RETRACTED ARTICLE: Virus-induced gene silencing for functional analysis of selected genes

Virus-induced gene silencing (VIGS) is a technology that exploits an RNA-mediated antiviral defense mechanism and has been shown to be of great potential in plant reverse genetics. Circumvention of plant transformation, methodological simplicity, robustness, and speedy results makes VIGS an attractive alternative instrument in functional genomics, even in a high throughput fashion. The system is well established in Nicotiana benthamiana, and efforts are being made to improve VIGS in other species, including monocots. Here, we discuss the issues specific to the application of VIGS technology to determine gene function, which has revealed the roles of a variety of genes in disease resistance, abiotic stress, cellular signaling and secondary metabolite biosynthesis. M. R. Godge and A. Purkayastha made equal contributions and hence should be treated as joint first authors for this paper.     

A categorization approach to automated ontological function annotation

Automated function prediction (AFP) methods increasingly use knowledge discovery algorithms to map sequence, structure, literature, and/or pathway information about proteins whose functions are unknown into functional ontologies, typically (a portion of) the Gene Ontology (GO). While there are a growing number of methods within this paradigm, the general problem of assessing the accuracy of such prediction algorithms has not been seriously addressed. We present first an application for function prediction from protein sequences using the POSet Ontology Categorizer (POSOC) to produce new annotations by analyzing collections of GO nodes derived from annotations of protein BLAST neighborhoods. We then also present hierarchical precision and hierarchical recall as new evaluation metrics for assessing the accuracy of any predictions in hierarchical ontologies, and discuss results on a test set of protein sequences. We show that our method provides substantially improved hierarchical precision (measure of predictions made that are correct) when applied to the nearest BLAST neighbors of target proteins, as compared with simply imputing that neighborhood's annotations to the target. Moreover, when our method is applied to a broader BLAST neighborhood, hierarchical precision is enhanced even further. In all cases, such increased hierarchical precision performance is purchased at a modest expense of hierarchical recall (measure of all annotations that get predicted at all).     

Recurrent use of evolutionary importance for functional annotation of proteins based on local structural similarity

The annotation of protein function has not kept pace with the exponential growth of raw sequence and structure data. An emerging solution to this problem is to identify 3D motifs or templates in protein structures that are necessary and sufficient determinants of function. Here, we demonstrate the recurrent use of evolutionary trace information to construct such 3D templates for enzymes, search for them in other structures, and distinguish true from spurious matches. Serine protease templates built from evolutionarily important residues distinguish between proteases and other proteins nearly as well as the classic Ser-His-Asp catalytic triad. In 53 enzymes spanning 33 distinct functions, an automated pipeline identifies functionally related proteins with an average positive predictive power of 62%, including correct matches to proteins with the same function but with low sequence identity (the average identity for some templates is only 17%). Although these template building, searching, and match classification strategies are not yet optimized, their sequential implementation demonstrates a functional annotation pipeline which does not require experimental information, but only local molecular mimicry among a small number of evolutionarily important residues.     

利用基因表达谱挖掘差异表达功能类的稳健性

Gene Ontology广泛地应用于基于基因芯片数据的差异表达功能类分析。基因芯片技术存在检测缺失与检测误差等问题。本文探讨上述这二个因素对利用基因表达谱挖掘Gene Ontology中差异表达功能类的影响。结果显示,差异表达功能类对于检测缺失与检测误差干扰等有一定的稳健性。     

Structure-based functional inference in structural genomics

The dramatically increasing number of new protein sequences arising from genomics 4 proteomics requires the need for methods to rapidly and reliably infer the molecular and cellular functions of these proteins. One such approach, structural genomics, aims to delineate the total repertoire of protein folds in nature, thereby providing three-dimensional folding patterns for all proteins and to infer molecular functions of the proteins based on the combined information of structures and sequences. The goal of obtaining protein structures on a genomic scale has motivated the development of high throughput technologies and protocols for macromolecular structure determination that have begun to produce structures at a greater rate than previously possible. These new structures have revealed many unexpected functional inferences and evolutionary relationships that were hidden at the sequence level. Here, we present samples of structures determined at Berkeley Structural Genomics Center and collaborators laboratories to illustrate how structural information provides and complements sequence information to deduce the functional inferences of proteins with unknown molecular functions.Two of the major premises of structural genomics are to discover a complete repertoire of protein folds in nature and to find molecular functions of the proteins whose functions are not predicted from sequence comparison alone. To achieve these objectives on a genomic scale, new methods, protocols, and technologies need to be developed by multi-institutional collaborations worldwide. As part of this effort, the Protein Structure Initiative has been launched in the United States (PSI; www.nigms.nih.gov/funding/psi.html). Although infrastructure building and technology development are still the main focus of structural genomics programs [1–6], a considerable number of protein structures have already been produced, some of them coming directly out of semi-automated structure determination pipelines [6–10]. The Berkeley Structural Genomics Center (BSGC) has focused on the proteins of Mycoplasma or their homologues from other organisms as its structural genomics targets because of the minimal genome size of the Mycoplasmas as well as their relevance to human and animal pathogenicity (http://www.strgen.org). Here we present several protein examples encompassing a spectrum of functional inferences obtainable from their three-dimensional structures in five situations, where the inferences are new and testable, and are not predictable from protein sequence information alone.     

Large-scale T-DNA mutagenesis in Arabidopsis for functional genomic analysis

In planta Agrobacterium-mediated transformation combined with a soil-based herbicide selection for transgenic plants was used to recover large numbers of transgenic Arabidopsis plants for functional genomic studies. A tissue-culture-free system for generating transgenic plants was achieved by infiltrating Arabidopsis plants with Agrobacterium tumefaciens harboring a binary T-DNA vector containing the phosphinothricin acetyltransferase gene from Streptomyces hygroscopicus, and by selecting transgenic Arabidopsis growing in soil by foliar application of the herbicide Finale (phosphinothricin). Analysis of herbicide-resistant plants indicated that all were transgenic and that the T-DNA transformation process occurred late during flower development, resulting in a preponderance of independently derived T-DNA insertions. T-DNA insertions were usually integrated in a concatenated, rearranged form, and using linkage analysis, we estimated that T1 plants carried between one and five T-DNA loci. Using pooling strategies, both DNA and seed pools were generated from about 38,000 Arabidopsis plants representing over 115,000 independent T-DNA insertions. We show the utility of these transgenic lines for identifying insertion mutations using gene sequence and PCR-based screening. Electronic Publication     

High-throughput analysis of gene expression on tissue sections by in situ hybridization

Genome-scale sequencing projects, high-throughput RNAi screens, systematic gene targeting, and system-biology-based network predictions all depend on a validation of biological significance in order to understand the relevance of a particular finding. Such validation, for the most part, rests on low-throughput technologies. This article provides protocols that, in combination with suitable instrumentation, make possible a semi-automated analysis of gene expression on tissue sections by means of in situ hybridization. Knowledge of gene expression localization has the potential to aid, and thereby accelerate, the validation of gene functions.     

Quantitative determination of gene strand bias in prokaryotic genomes

Comparative genometrics of microorganisms is a relatively new area, in which genome properties are translated into numerical indexes. Such indexes can be used for a comprehensive and comparative analysis of microbial genomes, contributing to the understanding of their evolution. This work presents a new method for quantitative determination of gene strand bias in prokaryotic chromosomes, in which data transformation of gene position skew leads to a numerical index that can be applied to quantitative comparisons of genome organization. It was applied in the comparative analysis of 49 completely sequenced Firmicutes genomes, allowing the distinction of groups defined according to their patterns of gene strand preference. The resulting groups revealed that, regarding gene strand bias, reduced genomes are, in general, the more disordered among Firmicutes, while genomes of extremophile organisms comprehend those with the highest degree of genome organization in this phylum.     

Knowledge-based voting algorithm for automated protein functional annotation

Automated annotation of high-throughput genome sequences is one of the earliest steps toward a comprehensive understanding of the dynamic behavior of living organisms. However, the step is often error-prone because of its underlying algorithms, which rely mainly on a simple similarity analysis, and lack of guidance from biological rules. We present herein a knowledge-based protein annotation algorithm. Our objectives are to reduce errors and to improve annotation confidences. This algorithm consists of two major components: a knowledge system, called "RuleMiner," and a voting procedure. The knowledge system, which includes biological rules and functional profiles for each function, provides a platform for seamless integration of multiple sequence analysis tools and guidance for function annotation. The voting procedure, which relies on the knowledge system, is designed to make (possibly) unbiased judgments in functional assignments among complicated, sometimes conflicting, information. We have applied this algorithm to 10 prokaryotic bacterial genomes and observed a significant improvement in annotation confidences. We also discuss the current limitations of the algorithm and the potential for future improvement.     

Characterization and functional annotation of nested transposable elements in eukaryotic genomes

The movement of transposable elements (TE) in eukaryotic genomes can often result in the occurrence of nested TEs (the insertion of TEs into pre-existing TEs). We performed a general TE assessment using available databases to detect nested TEs and analyze their characteristics and putative functions in eukaryote genomes. A total of 802 TEs were found to be inserted into 690 host TEs from a total number of 11,329 TEs. We reveal that repetitive sequences are associated with an increased occurrence of nested TEs and sequence biased of TE insertion. A high proportion of the genes which were associated with nested TEs are predicted to localize to organelles and participate in nucleic acid and protein binding. Many of these function in metabolic processes, and encode important enzymes for transposition and integration. Therefore, nested TEs in eukaryotic genomes may negatively influence genome expansion, and enrich the diversity of gene expression or regulation.     

RNA干扰文库在功能基因组学研究中的发展及应用

RNA干扰（RNAi）是双链RNA分子在mRNA水平上诱发的序列特异性转录后基因表达沉默,从基因组水平设计针对多个靶基因的RNAi序列,建立RNAi文库进行系统性、大规模的筛选工作是功能基因组学研究的有力工具。目前RNAi文库主要包括质粒（或病毒）文库、siRNA表达盒文库、寡核苷酸文库和随机RNAi文库,已经被成功应用于基因功能鉴别、信号转导途径解析和药物靶标筛选等研究领域。近年来,这一领域发展迅速,本文就RNAi文库的发展应用以及存在的问题与展望进行综述。     

Conventional functional classification schemes underestimate the relationship with ecosystem functioning

Studies linking the functional diversity of a biota to ecosystem functioning typically employ a priori classifications of species into hypothetically complementary groups. However, multiple alternate classifications exist in which the number of functional groups, the number of species per functional group, and the grouping of species differ from the a priori scheme. Without assessing the relative precision, or ability of an a priori scheme to accurately predict ecosystem functioning relative to its many alternatives, the validity and utility of analyses based on a single a priori classification scheme remains unclear. We examine the precision of a priori classifications used in 10 experimental grassland systems in Europe and the United States that have found evidence for a significant role of functional plant diversity in governing ecosystem function. The predictive precision of the a priori classifications employed in these studies was seldom significantly higher than the precision of random classifications. Post-hoc classification schemes that performed well in predicting ecosystem function resembled each other more with regard to species composition than average classifications, but there was still considerable variability in the manner in which these classification schemes grouped species. These results suggest that we need a more nuanced understanding of how the diversity of functional traits of species in an assemblage affects ecosystem functioning.     

GoPipe: 批量序列的Gene Ontology 注释和统计分析

随着后基因组时代的到来,批量的测序,特别是 EST 的测序,逐渐成为普通实验室的日常工作 . 这些新的序列往往需要进行批量的 Gene Ontology (GO) 的注释及随后的统计分析 . 但是目前除了 Goblet 以外,并没有软件适合对未知序列进行批量的 GO 注释,而 GoBlet 因为具有上载量的限制,以及仅仅利用 BLAST 作为预测工具,所以仍有许多不足之处 . 开发了一个软件包 GoPipe ,通过整合 BLAST 和 InterProScan 的结果来进行序列注释,并提供了进一步作统计比较的工具 . 主程序接收任意个 BLAST 和 InterProScan 的结果文件,并依次进行文本分析、数据整合、去除冗余、统计分析和显示等工作 . 还提供了统计的工具来比较不同输入对 GO 的分布来挖掘生物学意义 . 另外,在交集工作模式下,程序取 InterProScan 和 BLAST 结果的交集, 在测试数据集中,其精确度达到 99.1% ,这大大超过了 InterProScan 本身对 GO 预测的精确度,而敏感度只是稍微下降 . 较高的精确度、较快的速度和较大的灵活性使它成为对未知序列进行批量 Gene Ontology 注释的理想的工具 . 上述软件包可以在网站 (http://gopipe.fishgenome.org/ ) 免费获得或者与作者联系获取 .     

Protein surface analysis for function annotation in high-throughput structural genomics pipeline

Structural genomics (SG) initiatives are expanding the universe of protein fold space by rapidly determining structures of proteins that were intentionally selected on the basis of low sequence similarity to proteins of known structure. Often these proteins have no associated biochemical or cellular functions. The SG success has resulted in an accelerated deposition of novel structures. In some cases the structural bioinformatics analysis applied to these novel structures has provided specific functional assignment. However, this approach has also uncovered limitations in the functional analysis of uncharacterized proteins using traditional sequence and backbone structure methodologies. A novel method, named pvSOAR (pocket and void Surface of Amino Acid Residues), of comparing the protein surfaces of geometrically defined pockets and voids was developed. pvSOAR was able to detect previously unrecognized and novel functional relationships between surface features of proteins. In this study, pvSOAR is applied to several structural genomics proteins. We examined the surfaces of YecM, BioH, and RpiB from Escherichia coli as well as the CBS domains from inosine-5'-monosphate dehydrogenase from Streptococcus pyogenes, conserved hypothetical protein Ta549 from Thermoplasm acidophilum, and CBS domain protein mt1622 from Methanobacterium thermoautotrophicum with the goal to infer information about their biochemical function.     

MAPLE 2.3.0: an improved system for evaluating the functionomes of genomes and metagenomes

MAPLE is an automated system for inferring the potential comprehensive functions harbored by genomes and metagenomes. To reduce runtime in MAPLE analyzing the massive amino acid datasets of over 1 million sequences, we improved it by adapting the KEGG automatic annotation server to use GHOSTX and verified no substantial difference in the MAPLE results between the original and new implementations.