Tests for gene clustering.

Comparing chromosomal gene order in two or more related species is an important approach to studying the forces that guide genome organization and evolution. Linked clusters of similar genes found in related genomes are often used to support arguments of evolutionary relatedness or functional selection. However, as the gene order and the gene complement of sister genomes diverge progressively due to large scale rearrangements, horizontal gene transfer, gene duplication and gene loss, it becomes increasingly difficult to determine whether observed similarities in local genomic structure are indeed remnants of common ancestral gene order, or are merely coincidences. A rigorous comparative genomics requires principled methods for distinguishing chance commonalities, within or between genomes, from genuine historical or functional relationships. In this paper, we construct tests for significant groupings against null hypotheses of random gene order, taking incomplete clusters, multiple genomes, and gene families into account. We consider both the significance of individual clusters of prespecified genes and the overall degree of clustering in whole genomes.     

Evolutionary trajectory of phytoalexin biosynthetic gene clusters in rice

Plants frequently possess operon‐like gene clusters for specialized metabolism. Cultivated rice, Oryza sativa, produces antimicrobial diterpene phytoalexins represented by phytocassanes and momilactones, and the majority of their biosynthetic genes are clustered on chromosomes 2 and 4, respectively. These labdane‐related diterpene phytoalexins are biosynthesized from geranylgeranyl diphosphate via ent‐copalyl diphosphate or syn‐copalyl diphosphate. The two gene clusters consist of genes encoding diterpene synthases and chemical‐modification enzymes including P450s. In contrast, genes for the biosynthesis of gibberellins, which are labdane‐related phytohormones, are scattered throughout the rice genome similar to other plant genomes. The mechanism of operon‐like gene cluster formation remains undefined despite previous studies in other plant species. Here we show an evolutionary insight into the rice gene clusters by a comparison with wild Oryza species. Comparative genomics and biochemical studies using wild rice species from the AA genome lineage, including Oryza barthii, Oryza glumaepatula, Oryza meridionalis and the progenitor of Asian cultivated rice Oryza rufipogon indicate that gene clustering for biosynthesis of momilactones and phytocassanes had already been accomplished before the domestication of rice. Similar studies using the species Oryza punctata from the BB genome lineage, the distant FF genome lineage species Oryza brachyantha and an outgroup species Leersia perrieri suggest that the phytocassane biosynthetic gene cluster was present in the common ancestor of the Oryza species despite the different locations, directions and numbers of their member genes. However, the momilactone biosynthetic gene cluster evolved within Oryza before the divergence of the BB genome via assembly of ancestral genes.     

Computational analysis of Ciona intestinalis operons

Operons are clusters of genes that are co-regulated from a common promoter. Operons are typically associated with prokaryotes, although a small number of eukaryotes have been shown to possess them. Among metazoans, operons have been extensively characterized in the nematode Caenorhabditis elegans in which ～15% of the total genes are organized into operons. The most recent genome assembly for the ascidian Ciona intestinalis placed ～20% of the genes (2909 total) into 1310 operons. The majority of these operons are composed of two genes, while the largest are composed of six. Here is reported a computational analysis of the genes that comprise the Ciona operons. Gene ontology (GO) terms were identified for about two-thirds of the operon-encoded genes. Using the extensive collection of public EST libraries, estimates of temporal patterns of gene expression were generated for the operon-encoded genes. Lastly, conservation of operons was analyzed by determining how many operon-encoded genes were present in the ascidian Ciona savignyi and whether these genes were organized in orthologous operons. Over 68% of the operon-encoded genes could be assigned one or more GO terms and 697 of the 1310 operons contained genes in which all genes had at least one GO term. Of these 697 operons, GO terms were shared by all of the genes within 146 individual operons, suggesting that most operons encode genes with unrelated functions. An analysis of operon gene expression from nine different EST libraries indicated that for 587 operons, all of the genes that comprise an individual operon were expressed together in at least one EST library, suggesting that these genes may be co-regulated. About 50% (74/146) of the operons with shared GO terms also showed evidence of gene co-regulation. Comparisons with the C. savignyi genome identified orthologs for 1907 of 2909 operon genes. About 38% (504/1310) of the operons are conserved between the two Ciona species. These results suggest that like C. elegans, operons in Ciona are comprised of a variety of genes that are not necessarily related in function. The genes in only 50% of the operons appear to be co-regulated, suggesting that more complex gene regulatory mechanisms are likely operating.     

基因本体论的蟾蜍bHLH转录因子功能富集分布

基因本体论是国际上标准的基因和蛋白质功能知识词汇.利用基因本体论的功能富集分布比较和分析了两种蟾蜍bHLH基因分子功能分布特点.结果发现,两种蟾蜍的bHLH基因均有显著富集分布的GO注释语句,其中转录调控活性( GO:0030528)、转录调控(GO:0045449)、DNA结合(GO:0003677)、RNA代谢过程调控(G0:0051252)、DNA依赖的转录调控(GO:0006355)、转录(G0:0006350)和转录因子活性(GO:0003700)等频率很高,表明这些GO注释是蟾蜍bHLH基因常见的功能;此外,蟾蜍bHLH基因在肌肉器官发育、神经管和眼发育等一些重要的发育或生理过程的基因表达调控中发挥着重要的作用.     

In silico determination of transposon-derived miRNAs and targets in Aegilops species

Abstract

Transposable elements (TEs) are found almost in all living organism, shaping organisms’ genomes. miRNAs are noncoding RNA types which are especially important in gene expression regulations. Many previously determined plant miRNAs are identical/homologous to transposons (TE-MIR). The aim of this study was computational characterization of novel TE-related miRNAs and their targets in Aegilops genome by using stringent criteria. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were performed by BLAST2GO. Seventeen novel TE-related miRNAs in Aegilops genome were identified for the first time. GO analyses indicated that 40 targets played different roles in biological processes, cellular components and molecular functions. Moreover, these genes were involved in 10 metabolic pathways such as purine metabolism, nitrogen metabolism, oxidative phosphorylation, etc. as a result of KEGG analyses. Identification of miRNAs and their targets are significant to understand miRNA-TEs relationships and even how TEs affect plant growth and development. Obtaining results of this study are expected to provide possible new insight into Aegilops and its related species, wheat, with respect to miRNAs evolution and domestication.

Communicated by Ramaswamy H. Sarma     

Identifying clusters of functionally related genes in genomes

MOTIVATION: An increasing body of literature shows that genomes of eukaryotes can contain clusters of functionally related genes. Most approaches to identify gene clusters utilize microarray data or metabolic pathway databases to find groups of genes on chromosomes that are linked by common attributes. A generalized method that can find gene clusters regardless of the mechanism of origin would provide researchers with an unbiased method for finding clusters and studying the evolutionary forces that give rise to them. RESULTS: We present an algorithm to identify gene clusters in eukaryotic genomes that utilizes functional categories defined in graph-based vocabularies such as the Gene Ontology (GO). Clusters identified in this manner need only have a common function and are not constrained by gene expression or other properties. We tested the algorithm by analyzing genomes of a representative set of species. We identified species-specific variation in percentage of clustered genes as well as in properties of gene clusters including size distribution and functional annotation. These properties may be diagnostic of the evolutionary forces that lead to the formation of gene clusters. AVAILABILITY: A software implementation of the algorithm and example output files are available at http://fcg.tamu.edu/C_Hunter/.     

Gene function prediction based on Gene Ontology Hierarchy Preserving Hashing

Gene Ontology (GO) uses structured vocabularies (or terms) to describe the molecular functions, biological roles, and cellular locations of gene products in a hierarchical ontology. GO annotations associate genes with GO terms and indicate the given gene products carrying out the biological functions described by the relevant terms. However, predicting correct GO annotations for genes from a massive set of GO terms as defined by GO is a difficult challenge. To combat with this challenge, we introduce a Gene Ontology Hierarchy Preserving Hashing (HPHash) based semantic method for gene function prediction. HPHash firstly measures the taxonomic similarity between GO terms. It then uses a hierarchy preserving hashing technique to keep the hierarchical order between GO terms, and to optimize a series of hashing functions to encode massive GO terms via compact binary codes. After that, HPHash utilizes these hashing functions to project the gene-term association matrix into a low-dimensional one and performs semantic similarity based gene function prediction in the low-dimensional space. Experimental results on three model species (Homo sapiens, Mus musculus and Rattus norvegicus) for interspecies gene function prediction show that HPHash performs better than other related approaches and it is robust to the number of hash functions. In addition, we also take HPHash as a plugin for BLAST based gene function prediction. From the experimental results, HPHash again significantly improves the prediction performance. The codes of HPHash are available at: http://mlda.swu.edu.cn/codes.php?name=HPHash.     

A test of the coordinated expression hypothesis for the origin and maintenance of the GAL cluster in yeast

Metabolic gene clusters--functionally related and physically clustered genes--are a common feature of some eukaryotic genomes. Two hypotheses have been advanced to explain the origin and maintenance of metabolic gene clusters: coordinated gene expression and genetic linkage. Here we test the hypothesis that selection for coordinated gene expression underlies the clustering of GAL genes in the yeast genome. We find that, although clustering coordinates the expression of GAL1 and GAL10, disrupting the GAL cluster does not impair fitness, suggesting that other mechanisms, such as genetic linkage, drive the origin and maintenance metabolic gene clusters.     

Bioinformatic identification and experimental validation of miRNAs from foxtail millet (Setariaitalica)

MiRNAs are a novel group of non-coding small RNAs that negatively regulate gene expression. Many miRNAs have been identified and investigated extensively in plant species with sequenced genomes. However, few miRNAs have been identified in foxtail millet (Setaria italica), which is an ancient cereal crop of great importance for dry land agriculture. In this study, 271 foxtail millet miRNAs belonging to 44 families were identified using a bioinformatics approach. Twenty-three pairs of sense/antisense miRNAs belonging to 13 families, and 18 miRNA clusters containing members of 8 families were discovered in foxtail millet. We identified 432 potential targets for 38 miRNA families, most of which were predicted to be involved in plant development, signal transduction, metabolic pathways, disease resistance, and environmental stress responses. Gene ontology (GO) analysis revealed that 101, 56, and 23 target genes were involved in molecular functions, biological processes, and cellular components, respectively. We investigated the expression patterns of 43 selected miRNAs using qRT-PCR analysis. All of the miRNAs were expressed ubiquitously with many exhibiting different expression levels in different tissues. We validated five predicted targets of four miRNAs using the RNA ligase mediated rapid amplification of cDNA end (5′-RLM-RACE) method.     

Grouping miRNAs of similar functions via weighted information content of gene ontology

Background

Regulation mechanisms between miRNAs and genes are complicated. To accomplish a biological function, a miRNA may regulate multiple target genes, and similarly a target gene may be regulated by multiple miRNAs. Wet-lab knowledge of co-regulating miRNAs is limited. This work introduces a computational method to group miRNAs of similar functions to identify co-regulating miRNAsfrom a similarity matrix of miRNAs.

Results

We define a novel information content of gene ontology (GO) to measure similarity between two sets of GO graphs corresponding to the two sets of target genes of two miRNAs. This between-graph similarity is then transferred as a functional similarity between the two miRNAs. Our definition of the information content is based on the size of a GO term’s descendants, but adjusted by a weight derived from its depth level and the GO relationships at its path to the root node or to the most informative common ancestor (MICA). Further, a self-tuning technique and the eigenvalues of the normalized Laplacian matrix are applied to determine the optimal parameters for the spectral clustering of the similarity matrix of the miRNAs.

Conclusions

Experimental results demonstrate that our method has better clustering performance than the existing edge-based, node-based or hybrid methods. Our method has also demonstrated a novel usefulness for the function annotation of new miRNAs, as reported in the detailed case studies.

     

Comparisons of graph-structure clustering methods for gene expression data

Although many numerical clustering algorithms have been applied to gene expression dataanalysis,the essential step is still biological interpretation by manual inspection.The correlation betweengenetic co-regulation and affiliation to a common biological process is what biologists expect.Here,weintroduce some clustering algorithms that are based on graph structure constituted by biological knowledge.After applying a widely used dataset,we compared the result clusters of two of these algorithms in terms ofthe homogeneity of clusters and coherence of annotation and matching ratio.The results show that theclusters of knowledge-guided analysis are the kernel parts of the clusters of Gene Ontology (GO)-Clustersoftware,which contains the genes that are most expression correlative and most consistent with biologicalfunctions.Moreover,knowledge-guided analysis seems much more applicable than GO-Cluster in a largerdataset.     

Clustering of main orthologs for multiple genomes

The identification of orthologous genes shared by multiple genomes is critical for both functional and evolutionary studies in comparative genomics. While it is usually done by sequence similarity search and reconciled tree construction in practice, recently a new combinatorial approach and high-throughput system MSOAR for ortholog identification between closely related genomes based on genome rearrangement and gene duplication has been proposed in Fu et al. MSOAR assumes that orthologous genes correspond to each other in the most parsimonious evolutionary scenario, minimizing the number of genome rearrangement and (postspeciation) gene duplication events. However, the parsimony approach used by MSOAR limits it to pairwise genome comparisons. In this paper, we extend MSOAR to multiple (closely related) genomes and propose an ortholog clustering method, called MultiMSOAR, to infer main orthologs in multiple genomes. As a preliminary experiment, we apply MultiMSOAR to rat, mouse, and human genomes, and validate our results using gene annotations and gene function classifications in the public databases. We further compare our results to the ortholog clusters predicted by MultiParanoid, which is an extension of the well-known program InParanoid for pairwise genome comparisons. The comparison reveals that MultiMSOAR gives more detailed and accurate orthology information, since it can effectively distinguish main orthologs from inparalogs.     

Genome comparison using Gene Ontology (GO) with statistical testing

Background  

Automated comparison of complete sets of genes encoded in two genomes can provide insight on the genetic basis of differences in biological traits between species. Gene ontology (GO) is used as a common vocabulary to annotate genes for comparison. Current approaches calculate the fold of unweighted or weighted differences between two species at the high-level GO functional categories. However, to ensure the reliability of the differences detected, it is important to evaluate their statistical significance. It is also useful to search for differences at all levels of GO.