In Transfer Path Analysis TPA : The loads from the source are identified The transfer functions between the source and the target are identified Understanding the source loads and the transfer functions allows the levels to be calculated at the target location. This is for a simplified, two path structureborne noise model. Figure 1: Transfer functions, or FRFs, are used to characterize the structure and target attached to the source. Acoustic transfer functions relate force F to sound pressure at a target P. These functions can be calculated by: Test: The force is often applied with an impact hammer or shaker.
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Homology information between human and the applicable target species was obtained from Homologene [ 31 ] and from EnsMart [ 22 ] for cow only Algorithm details in Supplemental Data. For simplicity we restricted the use of data from these resources to gene relationships between template and target species. This restriction reduces clutter in the converted MAPPs and avoids potentially ambiguous homology relationships.
Qualitatively, the conversion rates correlated with the expected conservation of biological processes across species. The MAPPs representing the central dogma of DNA replication, RNA transcription and translation, for example, were converted with high fidelity from human to each of the target species, whereas specialized signaling pathways failed to be translated beyond dog, cow and chicken. This strategy of utilizing public homology information, existing pathway information and the Converter function can be applied to any species with available homology information to a species for which pathway information exists.
As expected, highly conserved processes showed high conversion rates across species far left , while more specialized processes were homologous only among more closely related species. Full size image The development of homology MAPPs in GenMAPP builds upon similar efforts at other databases [ 27 ] and addresses the dearth of pathway content that can be queried computationally.
However, it is important to note that these MAPPs are not genuine species-specific pathways, but rather translations of human pathways where target species genes have been mapped based on homology.
This distinction is important since accurate pathway inference requires knowledge that the particular biological process and molecular interactions are conserved between organisms and that predicted homologues encode for gene products that perform the same biological function. Another current limitation is that, unlike several other resources [ 27 , 33 , 34 ], the reactions in a GenMAPP pathway are illustrations rather than computable networks that allow for identification of conserved interactions.
Furthermore, pathways for non-mammalian species are mapped from human rather than the most closely related organism. They nonetheless offer an immediate and concrete solution for many researchers studying organisms with minimally annotated genomes not supported by other analysis programs.
It is our hope that these pathways will serve to nucleate additional curated pathways. Furthermore, the information provided by pathway representations of known biology, especially for minimally annotated genomes, is crucial not only for analyzing large-scale datasets, but also for assigning gene function. Extending pathways The use of homology mapping addresses the critical need to extend biological representations across species.
Yet it is also necessary to expand the pathway content within a given species. The collection of curated pathways from the scientific community is a slow, iterative process that requires the synthesis of a variety of evidence. Such evidence is being cataloged in numerous databases as protein-protein interactions, genetic interactions, and coexpression patterns, which are rapidly expanding with the advent of large-scale, high-throughput assays.
But it remains a challenge to form meaningful networks from this data and grow our understanding of pathways. The unique gene content in the GenMAPP pathway archive is traced over time blue for the mouse genome in terms of the number of genes left axis and corresponding percentage of the genome right axis. For comparison, the unique gene content annotated by Gene Ontology is shown green. Significant gains in absolute gene content were made by collecting new pathways targeting new biology e.
Full size image To address this challenge, we created a new pathway resource, which incorporates additional genes into our existing set of pathways using prior evidence.
This method of pathway extension has been previously used to include new genes predicted to expand and enhance the content of existing pathways and gene sets [ 36 ]. The method can work with any type of data that can be modeled as pairs of linked genes. The most obvious example is protein-protein interactions, where the link between genes represents the physical association of two proteins. The link could also represent coexpression, transcriptional regulation, or literature search results.
The extension method is currently implemented as a set of in-house Perl scripts used as an accessory to GenMAPP to expand a given pathway.
Each pathway MAPP is processed individually. First, the gene IDs are extracted from the pathway and converted to a uniform ID system e. The resulting gene list is used to query one or more specified databases e. A threshold is set for including new genes e. Finally, the new genes are added to a side panel of the original MAPP, separate from the curated pathway, and the interaction partners are noted and stored in the remarks field of each involved gene Figure 4.
Figure 4 G1 to S cell-cycle control pathway extended with genes from a coexpression network. All genes assigned to the original pathway were queried against the coexpression network.
Yellow designates the genes found in the coexpression network and blue designates their coexpression partners that were extracted from the network and added to the pathway. Full size image Using this approach, we extended the GenMAPP curated pathway archives for mouse with two types of data: protein-protein interactions and coexpression data [ 37 ] see supplemental data.
The coexpression links were derived from a network analysis of correlated gene expression across multiple species networks [ 37 ] under the premise that genes that maintain an evolutionary conservation of coregulation often participate in a related biological process [ 38 , 39 ].
It is important to distinguish the added genes from those originally in the pathway since the added genes are not necessarily involved in the pathway; rather, they are related to the pathway by a particular type of evidence.
Having access to this related information in the same view as the pathway allows for simultaneous data visualization and statistical analysis using MAPPFinder. These extended pathways may also serve as launching points for improved pathway curation by the community and as a predictive method for identifying new pathway interactions. Examples of pathway analysis Here we explore three of the many examples of how GenMAPP version 2 can be used to analyze data from complex genomic experiments and the types of biological insights potentially gained.
Gene expression time course analysis In figure 5 , we display gene expression data from multiple time-point comparisons for the myometrium during gestation [ 40 ]. There are two baselines in this analysis: virgin non-pregnant NP myometrium and mid-pregnancy myometrium.
The comparison allows the user to simultaneously examine the effects of pregnancy as compared to non-pregnant animals and the specific temporal effects leading up to labor through postpartum. The prostaglandin synthesis and regulation pathway contains molecular interactions that are critical in the transition of the myometrium from a relatively quiescent tissue throughout pregnancy to a highly contractile tissue at term. By viewing multiple time-point comparisons in this pathway, one can easily see which genes are differentially expressed just prior to the onset of labor 18 days of pregnancy compared to mid-pregnancy 14 days of pregnancy e.
Ptgs2, Edn1 and Hsd11b1 alongside the relative expression of these genes at mid-pregnancy versus the virgin state first stripe. Making such comparisons in the new version of GenMAPP is relatively straightforward and flexible, supporting not only multiple data points, but also multiple types of data see SNP example, figure 6c. Figure 6 Striped view of multiple data types.
A Transcription and splicing data, collected on whole-genome exon tiling microarrays see supplemental methods , are represented by stripes of color on a functionally organized list of monoamine G-protein-coupled receptors. Transcriptional changes for 11 different human tissues are displayed as the center color of the gene box, and splicing for the gene across all tissues is displayed as the rim color.
B In the context of glycolysis and gluconeogenesis, mRNA and protein levels change in response to carbon source perturbation in Saccharomyces cerevisiae growing on galactose or ethanol.
The color on the left side of the gene box illustrates mRNA changes; the color on the right indicates corresponding protein-level changes. C A variety of SNP parameters can be viewed simultaneously using the striped view. Full size image Analysis of whole-genome exon array data As the feature size of DNA microarrays have decreased, the number of probes hybridizing to specific targets has increased by well over an order of magnitude.
In the example shown in Figure 6a , we examined a publicly available microarray dataset that measured the expression of all known and predicted exons from 11 different adult human tissues [ 41 ]. From these data, both gene expression changes between tissues and splicing scores can be calculated for all genes see supplemental methods.
GenMAPP version 2 can display this information in each gene box, with the central color stripes indicating relative expression change for each tissue red or blue and the rim color designating a threshold for the significance of an alternative splicing call green, gray, or white.
This strategy takes advantage of how GenMAPP prioritizes assignment of central and rim colors of a gene box based on the order of the underlying data. Viewing related identifiers to a given gene as a secondary rim criterion can provide critical information to the analysis and is a unique feature of GenMAPP. When viewed in the context of Monoamine G-protein coupled receptors, we can clearly identify in which tissues a gene is most highly expressed bright red center color and which genes have a significant alternative splicing call green rim color.
By creating a color set for each of the 11 tissues and selecting "all" for visualization, both the tissue specific regulation of gene expression and the likelihood of splicing can be assessed in a single view. The results from this dataset can be exported for any given set of pathways with web-ready images and HTML backpages for each and every gene.
The web export function allows researchers to navigate and effectively communicate the impact of both gene expression and splicing on specific pathways and genes see the GenMAPP website [ 42 ] for this example and others. Combining proteomic and gene expression data In another example, gene expression and proteomic data [ 43 ] is viewed concurrently as two adjacent stripes of color Figure 6b.
The example displays data from an experiment measuring both mRNA and protein levels in yeast in response to changes in carbon source. Simultaneously visualizing changes at the transcript and protein level in the context of pathways represents a more informative depiction of the system-level changes occurring in the organism than if either data was analyzed alone.
The flexibility of combining any number of disparate data types in a single view is a relatively uncommon feature in pathway analysis tools. To view two data types side by side, datasets are combined into a single spreadsheet before import into GenMAPP. There are no restrictions on the nature of data that can be viewed as independent, adjacent color sets, provided that the data links to the GenMAPP gene database.
Integrating genomic, phenotypic and structural information for polymorphism data One of the key principles of pathway analysis is the integration of multiple pieces of information in order to assess new data in the context of known biology.
In studying polymorphic, or SNP, differences that may contribute to disease, the ability to compare the distribution of polymorphisms in the population along with phenotypic and protein product effects in the context of biological pathways provides both a birds-eye view and detailed dissection of how specific changes might impact larger biological systems. An example of how these different types of biological data can be combined is shown in Figure 6c using data from a whole-genome myocardial infarction SNP array experiment [ 44 ].
Displaying data in this format highlights genes evidenced by association, experimental and bioinformatics predictions e. CETP, MTP as well as their relationship to each other and with other genes upstream and downstream of these components.
Display formats such as this allow access to multiple modes of gene regulation from a single display. Although these examples illustrate three possible methods for displaying complex results, users can customize such views and apply them to any combination of data types that have been merged and ordered before import to GenMAPP.
This feature provides a means to assess multiple modes of gene regulation and thus new avenues of insight into complex biological relationships. Although the new features of GenMAPP version 2 are a useful starting point for the analysis of complex microarray data, there are still a number of obstacles to overcome.
These obstacles include providing cross-platform tools for integrating pathway resources, representing gene features such as SNPs and splicing variation , and supporting structured pathway vocabularies for more efficient pathway migration, update, curation and exchange.
To accelerate development and take full advantage of the growing base of open source pathway tools we are actively working with the Cytoscape Consortium [ 45 , 46 ] and BioPAX [ 23 ] developers to implement GenMAPP-style visualization and analysis methods in a new software framework.
The primary aims are 1 to transition to a platform-independent Java code base that is readily integrated with online resources, 2 to support dynamically generated gene databases that not only organize identifiers and aliases, but also sub-gene entities such as transcripts, exons, and polymorphisms, and 3 to provide innovative analysis tools to preprocesses high-throughput datasets preparing them for integration with gene databases and statistical analyses, as well as for abstracted visualization at multiple levels of resolution.
We are also working on an XML-based pathway data format that captures relationships, coordinates, and annotations, as well as a Web tool that facilitates pathway content migration, and curation from the community. We anticipate that open source bioinformatics tools such as GenMAPP and Cytoscape will provide researchers with a new view of biology that integrates genomic data with our growing knowledgebase of pathways.
Conclusion GenMAPP version 2 represents a step towards fostering the critical link between the biologist and their data, providing powerful analyses and intuitive representations of increasingly large and complex high-throughput datasets. Availability and requirements.
GenMAPP 2: new features and resources for pathway analysis
GenMAPP 2: new features and resources for pathway analysis.
An Introduction to Transfer Path Analysis