Biol425 2014

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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

Course Schedule (All Wednesdays)

January 29. Course overview & Unix tools

  • Course Overview:
  • Learning Goals: (1) Understand the "Omics" files; (2) Review/Learn Unix tools
  • In-Class Tutorial: Unix file filters
  1. Without changing directory, long-list genome, transcriptome, and proteome files using ls
  2. Without changing directory, view genome, transcriptome, and proteome files using less -S
  3. Using grep, find out the size of Borrelia burgdorferi B31 genome in terms of the number of replicons ("GBB.1con" file)
  4. Using sort, sort the replicons by (a) contig id (3rd field, numerically); and (b) replicon type (4th field)
  5. Using cut, show only the (a) contig id (3rd field); (b) replicon type (4th field)
  6. Using tr, (a) replace "_" with "|"; (b) remove ">"
  7. Using sed, (a) replace "Borrelia" with abbreviation "B."; (b) remove "plasmid" from all lines
  8. Using paste -s, extract all contig ids and concatenate them with ";"
  9. Using a combination of cut and uniq, count how many circular plasmids ("cp") and how many linear plasmids ("lp")
  • In-Class Challenges
  1. 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
  1. 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:
  1. Without changing directory (i.e., remain in your home directory), locate and long-list the genbank file named "GBB.gb" in the course data directory
  2. Count the total number of lines, show the first and last 10 lines of the file. Using a combination of head and tail commands, show only the lines containing the translated protein sequence of the first gene
  3. Count the total number of replicans by extracting lines containing "LOCUS" (case sensitive); sort them by the total number of bases ("bp")
  4. Remove the string "(plasmid" from the above output
  5. Extract the second column (replicon names) from the above output. [Hint: these fields are delimited by an unequal number of spaces, not by tabs. Use tr -s to first squeeze to single space]
Read & Respond (5 pts)

Genome sequencing technologies: textbook, pg. 79-83

  1. Briefly list and describe three new sequencing technologies
  2. Contrast the "hierarchical" and "shotgun" strategies of whole-genome sequencing
  3. Explain the following sequencing terms: "reads", "contigs", "scaffolds", "assemblies"

February 5. Genomics (1): Gene-Finding

  • Lecture Slides:
  • Learning goals: (1) Running UNIX programs; (2) Parse text with Perl anonymous hash
  • In-Class Tutorials
  1. Identify ORFs in a prokaryote genome
    1. Go to NCBI ORF Finder page
    2. Paste in the GenBank Accession: AE000791.1 and click "orfFind"
    3. 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?
    4. Click "Six Frames" to show positions of stop codons (magenta) and start codons (cyan)
  2. Gene finder using GLIMMER
    1. Locate the GLIMMER executables: ls /data/biocs/b/bio425/bin/
    2. Locate Borrelia genome files: ls /data/biocs/b/bio425/data/GBB.1con-splitted/
    3. 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]
    4. Open output file with cat cp9.coord. Compare results with those from NCBI ORF Finder.
    5. 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]
  3. Complex data structure with references
    1. Type the code from slides and save it as a file "read-coord.pl".
    2. Check syntax with perl -c read-coord.pl
    3. Make it executable: chmod +x read-coord.pl
    4. Run the code: ./read-coord.pl cp9.coord
  • In-Class Challenge
  1. 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)
  1. Use GLIMMER to predict ORFs in ../../bio425/data/mystery_seq1.fas. Save resulting coord file as "mystery_seq1.coord".
  2. Write a PERL program (named "extract.pl") that does exactly the same as the program /data/biocs/b/bio425/bin/extract. Two input files: (1) "mystery_seq1.fas" (2) "mystery_seq1.coord". One output: standard out (screen output) of a single FASTA file with multiple ORF sequences. Bonus points for outputting sequences beginning with a start codon and ending with a stop codon, which requires reverse complement for ORFs encoded on the opposite strand.

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)
  1. Read Box 1.2 GenBank Files (pg 26-27) and answer the following questions:
    1. What is the accession, gene, and source of this sequence
    2. Why is "gene" longer than "CDS"? Define these two terms
  2. To prepare for the next session on BioPerl (which is based on the object orientation paradigm), watch this short video and define a "class" and an "object". In designing a "biological_sequence" class, what would be your choices of "properties" and "behavior/methods"? (list three or more properties AND methods).

{{#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:
  • 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
  1. Rewrite the "extract.pl" using BioPerl, including the use of Bio::SeqIO to read the genome FASTA file ("mystery_seq1.fas") and the use of Bio::Seq for obtaining coding sequences and translating sequences. Your output of protein sequences should not contain stop codons except as the last codon.

Sample code:

#!/usr/bin/perl
use strict;
use warnings;
use lib '/data/biocs/b/bio425/bioperl-live';
use Bio::SeqIO;
# ----------------------------------------
# File            : extract.pl
# Author          : WGQ
# Date            : February 20, 2014
# Description     : Emulate glimmer EXTRACT program
# Input           : A FASTA file with 1 DNA seq and coord file from LONG-ORF
# Output          : A FASTA file with translated protein sequences
# ----------------------------------------
die "Usage: $0 <FASTA_file> <coord_file>\n" unless @ARGV > 0;
my ($fasta_file, $coord_file) = @ARGV;
my $input = Bio::SeqIO->new(-file=>$fasta_file, -format=>'fasta'); # create a file handle to read sequences from a file
my $output = Bio::SeqIO->new(-file=>">$fasta_file".".out", -format=>'fasta'); # create a file handle to output sequences into a file
my $seq_obj = $input->next_seq();
# Read COORD file & extract sequences
open COORD, "<" . $coord_file;
while (<COORD>) {
  my $line = $_;
  chomp $line;
  next unless $line =~ /^(\d+)\s+(\d+)\s+(\d+)\s+\S+\s+\S+\s*/; # extract data using regex & skip all other lines
  my ($seq_id, $cor1, $cor2) = ($1, $2, $3);
  if ($cor1 < $cor2) {
    $output->write_seq($seq_obj->trunc($cor1, $cor2)->translate());
  } else {
    $output->write_seq($seq_obj->trunc($cor2, $cor1)->revcom()->translate());
  }
}
close COORD;
exit;
Read & Respond
  1. Based on Box 2.2 (Searching sequence databases using BLAST, on p.90-91), define (with your own words) the following BLAST terms: Query, BLAST database, e-value, identity
  2. Exercise 2.3. Perform a BLAST search online (p. 114). Show summary results (top section before individual alignments)

February 26. Genomics (3). Homology searching with BLAST

  • Lecture Slides:
  • Learning goals: Homology, BLAST, & Alignments
  • In-Class Challenge. 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 tutorial 1. A single unknown sequence against a reference genome
cp ../../bio425/data/GBB.pep ~/. # Copy the reference genome
cp ../../bio425/data/unknown.pep ~/. # Copy the query sequence
makeblastdb -in GBB.pep -dbtype prot -parse_seqids -out ref # make a database of reference genome
blastp -query unknown.pep -db ref # Run simple protein blast
blastp -query unknown.pep -db ref -evalue 1e-5 # filter by E values
blastp -query unknown.pep -db ref -evalue 1e-5 -outfmt 6 # concise output
blastp -query unknown.pep -db ref -evalue 1e-5 -outfmt 6 | cut -f2 > homologs-in-ref.txt # save a list of homologs
blastdbcmd -db ref -entry_batch homologs-in-ref.txt > homologs.pep # extract homolog sequences
  • BLAST tutorial 2. Find homologs within the new genome itself
cp ../../bio425/data/N40.pep ~/. # Copy the unknown genome
makeblastdb -in N40.pep -dbtype prot -parse_seqids -out N40 # make a database of the new genome
blastp -query unknown.pep -db N40 -evalue 1e-5 -outfmt 6 | cut -f2 > homologs-in-N40.txt # find homologs in the new genome
blastdbcmd -db N40 -entry_batch homologs-in-N40.txt >> homologs.pep # append to homolog sequences
  • BLAST tutorial 3. Multiple alignment & build a phylogeny
../../bio425/bin/muscle -in homologs.pep -out homologs.aln # align sequences
cat homologs.aln | tr ':' '_' > homologs2.aln
../../bio425/bin/FastTree homologs2.aln > homologs.tree # build a gene tree
../../bio425/figtree &  # view tree (works only in the lab; install your own copy if working remotely)
  • BLAST tutorial 4. Annotate the entire genome
blastp -query N40.pep -db ref -evalue 1e-5 -outfmt 6 > blast.fwd # foward blast
blastdbcmd -db ref -entry all > ref.pep
blastp -query ref.pep -db N40 -evalue 1e-5 -outfmt 6 > blast.rev # reverse blast
Assignment #4 (Finalized Sat 3/1)
BLAST exercise (5 pts)
  1. Run BLASTp to identify all homologs of BBA18 in the reference genome ("ref"). First, obtain BBA18 peptide sequence using blastdbcmd -db ref -entry 'BBA18'. Then, run blastp against the "ref" database with the following e-value cutoffs: 1e-5, 1e-3, and 1e-1. Submit the results for 1e-3 with the "-outfmt 6" option. [Note: If you work remotely, you have to first ssh into "enaic.cs.hunter.cuny.edu" and then ssh again into "cslab1" or other workstations to access the blast programs.]
BioPerl exercise (5 pts)
  1. Write a BioPerl-based script ("pick-by-id.pl") to extract sequences by id. It will emulate the "blastdbcmd -db -entry" command. Use "GBB.pep" and "BBA15" as the two arguments. Usage: ./pick-by-id.pl GBB.pep BBA15. Your script should handle exceptions gracefully, e.g., by printing a warning message if the sequence is not found.
Read & Respond (5 pts)
  1. Read pg 117 (last paragraph)-118 (1st paragraph) & define "ortholog" and "paralog". Draw Figure 2.21(B) by hand and indicate (on the graph) a pair of ortholog and a pair of paralog.
  2. Read Box 2.4 (pg 120), introduction & the section labeled as "General principles". Define "homology", "phylogeny", and "branch length".

March 5: Genomics (4). Molecular phylogenetics

  • Lecture Slides:
  • Learning goal: how to interpret, build, and test phylogeny
  • Perl challenge: Write a BioPerl script ("get-seq-from-gb.pl") to retrieve a sequence from the GenBank. Follow the example in Bio::DB::GenBank. Input: a GenBank accession (e.g., "J00522"). Output: a file in "genbank" format. Note: include this statement: use lib '/data/biocs/b/bio425/perl5';
#!/usr/bin/perl -w
use strict;
use lib '/data/biocs/b/bio425/bioperl-live';
use lib '/data/biocs/b/bio425/perl5';
use Bio::DB::GenBank;
use Bio::SeqIO;
die "Usage: $0 <genbank_accession, e.g., J00522>\n" unless @ARGV == 1;
my $acc = shift @ARGV;
my $gb = Bio::DB::GenBank->new();
my $seq = $gb->get_Seq_by_acc($acc);
my $out = Bio::SeqIO->new(-file=>">" . $acc.".gb", -format=>'genbank');
$out->write_seq($seq);
exit;
  • Tutorials
  1. Tree Quizzes:
  2. Maximum Parsimony: Exercise 2.4 (pg. 123)
Assignment #5 (Finalized, 3/7 Friday 9pm)
Phylogeny questions (5 pts)
Referring to the tree at right, answer the following questions. Explain your answer.
Baum-fig2.png
  1. Which of the five marks corresponds to the most recent common ancestor of the mushroom and the sponge? [d]
  2. Is Mouse more related to Sponge or to Mushroom? [Mouse. MRCA of Sponge and Mouse is at e, which is more recent than d, the MRCA of Mouse and Mushroom]
  3. Is Mouse more related to Ferm or to Tomato? [Equally related, since MRAC(Mouse-Fern) is at b, so is MRAC(Mouse-Tomato)]
  4. Indicate on the tree graph a "clade" and a pair of "sister taxa" [e.g., Clade & Sister: Sponge-Mouse]
BLAST and phylogeny exercise (5 pts)

Identify all homologs of BBA68 in the ref and N40 genomes using BLASTp. Construct a phylogeny of BBA68 homologs using MUSCLE and FastTree. View and edit the tree using FigTree (download and install your own copy if you are not using the lab computers). Root the tree on the mid-point by following the right-side panel "Trees"->"Root tree"->"midpoint". Based on the phylogeny, identify N40 orthologs of the following B31 genes: BBA68, BBA69, BBA71, BBA72, and BBA73. (Bonus +2 pts) What are the two possible evolutionary mechanisms that there is no ortholog of BBA70 in the N40 genome? Your answer should consist of the following parts:

  1. All BLAST commands
  2. A tree rooted on the mid-point as exported by FigTree
  3. Labels Indicating each ortholog on the tree
  4. (+2 pts) Answer to the bonus question above

March 12: Putting it together: Annotating a new genome

March 19. Midterm Practicum

Assignment #6 (Tentative)
Perl Challenges (5 pts)
  • Write a program to read a BLAST "outfmt 6" output and print the top hit for each gene.
  • Explain what each statement does for the "gb2pep.pl" program
Read & Respond (5 pts)
  1. Gene Ontology (Box 2.5, pg 126-127). Describe what is "ontology" and what is "Gene Ontology". For the supplied example of the "Gap1" gene, list a GO term for each of the three GO categories (BP, MF, and CC). Example: Biological Process (BP): "mitosis".

March 26 (No Class)

Do assignments (see above)

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:
  1. Homology search using BLAST
  2. Multiple alignment using clustalw
  3. Distance-based phylogeny

May 7: Molecular Phylogenetics (Part 2)

  • Learning goals:
  1. Learn to read a phylogenetic tree
  2. Phylogenomics: identification of orthologous and paralogous genes
  • In-Class Exercise 1.

May 14: Final Project (Session I)

  • Goals:
  1. 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

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.

Regular Expression

Bioperl

SQL

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

Other Resources

© Weigang Qiu, Hunter College, Last Update ~~