Biol425 2013

Computational Molecular Biology (BIOL 425/790.49, Spring 2013) Instructors: Weigang Qiu (Associate Professor of Biology) & Che Martin (Assistant) Room:1000G HN (10th Floor, North Building, Computer Science Department, Linux Lab FAQ) Hours: Wednesdays 10:10 am-12:40 pm Office Hours: Room 839 HN; Wednesdays 5-7pm or by appointment Contacts: Dr Qiu: weigang@genectr.hunter.cuny.edu, 212-772-5296; Che: cmartin@gc.cuny.edu, 917-684-0864

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: Krane & Raymer (2003). Fundamental Concepts of Bioinformatics. Pearson Education, Inc. (ISBN 0-8053-4633-3)
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

Course Schedule (All Wednesdays)

January 30. Browse Genome & Transcriptome Files with Unix Tools

Course Overview
Learning Goal: The power of Unix text filters
In-Class Exercises:

You will need these 2 files for the following questions: /data/biocs/b/bio425/data/GBB.1con, GBB.seq
1. What is a genome? What does a bacterial genome typically consist of? Explain the following terms: chromosome, plasmids, and contig
2. Specify the FASTA file format
3. What is the size of the Borrelia burgdorferi (the Lyme disease pathogen) B31 genome in terms of
  1. Number of replicons: From your home directory, run:
```
grep -c "^>" ../../bio425/data/GBB.1con [Answer: N=22 replicons] 
```
  2. Number of genes: From your home directory, run:
```
grep -c "^>" ../../bio425/data/GBB.seq [Answer: N=1,738 genes] 
```
  3. Number of bases: First filter out FASTA headers using "grep -v" and then remove newline characters using "tr -d":
```
grep -v "^>" ../../bio425/data/GBB.1con | tr -d '\n' | wc -m [Answer: N=1,519,856 bases] 
```

Based on the file /data/biocs/b/bio425/data/ge-breast-cancer-cell-lines.dat, answer the following questions:

What is a transcriptome?

How many unique genes are represented in this chip?

cut -f1 ../../bio425/data/ge-breast-cancer-cell-lines.dat | grep -vc "^Description" [Answer: N=18,900 genes]

How many cell lines in the file?

grep "^Description" ../../bio425/data/ge-breast-cancer-cell-lines.dat| cut -f2- | wc -w [Answer: N=59 cells]

Extract gene expression values at 3 breast cancer clinical markers: ERBB2, ESR1, and PGR

grep -wP "ERBB2|ESR1|PGR" ../../bio425/data/ge-breast-cancer-cell-lines.dat [You should see 3 rows of gene expression values]

Assignment #1 (Final Version; Q4 file name corrected)
Unix Text Filters Display the absolute path of your home directory List files in your home directory in long format & ordered by their time stamps List files and directories in the "/data/biocs/b/bio425/" directory from your home directory Count the number of plasmids in the B. burgdorferi genome using the file "/data/biocs/b/bio425/GBB.1con" Show the first five lines of the file "GBB.seq" & save it to a file with arbitrary name Show your last ten commands using "history"
Read Chapter 1

February 6. Central Dogma with PERL: Replication & Transcription

Learning goals: String manipulations using BASH & PERL
In-Class Exercises:

Find out base composition (%A, %T, %C, and %G) of a DNA sequence
1. In the folder /data/biocs/b/bio425/data/GBB.1con-splitted, the whole B. burgdorferi genome has been splitted into files each of which containing a single replicon. Write a short BASH script to count how many bases in each replicon.
```
for f in ../../bio425/data/GBB.1con-splitted/*.fas; do echo -ne "$f\t"; grep -v "^>" $f | tr -d "\n" | wc -m; done
```
2. Write a PERL script to count GC% given a FASTA sequence.
```
Script posted at /data/biocs/b/bio425/scripts/gc-counter.pl
```
3. Run the GC-counter for each replicon using a BASH loop.
```
for f in ../../bio425/data/GBB.1con-splitted/*.fas; do perl /data/biocs/b/bio425/scripts/gc-counter.pl $f; done
```
Central Dogma
1. Illustrate the Central Dogma using lines representing DNA/RNA/Protein. Indicate directionality of all parts.
```
DNA replication, Transcription & Translation: all 5' to 3'
```
2. Define cDNA. What are some of the common uses of cDNA?
```
Identify alternative-splicing variants (isoforms); Quantify gene expression levels (RNA-SEQ)
```
DNA replication
1. What is the implied directionality of a DNA sequence if none is specified?
```
5' to 3'
```
2. What is the complement strand of this DNA sequence gatactaatgaagtat in 5' to 3' direction?
```
atacttcattagtatc
```
3. Write a PERL script to output the complementary strand of the ospC gene /data/biocs/b/bio425/data/ospC.seq in 5' to 3' direction. Save the result in a FASTA file ospC.revcom.fas
```
Script posted at /data/biocs/b/bio425/scripts/revcom.pl
```

Assignment #2 (Final)
Modify the gc-counter code /data/biocs/b/bio425/scripts/gc-counter.pl to (1) count only a,t,c,g's (not other letters, e.g., amino acids); `see below` (2) count bases in both upper-case AND lower-case letters; `if ($base =~ /[ATCG]/i) { $base =~ tr/atcg/ATCG/; $count{$base}++; $total_valid++}` (3) output GC% (i.e., percentage of G+C among the total bases; formated to no more than 2 decimal points, by using the printf function). `my $gc_percent=($count{"G"}+$count{"C"})/$total_valid; printf "%.2f\n", $gc_percent;`Show results for the file /data/biocs/b/bio425/data/cp9.seq. `23.69%` Add comments to the revcom script /data/biocs/b/bio425/scripts/revcom.pl to explain what each statement does. Chapter 1. Problems (pg.31-32): 1.4. Would you expect the amino acids containing hydroxyl groups (-OH) hydrophobic or hydrophilic? Why? `Hydrophilic, because it forms hydrogen bond with the water molecule` 1.6. How does a cDNA library differ from a genomic library? `cDNA library does not contain intronic sequences while genomic DNA does.`

February 13. Central Dogma with PERL: Translation & Gene Prediction

Learning goals: DNA translation & ORF Identification
In-Class Exercises:

Six-frame translation

Define ORF and reading frame. How many possible reading frames are there for a given sequence?

ORF: Hypothetical, computer-predicted protein-coding sequences. An ORF needs experimental data (e.g., mRNA) to be verified as a true gene. There are six possible reading frames for a given DNA sequence: three (+1, +2, +3) on one strand and another three (-1, -2, -3) on the reverse-complement strand.

How does a cell normally choose only one reading frame out of other possibilities for any given parts of a genome?

In a prokaryotic cell, transcription factors (TFs) bind to a particular sequence (called "promoters") at the 5'-upstream of an ORF.  TFs recruits RNA polymerase to initiative the transcription of a gene. After the transcription, ribosomes bind to another particular 5'-upstream sequence (SD sequence, normally "AGGAG") to start the translation of a protein.

Using a DNA genetic code table to manually translate the following sequence in all possible reading frames: gatactaatgaagtat . Which reading frame do you think is the most likely one?
```
+1: DTNEV; +2: ILMKY; +3:Y**S; -1:ILH*Y; -2:YFISI; -3:TSLV. Frames +3 and -1 could be eliminated.
```

ORF identification in a bacterial genome

Pick a plasmid sequence from /data/biocs/b/bio425/data/GBB.1con-splitted

Run ls -lh ../../bio425/data/GBB.1con-splitted/ and choose a small plasmid file, e.g., Borrelia_burgdorferi_4041_cp9_plasmid_C.fas

Identify ORFs on the plasmid by running the program /data/biocs/b/bio425/bin/long-orfs

/data/biocs/b/bio425/bin/long-orfs /data/biocs/b/bio425/data/GBB.1con-splitted/Borrelia_burgdorferi_4041_cp9_plasmid_C.fas cp9.coord

Extract ORF sequences using the program /data/biocs/b/bio425/bin/extract

/data/biocs/b/bio425/bin/extract /data/biocs/b/bio425/data/GBB.1con-splitted/Borrelia_burgdorferi_4041_cp9_plasmid_C.fas cp9.coord

Assignment #3 (Final Version)
(10 pts) Write a PERL program to perform 6-frame translation given a FASTA sequence (output: a FASTA file with 6 protein sequences; single-letter amino acid code; * for stop codon). Show results for the input file /data/biocs/b/bio425/data/mystery_seq1.fas. How many long (>=200 bases) ORFs you see? What are their reading frames? (5 pts) Run the "long-orf" program on the above mystery sequence using the following 2 options: "ATG" as start codon and a minimum ORF length of 200 bases. Save the coordinate file as "mystery_seq1.coord". (10 pts) Write a PERL program that does exactly the same as the program /data/biocs/b/bio425/bin/extract. Input files: (1) "mystery_seq1.fas" (2) "mysterm_seq1.coord". Output: A single FASTA file with multiple ORF sequences.

February 20 (No Class)

Monday Schedule

February 27. Genomics with BioPerl (Part 1)

Learning goals: Introduction to Objects
In-Class Exercises:

Construct a sequence object with a hash
1. Select and write down at least five features (e.g., strain, gene) associated with a DNA sequence based on this GenBank record. Include a sequence label and the sequence itself as two of the features.
2. Write a PERL program named "seq_object.pl", which captures these five features with a hash by using feature names as keys and features as values. Example: my %seq_obj=("strain"=>"SD91, "gene"=>"ospC"); Print the hash with Data::Dumper.
```
my $seq_hash=("id"=>"SD1_ospC", "seq"=>"tgtaataattcaggaaaaga", "organism"=>"Borrelia burgdorferi", "strain"=>"SD1", "gene"=>"ospC"); print Dumper(\%seq_hash)
```
3. Create a scalar variable called "$ref_seq_obj", which is a reference to the hash %seq_obj. Print the hash reference with Data::Dumper.
```
my $ref_seq_obj=\%seq_obj; print Dumper($ref_seq_obj)
```
4. Name some of the differences between a sequence and a sequence object.
```
Sequence refers to a string of nucleotides or amino acids. A sequence is usually represented by a string variable. A sequence object includes biological features associated with a sequence besides the sequence itself. A sequence object is commonly represented by a hash reference in Perl.
```
Construct a sequence object with Bio::Seq
1. Modify your program by calling the Bioperl Bio::Seq module:
```
use lib '/data/biocs/b/bio425/bioperl-live'; use Bio::Seq;
```
2. Convert your hash-based sequence object into a Bio::Seq object:
```
my $seqobj = Bio::Seq->new( -display_id => "SD1_ospC", -seq =>"tgtaataattcaggaaaaga" );
```
3. Print the Bio::Seq object with Data::Dumper
```
print Dumper($seqobj);
```

Apply Bio::Seq methods

Find the names of methods for the following operations on the Bio::Seq object: reverse complement, length, id, seq, translate, sub-sequence by running
```
perldoc /data/biocs/b/bio425/bioperl-live/Bio/Seq.pm
```

Add the above methods into your script & print all results

my $seq_rev=$seqobj->revcom()->seq(); my $eq_length=$seqobj->length(); my $seq_id=$seqobj->display_id(); my $seq_string=$seq_obj->seq(); my $seq_translate=$seqobj->translate()->seq(); my $subseq = $seqobj->subseq(1,10)

Discussion Questions
1. What is Object-oriented Programming?
2. What is an Object and what is a Method?
3. What are the advantages and disadvantages of Object-Oriented programming in comparison with traditional programming based on subroutines and functions?

Assignment #4 (Not Final)
(10 pts) Write a Bio::Seq-based PERL program to perform 6-frame translation of this sequence "/data/biocs/b/bio425/data/mystery_seq1.fas".

March 6: Genomics with BioPerl (Part 2)

Learning goals: File I/O with BioPerl
In-Class Exercises:

(to be filled)

Assignment #5 (Not Final)
(10 pts) Write a BioPerl version of extract

March 13: Multiple Alignment

(TBA)

March 20. Midterm Practicum

(TBA)

March 27 (No Class)

Spring Break

April 3: Phylogenetics I

(TBA)

April 10: Phylogenetics II

(TBA)

April 17: Transcriptome with R

(TBA)

April 24: Transcriptome with R/BioConductor

(TBA)

May 1: Transcriptome with R/BioConductor

(TBA)

May 8: Relational Database with SQL

(TBA)

May 15: Review

(TBA)

May 22: Final Project Due

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.