Improve Genome Annotation Research Articles

A manual curation strategy to improve genome annotation: application to a set of haloarchael genomes.

Genome annotation errors are a persistent problem that impede research in the biosciences. A manual curation effort is described that attempts to produce high-quality genome annotations for a set of haloarchaeal genomes (Halobacterium salinarum and Hbt. hubeiense, Haloferax volcanii and Hfx. mediterranei, Natronomonas pharaonis and Nmn. moolapensis, Haloquadratum walsbyi strains HBSQ001 and C23, Natrialba magadii, Haloarcula marismortui and Har. hispanica, and Halohasta litchfieldiae). Genomes are checked for missing genes, start codon misassignments, and disrupted genes. Assignments of a specific function are preferably based on experimentally characterized homologs (Gold Standard Proteins). To avoid overannotation, which is a major source of database errors, we restrict annotation to only general function assignments when support for a specific substrate assignment is insufficient. This strategy results in annotations that are resistant to the plethora of errors that compromise public databases. Annotation consistency is rigorously validated for ortholog pairs from the genomes surveyed. The annotation is regularly crosschecked against the UniProt database to further improve annotations and increase the level of standardization. Enhanced genome annotations are submitted to public databases (EMBL/GenBank, UniProt), to the benefit of the scientific community. The enhanced annotations are also publically available via HaloLex.

An improved poly(A) motifs recognition method based on decision level fusion

Polyadenylation is the process of addition of poly(A) tail to mRNA 3′ ends. Identification of motifs controlling polyadenylation plays an essential role in improving genome annotation accuracy and better understanding of the mechanisms governing gene regulation. The bioinformatics methods used for poly(A) motifs recognition have demonstrated that information extracted from sequences surrounding the candidate motifs can differentiate true motifs from the false ones greatly. However, these methods depend on either domain features or string kernels. To date, methods combining information from different sources have not been found yet. Here, we proposed an improved poly(A) motifs recognition method by combing different sources based on decision level fusion. First of all, two novel prediction methods was proposed based on support vector machine (SVM): one method is achieved by using the domain-specific features and principle component analysis (PCA) method to eliminate the redundancy (PCA–SVM); the other method is based on Oligo string kernel (Oligo-SVM). Then we proposed a novel machine-learning method for poly(A) motif prediction by marrying four poly(A) motifs recognition methods, including two state-of-the-art methods (Random Forest (RF) and HMM-SVM), and two novel proposed methods (PCA–SVM and Oligo-SVM). A decision level information fusion method was employed to combine the decision values of different classifiers by applying the DS evidence theory. We evaluated our method on a comprehensive poly(A) dataset that consists of 14,740 samples on 12 variants of poly(A) motifs and 2750 samples containing none of these motifs. Our method has achieved accuracy up to 86.13%. Compared with the four classifiers, our evidence theory based method reduces the average error rate by about 30%, 27%, 26% and 16%, respectively. The experimental results suggest that the proposed method is more effective for poly(A) motif recognition.

Integration of mass spectrometry and RNA‐Seq data to confirm human ab initio predicted genes and lncRNAs

MS/MS has been used to improve genome annotation in various organisms. The classical approach is to construct comprehensive theoretical peptide database with six frame translation model from the whole ORF of a genome and search against this database with real MS/MS spectra. In this work we took a more focused approach, we constructed a database containing only peptides from the ab initio predicted genes from current human genome annotation, and all theoretical peptides from currently annotated lncRNAs, and searched such a database with MS/MS data from human Hela cell line. The purpose of this design is to find translation evidence for ab initio predicted genes and to rule out possible wrongly defined lncRNAs in a systematic proteogenomics effort. To validate proteogenomics results, we integrated RNA-Seq data analysis for the same Hela cell line which generated MS/MS data, and performed MRM experiment on self-cultured Hela cell line samples. Six peptides were found to support ab initio predicted genes with both RNA-Seq and MRM validations, while none was found to support a translated lncRNA. This workflow could be flexibly applied to other human samples and datasets to help further improve human gene annotation.

Transcriptome sequencing of two wild barley (Hordeum spontaneum L.) ecotypes differentially adapted to drought stress reveals ecotype-specific transcripts.

BackgroundWild barley is adapted to highly diverse environments throughout its geographical distribution range. Transcriptome sequencing of differentially adapted wild barley ecotypes from contrasting environments contributes to the identification of genes and genetic variation involved in abiotic stress tolerance and adaptation.ResultsTwo differentially adapted wild barley ecotypes from desert (B1K2) and Mediterranean (B1K30) environments were analyzed for drought stress response under controlled conditions. The desert ecotype lost more water under both irrigation and drought, but exhibited higher relative water content (RWC) and better water use efficiency (WUE) than the coastal ecotype. We sequenced normalized cDNA libraries from drought-stressed leaves of both ecotypes with the 454 platform to identify drought-related transcripts. Over half million reads per ecotype were de novo assembled into 20,439 putative unique transcripts (PUTs) for B1K2, 21,494 for B1K30 and 28,720 for the joint assembly. Over 50% of PUTs of each ecotype were not shared with the other ecotype. Furthermore, 16% (3,245) of B1K2 and 17% (3,674) of B1K30 transcripts did not show orthologous sequence hits in the other wild barley ecotype and cultivated barley, and are candidates of ecotype-specific transcripts. Over 800 unique transcripts from each ecotype homologous to over 30 different stress-related genes were identified. We extracted 1,017 high quality SNPs that differentiated the two ecotypes. The genetic distance between the desert ecotype and cultivated barley was 1.9-fold higher than between the Mediterranean ecotype and cultivated barley. Moreover, the desert ecotype harbored a larger proportion of non-synonymous SNPs than the Mediterranean ecotype suggesting different demographic histories of these ecotypes.ConclusionsThe results indicate a strong physiological and genomic differentiation between the desert and Mediterranean wild barley ecotypes and a closer relationship of the Mediterranean to cultivated barley. A significant number of novel transcripts specific to wild barley were identified. The higher SNP density and larger proportion of SNPs with functional effects in the desert ecotype suggest different demographic histories and effects of natural selection in Mediterranean and desert wild barley. The data are a valuable genomic resource for an improved genome annotation, transcriptome studies of drought adaptation and a source of new genetic markers for future barley improvement.Electronic supplementary materialThe online version of this article (doi:10.1186/1471-2164-15-995) contains supplementary material, which is available to authorized users.

Sequencing, annotation and analysis of the Syrian hamster (Mesocricetus auratus) transcriptome.

BackgroundThe Syrian hamster (golden hamster, Mesocricetus auratus) is gaining importance as a new experimental animal model for multiple pathogens, including emerging zoonotic diseases such as Ebola. Nevertheless there are currently no publicly available transcriptome reference sequences or genome for this species.ResultsA cDNA library derived from mRNA and snRNA isolated and pooled from the brains, lungs, spleens, kidneys, livers, and hearts of three adult female Syrian hamsters was sequenced. Sequence reads were assembled into 62,482 contigs and 111,796 reads remained unassembled (singletons). This combined contig/singleton dataset, designated as the Syrian hamster transcriptome, represents a total of 60,117,204 nucleotides. Our Mesocricetus auratus Syrian hamster transcriptome mapped to 11,648 mouse transcripts representing 9,562 distinct genes, and mapped to a similar number of transcripts and genes in the rat. We identified 214 quasi-complete transcripts based on mouse annotations. Canonical pathways involved in a broad spectrum of fundamental biological processes were significantly represented in the library. The Syrian hamster transcriptome was aligned to the current release of the Chinese hamster ovary (CHO) cell transcriptome and genome to improve the genomic annotation of this species. Finally, our Syrian hamster transcriptome was aligned against 14 other rodents, primate and laurasiatheria species to gain insights about the genetic relatedness and placement of this species.ConclusionsThis Syrian hamster transcriptome dataset significantly improves our knowledge of the Syrian hamster's transcriptome, especially towards its future use in infectious disease research. Moreover, this library is an important resource for the wider scientific community to help improve genome annotation of the Syrian hamster and other closely related species. Furthermore, these data provide the basis for development of expression microarrays that can be used in functional genomics studies.

Polymorphism identification and improved genome annotation of Brassica rapa through Deep RNA sequencing.

The mapping and functional analysis of quantitative traits in Brassica rapa can be greatly improved with the availability of physically positioned, gene-based genetic markers and accurate genome annotation. In this study, deep transcriptome RNA sequencing (RNA-Seq) of Brassica rapa was undertaken with two objectives: SNP detection and improved transcriptome annotation. We performed SNP detection on two varieties that are parents of a mapping population to aid in development of a marker system for this population and subsequent development of high-resolution genetic map. An improved Brassica rapa transcriptome was constructed to detect novel transcripts and to improve the current genome annotation. This is useful for accurate mRNA abundance and detection of expression QTL (eQTLs) in mapping populations. Deep RNA-Seq of two Brassica rapa genotypes—R500 (var. trilocularis, Yellow Sarson) and IMB211 (a rapid cycling variety)—using eight different tissues (root, internode, leaf, petiole, apical meristem, floral meristem, silique, and seedling) grown across three different environments (growth chamber, greenhouse and field) and under two different treatments (simulated sun and simulated shade) generated 2.3 billion high-quality Illumina reads. A total of 330,995 SNPs were identified in transcribed regions between the two genotypes with an average frequency of one SNP in every 200 bases. The deep RNA-Seq reassembled Brassica rapa transcriptome identified 44,239 protein-coding genes. Compared with current gene models of B. rapa, we detected 3537 novel transcripts, 23,754 gene models had structural modifications, and 3655 annotated proteins changed. Gaps in the current genome assembly of B. rapa are highlighted by our identification of 780 unmapped transcripts. All the SNPs, annotations, and predicted transcripts can be viewed at http://phytonetworks.ucdavis.edu/.

Flexible and accessible workflows for improved proteogenomic analysis using the Galaxy framework.

Proteogenomics combines large-scale genomic and transcriptomic data with mass-spectrometry-based proteomic data to discover novel protein sequence variants and improve genome annotation. In contrast with conventional proteomic applications, proteogenomic analysis requires a number of additional data processing steps. Ideally, these required steps would be integrated and automated via a single software platform offering accessibility for wet-bench researchers as well as flexibility for user-specific customization and integration of new software tools as they emerge. Toward this end, we have extended the Galaxy bioinformatics framework to facilitate proteogenomic analysis. Using analysis of whole human saliva as an example, we demonstrate Galaxy’s flexibility through the creation of a modular workflow incorporating both established and customized software tools that improve depth and quality of proteogenomic results. Our customized Galaxy-based software includes automated, batch-mode BLASTP searching and a Peptide Sequence Match Evaluator tool, both useful for evaluating the veracity of putative novel peptide identifications. Our complex workflow (approximately 140 steps) can be easily shared using built-in Galaxy functions, enabling their use and customization by others. Our results provide a blueprint for the establishment of the Galaxy framework as an ideal solution for the emerging field of proteogenomics.

The Babesia bovis gene and promoter model: an update from full-length EST analysis.

BackgroundBabesia bovis is an apicomplexan parasite that causes babesiosis in infected cattle. Genomes of pathogens contain promising information that can facilitate the development of methods for controlling infections. Although the genome of B. bovis is publically available, annotated gene models are not highly reliable prior to experimental validation. Therefore, we validated a preproposed gene model of B. bovis and extended the associated annotations on the basis of experimentally obtained full-length expressed sequence tags (ESTs).ResultsFrom in vitro cultured merozoites, 12,286 clones harboring full-length cDNAs were sequenced from both ends using the Sanger method, and 6,787 full-length cDNAs were assembled. These were then clustered, and a nonredundant referential data set of 2,115 full-length cDNA sequences was constructed. The comparison of the preproposed gene model with our data set identified 310 identical genes, 342 almost identical genes, 1,054 genes with potential structural inconsistencies, and 409 novel genes. The median length of 5' untranslated regions (UTRs) was 152 nt. Subsequently, we identified 4,086 transcription start sites (TSSs) and 2,023 transcriptionally active regions (TARs) by examining 5' ESTs. We identified ATGGGG and CCCCAT sites as consensus motifs in TARs that were distributed around -50 bp from TSSs. In addition, we found ACACA, TGTGT, and TATAT sites, which were distributed periodically around TSSs in cycles of approximately 150 bp. Moreover, related periodical distributions were not observed in mammalian promoter regions.ConclusionsThe observations in this study indicate the utility of integrated bioinformatics and experimental data for improving genome annotations. In particular, full-length cDNAs with one-base resolution for TSSs enabled the identification of consensus motifs in promoter sequences and demonstrated clear distributions of identified motifs. These observations allowed the illustration of a model promoter composition, which supports the differences in transcriptional regulation frameworks between apicomplexan parasites and mammals.Electronic supplementary materialThe online version of this article (doi:10.1186/1471-2164-15-678) contains supplementary material, which is available to authorized users.

Discovery of Novel Genes and Gene Isoforms by Integrating Transcriptomic and Proteomic Profiling from Mouse Liver

Comprehensively identifying gene expression in both transcriptomic and proteomic levels of one tissue is a prerequisite for a deeper understanding of its biological functions. Alternative splicing and RNA editing, two main forms of transcriptional processing, play important roles in transcriptome and proteome diversity and result in multiple isoforms for one gene, which are hard to identify by mass spectrometry (MS)-based proteomics approach due to the relative lack of isoform information in standard protein databases. In our study, we employed MS and RNA-Seq in parallel into mouse liver tissue and captured a considerable catalogue of both transcripts and proteins that, respectively, covered 60 and 34% of protein-coding genes in Ensembl. We then developed a bioinformatics workflow for building a customized protein database that for the first time included new splicing-derived peptides and RNA-editing-caused peptide variants, allowing us to more completely identify protein isoforms. Using this experimentally determined database, we totally identified 150 peptides not present in standard biological databases at false discovery rate of <1%, corresponding to 72 novel splicing isoforms, 43 new genetic regions, and 15 RNA-editing sites. Of these, 11 randomly selected novel events passed experimental verification by PCR and Sanger sequencing. New discoveries of gene products with high confidence in two omics levels demonstrated the robustness and effectiveness of our approach and its potential application into improve genome annotation. All the MS data have been deposited to the iProx ( http://ww.iprox.org ) with the identifier IPX00003601.

Predicting network functions with nested patterns

Identifying suitable patterns in complex biological interaction networks helps understanding network functions and allows for predictions at the pattern level: by recognizing a known pattern, one can assign its previously established function. However, current approaches fail for previously unseen patterns, when patterns overlap and when they are embedded into a new network context. Here we show how to conceptually extend pattern-based approaches. We define metabolite patterns in metabolic networks that formalize co-occurrences of metabolites. Our probabilistic framework decodes the implicit information in the networks' metabolite patterns to predict metabolic functions. We demonstrate the predictive power by identifying 'indicator patterns', for instance, for enzyme classification, by predicting directions of novel reactions and of known reactions in new network contexts, and by ranking candidate network extensions for gap filling. Beyond their use in improving genome annotations and metabolic network models, we expect that the concepts transfer to other network types.

Characterization and prediction of mRNA alternative polyadenylation sites in rice genes.

Polyadenylation [poly(A)] of mRNA is a critical step during gene expression, which plays an important role in the termination of transcription. Prediction of poly(A) sites can help identify 3' ends of genes and improve genome annotation. Due to the limited knowledge of poly(A) signals in plants, predictive modeling of poly(A) sites in agricultural crops remains challenging. Recent studies have uncovered widespread occurrences of alternative poly(A) (APA) sites in intron and coding sequence (CDS), whereas the study on the prediction of these APA sites is scarce. In this study, four feature representation methods, involving a position weight matrix, the k-gram frequency, core hexamers, and a transition matrix, were adopted to characterize poly(A) signals surrounding APA sites. The classification model was built to predict each group of APA sites. Experimental results showed that this model was effective in the identification of APA sites located in different genomic regions, with a compromise between sensitivity and specificity higher than 87%. Compared with previous model PASS rice, accuracies for the prediction of APA sites in 3'-UTR, intron and CDS were enhanced by 5%, 7%, and 27%, respectively. This model will contribute to genetic engineering by enabling researchers to control poly(A) site selection.

Transcriptome analysis of the filamentous fungus Aspergillus nidulans directed to the global identification of promoters.

BackgroundThe filamentous fungus Aspergillus nidulans has been a tractable model organism for cell biology and genetics for over 60 years. It is among a large number of Aspergilli whose genomes have been sequenced since 2005, including medically and industrially important species. In order to advance our knowledge of its biology and increase its utility as a genetic model by improving gene annotation we sequenced the transcriptome of A. nidulans with a focus on 5′ end analysis.ResultsStrand-specific whole transcriptome sequencing showed that 80-95% of annotated genes appear to be expressed across the conditions tested. We estimate that the total gene number should be increased by approximately 1000, to 11,800. With respect to splicing 8.3% of genes had multiple alternative transcripts, but alternative splicing by exon-skipping was very rare. 75% of annotated genes showed some level of antisense transcription and for one gene, meaB, we demonstrated the antisense transcript has a regulatory role. Specific sequencing of the 5’ ends of transcripts was used for genome wide mapping of transcription start sites, allowing us to interrogate over 7000 promoters and 5′ untranslated regions.ConclusionsOur data has revealed the complexity of the A. nidulans transcriptome and contributed to improved genome annotation. The data can be viewed on the AspGD genome browser.Electronic supplementary materialThe online version of this article (doi:10.1186/1471-2164-14-847) contains supplementary material, which is available to authorized users.

Poly(A) motif prediction using spectral latent features from human DNA sequences

Motivation: Polyadenylation is the addition of a poly(A) tail to an RNA molecule. Identifying DNA sequence motifs that signal the addition of poly(A) tails is essential to improved genome annotation and better understanding of the regulatory mechanisms and stability of mRNA.Existing poly(A) motif predictors demonstrate that information extracted from the surrounding nucleotide sequences of candidate poly(A) motifs can differentiate true motifs from the false ones to a great extent. A variety of sophisticated features has been explored, including sequential, structural, statistical, thermodynamic and evolutionary properties. However, most of these methods involve extensive manual feature engineering, which can be time-consuming and can require in-depth domain knowledge.Results: We propose a novel machine-learning method for poly(A) motif prediction by marrying generative learning (hidden Markov models) and discriminative learning (support vector machines). Generative learning provides a rich palette on which the uncertainty and diversity of sequence information can be handled, while discriminative learning allows the performance of the classification task to be directly optimized. Here, we used hidden Markov models for fitting the DNA sequence dynamics, and developed an efficient spectral algorithm for extracting latent variable information from these models. These spectral latent features were then fed into support vector machines to fine-tune the classification performance.We evaluated our proposed method on a comprehensive human poly(A) dataset that consists of 14 740 samples from 12 of the most abundant variants of human poly(A) motifs. Compared with one of the previous state-of-the-art methods in the literature (the random forest model with expert-crafted features), our method reduces the average error rate, false-negative rate and false-positive rate by 26, 15 and 35%, respectively. Meanwhile, our method makes ∼30% fewer error predictions relative to the other string kernels. Furthermore, our method can be used to visualize the importance of oligomers and positions in predicting poly(A) motifs, from which we can observe a number of characteristics in the surrounding regions of true and false motifs that have not been reported before.Availability: http://sfb.kaust.edu.sa/Pages/Software.aspxContact: lsong@cc.gatech.edu or xin.gao@kaust.edu.saSupplementary information: Supplementary data are available at Bioinformatics online.

Characterization of Transposable Elements in the Ectomycorrhizal Fungus Laccaria bicolor

BackgroundThe publicly available Laccaria bicolor genome sequence has provided a considerable genomic resource allowing systematic identification of transposable elements (TEs) in this symbiotic ectomycorrhizal fungus. Using a TE-specific annotation pipeline we have characterized and analyzed TEs in the L. bicolor S238N-H82 genome.Methodology/Principal FindingsTEs occupy 24% of the 60 Mb L. bicolor genome and represent 25,787 full-length and partial copy elements distributed within 171 families. The most abundant elements were the Copia-like. TEs are not randomly distributed across the genome, but are tightly nested or clustered. The majority of TEs exhibits signs of ancient transposition except some intact copies of terminal inverted repeats (TIRS), long terminal repeats (LTRs) and a large retrotransposon derivative (LARD) element. There were three main periods of TE expansion in L. bicolor: the first from 57 to 10 Mya, the second from 5 to 1 Mya and the most recent from 0.5 Mya ago until now. LTR retrotransposons are closely related to retrotransposons found in another basidiomycete, Coprinopsis cinerea.ConclusionsThis analysis 1) represents an initial characterization of TEs in the L. bicolor genome, 2) contributes to improve genome annotation and a greater understanding of the role TEs played in genome organization and evolution and 3) provides a valuable resource for future research on the genome evolution within the Laccaria genus.

Phylogenomics of Prokaryotic Ribosomal Proteins

Archaeal and bacterial ribosomes contain more than 50 proteins, including 34 that are universally conserved in the three domains of cellular life (bacteria, archaea, and eukaryotes). Despite the high sequence conservation, annotation of ribosomal (r-) protein genes is often difficult because of their short lengths and biased sequence composition. We developed an automated computational pipeline for identification of r-protein genes and applied it to 995 completely sequenced bacterial and 87 archaeal genomes available in the RefSeq database. The pipeline employs curated seed alignments of r-proteins to run position-specific scoring matrix (PSSM)-based BLAST searches against six-frame genome translations, mitigating possible gene annotation errors. As a result of this analysis, we performed a census of prokaryotic r-protein complements, enumerated missing and paralogous r-proteins, and analyzed the distributions of ribosomal protein genes among chromosomal partitions. Phyletic patterns of bacterial and archaeal r-protein genes were mapped to phylogenetic trees reconstructed from concatenated alignments of r-proteins to reveal the history of likely multiple independent gains and losses. These alignments, available for download, can be used as search profiles to improve genome annotation of r-proteins and for further comparative genomics studies.

Novel methodologies for spectral classification of exon and intron sequences

Digital processing of a nucleotide sequence requires it to be mapped to a numerical sequence in which the choice of nucleotide to numeric mapping affects how well its biological properties can be preserved and reflected from nucleotide domain to numerical domain. Digital spectral analysis of nucleotide sequences unfolds a period-3 power spectral value which is more prominent in an exon sequence as compared to that of an intron sequence. The success of a period-3 based exon and intron classification depends on the choice of a threshold value. The main purposes of this article are to introduce novel codes for 1-sequence numerical representations for spectral analysis and compare them to existing codes to determine appropriate representation, and to introduce novel thresholding methods for more accurate period-3 based exon and intron classification of an unknown sequence. The main findings of this study are summarized as follows: Among sixteen 1-sequence numerical representations, the K-Quaternary Code I offers an attractive performance. A windowed 1-sequence numerical representation (with window length of 9, 15, and 24 bases) offers a possible speed gain over non-windowed 4-sequence Voss representation which increases as sequence length increases. A winner threshold value (chosen from the best among two defined threshold values and one other threshold value) offers a top precision for classifying an unknown sequence of specified fixed lengths. An interpolated winner threshold value applicable to an unknown and arbitrary length sequence can be estimated from the winner threshold values of fixed length sequences with a comparable performance. In general, precision increases as sequence length increases. The study contributes an effective spectral analysis of nucleotide sequences to better reveal embedded properties, and has potential applications in improved genome annotation.

TnpPred: A Web Service for the Robust Prediction of Prokaryotic Transposases

Transposases (Tnps) are enzymes that participate in the movement of insertion sequences (ISs) within and between genomes. Genes that encode Tnps are amongst the most abundant and widely distributed genes in nature. However, they are difficult to predict bioinformatically and given the increasing availability of prokaryotic genomes and metagenomes, it is incumbent to develop rapid, high quality automatic annotation of ISs. This need prompted us to develop a web service, termed TnpPred for Tnp discovery. It provides better sensitivity and specificity for Tnp predictions than given by currently available programs as determined by ROC analysis. TnpPred should be useful for improving genome annotation. The TnpPred web service is freely available for noncommercial use.

Analysis of porcine MHC using microarrays

The major histocompatibility complex (MHC) in Mammals is one of the most gene dense regions of the genome and contains the polymorphic histocompatibility gene families known to be involved in pathogen response and control of auto-immunity. The MHC is a complex genetic system that provides an interesting model system to study genome expression regulation and genetic diversity at the megabase scale. The pig MHC or SLA (Swine Leucocyte Antigen) complex spans 2.4 megabases and 151 loci have been annotated. We will review key results from previous RNA expression studies using microarrays containing probes specific to annotated loci within SLA and in addition present novel data obtained using high-density tiling arrays encompassing the whole SLA complex. We have focused on transcriptome modifications of porcine peripheral blood mononuclear cells stimulated with a mixture of phorbol myristate acetate and ionomycin known to activate B and T cell proliferation. Our results show that numerous loci mapping to the SLA complex are affected by the treatment. A general decreased level of expression for class I and II genes and an up-regulation of genes involved in peptide processing and transport were observed. Tiling array-based experiments contributed to refined gene annotations as presented for one SLA class I gene referred to as SLA-11. In conclusion, high-density tiling arrays can serve as an excellent tool to draw comprehensive transcription maps, and improve genome annotations for the SLA complex. We are currently studying their relevance to characterize SLA genetic diversity in combination with high throughput next generation sequencing.

An Integrated Approach for Finding Overlooked Genes in Shigella

BackgroundThe completion of numerous genome sequences introduced an era of whole-genome study. However, many genes are missed during genome annotation, including small RNAs (sRNAs) and small open reading frames (sORFs). In order to improve genome annotation, we aimed to identify novel sRNAs and sORFs in Shigella, the principal etiologic agents of bacillary dysentery.Methodology/Principal FindingsWe identified 64 sRNAs in Shigella, which were experimentally validated in other bacteria based on sequence conservation. We employed computer-based and tiling array-based methods to search for sRNAs, followed by RT-PCR and northern blots, to identify nine sRNAs in Shigella flexneri strain 301 (Sf301) and 256 regions containing possible sRNA genes. We found 29 candidate sORFs using bioinformatic prediction, array hybridization and RT-PCR verification. We experimentally validated 557 (57.9%) DOOR operon predictions in the chromosomes of Sf301 and 46 (76.7%) in virulence plasmid.We found 40 additional co-expressed gene pairs that were not predicted by DOOR.Conclusions/SignificanceWe provide an updated and comprehensive annotation of the Shigella genome. Our study increased the expected numbers of sORFs and sRNAs, which will impact on future functional genomics and proteomics studies. Our method can be used for large scale reannotation of sRNAs and sORFs in any microbe with a known genome sequence.

Phylogenetics and evolution of Su(var)3-9 SET genes in land plants: rapid diversification in structure and function

BackgroundPlants contain numerous Su(var)3-9 homologues (SUVH) and related (SUVR) genes, some of which await functional characterization. Although there have been studies on the evolution of plant Su(var)3-9 SET genes, a systematic evolutionary study including major land plant groups has not been reported. Large-scale phylogenetic and evolutionary analyses can help to elucidate the underlying molecular mechanisms and contribute to improve genome annotation.ResultsPutative orthologs of plant Su(var)3-9 SET protein sequences were retrieved from major representatives of land plants. A novel clustering that included most members analyzed, henceforth referred to as core Su(var)3-9 homologues and related (cSUVHR) gene clade, was identified as well as all orthologous groups previously identified. Our analysis showed that plant Su(var)3-9 SET proteins possessed a variety of domain organizations, and can be classified into five types and ten subtypes. Plant Su(var)3-9 SET genes also exhibit a wide range of gene structures among different paralogs within a family, even in the regions encoding conserved PreSET and SET domains. We also found that the majority of SUVH members were intronless and formed three subclades within the SUVH clade.ConclusionsA detailed phylogenetic analysis of the plant Su(var)3-9 SET genes was performed. A novel deep phylogenetic relationship including most plant Su(var)3-9 SET genes was identified. Additional domains such as SAR, ZnF_C2H2 and WIYLD were early integrated into primordial PreSET/SET/PostSET domain organization. At least three classes of gene structures had been formed before the divergence of Physcomitrella patens (moss) from other land plants. One or multiple retroposition events might have occurred among SUVH genes with the donor genes leading to the V-2 orthologous group. The structural differences among evolutionary groups of plant Su(var)3-9 SET genes with different functions were described, contributing to the design of further experimental studies.