The DAWGPAWS pipeline for the annotation of genes and transposable elements in plant genomes
© Estill and Bennetzen; licensee BioMed Central Ltd. 2009
Received: 30 April 2009
Accepted: 19 June 2009
Published: 19 June 2009
High quality annotation of the genes and transposable elements in complex genomes requires a human-curated integration of multiple sources of computational evidence. These evidences include results from a diversity of ab initio prediction programs as well as homology-based searches. Most of these programs operate on a single contiguous sequence at a time, and the results are generated in a diverse array of readable formats that must be translated to a standardized file format. These translated results must then be concatenated into a single source, and then presented in an integrated form for human curation.
We have designed, implemented, and assessed a Perl-based workflow named DAWGPAWS for the generation of computational results for human curation of the genes and transposable elements in plant genomes. The use of DAWGPAWS was found to accelerate annotation of 80–200 kb wheat DNA inserts in bacterial artificial chromosome (BAC) vectors by approximately twenty-fold and to also significantly improve the quality of the annotation in terms of completeness and accuracy.
The DAWGPAWS genome annotation pipeline fills an important need in the annotation of plant genomes by generating computational evidences in a high throughput manner, translating these results to a common file format, and facilitating the human curation of these computational results. We have verified the value of DAWGPAWS by using this pipeline to annotate the genes and transposable elements in 220 BAC insertions from the hexaploid wheat genome (Triticum aestivum L.). DAWGPAWS can be applied to annotation efforts in other plant genomes with minor modifications of program-specific configuration files, and the modular design of the workflow facilitates integration into existing pipelines.
Bacterial Artificial Chromosome
Command Line Interface
Expressed Sequence Tag
full-length complementary DNA
General Feature Format
General Public License
Hidden Markov Model
Long Terminal Repeat
Open Reading Frame
Profile Hidden Markov Model
Genomic sequence assemblies are rapidly being published for a great number of species [1, 2]. The sequence data used to produce genome assemblies are being generated at ever-increasing rates for reduced costs , indicating that the genomes of many more plant species will be de novo sequenced in coming years. The relative value of these sequencing efforts is a direct function of the accuracy of the annotation of the resultant sequence assemblies. Genome annotation seeks to delineate the sequence features that occur on the genome, thereby permitting definition of the biological processes responsible for these features . In plants, the sequence characteristics that are most critical to our interpretation of gene function and genome evolution include both genes and transposable elements (TEs) [5, 6].
Identification of the genes that have been uncovered in assembled genome sequence data can utilize evidence from both ab initio gene annotation programs as well as sequence similarity searches against databases of previously identified proteins and expressed RNA [4, 7, 8]. The ab initio gene finding programs derive full gene models from DNA sequence data based solely on knowledge of the sequence features associated with protein coding domains. Sequence alignments can refine the exon-intron boundaries of these models and provide evidence that computationally predicted genes are actually transcribed in vivo. Existing software can automatically synthesize these data to derive combined evidence gene models [9, 10].
While this combination of ab initio and homology-based approaches have been used to accurately annotate genes in a number of eukaryotic genomes, plant genome annotation efforts cannot focus solely on the annotation of genes due to the risk of conflating genes with transposable elements . Many TEs contain open reading frames (ORFs) that generate the proteins required for TE transposition. The ab initio gene annotation programs will often annotate these TE ORFs as genes. Since most TE genes are expressed and represented in cDNA libraries, homology-based searches will indicate that these ORFs are transcribed and they thus may be considered legitimate gene predictions. Simply removing the high-copy-number candidate genes does not alleviate this problem because some true gene families are highly abundant while not all transposable elements are highly repetitive . These erroneous gene annotations are especially problematic in plant genomes where transposable elements make up the majority of sequenced genome space. Since these false positive gene predictions cannot be mitigated by gene prediction methods alone, plant genome annotation must directly annotate TEs in order to remove them from the gene candidate list.
Similar to the prediction of genes, accurate identification of the TEs in genomic sequence data combines homology-based searches and ab initio results [13–15]. Tools for ab initio transposable element discovery can exploit the fact that many families of TEs occur in high copy number within a host genome [16–18], or they can utilize diagnostic structural features such as tandem inverted repeats (TIRs) or long terminal repeats (LTRs) that delineate an individual TE insertion [19–21]. Homology-based searches of transposable elements are facilitated by specialized tools [22–25] that make use of databases of previously identified TEs [26–29] or leverage repetitive data from the sequenced genome [30–32].
The gold standard of genome annotation is the integration and curation of multiple computational results by a knowledgeable biologist . This approach has been advocated for the structural annotation of genes [4, 11], as well as transposable elements . A limitation of the manually-curated multiple-evidences approach is that the process requires the combination of computational results from a disparate set of independent annotation programs. The output of this software has been designed to maximize readability by humans and not to facilitate integration of results across programs. Furthermore, these tools are often designed to work on a single contiguous sequence (contig) at a time, while many annotation efforts require the generation of computational results for thousands of assembled contigs. Computational workflow suites that seek to aid in plant genome annotation must therefore overcome these limitations while facilitating the human interpretation of the computational results contributing to a biological annotation.
Here, we introduce an annotation suite that allows for computational evidences to be generated in an automated fashion, integrates the results from multiple programs and facilitates the human curation of these computational results. This suite was designed to assist a Distributed Annotation Working Group (DAWG) approach for a Pipeline to Annotate Wheat Sequences (PAWS), and we hereafter refer to this effort as DAWGPAWS.
A common thread to each component of the DAWGPAWS package is that computational evidences are translated from the native annotation program output into the standard general feature format (GFF) . The GFF file format facilitates integration of multiple computational results. This format can be directly curated by any biologist using standard sequence curation and visualization tools such as Apollo , Artemis , GBrowse , the UCSC genome browser  or the Ensembl Genome Browser . The GFF files also provide a standard format for loading annotation results to relational database schemas such as BioSQL  or CHADO .
DAWGPAWS annotation scripts for generating computational annotation results in batch mode.
Gene ab initio and automated combined evidence
Gene ab initio
Gene ab initio
Gene ab initio
TE ab initio
TE ab initio
TE ab initio
TE ab initio
TE ab initio
Tandem Repeats Finder 
Repeat ab initio
TE and gene homology
DAWGPAWS scripts for conversion of annotation results from native program output to GFF.
Gene ab initio
Gene ab initio
TE ab initio
TE ab initio
TE ab initio
TE ab initio
TE ab initio
TE and gene homology
The DAWGPAWS suite also includes specialized tools for TE annotation. For identification of the highly repetitive regions of a contig, the seq_oligocount.pl program can count the occurrence of oligomers in the query sequence against an index of random shotgun sequences. This program generates all oligomers of length k from the query sequence, and uses the vmatch program  to determine the number of these k-mers that occur in a random shotgun sequence data set generated by mkvtree . The output of this program is a GFF file indicating the count of these k-mers in the shotgun sequence dataset. These results may be used to identify the mathematically defined repeats in the query sequence, as well as provides a means to visualize low-copy-number runs in the query sequence .
Additional helper scripts included in the DAWGPAWS package.
Converts GFF files to the game.xml format.
Converts game.xml files to the GFF3 format.
Given a directory of lowercase masked sequence files, this will replace lowercase residues with an N or X to indicate masking.
Given annotation results scattered across multiple directories, this program can merge the results into subdirectories in a single parent directory.
Given GFF annotation results from multiple methods, this program generates a Euler Diagram of these features using the VennMaster program 
This program will annotate gaps in the query sequences in the input directory.
This program writes shell scripts to run DAWGPAWS in a cluster environment running the Platform LSF queuing system.
Given a FASTA file with multiple sequence files, this program generates a separate FASTA file for each sequence record. The sequence files produced are named using the sequence ID in the FASTA header in the input file.
This program merges all FASTA files in a directory into a single FASTA file.
This program shortens the FASTA header by limiting the header length, or splitting the header by a delimiting character. Some annotation programs are limited by the length of the FASTA header that is accepted, and this programs allows input files to meet this limitation.
Fetches multiple results from the Plant GDB TEnest server and converts the results to GFF.
Given a GFF file that contains point or segment data, this will extract segments with score values that exceed a threshold value.
Because the LTR_STRUC program only runs under the windows environment, this program converts FASTA sequences in UNIX to DOS line endings and generates the files name and flist file required for LTR_STRUC.
This program allows for the generation of a GFF file that counts the number of times an oligomer in the genomic contig occurs in a reference shotgun sequence database.
Common command line options used throughout the DAWGPAWS suite of programs.
For batch scripts, this indicates the input directory containing the FASTA files to annotate. For conversion scripts, this indicates the input file to convert from the native format to the GFF format.
For batch scripts, this indicates the output directory containing the annotation results for the program and the GFF results.
For conversion scripts, this indicates the path to the GFF output file.
For programs that make use of a configuration file, this indicates the path to the configuration file to use.
For conversion scripts, this indicates the sequence id to use in the GFF output file.
For conversion scripts, this indicates the name of that parameter set used with the annotation program. This option allows the user to distinguish among multiple parameter sets for the same annotation program, and this parameter name is appended to the source column of the GFF output file.
For conversion scripts, this indicates the name of the program used to generate the annotation result.
Print the current version of the script.
Print a short program usage message.
Print a short help message including the common usage and all program options available at the command line.
Print the full program manual.
This will run the program with maximum verbosity. This option will generate status updates while the program is running, and will maximize the error reporting functions of the script. All verbose statements are written to the standard error output stream.
Results and discussion
In addition to visualization and curation of the annotated DNA, it is also possible to transfer the DAWGPAWS results into existing database schema. For example, the CHADO database  can make use of the gmod_bulk_load_gff3.pl program  that can load GFF3 format files into a CHADO database. In the DAWGPAWS package, the GFF3 format files from curated results can be generated with the cnv_game2gff3.pl program. These curated results could then be stored in a local implementation of the CHADO database. The BioSQL database schema  also includes a bp_load_gff.pl script that can load GFF results into the database schema.
The DAWGPAWS annotation framework has a number of features that make it a good choice to facilitate the workflow in plant genome annotation. The use of configuration files makes it fairly easy to modify the annotation workflow for the species of interest. The configuration files also makes it quite easy to generate results with multiple parameter sets for an individual program. Using multiple parameter sets will be especially useful when working with a genome that has not been annotated before, and for which appropriate annotation parameters have not been identified. Also, while previous annotation pipelines have focused on gene annotation, the DAWGPAWS suite maximizes the quality of TE annotation results. Most plants contain genomes with sizes > 5000 Mb , and are therefore expected to contain more than 80% TEs , so efficiently dealing with this large number and diverse set of mobile DNAs is necessary for effective genome annotation.
The current focus of DAWGPAWS in our laboratory is the structural annotation of the genes and TEs in a genome using methods and applications tuned to the Triticeae. In annotation of 220 BACs from hexaploid bread wheat, we found that the DAWGPAWS pipeline increased the rate of individual BAC annotations by twenty-fold. Due to the time required to manually generate annotation results, this previous annotation effort was limited to using the FGENESH annotation program combined with a BLAST search of predicted models against known transposable elements and protein databases . Using this method, annotators could annotate a single BAC in one to two days. The implementation of the DAWGPAWS pipeline increased the speed of annotation to ten-fifteen BACs per person per day. Furthermore, the quality of both TE and gene prediction were also seen to improve with the use of DAWGPAWS. This was due, at least in part, to the larger number of complementary programs for TE and gene discovery that could be conveniently employed in each BAC annotation. Specifically, the inclusion of ab initio TE prediction programs allowed for the identification of new families of LTR retrotransposons that would have been missed in our previous annotation efforts. Predicted gene models that span these newly discovered families would not have been identified as TEs in the exclusively homology-based searches that were previously used.
Future development of DAWGPAWS will incorporate tools for the functional annotation of the predicted genes. Currently, functional annotation can be done within the Apollo program by manually selecting individual gene models and BLASTing these results against appropriate databases. A batch run support for additional local alignment search tools will also be added. The use of NCBI-BLAST is sufficient for most comparisons of sequence contigs against reference databases, but programs such as BLAT  or sim4  are designed specifically to align ESTs and flcDNAs against assembled genomes. While output from these local alignment tools can be converted to GFF using the existing cnv_blast2gff.pl program in DAWGPAWS, it would be useful to use these packages in a batch run framework similar to the batch_blast.pl program.
Support for additional ab initio gene annotation programs will also be added to future releases of DAWGPAWS. Augustus  is an ab initio annotation program that will be useful for gene annotation that seeks to identify all transcripts derived from a single locus. Support for GENEZILLA  and GlimmerHMM  gene annotation packages will also be added to future releases of DAWGPAWS. The SNAP program  will be added to support the annotation of genomes that have been sequenced de novo and lack species-specific HMM model parameterizations. The addition of the PASA  program would assist in the annotation of genomes that have large transcript databases that can assist genome annotation. As additional fully-sequenced genomes are added to the plant genomics literature, we can make use of syntenic comparisons and multiple alignments to aid in gene annotation  as well as TE annotation . Future development of DAWGPAWS will incorporate syntenic alignment and prediction programs such as SGP2 , SLAM , and TWINSCAN  as they become increasingly relevant to plant genome annotation.
The DAWGPAWS annotation workflow provides a suite of command line interface programs that can generate computational evidences for human curation in a high-throughput fashion. We have used the DAWGPAWS pipeline to annotate 220 randomly selected BACs with wheat DNA inserts for both gene and TE content. Our curation efforts on the DAWGPAWS output are implemented in the Apollo program. The tiers file used for visualization of this curation are available as part of the DAWGPAWS package.
DAWGPAWS represents an efficient tool for genome annotation in the Triticeae, and can be used in its current form to generate gene and TE computational results for other grass genomes. Minor modifications to the configuration files used by DAWGPAWS can make this program suitable to the generation of computational annotation results for any plant genome. The TE annotation capabilities of DAWGPAWS exceeds any other current genome annotation suite, and makes this package particularly valuable for the great majority of plant genomes, such as wheat or maize, that contain a diverse arrays of TEs that comprise the majority of the nuclear genome.
The DAWGPAWS program has been specifically designed to facilitate use of individual component scripts outside of the entire package. Each script can function independently of all other applications in the package, and programs make use of standard input and standard output streams when possible to facilitate integration into existing pipelines. Since this package is being released under the open source GPL (version 3), the suite and its individual components can be used and modified under the terms of the GPL. Template batch run and conversion scripts are provided in a boilerplate format to facilitate extending DAWGPAWS to additional annotation tools. Furthermore, since we have selected the Perl language for the implementation of our package, the addition of new annotation tools can leverage existing modules in the BioPerl toolkit . These modules include parsers for computational tools useful for predicting alternative splicing [62, 61] as well as interfaces for transfer RNA prediction . We also formally invite collaboration in the development of additional DAWGPAWS applications under the auspices of the GNU GPL, as facilitated by the SourceForge subversion repository of the DAWGPAWS source code. Interested collaborators may contact the authors or become member developers of the DAWGPAWS SourceForge project .
Availability and requirements
Project Name: DAWGPAWS Plant Genome Annotation Pipeline
Project Home Page: http://dawgpaws.sourceforge.net/
Operating System: Platform Independent
Programming Language: Perl
Other Requirements: BioPerl 1.4, as well as the annotation programs that scripts are dependent upon.
License: GNU General Public License 3
Any restrictions to use by non-academics: No restrictions
The authors would like to thank Katrien Devos, Antonio Costa de Oliveira, Xiangyang Xu, Ansuya Jogi, and Jennifer Hawkins for their useful feedback that has been incorporated in the implementation of DAWGPAWS. Xiangyang Xu provided helpful comments on a draft version of this manuscript. The submitted version of this manuscript was refined with constructive comments from two anonymous peer reviewers. This work was supported by NSF grants DBI-0501814 and DBI-0607123.
- Liolios K, Mavromatis K, Tavernarakis N, Kyrpides NC: The Genomes On Line Database (GOLD) in 2007: status of genomic and metagenomic projects and their associated metadata. Nucleic Acids Res. 2008, 36: D475-479.PubMed CentralView ArticlePubMed
- Wheeler DL, Barrett T, Benson DA, Bryant SH, Canese K, Chetvernin V, Church DM, Dicuccio M, Edgar R, Federhen S: Database resources of the National Center for Biotechnology Information. Nucleic Acids Res. 2008, 36: D13-21.PubMed CentralView ArticlePubMed
- Pop M, Salzberg SL: Bioinformatics challenges of new sequencing technology. Trends Genet. 2008, 24: 142-149.PubMed CentralView ArticlePubMed
- Stein L: Genome annotation: from sequence to biology. Nat Rev Genet. 2001, 2: 493-503.View ArticlePubMed
- Bennetzen JL: Transposable element contributions to plant gene and genome evolution. Plant Mol Biol. 2000, 42: 251-269.View ArticlePubMed
- Bennetzen JL: Transposable elements, gene creation and genome rearrangement in flowering plants. Curr Opin Genet Dev. 2005, 15: 621-627.View ArticlePubMed
- Wang Z, Chen Y, Li Y: A brief review of computational gene prediction methods. Genomics Proteomics Bioinformatics. 2004, 2: 216-221.PubMed
- Do JH, Choi DK: Computational approaches to gene prediction. J Microbiol. 2006, 44: 137-144.PubMed
- Schiex T, Moisan A, Rouzé P: EuGene: An Eucaryotic Gene Finder that combines several sources of evidence. Computational Biology. Edited by: Gascuel O, Sagot M-F. 2001, 111-125.View Article
- Allen JE, Salzberg SL: JIGSAW: integration of multiple sources of evidence for gene prediction. Bioinformatics. 2005, 21: 3596-3603.View ArticlePubMed
- Bennetzen JL, Coleman C, Liu R, Ma J, Ramakrishna W: Consistent over-estimation of gene number in complex plant genomes. Curr Opin Plant Biol. 2004, 7: 732-736.View ArticlePubMed
- Sanmiguel P, Bennetzen JL: Evidence that a recent increase in maize genome size was caused by the massive amplification of intergene retrotransposons. Annals of Botany. 1998, 82: 37-44.View Article
- Bergman CM, Quesneville H: Discovering and detecting transposable elements in genome sequences. Brief Bioinform. 2007, 8: 382-392.View ArticlePubMed
- Feschotte C, Pritham EJ: Computational analysis and paleogenomics of interspersed repeats in eukaryotes. Computational Genomics: Current Methods. 2007, Stojanovic N: Taylor and Francis, 31-54.
- Saha S, Bridges S, Magbanua ZV, Peterson DG: Computational Approaches and Tools Used in Identification of Dispersed Repetitive DNA Sequences. Tropical Plant Biology. 2008, 1: 85-96.View Article
- Bao Z, Eddy SR: Automated de novo identification of repeat sequence families in sequenced genomes. Genome Res. 2002, 12: 1269-1276.PubMed CentralView ArticlePubMed
- Edgar RC, Myers EW: PILER: identification and classification of genomic repeats. Bioinformatics. 2005, 21 (Suppl 1): i152-158.View ArticlePubMed
- Price AL, Jones NC, Pevzner PA: De novo identification of repeat families in large genomes. Bioinformatics. 2005, 21 (Suppl 1): i351-358.View ArticlePubMed
- Tu Z: Eight novel families of miniature inverted repeat transposable elements in the African malaria mosquito, Anopheles gambiae. Proc Natl Acad Sci USA. 2001, 98: 1699-1704.PubMed CentralView ArticlePubMed
- McCarthy EM, McDonald JF: LTR_STRUC: a novel search and identification program for LTR retrotransposons. Bioinformatics. 2003, 19: 362-367.View ArticlePubMed
- Xu Z, Wang H: LTR_FINDER: an efficient tool for the prediction of full-length LTR retrotransposons. Nucleic Acids Res. 2007, 35: W265-268.PubMed CentralView ArticlePubMed
- Smit A, Hubley R, Green P: RepeatMasker Open-3.0. 1996–2004. [http://www.repeatmasker.org/cgi-bin/WEBRepeatMasker]
- Kohany O, Gentles AJ, Hankus L, Jurka J: Annotation, submission and screening of repetitive elements in Repbase: RepbaseSubmitter and Censor. BMC Bioinformatics. 2006, 7: 474PubMed CentralView ArticlePubMed
- Kronmiller BA, Wise RP: TEnest: Automated chronological annotation and visualization of nested plant transposable elements. Plant Physiol. 2008, 146: 45-59.PubMed CentralView ArticlePubMed
- Pereira V: Automated paleontology of repetitive DNA with REANNOTATE. BMC Genomics. 2008, 9: 614PubMed CentralView ArticlePubMed
- Wicker T, Matthews DE, Keller B: TREP: a database for Triticeae repetitive elements. Trends in Plant Science. 2002, 7: 561-562.View Article
- Ouyang S, Buell CR: The TIGR Plant Repeat Databases: a collective resource for the identification of repetitive sequences in plants. Nucleic Acids Res. 2004, 32: D360-363.PubMed CentralView ArticlePubMed
- Jurka J, Kapitonov VV, Pavlicek A, Klonowski P, Kohany O, Walichiewicz J: Repbase Update, a database of eukaryotic repetitive elements. Cytogenet Genome Res. 2005, 110: 462-467.View ArticlePubMed
- Spannagl M, Noubibou O, Haase D, Yang L, Gundlach H, Hindemitt T, Klee K, Haberer G, Schoof H, Mayer KF: MIPSPlantsDB – plant database resource for integrative and comparative plant genome research. Nucleic Acids Res. 2007, 35: D834-840.PubMed CentralView ArticlePubMed
- Li R, Ye J, Li S, Wang J, Han Y, Ye C, Yang H, Yu J, Wong GK: ReAS: Recovery of ancestral sequences for transposable elements from the unassembled reads of a whole genome shotgun. PLoS Comput Biol. 2005, 1: e43PubMed CentralView ArticlePubMed
- DeBarry JD, Liu R, Bennetzen JL: Discovery and assembly of repeat family pseudomolecules from sparse genomic sequence data using the Assisted Automated Assembler of Repeat Families (AAARF) algorithm. BMC Bioinformatics. 2008, 9: 235PubMed CentralView ArticlePubMed
- Kurtz S, Narechania A, Stein JC, Ware D: A new method to compute K-mer frequencies and its application to annotate large repetitive plant genomes. BMC Genomics. 2008, 9: 517PubMed CentralView ArticlePubMed
- Quesneville H, Bergman CM, Andrieu O, Autard D, Nouaud D, Ashburner M, Anxolabehere D: Combined evidence annotation of transposable elements in genome sequences. PLoS Comput Biol. 2005, 1: 166-175.View ArticlePubMed
- GFF Format Specifications. [http://www.sanger.ac.uk/Software/formats/GFF/GFF_Spec.shtml]
- Lewis SE, Searle SM, Harris N, Gibson M, Lyer V, Richter J, Wiel C, Bayraktaroglir L, Birney E, Crosby MA: Apollo: a sequence annotation editor. Genome Biol. 2002, 3: RESEARCH0082PubMed CentralView ArticlePubMed
- Rutherford K, Parkhill J, Crook J, Horsnell T, Rice P, Rajandream MA, Barrell B: Artemis: sequence visualization and annotation. Bioinformatics. 2000, 16: 944-945.View ArticlePubMed
- Donlin MJ: Using the Generic Genome Browser (GBrowse). Curr Protoc Bioinformatics. 2007, Chapter 9 (Unit 9): 9PubMed
- Kuhn RM, Karolchik D, Zweig AS, Wang T, Smith KE, Rosenbloom KR, Rhead B, Raney BJ, Pohl A, Pheasant M: The UCSC Genome Browser Database: update 2009. Nucleic Acids Res. 2009, 37: D755-761.PubMed CentralView ArticlePubMed
- Stalker J, Gibbins B, Meidl P, Smith J, Spooner W, Hotz HR, Cox AV: The Ensembl Web site: mechanics of a genome browser. Genome Res. 2004, 14: 951-955.PubMed CentralView ArticlePubMed
- BioSQL. [http://www.biosql.org]
- Zhou P, Emmert D, Zhang P: Using Chado to store genome annotation data. Curr Protoc Bioinformatics. 2006, Chapter 9 (Unit 9): 6PubMed
- Parra G, Blanco E, Guigo R: GeneID in Drosophila. Genome Res. 2000, 10: 511-515.PubMed CentralView ArticlePubMed
- Lukashin AV, Borodovsky M: GeneMark.hmm: new solutions for gene finding. Nucleic Acids Res. 1998, 26: 1107-1115.PubMed CentralView ArticlePubMed
- Burge C, Karlin S: Prediction of complete gene structures in human genomic DNA. J Mol Biol. 1997, 268: 78-94.View ArticlePubMed
- Rho M, Choi JH, Kim S, Lynch M, Tang H: De novo identification of LTR retrotransposons in eukaryotic genomes. BMC Genomics. 2007, 8: 90PubMed CentralView ArticlePubMed
- Kalyanaraman A, Aluru S: Efficient algorithms and software for detection of full-length LTR retrotransposons. J Bioinform Comput Biol. 2006, 4: 197-216.View ArticlePubMed
- Benson G: Tandem repeats finder: a program to analyze DNA sequences. Nucleic Acids Res. 1999, 27: 573-580.PubMed CentralView ArticlePubMed
- Eddy SR: Profile hidden Markov models. Bioinformatics. 1998, 14: 755-763.View ArticlePubMed
- Altschul SF, Gish W, Miller W, Myers EW, Lipman DJ: Basic local alignment search tool. J Mol Biol. 1990, 215: 403-410.View ArticlePubMed
- Solovyev VV, Salamov AA, Lawrence CB: Identification of human gene structure using linear discriminant functions and dynamic programming. Proc Int Conf Intell Syst Mol Biol. 1995, 3: 367-375.PubMed
- Salamov AA, Solovyev VV: Ab initio gene finding in Drosophila genomic DNA. Genome Res. 2000, 10: 516-522.PubMed CentralView ArticlePubMed
- Achaz G, Boyer F, Rocha EP, Viari A, Coissac E: Repseek, a tool to retrieve approximate repeats from large DNA sequences. Bioinformatics. 2007, 23: 119-121.View ArticlePubMed
- vmatch. [http://www.vmatch.de/]
- Wicker T, Narechania A, Sabot F, Stein J, Vu GT, Graner A, Ware D, Stein N: Low-pass shotgun sequencing of the barley genome facilitates rapid identification of genes, conserved non-coding sequences and novel repeats. BMC Genomics. 2008, 9: 518PubMed CentralView ArticlePubMed
- Kestler HA, Muller A, Gress TM, Buchholz M: Generalized Venn diagrams: a new method of visualizing complex genetic set relations. Bioinformatics. 2005, 21: 1592-1595.View ArticlePubMed
- DAWGPAWS User Manual. [http://dawgpaws.sourceforge.net/man.html]
- How to Load GFF Into Chado. [http://gmod.org/wiki/Load_GFF_Into_Chado]
- Zonneveld BJM, Leitch IJ, Bennett MD: First nuclear DNA amounts in more than 300 angiosperms. Annals of Botany. 2005, 96: 229-244.PubMed CentralView ArticlePubMed
- Flavell RB, Bennett MD, Smith JB, Smith DB: Genome Size and Proportion of Repeated Nucleotide-Sequence DNA in Plants. Biochemical Genetics. 1974, 12: 257-269.View ArticlePubMed
- Devos KM, Costa de Oliveira A, Xu X, Estill JC, Estep M, Jogi A, Morales M, Pinheiro J, SanMiguel P, Bennetzen JL: Structure and organization of the wheat genome – the number of genes in the hexaploid wheat genome. 11th International Wheat Genetics Symposium 2008 Proceedings. Edited by: Rudi Appels, Russell Eastwood, Evans Lagudah, Peter Langridge, Michael Mackay, Lynne McIntyre, Peter Sharp. 2008, Sydney University Press, 1-5. [http://ses.library.usyd.edu.au/bitstream/2123/3389/1/O25.pdf]
- Kent WJ: BLAT – the BLAST-like alignment tool. Genome Res. 2002, 12: 656-664.PubMed CentralView ArticlePubMed
- Florea L, Hartzell G, Zhang Z, Rubin GM, Miller W: A computer program for aligning a cDNA sequence with a genomic DNA sequence. Genome Res. 1998, 8: 967-974.PubMed CentralPubMed
- Stanke M, Keller O, Gunduz I, Hayes A, Waack S, Morgenstern B: AUGUSTUS: ab initio prediction of alternative transcripts. Nucleic Acids Res. 2006, 34: W435-439.PubMed CentralView ArticlePubMed
- Majoros WH, Pertea M, Salzberg SL: TigrScan and GlimmerHMM: two open source ab initio eukaryotic gene-finders. Bioinformatics. 2004, 20: 2878-2879.View ArticlePubMed
- Korf I: Gene finding in novel genomes. BMC Bioinformatics. 2004, 5: 59PubMed CentralView ArticlePubMed
- Haas BJ, Delcher AL, Mount SM, Wortman JR, Smith RK, Hannick LI, Maiti R, Ronning CM, Rusch DB, Town CD: Improving the Arabidopsis genome annotation using maximal transcript alignment assemblies. Nucleic Acids Res. 2003, 31: 5654-5666.PubMed CentralView ArticlePubMed
- Dubchak I: Comparative analysis and visualization of genomic sequences using VISTA browser and associated computational tools. Methods Mol Biol. 2007, 395: 3-16.View ArticlePubMed
- Caspi A, Pachter L: Identification of transposable elements using multiple alignments of related genomes. Genome Res. 2006, 16: 260-270.PubMed CentralView ArticlePubMed
- Parra G, Agarwal P, Abril JF, Wiehe T, Fickett JW, Guigo R: Comparative gene prediction in human and mouse. Genome Res. 2003, 13: 108-117.PubMed CentralView ArticlePubMed
- Alexandersson M, Cawley S, Pachter L: SLAM: cross-species gene finding and alignment with a generalized pair hidden Markov model. Genome Res. 2003, 13: 496-502.PubMed CentralView ArticlePubMed
- Korf I, Flicek P, Duan D, Brent MR: Integrating genomic homology into gene structure prediction. Bioinformatics. 2001, 17 (Suppl 1): S140-148.View ArticlePubMed
- Stajich JE, Block D, Boulez K, Brenner SE, Chervitz SA, Dagdigian C, Fuellen G, Gilbert JG, Korf I, Lapp H: The Bioperl toolkit: Perl modules for the life sciences. Genome Res. 2002, 12: 1611-1618.PubMed CentralView ArticlePubMed
- Lowe TM, Eddy SR: tRNAscan-SE: a program for improved detection of transfer RNA genes in genomic sequence. Nucleic Acids Res. 1997, 25: 955-964.PubMed CentralView ArticlePubMed
- DAWGPAWS SourceForge Project Page. [http://sourceforge.net/projects/dawgpaws/]
This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.