-Class Exercises:
- Phylip
- FastTree
March 12: Mid-Term Review
- In-class Exercise: Write a BASH script named as "get-long-orfs.bash" to output long ORFs and their translated protein sequences given a genome sequence. Your script will combine commands and scripts you have previously composed into a single-command utility.
- Input file: Locate whole plasmid sequences in the "/data/biocs/b/bio425/data/GBB.1con-splitted/" folder:
- Group 1: use "Borrelia_burgdorferi_4075_lp54_plasmid_A.fas"
- Group 2: use "Borrelia_burgdorferi_4091_cp26_plasmid_B.fas"
- Group 3: use "Borrelia_burgdorferi_4041_cp9_plasmid_C.fas"
- Group 4: use "Borrelia_burgdorferi_4076_lp17_plasmid_D.fas"
- Group 5: use "Borrelia_burgdorferi_4005_lp25_plasmid_E.fas"
- Output files: two files (2 bonus points if your output file names are generated dynamically, not hard-coded)
- "plasmid_X.nuc": ORF sequences in FASTA
- "plasmid_X.pep": Protein sequences in FASTA
- Input file: Locate whole plasmid sequences in the "/data/biocs/b/bio425/data/GBB.1con-splitted/" folder:
March 19. Midterm Practicum
March 26
Spring Break
April 2: Transcriptome with R (Part 1)
- Learning goal: Introduction to R
- R resources
April 9: Transcriptome with R (Part 2)
- Learning goal: Classification of breast-cancer subtypes
- In-Class Exercises: Part 1. Gene filtering
April 16 (No Class)
- Spring Break
April 23: Transcriptome with R (Part 3)
- Learning goal: Biomarker Discovery of Cancer Drugs
- Discussion Questions
April 30: Molecular Phylogenetics (Part 1)
- Learning goals:
- Homology search using BLAST
- Multiple alignment using clustalw
- Distance-based phylogeny
May 7: Molecular Phylogenetics (Part 2)
- Learning goals:
- Learn to read a phylogenetic tree
- Phylogenomics: identification of orthologous and paralogous genes
- In-Class Exercise 1.
May 14: Final Project (Session I)
- Goals:
- Claim your individual project
May 21: Final Project Due (5pm in my office @HN839)
- Sample Projects
General Information
Course Description
- Background: Biomedical research is becoming a high-throughput science. As a result, information technology plays an increasingly important role in biomedical discovery. Bioinformatics is a new interdisciplinary field formed between molecular biology and computer science.
- Contents: This course will introduce both bioinformatics theories and practices. Topics include: database searching, sequence alignment, molecular phylogenetics, structure prediction, and microarray analysis. The course is held in a UNIX-based instructional lab specifically configured for bioinformatics applications.
- Problem-based Learning (PBL): For each session, students will work in groups to solve a set of bioinformatics problems. Instructor will serve as the facilitator rather than a lecturer. Evaluation of student performance include both active participation in the classroom work as well as quality of assignments (see #Grading Policy).
- Learning Goals: After competing the course, students should be able to perform most common bioinformatics analysis in a biomedical research setting. Specifically, students will be able to
- Approach biological questions evolutionarily ("Tree-thinking")
- Evaluate and interpret computational results statistically ("Statistical-thinking")
- Formulate informatics questions quantitatively and precisely ("Abstraction")
- Design efficient procedures to solve problems ("Algorithm-thinking")
- Manipulate high-volume textual data using UNIX tools, Perl/BioPerl, R, and Relational Database ("Data Visualization")
- Pre-requisites: This 3-credit course is designed for upper-level undergraduates and graduate students. Prior experiences in the UNIX Operating System and at least one programming language are required. Hunter pre-requisites are CSCI132 (Practical Unix and Perl Programming) and BIOL300 (Biochemistry) or BIOL302 (Molecular Genetics), or permission by the instructor. Warning: This is a programming-based bioinformatics course. Working knowledge of UNIX and Perl is required for successful completion of the course.
- Textbook: Gibson & Muse (2009). A Primer of Genome Science (Third Edition). Sinauer Associates, Inc.
- Academic Honesty: Hunter College regards acts of academic dishonesty (e.g., plagiarism, cheating on examinations, obtaining unfair advantage, and falsification of records and official documents) as serious offenses against the values of intellectual honesty. The College is committed to enforcing the CUNY Policy on Academic Integrity and will pursue cases of academic dishonesty according to the Hunter College Academic Integrity Procedures.
Grading Policy
- Treat assignments as take-home exams. Student performance will be evaluated by weekly assignments and projects. While these are take-home projects and students are allowed to work in groups and answers to some of the questions are provided in the back of the textbook, students are expected to compose the final short answers, computer commands, and code independently. There are virtually an unlimited number of ways to solve a computational problem, as are ways and personal styles to implement an algorithm. Writings and blocks of codes that are virtually exact copies between individual students will be investigated as possible cases of plagiarism (e.g., copies from the Internet, text book, or each other). In such a case, the instructor will hold closed-door exams for involved individuals. Zero credits will be given to ALL involved individuals if the instructor considers there is enough evidence for plagiarism. To avoid being investigated for plagiarism, Do NOT copy from others or let others copy your work.
- Submit assignments in Printed Hard Copies. Email attachments will NOT be accepted. Each assignment will be graded based on timeliness (10%), whether executable or having major errors (50%), algorithm efficiency (10%), and readability in programming styles (30%, see #Assignment Expectations).
- The grading scheme
- Assignments (100 pts): 10 exercises.
- Mid-term (50 pts).
- Final Project (50 pts)
- Classroom performance (50 pts): Active engagement in classroom exercises and discussions
- Attendance (50 pts): 1 unexcused absences = 40; 2 absences = 30; More than 2 = 0.
Assignment Expectations
- Use a programming editor (e.g., vi or emacs) so you could have features like automatic syntax highlighting, indentation, and matching of quotes and parenthesis.
- All PERL code must begin with "use strict; and use warnings;" statements. For each assignment, unless otherwise stated, I would like the full text of the source code. Since you cannot print using the text editor in the lab (even if you are connected from home), you must copy and paste the code into a word processor or a local text editor. If you are using a word processor, change the font to a fixed-width/monospace font. On Windows, this is usually Courier.
- Also, unless otherwise stated, both the input and the output of the program must be submitted as well. This should also be in fixed-width font, and you should label it in such a way so that I know it is the program's input/output. This is so that I know that you've run the program, what data you have used, and what the program produced. If you are working from the lab, one option is to email the code to yourself, change the font, and then print it somewhere else as there is no printer in the lab.
- Recommended Style
- Bad Style
Useful Links
Unix Tutorials
- A very nice UNIX tutorial (you will only need up to, and including, tutorial 4).
- FOSSWire's Unix/Linux command reference (PDF). Of use to you: "File commands", "SSH", "Searching" and "Shortcuts".
Perl Help
- Professor Stewart Weiss has taught CSCI132, a UNIX and Perl class. His slides go into much greater detail and are an invaluable resource. They can be found on his course page here.
- Perl documentation at perldoc.perl.org. Besides that, running the perldoc command before either a function (with the -f option ie, perldoc -f substr) or a perl module (ie, perldoc Bio::Seq) can get you similar results without having to leave the terminal.
Bioperl
- BioPerl's HOWTOs page.
- BioPerl-live developer documentation. (We use bioperl-live in class.)
- Yozen's tutorial on installing bioperl-live on your own Mac OS X machine. (Let me know if there are any issues!).
- A small table showing some methods for BioPerl modules with usage and return values.
SQL
- SQL Primer, written by Yozen.
R Project
- Install location and instructions for Windows
- Install location and instructions for Mac OS X
- Install R-Studio
- For users of Ubuntu/Debian:
sudo apt-get install r-base-core
Utilities
- An RSS button extension for chrome. Can add feeds to Google Reader and others.
- A similar extension which adds a "Live bookmarks"-like feature to Chrome (like Firefox's RSS bookmarks).
Other Resources
- Information Theory Primer by Thomas D. Schneider. Useful in understanding sequence logo maps.
© Weigang Qiu, Hunter College, Last Update ~~