In prokaryotes, Shine–Dalgarno (SD) sequences, nucleotides upstream from start codons on messenger RNAs (mRNAs) that are complementary to ribosomal RNA (rRNA), facilitate the initiation of protein synthesis. The location of SD sequences relative to start codons and the stability of the hybridization between the mRNA and the rRNA correlate with the rate of synthesis. Thus, accurate characterization of SD sequences enhances our understanding of how an organism's transcriptome relates to its cellular proteome. We implemented the Individual Nearest Neighbor Hydrogen Bond model for oligo–oligo hybridization and created a new metric, relative spacing (RS), to identify both the location and the hybridization potential of SD sequences by simulating the binding between mRNAs and single-stranded 16S rRNA 3′ tails. In 18 prokaryote genomes, we identified 2,420 genes out of 58,550 where the strongest binding in the translation initiation region included the start codon, deviating from the expected location for the SD sequence of five to ten bases upstream. We designated these as RS+1 genes. Additional analysis uncovered an unusual bias of the start codon in that the majority of the RS+1 genes used GUG, not AUG. Furthermore, of the 624 RS+1 genes whose SD sequence was associated with a free energy release of less than −8.4 kcal/mol (strong RS+1 genes), 384 were within 12 nucleotides upstream of in-frame initiation codons. The most likely explanation for the unexpected location of the SD sequence for these 384 genes is mis-annotation of the start codon. In this way, the new RS metric provides an improved method for gene sequence annotation. The remaining strong RS+1 genes appear to have their SD sequences in an unexpected location that includes the start codon. Thus, our RS metric provides a new way to explore the role of rRNA–mRNA nucleotide hybridization in translation initiation. More than 30 years ago researchers first discovered a sequence of messenger RNA (mRNA) nucleotides in bacteria that ribosomes recognize as a signal for where to begin protein synthesis. Today, genome annotation software takes advantage of this finding and uses it to help identify the location of start codons. Because these sequences, now referred to as Shine–Dalgarno (SD) sequences, are always upstream from start codons, annotation programs look for them in the region 5′ to these candidate sites. In a comprehensive analysis of 18 bacterial genomes, the authors show that when looking for SD sequences, it sometimes pays off to analyze unlikely locations. By examining the region that immediately surrounds the start codon for SD sequences, the authors identify many mis-annotated genes and in so doing offer a method to help check for these in future annotation projects. Escherichia coli's 16S ribosomal RNA (rRNA) and observed that part of the sequence, 5′–ACCUCC–3′, was complementary to a motif, 5′–GGAGGU–3′, located 5′ of the initiation codons in several messenger RNAs (mRNAs). They combined this observation with previously published experimental evidence and suggested that complementarity between the 3′ tail of the 16S rRNA and the region 5′ of the start codon on the mRNA was sufficient to create a stable, double-stranded structure that could position the ribosome correctly on the mRNA during translation initiation. The motif on the mRNAs, 5′–GGAGGU–3′, and variations on it that are also complementary to parts of the 3′ 16S rRNA tail, have since been referred to as the Shine–Dalgarno (SD) sequence. Shine and Dalgarno's theory was bolstered by Steitz and Jakes in 1975 [In 1974 Shine and Dalgarno [ in 1975  and evenSince Shine and Dalgarno's publication, two different approaches have been used to identify and position SD sequences in prokaryotes: sequence similarity and free energy calculations.Methods based on sequence similarity include searching upstream from start codons for sub-strings of the SD sequences that are at least three nucleotides long . IdentifG °) [G ° values for progressive alignments of the rRNA tail with the mRNA in the region upstream of the start codon [G ° upstream of the start codon whose location is largely coincident with the SD consensus sequence. This second approach can both identify the SD sequence and pinpoint its exact location as that having the minimal ΔG ° value. However, the exact location of the SD sequence is dependent on the nucleotide indexing scheme of the algorithm, i.e., on which nucleotide is designated as the “0” position.The second approach, using free energy calculations, is based on thermodynamic considerations of the proposed mechanism of 30S binding to the mRNA and overcomes the limitations of sequence analysis. Watson–Crick hybridization occurs between the 3′-terminal, single-stranded nucleotides of the 16S rRNA (the rRNA tail) and the SD sequence in the mRNA and has a significant effect on translation . The forG °) –14. Thisrt codon ,6,15,16.relative spacing (RS). This metric localizes binding across the entire translation initiation region (TIR), relative to the rRNA tail, enabling us to characterize binding that involves the start codon as well as sequences downstream. RS is also independent of the length of the rRNA tail, and this property allows for comparison of binding locations between species.To normalize indexing and to further extend free energy analysis through the start codon and into the coding region of genes, we created a new metric, leaderless mRNAs [By examining sequences downstream from start codons, we could explore mRNAs that lack any upstream region, the ss mRNAs –22. The ss mRNAs , has beess mRNAs . Thus, tss mRNAs ,25.G ° troughs for genes of 18 species of prokaryotes as a test of its usefulness as a means to improve existing annotation tools, i.e., by identifying SD sequences. We observe 2,420 genes where the strongest binding in the entire TIR takes place one nucleotide downstream from the start codon, at RS+1. Of these, 624 genes have unusually strong binding . We then determine if these 624 genes were mis-annotated and conclude that 384 are.In this study we use the RS metric to identify the positions of minimal ΔG ° value at each position of the TIR for each species is shown in G ° troughs upstream from RS 0 are consistent with previous experimental studies on the location of the SD sequence [G ° trough immediately after the first base in the initiation codon, at RS+1, is unexpected, but present in a significant portion of genes in all species examined. The histograms of G ° < −3.4535, see the Materials and Methods section for more details) in each TIR for all genes within a species. For all genes that contain an SD-like sequence, we will call genes where the lowest ΔG ° value is at RS+1, +1 genes, and +1 genes where ΔG ° < − 8.4 kcal/mol, strong +1 genes. Genes where the strongest SD-like sequence is between RS-20 and RS-1, inclusive, are designated upstream genes, and similarly, downstream genes are genes where the strongest SD-like sequence is between RS+1 and RS+20, keeping in mind that these designations do not imply that other SD-like sequences do not exist in the TIR, but only that they do not bind with as low a ΔG ° value to the rRNA. If a trough of minimal free energy can be definitive of the SD sequence, a site whose location is presumed to be upstream from the coding region, the +1 genes are unexpected in that they exist within, not upstream from, the coding region. Our study focuses on the characterization of the sequence interactions that give rise to strong +1 genes and on possible explanations for their presence; we have reserved the downstream genes for future analysis.The average Δsequence ,8, as wesequence ,26 or ussequence . The ΔG We thought of four hypotheses to explain the unexpected RS+1 result. 1) The +1 site is an artifact of our model or implementation. 2) The +1 trough could result from known sequence bias around the start codon, assuming the start codon annotation is correct. 3) The start codon annotation could be incorrect: the presence of in-frame start codons downstream of the annotated start codons would be consistent with this interpretation. 4) If there were sequence errors in the start codon, they could potentially change the free energy calculation for alignments in which the three nucleotides of the start codon participated. All four of these hypotheses were examined.We were quickly able to dispose of our first hypothesis. The +1 site is not an artifact of the individual nearest neighbor–hydrogen bond (INN-HB) model or its implementation. Both the individual nearest neighbor (INN) and the INN-HB RNA secondary structure models are based on thermodynamics and use experimentally derived parameters. Implementations of INN models using dynamic programming have a well-established history of accurately predicting secondary structures for short RNA sequences ,14,28, aE. coli have shown considerable bias in the second codon, too [http://weblogo.berkeley.edu/) were created for the region of mRNA that would be aligned with the rRNA tail for RS+1 . E. coli genes that includes the first two codons. This logo was representative of the sequence logos for all 18 organisms (unpublished data). For E. coli, the sequence logo gives two options for relatively abundant sequences that could bind to the rRNA tail: AUGA and GUGA. AUGA has a positive ΔG ° value of 0.21 kcal/mol and cannot explain the trough of ΔG °. The alternate sequence, GUGA, has a negative ΔG ° value of −1.88 kcal/mol. However, if all 570 E. coli genes whose start codons are GUG had this value, the total would be too small to cause the average value of the 4,254 E. coli genes to be −0.79 kcal/mol. Using the same approach with the sequence logos for the remaining 17 organisms, sequence bias of the first two codons also failed to explain the average negative free energy trough associated with the RS+1 alignment.The second hypothesis assumes that the significant negative free energy value at RS+1 results primarily from nucleotide biases in the first two codons of the coding region. Obviously there is extreme codon bias in the start codon for all genes and, therefore, for all species examined, as shown in don, too –37. To edon, too ,39 . In the case of the remaining genes, the changes resulted in many more of the initiation regions having their most stable binding at RS+1. However, the ΔG ° value at RS+1 in these modified start codon sequences was only marginally stronger than the free energy trough still present at the upstream SD site. The small difference in energy values between the upstream SD site and the RS+1 site contrasts with that seen using the actual sequences of RS+1 genes. In those cases, the difference in energy values is quite large, as seen in The fourth hypothesis proposes that sequence errors might account for the presence of a minimal free energy trough at the RS+1 alignment. To examine this idea further, There is a long history of investigating SD sequences using approaches grounded in thermodynamics ,15,16,26Three major differences separate our method from prior methods. The primary difference is that we are examining both upstream and downstream sequences. Investigating downstream sequences allowed us to observe the large number of hybridization sites that include the start codon. The second main difference is our use of RS as a means to compare hybridization locations among species. The third difference is our use of the INN-HB model instead of the INN model.There are also many minor differences between our method and its predecessors. The most common are discrepancies in rRNA tail selection. We defined the 16S rRNA tails based on proposed secondary structures and conserved single-stranded 16S rRNA motifs. The sequences we used are the maximum number of single-stranded nucleotides available for hybridization based on accepted models of rRNA secondary structure. Osada et al. used the last 20 nucleotides of the 16S rRNA sequence without consideration of secondary structure models and the intramolecular helix formation that a significant portion of their 5′ bases are involved in . On the G ° trough indicative of SD sequences in Synechocystis [G ° ≥ 0 kcal/mol, and thus no discernible binding site for the rRNA tail, we were able to identify eight as +1 genes, and two as having stronger than average SD sequences between five and ten bases upstream from the start codons. Of the eight +1 genes, two had in-frame start codons within 12 bases downstream from the annotated start codon. The remaining 28 genes were able to bind to the rRNA tail farther downstream from the annotated start codon. These results show the benefit of our approach by providing more resolution of the TIR in genes that have unusual nucleotide sequences relative to previous methods.As a result of these differences, our method improves SD sequence characterization. hocystis . Our methocystis  shows beE. coli, none are experimentally verified, and they have no assigned function, making it likely that they are not true genes, but only vestigial ORFs.Our method is also useful for detecting errors in sequence annotation. B. longum's strong +1 gene rnpA, a ribonuclease P protein component, does not contain an in-frame start codon downstream from the annotated start site. CTC02285, a strong +1 gene in Clostridium tetani that codes for protein translation initiation factor 3 (IF3), is also without a downstream initiation codon. Bradyrhizobium japonicum has many strong +1 genes without downstream start codons: polE, which codes for the polymerase epsilon subunit, cycK, nah, and 52 others. Thus, while a large percentage of the strong +1 genes appears to be the result of sequence annotation errors, there remains a significant number that require an alternative explanation.That said, it is harder to understand the strong +1 genes that do not appear to be the result of annotation errors in the 17 other organisms we studied. For example, Two possible explanations for strong +1 genes that do not seem to be artifacts of annotation errors are: 1) the +1 site could stimulate translation initiation on leaderless genes, and 2) the binding site at RS+1 could be used as a translational standby site, i.e., sequences that hold the 16S rRNA close to the SD sequence . In the E. coli with more than 100 amino acids contained upstream SD sequences. The two cyanobacteria in our study, Nostoc and Synechocystis, both have relatively small percentages of upstream SD sequences. These two organisms are believed to be closely related to the free living predecessor of chloroplasts, which are thought to use SD sequences as well as alternative mechanisms to recruit ribosomes for translation . All genome sequences were downloaded from the National Center for Biotechnology Information (NCBI) GenBank database . Several observations determined this sequence window. In the majority of cases examined, SD sequences were within 10 nucleotides of the start codon. Although the hypothesis that a downstream box interacted with rRNA during translation initiation  was rejehttp://www.rna.icmb.utexas.edu). We defined the 3′ tail as the single-stranded terminal 3′ nucleotides, and then, to verify consistency, compared these sequences with all annotated copies of the 16S rRNA in the genome.To determine the 3′ tails for the 16S rRNAs, we downloaded predicted secondary structures from The Comparative RNA Web Site   is the free energy released by the hybridization of a particular nearest neighbor doublet, and jn is its number of occurrences in the duplex. mterm−AU is the number of terminal AU pairs, and ΔG term−AU ° is the free energy penalty for having a terminal AU pair. Finally, ΔGsym ° is the penalty for internal symmetry and Loopk the penalty for the kth internal loop. free_scan's hybridization parameter values for Watson-Crick binding are from Xia et al. [G °. Bulges, where one of the two strands of RNA has intervening nucleotides between bases that bond with the other strand, as well as secondary structures involving only one of the two strands of RNA, are ignored due to uncertainty about how much space is available within the 30S ribosomal complex to accommodate these structures, as well as the limitations they put on our ability to calculate RS. Dangling 5′ or 3′ ends are not considered because of ambiguities about what constitutes a dangling end on the mRNA sequences and on the 5′ end of the 16S rRNA tail.In this formula, Δa et al. , G/U misa et al. , and looa et al. . free_scG ° values to be computed before the start codon, one at the start codon, and 35 ΔG ° values after.After the free energy value for the first alignment in the mRNA is calculated, free_scan shifts the rRNA tail downstream one base, and the second alignment is examined. This process, illustrated in G ° estimates obtained using the prior INN models [Xia et al. created the INN-HB model  to improN models ,51–54. TN models .G ° value for each RS position . The INN-HB, however, does not assign all three hybridizations the same Δhttp://sourceforge.net/projects/free2bind.Our programs, free_scan and free_align are available at Source Forge: G ° value calculated within the initiation region. If ΔG ° > −3.4535 kcal/mol, then the gene was assumed not to have an SD sequence. This threshold is based on the work of Ma et al. [We located the SD sequence by the position of the lowest Δa et al.   for genes mentioned in this paper are: radC (948968); rnpA (1023245); CTC02285 (1060453); polE (1051409); cycK (1053038); nah (1053188); rpsA (945536); wecF (2847677); argD (947864); and hcaF (946997).Accession numbers from the National Center for Biotechnology Information (NCBI) GenBank database (


To identify as many different transcripts/genes in the Atlantic salmon genome as possible, it is crucial to acquire good cDNA libraries from different tissues and developmental stages, their relevant sequences (ESTs or full length sequences) and attempt to predict function. Such libraries allow identification of a large number of different transcripts and can provide valuable information on genes expressed in a particular tissue at a specific developmental stage. This data is important in constructing a microarray chip, identifying SNPs in coding regions, and for future identification of genes in the whole genome sequence. An important factor that determines the usefulness of generated data for biologists is efficient data access. Public searchable databases play a crucial role in providing such service.Twenty-three Atlantic salmon cDNA libraries were constructed from 15 tissues, yielding nearly 155,000 clones. From these libraries 58,109 ESTs were generated, of which 57,212 were used for contig assembly. Following deletion of mitochondrial sequences 55,118 EST sequences were submitted to GenBank. In all, 20,019 unique sequences, consisting of 6,424 contigs and 13,595 singlets, were generated. The Norwegian Salmon Genome Project Database has been constructed and annotation performed by the annotation transfer approach. Annotation was successful for 50.3%  of the sequences and 6,113 sequences (30.5%) were annotated with Gene Ontology terms for molecular function, biological process and cellular component.Salmo salar), EST sequencing, clustering, and annotation by assigning putative function to the transcripts. These sequences represents 97% of all sequences submitted to GenBank from the pre-smoltification stage. The data has been grouped into datasets according to its source and type of annotation. Various data query options are offered including searches on function assignments and Gene Ontology terms. Data delivery options include summaries for the datasets and their annotations, detailed self-explanatory annotations, and access to the original BLAST results and Gene Ontology annotation trees. Potential presence of a relatively high number of immune-related genes in the dataset was shown by annotation searches.We describe the construction of cDNA libraries from juvenile/pre-smolt Atlantic salmon ( The role of aquaculture in world food industry has rapidly become more important in the last 20 years. Atlantic salmon is an important aquaculture species with an interesting biology. It spawns in fresh water and develops through several stages before migrating to the sea to feed, a dramatic change of habitat that requires physiological, morphological and behavioural changes. In addition the salmonids did undergo a duplication event 25–100 Myr ago . As an iThe major goal of all farm animal genome projects is to identify the genetic mechanisms responsible for important and commercially interesting traits, such as disease resistance, growth, meat colour, fat deposition etc. in order to implement these results in the breeding and management programmes. Compared to most other farmed animals there is still a large stock of wild fish for most aquaculture species. This means that there is also a great need for managing the wild populations. To identify these genetic mechanisms one needs access to various tools such as a genetic and physical map, polymorphic markers (both microsatellites and SNPs), cDNA libraries, ESTs and full-length gene sequences, and preferably the whole genome sequence. In addition bioinformatics tools and databases are needed to extract biologically meaningful results from this large amount of data.For Atlantic salmon some of these resources have been developed. They include genetic markers and maps -4, a BACSalmo Salar EST sequences in GenBank, of these 55,118 EST sequences have been generated and submitted as part of the Salmon Genome Project. The DFCI (TIGR) annotated Atlantic salmon Gene Index (AsGI) . Table We performed 5' sequencing for approximately 75,000 pre-smolt cDNA clones from these libraries. Approximately 68,500 sequences (raw data) were loaded into the Salmon Genome Project (SGP) database as described in Construction and content. After loading, all sequences were subjected to pre-processing in order to clean out poor quality sequences and to trim off vector and linker sequences. After pre-processing there remained 58,109 (84%) high quality sequences, which have been marked as pre-processed ("passed") in the SGP database. The pre-processed sequences were submitted to the GenBank dbEST  after reThe project relies heavily on bioinformatics data processing and analysis and we have constructed the SGP data resource (Construction and content), which was used for the sequence processing, contig assembly, annotation, and project data hosting. All sequences, as well as other data and results, can be accessed through the SGP data resource as described in Table salmonidae. The matches in PDB and SWISS-PROT databases were considered to be potentially more informative. A match in the PDB database leads to the PDB entry, which contains a link to the UNIPROT entry and, depending on the length of aligned query sequence opens a possibility of further function prediction by protein structure modelling. A SWISS-PROT match allowed access to detailed annotation data for the match sequence verified to high quality standards, including function assignment and cross-links with other databases. Where sequences in SWISS-PROT had GO terms [salmonidae-specific search with the SGP dataset was performed on the NCBI NT database as an attempt to identify the SGP sequences which are similar to those that had previously been annotated as belonging to salmonidae. The results of this automatic annotation, loaded in the SGP database as the SGP-Sal dataset inevitably include some mismatches in its 1,768 hits, but provide a useful estimate of the possibly known salmonidae genes in the SGP data. Another annotation was performed where the sequences with salmonidae hits were excluded from the SGP dataset. The dataset identified as SGP-noSal was also loaded. These annotation datasets are available under the Clustered data datasets, Clustered data summary and Annotations menus, as well as via searches in the SGP database.Contig consensus sequences and singlet sequences were annotated by the BLAST-GO automatic annotation pipeline. The annotation focused on the specialised, detailed results, which provide novel information and extend the currently available annotations data for GO terms  assignedGO terms ,25, a puThere are three major ways of accessing the SGP data resource – by the Clustered data datasets and Clustered data summary menus, and via the SGP database searches. The Clustered data datasets menu Figure  providesThe detailed annotation output format includes length of the matched segment of the query and per cent identity with the aligned sequence Figure . TherefoAnnotation statistics are shown in Table Putative assignments of the GO terms (Construction and content) referred to here as GO-GOA annotation were made for 30% of the SGP contigs and singlets, with almost 50% of the contigs returning GO-GOA hits. The GO-GOA annotations present a very uneven picture, Figure Important function categories such as development, immune response and other were selected to assess the difference between annotations searches with the keywords representing broader search terms and alternatively exact GO terms. Results, listed in Table Salmo salar). Subsequent EST sequencing and clustering yielded 6,424 contigs and 13,595 singlets, resulting in a total of 20,119 unique sequences.We have constructed 23 tissue specific cDNA libraries from pre-smolt Atlantic salmon  terms for molecular function, biological process and cellular component.All data on ESTs, clustering and annotation can be accessed via the SGP data resource . There iOn the whole, annotation searches in the SGP database and access to annotations as datasets summary or as detailed results offer a powerful tool for exploring, at different levels of granularity, biological features reflected in the EST data. A database search, which can be done using sophisticated keywords search options, will produce an overview of the highly reliable sequence similarities ("best hits") and their gene and function annotations including GO assignments. For each of the "best hits" displayed in the overview, a separate link will produce a detailed annotation output presented in a user-friendly format, listing all significant hits in all databases used in the annotation. Users wishing to explore further annotation details can do this via links to the source EST sequences, dissected alignments in the original BLAST format, target (hit) sequences in the source databases, and the original GO annotation tree..The Salmon Genome Project (SGP) data resource is available at The web access is optimised for Netscape 8 and Internet Explorer.AAA supervised SGP bioinformatics, designed software and data processing techniques and developed the data resource. He carried out software development and data processing and drafted part of the manuscript.AVV, TAR, and JKL worked on software design, carried out development, implementation and data processing. AVV in addition were responsible for web design.HH-L did the construction and sequencing of the SSH as well as the normal gills and intestine libraries.BH conceived, headed and coordinated the project and performed the construction and sequencing of all cDNA libraries except those done by HH-L. He loaded all sequences into the DB and performed the sequence processing using the preAssemble pipeline. He drafted part of the manuscript.All authors have read and approved the final manuscript.


Drosophila melanogaster Release 4 genomic sequences using the combined computational evidence derived from RepeatMasker, BLASTER, TBLASTX, all-by-all BLASTN, RECON, TE-HMM and the previous Release 3.1 annotation. Our system is designed for use with the Apollo genome annotation tool, allowing automatic results to be curated manually to produce reliable annotations. The euchromatic TE fraction of D. melanogaster is now estimated at 5.3% (cf. 3.86% in Release 3.1), and we found a substantially higher number of TEs  than previously identified . Most of the new TEs derive from small fragments of a few hundred nucleotides long and highly abundant families not previously annotated . We also estimated that 518 TE copies (8.6%) are inserted into at least one other TE, forming a nest of elements. The pipeline allows rapid and thorough annotation of even the most complex TE models, including highly deleted and/or nested elements such as those often found in heterochromatic sequences. Our pipeline can be easily adapted to other genome sequences, such as those of the D. melanogaster heterochromatin or other species in the genus Drosophila.Transposable elements (TEs) are mobile, repetitive sequences that make up significant fractions of metazoan genomes. Despite their near ubiquity and importance in genome and chromosome biology, most efforts to annotate TEs in genome sequences rely on the results of a single computational program, RepeatMasker. In contrast, recent advances in gene annotation indicate that high-quality gene models can be produced from combining multiple independent sources of computational evidence. To elevate the quality of TE annotations to a level comparable to that of gene models, we have developed a combined evidence-model TE annotation pipeline, analogous to systems used for gene annotation, by integrating results from multiple homology-based and de novo TE identification methods. As proof of principle, we have annotated “TE models” in  A first step in adding value to the large-scale DNA sequences generated by genome projects is the process of annotation—marking biological features on the raw string of adenines, cytosines, guanines, and thymines. The predominant goal in genome annotation thus far has been to identify gene sequences that encode proteins; however, many functional sequences exist in non-protein-coding regions and their annotation remains incomplete. Mobile, repetitive DNA segments known as transposable elements (TEs) are one class of functional sequence in non-protein-coding regions, which can make up large fractions of genome sequences  and can play important roles in gene and chromosome structure and regulation. As a consequence, there has been increasing interest in the computational identification of TEs in genome sequences. Borrowing current ideas from the field of gene annotation, the authors have developed a pipeline to predict TEs in genome sequences that combines multiple sources of evidence from different computational methods. The authors' combined-evidence pipeline represents an important step towards raising the standards of TE annotation to the same quality as that of genes, and should help catalyze their understanding of the biological role of these fascinating sequences. Transposable elements (TEs) are mobile, repetitive DNA sequences that constitute a structurally dynamic component of genomes. The taxonomic distribution of TEs is virtually ubiquitous: they have been found in nearly all eukaryotic organisms studied, with few exceptions. TEs represent quantitatively important components of genome sequences , and thhttp://www.repeatmasker.org/), which recent studies indicate may be “neither the most efficient nor the most sensitive approach” for TE annotation ) tail. This occurs because the reference sequence has a shorter poly(A) tail than a particular genomic copy. In general, these cases are easily identified by observing an overlapping poly(A) simple repeat at the 3′ end of the element. One solution to this problem is to extend the poly(A) tail of non-LTR retrotransposons in the reference set to the length of the longest observed genomic copy.The biggest pitfall we have encountered is the problem posed by simple repeats that exist in TE reference sequences. Without a specific treatment of this problem we would have included 3,040 spurious hits—approximately one-third of our original set of annotations. Filtering simple repeats on the genomic or reference sequences without affecting the sensitivity of TE detection is not easy. We have developed an effective (but ad hoc) two-step filtering strategy, but the magnitude of this problem leaves room for future improvements. Currently we employ RM to detect simple repeats, although refined parameter optimization may reveal that other more specialized simple repeat detection software, such as TRF , Mreps , or otheRegardless of the best method or criteria to detect simple repeats, the existence of simple repeats in TE reference sequences raises an important problem, since it is difficult to unambiguously determine whether a simple repeat with homology to a TE is a spurious hit or reflects a true remnant of that TE in the genome. Our methods guarantee that if we leave a spurious hit in the annotation because of homology with a simple repeat, it is more than 170 bp long. Moreover, any potentially real TE labeled as spurious that did not survive our rescue strategy bears no unique hallmarks of being generated by a TE. Nevertheless, the possibility of the involvement of TEs in the genesis of microsatellites  highlighD. melanogaster. Our automated pipeline allows us to annotate TEs on a genomic scale quickly and accurately, and the integration of our pipeline with the Apollo annotation tool also allows rapid evaluation and manual editing of TE annotations for even complex TE models. Based on the lessons learned in this study, we are continuing to develop and improve our pipeline. We are automating several classes of the manual edits that we have identified and expect that progressively fewer manual edits will be necessary in the future, allowing application of our pipeline to larger genome sequences such as the human sequence. One possible solution to the simple repeat problem is to develop a “combined sensor” model that would seek to resolve competing signals between simple repeats and TE models. It may also be possible to predict nested elements that require manual edits by using a stochastic context-free grammar [We have shown in this work that a combined-evidence framework can improve the quality and confidence of TE annotations in  grammar  approachD. melanogaster genome. Since the methods that support these predictions potentially suffer from a high false positive rate, we have chosen not to include them in our current annotation, since more work needs to be done to validate these potential new TE families. Nevertheless the combined evidence for some of these elements is compelling and such cases are available for mining in our current results.We have observed several cases in the genome annotation where one or more de novo methods  simultaneously support a potential sequence belonging to a new TE family. In addition, results of our analyses with tools that detect anonymous TEs see  suggest Drosophila. We hope that the TE annotations presented here will serve to further the development and refinement of TE discovery and annotation methods in general, as the Release 3.1 annotations have served for the development of our current methods.In general, the problem of TE discovery remains a major challenge for TE annotation. A good TE annotation relies critically on an expertly assembled reference sequence set, data that currently cannot be obtained in an automatic fashion. This crucial step is now the bottleneck in any method or pipeline to annotate TEs in genome sequences . The taFinally, we are also developing our pipeline to include methods for the detailed annotation of the structural features  in TE sequences. Development of such detailed annotation methodologies will allow a detailed evaluation of the coding and expression potential of individual TE annotations in genomic sequences. Moreover, the ability to automatically annotate structural features of TEs will facilitate the manual curation and validation of candidate TE sequences resulting from one or several different de novo TE discovery methods –16. ContD. melanogaster genomic sequences and TE reference sets are available from BDGP (http://www.fruitfly.org/). The Release 3.1 D. melanogaster genomic sequences and their TE annotations have been extracted from the GAME-XML files. The Release 4 D. melanogaster genomic sequences have been downloaded as fasta files. TE reference sequence sets v.7.1  and v.9http://www.girinst.org). We used them to detect unknown families by similarity with TEs from other species.Sequences of the TEs were also obtained from the Repbase Update database release 8.12 , which cftp://ftp.ncbi.nlm.nih.gov/blast/) programs were used with default parameters, using as a query genomic fragments of 50 kb, overlapping by 100 bp.We have improved three C++ programs: BLASTER, MATCHER, and GROUPER, previously presented in Quesneville et al. . BLASTERE-value greater than 1 × 10−10 or length of 20 or less are eliminated.MATCHER has been developed to map match results onto query sequences by first filtering overlapping hits. When two matches overlap on the genomic (query) sequence, the one with the best alignment score is kept; the other is truncated so that only nonoverlapping regions remain on the match. As a result of this procedure a match is totally removed only if it is included in a longer one with a best score. All matches that have Long insertions (or deletions) in the query or subject could result in two matches, instead of one with a long gap. Thus, the remaining matches are chained by dynamic programming. A score is calculated by summing match scores and subtracting a gap penalty (0.05 times the gap length) and a mismatch penalty (0.2 times the mismatch length region) as in .The chaining algorithm  to gather similar sequences into groups by simple link clustering. A match belongs to a group if one of the two matching sequence coordinates overlaps a sequence coordinate of this group by more than a given length coverage percentage threshold (a program parameter). If the two matches overlap with this constraint, their coordinates are merged, taking the extremum of the both. Groups that share sequence locations—not previously grouped because of a too low length coverage percentage—are regrouped into what we call a cluster. As a result of these procedures, each group contains sequences that are homogeneous in length. A given region may belong to several groups, but all of these groups belong to the same cluster.http://www.repeatmasker.org) screens for TEs and low-complexity DNA sequences. It detects TEs in nucleic acid sequences by nucleic sequence alignment with previously characterized elements using the program Cross_match (http://www.phrap.org/phredphrapconsed.html) or WU-BLAST (http://blast.wustl.edu) with the script MaskerAid [RepeatMasker  and (2) and the counts of true positive (TP—correctly annotated as belonging to a TE), false positive , true negative (TN—correctly annotated as not belonging to a TE), and false negative  nucleotides.A high sensitivity indicates that a method misses few TE nucleotides . A high specificity indicates that a method finds few false positive nucleotides.The second Python script compared the boundaries of predictions to the boundaries of the reference annotations. For each prediction under test, we searched the reference annotations that overlapped on the same genomic region. Different cases could be distinguished according to one-to-one, one-to-many, many-to-one, or many-to-many relationships  had a large insertion or deletion. In this case, the two fragments (flanking the indel) were predicted as two separate copies, and the fragments were not joined. We called this error class “method not joined”. We also found cases in which two predictions were falsely considered as one in the reference annotation. Here, a long region of mismatch separated two fragments and the most parsimonious explanation was the independent insertion of two copies. These were “annotation over-joined” cases. We also found cases considered as one copy by the reference annotation, but that were in fact copies with a self-duplicated region. If the duplication was nested we call it “same TE nested”, or if not nested, “TE duplication”.One-to-many relationships were cases in which two annotations in the reference were found joined by the method. We called this “annotation not joined”.One-to-zero relationships corresponded to cases in which a prediction did not correspond to a reference annotation. “New TE” cases were copies identified by the method under test but not present in the reference annotation, and “different TE” cases were those overlapping a reference annotation but with a different TE family name. A TE prediction included in a prediction of a different family already involved in a given relationship with reference annotations, was called “new nest” if no corresponding reference annotation could be found. Annotation correspondence of the same TE family but on different strand was called “other strand” if the relationship was one-to-one; otherwise they were “new TE”.Finally we had a “complex structure” case when the relation was many-to-many.The script could be also used in an anonymous mode to test boundaries of de novo predictions that do not use a specific reference sequence. The information used for such comparisons is of poorer quality since we do not have alignment coordinates on the reference sequence , which renders several categories meaningless .


Picea glauca [Moench] Voss).The sequencing and analysis of ESTs is for now the only practical approach for large-scale gene discovery and annotation in conifers because their very large genomes are unlikely to be sequenced in the near future. Our objective was to produce extensive collections of ESTs and cDNA clones to support manufacture of cDNA microarrays and gene discovery in white spruce  also matched rice or Arabidopsis genomes. We used several sequence similarity search approaches for assignment of putative functions, including blast searches against general and specialized databases , Gene Ontology term assignation and Hidden Markov Model searches against PFAM protein families and domains. In total, 70% of the spruce transcripts displayed matches to proteins of known or unknown function in the Uniref100 database . We identified multigenic families that appeared larger in spruce than in the Arabidopsis or rice genomes. Detailed analysis of translationally controlled tumour proteins and S-adenosylmethionine synthetase families confirmed a twofold size difference. Sequences and annotations were organized in a dedicated database, SpruceDB. Several search tools were developed to mine the data either based on their occurrence in the cDNA libraries or on functional annotations.We produced 16 cDNA libraries from different tissues and a variety of treatments, and partially sequenced 50,000 cDNA clones. High quality 3' and 5' reads were assembled into 16,578 consensus sequences, 45% of which represented full length inserts. Consensus sequences derived from 5' and 3' reads of the same cDNA clone were linked to define 14,471 transcripts. A large proportion (84%) of the spruce sequences matched a pine sequence, but only 68% of the spruce transcripts had homologs in This report illustrates specific approaches for large-scale gene discovery and annotation in an organism that is very distantly related to any of the fully sequenced genomes. The ArboreaSet sequences and cDNA clones represent a valuable resource for investigations ranging from plant comparative genomics to applied conifer genetics. Pinus taeda ranged from 11 pg  Voss). This collection of ESTs constitutes an important new resource for the genomics of white spruce and related species. In this paper, we report the sequence analysis of around 71,000 sequence reads obtained through 3' and 5' sequencing of cDNAs. Comparative analyses were conducted to assign a functional annotation based upon similarities. Spruce contigs were also correlated with terms derived from the Gene Ontology  and [Genbank:CO472624-CO490610].Sequence traces from the spruce EST libraries were analyzed with the equences . Peaks w.990329) . Phrap cormation  under acace file generated by Phrap. From the ace file, several important characteristics of a consensus sequence and its member sequences can be determined. The first characteristic used in this process is the "shape" of the consensus sequence, or how the assembled reads overlap each other. This can be thought of as the profile of the consensus sequence member distribution. Consensus sequences are classified as being of block, staircase, or dumbell shape. Contigs with a dumbell shape are candidates for additional evaluation.The quality control of resulting consensus sequences used a system developed at the CCGB. This system uses information that is included in the contig Phrap provides information on the quality regions of assembled sequences, which is used for this step. If the high quality region of the read (as defined by the Phrap ace file) has less than 95% consistency with the consensus sequence of the contig, or has more than 5 mismatched bases relative to the consensus, the read is flagged as a suspected chimera, provided it also shows evidence of either a polyA or polyT region.Reads within a dumbell shaped contig are evaluated for their similarity to the consensus sequence of the contig. blast hits to different proteins are found to be adjacent in the read. The process of chimera detection and removal is often repeated numerous times before arriving at a finished assembly.The final step of the quality control process is to examine the flagged reads visually to find chimeric qualities. Chimeric reads are selected and removed based on their similarity to the consensus sequence and to the individual reads in the contig. A chimeric read may also be indicated if tblastx or blastx programs [Arabidopsis (AGI11), rice (OGI16) and pine (PGI5.0), retrieved from the TIGR web site [Cycas EST assembly [Blast searches were conducted against several databases: the NCBI non redundant database (nr), the Uniref100 peptides set [Arabidopsis and rice coding sequences were downloaded from the TAIR web site [Similarity searches were performed with the programs  against web site  and agaiassembly , retrievassembly . Blast sides set , and theides set . HMM seaweb site  and the web site , respectArabidopsis proteins were analysed. For each spruce consensus sequence, the blastx hits with a minimum similarity value of 0.75 and a minimum coverage of 0.5 were used in the GO assignment procedure. Similarity was defined as hsp positive/hsp alignment length (hsp : high scoring pair). Coverage was defined as the high scoring pair alignment length × 3/ query length. Among the retained hits, whenever a spruce sequence matched a protein with an associated GO term, this term was transferred to the spruce consensus sequence. Two GO annotation lists were completed: one including evidence codes Inferred from Electronic Annotation (IEA) evidence codes and one excluding IEA evidence.To correlate the spruce consensus sequences to a Gene Ontology (GO) molecular function term, the annotations of homologous Uniref100 and NP, coordination of bioinformatics activities, data analysis, preparation of the manuscript; CP, LP, JC, JEJ, ER, sequence processing, assembly and annotation, web publishing and database development; MJM, JC, ASé, plant material production, library synthesis, and evaluation; EN, CGC, protein family sequence analyses; YB, SB, GY, JS, ASi, RH, MM, high-throughput EST sequencing and quality assurance; CP, JB, preparation of manuscript; JM, overall project supervision, preparation of manuscript.Description of tissues used for cDNA library synthesis: genotype, treatments , organ, tissue and developmental stage.Click here for fileAnnotation of proteins related to the cell wall based on similarities with sequences from the Cell Wall Navigator Database [and lignin biosynthesis enzymes [tblastx searches with e-value < 1e-10.Database and ligni enzymes . Spruce Click here for file


With the exponential increase in genomic sequence data there is a need to develop automated approaches to deducing the biological functions of novel sequences with high accuracy. Our aim is to demonstrate how accuracy benchmarking can be used in a decision-making process evaluating competing designs of biological function predictors. We utilise the Gene Ontology, GO, a directed acyclic graph of functional terms, to annotate sequences with functional information describing their biological context. Initially we examine the effect on accuracy scores of increasing the allowed distance between predicted and a test set of curator assigned terms. Next we evaluate several annotator methods using accuracy benchmarking. Given an unannotated sequence we use the Basic Local Alignment Search Tool, BLAST, to find similar sequences that have already been assigned GO terms by curators. A number of methods were developed that utilise terms associated with the best five matching sequences. These methods were compared against a benchmark method of simply using terms associated with the best BLAST-matched sequence (best BLAST approach).The precision and recall of estimates increases rapidly as the amount of distance permitted between a predicted term and a correct term assignment increases. Accuracy benchmarking allows a comparison of annotation methods. A covering graph approach performs poorly, except where the term assignment rate is high. A term distance concordance approach has a similar accuracy to the best BLAST approach, demonstrating lower precision but higher recall. However, a discriminant function method has higher precision and recall than the best BLAST approach and other methods shown here.Allowing term predictions to be counted correct if closely related to a correct term decreases the reliability of the accuracy score. As such we recommend using accuracy measures that require exact matching of predicted terms with curator assigned terms. Furthermore, we conclude that competing designs of BLAST-based GO term annotators can be effectively compared using an accuracy benchmarking approach. The most accurate annotation method was developed using data mining techniques. As such we recommend that designers of term annotators utilise accuracy benchmarking and data mining to ensure newly developed annotators are of high quality. Genomics research is generating enormous quantities of DNA and protein sequence data. GenBank, a major repository of genomic data, reports an exponential increase in sequence data, in the last 10 years the quantity of data has increased more than two-hundred-fold          terms were all assigned to a single sequence         While observed number < 5            Drop lowest scoring term based on assignment method            observed = number of occurrences where term-            combination [i] terms were all assigned to a single            sequence         End while1/max)*(n2/max)... *(nn/max)) * max [where max         expected =       While test statistic < chi-square critical value AND terms > 1      Assign these terms to sequence   End IfEnd forCEJ undertook initial study design, software implementation, statistical analysis and interpretation, and drafted the initial manuscript. UB and ALB participated in the final study design, coordinated the study and contributed to the final manuscript.


Genome annotation can be viewed as an incremental, cooperative, data-driven, knowledge-based process that involves multiple methods to predict gene locations and structures. This process might have to be executed more than once and might be subjected to several revisions as the biological (new data) or methodological (new methods) knowledge evolves. In this context, although a lot of annotation platforms already exist, there is still a strong need for computer systems which take in charge, not only the primary annotation, but also the update and advance of the associated knowledge. In this paper, we propose to adopt a blackboard architecture for designing such a systemWe have implemented a blackboard framework  for developing automatic annotation systems. The system is not bound to any specific annotation strategy. Instead, the user will specify a blackboard structure in a configuration file and the system will instantiate and run this particular annotation strategy. The characteristics of this framework are presented and discussed. Specific adaptations to the classical blackboard architecture have been required, such as the description of the activation patterns of the knowledge sources by using an extended set of Allen's temporal relations. Although the system is robust enough to be used on real-size applications, it is of primary use to bioinformatics researchers who want to experiment with blackboard architectures.In the context of genome annotation, blackboards have several interesting features related to the way methodological and biological knowledge can be updated. They can readily handle the cooperative  and opportunistic (the flow of execution depends on the state of our knowledge) aspects of the annotation process. H. influenzae, was obtained in 1995. Ten years after, the number of fully sequenced genomes is steadily increasing: more than 350 bacterial and archeabacterial genomes and 20 eukaryotic genomes are presently available in public databases. However, the availability of the sequence is merely a starting point. The real challenge actually consists in interpreting and annotating the genomic text. When annotating a genome, biologists are especially looking for the genes, i.e. the regions of the chromosome containing the information to produce proteins or RNA, as well as regulatory signals. Finding all genes and regulatory signals on a complete raw genomic sequence is still an open problem, especially in the case of eukaryotic genomes where the coding regions are interspersed with non-coding regions called introns. Moreover, finding genes and signals is just the first step of the process. Once this has been done, the biologist should face the question to assign a putative function to the gene's product. This is done, for instance, by scanning databases of known proteins in order to pickup those that most resemble the protein to identify. Finally, once all these information have been collected, new and more complex questions arise, such as positioning the protein within its metabolic or gene regulation networks. All these steps compose the annotation process and involve computer programs as well as a lot of human expertise.The first complete genomic sequence of a living organism, the bacterium Genome annotation can be seen as an incremental, cooperative, data-driven, knowledge-based process . The preThe annotation is thus a long and tedious interpretation process. Moreover, it might have to be executed more than once and subjected to revisions. First of all, sequencing errors may be reported or manual corrections may be supplied by experts and will ask for the reannotation of the corrected regions. Moreover, as new prediction methods appear, they should be applied on the already annotated genomes to produce up-to-date annotations. There is therefore a strong need for computer systems which take in charge, not only the primary detection of genomic features, but the whole incremental annotation process. In this paper, we propose to adopt a blackboard architecture for designing such a system.To our knowledge, no annotation software has ever been designed as a blackboard system, but several existing automatic annotation platforms have adopted well-recognized architectures. Our purpose is not to list hereafter all the existing platforms , but Biopipe  has been• A set of "analyses", which describe how a method can be accessed and what are the adequate parameter values;• A set of rules, which specify when and how a method has to be executed; the set of rules thus defines the possible sequences of analysis methods;• A manager, which is in charge of accessing the data.Taverna  has adopIn the ImaGene  object-oIn rule-based systems, the biological data are represented by facts and the methodological knowledge is represented by rules. Rules express how facts in the current state of the system allow to infer new facts, which in turn allow the activation of rules, and so on. MagPie  is a gooIn GeneWeaver , the ann• The Primary database agents maintain a shared sequence database up to date so that it can be read and used by the agents which need these information;• The Non-redundant database agents rely on the information provided by the Primary database agents to maintain a curated non-redundant database.• The Genome agents manage the information related to a particular genome;• The Calculation agents are associated to sequence analysis methods;• The Broker agents register and manage the information on all the other agents so as to facilitate their working.One of the limitations of GeneWeaver is that it has never actually been deployed as a truly operational annotation system. However it recently gave rise to AGMIAL , a systeOther systems using multi-agent concepts have been proposed: including BioMAS/DECAF  and EDIT• The Information Extraction agents provide access to databases as well as some calculation services (such as sequence similarity search or feature predictions)• The Task agents are mostly generic middle agents except for the Annotation Agent that orchestrates the collection of information for each sequence and, therefore, provides some reasoning capabilities about sequence features.• The interface agents communicate with other agents and provide user interface to manual annotation and database querying.These various architectures can be classified into two main categories. The first category, which includes pipeline, workflow and task-based systems, is characterized by a sequential method invocation scheme. The second class, which includes multi-agent, blackboard-based and rule-based systems, is characterized by an opportunistic, event-driven, method invocation scheme. The advantages and drawbacks of these various approaches will be discussed later on in the Discussion section.Blackboard systems exhibit several similarities with aforementioned rule-based systems: a shared working memory, a procedural representation of knowledge and an inference cycle. The blackboard architecture is well known to support cooperative data-driven interpretation processes . Indeed,A blackboard system has three main components : the blaThe blackboard is a shared working space, hierarchically structured into layers. Each layer receives domain entities. These entities are produced by knowledge sources acting on entities belonging to lower layers. The bottom layer is directly populated with input data. In Hearsay, the bottom layer contains the raw signal coming from the microphone; the second layer receives the segments into which this continuous signal was decomposed; the third layer stores the hypothetical phonemes associated to each segment; on the fourth layer, these phonemes are grouped into syllables and so on, up to the last layer which contains the formal database query.Since a knowledge source may produce more than one entity for a given set of input data, the entities are given an hypothesis status. Some of them will be confirmed later on and merged into new entities stored in upper layers. Some others will be further discarded. The management of hypotheses is therefore an important feature of a blackboard system.All layers of the blackboard are observed by knowledge sources (KS). A KS takes as input the entities of one or more layers and will infer new entities to be stored on one or more higher layers. Inference of a new entity may be the result of an algorithm, a set of expert rules, a formal neural network, or any executable code. From the system point of view, a KS is actually a black box which is only known by the pattern of entities it expects as input (the activation condition) and the type of entities its produces as output. In Hearsay, the KS working on the lowest layer is a signal processing method; other KSs deal with the succession of phonemes and attempt to merge them into syllables; other KSs look up lexicons to predict words from syllable or check the syntax of a predicted sentence.The inference process of a blackboard system follows a cycle. First, as an event, such as the creation of an entity, occurs on the blackboard, the controller will inform all the KSs that are concerned by this event. Each selected KS then checks if its activation condition is satisfied or not. The resulting list of activable KSs is further sorted by the controller in order to prioritize the KSs that must be activated first. The further execution of these KSs will then produce new entities on the blackboard, thus triggering new events and a new cycle begins. The process ends when no KS can be further activated so that the state of the blackboard remains unchanged. The role of the controller is essential in focusing the inference process. It maintains an agenda of the pending KSs and may change the priorities of the agenda entries according to any specified criteria. The efficiency of the overall problem solving process may strongly depends upon the strategy used by the controller to order the KSs.Born as an AI architecture, the blackboard presents indeed several interesting features from the knowledge and software engineering points of view. Most of these properties derive from the existence of a shared working space, which represents, at any time, the state of the system. First of all, the KSs do not interact with each other. A KS is only concerned by the events occurring on one (or more) layer(s). At this time, it checks whether some patterns of entities match its own input pattern and declares itself as applicable to the controller. It is eventually executed when requested by the controller and finally writes its results onto its associated output layers. A KS can therefore be added or removed from the overall system without affecting the other KSs. Moreover, the inner part of a KS can be modified without affecting the system, as long as its activation pattern is not modified. Alternatively, the strategy of the controller can be independently modified and tuned up in order to make the inference process more efficient.The inference process is said to be opportunistic: the sequence of method invocations is not explicitly expressed before run time, but is decided according to the state of the system. As a striking illustration of this opportunistic behavior, if data produced by external sources are laid onto the blackboard, they are taken into account as if internally produced by the KSs and will affect the inference process accordingly.Blackboard systems have been built for a large spectrum of applications, for which the problems to be solved could not be linearly or hierarchically decomposed into sub- problems. This is the case of most interpretation problems, such as the seminal example of speech analysis and understanding. Examples of application of blackboards in biology include Protean , which wApart from the striking similarities between genomic sequence annotation and speech analysis, the decision to adopt a blackboard architecture for an annotation system has been motivated by the very nature of the annotation process. As explained previously, the annotation process relies on multiple methods, the execution of which provides different clues on the presence of coding sequences and regulatory signals. These clues have to be confronted, possibly discarded, but hopefully merged at different levels to finally predict the location and the structure of genes. The annotation process can thus be seen as a cooperative , opportunistic , knowledge-based (the conditions under which a method may be applied has to be explicitly expressed) and data-driven (the problem solving process is directed by the occurrence of patterns on the input DNA sequence) problem solving process.This latest point is probably the most important and the most characteristic of blackboard systems as compared to more traditional architectures. In blackboard systems each KS is autonomous and responsible of recognizing a particular state of the shared working space (its activation pattern) and declaring itself as activable. Therefore, the sequence of execution of methods is not programmed in a procedural manner but depends upon the current state of the blackboard. This aspect sometimes causes trouble to developers who want to have full control on the sequence of execution. With blackboards, one should better think in terms of event-driven programming.We have implemented a blackboard framework  for developing automatic annotation systems. By framework, we mean that the system is not bound to any specific annotation strategy or to any particular KSs. Instead, the user can specify a blackboard structure (layers and KSs) in a single configuration file and the system will actually instantiate and run this particular annotation strategy. This allows designing several strategies to target specific biological applications. However, all these strategies will share common mechanisms and properties that will be described by using the very simple prokaryotic annotation strategy depicted in Figure Stop triplet, a ribosome binding site (RBS) or a coding region (CDS), is described by its origin, its end and its type, i.e. as an oriented and typed interval over the sequence.The blackboard itself is structured into a hierarchy of layers, all collinear with the input sequence Figure . The lowKS:HypoCDS in Figure hypothetical CDS starting from an ORF and an in-frame Start triplet located within this ORF. More generally, it turns out that a large number of such annotation rules can be expressed by considering the relative position of the intervals representing the features. That is, by considering, for instance, that a given interval is located "before" or "after", or "overlaps" another interval. More formally, the activation pattern of a KS can be expressed by using a set of relations between intervals adapted from Allen's work on temporal relations [KS:HypoCDS states that this source should be activated when it exists an in-frame Start "during" an ORF. In the same way, KS:AnnotCDS will be activated when it exists a database hit "during" a predicted CDS. This KS will further ensure that the characteristic of the overlap (e.g. the sequence identity) is sufficient to produce an annotated CDS in the upper layer. When the activation pattern of a KS matches a pattern of features, the KS is said to be applicable. It will be further triggered and its output (i.e. a new feature) will be deposited on the corresponding layer.Some KSs integrate feature detection methods relying on bioinformatics algorithms such as Markov modeling or pattern searching, while others merely confront and merge features into more complex structures. An example of the latter is elations  . This approach will be further illustrated in the Results section.In practice both approaches may be mixed, e.g. by packing features in the lowest layers  and by working with individual features at the higher levels  throughThe prototype has not been developed with the ambition to overcome existing automatic annotation systems, but to demonstrate the appropriateness of the blackboard architecture for the development of genome annotation systems, both from the knowledge engineering and the software engineering points of view. Although the system is robust enough to be used on real-size applications, it is of primary use to bioinformatics researchers who want to experiment with blackboard architectures. To this purpose we provide, in addition to the core system, a graphical user interface allowing to load a blackboard configuration, to run it (possibly step by step) and to graphically visualize the creation of features on the chromosome during the execution.i.e. their input and output patterns and their executable body. This process will be exemplified now on the real annotation strategy depicted in Figure KS:FindORFs) for long ORFs (ALL_ORFS). These ORFs are used to build (KS:LearnMatrix) a Markov transition matrix (matrix). This matrix is further used to actually find (KS:FindCDS) CDSs. These CDSs are then checked (KS:SearchDB) against an enzyme database (database) whose entries are annotated with EC numbers (an EC number characterizes the function of an enzyme). Finally, when a CDS gets sufficient matches with annotated enzymes and when the majority of these matches have the same EC number, then this EC number is transferred to the CDS's product to eventually yield an enzymatic gene (ENZGENE). It is important to note that, in this example, we stopped at the gene level but one could imagine to continue using these genes in higher layers of the blackboard, representing for instance bacterial operons (sets of co-transcribed genes) or metabolic pathways .To instantiate a blackboard, the designer must declare, in a configuration file, the number of layers, the types of the different entities, the KSs, sequence, matrix, database and ALL_ORFS) are implemented as lists. They represent respectively, the raw sequence, the Markov matrix, the database to scan  and the list of all ORFs . The last three layers  are implemented as intervals (TimeLine) and represent individual features : CDSs, Blast hits associated to one CDS and validated enzyme genes respectively. The second part of the configuration file deals with the declaration of the KSs. Each KS has two parts: the activation pattern (<conditions> tag) and the executable body (<actions> tag). Moreover, the type of entities produced by a KS is specified by the <source> tag attribute create. For instance, KS:FindORFs in figure Figure sequence". It produces (a single packed) entity of type ALL_ORFS through the call of the executable pkorf declared in the <actions> tag. All sub-tags of the <actions> tag are associated to a piece of Java code. Some of them perform internal operations (e.g. <writeln> will write some text on the console) and others may call external executables (e.g. <pkorf>). When adding a new KS to the system, one will have also to define the associated tag. A more sophisticated KS example is given on Figure KS:SearchDB is responsible of computing Blast hits associated to each CDS. Its activation pattern reads as "select all couples composed of one CDS and the database. This CDS will then be scanned (<blastp>) on the database and the resulting hits will be further packed . Finally, an example of Allen relation is given on Figure KS:ECAnnotator activates on each couple composed of one CDS and a set of associated hits (DBHITS) that is included in ("during") the CDS.To configure a blackboard implementing this strategy, one has to edit an XML configuration file such as the one described in Figure CDS, DBHITS and ENZGENE). As a real-sized test case, we ran this particular annotation strategy on the whole chromosome of B. subtilis (~4 Mb). More than 97% of the actual genes (4106) were correctly found (with 7% of over-predicted genes). 399 genes were further annotated with EC numbers, most of them (95%) beeing correct as compared to the published annotation. On the other hand, 494 genes with EC annotations remained unpredicted, indicating that this particular strategy (or its parameters) were probably too conservative. The total running time on the whole chromosome was 9 hours , most of the time (99%) being actually spent in the Blast steps.As mentioned earlier, the Genepi standalone applications also provides a graphical interface allowing to follow step by step the execution of the blackboard. Figure As already mentioned in the Background section, two main classes of architecture of automatic annotation systems can be identified. The first class, which includes pipeline, workflow and task-based systems, is characterized by a sequential method invocation scheme. The order in which the analysis methods have to be executed is static and predefined. Some variations may be accepted if alternative sequences can be described. This is often the case in task-based systems, which allow the next task to be chosen according to the results of the previous one. The main drawback of such a sequential scheme is the lack of flexibility regarding the maintenance and the modification of the system, especially when new methods are to be added. On the other hand, the end user can easily follow the execution of the system. The second class, which includes multi-agent, blackboard-based and rule-based systems, is characterized by an opportunistic method invocation scheme. The order in which the methods are called is not preset but is determined at runtime by the state of the system. The major advantage is that a new knowledge chunk  corresponding to a new method can be easily added or removed without much disturbing the other parts of the knowledge base. If the conditions of a method invocation have been properly defined, the method will be appropriately called when the corresponding state will occur in the system. Among these systems, blackboard architectures present determinant advantages: a shared working memory which is structured in layers in adequacy with the levels of hypothesis setting, a centralized control strategy which is easy to follow, an intuitive description of the methods and their activation patterns as independent knowledge sources.i.e. the agents, communicate directly one with the others by sending messages: the control is therefore also distributed. On the contrary, in blackboards, KSs never communicate directly. They find their input on the blackboard and deposit the products of their execution on the same blackboard, which can thus be seen as a shared working memory. In both cases, the advantages of the architecture result from the modularity it induces from both the software and knowledge engineering points of view. However, we consider that the existence of a shared and structured working memory, together with a central controller, produce a reasoning system which is much easier to follow and understand, and therefore easier to maintain, to tune and to extend. Moreover, the KSs are truly independent modules that can be added or removed without affecting the others. On the other hand, the multi-agent architectures are better suited to distributed environments. Despite their qualities, blackboard systems seem to be presently much less popular that multi-agent systems. An explanation for this situation has probably to be searched in the development of object-oriented techniques, which have provided the technology to efficiently implement the agents as interacting concurrent processes. In the same time, the complexity of blackboard systems increased (it was not uncommon to read about systems which included multiple blackboards and highly sophisticated control strategies) and thus lost some of their most appreciated properties.Multi-agent and blackboard systems are both part of the so-called distributed AI systems in which the processing capacity is distributed among multiple entities: the KSs of the blackboard systems and the agents of the multi-agent systems. The main difference lies in the way the entities communicate. In a multi-agent system, the entities, via the blackboard, these additions are simple because they do not interfere with the ones already integrated. From this point of view, the annotation of a genomic sequence can thus be updated after a new analysis method has been integrated. Conversely, if a sequencing error has been detected and corrected on the raw sequence, all the KS executions for which the corrected region was involved can be forgotten, their output erased from the layers on the blackboard and the inference cycle reactivated. Finally, we would like to mention another important case where the update facilities offered by a blackboard architecture could be put into play. Usually, after a first pass of fully automatic annotation, the genomic features need to be manually reviewed by experts. For instance, the Start position, the functional annotation or simply the presence of a gene may be modified. These manual modifications may have consequences on the overall annotation and need to be propagated, for instance if the modified CDS is involved in higher structures like operons or pathways. In the blackboard view, this means that the human expert plays the role of a new KS . The propagation and update of the modification can then be handled by the architecture.In the context of genome annotation, blackboards have several interesting features related to the way the methodological and biological knowledge can be updated. As research in bioinformatics produces new methods, they can be added to the system, wrapped as new KSs. Since KS never communicate directly but Of course, besides these advantages, blackboard (and multi-agent) systems also have some known drawbacks. The first one is the difficulty of making them do specific calculation in ordered tasks. As explained before, this is a natural consequence of their opportunistic behavior. Developers should therefore think in terms of event-driven actions rather than strictly ordered tasks. However, if such a pipeline behavior is desired then a solution is to embed the ordered tasks within a single KS. Indeed, the <action> part of a KS can be seen as a small pipeline. Of course, this leads to a less declarative system where a part of the annotation strategy becomes hidden in the KS. Depending upon the problem to be solved, there is therefore a tradeoff to find between "pure" blackboard and pipeline behavior. Another known, more technical, difficulty is related to the debugging of the system. Again, because of the event-driven method invocation scheme, it may be sometimes difficult to pinpoint the source of a potential problem.The question of the reliability of bioinformatics software takes a slightly different form depending whether one considers a single piece of software or more complicated systems such as integrated platforms. In the first case, as long as the software correctly implements algorithms that are well known and understood, the software designers may consider that the results do not require to be further explained or justified. On the other hand, for genome annotation platforms, the execution of sequence analysis algorithms merely provide clues that have to be confronted, filtered and merged according to some methodological knowledge. This knowledge can be either directly provided by the user or formally expressed and integrated into the system. However, the possibility to formally express this knowledge, as rules, objects, tasks or any other modeling entities, does not mean that the resulting system will yield pertinent results. Indeed, this highly depends upon the expertise of the designers and the results may be further discussed and possibly refuted by the end users. In this context, we believe that an annotation system should not only allow the formal expression and integration of the methodological knowledge; it must also provide facilities for the user to follow and understand the annotation process, and to tune, adapt or even refute the content of the methodological knowledge base. The blackboard architecture appears to offer most of these software and knowledge engineering properties.. The distribution includes all the java sources as well as blackboard samples. The core system and graphical interface run on any platform supporting JavaVM. It has been tested on Linux and MacOSX. Some KSs (like Prokov or Blast) need external executables. These executable are provided in the distribution for MacOSX and Linux platforms.The Genepi protoptype has been implemented in Java and is freely available for download at the following url : AV and FR initiated the project. SDD, DZ and FR designed the architecture and software requirements. SDD wrote most of the Java code and AV provided the external toolbox. All authors participated in testing the software and in editing and proofreading the manuscript. All authors read and approved the final manuscript.