Transcriptomics and proteomics. Applications to ecotoxicology

Researchers from Europe and the USA met at the Joint Research Center (JRC) of the European Commission to discuss how to integrate gene and protein expression analyses with bioinformatic tools in the field of ecotoxicology and how this new approach could be translated in improved risk assessment procedures. The measurements of gene and/or protein expression levels, upon exposure to a chemical or a stressor, can be used to develop robust molecular biomarkers that will allow the early detection of environmental stress, study long-term exposure and infer the mechanism of action. These molecular biomarkers should be linked to phenotypic end points of exposure such as adverse effects in growth and reproduction in single organisms and populations. At environmentally realistic exposure levels there could be “non-linear” dose-response curves, which should be accounted for in the experimental design and in the analyses of microarray and proteomic data. The application of gene and protein expression profiling in ecotoxicology will have a significant impact on the ecotoxicology field in the near future and international collaborations will play an important role in accelerating the application of those techniques.     

Conservation of gene function in behaviour

Behaviour genetic research has shown that a given gene or gene pathway can influence categorically similar behaviours in different species. Questions about the conservation of gene function in behaviour are increasingly tractable. This is owing to the surge of DNA and 'omics data, bioinformatic tools, as well as advances in technologies for behavioural phenotyping. Here, we discuss how gene function, as a hierarchical biological phenomenon, can be used to examine behavioural homology across species. The question can be addressed independently using different levels of investigation including the DNA sequence, the gene's position in a genetic pathway, spatial-temporal tissue expression and neural circuitry. Selected examples from the literature are used to illustrate this point. We will also discuss how qualitative and quantitative comparisons of the behavioural phenotype, its function and the importance of environmental and social context should be used in cross-species comparisons. We conclude that (i) there are homologous behaviours, (ii) they are hard to define and (iii) neurogenetics and genomics investigations should help in this endeavour.     

Bioinformatic tools for DNA/protein sequence analysis, functional assignment of genes and protein classification

The development of efficient DNA sequencing methods has led to the achievement of the DNA sequence of entire genomes from (to date) 55 prokaryotes, 5 eukaryotic organisms and 10 eukaryotic chromosomes. Thus, an enormous amount of DNA sequence data is available and even more will be forthcoming in the near future. Analysis of this overwhelming amount of data requires bioinformatic tools in order to identify genes that encode functional proteins or RNA. This is an important task, considering that even in the well-studied Escherichia coli more than 30% of the identified open reading frames are hypothetical genes. Future challenges of genome sequence analysis will include the understanding of gene regulation and metabolic pathway reconstruction including DNA chip technology, which holds tremendous potential for biomedicine and the biotechnological production of valuable compounds. The overwhelming volume of information often confuses scientists. This review intends to provide a guide to choosing the most efficient way to analyze a new sequence or to collect information on a gene or protein of interest by applying current publicly available databases and Web services. Recently developed tools that allow functional assignment of genes, mainly based on sequence similarity of the deduced amino acid sequence, using the currently available and increasing biological databases will be discussed.     

Correlation between gene expression and GO semantic similarity

This research analyzes some aspects of the relationship between gene expression, gene function, and gene annotation. Many recent studies are implicitly based on the assumption that gene products that are biologically and functionally related would maintain this similarity both in their expression profiles as well as in their gene ontology (GO) annotation. We analyze how accurate this assumption proves to be using real publicly available data. We also aim to validate a measure of semantic similarity for GO annotation. We use the Pearson correlation coefficient and its absolute value as a measure of similarity between expression profiles of gene products. We explore a number of semantic similarity measures (Resnik, Jiang, and Lin) and compute the similarity between gene products annotated using the GO. Finally, we compute correlation coefficients to compare gene expression similarity against GO semantic similarity. Our results suggest that the Resnik similarity measure outperforms the others and seems better suited for use in gene ontology. We also deduce that there seems to be correlation between semantic similarity in the GO annotation and gene expression for the three GO ontologies. We show that this correlation is negligible up to a certain semantic similarity value; then, for higher similarity values, the relationship trend becomes almost linear. These results can be used to augment the knowledge provided by clustering algorithms and in the development of bioinformatic tools for finding and characterizing gene products.     

基于RNA-Seq的长非编码RNA预测

随着新一代生物技术和生物信息学的发展,研究发现,在真核生物转录组中存在大量长非编码RNA(long non-codingRNA,lncRNA),而这些lncRNA可能在基因表达调控过程中起到关键性的功能作用.当前lncRNA研究主要采用高通量RNA-Seq测序技术,并通过生物信息学方法对测序数据进行处理和分析,以挖掘其中lncRNA的序列、结构、表达及功能等信息.本文将对基于RNA-Seq的lncRNA预测流程进行介绍,对其中涉及的生物信息学方法进行较为全面的综述,就相关问题和挑战展开讨论,并对研究进行展望.     

Co-expression tools for plant biology: opportunities for hypothesis generation and caveats

Gene co‐expression analysis has emerged in the past 5 years as a powerful tool for gene function prediction. In essence, co‐expression analysis asks the question ‘what are the genes that are co‐expressed, that is, those that show similar expression profiles across many experiments, with my gene of interest?’. Genes that are highly co‐expressed may be involved in the biological process or processes of the query gene. This review describes the tools that are available for performing such analyses, how each of these perform, and also discusses statistical issues including how normalization of gene expression data can influence co‐expression results, calculation of co‐expression scores and P values, and the influence of data sets used for co‐expression analysis. Finally, examples from the literature will be presented, wherein co‐expression has been used to corroborate and discover various aspects of plant biology.     

植物病毒诱导的基因沉默在基因功能研究中的应用

传统的植物遗传转化方法周期长、工作量大、过程繁琐,不利于基因功能的快速高通量鉴定．近年来随着基因沉默机制研究的深入和不断发展,利用病毒诱导的基因沉默(Virus induced gene silencing,VIGS)进行植物功能基因组研究作为一种快速、高通量的反向遗传学工具已被广泛应用在烟草、马铃薯、番茄等植物中, 在大规模的植物基因组功能鉴定中展示了广阔的应用前景．综述了 VIGS 的作用机制、植物病毒栽体、转化方法以及在植物基因功能研究等方面的应用及前景．     

Mining the genomes of plant pathogenic bacteria: how not to drown in gigabases of sequence

Hundreds of bacterial genomes including the genomes of dozens of plant pathogenic bacteria have been sequenced. These genomes represent an invaluable resource for molecular plant pathologists. In this review, we describe different approaches that can be used for mining bacterial genome sequences and examples of how some of these approaches have been used to analyse plant pathogen genomes so far. We review how genomes can be mined one by one and how comparative genomics of closely related genomes releases the true power of genomics. Databases and tools useful for genome mining that are publicly accessible on the Internet are also described. Finally, the need for new databases and tools to efficiently mine today's plant pathogen genomes and hundreds more in the near future is discussed.     

Capture and analysis of quantitative proteomic data

Whilst the array of techniques available for quantitative proteomics continues to grow, the attendant bioinformatic software tools are similarly expanding in number. The data capture and analysis of such quantitative data is obviously crucial to the experiment and the methods used to process it will critically affect the quality of the data obtained. These tools must deal with a variety of issues, including identification of labelled and unlabelled peptide species, location of the corresponding MS scans in the experiment, construction of representative ion chromatograms, location of the true peptide ion chromatogram start and end, elimination of background signal in the mass spectrum and chromatogram and calculation of both peptide and protein ratios/abundances. A variety of tools and approaches are available, in part restricted by the nature of the experiment to be performed and available instrumentation. Currently, although there is no single consensus on precisely how to calculate protein and peptide abundances, many common themes have emerged which identify and reduce many of the key sources of error. These issues will be discussed, along with those relating to deposition of quantitative data. At present, mature data standards for quantitative proteomics are not yet available, although formats are beginning to emerge.     

A hybrid SOM-SVM approach for the zebrafish gene expression analysis

Microarray technology can be employed to quantitatively measure the expression of thousands of genes in a single experiment. It has become one of the main tools for global gene expression analysis in molecular biology research in recent years. The large amount of expression data generated by this technology makes the study of certain complex biological problems possible, and machine learning methods are expected to play a crucial role in the analysis process. In this paper, we present our results from integrating the self-organizing map （SOM） and the support vector machine （SVM） for the analysis of the various functions of zebrafish genes based on their expression. The most distinctive characteristic of our zebrafish gene expression is that the number of samples of different classes is imbalanced. We discuss how SOM can be used as a data-filtering tool to improve the classification performance of the SVM on this data set.     

Genomic tools and cDNA derived markers for butterflies

The Lepidoptera have long been used as examples in the study of evolution, but some questions remain difficult to resolve due to a lack of molecular genetic data. However, as technology improves, genomic tools are becoming increasingly available to tackle unanswered evolutionary questions. Here we have used expressed sequence tags (ESTs) to develop genetic markers for two Müllerian mimic species, Heliconius melpomene and Heliconius erato. In total 1363 ESTs were generated, representing 330 gene objects in H. melpomene and 431 in H. erato. User-friendly bioinformatic tools were used to construct a nonredundant database of these putative genes (available at http://www.heliconius.org), and annotate them with blast similarity searches, InterPro matches and Gene Ontology terms. This database will be continually updated with EST sequences for the Papilionideae as they become publicly available, providing a tool for gene finding in the butterflies. Alignments of the Heliconius sequences with putative homologues derived from Bombyx mori or other public data sets were used to identify conserved PCR priming sites, and develop 55 markers that can be amplified from genomic DNA in both H. erato and H. melpomene. These markers will be used for comparative linkage mapping in Heliconius and will have applications in other phylogenetic and genomic studies in the Lepidoptera.