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Overview

The GeneList tool allows the user to search for genes using gene IDs, descriptions, experiments, GO ids and different annotations, and then saves the result in a list that can be used by other tools.

Basic Usage Simply type in gene ids, descriptions, or different annotations. The matching genes will be displayed with selected annotations. The result can be customized by clicking the Select Displayed Annotations button. There are three buttons to “Save all to Gene List”, “Remove selected from Gene List” or “Empty Gene List”. The “Share table” button allows sharing the current GeneList with other users by way of an auto generated URL. The GeneList tool is the starting point for most PlantGenIE workflow.

Multiple GeneList The GeneList tool is capable of holding several named gene lists for use in other tools. These lists can be Added, Renamed or Deleted. Once clicking on a GeneList name, it will become the active GeneList and will be displayed in all other tools. GeneLists will remain for seven days while shared GeneList will be saved for 30 days.

Data

We use both PostgresSQL and MySQL to store annotation data. The GeneLists tool uses both in house annotation data and data from Phytozome and Plaza.

Implementation

This tool uses JavaScript, PHP, MySQL, PostgreSQL, JQuery, Datatables and Toastr libraries. SQL views and tmp tables gave additional speed to the tool.

Overview

The BLAST (Basic Local Alignment Search Tool) tool compares input sequences to PlantGenIE sequence databases to identify homologous sequence matches.

Basic Usage

Simply paste your sequence (with or without a FASTA header) into the Query Sequence input text box. Alternative you can retrieve a transcript sequence by entering a gene ID into the Load example text box, or you can upload a sequence file (Less than 100 MB) using the upload file function. Having used one of these input options, click and select the desired dataset from the lists of available BLAST databases. Finally click the BLAST! button at the bottom of the page.

PlantGenIE BLAST uses standard default NCBI BLAST options. However users can change the following advanced options:

Option Description
Scoring matrix Substitution matrix that determines the cost of each possible residue mismatch between query and target sequence. See BLAST substitution matrices for more information.
Filtering Whether to remove low complexity regions from the query sequence.
E-value cutoff The maximum expectation value of retained alignments.
Query genetic code Genetic code to be used in blastx translation of the query.
DB genetic code Genetic code to be used in blastx translation of the datasets.
Frame shift penalty Out-of-frame gapping (blastx, tblastn only) [Integer] default = 0.
Number of results The maximum number of results to return.

BLAST results

The BLAST Results page will be automatically reloaded until the search results are successfully retrieved. BLAST results are organized into a table containing Query ID, Hit ID, Average bit score (top), Average e-value (lowest), Average identity (av. similarity) and Links. Clickable BLAST results display the corresponding region of identified homology within the GBrowse tool, where the matching region is shown.

Data

The BLAST tool uses public genome assemblies, early release de novo assemblies from UPSC and data from [Phytozome] (http://www.phytozome.net/) and Plaza.

Implementation

PlantGenIE BLAST search is implemented using NCBI Blast (v2.2.26) and a backend PostgresSQL Chado database. We use PHP, JavaScript, XSL, Perl and d3js, Drupal libraries to improve Open Source GMOD Bioinformatic Software Bench server to provide a graphical user interface.


Overview
GBrowse is an open-source, genome annotation viewer.

Basic Usage
To find particular region of the chromosome, type a gene name, a short sequence (minimum of 15 bp), or a nucleotide range in the Landmark or Region box located near the top left of the page and click on the Search button. The area shown in the Details panel is highlighted by a box. You can grab the box and slide it left or right within limits (it can't slide over the whole genome). Once you get to a particular location, you can fine-tune the view with the Scroll/Zoom buttons to move along the chromosome or change magnification.

Data
GBrowse uses in house annotation data and data from Phytozome and Plaza.

Implementation
PlantGenIE GBrowse uses customized version of Generic Genome Browser version 2.49. We use dedicated GBrowse servers for each of our PlantGenIE resources.


Overview

exNet (expression Network) is an interactive tool for exploring co-expression networks.

Basic Usage

exNet visualizes co-expression between genes in the active gene list. Co-expression is visualized by drawing a co-expression network where genes are displayed as nodes and co-expression between genes is indicated by connecting nodes with an edge (i.e. if two genes have a line connecting them, they are co-expressed above the selected threshold). Genes are co-expressed if their profiles are correlated above a set co-expression threshold, and both the correlation measure and the co-expression threshold can be changed by the user. There are options to expand the network to include all co-expressed neighbours of genes currently represented in the network, or to remove genes (e.g. unconnected nodes). There are also options to color genes either individually or using pre-defined groups such as clusters or Gene Ontology categories. It is also possible to change the node shapes. Users can select a subset of genes within the network (selected by either mouse-dragging over the desired genes or by SHIFT-select and clicking on multiple nodes) and the  tool will then display (below the network) the expression profiles of the subset as a line plot or a heatmap. For pairs of genes, the tool can visualize their co-expression as a scatterplot.

Example workflow:

  1. Add a gene or genes to the currently active gene list.
  2. Go to the exNet tool and display the network.
  3. Select a gene or genes and right click, choose expand (to add co-expression neighbors).
  4. After expansion, add new genes (yellow) to your gene list by selecting them and clicking the 'Add' button in the "GeneList actions:" section of the controls below the network.
  5. You can then use the updated gene list in other PlantGenIE tools, e.g. Enrichment, exImage, exPlot or ComPlEx.

 

exNet display panel

The various elements of the exNet interface are shown Figure 1 and will be explained in detail. To view a network, exNet requires an active list of genes selected using the gene list tool. The network display shows an editable co-expression network of the current gene selection and allows various operations. Some of these operations are available by selecting genes and right clicking on the selection to access the selection menu. The actual network being displayed is controlled from the display settings panel. Various plots can be interactively displayed as the user selects network elements, and can be opened in their own specialized tools.

interface

Figure 1. ExNet

Display settings

This panel allows users to choose different co-expression measures (correlation measures). Co-expression can be displayed as either CLR values or as ordinary Pearson correlations (Pear). The CLR values are based on Mutual Information (MI); a pair-wise measure of mutual dependence. The Context Likelihood of Relatedness (CLR) approach transforms each MI value into a z-score indicating how much higher (in standard deviations) that MI value is than the average MI value in the network neighborhood. Hence the CLR value indicate the significance of the co-expression between two genes. The threshold boxes control the network size. For example, a lower threshold for display will result in more links between the selected genes (nodes) in the network panel. A lower threshold for expansion will include more co-expressed genes to be shown when selected genes are expanded (Figure 3a). You can also change the shapes of the nodes and you can use separate shapes for the genes annotated as transcription factors.

display settings

Figure 2. Display settings panel

Co-expression values between all pairs of genes across all samples are precomputed and stored in the database. However, it is sometimes necessary to compute co-expression for custom selections of samples (selected using the sample list selector in the menu, note that sample selection is currently not available for all datasets). If the checkbox for sub-network is on, a custom co-expression network of the type specified in the correlation drop-box will be computed on the fly, using the active sample list. By specifying the minimum co-expression level in the attached ‘thresh’ box you can filter the number of links. CLR cannot be computed for custom sample selections so for this option the network will show MI correlations.

Co-expression network actions

Checking the gene profiles box will show gene expression profiles inside the network nodes for the active sample list. Note, however, that these gene profiles may not always look similar (even for highly co-expressed genes) because the default co-expression network is computed across all samples while the profile is shown for the selected subset of samples. To draw a network for the selected samples, use the sub-network functionality.

network display

Figure 3. Network display panel: a) Selection menu. b) Selection method.

The selection menu (Figure 3a) appears by right-clicking a node selection and allows expanding the network and selecting pathway nodes.

Expanding the network. A node selection can be expanded to include all co-expressed genes at a predefined co-expression threshold (specified by changing the expansion threshold in the display settings panel). The network display panel cannot display networks of unlimited size (it is designed to display a few hundred elements (nodes + links), this is due to restrictions on the user’s web client memory). If the network exceeds a certain number of elements, a warning will be displayed and the user will have to raise the expansion threshold before trying again.

Pathway nodes. After selecting genes in two or more regions of the displayed network, the Select ptw nodes - option selects the genes in the shortest paths between these initial selections. The initially selected subnetworks can for example be two Gene Ontology categories and the pathway genes are the genes connecting them.

pathway nodes

Figure 4. Pathway nodes. In this example the shortest paths are computed between two genes and pathway genes are selected.

Gene list and export panel

GeneList actions. The gene list and export panel can be used to change the gene list based on network selections. Selected genes can be added/removed from the active gene list (marked with red in the [master menu]), replace the active list or saved into a new list (input the name of the new list and select Save all or Save selected).

edit export

Figure 5. Gene list and export panel.

Export options. The Genes button exports the gene names in the network as a text file. The ‘SVG’ button exports the current network as a publication-quality figure. The ‘Graphml’ button exports the network in the graphml format so that it can be further edited in graph editing programs such as Cytoscape and yEd.

Plotting panel

Three types of plots can be generated for the current network selection: expression profiles (‘geneprofile’), scatter-plots and heatmaps. These plots can also be saved (selecting the corresponding button will open the image in a new tab). These plots will automatically refresh as the user changes the gene selection.

Gene profile. This button plots the expression profile of the selected genes for the active sample list. The [‘ExPlot’ tool] can be opened for a more detailed analysis of the expression profiles.

plot profile

Figure 6. Profile plot.

Gene scatter plot. This button produces a scatterplot of the expression values of two genes for the current selection of samples. Clicking a link in the network will also produce a scatter plot, but for all samples used to build the network. The scatter plot tool is limited to two genes.

scatter plot

Figure 7. Scatter plot.

Heatmap. The heatmap tool displays a heatmap for the active sample list and the selected genes. A link is also provided for downloading the corresponding expression table. This plot also has a dedicated tool called ‘exHeatmap’, with additional settings and options to generate publication-quality figures.

heatmap

Figure 8. Heatmap.

The Color panel

The color panel can be used to color named genes in the network or to color genes from the same GO categories. The ‘GO enrichment’ tool can be used to test the statistical enrichment of GO categories in a selection.

"This textbox allows the user to select a number of categories for colouring the genes, including GO terms, chip_Seq annotations and gene ids. There are some limitation for this tool, the maximum number of single genes to be coloured is 500 and the maximum number of different annotation categories are eight. There are also a limitations in the colouring when the same gene id exists in two or more categories, in this case the colour will be coloured and then overwritten in in the order 1. GO term (first to last) 2. chipSeq chip (first to last) 3. Gene id. First to last means the order in which they were written in the textbox. In the case when colouring several terms from the same gene ontology tree for example it is recommended write the terms in order from parent to child."

GO list

 

Figure 9. Color menu.

The textbox allows the user to type/paste nodes that should be colored, including GO terms, ChIP-Seq annotations and gene IDs. This is limited to 500 single genes and 8 categories. Note that if a single gene exists in two or more categories, the color will be overwritten in this order: 1. GO term (in the order they were written) 2. ChIP-Seq (in the order they were written) 3. Gene ID.

"When the colour genes button is clicked, genes belonging to selected categories will be coloured in the network panel. The term colour will be listed in the plots and color display."

colorpanel

Figure 10. Color display.

Data

There are currently two co-expression networks in our database for PopGenIE (All affymetrix or Asp201 Expression Atlas) and one each for AtGenIE and ConGenIE.

Implementation

exNet uses Cytoscape Web (Flash) as the core for the network layout and visualization; the web page is coded in HTML, JavaScript and PHP. For structuring and printing the network information, a python script is used. The data is stored in a MySQL database and PHP mysql and Python MySQLdb packages are used to access the data.

Overview
exImage provides an intuitive pictographic view of expression data across a diverge range of PlantGenIE datasets.

Basic Usage
Users can either enter a gene ID in the input text area (and hit the "GO" button) or create a gene list which then will appear as an interactive list in the tool. exImage will shade the samples according to expression levels across multiple samples using either absolute or relative values. Relative values displays expression relative to the mean expression across all samples. The current view can be exported in various vector formats including publication ready PDFs or as expression values. The ‘Take a tour’ feature will provide a brief introduction to the basic functionalities in exImage.

Data
exImage uses VST (Variance-Stabilizing Transformation) values for absolute expression, and no unit for the relative values. Absolute expression values were generated by aligning RNA-Seq reads to the reference genome and gene annotation with aligned read numbers then used to calculate VST values using XXXXX .

Implementation
exImage was developed using PHP, Javascript, d3js, rsvg-convert, imagemacgick, librvg and batik. exImage uses a MySQL database as a backend data source. exImage was inspired by the eFP resource.

Overview
exPlot is an interactive plotting tool visualize expression profiles as line graphs for selected genes and experiments.

Basic Usage
Type in multiple gene IDs in the input text area separated by comma, space, tab or new line and hit the "Search" button. Alternatively, you can create a gene list, in which case genes from the currently active list will be displayed. The tool plots VST normalized gene expression values across the selected samples or pre-defined sets of samples for the input genes. Different sample sets are available in the ‘SampleList’ in the top-right corner of the page. The plot is interactive and allows the user to select a subset of the displayed genes and to create a new GeneList containing only these genes. Publication-ready figures, in PDF or SVG format, can be exported

The ‘Take a tour’ feature will provide a brief introduction to the basic functionalities available in exPlot.

Data
exPlot uses the same VST(variance-stabilizing transformation) datasets stored in MySQL database.

Implementation
exPlot was developed using JavaScript, PHP and MySQL. It uses Highchart open source framework to visualize, draw and export charts interactively.

Overview
Enrichment – A tool that calculate gene function enrichment for a selected gene set. Ther tool displays Gene Ontology enrichments as a tree.

Example work flow:

  1. Add a group of genes to the gene list.
  2. Go to the tool and display the enrichments.
  3. Scroll down to view all enrichment tests.
  4. Select the GO details tab to view more options for gene ontology enrichment.
  5. Choose one of the three domains and calculate enrichment.
  6. Select a minimum p-value and minimum number of genes in a category to reduce the number of results.
  7. Change the layout to tree view.

Basic Usage
The enrichment tool calculates the statistical enrichment of a set of functional annotations, currently Gene ontology, miRNA, and Pfam, in a gene list. There is one page with an overview of all enrichment tests, and a GO details tab with advanced settings for GO enrichment as well as a visualization of the Gene Ontology trees (biological process, molecular function and cellular component). The results can be filtered by specifying a p-value threshold (default is 0.05) and a minimum number of genes annotated with the specific function (default 2). False Discovery Rate (FDR) corrected p-values are reported by default, but other corrections are available. Under-enrichment, that is significantly fewer annotations in the gene list than one would expect by chance, is hidden by default.

Implementation
The Enrichment tool uses Cytoscape Web (flash) to display the Gene Ontology tree. The webpage is coded with PHP, HTML and JavaScript, the calculations are coded in Python, and the statistical test (fisher´s exact test) is calculated with the fisher 0.1.4 package.
Ref http://github.com/brentp/fishers_exact_test

Overview
The Chromosome diagram tool plots the location of genes in the active gene list.

Basic Usage
Type in multiple gene ids inside the input text area separated by comma, space, tab or new line and hit the "Submit" button. Click the padlock icon to enable zoom in function. You can scroll or use the zoom slider to zoom in or zoom out the chromosome diagram. When you mouse over the gene location, it will show the detailed information popup and link to the Gene Information page. You can simply drag and select the favorite gene locations and export as TSV or GFF3; or visualize in Phytozome or Agrigo. The Chromosome diagram tool allows users to upload a gene list and display the chromosomal location of those genes. It has controllers to change the color of the output diagram and also generates publication-ready plot that can be exported in common file formats including PDF.

Data
The Chromosome diagram tool uses basic annotation data from PlantGenIE MySQL database and you can upload custom files.

Implementation
The Chromosome diagram tool was built using Action script, PHP and MySQL.

exNorthern(Depreciated)
Digital Northern heat map representing the library distribution of ESTs representing gene models within PopulusDB as described in this paper(http://www.biomedcentral.com/1471-2164/9/589/).Which is based on EST frequencies in different libraries (Sterky et al. 2004) – of the OPLS-generated leaf gene list and shows that the greatest prevalence of genes was found in the shoot meristem, young leaf and apical shoot libraries. New RIA(Rich Internet Application) based Digital Northern tool  developed using Adobe Flash technology.Main goal is to enhance simplicity,efficiency and  real time interaction to user.
How to use?
Type in some Poplar gene ids inside the right side text input box by separating comma, newline or tab .Then change the variable drop down lists(Plot gene names?, Plot dendrogram?, Clustering method ...) ,Color picker(Background colour) or slider values(Max genes for colour, Min gene frequency) according to your requirement and it will simultaneously update the Heatmap otherwise click Submit button.Finally you can download the PDF file by clicking Download PDF link.

cDNA librarys
Cambial zone (A + B)   Populus tremula x tremuloides T89. Bark was peeled, and tissue scraped from both exposed surfaces with a scalpel. Sample includes developing xylem, cambial zone and mature phloem.
 
Active cambium (UB)   Populus tremula. Stem samples were collected from 3 different trees growing south of UmeŒ on July 10th 2001. 30-micrometer section were obtained by cryosectioning and used for RNA preparation.
 
Dormant cambium (UA)   Populus tremula. Stem samples were collected from 3 different trees growing south of UmeŒ on October 5th 2001. 30-micrometer section were obtained by cryosectioning and used for RNA preparation.
 
Tension wood (G)   Populus tremula x tremuloides T89. Wood scrapings of a tree inclined for 3 weeks in the greenhouse. Tissues should mainly contain wood cells that are actively forming secondary cell wall and a G-layer
 
Wood cell death (X)   Populus tremula x tremuloides T89. Sample was taken from stem and included xylem cells that started secondary cell wall formation but mainly those where cell wall was fully developed. The sample also included cells that had died.
 
Young leaves (C)   Populus tremula x tremuloides T89. Library described in Larsson S, Bjorkbacka H, Forsman C, Samuelsson G, Olsson O. (1997). Molecular cloning and biochemical characterization of carbonic anhydrase from Populus tremula x tremuloides. Plant Molecular Biology 34: 583-592. Trees cultured in greenhouse in fertilized peat under natural light supplemented with metal l halogen lamps at a PPF of 150 microE. Photoperiod 18 hrs, 20/15 C. watered daily and fertilized once a week.
 
Senescing leaves (I)   Populus tremula, Library described in Bhalerao et al. (2003) Gene expression in autumn leaves. Plant Physiol. 131: 430Ð442. Sample collected from one wild tree on the UmeŒ University campus. Sampled September 14th 1999 (few days before visible leaf senescence was observed) at 11.00. Mid-rib were removed.
 
Cold stressed leaves (L)   Populus tremula x tremuloides T89. Greenhouse plants were transferred to 5¡C. Fully developed leaves were sampled 3 and 4 days after transfer and pooled.
 
Dormant buds (Q)   Populus tremula. The same tree as in the senescing leaves library. Dormant buds were collected in February.
 
Petioles (P)   Populus tremula. Petioles was collected from several individuals, growing in long days conditions and stressed in different ways were pooled. Stress treatments were 1) Mechanical stress: A tree was hit every second for 20 hours, resulting in trembling of the whole tree 2) Nutrient stress: A tree was planted in perlit and grown without nutrients for two weeks 3) Biotic stress: A tree infested with (ticks) 4) Cold stress: A tree was exposed to 5¡C(under short day conditions and sampled after 15, 10, 20 and 37 days.
 
Virus/fugus-infected leaves (Y)   Populus tremula. Leaves from different stages infected either with 1) Poplar Mosaic Virus or 2) Venturia tremulae were sampled and pooled. Healthy non-infected leaves were sampled and used as driver pool in a partial subtraction step of the cDNA synthesis. Sequences Y001-Y004 are from the Virus infected and Y005-Y024 from the fungi infected partial subtractive library.
 
Flower buds (F)   Populus trichocarpa. Library described in Rottmann, W.H. et al. (2000). Diverse effects of overexpression of AFY and PTLF, a poplar (Populus) homolog of LEAFY/FLORICAULA, in transgenic poplar and Arabidopsis. Plant J. 22: 235-246. Immature female inflorescence tissue was collected in mid to late May from wild trees growing in the vicinity of Corvallis, Oregon. Reproductive buds were dissected to remove the young bud scales and the entire inflorescences were collected.
 
Female catkins (M)   Populus trichocarpa. Flushing catkins were collected in early spring (around March 1) from wild trees growing in the vicinity of Corvallis, Oregon.
 
Male catkins (V)   Populus trichocarpa. Flushing catkins were collected in early spring (around March 1) wild trees growing in the vicinity of Corvallis, Oregon
 
Apical shoot (K)   Populus tremula x tremuloides T89. 150 apical shoots (top 3 mm, biggest leaf ca. 5 mm, weight ca 4 mg) from 3-month-old greenhouse-grown plants were collected and pooled.
 
Shoot meristem (T)   Populus tremula x tremuloides T89. Shoot apexes were dissected under microscope .
 
Bark (N)   Populus tremula x tremuloides T89. Long-day treated plants (about 3 m). Bark was sampled from under the "crown" and 75 cm downwards. The sample was peeled off with a "potato peeler", buds were avoided and the cells were inspected in the microscope.
 
Roots (R)   Populus tremula x tremuloides T89. Plants grown in agar under sterile conditions. The whole root system (primary roots) up to 0.5-1 cm from the stem was used. Roots were still white.
 
Imbibed seeds (S)   Populus tremula. Seeds from a seed lot were imbibed and samples were taken 1) right after imbibition and 2) after 24 hours and pooled.
 

eXHeatmap
This tool is generates a heatmap plot, useful for clustering and for analyzing the expression of genes relative to each other. The network analysis tool (Popnet) is a useful alternative to clustering, while the expression plotting tool (exPlot) can be a useful alternative for plotting expression profiles. This tool uses the current gene list and sample list available in the Master Menu, so if those lists are empty, users must first fill them up from a set of dedicated tools.

Clustering with the heatmap
The genes are clustered based on the choice of a distance function and the result of the clustering is shown by means of a dendogram, that can be places on either of x and y axes. The color scale indicates how far the actual expression values are from the local consensus. Distance functions are quantifying how similar is the expression of two genes/samples. For more accurate estimators of gene expression similarity use the PopNet tool. Based on the all-pair distance estimations the genes are clustered together using a chosen variety of the hierarchical clustering algorithm. The sample information is selectable from the command panel. By clicking on the heatmap itself you will open a publishing-ready pdf, or you can export the heatmap data from the command panel and import it into your favorite plotting program.

Best tips to try before you contact us!
We have found that many apparent problems with tools in PopGenIE can result from previous results that have been cached. Before reporting a bug/problem we would request that you first clear your browser cache, quit the browser, again clear the cache when you re-open the browser and then finally check that the problems remains.

How can I find PopGenIE old versions?
http://v2.popgenie.org
http://v1.popgenie.org

How can I convert Populus trichocarpa version 1 gene ids into version 2 gene ids?
Please go to http://v2.popgenie.org/flashbulktools and choose the ID conversion option. Then select the Populus Genome from dropd down menu. Finally paste your old gene ids and click GO button.

How can I convert Populus trichocarpa version 2 gene ids into version 3 gene ids?
Please go to Gene Search tool and paste your old gene ids.

How can I download all protein/nucleotide sequences from my active gene list?
Please use the Sequence Search tool, which is listed in the Analysis section. In there you can select P. trichocarpa peptide/nucleotide, click on the search button and then on the download button. You will then have a FASTA file of the protein/nucleotide sequences for the genes in your active gene list.

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