Pathway analysis

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Pathway resources and types of pathway analysis using databases like KEGG, Reactome and WikiPathways. Fgene-10-01203-g002.jpg
Pathway resources and types of pathway analysis using databases like KEGG, Reactome and WikiPathways.

Pathway is the term from molecular biology for a curated schematic representation of a well characterized segment of the molecular physiological machinery, such as a metabolic pathway describing an enzymatic process within a cell or tissue or a signaling pathway model representing a regulatory process that might, in its turn, enable a metabolic or another regulatory process downstream. A typical pathway model starts with an extracellular signaling molecule that activates a specific receptor, thus triggering a chain of molecular interactions. [2] A pathway is most often represented as a relatively small graph with gene, protein, and/or small molecule nodes connected by edges of known functional relations. While a simpler pathway might appear as a chain, [3] complex pathway topologies with loops and alternative routes are much more common. Computational analyses employ special formats of pathway representation. [4] [5] In the simplest form, however, a pathway might be represented as a list of member molecules with order and relations unspecified. Such a representation, generally called Functional Gene Set (FGS), can also refer to other functionally characterised groups such as protein families, Gene Ontology (GO) and Disease Ontology (DO) terms etc. In bioinformatics, methods of pathway analysis might be used to identify key genes/ proteins within a previously known pathway in relation to a particular experiment / pathological condition or building a pathway de novo from proteins that have been identified as key affected elements. By examining changes in e.g. gene expression in a pathway, its biological activity can be explored. However most frequently, pathway analysis refers to a method of initial characterization and interpretation of an experimental (or pathological) condition that was studied with omics tools or genome-wide association study. [6] Such studies might identify long lists of altered genes. A visual inspection is then challenging and the information is hard to summarize, since the altered genes map to a broad range of pathways, processes, and molecular functions (with a large gene fraction lacking any annotation). In such situations, the most productive way of exploring the list is to identify enrichment of specific FGSs in it. The general approach of enrichment analyses is to identify FGSs, members of which were most frequently or most strongly altered in the given condition, in comparison to a gene set sampled by chance. In other words, enrichment can map canonical prior knowledge structured in the form of FGSs to the condition represented by altered genes.

Contents

Use

The data for pathway analysis come from high throughput biology. This includes high throughput sequencing data and microarray data. Before pathway analysis can be done, each gene's alteration should be evaluated using the omics dataset in either quantitative (differential expression analysis) or qualitative (detection of somatic point mutations or mapping neighbor genes to a disease-associated SNP). It is also possible to combine datasets from different research groups or multiple omics platform with a meta-analysis and cross-platform regularization. [7] [8] Further, a list where gene identifiers are accompanied by the alteration attributes is subjected to a pathway analysis. By using pathway analysis software, researchers can determine which FGSs are enriched with the altered experimental genes [9] [10] For example, pathway analysis of several independent microarray experiments (meta-analysis) helped to discover potential biomarkers in a single pathway important for fast-to-slow switch fiber type transition in Duchenne muscular dystrophy. [11] In another study meta-analysis identified two biomarkers in blood of patients with Parkinson's disease, which can be useful for monitoring the disease. [12] Candidate gene alleles causative of Alzheimer's disease and elderly dementia where first discovered via genome-wide association study and further validated with network enrichment analysis against FGS consisting of known Alzheimer's genes. [13] [14]

Databases

Pathway collections and interaction networks constitute the knowledge base required for a pathway analysis. Pathway content, structure, format, and functionality vary between different database resources such as KEGG, [15] WikiPathways, or Reactome. [16] Also exist proprietary pathways collections used by e.g. Pathway Studio [17] and Ingenuity Pathway Analysis [18] tools. Public online tools can provide pre-compiled and ready-to-go menus of pathways and networks from different open sources (e.g. EviNet).

Methods and software

Pathway analysis software can be found in the form of desktop programs, web-based applications, or packages coded in such languages as R and Python and shared openly through the BioConductor [19] and GitHub [20] projects. The methodology of pathway analysis evolves fast and the classification is still discussable, [21] [22] with the following main categories of pathway enrichment analysis applicable to high-throughput data: [21]

Over-representation analysis (ORA)

This method measures the overlap between, on the one hand, a set of genes (or proteins) in an FGS and, on the other hand, a list of most altered genes generally called Altered Gene Sets (AGS). A typical AGS example is a list of top N differentially expressed genes from an RNA-Seq assay. The basic assumption behind ORA is that a biologically relevant pathway can be identified by excess of AGS genes in it compared to the number expected by chance. The aim of ORA is to identify such enriched pathways, judging by statistical significance of the overlap between FGS and AGS as determined either by an appropriate statistic, such as Jaccard index or by a statistical test producing p-values (Fisher's exact test or the test using hypergeometric distribution).

Functional class scoring (FCS)

This method identifies FGS by considering their relative positions in the full list of genes studied in the experiment. This full list should be therefore ranked in advance by a statistic (such as mRNA expression fold-change, Student's t-test etc.) or a p-value - while watching the direction of fold change, since p-values are non-directional. Thus FCS takes into account every FGS gene regardless of its statistical significance and does not require pre-compiled AGS. One of the first and most popular methods deploying the FCS approach was the Gene Set Enrichment Analysis (GSEA). [10]

Pathway topology analysis (PTA)

Similarly to FCS, PTA accounts for high-throughput data for every FGS gene. [23] In addition, specific topological information is used about role, position, and interaction directions of the pathway genes. This requires additional input data from a pathway database in a pre-specified format, such as KEGG Markup Language (KGML). Using this information, PTA estimates a pathway significance by considering how much each individual gene alteration might have affected the whole pathway. Multiple alteration types can be used in parallel (somatic copy-number variations, point mutations etc.) when available. [21] The set of PTA methods includes the Impact Analysis, [24] [25] EnrichNet, [26] GGEA, [27] and TopoGSA. [28]

Network enrichment analysis (NEA)

Network enrichment analysis (NEA) has been an extension of gene-set enrichment analysis to the domain of global gene networks [29] [30] [31] [32] The major principle of NEA can be understood in comparison with ORA, where enrichment of FGS in genes of the AGS is determined by how many genes are directly shared by AGS and FGS. In NEA, on the contrary, the global network is searched for network edges that connect any genes of AGS with any genes of FGS. Since enrichment significance is influenced by the highly variable node degrees of individual AGS and FGS genes, it should be determined by a dedicated statistical test, which compares the observed number of network edges to the number expected by chance in the same network context. Some valuable properties of NEA are that:

  1. it is more robust to biological and technical variability between sample replicates; [8] [33]
  2. AGS genes may not necessarily be annotated as pathway members; [34]
  3. FGS members do not have to be altered themselves, but still are accounted for due to possessing network links to AGS genes. [35]

Commercial solutions

Beyond open-source tools, such as STRING or Cytoscape, a number of companies sell licensed software products to analyse gene sets. While most of the publicly available solutions use online and public pathway collections, the commercial products mostly promote own, proprietary pathways and networks. The choice of such products might be driven by customers' skills, financial and time resources, and needs. [6] Ingenuity, for example, maintains a knowledge base for comparative analysis of gene expression data. [36] Pathways Studio [37] is commercial software which allows searching for biologically relevant facts, analyze experiments, and create pathways. Pathways Studio Viewer [38] is a free resource from the same company for presenting the Pathway Studio interactive pathway collection and database. Two commercial solutions offer PTA: iPathwayGuide from Advaita Corporation and MetaCore from Thomson Reuters. [39] Advaita uses the peer reviewed Impact Analysis method [24] [25] while the MetaCore method is unpublished. [39]

Limitations

Lack of annotations

Application of pathway analysis methods depends on annotations found in existing databases, such as gene set membership in pathways, pathway topology, presence of genes in the global network etc. These annotations, however, are far from being complete and have highly variable degrees of confidence. In addition, such information is usually general, i.e. deprived of e.g. cell type, compartment, or developmental context. Therefore, interpretation of pathway analysis results for omics datasets should be done with caution [22] Partially, the problem can be addressed by analysing larger gene sets in a more, such as big pathway collections or global interaction networks. [40]

See also

Related Research Articles

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Bioinformatics is an interdisciplinary field of science that develops methods and software tools for understanding biological data, especially when the data sets are large and complex. Bioinformatics uses biology, chemistry, physics, computer science, computer programming, information engineering, mathematics and statistics to analyze and interpret biological data. The subsequent process of analyzing and interpreting data is referred to as computational biology.

<span class="mw-page-title-main">DNA microarray</span> Collection of microscopic DNA spots attached to a solid surface

A DNA microarray is a collection of microscopic DNA spots attached to a solid surface. Scientists use DNA microarrays to measure the expression levels of large numbers of genes simultaneously or to genotype multiple regions of a genome. Each DNA spot contains picomoles of a specific DNA sequence, known as probes. These can be a short section of a gene or other DNA element that are used to hybridize a cDNA or cRNA sample under high-stringency conditions. Probe-target hybridization is usually detected and quantified by detection of fluorophore-, silver-, or chemiluminescence-labeled targets to determine relative abundance of nucleic acid sequences in the target. The original nucleic acid arrays were macro arrays approximately 9 cm × 12 cm and the first computerized image based analysis was published in 1981. It was invented by Patrick O. Brown. An example of its application is in SNPs arrays for polymorphisms in cardiovascular diseases, cancer, pathogens and GWAS analysis. It is also used for the identification of structural variations and the measurement of gene expression.

<span class="mw-page-title-main">Systems biology</span> Computational and mathematical modeling of complex biological systems

Systems biology is the computational and mathematical analysis and modeling of complex biological systems. It is a biology-based interdisciplinary field of study that focuses on complex interactions within biological systems, using a holistic approach to biological research.

<span class="mw-page-title-main">Functional genomics</span> Field of molecular biology

Functional genomics is a field of molecular biology that attempts to describe gene functions and interactions. Functional genomics make use of the vast data generated by genomic and transcriptomic projects. Functional genomics focuses on the dynamic aspects such as gene transcription, translation, regulation of gene expression and protein–protein interactions, as opposed to the static aspects of the genomic information such as DNA sequence or structures. A key characteristic of functional genomics studies is their genome-wide approach to these questions, generally involving high-throughput methods rather than a more traditional "candidate-gene" approach.

The transcriptome is the set of all RNA transcripts, including coding and non-coding, in an individual or a population of cells. The term can also sometimes be used to refer to all RNAs, or just mRNA, depending on the particular experiment. The term transcriptome is a portmanteau of the words transcript and genome; it is associated with the process of transcript production during the biological process of transcription.

A biochemical cascade, also known as a signaling cascade or signaling pathway, is a series of chemical reactions that occur within a biological cell when initiated by a stimulus. This stimulus, known as a first messenger, acts on a receptor that is transduced to the cell interior through second messengers which amplify the signal and transfer it to effector molecules, causing the cell to respond to the initial stimulus. Most biochemical cascades are series of events, in which one event triggers the next, in a linear fashion. At each step of the signaling cascade, various controlling factors are involved to regulate cellular actions, in order to respond effectively to cues about their changing internal and external environments.

Computational genomics refers to the use of computational and statistical analysis to decipher biology from genome sequences and related data, including both DNA and RNA sequence as well as other "post-genomic" data. These, in combination with computational and statistical approaches to understanding the function of the genes and statistical association analysis, this field is also often referred to as Computational and Statistical Genetics/genomics. As such, computational genomics may be regarded as a subset of bioinformatics and computational biology, but with a focus on using whole genomes to understand the principles of how the DNA of a species controls its biology at the molecular level and beyond. With the current abundance of massive biological datasets, computational studies have become one of the most important means to biological discovery.

<span class="mw-page-title-main">Metabolic network modelling</span> Form of biological modelling

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<span class="mw-page-title-main">KEGG</span> Collection of bioinformatics databases

KEGG is a collection of databases dealing with genomes, biological pathways, diseases, drugs, and chemical substances. KEGG is utilized for bioinformatics research and education, including data analysis in genomics, metagenomics, metabolomics and other omics studies, modeling and simulation in systems biology, and translational research in drug development.

<span class="mw-page-title-main">Gene expression profiling</span>

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<span class="mw-page-title-main">Microarray analysis techniques</span>

Microarray analysis techniques are used in interpreting the data generated from experiments on DNA, RNA, and protein microarrays, which allow researchers to investigate the expression state of a large number of genes - in many cases, an organism's entire genome - in a single experiment. Such experiments can generate very large amounts of data, allowing researchers to assess the overall state of a cell or organism. Data in such large quantities is difficult - if not impossible - to analyze without the help of computer programs.

<span class="mw-page-title-main">MicrobesOnline</span>

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DAVID is a free online bioinformatics resource developed by the Laboratory of Human Retrovirology and Immunoinformatics. All tools in the DAVID Bioinformatics Resources aim to provide functional interpretation of large lists of genes derived from genomic studies, e.g. microarray and proteomics studies. DAVID can be found at https://david.ncifcrf.gov/

<span class="mw-page-title-main">RNA-Seq</span> Lab technique in cellular biology

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<span class="mw-page-title-main">Gene set enrichment analysis</span> Bioinformatics method

Gene set enrichment analysis (GSEA) (also called functional enrichment analysis or pathway enrichment analysis) is a method to identify classes of genes or proteins that are over-represented in a large set of genes or proteins, and may have an association with different phenotypes (e.g. different organism growth patterns or diseases). The method uses statistical approaches to identify significantly enriched or depleted groups of genes. Transcriptomics technologies and proteomics results often identify thousands of genes which are used for the analysis.

<span class="mw-page-title-main">Multiomics</span>

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<span class="mw-page-title-main">Sorin Draghici</span> Researcher

Sorin Drăghici is a Romanian-American computer scientist and a program director in the Division of Information and Intelligent Systems (IIS) of the Directorate for Computer and Information Science and Engineering (CISE) at the National Science Foundation (NSF). Previous positions include: Associate Dean for Entrepreneurship and Innovation of Wayne State University's College of Engineering, the Director of the Bioinformatics and Biostatistics Core at Karmanos Cancer Institute, and the Director of the James and Patricia Anderson Engineering Ventures Institute. Draghici was elected a Fellow of the Institute of Electrical and Electronics Engineers (IEEE) in 2022, for contributions to the analysis of high-throughput genomics and proteomics data

<span class="mw-page-title-main">Machine learning in bioinformatics</span>

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