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Merge pull request #26 from BackofenLab/tour-update
Update graphclust_step_by_step.yaml
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tours/graphclust_step_by_step.yaml

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with set of tool necessary for GraphCLust process. <br>
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The first step of the process is <b>Preprocessing</b>."
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position: "right"
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- title: "<b>Pre-processing</b>"
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intro: "This tool takes as an input file of sequences in Fasta format
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and creates the final input for GraphCLust based on given parameters. Parameters allows us to
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<br><br>"
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position: "left"
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- title: "<b>Preprocessing : DONE</b>"
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intro: "Once preprocessing of the data is done we can move on to the next step :create a graph from FASTA file.<br>"
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position: "right"
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- title: "<b>Creating a graph from FASTA</b>"
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intro: "Next step is to create a graph from FASTA file.<br>"
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intro: "To create a graph we need to use a tool called <b>fasta_to_gspan</b> which you
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can find in GraphClust section of tool panel.<br>"
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position: "right"
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postclick:
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- "#gspan > div.toolTitle > div > a"
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intro: "Once tool is executed it will produce <b>gspan.zip file</b>.<br>"
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position: "top"
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- title: "<b>Creating a sparse vector</b>"
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intro: "Next comes creation of explicit sparse feature encoding usinf <b>NSPDK</b>.<br>"
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- title: "<b>Creating a graph from FASTA : DONE</b>"
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intro: "Now we have <b>gspan.zip</b> file and can move on the next step.<br>
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The next step is to create sparse vector using NSPDK. <br>
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From GraphClust tools choose <b>NSPDK_sparseVect</b> to start the process. <br>"
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position: "top"
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postclick:
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- "#nspdk_sparse > div.toolTitle > div > a"
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- title: "<b>Creating a sparse vector</b>"
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intro: "This tool will create explicit sparse feature encoding using <b>NSPDK</b>.<br>"
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position: "top"
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- title: "<b>Creating a sparse vector</b>"
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intro: "This tools requires 2 input files:
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<br>
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intro: "After tool is exec it will produce <b>data_svector</b> which we will use in the next step."
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position: "top"
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- title: "<b>Creating a sparse vector: DONE</b>"
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intro: "Created <b>data_svector</b> file will be used in the next step for computing
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candidate cluster. For that click on <b>NSPDK_candidateClusters</b> tool."
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position: "top"
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postclick:
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- "#NSPDK_candidateClust > div.toolTitle > div > a"
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- title: "<b>Computing candidate clusters</b>"
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intro: "During this step we will compute global feature index and get top dense sets.
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The candidate clusters are chosen as the top ranking neighborhoods provided that the
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size of their overlap is below a specified threshold."
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position: "top"
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postclick:
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- "#NSPDK_candidateClust > div.toolTitle > div > a"
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- title: "<b>Computing candidate clusters</b>"
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of the single iteration it's empty. <br>"
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position: "top"
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- title: "<b>Preprocessing data for computing best subtree</b>"
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intro: "Next step will do some pre-processing for computing best subtrees.<br>"
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- title: "<b>Computing candidate clusters : DONE</b>"
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intro: "Once output files are ready we can move to the nexr step by clicking on
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<b>premlocarna</b> tool.<br>"
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position: "top"
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postclick:
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- "#preMloc > div.toolTitle > div > a"
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- title: "<b>Preprocessing data for computing best subtree</b>"
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intro: "This tool will do some pre-processing for computing best subtrees.<br>"
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position: "top"
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- title: "<b>Preprocessing data for computing best subtree</b>"
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intro: "This step needs 4 input files:
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<br>
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<br>"
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position: "top"
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- title: "<b>Preprocessing data for computing best subtree : DONE</b>"
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intro: "Preprocessing for the best tree computation is done, so we can now do the actual computation
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of the best subtree. For that we need the tool called <b>locarna_best_subtree</b>.<br>"
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position: "top"
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postclick:
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- "#locarna_best_subtree > div.toolTitle > div > a"
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- title: "<b>Computing best subtree using LocaRna</b>"
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intro: "This step computes a multiple sequence-structure alignment of RNA sequences using <b>LocaRna</b>.
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It uses tree file (from previous step) with guide tree in NEWICK format.
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The given tree is used as guide tree for the progressive alignment. It saves the calculation
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of pairwise all-vs-all similarities and construction of the guide tree. And at the end return the best subtree<br>"
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position: "top"
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postclick:
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- "#locarna_best_subtree > div.toolTitle > div > a"
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- title: "<b>Computing best subtree using LocaRna</b>"
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intro: "This step takes as an input following files:
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be used in next step to find consensus motives.<br>"
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position: "top"
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- title: "<b>Computing best subtree using LocaRna : DONE</b>"
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intro: "Now we have <b>model.tree.stk</b> so we can find consensus motives using the next tool -
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<b>CMFinder_v0</b>.<br>"
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position: "top"
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postclick:
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- "#cmFinder > div.toolTitle > div > a"
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- title: "<b>Finding consensus motives</b>"
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intro: "During this step conversion from CLUSTAL format files to STOCKHOLM format is done.
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Then using CMFinder we determine consensus motives for sequences.<br>"
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position: "top"
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postclick:
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- "#cmFinder > div.toolTitle > div > a"
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- title: "<b>Finding consensus motives</b>"
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intro: "This tool takes as an input the following files:
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<li>model_tree_stk : from previous step</li>
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<li>cmfinder_fa : from 'Preprocessing data for computing best subtree' step </li>
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<li>tree_matrix</li>
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</b>
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</dir>
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<br>"
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intro: "Output of the tool is in STOCKHOLM format and contains the consensus structure. <br>"
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- title: "<b>Finding consensus motives : DONE</b>"
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intro: "Once we have consensus structure we can build covariance model with <b>cmbuild</b> tool.
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For that click on the tool named <b>Build covariance models </b><br>"
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position: "top"
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postclick:
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- "#infernal > div:nth-child(3) > div > a "
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- title: "<b>Building Covariance Model using cmbuild</b>"
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intro: "In this step we cm build a covariance model of an RNA multiple alignment.
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<b>cmbuild</b> uses the consensus structure to determine the architecture of the covariance model. <br>"
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position: "top"
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postclick:
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- "#infernal > div:nth-child(3) > div > a "
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- title: "<b>Building Covariance Model using cmbuild</b>"
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intro: "As an input for this tool we give 'model_cmfinder_stk' file containing consensus
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intro: "Run the tool by pressing <b>'Execute' </b> button.<br>"
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position: "top"
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- title: "<b>Searching homologous sequences using cmsearch</b>"
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intro: "After covariance model is built we can move on to <cmsearch>. <br>"
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- title: "<b>Building Covariance Model using cmbuild : DONE</b>"
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intro: "After covariance model is built we can move on to <cmsearch>.
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Simply click on <b>Search covariance model(s)</b>.<br>"
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postclick:
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- "#infernal > div:nth-child(4) > div > a"
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intro: "Run the tool by pressing <b>'Execute' </b> button.<br>"
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- title: "<b>Searching homologous sequences using cmsearch : DONE</b>"
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intro: "Finally we reached the last step of our workflow!
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Click on <b>Report_Results</b> to do the final step. <br>"
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position: "top"
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postclick:
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- "#glob_report > div.toolTitle > div > a"
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- title: "<b>Post-processing</b>"
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intro: "Final step of our workflow is <b>Post-processing</b>.<br>
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In this step we will report clusters and merge them if needed."
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position: "top"
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postclick:
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- "#glob_report > div.toolTitle > div > a"
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- title: "<b>Post-processing</b>"
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intro: "This tool takes as an input the following files:
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<li>cmsearch_results : from previous step </li>
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and
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<li>model_tree_files : from <b>'Preprocessing data for computing best subtree'</b> step</li>
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</b>
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</dir>
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<br>"
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position: "top"

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