Genome-wide identification of heat shock proteins (Hsps) and Hsp interactors in rice: Hsp70s as a case study

Background

Heat shock proteins (Hsps) perform a fundamental role in protecting plants against abiotic stresses. Although researchers have made great efforts on the functional analysis of individual family members, Hsps have not been fully characterized in rice (Oryza sativa L.) and little is known about their interactors.

Results

In this study, we combined orthology-based approach with expression association data to screen rice Hsps for the expression patterns of which strongly correlated with that of heat responsive probe-sets. Twenty-seven Hsp candidates were identified, including 12 small Hsps, six Hsp70s, three Hsp60s, three Hsp90s, and three clpB/Hsp100s. Then, using a combination of interolog and expression profile-based methods, we inferred 430 interactors of Hsp70s in rice, and validated the interactions by co-localization and function-based methods. Subsequent analysis showed 13 interacting domains and 28 target motifs were over-represented in Hsp70s interactors. Twenty-four GO terms of biological processes and five GO terms of molecular functions were enriched in the positive interactors, whose expression levels were positively associated with Hsp70s. Hsp70s interaction network implied that Hsp70s were involved in macromolecular translocation, carbohydrate metabolism, innate immunity, photosystem II repair and regulation of kinase activities.

Conclusions

Twenty-seven Hsps in rice were identified and 430 interactors of Hsp70s were inferred and validated, then the interacting network of Hsp70s was induced and the function of Hsp70s was analyzed. Furthermore, two databases named Rice Heat Shock Proteins (RiceHsps) and Rice Gene Expression Profile (RGEP), and one online tool named Protein-Protein Interaction Predictor (PPIP), were constructed and could be accessed at http://bioinformatics.fafu.edu.cn/.

Electronic supplementary material

The online version of this article (doi:10.1186/1471-2164-15-344) contains supplementary material, which is available to authorized users.     

Web Watch

Plant biology - with an emphasis on photosynthesis

The Great Plant Escape http://www.urbanext.uiuc.edu/gpe/gpe.html

The Power of Green.http://researchmag.asu.edu/stories/power.html

Photosynthesis http://biology.ck.uc.edu/courses/biol04/photosyn.htm

An Introduction to Photosynthesis and its Applicationhttp://photoscience.la.asu.edu/photosyn/education/photointro.html

Aliens Explorers - Photosynthesis http://www.alienexplorer.com/ecology/topic3.html

The Photosynthesis Processhttp://www.ifmt.nf.ca/mi-net/enviro/photo3.htm     

Prediction of protein kinase-specific phosphorylation sites in hierarchical structure using functional information and random forest

Reversible protein phosphorylation is one of the most important post-translational modifications, which regulates various biological cellular processes. Identification of the kinase-specific phosphorylation sites is helpful for understanding the phosphorylation mechanism and regulation processes. Although a number of computational approaches have been developed, currently few studies are concerned about hierarchical structures of kinases, and most of the existing tools use only local sequence information to construct predictive models. In this work, we conduct a systematic and hierarchy-specific investigation of protein phosphorylation site prediction in which protein kinases are clustered into hierarchical structures with four levels including kinase, subfamily, family and group. To enhance phosphorylation site prediction at all hierarchical levels, functional information of proteins, including gene ontology (GO) and protein–protein interaction (PPI), is adopted in addition to primary sequence to construct prediction models based on random forest. Analysis of selected GO and PPI features shows that functional information is critical in determining protein phosphorylation sites for every hierarchical level. Furthermore, the prediction results of Phospho.ELM and additional testing dataset demonstrate that the proposed method remarkably outperforms existing phosphorylation prediction methods at all hierarchical levels. The proposed method is freely available at http://bioinformatics.ustc.edu.cn/phos_pred/.     

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.     

Genetics web sites

Human Genome Project Information http://www.ornl.gov/TechResources/Human_Genome/home.html

Access Excellence http://www.accessexcellence.org/

Genetics Science Learning Centre at the Eccles Institute of Human Genetics, University of Utah http://gslc.genetics.utah.edu/

Blazing a Genetic Trial http://www.hhmi.org/GeneticTrail/

A Hypermedia Glossary of Genetic Terms prepared and presented by Birgid Schlindwein http://www.weihenstephan.de/~schlind/genglos.html

Virtual Flylab http://vcourseware5.calstatela.edu/VirtualFlyLab/IntroVflyLab.html

Web watch topics coming up…     

IHEC_RAAC: a online platform for identifying human enzyme classes via reduced amino acid cluster strategy

Enzymes have been proven to play considerable roles in disease diagnosis and biological functions. The feature extraction that truly reflects the intrinsic properties of protein is the most critical step for the automatic identification of enzymes. Although lots of feature extraction methods have been proposed, some challenges remain. In this study, we developed a predictor called IHEC_RAAC, which has the capability to identify whether a protein is a human enzyme and distinguish the function of the human enzyme. To improve the feature representation ability, protein sequences were encoded by a new feature-vector called ‘reduced amino acid cluster’. We calculated 673 amino acid reduction alphabets to determine the optimal feature representative scheme. The tenfold cross-validation test showed that the accuracy of IHEC_RAAC to identify human enzymes was 74.66% and further discriminate the human enzyme classes with an accuracy of 54.78%, which was 2.06% and 8.68% higher than the state-of-the-art predictors, respectively. Additionally, the results from the independent dataset indicated that IHEC_RAAC can effectively predict human enzymes and human enzyme classes to further provide guidance for protein research. A user-friendly web server, IHEC_RAAC, is freely accessible at http://bioinfor.imu.edu.cn/ihecraac.

     

Gene Re-annotation in Genome of the Extremophile Pyrobaculum Aerophilum by Using Bioinformatics Methods

Abstract

In this paper, we re-annotated the genome of Pyrobaculum aerophilum str. IM2, particularly for hypothetical ORFs. The annotation process includes three parts. Firstly and most importantly, 23 new genes, which were missed in the original annotation, are found by combining similarity search and the ab initio gene finding approaches. Among these new genes, five have significant similarities with function-known genes and the rest have significant similarities with hypothetical ORFs contained in other genomes. Secondly, the coding potentials of the 1645 hypothetical ORFs are re-predicted by using 33 Z curve variables combined with Fisher linear discrimination method. With the accuracy being 99.68%, 25 originally annotated hypothetical ORFs are recognized as non-coding by our method. Thirdly, 80 hypothetical ORFs are assigned with potential functions by using similarity search with BLAST program. Re-annotation of the genome will benefit related researches on this hyperthermophilic crenarchaeon. Also, the re-annotation procedure could be taken as a reference for other archaeal genomes. Details of the revised annotation are freely available at http://cobi.uestc.edu.cn/resource/paero/     

Using Over-Represented Tetrapeptides to Predict Protein Submitochondria Locations

The mitochondrion is a key organelle of eukaryotic cell that provides the energy for cellular activities. Correctly identifying submitochondria locations of proteins can provide plentiful information for understanding their functions. However, using web-experimental methods to recognize submitochondria locations of proteins are time-consuming and costly. Thus, it is highly desired to develop a bioinformatics method to predict the submitochondria locations of mitochondrion proteins. In this work, a novel method based on support vector machine was developed to predict the submitochondria locations of mitochondrion proteins by using over-represented tetrapeptides selected by using binomial distribution. A reliable and rigorous benchmark dataset including 495 mitochondrion proteins with sequence identity ≤25 % was constructed for testing and evaluating the proposed model. Jackknife cross-validated results showed that the 91.1 % of the 495 mitochondrion proteins can be correctly predicted. Subsequently, our model was estimated by three existing benchmark datasets. The overall accuracies are 94.0, 94.7 and 93.4 %, respectively, suggesting that the proposed model is potentially useful in the realm of mitochondrion proteome research. Based on this model, we built a predictor called TetraMito which is freely available at http://lin.uestc.edu.cn/server/TetraMito.     

Identifying micro-inversions using high-throughput sequencing reads

Background

The identification of inversions of DNA segments shorter than read length (e.g., 100 bp), defined as micro-inversions (MIs), remains challenging for next-generation sequencing reads. It is acknowledged that MIs are important genomic variation and may play roles in causing genetic disease. However, current alignment methods are generally insensitive to detect MIs. Here we develop a novel tool, MID (Micro-Inversion Detector), to identify MIs in human genomes using next-generation sequencing reads.

Results

The algorithm of MID is designed based on a dynamic programming path-finding approach. What makes MID different from other variant detection tools is that MID can handle small MIs and multiple breakpoints within an unmapped read. Moreover, MID improves reliability in low coverage data by integrating multiple samples. Our evaluation demonstrated that MID outperforms Gustaf, which can currently detect inversions from 30 bp to 500 bp.

Conclusions

To our knowledge, MID is the first method that can efficiently and reliably identify MIs from unmapped short next-generation sequencing reads. MID is reliable on low coverage data, which is suitable for large-scale projects such as the 1000 Genomes Project (1KGP). MID identified previously unknown MIs from the 1KGP that overlap with genes and regulatory elements in the human genome. We also identified MIs in cancer cell lines from Cancer Cell Line Encyclopedia (CCLE). Therefore our tool is expected to be useful to improve the study of MIs as a type of genetic variant in the human genome. The source code can be downloaded from: http://cqb.pku.edu.cn/ZhuLab/MID.

     

FragIdent - Automatic identification and characterisation of cDNA-fragments

Background

The ubiquitin 26S/proteasome system (UPS), a serial cascade process of protein ubiquitination and degradation, is the last step for most cellular proteins. There are many genes involved in this system, but are not identified in many species. The accumulating availability of genomic sequence data is generating more demands in data management and analysis. Genomics data of plants such as Populus trichocarpa, Medicago truncatula, Glycine max and others are now publicly accessible. It is time to integrate information on classes of genes for complex protein systems such as UPS.

Results

We developed a database of higher plants' UPS, named 'plantsUPS'. Both automated search and manual curation were performed in identifying candidate genes. Extensive annotations referring to each gene were generated, including basic gene characterization, protein features, GO (gene ontology) assignment, microarray probe set annotation and expression data, as well as cross-links among different organisms. A chromosome distribution map, multi-sequence alignment, and phylogenetic trees for each species or gene family were also created. A user-friendly web interface and regular updates make plantsUPS valuable to researchers in related fields.

Conclusion

The plantsUPS enables the exploration and comparative analysis of UPS in higher plants. It now archives > 8000 genes from seven plant species distributed in 11 UPS-involved gene families. The plantsUPS is freely available now to all users at http://bioinformatics.cau.edu.cn/plantsUPS.     

基层医疗机构人才队伍建设的探讨

??????? 在医改中硬件是基础,软件是根本,基层医疗机构人才队伍建设问题至关重要,本文就如何吸引毕业生下沉到基层、如何提升基层现存医疗队伍的技术水平提出建议,并为如何实现2020年培养30万名全科医生的总体目标,提出利用社会融资方法培养农村全科医生的构想。     

Applications of Tree-Maps to hierarchical biological data

SUMMARY: A brief overview of Tree-Maps provides the basis for understanding two new implementations of Tree-Map methods. TreeMapClusterView provides a new way to view microarray gene expression data, and GenePlacer provides a view of gene ontology annotation data. We also discuss the benefits of Tree-Maps to visualize complex hierarchies in functional genomics. AVAILABILITY: Java class files are freely available at http://mendel.mc.duke.edu/bioinformatics/ CONTACT: mccon012@mc.duke.edu SUPPLEMENTARY INFORMATION: For more information on TreeMapClusterView (see http://mendel.mc.duke.edu/bioinformatics/software/boxclusterview/), and http://mendel.mc.duke.edu/bioinformatics/software/geneplacer/).     

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.     

AraPPISite: a database of fine-grained protein–protein interaction site annotations for Arabidopsis thaliana

Knowledge about protein interaction sites provides detailed information of protein–protein interactions (PPIs). To date, nearly 20,000 of PPIs from Arabidopsis thaliana have been identified. Nevertheless, the interaction site information has been largely missed by previously published PPI databases. Here, AraPPISite, a database that presents fine-grained interaction details for A. thaliana PPIs is established. First, the experimentally determined 3D structures of 27 A. thaliana PPIs are collected from the Protein Data Bank database and the predicted 3D structures of 3023 A. thaliana PPIs are modeled by using two well-established template-based docking methods. For each experimental/predicted complex structure, AraPPISite not only provides an interactive user interface for browsing interaction sites, but also lists detailed evolutionary and physicochemical properties of these sites. Second, AraPPISite assigns domain–domain interactions or domain–motif interactions to 4286 PPIs whose 3D structures cannot be modeled. In this case, users can easily query protein interaction regions at the sequence level. AraPPISite is a free and user-friendly database, which does not require user registration or any configuration on local machines. We anticipate AraPPISite can serve as a helpful database resource for the users with less experience in structural biology or protein bioinformatics to probe the details of PPIs, and thus accelerate the studies of plant genetics and functional genomics. AraPPISite is available at http://systbio.cau.edu.cn/arappisite/index.html.     

FANSe2: A Robust and Cost-Efficient Alignment Tool for Quantitative Next-Generation Sequencing Applications

Correct and bias-free interpretation of the deep sequencing data is inevitably dependent on the complete mapping of all mappable reads to the reference sequence, especially for quantitative RNA-seq applications. Seed-based algorithms are generally slow but robust, while Burrows-Wheeler Transform (BWT) based algorithms are fast but less robust. To have both advantages, we developed an algorithm FANSe2 with iterative mapping strategy based on the statistics of real-world sequencing error distribution to substantially accelerate the mapping without compromising the accuracy. Its sensitivity and accuracy are higher than the BWT-based algorithms in the tests using both prokaryotic and eukaryotic sequencing datasets. The gene identification results of FANSe2 is experimentally validated, while the previous algorithms have false positives and false negatives. FANSe2 showed remarkably better consistency to the microarray than most other algorithms in terms of gene expression quantifications. We implemented a scalable and almost maintenance-free parallelization method that can utilize the computational power of multiple office computers, a novel feature not present in any other mainstream algorithm. With three normal office computers, we demonstrated that FANSe2 mapped an RNA-seq dataset generated from an entire Illunima HiSeq 2000 flowcell (8 lanes, 608 M reads) to masked human genome within 4.1 hours with higher sensitivity than Bowtie/Bowtie2. FANSe2 thus provides robust accuracy, full indel sensitivity, fast speed, versatile compatibility and economical computational utilization, making it a useful and practical tool for deep sequencing applications. FANSe2 is freely available at http://bioinformatics.jnu.edu.cn/software/fanse2/.