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Adds more FAQ entries -- #27
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chrtannus committed Dec 5, 2024
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332 changes: 215 additions & 117 deletions src/client/components/home/faq.js
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import React from 'react';

import PropTypes from 'prop-types';
import makeStyles from '@mui/styles/makeStyles';

import LinkOut from './link-out';
import { Typography } from '@mui/material';


const ulStyle = { marginTop: '0.5rem' };

const faqs = [
[
// {
// question: <>What is an EnrichmentMap network?</>,
// answer: <>
// EnrichmentMap performs gene set enrichment analysis on a gene list then visualizes the results as a network.
// Nodes represent gene sets &#40;pathways&#41; and edges represent similarity &#40;overlap&#41; between the gene sets.
// The network is then structured so that highly redundant gene sets are grouped together as clusters,
// dramatically improving the capability to navigate and interpret enrichment results.
// </>,
// },
// {
// question: <>What files can I upload?</>,
// answer: <>
// Currently the EnrichmentMap web app only supports gene lists as input.
// You can upload either a gene list that already has ranks or an RNA-seq expression file that contains read counts.<br />
// The file type must be <code>Excel</code>, <LinkOut href="https://en.wikipedia.org/wiki/Comma-separated_values"><code>CSV</code></LinkOut>&nbsp;
// or <LinkOut href="https://en.wikipedia.org/wiki/Tab-separated_values"><code>TSV</code></LinkOut>.<br />
// The gene names must be the identifiers from&nbsp;
// <LinkOut href="https://www.ensembl.org/Homo_sapiens/Info/Index">Ensembl</LinkOut> or&nbsp;
// <LinkOut href="https://www.genenames.org/">HGNC</LinkOut> for human only.
// </>,
// },
// {
// question: <>Can I upload GSEA and g:Profiler files?</>,
// answer: <>
// No, if you want to use&nbsp;
// <LinkOut href="https://www.gsea-msigdb.org/gsea/index.jsp">GSEA</LinkOut> or&nbsp;
// <LinkOut href="https://biit.cs.ut.ee/gprofiler">g:Profiler</LinkOut> results to create an EnrichmentMap network,
// you can use the <LinkOut href="https://apps.cytoscape.org/apps/enrichmentmap">EnrichmentMap App</LinkOut> for&nbsp;
// <LinkOut href="https://cytoscape.org/">Cytoscape</LinkOut>&mdash;<LinkOut href="https://enrichmentmap.readthedocs.io/en/latest/Gsea.html">more info</LinkOut>.<br />
// However, if you still have the original gene list file used as input to GSEA,
// you can upload the file to EnrichmentMap web and perform a new enrichment analysis.
// The results will be available much faster than with GSEA.<br />
// Unranked gene list files typically used as input for g:Profiler are currently not supported by this web app.
// </>,
// },
{
question: <>How long does the iRegulon query take?</>,
question: <>What is iRegulon?</>,
answer: <>
iRegulon is a bioinformatics tool that predicts the master regulators and their target genes within a set of co-expressed genes.
It uses a comprehensive collection of transcription factor binding site motifs and ChIP-Seq data to identify enriched regulatory
elements and infers potential regulatory networks.
</>,
},
{
question: <>How does iRegulon work?</>,
answer: <>
iRegulon employs a &quot;ranking-and-recovery&quot; approach.<br />
<UnorderedList
items={[
<>
<b>Ranking:</b> Each gene in the human genome &#40;and orthologous genes in other vertebrate genomes&#41; is ranked based
on the presence and strength of potential transcription factor binding sites &#40;TFBS&#41; in its regulatory regions.
This creates a ranked list for each TFBS motif.
</>
,
<>
<b>Recovery:</b> A user-provided set of co-expressed genes is then analyzed for enrichment in each of the ranked lists.
Motifs showing significant enrichment suggest potential regulators of the input gene set.
</>
]}
/>
iRegulon also uses a &quot;motif2TF&quot; approach to link enriched motifs to specific transcription factors.
This database leverages direct motif annotations, TF homology, and motif similarity to provide candidate TFs for a given enriched motif.
</>,
},
{
question: <>What are the supported organisms?</>,
answer: <>
Human, mouse and drosophila.
</>,
},
{
question: <>What are the supported gene IDs?</>,
answer: <>
<UnorderedList
items={[
<><LinkOut href="https://www.genenames.org/tools/multi-symbol-checker/">HGNC symbols</LinkOut> for human</>,
<><LinkOut href="https://www.informatics.jax.org/mgihome/nomen/">MGI symbols</LinkOut> for mouse</>,
<>CG numbers and <LinkOut href="https://wiki.flybase.org/wiki/FlyBase:Nomenclature">Flybase names</LinkOut> for drosophila</>,
]}
/>
</>,
},
{
question: <>What types of gene sets can I use with iRegulon?</>,
answer: <>
iRegulon can be applied to various gene sets, including:
<UnorderedList
items={[
<>Co-expressed genes from gene expression profiling experiments</>,
<>Genes involved in specific pathways &#40;e.g., KEGG, Reactome, Gene Ontology&#41;</>,
<>Genes connected in biological networks &#40;e.g., GeneMania, STRING&#41;</>,
<>Shared targets of microRNAs</>,
]}
/>
</>,
},
{
question: <>How does iRegulon handle noisy gene sets?</>,
answer: <>
iRegulon is designed to be robust to noisy gene sets, meaning it can still identify relevant regulators
even if the input set contains genes that are not directly regulated by the same TF.
It achieves this by focusing on the most highly ranked genes for each motif and using statistical methods
to assess enrichment.
</>,
},
{
question: <>Can iRegulon predict direct and indirect targets?</>,
answer: <>
Yes, iRegulon can distinguish between direct and indirect targets within a co-expressed gene set.
It does this by identifying the &quot;leading edge&quot; of target genes, which are those genes most highly ranked
for a given motif and significantly above the background ranking.
Genes beyond the leading edge are considered potential indirect targets.
</>,
},
{
question: <>How can I validate the predictions made by iRegulon?</>,
answer: <>
iRegulon predictions can be validated using several approaches:
<UnorderedList
items={[
<>
<b>Comparison with ChIP-Seq data:</b> If available, ChIP-Seq data for the predicted regulator can be used
to confirm the binding of the TF to the predicted target genes.
</>,
<>
<b>Experimental validation:</b> Techniques such as luciferase reporter assays can be used to test the regulatory activity
of predicted enhancer regions containing the enriched motifs.
</>,
<>
<b>Meta-analysis:</b> iRegulon can be applied to multiple gene signatures to identify recurrently predicted targets,
providing further evidence for the regulatory interactions.
</>,
]}
/>
</>,
},
{
question: <>What are the advantages of using iRegulon compared to other motif discovery tools?</>,
answer: <>
On average it takes about 2 minutes to create a network from a list of genes.
</>,
},
// {
// question: <>How is the enrichment analysis performed?</>,
// answer: <>
// EnrichmentMap performs the gene set enrichment analysis by using an <code>R</code> package called&nbsp;
// <LinkOut href="https://bioconductor.org/packages/release/bioc/html/fgsea.html">FGSEA</LinkOut> &#40;Fast Gene Set Enrichment Analysis&#41;.
// The input to FGSEA is a ranked gene list. If you have a gene list that already has ranks then it can be used directly as input for FGSEA.
// If not, EnrichmentMap also accepts RNA-seq expression files that contain read counts.
// In this case, the replicates must be grouped into two experimental conditions &#40;e.g. treatment vs control&#41;.
// The read counts per gene are tested for differential expression and a rank is calculated for each gene.
// The resulting ranked gene list is then given to FGSEA.
// EnrichmentMap will provide the results of the gene rank calculations as well as the enrichment pathways.
// </>,
// },
// {
// question: <>What are the analysis parameters?</>,
// answer: <>
// The gene set filtering parameters are cutoff parameters used to filter the results of an enrichment analysis.<br />
// Please download the network images and data and then check the <code>README</code> file for the applied parameters.
// </>,
// },
// {
// question: <>What data does the app use?</>,
// answer: <>
// The enrichment analysis is performed against a <LinkOut href="https://baderlab.org/GeneSets">database of known pathways</LinkOut>&nbsp;
// for human, which has been curated from several sources by Bader Lab at the University of Toronto.
// </>,
// },
iRegulon offers several advantages over other motif discovery tools:
<UnorderedList
items={[
<>
<b>Large motif collection:</b> It utilizes a vast collection of over 9,000 TFBS motifs from multiple species,
increasing the chances of finding the correct regulator.
</>,
<>
<b>Motif2TF mapping:</b> This unique feature links enriched motifs to specific transcription factors,
even if the motif itself is not annotated for a human TF.
</>,
<>
<b>Integration with Cytoscape:</b> iRegulon is also available as a user-friendly <LinkOut href="https://apps.cytoscape.org/apps/iregulon">Cytoscape plugin</LinkOut>,
allowing seamless integration of predicted regulatory networks with other biological networks and data analysis tools.
</>,
]}
/>
</>,
},
], [
{
question: <>How long does the iRegulon query take?</>,
answer: <>
On average, it takes about 2 minutes to create a network from a list of genes.
</>,
},
{
question: <>How can the same transcription factor be detected in two different clusters?</>,
answer: <>
The Transcription Factor view relies on the motif clustering which is performed only on the enriched motifs using STAMP.
So a TF can be assigned to a cluster of motifs that are similar, but if such TF is annotated for a motif
which varies enough to be clustered with other motifs, then you can find the same TF associated to another cluster
because of a different motif.
</>,
},
{
question: <>The transcription factor or motif I&apos;m looking for has a low ranking, but has clustercode 1. Is this a good result?</>,
answer: <>
This depends of your research. A low ranking doesn&apos;t give a bad result. Like here the clustercode is 1,
so the motif of this transcription factor is clustered in the same cluster as the top motif.
This means that the motifs has a lot in common, and that is a good result.
</>,
},
{
question: <>A motif can be associated to different transcription factors, which one has the highest rank?</>,
answer: <>
The TFs are ordered by the motif2TF algorithm by decreasing order of preference.
The best transcription factors are those that are directly annotated &#40;&quot;Direct&quot;&#41; and a preference will be
for the TF that are from the input.
Then, the motif2TF algorithm will give preference to the TF associated by orthology,
then the TF associated by motif similarity.
</>,
},
{
question: <>A transcription factor has orthologous and the motif below that &#40;same cluster&#41; has a perfect match, is the second transcription factor better?</>,
answer: <>
The NES score of the motif decides the rank. Transcription factors are ranked for each motif.
So the motifs are ranked and not the transcription factors.
It is very difficult to decide if the second transcription factor is better or not.
But it&apos;s certain that the highest motif is better than the second.
</>,
},
{
question: <>How do I save my network?</>,
answer: <>
Accounts are not supported right now. In order to save your results, you have the following options:
<ul style={ulStyle}>
<li>Create a <LinkOut href="https://www.pcmag.com/how-to/how-to-organize-sync-web-browser-bookmarks-chrome-edge-firefox">bookmark</LinkOut> using your web browser.</li>
<li>Copy the URL in the browser address bar and save it.</li>
<li>Download the network images and data&mdash;the <code>README</code> file contains the permanent link to the network.</li>
<li>If you use the same browser, the home page shows the last 20 networks you opened.</li>
</ul>
<UnorderedList
items={[
<>Create a <LinkOut href="https://www.pcmag.com/how-to/how-to-organize-sync-web-browser-bookmarks-chrome-edge-firefox">bookmark</LinkOut> using your web browser.</>,
<>Copy the URL in the browser address bar and save it.</>,
<>Download the network images and data&mdash;the <code>README</code> file contains the permanent link to the network.</>,
<>If you use the same browser, the home page shows the last 20 networks you opened.</>,
]}
/>
</>,
},
{
question: <>How do I share my network?</>,
answer: <>
<ul style={ulStyle}>
<li>To share your results with others, copy the URL in the browser address bar and send it via email or text.</li>
<li>The network URL contains a unique code that allows access to your results. There is no way to access your results without its URL.</li>
<li>Anyone with the URL will be able to see your results and make changes to the network layout.</li>
</ul>
<UnorderedList
items={[
<>To share your results with others, copy the URL in the browser address bar and send it via email or text.</>,
<>The network URL contains a unique code that allows access to your results. There is no way to access your results without its URL.</>,
<>Anyone with the URL will be able to see your results and make changes to the network layout.</>,
]}
/>
</>,
},
{
question: <>How is my data stored?</>,
answer: <>
<ul style={ulStyle}>
<li>Your query parameters and the resulting analysis data is stored on our servers. We will not share this data with anyone,
and there is nothing connecting the data with your personal information. The data will be shared over time to make it conveniently accessible
to you, but older results may be deleted if we need to free space for new analyses.
</li>
<li>We use industry-standard technology to protect the security of the app and user data.</li>
<li>Your data is private by default. Others can access your data or results only if you share the URL with them.</li>
</ul>
<UnorderedList
items={[
<>
Your query parameters and the resulting analysis data is stored on our servers. We will not share this data with anyone,
and there is nothing connecting the data with your personal information.
The data will be shared over time to make it conveniently accessible to you, but older results may be deleted
if we need to free space for new analyses.
</>,
<>
We use industry-standard technology to protect the security of the app and user data.
</>,
<>
Your data is private by default. Others can access your data or results only if you share the URL with them.
</>,
]}
/>
</>,
},
{
question: <>Can I import my network into Cytoscape?</>,
answer: <>
Yes, you first need to download and install <LinkOut href="https://cytoscape.org/download.html">Cytoscape</LinkOut> and then
install the <LinkOut href="https://apps.cytoscape.org/apps/iregulon">iRegulon App</LinkOut> for Cytoscape.<br />
You can find the instructions in the <code>README</code> file&mdash;included when you download the network images and data.
</>,
},
// {
// question: <>How do I interpret the EnrichmentMap network?</>,
// answer: <ul style={ulStyle}>
// <li>Nodes &#40;circles&#41; represent highly enriched gene sets &#40;pathways&#41;.</li>
// <li>Edges &#40;links between nodes&#41; represent similarity &#40;overlaps&#41; between gene sets.</li>
// <li>Groups of highly similar pathways are represented as tight clusters, which can be expanded to see their individual pathways.</li>
// <li>The network is laid out so that similar pathways are close together.</li>
// <li>Node color represents the NES of each pathway&mdash;blue for positive NES value and red for negative.</li>
// </ul>,
// },
// {
// question: <>What does NES mean?</>,
// answer: <>
// NES is the Normalised Enrichment Score of a pathway. It may be:
// <ul style={ulStyle}>
// <li>Positive: when the pathway is up-regulated &#40;i.e. the pathway is more enriched in the experiment vs the control&#41;.</li>
// <li>Negative: when the pathway is down-regulated &#40;i.e. the pathway is less enriched in the experiment vs the control&#41;.</li>
// </ul>
// </>,
// },
// {
// question: <>Can I import my network into Cytoscape?</>,
// answer: <>
// Yes, you first need to download and install <LinkOut href="https://cytoscape.org/download.html">Cytoscape</LinkOut> and then
// install the <LinkOut href="https://apps.cytoscape.org/apps/enrichmentmap">EnrichmentMap App</LinkOut> for Cytoscape.<br />
// You can find the instructions in the <code>README</code> file&mdash;included when you download the network images and data.
// </>,
// },
],
];

Expand Down Expand Up @@ -204,4 +291,15 @@ export function Faq() {
);
}

function UnorderedList({ items }) {
return (
<ul style={{ marginTop: '0.5rem', marginBottom: '0.5rem' }}>
{ items.map((item, idx) => <li key={idx}>{item}</li>) }
</ul>
);
}
UnorderedList.propTypes = {
items: PropTypes.array.isRequired,
};

export default Faq;

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