Bioinformatics 生物信息学理论和实践 唐继军 jtang@cse.sc 13928761660

Bioinformatics生物信息学理论和实践

唐继军

jtang@cse.sc.edu13928761660

!/usr/bin/perl -wuse Bio;use strict;use warnings;

my $DNA = fasta_read();

print "First ", dna2peptide($DNA), "\n";print "Second ", dna2peptide(substr($DNA, 1)), "\n";print "Third ", dna2peptide(substr($DNA, 2)), "\n";

$DNA = reverse $DNA;$DNA =~ tr/ACGTacgt/TGCAtgca/;

print "Fourth ", dna2peptide($DNA), "\n";print "Fifth ", dna2peptide(substr($DNA, 1)), "\n";print "Sixth ", dna2peptide(substr($DNA, 2)), "\n";

my $x = 10;

for (my $x = 0; $x < 5; $x++) { Scope(); print $x, "\n";}

print $x, "\n";

sub Scope { my $x = 0;}

my $x = 10;

for (my $x = 0; $x < 5; $x++) { Scope(); print $x, "\n";}

print $x, "\n";

sub Scope { $x = 0;}

sub extract_sequence_from_fasta_data {

my(@fasta_file_data) = @_; my $sequence = '';

foreach my $line (@fasta_file_data) {

if ($line =~ /^\s*$/) { next; } elsif($line =~ /^\s*#/) { next; } elsif($line =~ /^>/) { next; } else { $sequence .= $line; } }

# remove non-sequence data (in this case, whitespace) from $sequence string $sequence =~ s/\s//g;

return $sequence;}

Molecular Scissors

Molecular Cell Biology, 4th edition

R = G or A Y = C or T M = A or C K = G or T S = G or C W = A or T B = not A (C or G or T) D = not C (A or G or T) H = not G (A or C or T) V = not T (A or C or G) N = A or C or G or T

sub IUB_to_regexp { my($iub) = @_; my $regular_expression = ‘’; my %iub2character_class = (

A => 'A', C => 'C', G => 'G', T => 'T', R => '[GA]', Y => '[CT]', M => '[AC]', K => '[GT]', S => '[GC]', W => '[AT]', B => '[CGT]', D => '[AGT]', H => '[ACT]', V => '[ACG]', N => '[ACGT]', );

$iub =~ s/\^//g;

for ( my $i = 0 ; $i < length($iub) ; ++$i ) { $regular_expression .= $iub2character_class{substr($iub, $i, 1)}; } return $regular_expression;}

Hash

• Initialize: my %hash = ();• Add key/value pair: $hash{$key} = $value;• Add more keys:

• %hash = ( 'key1', 'value1', 'key2', 'value2 );• %hash = ( key1 => 'value1', key2 => 'value2', );

• Delete: delete $hash{$key};

while ( my ($key, $value) = each(%hash) ) { print "$key => $value\n"; }

foreach my $key ( keys %hash ) { my $value = $hash{$key}; print "$key => $value\n"; }

sub parseREBASE { my($rebasefile) = @_; my @rebasefile = ( ); my %rebase_hash = ( ); my $name; my $site; my $regexp;

open($rebase_filehandle, $rebasefile) or die "Cannot open file\n";

while(<$rebase_filehandle>) {

# Discard header lines ( 1 .. /Rich Roberts/ ) and next;

# Discard blank lines /^\s*$/ and next; # Split the two (or three if includes parenthesized name) fields my @fields = split( " ", $_);

$name = shift @fields;

$site = pop @fields;

# Translate the recognition sites to regular expressions $regexp = IUB_to_regexp($site);

# Store the data into the hash $rebase_hash{$name} = "$site $regexp"; }

# Return the hash containing the reformatted REBASE data return %rebase_hash;}

Range

• ( 1 .. /Rich Roberts/ ) and next• from first line till some line containing Rich Roberts• If that is true, it will check the statement after "and"• If that is not true, it will not check the statement after

"and"• open(…) or die

• If can open, the statement is already true, no need to check the statement after "or"

• If cannot open, the statement is false, need to check the statement after "or" to see if it can be true

Array operators

• push and pop (right-most element)• @mylist = (1,2,3); push(@mylist,4,5,6);• $oldvalue = pop(@mylist);

• shift and unshift (left-most element)• @fred = (5,6,7); unshift(@fred,2,3,4);

• $x = shift(@fred); • reverse: @a = (7,8,9); @b = reverse(@a);• sort: @a = (7,9,9); @b = sort(@a);

sub match_positions {

my($regexp, $sequence) = @_;

use BeginPerlBioinfo;

my @positions = ( );

while ( $sequence =~ /$regexp/ig ) {

push ( @positions, pos($sequence) - length($&) + 1); }

return @positions;}

use BeginPerlBioinfo;

my %rebase_hash = ( ); my @file_data = ( ); my $query = ''; my $dna = ''; my $recognition_site = '';my $regexp = ''; my @locations = ( );

@file_data = get_file_data("sample.dna");$dna = extract_sequence_from_fasta_data(@file_data);%rebase_hash = parseREBASE('bionet');

do { print "Search for what restriction site for (or quit)?: "; $query = <STDIN>; chomp $query; if ($query =~ /^\s*$/ ) { exit; } if ( exists $rebase_hash{$query} ) { ($recognition_site, $regexp) = split ( " ", $rebase_hash{$query}); @locations = match_positions($regexp, $dna); if (@locations) { print "Searching for $query $recognition_site $regexp\n"; print "Restriction site for $query at :", join(" ", @locations), "\n"; } else { print "A restriction enzyme $query is not in the DNA:\n"; } }} until ( $query =~ /quit/ );

exit;

Print to file

• Open a file to print• open FILE, ">filename.txt";• open (FILE, ">filename.txt“);

• Print to the file• print FILE $str;

#write new fileopen(FILE, ">out") or die "Cannot open file to write";

print FILE "Test\n";

close FILE;exit;

#Appendopen(FILE, ">>out") or die "Cannot open file to write";

print FILE "Test\n";

close FILE;exit;

#!/usr/bin/perlprint "My name is $0 \n";print "First arg is: $ARGV[0] \n";print "Second arg is: $ARGV[1] \n";print "Third arg is: $ARGV[2] \n";

$num = $#ARGV + 1; print "How many args? $num \n";print "The full argument string was: @ARGV \n";

use BeginPerlBioinfo;

my %rebase_hash = ( ); my @file_data = ( ); my $query = ''; my $dna = ''; my $recognition_site = '';my $regexp = ''; my @locations = ( );

@file_data = get_file_data($ARGV[0]);$dna = extract_sequence_from_fasta_data(@file_data);%rebase_hash = parseREBASE('bionet');

do { print "Search for what restriction site for (or quit)?: "; $query = <STDIN>; chomp $query; if ($query =~ /^\s*$/ ) { exit; } if ( exists $rebase_hash{$query} ) { ($recognition_site, $regexp) = split ( " ", $rebase_hash{$query}); @locations = match_positions($regexp, $dna); if (@locations) { print "Searching for $query $recognition_site $regexp\n"; print "Restriction site for $query at :", join(" ", @locations), "\n"; } else { print "A restriction enzyme $query is not in the DNA:\n"; } }} until ( $query =~ /quit/ );

exit;

use BeginPerlBioinfo;

my %rebase_hash = ( ); my @file_data = ( ); my $query = ''; my $dna = ''; my $recognition_site = '';my $regexp = ''; my @locations = ( );

@file_data = get_file_data($ARGV[0]);$dna = extract_sequence_from_fasta_data(@file_data);%rebase_hash = parseREBASE($ARGV[1]);

do { print "Search for what restriction site for (or quit)?: "; $query = <STDIN>; chomp $query; if ($query =~ /^\s*$/ ) { exit; } if ( exists $rebase_hash{$query} ) { ($recognition_site, $regexp) = split ( " ", $rebase_hash{$query}); @locations = match_positions($regexp, $dna); if (@locations) { print "Searching for $query $recognition_site $regexp\n"; print "Restriction site for $query at :", join(" ", @locations), "\n"; } else { print "A restriction enzyme $query is not in the DNA:\n"; } }} until ( $query =~ /quit/ );

exit;

Regular Expression• ^ beginning of string • $ end of string • . any character except newline • * match 0 or more times • + match 1 or more times • ? match 0 or 1 times; • | alternative • ( ) grouping; “storing” • [ ] set of characters • { } repetition modifier • \ quote or special

Repeats

• a*zero or more a’s • a+one or more a’s • a?zero or one a’s (i.e., optional a) • a{m}exactly m a’s • a{m,}at least m a’s • a{m,n}at least m but at most n a’s

\

[]

Perl tr/// function• tr means transliterate – replaces a character with

another character• $dna =~ tr/a/c/ replaces all “a” with “c” in in $dna• It also works on a range:

$dna =~ tr/a-z/A-Z/ replaces all lower case letters with upper case

• tr also counts$count = ($string =~ tr/A//)(you might think this also deletes all “A” from the string, but it doesn’t)

Wildcards• Perl has a set of wildcard characters for Reg. Exps.

that are completely different than the ones used by Unix • the dot (.) matches any character• \d matches any digit (a number from 0-9)• \w matches any text character

(a letter or number, not punctuation or space)

• \s matches white space (any amount)• ^ matches the beginning of a line• $ matches the end of a line

(Yes, this is very confusing!)

Repeat for a count

• Use curly brackets to show that a character repeats a specific number (or range) of times:

• find an EcoRI fragment of 100-500 bp length (two EcoRI sites with any other sequence between):

if $ecofrag =~ /GAATTC[GATC]{100,500}GAATTC/

• The + sign is used to indicate an unlimited number of repeats (occurs 1 or more times)

my $mystring; $mystring = "Hello world!";

if($mystring =~ m/World/) { print "Yes"; }

if($mystring =~ m/World/i) { print "Yes"; }

Grabbing parts of a string• Regular expressions can do more than just ask ‘if”

questions• They can be used to extract parts of a line of text

into variables; Check this out:/^>(\w+)\s(. +)$/;

Complete gibberish, right?• It means:

-look for the > sign at the beginning of a FASTA formatted sequence file

-dump the first word (\w+) into variable $1 (the sequence ID) -after a space, dump the rest of the line (.+), until you

reach the end of line $, into variable $2 (the description)

$mystring = "[2004/04/13] The date of this article.";

if($mystring =~ m/(\d)/) { print "The first digit is $1.";}

if($mystring =~ m/(\d+)/) { print "The first number is $1.";}

if($mystring =~ m/(\d+)\/(\d+)\/(\d+)/) { print "The date is $1-$2-$3";}

while($mystring =~ m/(\d+)/g) { print "Found number $1."; }

@myarray = ($mystring =~ m/(\d+)/g); print join(",", @myarray);

Working with Single DNA Sequences

Learning Objectives

• Discover how to manipulate your DNA sequence on a computer, analyze its composition, predict its restriction map, and amplify it with PCR

• Find out about gene-prediction methods, their potential, and their limitations

• Understand how genomes and sequences and assembled

Outline

1. Cleaning your DNA of contaminants2. Digesting your DNA in the computer3. Finding protein-coding genes in your DNA

sequence4. Assembling a genome

Cleaning DNA Sequences• In order to sequence genomes, DNA sequences are often

cloned in a vector (plasmid, YAC, or cosmide) • Sequences of the vector can be mixed with your DNA sequence• Before working with your DNA sequence, you should always

clean it with VecScreen

VecScreen• http://www.ncbi.nlm.nih.gov/

VecScreen/VecScreen.html• Runs a special version of Blast• A system for quickly identifying

segments of a nucleic acid sequence that may be of vector origin

What to do if hits found• If hits are in the extremity, can just

remove them• If in the middle, or vectors are not what

you are using, the safest thing is to throw the sequence away

Computing a Restriction Map• It is possible to cut DNA sequences using restriction enzymes

• Each type of restriction enzyme recognizes and cuts a different sequence:

• EcoR1: GAATTC

• BamH1: GGATCC

• There are more than 900 different restriction enzymes, each with a different specificity

• The restriction map is the list of all potential cleavage sites in a DNA molecule

• You can compile a restriction map with www.firstmarket.com/cutter

Cannot get it work!

http://biotools.umassmed.edu/tacg4

Making PCR with a Computer• Polymerase Chain Reaction (PCR) is a method for amplifying DNA

• PCR is used for many applications, including• Gene cloning

• Forensic analysis

• Paternity tests

• PCR amplifies the DNA between two anchors

• These anchors are called the PCR primer

Designing PCR Primers• PCR primes are typically 20 nucleotides long

• The primers must hybridize well with the DNA

• On biotools.umassmed.edu, find the best location for the primers: • Most stable

• Longest extension

Analyzing DNA Composition• DNA composition varies a lot• Stability of a DNA sequence depends on its G+C

content (total guanine and cytosine)• High G+C makes very stable DNA molecules• Online resources are available to measure the

GC content of your DNA sequence• Also for counting words and internal repeats

http://helixweb.nih.gov/emboss/html/

Counting words

• ATGGCTGACT• A, T, G, G, C, T, G, A, C, T• AT, TG, GG, GC, CT, TG, GA, AC, CT• ATG, TGG, GGC, GCT, CTG, TGA, GAC, ACT

www.genomatix.de/cgi-bin/tools/tools.pl

EMBOSS servers

• European Molecular Biology Open Software Suite

• http://pro.genomics.purdue.edu/emboss/

ORF

• EMBOSS• NCBI

ncbi.nlm.nih.gov/gorf/gorf.html

Internal repeats

• A word repeated in the sequence, long enough to not occur by chance

• Can be imperfect (regular expression)• Dot plot is the best way to spot it

arbl.cvmbs.colostate.edu/molkit

Predicting Genes

• The most important analysis carried out on DNA sequences is gene prediction

• Gene prediction requires different methods for eukaryotes and prokaryotes

• Most gene-prediction methods use hidden Markov Models

Predicting Genes in Prokaryotic Genome

• In prokaryotes, protein-coding genes are uninterrupted• No introns

• Predicting protein-coding genes in prokaryotes is considered a solved problem• You can expect 99% accuracy

Finding Prokaryotic Genes with GeneMark

• GeneMark is the state of the art for microbial genomes

• GeneMark can• Find short proteins• Resolve overlapping genes• Identify the best start codon

• Use exon.gatech.edu/GeneMark

• Click the “heutistic models”

Predicting Eukaryotic Genes

• Eukaryotic genes (human, for example) are very hard to predict

• Precise and accurate eukaryotic gene prediction is still an open problem• ENSEMBL contains 21,662 genes for the human genome

• There may well be more genes than that in the genome, as yet unpredicted

• You can expect 70% accuracy on the human genome with automatic methods

• Experimental information is still needed to predict eukaryotic genes

Finding Eukaryotic Genes with GenomeScan

• GenomeScan is the state of the art for eukaryotic genes

• GenomeScan works best with• Long exons• Genes with a low GC content

• It can incorporate experimental information

• Use genes.mit.edu/genomescan

Producing Genomic Data• Until recently, sequencing an entire genome was very

expensive and difficult

• Only major institutes could do it

• Today, scientists estimate that in 10 years, it will cost about $1000 to sequence a human genome

• With sequencing so cheap, assembling your own genomes is becoming an option

• How could you do it?

Sequencing and Assembling a Genome (I)

• To sequence a genome, the first task is to cut it into many small, overlapping pieces

• Then clone each piece

Sequencing and Assembling a Genome (II)

• Each piece must be sequenced• Sequencing machines cannot do an entire sequence at once

• They can only produce short sequences smaller than 1 Kb• These pieces are called reads

• It is necessary to assemble the reads into contigs

Sequencing and Assembling a Genome (III)

• The most popular program for assembling reads is PHRAP • Available at www.phrap.org

• Other programs exist for joining smaller datasets• For example, try CAP3 at pbil.univ-lyon1.fr/cap3.php