Predicting gene function by conserved co-expression

We show that gene co-expression, which generally provides only a very weak signal for the prediction of functional interactions, can provide a reliable signal by exploiting evolutionary conservation. The encoded proteins of conserved co-expressed gene pairs are highly likely to be part of the same pathway not only after speciation (98%), but also after parallel gene duplication (97%). Conserved co-expression combined with homology data enables us to predict specific gene functions. The use of conservation between parallel duplicated gene pairs to predict function is especially promising given that gene duplication is common in eukaryotes, and that data from only a single organism can be used.     

Unraveling gene function in agricultural species using gene co-expression networks

Co-expression networks have been shown to be a powerful tool for inferring a gene's function when little is known about it. With the advent of next generation sequencing technologies, the construction and analysis of co-expression networks is now possible in non-model species, including those with agricultural importance. Here, we review fundamental concepts in the construction and application of co-expression networks with a focus on agricultural crops. We survey past and current applications of co-expression network analysis in several agricultural species and provide perspective on important considerations that arise when analyzing network relationships. We conclude with a perspective on future directions and potential challenges of utilizing this powerful approach in crops. This article is part of a Special Issue entitled: Plant Gene Regulatory Mechanisms and Networks, edited by Dr. Erich Grotewold and Dr. Nathan Springer.     

Network aggregation improves gene function prediction of grapevine gene co-expression networks

Plant Molecular Biology - Aggregation across multiple networks highlights robust co-expression interactions and improves the functional connectivity of grapevine gene co-expression networks. In...     

Quantifying protein function specificity in the gene ontology

Quantitative or numerical metrics of protein function specificity made possible by the Gene Ontology are useful in that they enable development of distance or similarity measures between protein functions. Here we describe how to calculate four measures of function specificity for GO terms: 1) number of ancestor terms; 2) number of offspring terms; 3) proportion of terms; and 4) Information Content (IC). We discuss the relationship between the metrics and the strengths and weaknesses of each.     

离子通道亚型与其基因共表达的关联研究

离子通道亚型与其基因共表达的关联对研究离子通道功能有重要意义。文章采用主成分分析和模糊C-均值聚类算法对数据进行分析, 将方法应用到人类和小鼠两套表达谱数据, 结果发现离子通道亚型中钾离子通道、钙离子通道、氯离子通道和受体激活型离子通道的表达谱聚类结果与生物学分类有较好的一致性, 体现了离子通道亚型在mRNA水平上的共表达, 并证实了通过离子通道表达谱能很好的对离子通道的功能亚型进行分类。     

Relationship between gene co-expression and probe localization on microarray slides

Background  

Microarray technology allows simultaneous measurement of thousands of genes in a single experiment. This is a potentially useful tool for evaluating co-expression of genes and extraction of useful functional and chromosomal structural information about genes.     

Quantifying the relationship between sequence and three-dimensional structure conservation in RNA

Background  

In recent years, the number of available RNA structures has rapidly grown reflecting the increased interest on RNA biology. Similarly to the studies carried out two decades ago for proteins, which gave the fundamental grounds for developing comparative protein structure prediction methods, we are now able to quantify the relationship between sequence and structure conservation in RNA.     

Quantifying the relationship between airborne pollen and vegetation in the urban environment

The goal of this study was to quantitatively assess the relationship linking vegetation and airborne pollen. For this, we established six sampling stations in the city of Thessaloniki, Greece. Once every week for 2 years, we recorded airborne pollen in them, at breast height, by use of a portable volumetric sampler. We also made a detailed analysis of the vegetation in each station by counting all existing individuals of the woody species contributing pollen to the air, in five zones of increasing size, from 4 to 40 ha. We found the local vegetation to be the driver of the spatial variation of pollen in the air of the city. Even at very neighbouring stations, only 500 m apart, considerable differences in vegetation composition were expressed in the pollen spectrum. We modelled the pollen concentration of each pollen taxon as a function of the abundance of the woody species corresponding to that taxon by use of a Generalized Linear Model. The relationship was significant for the five most abundantly represented taxa in the pollen spectrum of the city. It is estimated that every additional individual of Cupressaceae, Pinaceae, Platanus, Ulmus and Olea increases pollen in the air by approximately 0.7, 0.2, 2, 6 and 5%, respectively. Whether the relationships detected for the above pollen taxa hold outside the domain for which we have data, as well as under different environmental conditions and/or with different assemblages of species representing them are issues to be explored in the future.     

The relationship between gene dosage,gene expression,and sex in Drosophila

In Drosophila, the ratio of the number of X chromosomes to sets of other chromosomes initiates a series of events which result in sexual differentiation. In addition, this ratio establishes dosage compensation, a mechanism which equalizes the products of X-linked genes in males and females. The present review discusses possible genetic entities responsible for the interpretation of chromosomal sex and subsequent sex-mediated regulation during development.     

Combining pattern discovery and discriminant analysis to predict gene co-regulation

MOTIVATION: Several pattern discovery methods have been proposed to detect over-represented motifs in upstream sequences of co-regulated genes, and are for example used to predict cis-acting elements from clusters of co-expressed genes. The clusters to be analyzed are often noisy, containing a mixture of co-regulated and non-co-regulated genes. We propose a method to discriminate co-regulated from non-co-regulated genes on the basis of counts of pattern occurrences in their non-coding sequences. METHODS: String-based pattern discovery is combined with discriminant analysis to classify genes on the basis of putative regulatory motifs. RESULTS: The approach is evaluated by comparing the significance of patterns detected in annotated regulons (positive control), random gene selections (negative control) and high-throughput regulons (noisy data) from the yeast Saccharomyces cerevisiae. The classification is evaluated on the annotated regulons, and the robustness and rejection power is assessed with mixtures of co-regulated and random genes.     

Quantifying the association between gene expressions and DNA-markers by penalized canonical correlation analysis

Multiple changes at the DNA level are at the basis of complex diseases. Identifying the genetic networks that are influenced by these changes might help in understanding the development of these diseases. Canonical correlation analysis is used to associate gene expressions with DNA-markers and thus reveals sets of co-expressed and co-regulated genes and their associating DNA-markers. However, when the number of variables gets high, e.g. in the case of microarray studies, interpretation of these results can be difficult. By adapting the elastic net to canonical correlation analysis the number of variables reduces, and interpretation becomes easier, moreover, due to the grouping effect of the elastic net co-regulated and co-expressed genes cluster. Additionally, our adaptation works well in situations where the number of variables exceeds by far the number of subjects.     

Mutual co-regulation between GPI-N-acetylglucosaminyltransferase and ergosterol biosynthesis in Candida albicans

A novel co-regulation exists between the first step of GPI (glycosylphosphatidylinositol) anchor biosynthesis and the rate-determining step of ergosterol biosynthesis in Candida albicans. Depleting CaGpi19p, an accessory subunit of the enzyme complex that initiates GPI biosynthesis, down-regulates ERG11, altering ergosterol levels and drug response. This effect is specific to CaGpi19p depletion and is not due to cell wall defects or GPI deficiency. Additionally, down-regulation of ERG11 down-regulates CaGPI19 and GPI biosynthesis.     

Study of gene function based on spatial co-expression in a high-resolution mouse brain atlas

Background  

The Allen Brain Atlas (ABA) project systematically profiles three-dimensional high-resolution gene expression in postnatal mouse brains for thousands of genes. By unveiling gene behaviors at both the cellular and molecular levels, ABA is becoming a unique and comprehensive neuroscience data source for decoding enigmatic biological processes in the brain. Given the unprecedented volume and complexity of the in situ hybridization image data, data mining in this area is extremely challenging. Currently, the ABA database mainly serves as an online reference for visual inspection of individual genes; the underlying rich information of this large data set is yet to be explored by novel computational tools. In this proof-of-concept study, we studied the hypothesis that genes sharing similar three-dimensional expression profiles in the mouse brain are likely to share similar biological functions.     

Acetylcarnitine: on the relationship between structure and function

Acetylcarnitine chloride, a molecule with cholinergic properties, has been studied by X-ray crystallographic techniques. Results show that a portion of the acetylcarnitine molecule is in the same configuration as the functionally similar acetylcholine molecule and other cholinergic molecules.