A MATLAB tool for pathway enrichment using a topology-based pathway regulation score

Pathways Enrichment Analysis of Gene Expression Data in Type 2 Diabetes

Profiling genome-wide transcriptional changes with advanced high-throughput transcriptional profiling techniques has led to a revolution in biomedical science. It has been challenging to handle the massive data generated by these techniques and draw meaningful conclusions from it. Therefore, computational biologists have developed a number of innovative methods of varying complexity and effectiveness to analyze such complex data. Over the past decade, rich information in pathway repositories has attracted and motivated researchers to incorporate such existing biological knowledge into computational analysis tools to develop what is known as pathway enrichment analysis tools. This chapter describes a new sophisticated pathway enrichment tool that exploits topology of pathway as well as expression of significantly changed genes to identify biologically significant pathways for high-dimensional gene expression datasets. Also, we demonstrate the use of this tool to analyze gene expression data from a type 2 diabetes dataset to identify a list of significantly enriched metabolic pathways.

KEDDY: a knowledge-based statistical gene set test method to detect differential functional protein-protein interactions

MOTIVATION

Identifying differential patterns between conditions is a popular approach to understanding the discrepancy between different biological contexts. Although many statistical tests were proposed for identifying gene sets with differential patterns based on different definitions of differentiality, few methods were suggested to identify gene sets with differential functional protein networks due to computational complexity.

RESULTS

We propose a method of Knowledge-based Evaluation of Dependency DifferentialitY (KEDDY), which is a statistical test for differential functional protein networks of a set of genes between two conditions with utilizing known functional protein-protein interaction information. Unlike other approaches focused on differential expressions of individual genes or differentiality of individual interactions, KEDDY compares two conditions by evaluating the probability distributions of functional protein networks based on known functional protein-protein interactions. The method has been evaluated and compared with previous methods through simulation studies, where KEDDY achieves significantly improved performance in accuracy and speed than the previous method that does not use prior knowledge and better performance in identifying gene sets with differential interactions than other methods evaluating changes in gene expressions. Applications to cancer data sets show that KEDDY identifies alternative cancer subtype-related differential gene sets compared to other differential expression-based methods, and the results also provide detailed gene regulatory information that drives the differentiality of the gene sets.

AVAILABILITY AND IMPLEMENTATION

The Java implementation of KEDDY is freely available to non-commercial users at https://sites.google.com/site/sjunggsm/keddy.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Pathway and Network Analysis of Differentially Expressed Genes in Transcriptomes

In recent years, transcriptome sequencing has become very popular, encompassing a wide variety of applications from simple mRNA profiling to discovery and analysis of the entire transcriptome. One of the most common aims of transcriptome sequencing is to identify genes that are differentially expressed (DE) between two or more biological conditions, and to infer associated pathways and gene networks from expression profiles. It can provide avenues for further systematic investigation into potential biologic mechanisms. Gene Set (GS) enrichment analysis is a popular approach to identify pathways or sets of genes that are significantly enriched in the context of differentially expressed genes. However, the approach considers a pathway as a simple gene collection disregarding knowledge of gene or protein interactions. In contrast, topology-based methods integrate the topological structure of a pathway and gene network into the analysis. To provide a panoramic view of such approaches, this chapter demonstrates several recent computational workflows, including gene set enrichment and topology-based methods, for analysis of the DE pathways and gene networks from transcriptome-wide sequencing data.

Kinetic changes of intestinal microbiota in the course of intestinal sensitization

Food allergy (FA) is an adverse immune response to certain innocent food. It is estimated about 2% to 6% of the general population suffer from FA. Symptoms of a food allergic reaction may involve the gastrointestinal tract or/and other organs. The gut microbiota plays a critical role in diet-induced health problems. Whether the changes in the composition of the intestinal microbiota regulate allergic responses to food remains poorly understood. Thus, we created an FA animal model, sequenced the V4-V5 regions of 16S rRNA genes to characterize the genera abundance of gut microbiota. The results showed that mice under FA condition showed different gut bacterial structures. Diverse distribution of the bacterial species was identified between FA and control groups. FA altered the components of intestinal Microbiota in mice. The dysbiosis of the gut metagenome correlated with the development of the FA.

Developing Network Models of Multiscale Host Responses Involved in Infections and Diseases

Complex interactions involved in host response to infections and diseases require advanced analytical tools to infer drivers of the response in order to develop strategies for intervention. This chapter discusses approaches to assemble interactions ranging from molecular to cellular levels and their analysis to investigate the cross talk between immune pathways. Particularly, construction of immune networks by either data-driven or literature-driven methods is explained. Next, graph theoretic approaches for probing static network properties as well as visualization of networks are discussed. Finally, development of Boolean models for simulation of network dynamics to investigate cross talk and emergent properties are considered along with Boolean-like models that may compensate for some of the limitations encountered in Boolean simulations. In conclusion, the chapter will allow readers to construct and analyze multiscale networks involved in immune responses.

Pathway-Informed Classification System (PICS) for Cancer Analysis Using Gene Expression Data

We introduce Pathway-Informed Classification System (PICS) for classifying cancers based on tumor sample gene expression levels. PICS is a computational method capable of expeditiously elucidating both known and novel biological pathway involvement specific to various cancers and uses that learned pathway information to separate patients into distinct classes. The method clearly separates a pan-cancer dataset by tissue of origin and also sub-classifies individual cancer datasets into distinct survival classes. Gene expression values are collapsed into pathway scores that reveal which biological activities are most useful for clustering cancer cohorts into subtypes. Variants of the method allow it to be used on datasets that do and do not contain noncancerous samples. Activity levels of all types of pathways, broadly grouped into metabolic, cellular processes and signaling, and immune system, are useful for separating the pan-cancer cohort. In the clustering of specific cancer types, certain pathway types become more valuable depending on the site being studied. For lung cancer, signaling pathways dominate; for pancreatic cancer, signaling and metabolic pathways dominate; and for melanoma, immune system pathways are the most useful. This work suggests the utility of pathway-level genomic analysis and points in the direction of using pathway classification for predicting the efficacy and side effects of drugs and radiation.

Identification of mutated driver pathways in cancer using a multi-objective optimization model

New-generation high-throughput technologies, including next-generation sequencing technology, have been extensively applied to solve biological problems. As a result, large cancer genomics projects such as the Cancer Genome Atlas (TCGA) and the International Cancer Genome Consortium are producing large amount of rich and diverse data in multiple cancer types. The identification of mutated driver genes and driver pathways from these data is a significant challenge. Genome aberrations in cancer cells can be divided into two types: random 'passenger mutation' and functional 'driver mutation'. In this paper, we introduced a Multi-objective Optimization model based on a Genetic Algorithm (MOGA) to solve the maximum weight submatrix problem, which can be employed to identify driver genes and driver pathways promoting cancer proliferation. The maximum weight submatrix problem defined to find mutated driver pathways is based on two specific properties, i.e., high coverage and high exclusivity. The multi-objective optimization model can adjust the trade-off between high coverage and high exclusivity. We proposed an integrative model by combining gene expression data and mutation data to improve the performance of the MOGA algorithm in a biological context.

ToPASeq: an R package for topology-based pathway analysis of microarray and RNA-Seq data

BACKGROUND

Pathway analysis methods, in which differentially expressed genes are mapped to databases of reference pathways and relative enrichment is assessed, help investigators to propose biologically relevant hypotheses. The last generation of pathway analysis methods takes into account the topological structure of a pathway, which helps to increase both specificity and sensitivity of the findings. Simultaneously, the RNA-Seq technology is gaining popularity and becomes widely used for gene expression profiling. Unfortunately, majority of topological pathway analysis methods remains without implementation and if an implementation exists, it is limited in various factors.

RESULTS

We developed a new R/Bioconductor package ToPASeq offering uniform interface to seven distinct topology-based pathway analysis methods, of which three we implemented de-novo and four were adjusted from existing implementations. Apart this, ToPASeq offers a set of tailored visualization functions and functions for importing and manipulating pathways and their topologies, facilitating the application of the methods on different species. The package can be used to compare the differential expression of pathways between two conditions on both gene expression microarray and RNA-Seq data. The package is written in R and is available from Bioconductor 3.2 using AGPL-3 license.

CONCLUSION

ToPASeq is a novel package that offers seven distinct methods for topology-based pathway analysis, which are easily applicable on microarray as well as RNA-Seq data, both in human and other species. At the same time, it provides specific tools for visualization of the results.