2.1 Core module

The core module provides classes to model nucleotide and amino acid sequences and their inherent relationships. Emphasis was placed on using Java classes and method names to describe sequences that would be familiar to the biologist and provide a concrete representation of the steps in going from a gene sequence to a protein sequence to the computer scientist.

BioJava 3 leverages recent innovations in Java. A sequence is defined as a generic interface, allowing the framework to build a collection of utilities which can be applied to any sequence such as multiple ways of storing data. In order to improve the framework’s usability to biologists, we also define specific classes for common types of sequences, such as DNA and proteins. One area that highlights this work is the translation engine, which allows the interconversion of DNA, RNA and amino acid sequences. The engine can handle details such as choosing the codon table, converting start codons to a methionine, trimming stop codons, specifying the reading frame and handling ambiguous sequences (‘R’ for purines, for example). Alternatively, the user can manually override defaults for any of these.

The storage of sequences is designed to minimize memory usage for large collections using a ‘proxy’ storage concept. Various proxy implementations are provided which can store sequences in memory, fetch sequences on demand from a web service such as UniProt or read sequences from a FASTA file as needed. The latter two approaches save memory by not loading sequence data until it is referenced in the application. This concept can be extended to handle very large genomic datasets, such as NCBI GenBank or a proprietary database.

2.2 Protein structure modules

The protein structure modules provide tools for representing and manipulating 3D biomolecular structures, with the particular focus on protein structure comparison. It contains Java ports of the FATCAT algorithm (Ye and Godzik, 2003) for flexible and rigid body alignment, a version of the standard Combinatorial Extension (CE) algorithm (Shindyalov and Bourne, 1998) as well as a new version of CE that can detect circular permutations in proteins (Bliven and Prlić, 2012). These algorithms are used to provide the RCSB Protein Data Bank (PDB) (Rose et al., 2011) Protein Comparison Tool as well as systematic comparisons of all proteins in the PDB on a weekly basis (Prlić et al., 2010).

Parsers for PDB and mmCIF file formats (Bernstein et al., 1977; Fitzgerald et al., 2006) allow the loading of structure data into a reusable data model. Notably, this feature is used by the SIFTS project to map between UniProt sequences and PDB structures (Velankar et al., 2005). Information from the RCSB PDB can be dynamically fetched without the need to manually download data. For visualization, an interface to the 3D viewer Jmol (Hanson, 2010) http://www.jmol.org/ is provided. Work is underway for better interaction with the RCSB PDB viewers (Moreland et al., 2005).

2.3 Genome and sequencing modules

The genome module is focused on the creation of gene sequence objects from the core module by supporting the parsing of GTF files generated by GeneMark (Besemer and Borodovsky, 2005), GFF2 files generated by GeneID (Blanco and Abril, 2009) and GFF3 files generated by Glimmer (Kelley et al., 2011). The gene sequences can then be written out as a GFF3 format for importing into GMOD (Stein et al., 2002). A separate sequencing module provides memory efficient, low level and streaming I/O support for several common variants of the FASTQ file format from next generation sequencers (Cock et al., 2010).

2.4 Alignment module

The alignment module supplies standard algorithms for sequence alignment and establishes a foundation to perform progressive multiple sequence alignments. For pairwise alignments, an implementation of the Needleman–Wunsch algorithm computes the optimal global alignment (Needleman and Wunsch, 1970) and the Smith–Waterman algorithm calculates local alignments (Smith and Waterman, 1981). In addition to these standard pairwise algorithms, the module includes the Guan–Uberbacher algorithm to perform global sequence alignment efficiently using only linear memory (Guan and Uberbacher, 1996). This routine also allows predefined anchors to be manually specified that will be included in the alignment produced. Any of the pairwise routines can also be used to perform progressive multiple sequence alignment. Both pairwise and multiple sequence alignments output to standard alignment formats for further processing or visualization.

2.5 ModFinder module

The ModFinder module provides new methods to identify and classify protein modifications in protein 3D structures. More than 400 different types of protein modifications (phosphorylation, glycosylation, disulfide bonds metal chelation, etc.) were collected and curated based on annotations in PSI-MOD (Montecchi-Palazzi et al., 2008), RESID (Garavelli, 2004) and RCSB PDB (Berman et al., 2000). The module provides an API for detecting protein modifications within protein structures. Figure 1 shows a web-based interface for displaying modifications which was created using the ModFinder module. Future developments are planned to include additional protein modifications by integrating other resources such as UniProt (Farriol-Mathis et al., 2004).

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Fig. 1.

An example application using the ModFinder module and the protein structure module. Protein modifications are mapped onto the sequence and structure of ferredoxin I (PDB ID 1GAO; Chen et al., 2002). Two possible iron–sulfur clusters are shown on the protein sequence (3Fe–4S (F3S): orange triangles/lines; 4Fe–4S (SF4): purple diamonds/lines). The 4Fe–4S cluster is displayed in the Jmol structure window above the sequence display

2.6 Amino acid properties module

The goal of the amino acid properties module is to provide a range of accurate physicochemical properties for proteins. The following peptide properties can currently be calculated: molecular weight, extinction coefficient, instability index, aliphatic index, grand average of hydropathy, isoelectric point and amino acid composition.

To aid proteomic studies, the module includes precise molecular weights for common isotopically labeled or post-translationally modified amino acids. Additional types of PTMs can be defined using simple XML configuration files. This flexibility is especially valuable in situations where the exact mass of the peptide is important, such as mass spectrometry experiments.

2.7 Protein disorder module

BioJava now includes a port of the Regional Order Neural Network (RONN) predictor (Yang et al., 2005) for predicting disordered regions of proteins. BioJava’s implementation supports multiple threads, making it ~3.2-times faster than the original C implementation on a modern quad-core machine.

The protein disorder module is distributed both as part of the BioJava library and as a standalone command line executable. The executable is optimized for use in automated analysis pipelines to predict disorder in multiple proteins. It can produce output optimized for either human readers or machine parsing.

The authors thank everybody who contributed code, documentation or ideas, in particular A. Al-Hossary, R. Thornton, J. Warren, A. Draeger, G. Waldon and G. Barton. Each contribution is appreciated, although the total list of contributors is too long to be reproduced here. They also thank the Open Bioinformatics Foundation for project hosting.

Funding: The RCSB PDB (NSF DBI 0829586 to A.P., P.W.R. and P.E.B.); Google Summer of Code in 2010 and 2011 (to J.G., M.C. and C.H.K.) and Scottish Universities Life Sciences Alliance (SULSA) (to P.T.).

Conflict of Interest: None declared.