Previous Annotation Research Articles

Analysis of mammalian gene function through broad-based phenotypic screens across a consortium of mouse clinics.

The function of the majority of genes in the mouse and human genomes remains unknown. The mouse ES cell knockout resource provides a basis for characterisation of relationships between gene and phenotype. The EUMODIC consortium developed and validated robust methodologies for broad-based phenotyping of knockouts through a pipeline comprising 20 disease-orientated platforms. We developed novel statistical methods for pipeline design and data analysis aimed at detecting reproducible phenotypes with high power. We acquired phenotype data from 449 mutant alleles, representing 320 unique genes, of which half had no prior functional annotation. We captured data from over 27,000 mice finding that 83% of the mutant lines are phenodeviant, with 65% demonstrating pleiotropy. Surprisingly, we found significant differences in phenotype annotation according to zygosity. Novel phenotypes were uncovered for many genes with unknown function providing a powerful basis for hypothesis generation and further investigation in diverse systems.

UniqTag: Content-Derived Unique and Stable Identifiers for Gene Annotation.

When working on an ongoing genome sequencing and assembly project, it is rather inconvenient when gene identifiers change from one build of the assembly to the next. The gene labelling system described here, UniqTag, addresses this common challenge. UniqTag assigns a unique identifier to each gene that is a representative k-mer, a string of length k, selected from the sequence of that gene. Unlike serial numbers, these identifiers are stable between different assemblies and annotations of the same data without requiring that previous annotations be lifted over by sequence alignment. We assign UniqTag identifiers to ten builds of the Ensembl human genome spanning eight years to demonstrate this stability. The implementation of UniqTag in Ruby and an R package are available at https://github.com/sjackman/uniqtag sjackman/uniqtag. The R package is also available from CRAN: install.packages ("uniqtag"). Supplementary material and code to reproduce it is available at https://github.com/sjackman/uniqtag-paper.

SmartAnnotator An Interactive Tool for Annotating Indoor RGBD Images

AbstractRGBD images with high quality annotations, both in the form of geometric (i.e., segmentation) and structural (i.e., how do the segments mutually relate in 3D) information, provide valuable priors for a diverse range of applications in scene understanding and image manipulation. While it is now simple to acquire RGBD images, annotating them, automatically or manually, remains challenging. We present SmartAnnotator, an interactive system to facilitate annotating raw RGBD images. The system performs the tedious tasks of grouping pixels, creating potential abstracted cuboids, inferring object interactions in 3D, and generates an ordered list of hypotheses. The user simply has to flip through the suggestions for segment labels, finalize a selection, and the system updates the remaining hypotheses. As annotations are finalized, the process becomes simpler with fewer ambiguities to resolve. Moreover, as more scenes are annotated, the system makes better suggestions based on the structural and geometric priors learned from previous annotation sessions. We test the system on a large number of indoor scenes across different users and experimental settings, validate the results on existing benchmark datasets, and report significant improvements over low‐level annotation alternatives. (Code and benchmark datasets are publicly available on the project page.)

Shade avoidance components and pathways in adult plants revealed by phenotypic profiling.

Shade from neighboring plants limits light for photosynthesis; as a consequence, plants have a variety of strategies to avoid canopy shade and compete with their neighbors for light. Collectively the response to foliar shade is called the shade avoidance syndrome (SAS). The SAS includes elongation of a variety of organs, acceleration of flowering time, and additional physiological responses, which are seen throughout the plant life cycle. However, current mechanistic knowledge is mainly limited to shade-induced elongation of seedlings. Here we use phenotypic profiling of seedling, leaf, and flowering time traits to untangle complex SAS networks. We used over-representation analysis (ORA) of shade-responsive genes, combined with previous annotation, to logically select 59 known and candidate novel mutants for phenotyping. Our analysis reveals shared and separate pathways for each shade avoidance response. In particular, auxin pathway components were required for shade avoidance responses in hypocotyl, petiole, and flowering time, whereas jasmonic acid pathway components were only required for petiole and flowering time responses. Our phenotypic profiling allowed discovery of seventeen novel shade avoidance mutants. Our results demonstrate that logical selection of mutants increased success of phenotypic profiling to dissect complex traits and discover novel components.

Quantitative gene profiling of long noncoding RNAs with targeted RNA sequencing.

We compared quantitative RT-PCR (qRT-PCR), RNA-seq and capture sequencing (CaptureSeq) in terms of their ability to assemble and quantify long noncoding RNAs and novel coding exons across 20 human tissues. CaptureSeq was superior for the detection and quantification of genes with low expression, showed little technical variation and accurately measured differential expression. This approach expands and refines previous annotations and simultaneously generates an expression atlas.

Modeling false positive error making patterns in radiology trainees for improved mammography education

IntroductionWhile mammography notably contributes to earlier detection of breast cancer, it has its limitations, including a large number of false positive exams. Improved radiology education could potentially contribute to alleviating this issue. Toward this goal, in this paper we propose an algorithm for modeling of false positive error making among radiology trainees. Identifying troublesome locations for the trainees could focus their training and in turn improve their performance. MethodsThe algorithm proposed in this paper predicts locations that are likely to result in a false positive error for each trainee based on the previous annotations made by the trainee. The algorithm consists of three steps. First, the suspicious false positive locations are identified in mammograms by Difference of Gaussian filter and suspicious regions are segmented by computer vision-based segmentation algorithms. Second, 133 features are extracted for each suspicious region to describe its distinctive characteristics. Third, a random forest classifier is applied to predict the likelihood of the trainee making a false positive error using the extracted features. The random forest classifier is trained using previous annotations made by the trainee. We evaluated the algorithm using data from a reader study in which 3 experts and 10 trainees interpreted 100 mammographic cases. ResultsThe algorithm was able to identify locations where the trainee will commit a false positive error with accuracy higher than an algorithm that selects such locations randomly. Specifically, our algorithm found false positive locations with 40% accuracy when only 1 location was selected for all cases for each trainee and 12% accuracy when 10 locations were selected. The accuracies for randomly identified locations were both 0% for these two scenarios. ConclusionsIn this first study on the topic, we were able to build computer models that were able to find locations for which a trainee will make a false positive error in images that were not previously seen by the trainee. Presenting the trainees with such locations rather than randomly selected ones may improve their educational outcomes.

Re-annotation of the woodland strawberry (Fragaria vesca) genome.

BackgroundFragaria vesca is a low-growing, small-fruited diploid strawberry species commonly called woodland strawberry. It is native to temperate regions of Eurasia and North America and while it produces edible fruits, it is most highly useful as an experimental perennial plant system that can serve as a model for the agriculturally important Rosaceae family. A draft of the F. vesca genome sequence was published in 2011 [Nat Genet 43:223,2011]. The first generation annotation (version 1.1) were developed using GeneMark-ES+[Nuc Acids Res 33:6494,2005]which is a self-training gene prediction tool that relies primarily on the combination of ab initio predictions with mapping high confidence ESTs in addition to mapping gene deserts from transposable elements. Based on over 25 different tissue transcriptomes, we have revised the F. vesca genome annotation, thereby providing several improvements over version 1.1.ResultsThe new annotation, which was achieved using Maker, describes many more predicted protein coding genes compared to the GeneMark generated annotation that is currently hosted at the Genome Database for Rosaceae (http://www.rosaceae.org/). Our new annotation also results in an increase in the overall total coding length, and the number of coding regions found. The total number of gene predictions that do not overlap with the previous annotations is 2286, most of which were found to be homologous to other plant genes. We have experimentally verified one of the new gene model predictions to validate our results.ConclusionsUsing the RNA-Seq transcriptome sequences from 25 diverse tissue types, the re-annotation pipeline improved existing annotations by increasing the annotation accuracy based on extensive transcriptome data. It uncovered new genes, added exons to current genes, and extended or merged exons. This complete genome re-annotation will significantly benefit functional genomic studies of the strawberry and other members of the Rosaceae.

Data Analytics for Rapid Mapping: Case Study of a Flooding Event in Germany and the Tsunami in Japan Using Very High Resolution SAR Images

In this paper, we present data analytics for a quantitative analysis in a rapid mapping scenario applied for damage assessment of the 2013 floods in Germany and the 2011 tsunami in Japan. These scenarios are created using preand postdisaster TerraSAR-X images and a semi-automated processing chain. All our dataset is tiled into patches and Gabor filters are applied as a primitive feature extraction method to each patch separately. A support vector machine with relevance feedback is implemented in order to group the features into categories. Once all categories are identified, these are semantically annotated using reference data as ground truth. In our investigation, nondamaged and damaged categories were retrieved with their specific taxonomies defined using our previous hierarchical annotation scheme. The classifier supports rapid mapping scenarios (e.g., floods in Germany and tsunami in Japan) and interactive mapping generation. The quantitative damages can be assessed by: 1) flooded agricultural areas (21.66% in the case of floods in Germany and 4.15% in the case of tsunami in Japan) and destroyed aquaculture (2.33% in the case of tsunami in Japan); 2) destroyed transportation infrastructures, such as airport (50% in case tsunami in Japan), bridges, and roads.; and 3) debris that appears in postdisaster images (3.81% in the case of tsunami after the aquaculture was destroyed). The first analysis envisages the floods of Elbe river in June 2013: 30% of the investigated area, about , including agricultural land, forest, river, and some residential and industrial areas close to the river, was covered by water. The second analysis, considering an area of affected by the tsunami, led us to conclude that 3 months after the tsunami, some of the categories returned to previous functionality-the airport, others return to partial functionality such as isolated residents, and some were totally destroyed-the aquaculture. The flooded area was about . The proposed approach goes beyond a simple annotation of the data and provides an intermediate product in order to produce a relevant visual analytics representation of the data. This makes it easier to compare datasets and different quantitative findings in a meaningful manner, accessible both to experts and regular users. Our paper presents an interactive and automatic, fast processing method applicable to large and complex datasets (such as image time series). In addition to enhancing the information content extraction (number of identified categories), this approach enables the discovery and analysis of these categories. The novelty of this paper resides in that this is the first time data analytics have been run on a large dataset and for different scenarios using a semiautomated processing chain.

Bioinformatics: the next frontier of metabolomics.

Bioinformatic tools are required to carry out essential functions such as statistical analyses and database functionalities. Now, they are also needed for one of the most difficult tasks, helping researchers decide which metabolites are the most biologically meaningful. This can be achieved through aiding the identification process, reducing feature redundancy, putting forward better candidates for tandem mass spectrometry (MS/MS), speeding up or automating the workflow, deconvolving the feature list through meta-analysis or multigroup analysis, or using stable isotopes and pathway mapping. This review thus focuses on the most recent and innovative bioinformatic advancements for identifying metabolites. A primary objective of metabolomics beyond biomarker discovery is to identify the most meaningful metabolites that correlate with disease pathogenesis or other perturbations of metabolism. Metabolites play important roles in biological pathways; their flux or differential regulation (dysregulation) can reveal novel insights into disease and environmental influences. Therefore, one of the most important goals of metabolomic analysis has been to assign metabolite identity so they can be used for further statistical and informed pathway analysis.1,2 Over the past few years, technologies for analyzing metabolites by untargeted or targeted metabolomics have undergone extensive improvements. Strides to establish the most efficient protocols for experimental design, sample extraction techniques, and data acquisition have paid off providing robust complex data sets.3−9 As more is being required of these data sets such as assigning identity and biological meaning to the features, bioinformatics is the area of metabolomics which is currently undergoing the most needed growth. It is often the case that metabolomic analysis results in a list of metabolites with low specificity for the disease or stimulus being studied (Figure ​(Figure1).1). Some of these metabolites seem to be dysregulated in a variety of diseases such as acylcarnitines10−13 and fatty acids.14−17 They may be more indicative of a perturbed systemic cause (appetite, physical activity, diurnal rhythm changes, etc..), sample contamination, or instrumental/bioinformatic noise, rather than a specific biomarker of disease. An example of this can be seen in the analysis of urinary biomarkers of ionizing radiation, where dicarboxylic acids were downregulated in the rat after radiation exposure. It was proven that this observation was actually caused by a decreased appetite after radiation exposure perturbing the β-oxidation pathway and not from radiation-induced cellular changes.18,19 Furthermore, dicarboxylic acids can leach out from plastics during the extraction process, further adding to the ambiguity of their role in ionizing radiation.20 Figure 1 Biomarkers that have high vs low disease specificity. As well as identifying the correct source of the biomarkers, it is also important to identify their physiological role and how to utilize them as therapeutic targets. This first has to start with the identification of the metabolite and is determined by filtering thresholds set by the user which is intrinsically biased. These thresholds include those for fold change and p-value, which are highly dependent on the experiment; in vitro experiments would exhibit lower variation between biological replicates than in vivo. The ease of identifying the metabolite is also determined by its concentration in the sample and previous annotation in metabolite databases. Filtering thresholds for metabolite intensity that are set too high may omit important biologically meaningful metabolites rather than noise. Furthermore, a metabolite that is novel or not curated in a database may not be taken into consideration based on the chemical knowledge of the researcher and what they deem as meaningful. In order to transform the complex list of identified metabolites into markers of disease, or assign what role they play, bioinformatic tools can aid in identifying the potential pathways that the metabolite may belong to. It is then that the researcher can use this knowledge surrounding the biology of the metabolite to probe the mechanism of the disease. Untargeted metabolomics has already been used in such a manner to find the source of neuropathic pain.21N,N-Dimethylsphingosine was dysregulated in a rat model of neuropathic pain, furthermore when dosed to control rats it induced mechanical hypersensitivity. This metabolite implicated the sphingomyelin-ceramide pathway as a potential therapeutic target. Antimetabolite inhibitors of enzymes in this pathway were tested and were able to ameliorate neuropathic pain (unpublished data). This study holds promise for other metabolomic studies to maximize the potential information contained within the data for finding therapeutics of disease rather than only providing lists of dysregulated metabolites.

Metabolic network prediction through pairwise rational kernels.

BackgroundMetabolic networks are represented by the set of metabolic pathways. Metabolic pathways are a series of biochemical reactions, in which the product (output) from one reaction serves as the substrate (input) to another reaction. Many pathways remain incompletely characterized. One of the major challenges of computational biology is to obtain better models of metabolic pathways. Existing models are dependent on the annotation of the genes. This propagates error accumulation when the pathways are predicted by incorrectly annotated genes. Pairwise classification methods are supervised learning methods used to classify new pair of entities. Some of these classification methods, e.g., Pairwise Support Vector Machines (SVMs), use pairwise kernels. Pairwise kernels describe similarity measures between two pairs of entities. Using pairwise kernels to handle sequence data requires long processing times and large storage. Rational kernels are kernels based on weighted finite-state transducers that represent similarity measures between sequences or automata. They have been effectively used in problems that handle large amount of sequence information such as protein essentiality, natural language processing and machine translations.ResultsWe create a new family of pairwise kernels using weighted finite-state transducers (called Pairwise Rational Kernel (PRK)) to predict metabolic pathways from a variety of biological data. PRKs take advantage of the simpler representations and faster algorithms of transducers. Because raw sequence data can be used, the predictor model avoids the errors introduced by incorrect gene annotations. We then developed several experiments with PRKs and Pairwise SVM to validate our methods using the metabolic network of Saccharomyces cerevisiae. As a result, when PRKs are used, our method executes faster in comparison with other pairwise kernels. Also, when we use PRKs combined with other simple kernels that include evolutionary information, the accuracy values have been improved, while maintaining lower construction and execution times.ConclusionsThe power of using kernels is that almost any sort of data can be represented using kernels. Therefore, completely disparate types of data can be combined to add power to kernel-based machine learning methods. When we compared our proposal using PRKs with other similar kernel, the execution times were decreased, with no compromise of accuracy. We also proved that by combining PRKs with other kernels that include evolutionary information, the accuracy can also also be improved. As our proposal can use any type of sequence data, genes do not need to be properly annotated, avoiding accumulation errors because of incorrect previous annotations.

A sparse kernel relevance model for automatic image annotation

In this paper, we introduce a new form of the continuous relevance model (CRM), dubbed the SKL-CRM, that adaptively selects the best performing kernel per feature type for automatic image annotation. Previous image annotation models apply a standard selection of kernels to model the distribution of image features. Popular examples include a Gaussian kernel for modelling GIST features or a Laplacian kernel for global colour histograms. In this work, we demonstrate that this standard assignment of kernels to feature types is sub-optimal and a substantially higher image annotation accuracy can be attained by adapting the kernel-feature assignment. We formulate an efficient greedy algorithm to find the best kernel-feature alignment and show that it is able to rapidly find a sparse subset of features that maximises annotation \(F_{1}\) score. In a second contribution, we introduce two data-adaptive kernels for image annotation—the generalised Gaussian and multinomial kernels—which we demonstrate can better model the distribution of image features as compared to standard kernels. Evaluation is conducted on three standard image datasets across a selection of different feature representations. The proposed SKL-CRM model is found to attain performance that is competitive to a suite of state-of-the-art image annotation models.

Understanding how and why the Gene Ontology and its annotations evolve: the GO within UniProt

The Gene Ontology Consortium (GOC) is a major bioinformatics project that provides structured controlled vocabularies to classify gene product function and location. GOC members create annotations to gene products using the Gene Ontology (GO) vocabularies, thus providing an extensive, publicly available resource. The GO and its annotations to gene products are now an integral part of functional analysis, and statistical tests using GO data are becoming routine for researchers to include when publishing functional information. While many helpful articles about the GOC are available, there are certain updates to the ontology and annotation sets that sometimes go unobserved. Here we describe some of the ways in which GO can change that should be carefully considered by all users of GO as they may have a significant impact on the resulting gene product annotations, and therefore the functional description of the gene product, or the interpretation of analyses performed on GO datasets. GO annotations for gene products change for many reasons, and while these changes generally improve the accuracy of the representation of the underlying biology, they do not necessarily imply that previous annotations were incorrect. We additionally describe the quality assurance mechanisms we employ to improve the accuracy of annotations, which necessarily changes the composition of the annotation sets we provide. We use the Universal Protein Resource (UniProt) for illustrative purposes of how the GO Consortium, as a whole, manages these changes.

The rRNA and tRNA transcripts of maternally and paternally inherited mitochondrial DNAs of Mytilus galloprovincialis suggest presence of a “degradosome” in mussel mitochondria and necessitate the re-annotation of the l-rRNA/CR boundary

Species of the genus Mytilus carry two mitochondrial genomes in obligatory coexistence; one transmitted though the eggs (the F type) and one through the sperm (the M type). We have studied the 3′ and 5′ ends of rRNA and tRNA transcripts using RT-PCR and RNA circularization techniques in both the F and M genomes of Mytilus galloprovincialis. We have found polyadenylated and non-adenylated transcripts for both ribosomal and transfer RNAs. In all these genes the 5′ ends of the transcripts coincided with the first nucleotide of the annotated genes, but the 3′ ends were heterogeneous. The l-rRNA 3′ end is 47 or 48 nucleotides upstream from the one assigned by a previous annotation, which makes the adjacent first domain (variable domain one, VD1) of the main control region (CR) correspondingly longer. We have observed s-rRNA and l-rRNA transcripts with truncated 3′ end and polyadenylated tRNA transcripts carrying the CCA trinucleotide. We have also detected polyadenylated RNA remnants carrying the sequences of the control region, which strongly suggests RNA degradation activity and thus presence of degradosomes in Mytilus mitochondria.

In Silico Proteomic Functional Re-annotation of <i>Escherichia coli</i> K-12 using Dynamic Biological Data Fusion Strategy

Escherichia coli , one of the favorite model organisms, was initially annotated in 1997 and re-annotated in 2007. Although years of intensive research is being carried out on E. coli genome, still complete and accurate functional information of this organism is not available. In E. coli , about 40% of the protein sequences have been annotated as hypothetical proteins, because of lack of information. Hence, such sequences require advanced computational strategies and derive clues on their biological role. Herein, we have carried out re-annotation of the complete genome of E. coli K-12 using “Dynamic biological data fusion method”. It is a computational strategy we typically applied for combining the heterogeneous biological data sources to maximize knowledge sharing and generating the intersection of data sets. Functional re-annotation results reported in this paper help us to present high quality data on complete proteome of E. coli K-12. We have updated all the protein coding genes from previous annotation work and tried to assign new or more precise functions, wherever possible. About 29% of the protein sequences of E. coli which have been previously annotated as unclear / unknown (hypothetical; without functions) have now been assigned with clear / known functions. Further, the analysis also resulted in the revision of the protein sequences that have been found to be false positive or poorly annotated. Information from this work is made available as a database, “REC-DB, which will remain a useful repository with accurate and updated functional information. Availability: REC-DB is publicly available at http://192.168.2.168/recdb/index.html

Simulation of Rollcage of an All Terrain Vehicle Attributing Differential Finite Element

Design and simulation of the chassis of any automobile for validation against crash and consequences is the dire need of designers in the automobile industry today. This paper presents a detailed methodology in this direction and conclusions drawn might be obliging by the basic constraints and loading conditions of the real time situations. The paper describes detailed analysis and loads of the considered roll cage model. In the current study importance is given for critical points for bent sections-using two elements which has shown better results than the one with single element discretization as in the previous literatures. The design considerations include several aspects of driver ergonomics, suspension geometry, vehicle dynamics, dynamic load transfer, roll centre variations and centre of gravity positions in dynamic modes, i.e., studying the anti-dive and anti-squat concepts keenly. The following sections of the paper detail the selection criteria and factors considered in the process. The chassis is deliberately tested for its behaviour in static and impact loading, drop and heave conditions. The results are further compared with the standards and experimental values obtained in the previous literatures and annotations.

NECTAR: a database of codon-centric missense variant annotations

NECTAR (Non-synonymous Enriched Coding muTation ARchive; http://nectarmutation.org) is a database and web application to annotate disease-related and functionally important amino acids in human proteins. A number of tools are available to facilitate the interpretation of DNA variants identified in diagnostic or research sequencing. These typically identify previous reports of DNA variation at a given genomic location, predict its effects on transcript and protein sequence and may predict downstream functional consequences. Previous reports and functional annotations are typically linked by the genomic location of the variant observed. NECTAR collates disease-causing variants and functionally important amino acid residues from a number of sources. Importantly, rather than simply linking annotations by a shared genomic location, NECTAR annotates variants of interest with details of previously reported variation affecting the same codon. This provides a much richer data set for the interpretation of a novel DNA variant. NECTAR also identifies functionally equivalent amino acid residues in evolutionarily related proteins (paralogues) and, where appropriate, transfers annotations between them. As well as accessing these data through a web interface, users can upload batches of variants in variant call format (VCF) for annotation on-the-fly. The database is freely available to download from the ftp site: ftp://ftp.nectarmutation.org.

RSNPBase: a database for curated regulatory SNPs.

In recent years, human regulatory SNPs (rSNPs) have been widely studied. Here, we present database rSNPBase, freely available at http://rsnp.psych.ac.cn/, to provide curated rSNPs that analyses the regulatory features of all SNPs in the human genome with reference to experimentally supported regulatory elements. In contrast with previous SNP functional annotation databases, rSNPBase is characterized by several unique features. (i) To improve reliability, all SNPs in rSNPBase are annotated with reference to experimentally supported regulatory elements. (ii) rSNPBase focuses on rSNPs involved in a wide range of regulation types, including proximal and distal transcriptional regulation and post-transcriptional regulation, and identifies their potentially regulated genes. (iii) Linkage disequilibrium (LD) correlations between SNPs were analysed so that the regulatory feature is annotated to SNP-set rather than a single SNP. (iv) rSNPBase provides the spatio-temporal labels and experimental eQTL labels for SNPs. In summary, rSNPBase provides more reliable, comprehensive and user-friendly regulatory annotations on rSNPs and will assist researchers in selecting candidate SNPs for further genetic studies and in exploring causal SNPs for in-depth molecular mechanisms of complex phenotypes.

Coreference, lexical givenness and prosody in German

In this article we discuss some empirical results concerning the impact of different levels of information status (i.e. referents and words, respectively) on the prosodic realisation of referential expressions in annotated corpora of read and spontaneous speech. Both at the referential and at the lexical level not only given and new but also intermediate classes of givenness/novelty have to be distinguished. We provide a brief introduction to our two-dimensional RefLex annotation scheme and discuss its application to a number of examples from the theoretical literature which cannot be described satisfactorily by means of previous annotation schemes. From these examples we derive hypotheses on the relationship between information status and accent position as well as accent type. The hypotheses are generally confirmed for read speech showing a stepwise increase in prosodic prominence from given to new items, predominantly ordered according to the information status at the lexical level. The results of the relationship between prosody and information status are found to be less clear in spontaneous speech, probably due to the production of shorter intonation phrases reducing the variability of accentuation in marking different levels of givenness.

Analysis of the Secretome and Identification of Novel Constituents from Culture Filtrate of Bacillus Calmette-Guérin Using High-resolution Mass Spectrometry

Tuberculosis (TB) is an infectious bacterial disease that causes morbidity and mortality, especially in developing countries. Although its efficacy against TB has displayed a high degree of variability (0%-80%) in different trials, Mycobacterium bovis bacillus Calmette-Guérin (BCG) has been recognized as an important weapon for preventing TB worldwide for over 80 years. Because secreted proteins often play vital roles in the interaction between bacteria and host cells, the secretome of mycobacteria is considered to be an attractive reservoir of potential candidate antigens for the development of novel vaccines and diagnostic reagents. In this study, we performed a proteomic analysis of BCG culture filtrate proteins using SDS-PAGE and high-resolution Fourier transform mass spectrometry. In total, 239 proteins (1555 unique peptides) were identified, including 185 secreted proteins or lipoproteins. Furthermore, 17 novel protein products not annotated in the BCG database were detected and validated by means of RT-PCR at the transcriptional level. Additionally, the translational start sites of 52 proteins were confirmed, and 22 proteins were validated through extension of the translational start sites based on N-terminus-derived peptides. There are 103 secreted proteins that have not been reported in previous studies on BCG [corrected] secretome and are unique to our study. The physicochemical characteristics of the secreted proteins were determined. Major components from the culture supernatant, including low-molecular-weight antigens, lipoproteins, Pro-Glu and Pro-Pro-Glu family proteins, and Mce family proteins, are discussed; some components represent potential predominant antigens in the humoral and cellular immune responses.

Young, intact and nested retrotransposons are abundant in the onion and asparagus genomes

Although monocotyledonous plants comprise one of the two major groups of angiosperms and include >65 000 species, comprehensive genome analysis has been focused mainly on the Poaceae (grass) family. Due to this bias, most of the conclusions that have been drawn for monocot genome evolution are based on grasses. It is not known whether these conclusions apply to many other monocots. To extend our understanding of genome evolution in the monocots, Asparagales genomic sequence data were acquired and the structural properties of asparagus and onion genomes were analysed. Specifically, several available onion and asparagus bacterial artificial chromosomes (BACs) with contig sizes >35 kb were annotated and analysed, with a particular focus on the characterization of long terminal repeat (LTR) retrotransposons. The results reveal that LTR retrotransposons are the major components of the onion and garden asparagus genomes. These elements are mostly intact (i.e. with two LTRs), have mainly inserted within the past 6 million years and are piled up into nested structures. Analysis of shotgun genomic sequence data and the observation of two copies for some transposable elements (TEs) in annotated BACs indicates that some families have become particularly abundant, as high as 4-5 % (asparagus) or 3-4 % (onion) of the genome for the most abundant families, as also seen in large grass genomes such as wheat and maize. Although previous annotations of contiguous genomic sequences have suggested that LTR retrotransposons were highly fragmented in these two Asparagales genomes, the results presented here show that this was largely due to the methodology used. In contrast, this current work indicates an ensemble of genomic features similar to those observed in the Poaceae.