Lincoln D. Stein


   use Bio::DB::BigBed 'binMean','binStdev';

   my $bed  = Bio::DB::BigBed->new(-bigbed=>'ExampleData/',

   # pull out all the features across a portion of chromosome 1
   my @features = $bed->features(-seq_id=>'chr1',
                                 -start => 11704300,
                                 -end   => 11914000);

   for my $f (@features) {
     my $start = $f->start;
     my $end   = $f->end;
     my $strand = $f->strand;
     my @CDS   = $f->get_SeqFeatures('CDS');
     my @UTRs  = $f->get_SeqFeatures('five_prime_UTR','three_prime_UTR');
     my $name  = $f->display_name;
     my $score = $f->score;
     my $itemRGB = $f->attributes('RGB');

   # same thing, but using a memory-efficient iterator
   my $iterator = $bed->get_seq_stream(-seq_id=>'chr1',
                                       -start => 11704300,
                                       -end   => 11914000);
   while (my $f = $iterator->next_seq) {
       my $start = $f->start;
       # etc

   # get statistical summaries using the "bin" feature type
   my @bin = $bed->features(-type  => 'bin:10',
                            -start => 11704300,
                            -end   => 11914000);
   for my $b (@bin) {
      my $start    = $b->start;
      my $end      = $b->end;
      my $coverage = $b->count;   # number of features in this bin
      my $minScore = $b->minVal;  # tally of BED score fields
      my $maxScore = $b->maxVal;
      my $meanScore = $b->meanVal;
      my $stdevScore=$b->stdev;

   # same thing, but using the "summary" feature type
   my ($summary) = $bed->features(-type => 'summary',
                                  -start => 11704300,
                                  -end   => 11914000);
   my $bins = $summary->statistical_summary(10);  
   my $start= $summary->start;
   my $len  = $summary->length;
   my $binwidth = $len/10;
   for my $b (@$bins) {
       my $coverage   = $b->{validCount};
       my $minScore   = $b->{minVal};
       my $maxScore   = $b->{maxVal};
       my $meanScore  = binMean($b);
       my $stdevScore = binStdev($b);
       $start += $binwidth;

   # getting feature counts across whole chromosomes
   my @chrom_bins = $bed->features(-type=>'bin');
   for my $summary (@chrom_summaries) {
       print $summary->seq_id,": ",$summary->count,"\n";       

   # same thing, but using the "summary" type
   my @chrom_bins = $bed->features(-type=>'summary');
   for my $summary (@chrom_summaries) {
       print $summary->seq_id,": ",$summary->score->{validCount},"\n";       

   # Fetching features via the "segment" interface
   my $segment  = $bed->segment('chr1',11704300 => 11914000);
   my @features = $segment->features;


This module provides a high-level interface to Jim Kent's BigBed files, a type of indexed genome feature database that can be randomly accessed across the network. Please see for information about creating these files.

For the low-level interface, please see Bio::DB::BigFile. BigWig files are supported by the module Bio::DB::BigWig.


Installation requires a compiled version of Jim Kent's source tree, including the main library, jkweb.a. Please see the README in the Bio-BigFile distribution directory for instructions.

BioPerl SeqFeature APi

This high-level interface places a BioPerl-compatible API on top of the native Bio::DB::BigFile interface. This API will be famiiar to users of the Bio::DB::SeqFeature and Bio::DB::GFF modules. You use the features() and get_seq_stream() method to query the database for features of various types. The features returned obey the Bio::SeqFeatureI interface, and provide methods for accessing the feature's type, coordinates, score, and subfeatures.

The BED format does not provide a way of identifying the type of features; however it provides for the ability to specify feature subparts ("blocks") and a boundary between the "thick" and "thin" portions of the feature when it is rendered on the UCSC Genome Browser. When this module encounters a feature whose blockCount field is greater than zero, the feature's primary_tag() method will default to "mRNA"; otherwise it defaults to "region". In the former case, blocks will be turned into subfeatures named "CDS", "five_prime_UTR" and "three_prime_UTR" based on the values in the thickStart and thickEnd columns. This heuristic may be inappropriate for BED lines that represent non-coding features such as cDNA alignments; in such cases, explicitly request a type of "feature" or "region" when fetching features from the database.

The Bio::DB::BigWig API is also compatible with Bio::DasI, in which one defines a region of the genome using the segment() method, and then fetches features from the segment by calling its features() method.

Please note that all genomic coordinates consumed or returned by this module are one-based closed intervals, identical to the BioPerl standard. This is not true of the low level interfaces.


The new() method allows you to create new instances of Bio::DB::BigBed.

$bw = Bio::DB::BigBed->new(-bigbed=>$bb_path,-fasta=>$fa_path) =item $bw = Bio::DB::BigBed->new($bw_path)

Create a new Bio::DB::BigBed object. The -bigbed argument (required) points to the path to the indexed BigBed file you wish to open. Alternatively, you may pass an http: or ftp: URL in order to open a remote BigBed file. A shorter version of new() allows you to pass a single argument consisting of the BigBed file path.

The optional -fasta argument provides a path to a FASTA file containing the genome sequence corresponding to the original BED file. All DNA sequences come from this file, so annoying and confusing things will happen if use the wrong genome build. The file must use chromosome/contig identifiers that match those in the BED file from which the BigBed was built.

This module uses the BioPerl Bio::DB::Fasta libary to build an index of the FASTA file, which means that the directory in which the FASTA file resides must be writable by the current process the first time you use it. Alternately, you can pass the -fasta option a previously-opened Perl object that supports a seq() method. This method takes three arguments consisting of the chromosome/contig name and the start and end coordinates of the region of interest in 1-based coordinates. It returns the DNA as a plain string.

    my $dna_string = $object->seq($seqid,$start,$end);

Suitable implementations include Bio::DB::SeqFeature::Store (part of BioPerl) and Bio::DB::Sam::Fai, part of the Bio::SamTools package. You are of course welcome to implement your own Fasta object.

When opening a remote file on an FTP or HTTP server, the directory returned by Bio::DB::BigFile->udcGetDefaultDir must be writable (usually '/tmp/udcCache'). The new() method will attempt to catch the case in which this directory is not writable and instead set the cache to /tmp/udcCache_###, where ### is the current username. For better control over this behavior, you may set the environment variable UDC_CACHEDIR before creating the BigWig file.


The following are public methods available to Bio::DB::BigBed objects.


Here are read-only accessors that give you limited access to the contents of the BigBed object. In the method synopses given below, $bigbed is a Bio::DB::BigBed object.

$bigfile = $bigbed->bf

Return the low-level Bio::DB::BigFile underlying the object.

$bigfile = $bigbed->bb

An alias for bf().

$fasta = $bigbed->fa

Return the DNA accessor (usually a Bio::DB::Fasta object) which the object uses to fetch the sequence of the reference genome.

Retrieving individual features

This section describes methods that return features corresponding individual BED lines.

@features = $bigbed->features(@args)

This method is the workhorse for retrieving various types of intervals and summary statistics from the BigBed database. It takes a series of named arguments in the format (-argument1 => value1, -argument2 => value2, ...) and returns a list of zero or more BioPerl Bio::SeqFeatureI objects.

The following arguments are recognized:

   Argument     Description                         Default
   --------     -----------                         -------

   -seq_id      Chromosome or contig name defining  All chromosomes/contigs.
                the range of interest.

   -start       Start of the range of interest.     1

   -end         End of the range of interest        Chromosome/contig end

   -type        Type of feature to retrieve         'region'
                    (see below). 

   -iterator    Boolean, which if true, returns     undef (false)
                an iterator across the list rather
                than the list itself.

Each returned feature supports the standard Bio::SeqFeatureI methods, including seq_id(), start(), end(), strand(), display_name(), primary_id(), primary_tag(), score(), and get_SeqFeatures() methods.

The -type argument selects the type of feature that will be returned from the database. If no type is specified, then this module will fetch all individual BED features and assign them a primary tag of either "region" or "mRNA" based on the blockCount heuristic as described earlier.

The features() (and related) calls recognize five feature types:


Features retrieved using any of these types represent the raw BED lines. There is no effective difference among the three, but they are provided as aliases to make code more readable. "region" is preferred for non-coding BED lines because it is a Sequence Ontology term. "mRNA" is preferred for coding features.


A type named "bin:" followed by an integer will divide each chromosome/contig into the indicated number of summary bins, and return one feature for each bin. For example, type "bin:100" will return 100 evenly-spaced bins across each chromosome/contig.


This feature type is similar to "bin" except that instead of returning one feature for each binned interval on the genome, it returns a single object from which you can retrieve summary statistics across fixed-width bins in a more memory-efficient manner.

Working with the features returned by each of these types is discussed under "Working with features".

The next few methods are basically convenience interfaces to the features() method:

@features = $bigbed->get_features_by_type($type)

This method returns all features of the specified type from the BigBed file without regard to their location

@features = $bigbed->get_features_by_location($seqid,$start,$end)
@features = $bigbed->get_features_by_location(@named_args)

get_features_by_location() retrieves features across a specified interval of the genome. In its three-argument form, it accepts the ID of the chromosome/contig, the start position and the end position of the desired range. If start or end are omitted, they default to the beginning and end of the chromosome respectively.

In the named-argument form, it behaves essentially identically to features().

Typical usage to fetch 100 bins across the region of chromosome "I" from 1Mb to 2Mb:

  my @bins = $bigbed->get_features_by_location(-seq_id => 'I',
                                               -start  => 1_000_000,
                                               -end    => 2_000_000,
                                               -type   => 'bins:100');

  foreach (@bins) {
      print $_->start,'..',$_->end,': ',$_->mean,"\n";
$feature = $bigbed->get_feature_by_id($id)

This method uses a BED line feature's primary ID to retrieve the feature. Because BED files don't actually use IDs, the ID is constructed from the feature's name (if any), chromosome coordinates, strand and block count. This is usually, but not necessarily, unique.

The feature's primary ID can be retrieved by calling its primary_id() method.

It is not possible (and not usually desired) to fetch features of type "bin" or "summary" in this manner.

@features = $bigbed->get_features_by_name($name)
@features = $bigbed->get_features_by_alias($name)
@features = $bigbed->get_features_by_attribute(%attributes)
$feature = $bigbed->get_feature_by_name($name)

These methods are supported for compatibility with like-named methods in BioPerl's Bio::DB::SeqFeature::Store class. However they do not do anything useful, as these are not properties of BED data.

$iterator = $bigbed->get_seq_stream(@args)

This method is identical to calling $bigbed->features(-iterator=>1,@args), and returns an iterator object that will fetch the result of the query by calling next_seq repeatedly:

  my $i = $bigbed->get_seq_stream(-seq_id => 'I',
                                  -start  => 1_000_000,
                                  -end    => 2_000_000,
                                  -type   => 'bins:100');

  while (my $b = $i->next_seq;
      print $b->start,'..',$b->end,': ',$b->mean,"\n";

Working with Features

The three types of features returned by this interface are "region" (also known as "feature" and "mRNA" depending on context), "bin" and "summary." The first type is used for retrieving information about individual BED lines. The second and third are used for obtaining statistical summary information about features spanning a region of the genome.

The Feature type

These features, which are retrieved by specifying a type of "region", "feature" or "mRNA", represent individual BED lines. These are also the type of features that are returned when you do not specify a type explicitly. They have the following useful methods:

  $feature->seq_id()       The chromosome/contig name
  $feature->start()        Start of the feature
  $feature->end()          End of the feature
  $feature->primary_tag()  Type of the feature ("region", "feature" or "mRNA")
  $feature->strand()       Feature strand (if present in the BED line)
  $feature->display_name() The feature name (if present)
  $feature->score()        Score for the feature (if present)
  $feature->primary_id()   Primary ID for the interval

In addition, features support the get_SeqFeatures() method, which is called like this:

  @subfeats = $feature->getSeqFeatures()

This will return a list of subfeatures corresponding to the block structure specified in the BED line. The boundaries of the blocks are found using the features' start() and end() methods:

  for my $block (@subfeats) {
     my $start = $block->start;
     my $end   = $block->end;
     print "$start..$end";

The start and end coordinates are given in chromosome coordinates.

The BioPerl API requires that features and subfeatures have primary tags. If the main (parent) feature is of type "mRNA" (either determined via the blocks heuristic or requested explicitly), then subfeatures will have primary tags "CDS", "five_prime_UTR" and/or "three_time_UTR". If the main feature has the type "region", then the subfeatures will have primary tags "thickregion" and "thinregion", based on where they are with respect to the thickStart and thinStart BED fields. Similarly, a feature of type "feature" will have subparts of type "thickfeature" and "thinfeature".

Notice that the module will split blocks in two at the thickStart and thickEnd positions.


A type named "bin:" followed by an integer will divide each chromosome/contig into the indicated number of summary bins, and return one feature for each bin. For example, type "bin:100" will return 100 evenly-spaced bins across each chromosome/contig.

The returned bins have all the same methods as those returned by the "region" type, except that the start() and end() methods return the boundaries of the bin rather than any individual interval reported in the BED file. Instead of returning a single integer value, the score() method returns a hash of reference to statistical summary information:

  Key            Value
  ---            ---------

  validCount     Number of intervals in the bin

  maxVal         Maximum value in the bin

  minVal         Minimum value in the bin

  sumData        Sum of the intervals in the bin

  sumSquares     Sum of the squares of the intervals in the bin

In addition, the bin objects add the following convenience methods:

 $bin->count()    Same as $bin->score->{validCount}
 $bin->minVal()   Same as $bin->score->{minVal}
 $bin->maxVal()   Same as $bin->score->{maxVal}
 $bin->mean()     The mean of values in the bin (from the formula above)
 $bin->variance() The variance of values in the bin (ditto)
 $bin->stdev()    The standard deviation of values in the bin (ditto)

From these values one can determine the mean, variance and standard deviation across one or more genomic intervals. The formulas are as follows:

 sub mean {
    my ($sumData,$validCount) = @_;
    return $sumData/$validCount;

 sub variance {
    my ($sumData,$sumSquares,$validCount) = @_;
    my $var = $sumSquares - $sumData*$sumData/$validCount;
    if ($validCount > 1) {
        $var /= $validCount-1;
    return 0 if $var < 0;
    return $var;

 sub stdev {
     my ($sumData,$sumSquares,$validCount) = @_;
     my $variance = variance($sumData,$sumSquares,$validCount);
     return sqrt($variance);

Note that in the calculation of variance, there is a chance of getting very small negative numbers in a tight distribution due to floating point rounding errors. Hence the check for variance < 0. To pool bins, simply sum the individual values.

For your convenience, this module optionally exports functions that perform these calculations for you. Please see "EXPORTED FUNCTIONS" below.

If no bin count is specified, then a value of 1 is assumed. This will return one bin spanning the entirety of the region specified. For example:

  my ($bin) = $bigbed->features(-seq_id=>'chr1',
                                -type => 'bin');
  print  "Features on chr1:1..120,000,000 : ",$bin->count,"\n";

  my ($bin) = $bigbed->features(-seq_id=>'chr1',-type=>'bin');
  print "Features on chr1: ",$bin->count,"\n";

  my @bins  = $bigbed->features(-type=>'bin'); # no position specified
  for my $bin (@bins) {
     my $chr = $bin->seq_id;
     print "Features on $chr: ",$bin->count,"\n";


This feature type is similar to "bin" except that instead of returning one feature for each binned interval on the genome, it returns a single object from which you can retrieve summary statistics across fixed-width bins in a more memory-efficient manner. Call the object's statistical_summary() method with the number of bins you need to get an array ref of bins length. Each element of the array will be a hashref containing the minVal, maxVal, sumData, sumSquares and validCount keys. The following code illustrates how this works:

 use Bio::DB::BigBed 'binMean','binStdev';

 my $bed    = Bio::DB::BigBed->new(-bigbed=>$path);
 my @chroms = $bed->features(-type=>'summary');

 for my $c (@chroms) {
    my $seqid   = $c->seq_id;
    my $c_start = $c->start;

    my $stats     = $c->statistical_summary(10);
    my $bin_width = $c->length/@$stats;
    my $start     = $c_start;

    for my $s (@$stats) {
        my $mean  = binMean($s);
        my $stdev = binStdev($s);
        my $end   = $start + $bin_width-1;
        print "$seqid:",int($start),'..',int($end),": $mean +/- $stdev\n";
    } continue {
       $start += $c_start;

The "summary" features also has a score() method which returns a statistical summary hash across the entire region.

Using BigBed objects with Bio::Graphics

Recent versions of the Bio::Graphics module (see Bio::Graphics) directly supports the "summary" feature type via the wiggle_whiskers glyph. This glyph uses different color intensities to summarize the mean, standard deviation, min and max of bins across the range. You do not have to specify the bin size -- the glyph will choose a bin that is most appropriate for the width of the drawing. Typical usage is like this:

 use Bio::DB::BigBed;
 use Bio::Graphics;

 my $path      = 'ExampleData/';
 my $bed       = Bio::DB::BigBed->new($path) or die;
 my ($summary) = $bed->features(-seq_id=>'chr1',

 my $panel     = Bio::Graphics::Panel->new(-width   => 800,
                                           -segment => $summary,
                                           -key_style => 'between',
                                           -pad_left => 10,
 # add the scalebar
                  -glyph => 'arrow',
                  -tick  => 2,
                  -label => 'chrI',

 # add the whisker
                  -glyph => 'wiggle_whiskers',
                  -height => 80,
                  -key   => 'Summary statistics',
 print $panel->png;

In addition, you can draw individual BED lines using the "region" or "mRNA" feature types in conjunction with the glyphs of your choice. This example displays the same BED data from chromosome 1 using the "generic", "segments" and "gene" glyphs:

 use Bio::DB::BigBed;
 use Bio::Graphics;

 my $path      = 'ExampleData/';
 my $bed       = Bio::DB::BigBed->new($path) or die;
 my $segment   = $bed->segment('chr1',11689000 => 11979000) or die;
 my @features  = $segment->features();

 my $panel     = Bio::Graphics::Panel->new(-width     => 800,
                                          -segment   => $segment,
                                          -key_style => 'between',
                                          -pad_left  => 10,
                                          -pad_right =>18,

 # add the scalebar
                  -glyph => 'arrow',
                  -tick  => 2,
                  -label => 'chrI',

 # add the data as a generic glyph
                  -glyph => 'box',
                  -key   => 'Generic representation',

 # add the data as a segments glyph
                  -glyph => 'segments',
                  -key   => 'Segments representation',

 # add the data as a gene glyph
                  -glyph => 'gene',
                  -key   => 'Gene representation',

 print $panel->png;

Using BigWig objects and GBrowse

The Generic Genome Browser version 2.0 ( can treat a BigWig file as a track database. A typical configuration will look like this:

 db_adaptor    = Bio::DB::BigWig
 db_args       = -bigwig /var/www/data/
                 -fasta  /var/www/data/elegans-ws190.fa

 feature  = summary
 database = BigWig
 glyph    = wiggle_whiskers
 min_score = -1
 max_score = +1.5
 key       = DPY-27 ChIP-chip


Bio::DB::BigFile, Bio::Perl, Bio::Graphics, Bio::Graphics::Browser2


Lincoln Stein <>. <>

Copyright (c) 2010 Ontario Institute for Cancer Research.

This package and its accompanying libraries is free software; you can redistribute it and/or modify it under the terms of the GPL (either version 1, or at your option, any later version) or the Artistic License 2.0. Refer to LICENSE for the full license text. In addition, please see DISCLAIMER.txt for disclaimers of warranty.

1 POD Error

The following errors were encountered while parsing the POD:

Around line 369:

You forgot a '=back' before '=head1'