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#!/usr/bin/perl -w
# written by
#
# Bob Freeman, Ph.D.
# Acorn Worm Informatics, Kirschner lab
# Dept of Systems Biology, Alpert 524
# Harvard Medical School
# 200 Longwood Avenue
# Boston, MA 02115
# 617/432.2294, vox
#
# bob_freeman@hms.harvard.edu
=from_Bob
I wrote a small script in Perl that gives a number of basic stats and can even generate a histogram of lengths for you. Script is attached. I typically use this for assessing the size / quality of my data.
For example, after trimming a set of reads (prior to assembly), I used the command:
fastq_stats.pl -i trimmed_reads.fastq -f "30,40,50,60,70,80,90,100"
to assess resulting data, and my output is:
Count27717551
Sum2347820942
Mean84.7052087
Min36
Max101
Median101
Q036
Q154
Q2101
Q3101
Q4101
36(min)
<= 30.0:0
<= 40.0:183537
<= 50.0:283964
<= 60.0:8209709
<= 70.0:339854
<= 80.0:458850
<= 90.0:460218
<= 100.0:1271776
I believe I've modified the file to identify fasta, fastq, and protein fasta ( *.fasta, *.fa, *.mfasta, *.pro, and *.pep). Output can be in table-format also ( -t ). And use the -h switch to print out the (scarce) help info. It does use Bioperl and the Perl modules Statistics::Descriptive.
Hope you find it useful!
Bob
=cut
use strict;
use Bio::SeqIO;
use Carp;
use Data::Dumper;
use English qw ( -no_match_vars );
use Statistics::Descriptive;
my $usage = qq (
fastq_stats.pl -i infile -f partition|bins -t
reads in fastq or fasta data and spits out some descripting stats
-i input file
-f frequency distribution, either # partitions or a quoted list of
comma-separated bins
-t print results in table (tab-delimited) format
);
# opens fastq or fasta data
# reads in each sequence, grabbing data about each
# then spits out output data
$OUTPUT_AUTOFLUSH = 1;
our @seqlen_data;
our @hist_bins;
our ($infile, $freqdist, $fdref);
our $table = 0;
parse_args();
read_seq_data2();
if (!$table) {
output_stats();
} else {
output_stats_table();
}
exit;
#
#
# END OF PROGRAM
#
sub parse_args {
#
while (scalar(@ARGV)) {
my $arg = shift(@ARGV);
if ($arg eq '-h') { die $usage; }
elsif ($arg eq '-i') { $infile = shift(@ARGV); }
elsif ($arg eq '-f') { $freqdist = shift(@ARGV); }
elsif ($arg eq '-t') { $table = 1; }
else { die "unknown argument '$arg'"; }
}
# check to ensure we have the required parameters
if (!defined $infile) {
print "Error from undefined input arguments!\n";
die $usage;
}
# parse frequency distribution stuff
if (defined $freqdist) {
$freqdist =~ s/ //g;
@hist_bins = split (/,/, $freqdist);
$freqdist = 1;
} else {
$freqdist = 0;
}
}
sub read_seq_data {
my ($file_suffix) = $infile =~ m/.+\.(\S+)$/;
# create input SeqIO object
my $seq_in = Bio::SeqIO->new( '-file' => "<$infile",
'-format' => $file_suffix );
while (my $inseq = $seq_in->next_seq()) {
push @seqlen_data, $inseq->length();
} #end of while
}
sub read_seq_data2 {
if ($infile =~ m/(\.fasta$)|(\.fa$)|(\.mfasta$)|(\.mfa$)|(\.pro$)|(\.pep$)/) {
read_fasta();
} elsif ($infile =~ m/(\.fastq$)|(\.fq$)/) {
read_fastq();
} else {
die "Error: Unknown file format for input file $infile!\n";
}
}
sub read_fasta {
# read in fasta data using BioPerl methods. These are pretty quick.
# create input SeqIO object
my $seq_in = Bio::SeqIO->new( '-file' => "<$infile",
'-format' => 'fasta' );
while (my $inseq = $seq_in->next_seq()) {
push @seqlen_data, $inseq->length();
} #end of while
}
sub read_fastq {
# read in fastq data in old-fashioned method, as it's faster than using BioPerl
open (my $INFILE, "<$infile") || die "Error opening file $infile for reading: $!\n";
while ( my $line = <$INFILE> ) {
#chomp $line;
#next if $line =~ m/^\s|#/;
if ($line =~ m/^@/) {
# skip to next line and push length
$line = <$INFILE>;
chomp $line;
push @seqlen_data, length($line);
}
}
close $INFILE;
}
sub output_stats {
print "\nSequence stats:\n";
my $stat = Statistics::Descriptive::Full->new();
$stat->add_data(@seqlen_data);
print "Count\t", $stat->count(), "\n";
print "Sum\t", $stat->sum(), "\n";
print "Mean\t", $stat->mean(), "\n\n";
print "Min\t", $stat->min(), "\n";
print "Max\t", $stat->max(), "\n";
print "Median\t", $stat->median(), "\n\n";
print "Q0\t", $stat->quantile(0), "\n";
print "Q1\t", $stat->quantile(1), "\n";
print "Q2\t", $stat->quantile(2), "\n";
print "Q3\t", $stat->quantile(3), "\n";
print "Q4\t", $stat->quantile(4), "\n";
# do frequency distribution output if indicated
if ($freqdist) {
# call proper method
if (scalar(@hist_bins) == 1) {
$fdref = $stat->frequency_distribution_ref($hist_bins[0]);
} else {
$fdref = $stat->frequency_distribution_ref(\@hist_bins);
}
# now print it out
print "\n", $stat->min(), "(min)\n";
for (sort {$a <=> $b} keys %$fdref) {
#sprintf "<= %.1f: %8u \n", $_, "<= $_, \tcount = $fdref->{$_}\n";
printf "<= %5.1f:\t%8u \n", $_, $fdref->{$_};
}
}
}
sub output_stats_table {
#
# do calculations
my $stat = Statistics::Descriptive::Full->new();
$stat->add_data(@seqlen_data);
if ($freqdist) {
# call proper method
if (scalar(@hist_bins) == 1) {
$fdref = $stat->frequency_distribution_ref($hist_bins[0]);
} else {
$fdref = $stat->frequency_distribution_ref(\@hist_bins);
}
}
# print header
print "#Sequence stats:\n";
print join("\t", "#","Count","Sum","Mean","Min","Max","Median","Q0","Q1","Q2","Q3","Q4");
if ($freqdist) {
for (sort {$a <=> $b} keys %$fdref) {
#sprintf "<= %.1f: %8u \n", $_, "<= $_, \tcount = $fdref->{$_}\n";
print "\t<=", $_;
}
}
print "\n";
#print results
print $infile, "\t";
print $stat->count(), "\t";
print $stat->sum(), "\t";
print $stat->mean(), "\t";
print $stat->min(), "\t";
print $stat->max(), "\t";
print $stat->median(), "\t";
print $stat->quantile(0), "\t";
print $stat->quantile(1), "\t";
print $stat->quantile(2), "\t";
print $stat->quantile(3), "\t";
print $stat->quantile(4);
if ($freqdist) {
# now print it out
for (sort {$a <=> $b} keys %$fdref) {
#sprintf "<= %.1f: %8u \n", $_, "<= $_, \tcount = $fdref->{$_}\n";
print "\t", $fdref->{$_};
}
}
print "\n";
}
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