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            <h4>Frequently Asked Questions</h4>
            <ol>
              <li>
                <h5>
                  Question: What is the workflow of using PMAP? How should we
                  use PMAP?
                </h5>
                <p>
                  Below is a typical work flow of analysis using PMAP. For
                  further details, please contact Gyan Srivastava
                  (gyan.srivastava@abbvie.com).
                </p>

                <div class="about_image">
                  <img src="./images/ques1.png" alt="" />
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              </li>
              <li>
                <h5>Question: which network I should use for analysis?</h5>
                <p>
                  For a given gene list, user can do enrichment test to select
                  significantly enriched networks. Please contact Gyan
                  Srivastava (gyan.srivastava@abbvie.com) for further details.
                </p>
                <p>
                  As shown below, there are multiple type of networks already
                  populated in the PMAP database. As the complexity of the
                  network analysis is proportion to square of network size, it
                  is recommended to avoid larger networks for analysis e.g. full
                  proteostasis networks consisting of ~5000 genes or large size
                  AMP-AD modules as shown in the information slide in the left
                  panel.
                </p>
                <div class="about_image">
                  <img src="./images/ques2.png" alt="" />
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              </li>
              <li>
                <h5>What kind of analysis can be performed in PMAP?</h5>
                <p>
                  Following figure explains various option of integrating
                  information to a selected network from PMAP. These options are
                  applicable and consistent to any selected network. Once user
                  understands these options, he/she can apply these results to
                  any network.
                </p>
                <div class="about_image">
                  <img src="./images/ques3.png" alt="" />
                </div>
                <p>
                  Here we provide an example of network analysis using PMAP. In
                  particular, we are using an AMP-AD module “DLPFCBrown” to show
                  various steps of analysis and derived scientific knowledge.
                </p>
              </li>
              <li>
                <h5>Example PMAP analysis using AMP-AD network module</h5>
                <p>
                  Here we provide an example analysis of an AMP-AD module to
                  discover hot-spots in the network and deep divining the
                  hub-genes. For illustration purpose, we chose “DLPFCBrown”
                  AMP-AD module, which is highly associated with amyloid
                  pathology. We created a functional protein-protein interaction
                  network amongst those genes derived from string-DB database.
                </p>
                <p>
                  A deep-dive into the network shows hot-spots of biological
                  interest. For example, we identified clusters of E3 ligases
                  and adapters as well as endocytosis but not lysosomes or
                  autophagy-related genes (Figure 2a).
                </p>
                <div class="about_image">
                  <img src="./images/fiq1.png" alt="" />
                </div>
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                  <img src="./images/fiq2.png" alt="" />
                </div>
                <p>
                  <b
                    >Figure 2: PPI network of 882 genes from DLPFCBrown
                    module,</b
                  >
                  A) DLPFCBrown module PPI network labeled with other pathway
                  genes in red color e.g. macroautophagy, lysosome etc. B) Genes
                  involved in endocytosis pathways are shown in red color.
                </p>
                <p>
                  We mapped differential expression summary on the network. As
                  shown in Figure 3, many genes from this module are
                  up-regulated in temporal cortex compared to cerebellum of AD
                  patients. Given the fact that temporal cortex is highly
                  affected in AD whereas Cerebellum is spared, these differences
                  indicate region and disease-specific pathway activity of these
                  genes. Moreover, genes that are down-regulated in cerebellum
                  but not changed in temporal cortex of AD brain might be
                  related to protective mechanism of this brain region against
                  AD progression.
                </p>
                <div class="about_image">
                  <img src="./images/fiq3.png" alt="" />
                </div>
                <p>
                  <b
                    >Figure 3. Differential expression of DLPFCBrown module in
                    AD vs control.</b
                  >
                  A) temporal cortex B) Cerebellum. Color of a gene represents
                  direction of differential expression i.e. Up-regulation in AD
                  (red); Down-regulation in AD (green). Size of the circle is
                  proportion to association (-log10 P-value) of genes to
                  cognitive decline.
                </p>
                <p>
                  Furthermore, we investigated endocytosis cluster and
                  interacting partners of ADAM10 from this network. We mapped
                  differential expression summary derived from 6 brain regions
                  (Figure 4) and AD traits association P-value (Figure 5). The
                  cluster shows brain region specific changes at transcriptional
                  level. Interestingly, SPP1 is the only gene up-regulated in
                  all 4 brain regions whereas other genes like NOTCH1 and DLL1
                  are up-regulated in temporal cortex but not in other brain
                  regions.
                </p>

                <div class="about_image">
                  <img src="./images/fiq4.png" alt="" />
                </div>

                <p>
                  <b
                    >Figure 4. Transcriptional changes atlas of Hub-gene ADAM10
                    and its direct interactors.
                  </b>
                  Size of the circle is proportion to association of genes to
                  cognitive decline, whereas color of the genes shows
                  differential expression in RNAseq
                </p>
                <p>
                  Additionally, ADAM10 transcriptional changes are more
                  associated with amyloid-plaques than neurofibrillary tangles.
                  However, direct interactors of ADAM10 shows different level of
                  association to AD traits. Specifically, SPP1, TMC6, RAP1A and
                  NOTCH1 are strongly correlated to amyloid-plaque and cognitive
                  decline but not with neurofibrillary tangles. In contrast,
                  LAMB2 shows a similar level of association with both amyloid
                  and tau pathology as well as cognitive decline.
                </p>

                <div class="about_image">
                  <img src="./images/fiq5.png" alt="" />
                </div>
                <p>
                  <b>Figure 5: Hub-gene ADAM10 and its direct interactors.</b>
                  Size of the circle is proportion to association (-log10
                  P-value) with AD-traits. Color represents Up (red) or Down
                  (green) regulation of the genes expression in temporal cortex
                  of AD brain.
                </p>
              </li>
              <li>
                <h5>
                  What species are covered in The Proteostasis Network Reference
                  Atlas (PNRA)?
                </h5>
                <p>
                  Currently, the Proteostasis Network Reference Atlas is focused
                  on Human genes and their Alzheimer’s association. Data from
                  other animal model will be incorporated in future versions of
                  the tool.
                </p>
              </li>
              <li>
                <h5>
                  Can user see their own gene network in Proteostasis Network
                  Reference Atlas (PNRA).
                </h5>
                <p>
                  Ans: Yes, user can upload an input in given format and
                  visualize the network and analyze the network’s gene assoction
                  with other available data at Proteostasis Co-Expresssion
                  Network.
                </p>
              </li>
              <li>
                <h5>How interconnection is established between two genes?</h5>
                <p>
                  The data from different modules were analyzed using
                  correlation network analysis with the help of Multiscale
                  Embedded Gene Co-expression Network Analysis package (Link:
                  https://doi.org/10.1371/journal.pcbi.1004574 | References)
                </p>
              </li>
            </ol>
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