Prediction of nitrogen metabolism-related genes in Anabaena by kernel-based network analysis

Prediction of molecular interaction networks from large-scale datasets in genomics and other omics experiments is an important task in terms of both developing bioinformatics methods and solving biological problems. We have applied a kernel-based network inference method for extracting functionally related genes to the response of nitrogen deprivation in cyanobacteria Anabaena sp. PCC 7120 integrating three heterogeneous datasets: microarray data, phylogenetic profiles, and gene orders on the chromosome. We obtained 1348 predicted genes that are somehow related to known genes in the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways. While this dataset contained previously known genes related to the nitrogen deprivation condition, it also contained additional genes. Thus, we attempted to select any relevant genes using the constraints of Pfam domains and NtcA-binding sites. We found candidates of nitrogen metabolism-related genes, which are depicted as extensions of existing KEGG pathways. The prediction of functional relationships between proteins rather than functions of individual proteins will thus assist the discovery from the large-scale datasets.     

Discovering tightly regulated and differentially expressed gene sets in whole genome expression data

MOTIVATION: Recently, a new type of expression data is being collected which aims to measure the effect of genetic variation on gene expression in pathways. In these datasets, expression profiles are constructed for multiple strains of the same model organism under the same condition. The goal of analyses of these data is to find differences in regulatory patterns due to genetic variation between strains, often without a phenotype of interest in mind. We present a new method based on notions of tight regulation and differential expression to look for sets of genes which appear to be significantly affected by genetic variation. RESULTS: When we use categorical phenotype information, as in the Alzheimer's and diabetes datasets, our method finds many of the same gene sets as gene set enrichment analysis. In addition, our notion of correlated gene sets allows us to focus our efforts on biological processes subjected to tight regulation. In murine hematopoietic stem cells, we are able to discover significant gene sets independent of a phenotype of interest. Some of these gene sets are associated with several blood-related phenotypes. AVAILABILITY: The programs are available by request from the authors.     

Inferring pairwise regulatory relationships from multiple time series datasets

MOTIVATION: Time series expression experiments have emerged as a popular method for studying a wide range of biological systems under a variety of conditions. One advantage of such data is the ability to infer regulatory relationships using time lag analysis. However, such analysis in a single experiment may result in many false positives due to the small number of time points and the large number of genes. Extending these methods to simultaneously analyze several time series datasets is challenging since under different experimental conditions biological systems may behave faster or slower making it hard to rely on the actual duration of the experiment. RESULTS: We present a new computational model and an associated algorithm to address the problem of inferring time-lagged regulatory relationships from multiple time series expression experiments with varying (unknown) time-scales. Our proposed algorithm uses a set of known interacting pairs to compute a temporal transformation between every two datasets. Using this temporal transformation we search for new interacting pairs. As we show, our method achieves a much lower false-positive rate compared to previous methods that use time series expression data for pairwise regulatory relationship discovery. Some of the new predictions made by our method can be verified using other high throughput data sources and functional annotation databases. AVAILABILITY: Matlab implementation is available from the supporting website: http://www.cs.cmu.edu/~yanxins/regulation_inference/index.html.     

A scalable method for integration and functional analysis of multiple microarray datasets

MOTIVATION: The diverse microarray datasets that have become available over the past several years represent a rich opportunity and challenge for biological data mining. Many supervised and unsupervised methods have been developed for the analysis of individual microarray datasets. However, integrated analysis of multiple datasets can provide a broader insight into genetic regulation of specific biological pathways under a variety of conditions. RESULTS: To aid in the analysis of such large compendia of microarray experiments, we present Microarray Experiment Functional Integration Technology (MEFIT), a scalable Bayesian framework for predicting functional relationships from integrated microarray datasets. Furthermore, MEFIT predicts these functional relationships within the context of specific biological processes. All results are provided in the context of one or more specific biological functions, which can be provided by a biologist or drawn automatically from catalogs such as the Gene Ontology (GO). Using MEFIT, we integrated 40 Saccharomyces cerevisiae microarray datasets spanning 712 unique conditions. In tests based on 110 biological functions drawn from the GO biological process ontology, MEFIT provided a 5% or greater performance increase for 54 functions, with a 5% or more decrease in performance in only two functions.     

Mining relational paths in integrated biomedical data

Much life science and biology research requires an understanding of complex relationships between biological entities (genes, compounds, pathways, diseases, and so on). There is a wealth of data on such relationships in publicly available datasets and publications, but these sources are overlapped and distributed so that finding pertinent relational data is increasingly difficult. Whilst most public datasets have associated tools for searching, there is a lack of searching methods that can cross data sources and that in particular search not only based on the biological entities themselves but also on the relationships between them. In this paper, we demonstrate how graph-theoretic algorithms for mining relational paths can be used together with a previous integrative data resource we developed called Chem2Bio2RDF to extract new biological insights about the relationships between such entities. In particular, we use these methods to investigate the genetic basis of side-effects of thiazolinedione drugs, and in particular make a hypothesis for the recently discovered cardiac side-effects of Rosiglitazone (Avandia) and a prediction for Pioglitazone which is backed up by recent clinical studies.     

The Ability of Different Imputation Methods to Preserve the Signi?cant Genes and Pathways in Cancer

Deciphering important genes and pathways from incomplete gene expression data could facilitate a better understanding of cancer. Different imputation methods can be applied to estimate the missing values. In our study, we evaluated various imputation methods for their performance in preserving signi?cant genes and pathways. In the ?rst step, 5% genes are considered in random for two types of ignorable and non-ignorable missingness mechanisms with various missing rates. Next,10 well-known imputation methods were applied to the complete datasets. The signi?cance analysis of microarrays(SAM) method was applied to detect the signi?cant genes in rectal and lung cancers to showcase the utility of imputation approaches in preserving signi?cant genes. To determine the impact of different imputation methods on the identi?cation of important genes, the chi-squared test was used to compare the proportions of overlaps between signi?cant genes detected from original data and those detected from the imputed datasets. Additionally, the signi?cant genes are tested for their enrichment in important pathways, using the Consensus Path DB. Our results showed that almost all the signi?cant genes and pathways of the original dataset can be detected in all imputed datasets, indicating that there is no signi?cant difference in the performance of various imputationmethods tested. The source code and selected datasets are available on http://pro?les.bs.ipm.ir/softwares/imputation_methods/.     

VIS-O-BAC: exploratory visualization of functional genome studies from bacteria

SUMMARY: The visualization-aided exploration of complex datasets will allow the research community to formulate novel functional hypotheses leading to a better understanding of biological processes at all levels. Therefore, we have developed a web resource termed VIS-O-BAC designed for the functional investigation of expression data for model systems, such as bacterial pathogens based on a graphical display. Genome-scale datasets derived from typical 'omic' approaches can directly be explored with respect to three biologically relevant aspects, the genome structure (operon organization), the organization of genes in pathways (KEGG) and the gene function with Gene Ontology (GO) terms. The integrated viewers can be used in parallel and combine expression data and functional annotations from different external data repositories. The graphical visualizations evidently accelerate both the validation of regulatory information and the detection of affected biological processes. AVAILABILITY: http://leger2.gbf.de/cgi-bin/vis-o-bac.pl. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.     

Kalman filtering for disease-state estimation from microarray data

MOTIVATION: In this paper, we propose using the Kalman filter (KF) as a pre-processing step in microarray-based molecular diagnosis. Incorporating the expression covariance between genes is important in such classification problems, since this represents the functional relationships that govern tissue state. Failing to fulfil such requirements may result in biologically implausible class prediction models. Here, we show that employing the KF to remove noise (while retaining meaningful covariance and thus being able to estimate the underlying biological state from microarray measurements) yields linearly separable data suitable for most classification algorithms. RESULTS: We demonstrate the utility and performance of the KF as a robust disease-state estimator on publicly available binary and multi-class microarray datasets in combination with the most widely used classification methods to date. Moreover, using popular graphical representation schemes we show that our filtered datasets also have an improved visualization capability.     

Analysis of sample set enrichment scores: assaying the enrichment of sets of genes for individual samples in genome-wide expression profiles

MOTIVATION: Gene expression profiling experiments in cell lines and animal models characterized by specific genetic or molecular perturbations have yielded sets of genes annotated by the perturbation. These gene sets can serve as a reference base for interrogating other expression datasets. For example, a new dataset in which a specific pathway gene set appears to be enriched, in terms of multiple genes in that set evidencing expression changes, can then be annotated by that reference pathway. We introduce in this paper a formal statistical method to measure the enrichment of each sample in an expression dataset. This allows us to assay the natural variation of pathway activity in observed gene expression data sets from clinical cancer and other studies. RESULTS: Validation of the method and illustrations of biological insights gleaned are demonstrated on cell line data, mouse models, and cancer-related datasets. Using oncogenic pathway signatures, we show that gene sets built from a model system are indeed enriched in the model system. We employ ASSESS for the use of molecular classification by pathways. This provides an accurate classifier that can be interpreted at the level of pathways instead of individual genes. Finally, ASSESS can be used for cross-platform expression models where data on the same type of cancer are integrated over different platforms into a space of enrichment scores. AVAILABILITY: Versions are available in Octave and Java (with a graphical user interface). Software can be downloaded at http://people.genome.duke.edu/assess.     

Transcriptome Profiling of Human Ulcerative Colitis Mucosa Reveals Altered Expression of Pathways Enriched in Genetic Susceptibility Loci

Human colonic mucosa altered by inflammation due to ulcerative colitis (UC) displays a drastically altered pattern of gene expression compared with healthy tissue. We aimed to understand the underlying molecular pathways influencing these differences by analyzing three publically-available, independently-generated microarray datasets of gene expression from endoscopic biopsies of the colon. Gene set enrichment analysis (GSEA) revealed that all three datasets share 87 gene sets upregulated in UC lesions and 8 gene sets downregulated (false discovery rate <0.05). The upregulated pathways were dominated by gene sets involved in immune function and signaling, as well as the control of mitosis. We applied pathway analysis to genotype data derived from genome-wide association studies (GWAS) of UC, consisting of 5,584 cases and 11,587 controls assembled from eight European-ancestry cohorts. The upregulated pathways derived from the gene expression data showed a highly significant overlap with pathways derived from the genotype data (33 of 56 gene sets, hypergeometric P = 1.49×10^–19). This study supports the hypothesis that heritable variation in gene expression as measured by GWAS signals can influence key pathways in the development of disease, and that comparison of genetic susceptibility loci with gene expression signatures can differentiate key drivers of inflammation from secondary effects on gene expression of the inflammatory process.     

产电微生物基因调控网络的构建和特异性通路分析

研究产电微生物胞外电子传递过程和机制,发现与产电效率相关的关键基因、通路和代谢物,是微生物燃料电池研究中的关键技术。为了发现在胞外电子传递过程中起到关键作用的基因以及通路,首先利用比较基因组学的方法,以模式微生物大肠杆菌和同属希瓦氏菌的其他菌株为参考,构建了Shewanella.onedensis MR-1的全基因组基因转录调控网络,大大扩展了目前已知的基因调控关系。然后以此网络为基础,结合基于蛋白质相互作用分析得到的胞外电子传递通路,构建了与胞外电子传递直接传递密切相关的细胞色素C编码基因及其相关调控基因构成的子网络,结合全基因组基因表达数据,研究了特异性条件下胞外电子传递的可能通路和基因调控过程。     

Identification of dominant signaling pathways from proteomics expression data

The availability of the results of high-throughput analyses coming from ‘omic’ technologies has been one of the major driving forces of pathway biology. Analytical pathway biology strives to design a ‘pathway search engine’, where the input is the ‘omic’ data and the output is the list of activated or dominant pathways in a given sample. Here we describe the first attempt to design and validate such a pathway search engine using as input expression proteomics data. The engine represents a specific workflow in computational tools developed originally for mRNA analysis (BMC Bioinformatics 2006, 7 (Suppl 2), S13). Using our own datasets as well as data from recent proteomics literature we demonstrate that different dominant pathways (EGF, TGF_β, stress, and Fas pathways) can be correctly identified even from limited datasets. Pathway search engines can find application in a variety of proteomics-related fields, from fundamental molecular biology to search for novel types of disease biomarkers.