Re-Annotation of Protein-Coding Genes in the Genome of Saccharomyces cerevisiae Based on Support Vector Machines

The annotation of the well-studied organism, Saccharomyces cerevisiae, has been improving over the past decade while there are unresolved debates over the amount of biologically significant open reading frames (ORFs) in yeast genome. We revisited the total count of protein-coding genes in S. cerevisiae S288c genome using a theoretical approach by combining the Support Vector Machine (SVM) method with six widely used measurements of sequence statistical features. The accuracy of our method is over 99.5% in 10-fold cross-validation. Based on the annotation data in Saccharomyces Genome Database (SGD), we studied the coding capacity of all 1744 ORFs which lack experimental results and suggested that the overall number of chromosomal ORFs encoding proteins in yeast should be 6091 by removing 488 spurious ORFs. The importance of the present work lies in at least two aspects. First, cross-validation and retrospective examination showed the fidelity of our method in recognizing ORFs that likely encode proteins. Second, we have provided a web service that can be accessed at http://cobi.uestc.edu.cn/services/yeast/, which enables the prediction of protein-coding ORFs of the genus Saccharomyces with a high accuracy.     

Enhancing protein-vitamin binding residues prediction by multiple heterogeneous subspace SVMs ensemble

Background

Vitamins are typical ligands that play critical roles in various metabolic processes. The accurate identification of the vitamin-binding residues solely based on a protein sequence is of significant importance for the functional annotation of proteins, especially in the post-genomic era, when large volumes of protein sequences are accumulating quickly without being functionally annotated.

Results

In this paper, a new predictor called TargetVita is designed and implemented for predicting protein-vitamin binding residues using protein sequences. In TargetVita, features derived from the position-specific scoring matrix (PSSM), predicted protein secondary structure, and vitamin binding propensity are combined to form the original feature space; then, several feature subspaces are selected by performing different feature selection methods. Finally, based on the selected feature subspaces, heterogeneous SVMs are trained and then ensembled for performing prediction.

Conclusions

The experimental results obtained with four separate vitamin-binding benchmark datasets demonstrate that the proposed TargetVita is superior to the state-of-the-art vitamin-specific predictor, and an average improvement of 10% in terms of the Matthews correlation coefficient (MCC) was achieved over independent validation tests. The TargetVita web server and the datasets used are freely available for academic use at http://csbio.njust.edu.cn/bioinf/TargetVita or http://www.csbio.sjtu.edu.cn/bioinf/TargetVita.

Electronic supplementary material

The online version of this article (doi:10.1186/1471-2105-15-297) contains supplementary material, which is available to authorized users.     

RADtyping: An Integrated Package for Accurate De Novo Codominant and Dominant RAD Genotyping in Mapping Populations

Genetic linkage maps are indispensable tools in genetic, genomic and breeding studies. As one of genotyping-by-sequencing methods, RAD-Seq (restriction-site associated DNA sequencing) has gained particular popularity for construction of high-density linkage maps. Current RAD analytical tools are being predominantly used for typing codominant markers. However, no genotyping algorithm has been developed for dominant markers (resulting from recognition site disruption). Given their abundance in eukaryotic genomes, utilization of dominant markers would greatly diminish the extensive sequencing effort required for large-scale marker development. In this study, we established, for the first time, a novel statistical framework for de novo dominant genotyping in mapping populations. An integrated package called RADtyping was developed by incorporating both de novo codominant and dominant genotyping algorithms. We demonstrated the superb performance of RADtyping in achieving remarkably high genotyping accuracy based on simulated and real mapping datasets. The RADtyping package is freely available at http://www2.ouc.edu.cn/mollusk/ detailen.asp?id=727.     

INDIGO – INtegrated Data Warehouse of MIcrobial GenOmes with Examples from the Red Sea Extremophiles

Background

The next generation sequencing technologies substantially increased the throughput of microbial genome sequencing. To functionally annotate newly sequenced microbial genomes, a variety of experimental and computational methods are used. Integration of information from different sources is a powerful approach to enhance such annotation. Functional analysis of microbial genomes, necessary for downstream experiments, crucially depends on this annotation but it is hampered by the current lack of suitable information integration and exploration systems for microbial genomes.

Results

We developed a data warehouse system (INDIGO) that enables the integration of annotations for exploration and analysis of newly sequenced microbial genomes. INDIGO offers an opportunity to construct complex queries and combine annotations from multiple sources starting from genomic sequence to protein domain, gene ontology and pathway levels. This data warehouse is aimed at being populated with information from genomes of pure cultures and uncultured single cells of Red Sea bacteria and Archaea. Currently, INDIGO contains information from Salinisphaera shabanensis, Haloplasma contractile, and Halorhabdus tiamatea - extremophiles isolated from deep-sea anoxic brine lakes of the Red Sea. We provide examples of utilizing the system to gain new insights into specific aspects on the unique lifestyle and adaptations of these organisms to extreme environments.

Conclusions

We developed a data warehouse system, INDIGO, which enables comprehensive integration of information from various resources to be used for annotation, exploration and analysis of microbial genomes. It will be regularly updated and extended with new genomes. It is aimed to serve as a resource dedicated to the Red Sea microbes. In addition, through INDIGO, we provide our Automatic Annotation of Microbial Genomes (AAMG) pipeline. The INDIGO web server is freely available at http://www.cbrc.kaust.edu.sa/indigo.     

Integrating Multiple Genomic Data to Predict Disease-Causing Nonsynonymous Single Nucleotide Variants in Exome Sequencing Studies

Exome sequencing has been widely used in detecting pathogenic nonsynonymous single nucleotide variants (SNVs) for human inherited diseases. However, traditional statistical genetics methods are ineffective in analyzing exome sequencing data, due to such facts as the large number of sequenced variants, the presence of non-negligible fraction of pathogenic rare variants or de novo mutations, and the limited size of affected and normal populations. Indeed, prevalent applications of exome sequencing have been appealing for an effective computational method for identifying causative nonsynonymous SNVs from a large number of sequenced variants. Here, we propose a bioinformatics approach called SPRING (Snv PRioritization via the INtegration of Genomic data) for identifying pathogenic nonsynonymous SNVs for a given query disease. Based on six functional effect scores calculated by existing methods (SIFT, PolyPhen2, LRT, MutationTaster, GERP and PhyloP) and five association scores derived from a variety of genomic data sources (gene ontology, protein-protein interactions, protein sequences, protein domain annotations and gene pathway annotations), SPRING calculates the statistical significance that an SNV is causative for a query disease and hence provides a means of prioritizing candidate SNVs. With a series of comprehensive validation experiments, we demonstrate that SPRING is valid for diseases whose genetic bases are either partly known or completely unknown and effective for diseases with a variety of inheritance styles. In applications of our method to real exome sequencing data sets, we show the capability of SPRING in detecting causative de novo mutations for autism, epileptic encephalopathies and intellectual disability. We further provide an online service, the standalone software and genome-wide predictions of causative SNVs for 5,080 diseases at http://bioinfo.au.tsinghua.edu.cn/spring.     

Somatic embryogenesis of a seedless sweet orange (<Emphasis Type="Italic">Citrus sinensis</Emphasis> (L.) Osbeck)

Somatic embryogenesis is an important in vitro technique used to obtain Citrus sinensis (L.) Osbeck (sweet orange) plantlets for conservation, sanitation, propagation, and breeding. The induction of somatic embryogenesis from adult tissues of sweet orange could be an alternative to in vitro organogenesis from epicotyl segments, especially in seedless cultivars, where the latter is not feasible. The aim of this study was to obtain plantlets from ovary-derived somatic embryos of sweet orange cv. ‘Washington Navel’, an important seedless cultivar for citrus fresh fruit production. The explants used were pistils from flower buds, pre-anthesis, from 20-y-old plants cultivated in the field. Forty plantlets from 47 somatic embryos were obtained, in vitro-grafted, and acclimatized in greenhouse conditions. Ploidy evaluation through flow cytometric analysis, as well as the results of target region amplification polymorphism (TRAP) molecular markers confirmed the somatic origin of embryos as genetically similar to donor plants. This technique could be used for obtaining embryogenic cell suspension cultures or regenerated plants from mature tissues other than seed-derived tissues, especially for seedless genotypes.     

DeTEXT: A Database for Evaluating Text Extraction from Biomedical Literature Figures

Hundreds of millions of figures are available in biomedical literature, representing important biomedical experimental evidence. Since text is a rich source of information in figures, automatically extracting such text may assist in the task of mining figure information. A high-quality ground truth standard can greatly facilitate the development of an automated system. This article describes DeTEXT: A database for evaluating text extraction from biomedical literature figures. It is the first publicly available, human-annotated, high quality, and large-scale figure-text dataset with 288 full-text articles, 500 biomedical figures, and 9308 text regions. This article describes how figures were selected from open-access full-text biomedical articles and how annotation guidelines and annotation tools were developed. We also discuss the inter-annotator agreement and the reliability of the annotations. We summarize the statistics of the DeTEXT data and make available evaluation protocols for DeTEXT. Finally we lay out challenges we observed in the automated detection and recognition of figure text and discuss research directions in this area. DeTEXT is publicly available for downloading at http://prir.ustb.edu.cn/DeTEXT/.     

Genome Sequence of Aggregatibacter actinomycetemcomitans Serotype c Strain D11S-1

Aggregatibacter actinomycetemcomitans is a major etiological agent of periodontitis. Here we report the complete genome sequence of serotype c strain D11S-1, which was recovered from the subgingival plaque of a patient diagnosed with generalized aggressive periodontitis.Aggregatibacter actinomycetemcomitans is a major etiologic agent of human periodontal disease, in particular aggressive periodontitis (12). The natural population of A. actinomycetemcomitans is clonal (7). Six A. actinomycetemcomitans serotypes are distinguished based on the structural and serological characteristics of the O antigen of LPS (6, 7). Three of the serotypes (a, b, and c) comprise >80% of all strains, and each serotype represents a distinct clonal lineage (1, 6, 7). Serotype c strain D11S-1 was cultured from a subgingival plaque sample of a patient diagnosed with generalized aggressive periodontitis. The complete genome sequencing of the strain was determined by 454 pyrosequencing (10), which achieved 25× coverage. Assembly was performed using the Newbler assembler (454, Branford, CT) and generated 199 large contigs, with 99.3% of the bases having a quality score of 40 and above. The contigs were aligned with the genome of the sequenced serotype b strain HK1651 (http://www.genome.ou.edu/act.html) using software written in house. The putative contig gaps were then closed by primer walking and sequencing of PCR products over the gaps. The final genome assembly was further confirmed by comparison of an in silico NcoI restriction map to the experimental map generated by optical mapping (8). The genome structure of the D11S-1 strain was compared to that of the sequenced strain HK1651 using the program MAUVE (2, 3). The automated annotation was done using a protocol similar to the annotation engine service at The Institute for Genomic Research/J. Craig Venter Institute with some local modifications. Briefly, protein-coding genes were identified using Glimmer3 (4). Each protein sequence was then annotated by comparing to the GenBank nonredundant protein database. BLAST-Extend-Repraze was applied to the predicted genes to identify genes that might have been truncated due to a frameshift mutation or premature stop codon. tRNA and rRNA genes were identified by using tRNAScan-SE (9) and a similarity search to our in-house RNA database, respectively.The D11S-1 circular genome contains 2,105,764 nucleotides, a GC content of 44.55%, 2,134 predicted coding sequences, and 54 tRNA and 19 rRNA genes (see additional data at http://expression.washington.edu/bumgarnerlab/publications.php). The distribution of predicted genes based on functional categories was similar between D11S-1 and HK1651 (http://expression.washington.edu/bumgarnerlab/publications.php). One hundred six and 86 coding sequences were unique to strain D11S-1 and HK1651, respectively (http://expression.washington.edu/bumgarnerlab/publications.php). Genomic islands were identified based on annotations for strain HK1651 and based on manual inspection of contiguous D11S-1 specific DNA regions with G+C bias (http://expression.washington.edu/bumgarnerlab/publications.php). Among 12 identified genomics islands, 5 (B, C, D, E and G; cytolethal distending toxin gene cluster, tight adherence gene cluster, O-antigen biosynthesis and transport gene cluster, leukotoxin gene cluster, and lipoligosaccharide biosynthesis enzyme gene, respectively) correspond to islands 2 to 5 and 8 of strain HK1651 (http://www.oralgen.lanl.gov/) (5). Island F (∼5 kb) is homologous to a portion of the 12.5-kb island 7 in HK1651. Five genomic islands (H to L) were unique to strain D11S-1. The remaining island (A) is a fusion of genomic islands 1 and 6, in strain HK1651. The genome of D11S-1 is largely in synteny with the genome of the sequenced serotype b strain HK1651 but contained several large-scale genomic rearrangements.Strain D11S-1 harbors a 43-kb bacteriophage and two plasmids of 31 and 23 kb (http://expression.washington.edu/bumgarnerlab/publications.php). Excluding an ∼9-kb region of low homology, the phage showed >90% nucleotide sequence identity with AaΦ23 (11). A 49-bp attB site (11) was identified at coordinates 2,024,825 to 2,024,873. The location of the inserted phage was identified in the optical map of strain D11S-1 and further confirmed by PCR amplification and sequencing of the regions flanking the insertion site. A closed circular form of the phage was also detected in strain D11S-1 by PCR analysis of the phage ends. The 23-kb plasmid is homologous to pVT745 (92% nucleotide identities). The 31-kb plasmid is a novel plasmid. It has significant homologies in short regions (<2 kb) to Haemophilus influenzae biotype aegyptius plasmid pF1947 and other plasmids.     

BEACON: automated tool for Bacterial GEnome Annotation ComparisON

Background

Genome annotation is one way of summarizing the existing knowledge about genomic characteristics of an organism. There has been an increased interest during the last several decades in computer-based structural and functional genome annotation. Many methods for this purpose have been developed for eukaryotes and prokaryotes. Our study focuses on comparison of functional annotations of prokaryotic genomes. To the best of our knowledge there is no fully automated system for detailed comparison of functional genome annotations generated by different annotation methods (AMs).

Results

The presence of many AMs and development of new ones introduce needs to: a/ compare different annotations for a single genome, and b/ generate annotation by combining individual ones. To address these issues we developed an Automated Tool for Bacterial GEnome Annotation ComparisON (BEACON) that benefits both AM developers and annotation analysers. BEACON provides detailed comparison of gene function annotations of prokaryotic genomes obtained by different AMs and generates extended annotations through combination of individual ones. For the illustration of BEACON’s utility, we provide a comparison analysis of multiple different annotations generated for four genomes and show on these examples that the extended annotation can increase the number of genes annotated by putative functions up to 27 %, while the number of genes without any function assignment is reduced.

Conclusions

We developed BEACON, a fast tool for an automated and a systematic comparison of different annotations of single genomes. The extended annotation assigns putative functions to many genes with unknown functions. BEACON is available under GNU General Public License version 3.0 and is accessible at: http://www.cbrc.kaust.edu.sa/BEACON/.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-015-1826-4) contains supplementary material, which is available to authorized users.     

ggComp enables dissection of germplasm resources and construction of a multiscale germplasm network in wheat

Accurate germplasm characterization is a vital step for accelerating crop genetic improvement, which remains largely infeasible for crops such as bread wheat (Triticum aestivum L.), which has a complex genome that undergoes frequent introgression and contains many structural variations. Here, we propose a genomic strategy called ggComp, which integrates resequencing data with copy number variations and stratified single-nucleotide polymorphism densities to enable unsupervised identification of pairwise germplasm resource-based Identity-By-Descent (gIBD) blocks. The reliability of ggComp was verified in wheat cultivar Nongda5181 by dissecting parental-descent patterns represented by inherited genomic blocks. With gIBD blocks identified among 212 wheat accessions, we constructed a multi-scale genomic-based germplasm network. At the whole-genome level, the network helps to clarify pedigree relationship, demonstrate genetic flow, and identify key founder lines. At the chromosome level, we were able to trace the utilization of 1RS introgression in modern wheat breeding by hitchhiked segments. At the single block scale, the dissected germplasm-based haplotypes nicely matched with previously identified alleles of “Green Revolution” genes and can guide allele mining and dissect the trajectory of beneficial alleles in wheat breeding. Our work presents a model-based framework for precisely evaluating germplasm resources with genomic data. A database, WheatCompDB (http://wheat.cau.edu.cn/WheatCompDB/), is available for researchers to exploit the identified gIBDs with a multi-scale network.

ggComp is a genomic-based approach that enables multi-scale germplasm network construction and promotes germplasm resource utilization in crop breeding.     

Evidence for host plant preference by Iphiseiodes quadripilis (Acari: Phytoseiidae) on Citrus

In this study, we present field and laboratory evidence on the preference of Iphiseiodes quadripilis (Banks) for grapefruit (Citrus paradisi Macfadyen) leaves compared with sweet orange (Citrus sinensis (L.) Osbeck) leaves. This preference was confirmed in four orchards whether leaf samples were taken from either border trees of contiguous grapefruit or sweet orange or interior row trees with both citrus species in adjacent rows. Iphiseiodes quadripilis was most abundant in grapefruit trees in spite of the greater abundance of the Texas citrus mite, Eutetranychus banksi (McGregor) (Acari: Tetranychidae) in sweet orange trees. Similar preference responses were observed in laboratory tests using a Y-tube olfactometer whether I. quadripilis were collected from sweet orange or grapefruit. Iphiseiodes quadripilis collected from grapefruit trees showed significant preference for grapefruit over sweet orange leaves in contact choice tests using an arena of alternating leaf strips (12 mm long × 2 mm wide) of sweet orange and grapefruit. However, I.␣quadripilis collected from sweet orange trees did not show preference for either grapefruit or sweet orange leaves. Based on these results, grapefruit leaves foster some unknown factor or factors that retain I. quadripilis in greater numbers compared with sweet orange leaves.     

Prediction of S-Glutathionylation Sites Based on Protein Sequences

S-glutathionylation, the reversible formation of mixed disulfides between glutathione(GSH) and cysteine residues in proteins, is a specific form of post-translational modification that plays important roles in various biological processes, including signal transduction, redox homeostasis, and metabolism inside cells. Experimentally identifying S-glutathionylation sites is labor-intensive and time consuming, whereas bioinformatics methods provide an alternative way to this problem by predicting S-glutathionylation sites in silico. The bioinformatics approaches give not only candidate sites for further experimental verification but also bio-chemical insights into the mechanism of S-glutathionylation. In this paper, we firstly collect experimentally determined S-glutathionylated proteins and their corresponding modification sites from the literature, and then propose a new method for predicting S-glutathionylation sites by employing machine learning methods based on protein sequence data. Promising results are obtained by our method with an AUC (area under ROC curve) score of 0.879 in 5-fold cross-validation, which demonstrates the predictive power of our proposed method. The datasets used in this work are available at http://csb.shu.edu.cn/SGDB.