1. Setup
<ol>
	<li>Install R, if it is not already installed. 
	NOTE: PAST is written in R and, therefore, requires that its users have R installed. At the time of this writing, installing PAST directly from Bioconductor requires R4.0.&#160; Older versions of PAST can be&#160;installed from Bioconductor for R3.6, and PAST can be installed from Github for users with&#160;R3.5. R installation instructions can be downloaded from the following link:&#160;https://www.r-project.org/.</li>
	<li>Install the latest version of RStudio Desktop or update RStudio (optional). 
	NOTE: RStudio is a helpful environment for working with the R language. Its installation is recommended, especially for those who choose to run PAST in the command line rather than through the Shiny GUI application. RStudio and its installation instructions can be found at the following link:&#160;<a href="https://rstudio.com/products/rstudio/">https://rstudio.com/products/rstudio/</a>.&#160;</li>
	<li>Install PAST from Bioconductor11 by following the instructions on Bioconductor. 
	NOTE: Installation through Bioconductor should handle the installation of PAST&#39;s dependencies. Additionally, PAST can be installed from Github12, but installing from Github will not install dependencies automatically.</li>
	<li>Install PAST Shiny (optional). Download the file &#34;app.R&#34; from the Releases page of the Github repository:&#160;https://github.com/IGBB/PAST/releases/, and remember where the downloaded file is located.&#160; 
	NOTE: PAST can be used by calling its methods directly with R, but users who are less familiar with R can run the PAST Shiny application, which provides a guided user interface. PAST Shiny is an R script available in the shiny_app branch of the PAST Github repository. PAST Shiny will attempt to install its dependencies during the first run.</li>
	<li>Begin analysis by starting the application in one of the three ways described below.
	<ol>
		<li>PAST Shiny with RStudio
		<ol>
			<li>Using RStudio, create a new project in the folder where app.R is located. Click File | New Project and select that folder.</li>
			<li>Once a new project has been created, open the app.R file downloaded earlier. RStudio recognizes that app.R is a Shiny app and creates a Run App button on the bar above the displayed source code. Click Run App. RStudio will then launch a window that displays the PAST Shiny application.</li>
		</ol>
		</li>
		<li>PAST Shiny with R Console
		<ol>
			<li>Launch R and run the following code to start the PAST Shiny application: shiny::runApp(&#39;path/to/folder/with/shiny/app.R&#39;. Replace the text in quotes with the folder to which app.R was downloaded, and keep the quotes.</li>
		</ol>
		</li>
		<li>PAST without R Shiny
		<ol>
			<li>Run library(PAST) in an R Console to load PAST.</li>
		</ol>
		</li>
	</ol>
	</li>
</ol>
2. Customize Shiny analysis (optional)
<ol>
	<li>Change the analysis title from &#34;New Analysis&#34; to something that better reflects the type of analysis being run which helps to keep track of multiple analyses (see Figure 1).</li>
</ol>
<img alt="Figure 1" class="xfigimg" src="/files/ftp_upload/61268/61268fig1.jpg" /> 
Figure 1. <a href="https://www-jove-com.bdigitaluss.remotexs.co/files/ftp_upload/61268/61268fig1large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<ol start="2">
	<li>Modify the number of cores and the mode. Set the number of cores to any number between 1 and the total number on the machine but be aware that devoting more resources to PAST may slow down other operations on the machine. Set the mode based on the description in section 6.</li>
</ol>
3. Load GWAS data
NOTE: Verify that the GWAS data is tab delimited. Ensure that the association file contains the following columns: trait, marker name, locus or chromosome, position on the chromosome, p-value, and R2 value for the marker. Ensure that the effects file contains the following columns: trait, marker name, locus or chromosome, position on the chromosome, and effect. The order of these columns is not important, as the user can specify the names of the columns when loading the data. Any additional columns are ignored. TASSEL13 can be used to produce these files.
<ol>
	<li>Load GWAS data with PAST Shiny.
	<ol>
		<li>Select an association file and an effects file by using the Association File and Effects File selection boxes. Change the column names in the Association Column Name and Effects Columns Name input boxes below the file selection boxes to reflect the column names in the data.</li>
	</ol>
	</li>
</ol>
<img alt="Figure 2" class="xfigimg" src="/files/ftp_upload/61268/61268fig2.jpg" /> 
Figure 2. <a href="https://www-jove-com.bdigitaluss.remotexs.co/files/ftp_upload/61268/61268fig2large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<ol start="2">
	<li>Load GWAS data with PAST in the R Console.
	<ol>
		<li>Modify and run the following code: 
		gwas_data = load_GWAS_data(&#34;path/to/association_file.tsv&#34;, &#34;path/to/effects_file.tsv&#34;, association_columns = c(&#34;Trait&#34;, &#34;Marker&#34;, &#34;Locus&#34;, &#34;Site&#34;, &#34;p&#34;, &#34;marker_R2&#34;), effects_columns = c(&#34;Trait&#34;, &#34;Marker&#34;, &#34;Locus&#34;, &#34;Site&#34;, &#34;Effect&#34;)</li>
	</ol>
	</li>
	<li>NOTE: Change the paths to the actual location of the GWAS files. The values provided for association_columns and effects_columns are the default values. If the names do not match the default values, specify the column names. Otherwise, these can be omitted.</li>
</ol>
4. Load linkage disequilibrium (LD) data
NOTE: Verify that the linkage disequilibrium (LD) data is tab delimited and contains the following types of data: Locus, Position1, Site1, Position2, Site2, Distance in base pairs between Position1 and Position2, and R2 value.
<ol>
	<li>Load LD data with PAST Shiny.
	<ol>
		<li>Select the file containing LD data. Change the column names in the LD Column Names input boxes below the file selection box to match the column names in the LD data if necessary.</li>
	</ol>
	</li>
</ol>
<img alt="Figure 3" class="xfigimg" src="/files/ftp_upload/61268/61268fig3.jpg" /> 
Figure 3. <a href="https://www-jove-com.bdigitaluss.remotexs.co/files/ftp_upload/61268/61268fig3large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<ol start="2">
	<li>Load LD Data with PAST in the R Console.
	<ol>
		<li>Modify and run the following code to load LD data: 
		LD = load_LD(&#34;path/to/LD.tsv&#34;, LD_columns = c(&#34;Locus1&#34;, &#34;Position1&#34;, &#34;Site1&#34;, &#34;Position2&#34;, &#34;Site2&#34;, &#34;Dist_bp&#34;, &#34;R.2&#34;) 
		NOTE: Change the path to the actual location of the LD file. The values provided for LD_columns are the default values. If the names do not match these defaults, specify the correct names of the columns; otherwise, these can be omitted.</li>
	</ol>
	</li>
</ol>
5. Assign SNPs to genes
NOTE: Download or otherwise locate annotations in GFF format. These annotations can often be found in online databases for specific organisms. Be cautious about low quality annotations, as the quality of the annotations data will affect the quality of the pathway analysis. Confirm that the first column of these annotations (the chromosome) matches the format of the locus/chromosome in the association, effects, and LD data. For example, the annotations should not call the first chromosome &#34;chr1&#34; if the GWAS and LD data files call the first chromosome &#34;1&#34;.
<ol>
	<li>Assign SNPs to genes with PAST Shiny. 
	NOTE: More information about determining an appropriate R2 cutoff can be found in Tang et al.6, in the section called &#34;SNP to gene algorithm for the pathway analysis&#34;.
	<ol>
		<li>Select the file containing GFF annotations. Consider what window size and R2 cutoff are most suitable for the species being considered and modify if the defaults do not suit the uploaded data. 
		NOTE: Default values in PAST primarily reflect values appropriate for maize. The number of cores set at the beginning of the PAST Shiny analysis (Step 2.2) is used in this step.</li>
	</ol>
	</li>
</ol>
<img alt="Figure 4" class="xfigimg" src="/files/ftp_upload/61268/61268fig4.jpg" /> 
Figure 4. <a href="https://www-jove-com.bdigitaluss.remotexs.co/files/ftp_upload/61268/61268fig4large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<ol start="2">
	<li>Assign SNPs to genes with PAST in the R Console.
	<ol>
		<li>Modify and run the following code to assign SNPs to genes: 
		genes = assign_SNPs_to_genes(gwas_data, LD, &#34;path/to/annotations.gff&#34;, c(&#34;gene&#34;), 1000, 0.8, 2) 
		NOTE: In this sample code, several default suggestions are provided: 1000 is the size of the window around the SNP to search for genes; 0.8 is the cutoff value for R2; 2 is the number of cores used for parallel processing. The path to the annotations should also be changed to the actual location of the annotations file.</li>
	</ol>
	</li>
</ol>
6. Discover significant pathways
NOTE: Verify that the pathways file contains the following data in tab delimited format, with one line for every gene in each pathway: pathway ID - an identifier such as &#34;PWY-6475-1&#34;; pathway description - a lengthier description of what the pathways do such as &#34;trans-lycopene biosynthesis&#34;; gene - a gene in the pathway, which should match the names provided in the annotations. Pathway information can likely be found in online databases for specific organisms, such as MaizeGDB. The second user-specified option is the mode. &#34;Increasing&#34; refers to phenotypes that reflect when an increasing value of the measured trait is desirable, such as yield, while &#34;decreasing&#34; refers to a trait where a decrease in the measured values is beneficial, such as insect damage ratings. The significance of pathways is tested using previously described methods4,6,14.
<ol>
	<li>Discover significant pathways with PAST Shiny.
	<ol>
		<li>Select the file containing pathways data and be sure that the mode is selected in the analysis options. If necessary, change the number of genes that must be in a pathway to retain it for the analysis and the number of permutations used to create the null distribution to test significance of effect.</li>
	</ol>
	</li>
</ol>
<img alt="Figure 5" class="xfigimg" src="/files/ftp_upload/61268/61268fig5.jpg" /> 
Figure 5. <a href="https://www-jove-com.bdigitaluss.remotexs.co/files/ftp_upload/61268/61268fig5large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
NOTE: The number of cores and the mode set at the beginning of the PAST Shiny analysis (Step 2.2) is used in this step. The default number of genes is currently set at 5 genes, so pathways with fewer known genes will be removed. The user can lower this value to 4 or 3, to include shorter pathways, but doing so will risk false positive results. Increasing this value can increase the power of the analysis but will remove more pathways from the analysis. Changing the number of permutations used&#160;increases and decreases the power of the test.
<ol start="2">
	<li>Discover significant pathways with PAST in the R Console.
	<ol>
		<li>Modify and run the following code to discover significant pathways: 
		rugplots_data &#60;- find_pathway_significance(genes, &#34;path/to/pathways.tsv&#34;, 5, &#34;increasing&#34;, 1000, 2) 
		&#8203;NOTE: In this sample code, several suggested defaults are provided. 5 is the minimum number of genes that must be in a pathway in order to keep the pathway in the analysis, increasing refers to an increasing amount of the measured trait (it is recommended that the user run both increasing and decreasing, regardless of trait; data interpretation will differ for the two, however), 1000 is the number of times to sample the effects to determine the null distribution, and 2 is the number of cores used for parallel processing. Change the path to the actual location of the pathways file.</li>
	</ol>
	</li>
</ol>
7. View Rugplots
<ol>
	<li>View Rugplots with PAST Shiny.
	<ol>
		<li>Once all inputs are uploaded and set, click Begin Analysis. A progress bar will appear and indicate which step of the analysis was last completed. When the analysis completes, PAST Shiny will switch to the Results tab. A table of results will be displayed in the left column (labeled &#34;pathways&#34;) and the Rugplots will be displayed in the right column (labeled &#34;plots&#34;).</li>
		<li>Use the slider to control the filtering parameters. When the filtering level is satisfactory, click the Download Results button at the bottom left to download all images and tables individually to a ZIP file that is named with the analysis title. This ZIP file contains the filtered table, the unfiltered table, and one image per pathway in the filtered table.</li>
	</ol>
	</li>
</ol>
<img alt="Figure 6" class="xfigimg" src="/files/ftp_upload/61268/61268fig6.jpg" /> 
Figure 6. <a href="https://www-jove-com.bdigitaluss.remotexs.co/files/ftp_upload/61268/61268fig6large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 7" class="xfigimg" src="/files/ftp_upload/61268/61268fig7.jpg" /> 
Figure 7. <a href="https://www-jove-com.bdigitaluss.remotexs.co/files/ftp_upload/61268/61268fig7large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<ol start="2">
	<li>View Rugplots with PAST in the R Console
	<ol>
		<li>Modify and run the following code to save the results: 
		plot_pathways(rugplots_data, &#34;pvalue&#34;, 0.02, &#34;increasing&#34;, &#34;output_folder&#34;) 
		NOTE: In this sample code, several suggested defaults are provided. pvalue provides the data that can be used for filtering insignificant pathways after a significance threshold is chosen by the user; 0.02 is the default value used in filtering, and increasing refers to an increasing amount of the measured trait (it is recommended that the user run both increasing and decreasing, regardless of trait; data interpretation will differ for the two, however); output_folder is the folder where the images and tables will be written (this folder must exist prior to running the function). A table of filtered results, the unfiltered results, and individual images for every pathway in the filtered results are written to this folder.</li>
	</ol>
	</li>
</ol>

<ol>
	<li>Rafalski, J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Association+genetics+in+crop+improvement.">Association genetics in crop improvement.</a> Current Opinion in Plant Biology. 13 (2), 174-180 (2010).</li><li>Yan, J., Warburton, M., Crouch, J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Association+Mapping+for+Enhancing+Maize+(Zea+mays+L.)+Genetic+Improvement.">Association Mapping for Enhancing Maize (Zea mays L.) Genetic Improvement.</a> Crop Science. 51 (2), 433-449 (2011).</li><li>Xiao, Y., Liu, H., Wu, L., Warburton, M., Yan, J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Genome-wide+Association+Studies+in+Maize:+Praise+and+Stargaze.">Genome-wide Association Studies in Maize: Praise and Stargaze.</a> Molecular Plant. 10 (3), 359-374 (2017).</li><li>Wang, K., Li, M., Bucan, M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Pathway-Based+Approaches+for+Analysis+of+Genomewide+Association+Studies.">Pathway-Based Approaches for Analysis of Genomewide Association Studies.</a> The American Journal of Human Genetics. 81 (6), 1278-1283 (2007).</li><li>Weng, L., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=SNP-based+pathway+enrichment+analysis+for+genome-wide+association+studies.">SNP-based pathway enrichment analysis for genome-wide association studies.</a> BMC Bioinformatics. 12 (1), 99 (2011).</li><li>Tang, J., Perkins, A., Williams, W., Warburton, M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Using+genome-wide+associations+to+identify+metabolic+pathways+involved+in+maize+aflatoxin+accumulation+resistance.">Using genome-wide associations to identify metabolic pathways involved in maize aflatoxin accumulation resistance.</a> BMC Genomics. 16 (1), 673 (2015).</li><li>Warburton, M., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Genome-Wide+Association+Mapping+of+Aspergillus+flavus+and+Aflatoxin+Accumulation+Resistance+in+Maize.">Genome-Wide Association Mapping of Aspergillus flavus and Aflatoxin Accumulation Resistance in Maize.</a> Crop Science. 55 (5), 1857-1867 (2015).</li><li>Warburton, M., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Genome-Wide+Association+and+Metabolic+Pathway+Analysis+of+Corn+Earworm+Resistance+in+Maize.">Genome-Wide Association and Metabolic Pathway Analysis of Corn Earworm Resistance in Maize.</a> The Plant Genome. 11 (1), 170069 (2018).</li><li>Li, H., Thrash, A., Tang, J., He, L., Yan, J., Warburton, M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Leveraging+GWAS+data+to+identify+metabolic+pathways+and+networks+involved+in+maize+lipid+biosynthesis.">Leveraging GWAS data to identify metabolic pathways and networks involved in maize lipid biosynthesis.</a> The Plant Journal. 98 (5), 853-863 (2019).</li><li>Thrash, A., Tang, J., DeOrnellis, M., Peterson, D., Warburton, M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=PAST:+The+Pathway+Association+Studies+Tool+to+Infer+Biological+Meaning+from+GWAS+Datasets.">PAST: The Pathway Association Studies Tool to Infer Biological Meaning from GWAS Datasets.</a> Plants. 9 (1), 58 (2020).</li><li>Adam, T., Mason, D. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=.">.</a> PAST: Pathway Association Study Tool (PAST). Bioconductor version: Release (3.10). , (2020).</li><li>Thrash, A., DeOrnellis, M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=.">.</a> IGBB/PAST. , (2019).</li><li>Bradbury, P., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=TASSEL:+software+for+association+mapping+of+complex+traits+in+diverse+samples.">TASSEL: software for association mapping of complex traits in diverse samples.</a> Bioinformatics. 23 (19), 2633-2635 (2007).</li><li>Subramanian, A., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Gene+set+enrichment+analysis:+A+knowledge-based+approach+for+interpreting+genome-wide+expression+profiles.">Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles.</a> Proceedings of the National Academy of Sciences U.S.A. 102, 15545-15550 (2005).</li></ol>

The authors have nothing to disclose.

A primary goal of PAST is to bring metabolic pathway analyses of GWAS data to a wider audience, especially for non-human and non-animal organisms. Alternative methods to PAST are often command-line programs that focus on humans or animals. User-friendliness was a primary goal in the development of PAST, both in choosing to develop a Shiny application and in choosing to use R and Bioconductor to release the application. Users do not need to learn how to compile programs in order to use PAST.
As with most types of analysis software, the results of PAST are only as good as the input data; if the input data has errors or is incorrectly formatted, PAST will fail to run or produce uninformative results. Ensuring that the GWAS data, LD data, annotations, and pathways files are correctly formatted is critical to receiving correct output from PAST. PAST only analyzes bi-allelic markers and can run only one trait for each set of input data. In addition, GWAS data produced by poor genotyping or incorrect or imprecise phenotyping is not likely to produce clear or repeatable results either. PAST can aid in the biological interpretation of GWAS results but is unlikely to clarify chaotic data sets if environmental variation, experimental error, or population structure was not properly accounted for.
Users can choose to change some parameters of the analysis, both in the Shiny application and by passing those parameters to PAST&#39;s functions in the R console. These parameters can change the results reported by PAST, and users should take care when modifying these from the defaults. Because LD is measured by the users, typically using the same marker data set that was also used in the GWAS, the LD measurements are specific to the population. For all studies, especially for species other than maize, (particularly self-pollinating, polyploid, or highly heterogenous species), changes in the defaults may be warranted.

An erratum was issued for: A Pathway Association Study Tool for GWAS Analyses of Metabolic Pathway Information. One of the affiliations was updated.
The second affiliation was updated from:
USDA-ARS Corn Host Plant Resistance Research Unit, Mississippi State University
to:
Corn Host Plant Resistance Research Unit, USDA-ARS

An erratum was issued for:&#160;<a href="https://www-jove-com.bdigitaluss.remotexs.co/t/61268">A Pathway Association Study Tool for GWAS Analyses of Metabolic Pathway Information</a>. One of the affiliations was updated.

Erratum: A Pathway Association Study Tool for GWAS Analyses of Metabolic Pathway Information

Genome-wide association studies (GWAS) are a popular method of studying complex traits and the genomic regions associated with them1,2,3. In this type of study, hundreds of thousands of single nucleotide polymorphism (SNP) markers are tested for their association with the trait, and the significance of the associations is assessed. Marker-trait associations that meet the false discovery rate (FDR) threshold (or some other type of significance threshold) are retained for the study, but true associations may be filtered out. For complex, polygenic traits, the effect of each gene might be small (and thus filtered out), and some alleles are only expressed in specific conditions that might not be present in the study3. Thus, while many SNPs may be retained as associated with the trait, each may have a very small effect. Too many SNP calls will be missing, and an interpretation of the biological meaning and genetic architecture of the trait may be incomplete and confusing. Metabolic pathway analysis can help to address some of these issues by focusing on the combined effects of genes grouped according to their biological function4,5,6.
Several studies were completed using a previous implementation of the method described in this article. Aflatoxin accumulation7, corn earworm resistance8, and oil biosynthesis9 were all studied with the previous implementation. While these analyses were successful, the process of analysis was complicated, time-consuming, and cumbersome, because the analysis tools were written in a combination of R, Perl, and Bash, and the pipeline was not automated. Because of the specialized knowledge required to modify this method for each analysis, a new method has now been developed that can be shared with other researchers.
The Pathway Association Study Tool (PAST)10 was designed to address the shortcomings of the previous method by requiring less knowledge of programming languages and by running analyses in a shorter period. While the method was tested with maize, PAST makes no species-specific assumptions. PAST can be run through the R console, as a Shiny app, and an online version is expected to soon be available on MaizeGDB.

<table><tbody><tr><td>Computer</td><td>NA</td><td>NA</td><td>Any computer with 8GB RAM should be sufficient</td></tr><tr><td>R</td><td>R Project</td><td>NA</td><td>R 4.0 or greater is required to install from Bioconductor 3.11</td></tr></tbody></table>

a pathway association study tool for gwas analyses of metabolic pathway information

Recently, a new implementation of a previously described method for interpreting genome-wide association study (GWAS) data using metabolic pathway analysis has been developed and released. The Pathway Association Study Tool (PAST) was developed to address concerns with user-friendliness and slow-running analyses. This new user-friendly tool has been released on Bioconductor and Github. In testing, PAST ran analyses in less than one hour that previously required twenty-four or more hours. In this article, we present the protocol for using either the Shiny application or the R console to run PAST.

If results are not produced following a run of the PAST software tool, check to be sure that all input files are correctly formatted. A successful run using the example data in the PAST package, which are based on a maize GWAS of grain color, is shown in Figure 8. This table and the resulting image can be downloaded using the Download Results button. An example of the downloaded image is shown in Figure 210. Incorrect settings might lead to results that do not make biological sense, but determining incorrectness must be up to the researcher, who should double check the validity of the chosen settings and consider all known evidence regarding the trait of interest.
Figure 910 shows the rugplot produced from the pathway analysis of GWAS results created with a maize panel of 288 inbred lines that had been phenotyped for grain color. This simplistic example, where the phenotypes were either &#34;white&#34; or &#34;yellow&#34;, was used because the pathway responsible for creating the bright yellow carotenoid pigments is known and should be responsible for most of the phenotype. Thus, we expected to see the trans-lycopene biosynthesis pathway (which produce carotenoids) to be significantly associated with grain color, which it is. Pathway ID and name are listed at the top of the graph. The horizontal axis of the graph ranks all genes that were included in the analysis, arranged from left to right in order of largest effect on the trait to smallest. However, only the genes in the trans-lycopene biosynthesis pathway are marked (at the top of the graph, as hatch marks, appearing in the gene rank of their effect as compared to all other genes in the analysis). There are 7 genes in this pathway. The running enrichment score (ES) is plotted along the vertical axis. The ES for each gene is added into the running total in order of effect and the total is adjusted to the number of genes analyzed. Thus, the score changes as one moves right along the horizontal axis and tends to increase as the larger effect genes are included, but at some point, the increase in the effect is smaller than the adjustment for having added another gene, and the entire score begins to decrease. The apex of the running ES line is marked with a dotted vertical line; this is the ES for the entire pathway and is used by the program to determine if the pathway is chosen and presented as a rugplot.&#160;
<img alt="Figure 8" class="xfigimg" src="/files/ftp_upload/61268/61268fig8.jpg" /> 
Figure 8: Completed run of PAST Shiny. <a href="https://www-jove-com.bdigitaluss.remotexs.co/files/ftp_upload/61268/61268fig8large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 9" class="xfigimg" src="/files/ftp_upload/61268/61268fig9.jpg" /> 
Figure 9: Pathway image from completed run of PAST (or downloaded from Shiny). This figure has been cited from Thrash et al.10. <a href="https://www-jove-com.bdigitaluss.remotexs.co/files/ftp_upload/61268/61268fig9large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>

Watch this Scientific Journal Video about A Pathway Association Study Tool for GWAS Analyses of Metabolic Pathway Information at JoVE.com

A Pathway Association Study Tool for GWAS Analyses of Metabolic Pathway Information

By running the Pathway Association Study Tool (PAST), either through the Shiny application or through the R console, researchers can gain a deeper understanding of the biological meaning of their genome-wide association study (GWAS) results by investigating the metabolic pathways involved.

Recently, a new implementation of a previously described method for interpreting genome-wide association study (GWAS) data using metabolic pathway ...

a-pathway-association-study-tool-for-gwas-analyses-metabolic-pathway

Nanyang Technological University

Research

JoVE Journal

Genetics

3.2K Views.  Mississippi State University. By running the Pathway Association Study Tool (PAST), either through the Shiny application or through the R console, researchers can gain a deeper understanding of the biological meaning of their genome-wide association study (GWAS) results by investigating the metabolic pathways involved. 

Video: A Pathway Association Study Tool for GWAS Analyses of Metabolic Pathway Information

Sample Preparation and Analysis of RNASeq-based Gene Expression Data from Zebrafish

The analysis of global gene expression changes is a valuable tool for identifying novel pathways underlying observed phenotypes. The zebrafish is an excellent model for rapid assessment of whole transcriptome from whole animal or individual cell populations due to the ease of isolation of RNA from large numbers of animals. Here a protocol for global gene expression analysis in zebrafish embryos using RNA sequencing (RNASeq) is presented. We describe preparation of RNA from whole embryos or from cell populations obtained using cell sorting in transgenic animals. We also describe an approach for analysis of RNASeq data to identify enriched pathways and Gene Ontology (GO) terms in global gene expression data sets. Finally, we provide a protocol for validation of gene expression changes using quantitative reverse transcriptase PCR (qRT-PCR). These protocols can be used for comparative analysis of control and experimental sets of zebrafish to identify novel gene expression changes, and provide molecular insight into phenotypes of interest.

This protocol presents an approach for whole transcriptome analysis from zebrafish embryos, larvae, or sorted cells. We include isolation of RNA, pathway analysis of RNASeq data, and qRT-PCR-based validation of gene expression changes.

The analysis of global gene expression changes is a valuable tool for identifying novel pathways underlying observed phenotypes. The zebrafish is an ...

Unsupervised Analysis of Multi-Omics Data in Coronary Syndromes Using MOFA

Application of Unsupervised Multi-Omic Factor Analysis to Uncover Patterns of Variation and Molecular Processes Linked to Cardiovascular Disease

Disease mechanisms are usually complex and governed by the interaction of several distinct molecular processes. Complex, multidimensional datasets are a valuable resource to generate more insights into those processes, but the analysis of such datasets can be challenging due to the high dimensionality resulting, for example, from different disease conditions, timepoints, and omics capturing the process at different resolutions.
Here, we showcase an approach to analyze and explore such a complex multiomics dataset in an unsupervised way by applying multi-omics factor analysis (MOFA) to a dataset generated from blood samples that capture the immune response in acute and chronic coronary syndromes. The dataset consists of several assays at differing resolutions, including sample-level cytokine data, plasma-proteomics and neutrophil prime-seq, and single-cell RNA-seq (scRNA-seq)&#160;data. Further complexity is added by having several different time points measured per patient and several patient subgroups.
The analysis workflow outlines how to integrate and analyze the data in several steps: (1) Data pre-processing and harmonization, (2) Estimation of the MOFA model, (3) Downstream analysis. Step 1 outlines how to process the features of the different data types, filter out low-quality features, and normalize them to harmonize their distributions for further analysis. Step 2 shows how to apply the MOFA model and explore the major sources of variance within the dataset across all omics and features. Step 3 presents several strategies for the downstream analysis of the captured patterns, linking them to the disease conditions and potential molecular processes governing those conditions.
Overall, we present a workflow for unsupervised data exploration of complex multi-omics datasets to enable the identification of major axes of variation composed of differing molecular features that can also be applied to other contexts and multi-omics datasets (including other assays as presented in the exemplary use case).

Author Spotlight: Integrated Multi-Omics Analysis for Unveiling Multicellular Immune Signatures in Clinical Heart Attack Cohorts

We present a flexible, extendible Jupyter-lab-based workflow for the unsupervised analysis of complex multi-omics datasets that combines different pre-processing steps, estimation of the multi-omics factor analysis model, and several downstream analyses.

Disease mechanisms are usually complex and governed by the interaction of several distinct molecular processes. Complex, multidimensional datasets are a ...

Large-Scale Multi-Omics Genome-Wide Association Studies (Mo-GWAS): Guidelines for Sample Preparation and Normalization

Both gas chromatography-mass spectrometry (GC-MS) and liquid chromatography-mass spectrometry (LC-MS) are widely used metabolomics approaches to detect and quantify hundreds of thousands of metabolite features. However, the application of these techniques to a large number of samples is subject to more complex interactions, particularly for genome-wide association studies (GWAS). This protocol describes an optimized metabolic workflow, which combines an efficient and fast sample preparation with the analysis of a large number of samples for legume crop species. This slightly modified extraction method was initially developed for the analysis of plant and animal tissues and is based on extraction in methyl tert-butyl ether: methanol solvent to allow the capture of polar and lipid metabolites. In addition, we provide a step-by-step guide for reducing analytical variations, which are essential for the high-throughput evaluation of metabolic variance in GWAS.

Large-Scale Multi-Omics Genome-Wide Association Studies Mo-GWAS: Guidelines for Sample Preparation and Normalization

In this protocol, we present an optimized workflow, which combines an efficient and fast sample preparation of many samples. In addition, we provide a step-by-step guide to reduce analytical variations for high-throughput evaluation of metabolic GWAS studies.

Both gas chromatography-mass spectrometry (GC-MS) and liquid chromatography-mass spectrometry (LC-MS) are widely used metabolomics approaches to detect ...

Biochemistry

A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports

The rapidly increasing and vast quantities of biomedical reports, each containing numerous entities and rich information, represent a rich resource for biomedical text-mining applications. These tools enable investigators to integrate, conceptualize, and translate these discoveries to uncover new insights into disease pathology and therapeutics. In this protocol, we present CaseOLAP LIFT, a new computational pipeline to investigate cellular components and their disease associations by extracting user-selected information from text datasets (e.g., biomedical literature). The software identifies sub-cellular proteins and their functional partners within disease-relevant documents. Additional disease-relevant documents are identified via the software's label imputation method. To contextualize the resulting protein-disease associations and to integrate information from multiple relevant biomedical resources, a knowledge graph is automatically constructed for further analyses. We present one use case with a corpus of ~34 million text documents downloaded online to provide an example of elucidating the role of mitochondrial proteins in distinct cardiovascular disease phenotypes using this method. Furthermore, a deep learning model was applied to the resulting knowledge graph to predict previously unreported relationships between proteins and disease, resulting in 1,583 associations with predicted probabilities &gt;0.90 and with an area under the receiver operating characteristic curve (AUROC) of 0.91 on the test set. This software features a highly customizable and automated workflow, with a broad scope of raw data available for analysis; therefore, using this method, protein-disease associations can be identified with enhanced reliability within a text corpus.

A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports

A computational protocol, CaseOLAP LIFT, and a use case are presented for investigating mitochondrial proteins and their associations with cardiovascular disease as described in biomedical reports. This protocol can be easily adapted to study user-selected cellular components and diseases.

The rapidly increasing and vast quantities of biomedical reports, each containing numerous entities and rich information, represent a rich resource for ...

Annotation of Plant Gene Function via Combined Genomics, Metabolomics and Informatics

Given the ever expanding number of model plant species for which complete genome sequences are available and the abundance of bio-resources such as knockout mutants, wild accessions and advanced breeding populations, there is a rising burden for gene functional annotation. In this protocol, annotation of plant gene function using combined co-expression gene analysis, metabolomics and informatics is provided (Figure 1). This approach is based on the theory of using target genes of known function to allow the identification of non-annotated genes likely to be involved in a certain metabolic process, with the identification of target compounds via metabolomics. Strategies are put forward for applying this information on populations generated by both forward and reverse genetics approaches in spite of none of these are effortless. By corollary this approach can also be used as an approach to characterise unknown peaks representing new or specific secondary metabolites in the limited tissues, plant species or stress treatment, which is currently the important trial to understanding plant metabolism.

Combination of genomics, co-expression gene analysis and the identification of target compounds via metabolism give gene functional annotation.

Given the ever expanding number of model plant species for which complete genome sequences are available and the abundance of bio-resources such as ...

Biology

Mapping Bacterial Functional Networks and Pathways in Escherichia Coli using Synthetic Genetic Arrays

Phenotypes are determined by a complex series of physical (e.g. protein-protein) and functional (e.g. gene-gene or genetic) interactions (GI)1. While physical interactions can indicate which bacterial proteins are associated as complexes, they do not necessarily reveal pathway-level functional relationships1. GI screens, in which the growth of double mutants bearing two deleted or inactivated genes is measured and compared to the corresponding single mutants, can illuminate epistatic dependencies between loci and hence provide a means to query and discover novel functional relationships2. Large-scale GI maps have been reported for eukaryotic organisms like yeast3-7, but GI information remains sparse for prokaryotes8, which hinders the functional annotation of bacterial genomes. To this end, we and others have developed high-throughput quantitative bacterial GI screening methods9, 10.
Here, we present the key steps required to perform quantitative E. coli Synthetic Genetic Array (eSGA) screening procedure on a genome-scale9, using natural bacterial conjugation and homologous recombination to systemically generate and measure the fitness of large numbers of double mutants in a colony array format. Briefly, a robot is used to transfer, through conjugation, chloramphenicol (Cm) - marked mutant alleles from engineered Hfr (High frequency of recombination) 'donor strains' into an ordered array of kanamycin (Kan) - marked F- recipient strains. Typically, we use loss-of-function single mutants bearing non-essential gene deletions (e.g. the 'Keio' collection11) and essential gene hypomorphic mutations (i.e. alleles conferring reduced protein expression, stability, or activity9, 12, 13) to query the functional associations of non-essential and essential genes, respectively. After conjugation and ensuing genetic exchange mediated by homologous recombination, the resulting double mutants are selected on solid medium containing both antibiotics. After outgrowth, the plates are digitally imaged and colony sizes are quantitatively scored using an in-house automated image processing system14. GIs are revealed when the growth rate of a double mutant is either significantly better or worse than expected9. Aggravating (or negative) GIs often result between loss-of-function mutations in pairs of genes from compensatory pathways that impinge on the same essential process2. Here, the loss of a single gene is buffered, such that either single mutant is viable. However, the loss of both pathways is deleterious and results in synthetic lethality or sickness (i.e. slow growth). Conversely, alleviating (or positive) interactions can occur between genes in the same pathway or protein complex2 as the deletion of either gene alone is often sufficient to perturb the normal function of the pathway or complex such that additional perturbations do not reduce activity, and hence growth, further. Overall, systematically identifying and analyzing GI networks can provide unbiased, global maps of the functional relationships between large numbers of genes, from which pathway-level information missed by other approaches can be inferred9.

Mapping Bacterial Functional Networks and Pathways in Escherichia Coli using Synthetic Genetic Arrays

Systematic, large-scale synthetic genetic (gene-gene or epistasis) interaction screens can be used to explore genetic redundancy and pathway cross-talk. Here, we describe a high-throughput quantitative synthetic genetic array screening technology, termed eSGA that we developed for elucidating epistatic relationships and exploring genetic interaction networks in Escherichia coli.

Phenotypes  are determined by a complex series of physical (e.g. protein-protein) and  functional (e.g. gene-gene or genetic) interactions (GI)1. While ...

A Web Tool for Generating High Quality Machine-readable Biological Pathways

PathWhiz is a web server built to facilitate the creation of colorful, interactive, visually pleasing pathway diagrams that are rich in biological information. The pathways generated by this online application are machine-readable and fully compatible with essentially all web-browsers and computer operating systems. It uses a specially developed, web-enabled pathway drawing interface that permits the selection and placement of different combinations of pre-drawn biological or biochemical entities to depict reactions, interactions, transport processes and binding events. This palette of entities consists of chemical compounds, proteins, nucleic acids, cellular membranes, subcellular structures, tissues, and organs. All of the visual elements in it can be interactively adjusted and customized. Furthermore, because this tool is a web server, all pathways and pathway elements are publicly accessible. This kind of pathway &#34;crowd sourcing&#34; means that PathWhiz already contains a large and rapidly growing collection of previously drawn pathways and pathway elements. Here we describe a protocol for the quick and easy creation of new pathways and the alteration of existing pathways. To further facilitate pathway editing and creation, the tool contains replication and propagation functions. The replication function allows existing pathways to be used as templates to create or edit new pathways. The propagation function allows one to take an existing pathway and automatically propagate it across different species. Pathways created with this tool can be &#34;re-styled&#34; into different formats (KEGG-like or text-book like), colored with different backgrounds, exported to BioPAX, SBGN-ML, SBML, or PWML data exchange formats, and downloaded as PNG or SVG images. The pathways can easily be incorporated into online databases, integrated into presentations, posters or publications, or used exclusively for online visualization and exploration. This protocol has been successfully applied to generate over 2,000 pathway diagrams, which are now found in many online databases including HMDB, DrugBank, SMPDB, and ECMDB.

PathWhiz is a comprehensive, online pathway drawing tool for generating biochemical and biological pathways. It uses publicly accessible databases and easily expandable palettes consisting of pre-drawn pathway components. This protocol describes how to easily build new pathways, replicate and edit existing pathways, and propagate previously drawn pathways to different organisms.

PathWhiz is a web server built to facilitate the creation of colorful, interactive, visually pleasing pathway diagrams that are rich in biological ...

A Protocol for Using Gene Set Enrichment Analysis to Identify the Appropriate Animal Model for Translational Research

Recent studies that compared transcriptomic datasets of human diseases with datasets from mouse models using traditional gene-to-gene comparison techniques resulted in contradictory conclusions regarding the relevance of animal models for translational research. A major reason for the discrepancies between different gene expression analyses is the arbitrary filtering of differentially expressed genes. Furthermore, the comparison of single genes between different species and platforms often is limited by technical variance, leading to misinterpretation of the con/discordance between data from human and animal models. Thus, standardized approaches for systematic data analysis are needed. To overcome subjective gene filtering and ineffective gene-to-gene comparisons, we recently demonstrated that gene set enrichment analysis (GSEA) has the potential to avoid these problems. Therefore, we developed a standardized protocol for the use of GSEA to distinguish between appropriate and inappropriate animal models for translational research. This protocol is not suitable to predict how to design new model systems a-priori, as it requires existing experimental omics data. However, the protocol describes how to interpret existing data in a standardized manner in order to select the most suitable animal model, thus avoiding unnecessary animal experiments and misleading translational studies.

We provide a standardized protocol for the use of gene set enrichment analysis of transcriptomic data to identify an ideal mouse model for translational research. 
This protocol can be used with DNA microarray and RNA sequencing data and can further be extended to other omics data if data are available.

Recent studies that compared transcriptomic datasets of human diseases with datasets from mouse models using traditional gene-to-gene comparison ...

Addressing Metabolomics Data Analysis Using Advanced Bioinformatics Tools

CorrelationCalculator and Filigree: Tools for Data-Driven Network Analysis of Metabolomics Data

A significant challenge in the analysis of omics data is extracting actionable biological knowledge. Metabolomics is no exception. The general problem of relating changes in levels of individual metabolites to specific biological processes is compounded by the large number of unknown metabolites present in untargeted liquid chromatography-mass spectrometry (LC-MS) studies. Further, secondary metabolism and lipid metabolism are poorly represented in existing pathway databases. To overcome these limitations, our group has developed several tools for data-driven network construction and analysis. These include CorrelationCalculator and Filigree. Both tools allow users to build partial correlation-based networks from experimental metabolomics data when the number of metabolites exceeds the number of samples. CorrelationCalculator supports the construction of a single network, while Filigree allows building a differential network utilizing data from two groups of samples, followed by network clustering and enrichment analysis. We will describe the utility and application of both tools for the analysis of real-life metabolomics data.

Author Spotlight: Emerging Technologies and Advanced Tools for Decoding Metabolomics Data Analysis 

We present CorrelationCalculator and Filigree, two tools for data-driven network construction and analysis of metabolomics data. CorrelationCalculator supports building a single interaction network of metabolites based on expression data, while Filigree allows building a differential network, followed by network clustering and enrichment analysis.

A significant challenge in the analysis of omics data is extracting actionable biological knowledge. Metabolomics is no exception. The general problem of ...

Analyzing Signaling Modules in Alzheimer's Disease with Deep Learning

DeepOmicsAE: Representing Signaling Modules in Alzheimer's Disease with Deep Learning Analysis of Proteomics, Metabolomics, and Clinical Data

Large omics datasets are becoming increasingly available for research into human health. This paper presents DeepOmicsAE, a workflow optimized for the analysis of multi-omics datasets, including&#160;proteomics, metabolomics, and clinical data. This workflow employs a type of neural network called autoencoder, to extract a concise set of features from the high-dimensional multi-omics input data. Furthermore, the workflow provides a method to optimize the key parameters needed to implement the autoencoder. To showcase this workflow, clinical data were analyzed from a cohort of 142 individuals who were either healthy or diagnosed with Alzheimer&#39;s disease, along with the proteome and metabolome of their postmortem brain samples. The features extracted from the latent layer of the autoencoder retain the biological information that separates healthy and diseased patients. In addition, the individual extracted features represent distinct molecular signaling modules, each of which interacts uniquely with the individuals&#39; clinical features,&#160;providing for a mean to integrate&#160;the proteomics, metabolomics, and clinical data.

Author Spotlight: Advancing Alzheimer's Research &#8211; Exploring Early Detection and Multi-Omics Approaches

DeepOmicsAE is a workflow centered on the application of a deep learning method (i.e., an autoencoder) to reduce the dimensionality of multi-omics data, providing a foundation for predictive models and signaling modules representing multiple layers of omics data.

Large omics datasets are becoming increasingly available for research into human health. This paper presents DeepOmicsAE, a workflow optimized for the ...

A Pathway Association Study Tool for GWAS Analyses of Metabolic Pathway Information

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Chapters in this video

Sample Preparation and Analysis of RNASeq-based Gene Expression Data from Zebrafish

Application of Unsupervised Multi-Omic Factor Analysis to Uncover Patterns of Variation and Molecular Processes Linked to Cardiovascular Disease

Large-Scale Multi-Omics Genome-Wide Association Studies (Mo-GWAS): Guidelines for Sample Preparation and Normalization

A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports

Annotation of Plant Gene Function via Combined Genomics, Metabolomics and Informatics

Mapping Bacterial Functional Networks and Pathways in Escherichia Coli using Synthetic Genetic Arrays

A Web Tool for Generating High Quality Machine-readable Biological Pathways

A Protocol for Using Gene Set Enrichment Analysis to Identify the Appropriate Animal Model for Translational Research

CorrelationCalculator and Filigree: Tools for Data-Driven Network Analysis of Metabolomics Data

DeepOmicsAE: Representing Signaling Modules in Alzheimer's Disease with Deep Learning Analysis of Proteomics, Metabolomics, and Clinical Data