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Initial release11 June 2002 (2002-06-11)
Stable release
1.7.2 / 29 November 2018; 2 years ago (2018-11-29)
Repository Edit this at Wikidata
Written inPerl
LicenseArtistic License and GPL

BioPerl[1][2] is a collection of Perl modules that facilitate the development of Perl scripts for bioinformatics applications. It has played an integral role in the Human Genome Project.[3]


BioPerl is an active open source software project supported by the Open Bioinformatics Foundation. The first set of Perl codes of BioPerl was created by Tim Hubbard and Jong Bhak[citation needed] at MRC Centre Cambridge, where the first genome sequencing was carried out by Fred Sanger. MRC Centre was one of the hubs and birth places of modern bioinformatics as it had a large quantity of DNA sequences and 3D protein structures. Hubbard was using the Perl library, which contained many useful Perl subroutines for bioinformatics. Bhak, Hubbard's first PhD student, created Bhak merged the two Perl subroutine libraries into The name BioPerl was coined jointly by Bhak and Steven Brenner at the Centre for Protein Engineering (CPE). In 1995, Brenner organized a BioPerl session at the Intelligent Systems for Molecular Biology conference, held in Cambridge. BioPerl had some users in coming months including Georg Fuellen who organized a training course in Germany. Fuellen's colleagues and students greatly extended BioPerl; this was further expanded by others, including Steve Chervitz who was actively developing Perl codes for his yeast genome database. The major expansion came when Cambridge student Ewan Birney joined the development team.[citation needed]

The first stable release was on 11 June 2002; the most recent stable (in terms of API) release is 1.7.2 from 07 September 2017. There are also developer releases produced periodically. Version series 1.7.x is considered to be the most stable (in terms of bugs) version of BioPerl and is recommended for everyday use.

In order to take advantage of BioPerl, the user needs a basic understanding of the Perl programming language including an understanding of how to use Perl references, modules, objects and methods.

Influence on the Human Genome Project

The Human Genome Project faced several challenges during its lifetime. A few of these problems were solved when many of the genomics labs started to use Perl. The process of analyzing all of the DNA sequences was one such problem. Some labs built large monolithic systems with complex relational databases that took forever to debug and implement, and got surpassed by new technologies. Other labs learned to build modular, loosely-coupled systems whose parts could be swapped in and out when new technologies arose. Many of the initial results from all of the labs were mixed. It was eventually discovered that many of the steps could be implemented as loosely coupled programs that were run with a Perl shell script. Another problem that was fixed was interchange of data. Each lab usually had different programs that they ran with their scripts, resulting in several conversions when comparing results. To fix this the labs collectively started using a super-set of data. One script was used to convert from super-set to each lab's set and one was used to convert back. This minimized the number of scripts needed and data exchange became simplified with Perl.

Features and examples

BioPerl provides software modules for many of the typical tasks of bioinformatics programming. These include:

  • Accessing nucleotide and peptide sequence data from local and remote databases

Example of accessing GenBank to retrieve a sequence:

use Bio::DB::GenBank;

$db_obj = Bio::DB::GenBank->new;

$seq_obj = $db_obj->get_Seq_by_acc( # Insert Accession Number );
  • Transforming formats of database/ file records

Example code for transforming formats

use Bio::SeqIO;

my $usage = " informat outfile outfileformat";
my $informat = shift or die $usage;
my $outfile = shift or die $usage;
my $outformat = shift or die $usage;

my $seqin = Bio::SeqIO->new( -fh  => *STDIN,  -format => $informat, );
my $seqout = Bio::SeqIO->new( -file  => ">$outfile",  -format => $outformat, );

while (my $inseq = $seqin->next_seq)
  • Manipulating individual sequences

Example of gathering statistics for a given sequence

use Bio::Tools::SeqStats;
$seq_stats = Bio::Tools::SeqStats->new($seqobj);

$weight = $seq_stats->get_mol_wt();
$monomer_ref = $seq_stats->count_monomers();

# for nucleic acid sequence
$codon_ref = $seq_stats->count_codons();
  • Searching for similar sequences
  • Creating and manipulating sequence alignments
  • Searching for genes and other structures on genomic DNA
  • Developing machine readable sequence annotations


In addition to being used directly by end-users,[4] BioPerl has also provided the base for a wide variety of bioinformatic tools, including amongst others:

  • SynBrowse[5]
  • GeneComber[6]
  • TFBS[7]
  • MIMOX[8]
  • BioParser[9]
  • Degenerate primer design[10]
  • Querying the public databases[11]
  • Current Comparative Table[12]

New tools and algorithms from external developers are often integrated directly into BioPerl itself:

  • Dealing with phylogenetic trees and nested taxa[13]
  • FPC Web tools[14]


BioPerl was one of the first biological module repositories that increased its usability. It has very easy to install modules, along with a flexible global repository. BioPerl uses good test modules for a large variety of processes.


There are many ways to use BioPerl, from simple scripting to very complex object programming. This makes the language not clear and sometimes hard to understand. For as many modules that BioPerl has, some do not always work the way they are intended.

Related libraries in other programming languages

Several related bioinformatics libraries implemented in other programming languages exist as part of the Open Bioinformatics Foundation, including:

  • Biopython
  • BioJava
  • BioRuby
  • BioPHP
  • BioJS
  • Bioconductor


  1. ^ Stajich, J. E.; Block, D.; Boulez, K.; Brenner, S.; Chervitz, S.; Dagdigian, C.; Fuellen, G.; Gilbert, J.; Korf, I.; Lapp, H.; Lehväslaiho, H.; Matsalla, C.; Mungall, C. J.; Osborne, B. I.; Pocock, M. R.; Schattner, P.; Senger, M.; Stein, L. D.; Stupka, E.; Wilkinson, M. D.; Birney, E. (2002). "The BioPerl Toolkit: Perl Modules for the Life Sciences". Genome Research. 12 (10): 1611–1618. doi:10.1101/gr.361602. PMC . PMID 12368254.
  2. ^ "Archived copy". Archived from the original on 2007-02-02. Retrieved 2007-01-21.CS1 maint: archived copy as title (link) A complete, up-to-date list of BioPerl references
  3. ^ Lincoln Stein (1996). "How Perl saved the human genome project". The Perl Journal. 1 (2). Archived from the original on 2007-02-02. Retrieved 2009-02-25.
  4. ^ Khaja R, MacDonald J, Zhang J, Scherer S (2006). "Methods for identifying and mapping recent segmental and gene duplications in eukaryotic genomes". Gene Mapping, Discovery, and Expression. Methods Mol Biol. 338. Totowa, N.J. : Humana Press. pp. 9–20. doi:10.1385/1-59745-097-9:9. ISBN 978-1-59745-097-3. PMID 16888347.
  5. ^ Pan, X.; Stein, L.; Brendel, V. (2005). "SynBrowse: A synteny browser for comparative sequence analysis". Bioinformatics. 21 (17): 3461–3468. doi:. PMID 15994196.
  6. ^ Shah, S. P.; McVicker, G. P.; MacKworth, A. K.; Rogic, S.; Ouellette, B. F. F. (2003). "GeneComber: Combining outputs of gene prediction programs for improved results". Bioinformatics. 19 (10): 1296–1297. doi:. PMID 12835277.
  7. ^ Lenhard, B.; Wasserman, W. W. (2002). "TFBS: Computational framework for transcription factor binding site analysis". Bioinformatics. 18 (8): 1135–1136. doi:. PMID 12176838.
  8. ^ Huang, J.; Gutteridge, A.; Honda, W.; Kanehisa, M. (2006). "MIMOX: A web tool for phage display based epitope mapping". BMC Bioinformatics. 7: 451. doi:10.1186/1471-2105-7-451. PMC . PMID 17038191.
  9. ^ Catanho, M.; Mascarenhas, D.; Degrave, W.; De Miranda, A. B. ?L. (2006). "BioParser". Applied Bioinformatics. 5 (1): 49–53. doi:. PMID 16539538.
  10. ^ Wei, X.; Kuhn, D. N.; Narasimhan, G. (2003). "Degenerate primer design via clustering". Proceedings. IEEE Computer Society Bioinformatics Conference. 2: 75–83. PMID 16452781.
  11. ^ Croce, O.; Lamarre, M. L.; Christen, R. (2006). "Querying the public databases for sequences using complex keywords contained in the feature lines". BMC Bioinformatics. 7: 45. doi:10.1186/1471-2105-7-45. PMC . PMID 16441875.
  12. ^ Landsteiner, B. R.; Olson, M. R.; Rutherford, R. (2005). "Current Comparative Table (CCT) automates customized searches of dynamic biological databases". Nucleic Acids Research. 33 (Web Server issue): W770–W773. doi:10.1093/nar/gki432. PMC . PMID 15980582.
  13. ^ Llabrés, M.; Rocha, J.; Rosselló, F.; Valiente, G. (2006). "On the Ancestral Compatibility of Two Phylogenetic Trees with Nested Taxa". Journal of Mathematical Biology. 53 (3): 340–364. arXiv:. doi:10.1007/s00285-006-0011-4. PMID 16823581. S2CID 1704494.
  14. ^ Pampanwar, V.; Engler, F.; Hatfield, J.; Blundy, S.; Gupta, G.; Soderlund, C. (2005). "FPC Web Tools for Rice, Maize, and Distribution". Plant Physiology. 138 (1): 116–126. doi:10.1104/pp.104.056291. PMC . PMID 15888684.

Edited: 2021-06-18 14:31:49