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Data for paper 'Formation of motile cell clusters in heterogeneous model tumors: The role of cell-cell alignment' (PRE, 2024)
<p>The data provided in this repository is generated for the publication ‘Formation of motile cell clusters in heterogeneous model tumors: The role of cell-cell alignment’ by Quirine J.S. Braat, Cornelis Storm and Liesbeth M.C. Janssen and published in Physical Review E.</p> <p>The data is generated using the Cellular Potts Model in CompuCell3D [1] that can be retrieved from GitHub. The simulations contain a more detailed description of the data, and the data provided here can be reproduced using the appropriate simulation code and parameters. These can be found on GitHub via <a href="https://github.com/QBraat/Cluster-Formation-Alignment">https://github.com/QBraat/Cluster-Formation-Alignment</a>.</p> <p>The data in this repository has been divided into the following sets: </p> <ol> <li><strong>EmptyLayer_Random.zip</strong>. this data set belongs to section III.A and section III.C and contains detailed information about the cells’ positions, orientation as a function of time for the active cells in free space.</li> <li><strong>EmptyLater_Random_single.zip</strong>. this data set belongs to section III.A and contains processed data about the order parameter and cluster size as a function of time for the active cells in free space.</li> <li><strong>ConfluentLayer_Random_single.zip</strong>. this data set belongs to section III.B and contains the processed data about the order parameter and cluster size as a function of time for the active cells in a confluent layer. </li> <li><strong>ConfluentLayer_Random_FiniteSize.zip</strong>. this data set belongs to the data in the supplementary information.</li> <li><strong>ConfluentLayer_Random_InitialBlock.zip</strong>. this data set belongs to the data in the supplementary information.</li> </ol> <p>Other than these sets, there is an additional data set ‘<strong>ConfluentLayer_Random.zip</strong>’, which contains the detailed information about the cells’ positions, orientation etc. as a function of time for the confluent layer simulations in section III.B and section III.C. This data set has not been included as it contains a significantly large amount of data, but can be received upon request from the authors.</p> <h3>Detailed information about the data set</h3> <p>Each data point in the paper is generated by running 200 simulations using the simulation code for a set of parameters. For each combination of parameters (tau, gamma), we either got the full dynamic information (Full) or only the dynamic evolution of the mean cluster size and the steady state values for the mean cluster size and order parameter (Single).</p> <p><strong>Confluent layer (fraction = 0.25</strong><strong>)</strong></p> <table> <tbody> <tr> <td> <p><strong>tau / gamma</strong></p> </td> <td> <p><strong>1.0</strong></p> </td> <td> <p><strong>0.5</strong></p> </td> <td> <p><strong>0.2</strong></p> </td> <td> <p><strong>0.1</strong></p> </td> <td> <p><strong>0.05</strong></p> </td> <td> <p><strong>0.02</strong></p> </td> <td> <p><strong>0.01</strong></p> </td> <td> <p><strong>0.005</strong></p> </td> <td> <p><strong>0.001</strong></p> </td> <td> <p><strong>0.001</strong></p> </td> </tr> <tr> <td> <p>500 mcs</p> </td> <td> <p>Full</p> </td> <td> <p>Single</p> </td> <td> <p>Single</p> </td> <td> <p>Full</p> </td> <td> <p>Single</p> </td> <td> <p>Single</p> </td> <td> <p>Full</p> </td> <td> <p>Full</p> </td> <td> <p>Single</p> </td> <td> <p>Full</p> </td> </tr> <tr> <td> <p>2500 mcs</p> </td> <td> <p>Full</p> </td> <td> <p>Single</p> </td> <td> <p>Single</p> </td> <td> <p>Full</p> </td> <td> <p>Single</p> </td> <td> <p>Single</p> </td> <td> <p>Full</p> </td> <td> <p>Full</p> </td> <td> <p>Single</p> </td> <td> <p>Full</p> </td> </tr> <tr> <td> <p>4000 mcs</p> </td> <td> <p>Full</p> </td> <td> <p>Single</p> </td> <td> <p>Single</p> </td> <td> <p>Full</p> </td> <td> <p>Single</p> </td> <td> <p>Single</p> </td> <td> <p>Full</p> </td> <td> <p>Full</p> </td> <td> <p>Single</p> </td> <td> <p>Full</p> </td> </tr> </tbody> </table> <p><strong> </strong></p> <p><strong>Empty layer (fraction = 0.25</strong><strong>) </strong></p> <table> <tbody> <tr> <td> <p><strong>tau / gamma</strong></p> </td> <td> <p><strong>1.0</strong></p> </td> <td> <p><strong>0.5</strong></p> </td> <td> <p><strong>0.2</strong></p> </td> <td> <p><strong>0.1</strong></p> </td> <td> <p><strong>0.05</strong></p> </td> <td> <p><strong>0.02</strong></p> </td> <td> <p><strong>0.01</strong></p> </td> <td> <p><strong>0.005</strong></p> </td> <td> <p><strong>0.001</strong></p> </td> <td> <p><strong>0.001</strong></p> </td> </tr> <tr> <td> <p>500 mcs</p> </td> <td> <p>Full</p> </td> <td> <p>Full</p> </td> <td> <p>Full</p> </td> <td> <p>Full</p> </td> <td> <p>Full</p> </td> <td> <p>Full</p> </td> <td> <p>Full</p> </td> <td> <p>Full</p> </td> <td> <p>Full</p> </td> <td> <p>Full</p> </td> </tr> <tr> <td> <p>2500 mcs</p> </td> <td> <p>Full</p> </td> <td> <p>Full</p> </td> <td> <p>Full</p> </td> <td> <p>Full</p> </td> <td> <p>Full</p> </td> <td> <p>Full</p> </td> <td> <p>Full</p> </td> <td> <p>Full</p> </td> <td> <p>Full</p> </td> <td> <p>Full</p> </td> </tr> <tr> <td> <p>4000 mcs</p> </td> <td> <p>Single</p> </td> <td> <p>Single</p> </td> <td> <p>Single</p> </td> <td> <p>Single</p> </td> <td> <p>Single</p> </td> <td> <p>Single</p> </td> <td> <p>Single</p> </td> <td> <p>Single</p> </td> <td> <p>Single</p> </td> <td> <p>Single</p> </td> </tr> </tbody> </table> <p> </p> <p><strong>Other simulations </strong></p> <p>Apart from the main results, we also ran simulations with different parameter settings to get more insights into the dynamic behavior.</p> <table> <tbody> <tr> <td> <p><strong>Simulations</strong></p> </td> <td> <p><strong>Settings</strong></p> </td> <td> <p><strong>Storage type</strong></p> </td> </tr> <tr> <td> <p>Different fraction active cells</p> </td> <td> <p>Confluent layer, fraction = 0.1, other parameters as before</p> </td> <td> <p>Single</p> </td> </tr> <tr> <td> <p>No alignment</p> </td> <td> <p>Confluent Layer + Empty Layer, fraction = 0.25, gamma = 0, tau = 2500 mcs</p> </td> <td> <p>Full</p> </td> </tr> <tr> <td> <p>Initially aligned cluster</p> </td> <td> <p>Fully aligned cluster, Confluent Layer + Empty Layer, gamma = 1.0, 0.01, 0.001 and tau = 2500 mcs</p> </td> <td> <p>Full</p> </td> </tr> <tr> <td> <p>Finite-size effects</p> </td> <td> <p>Confluent Layer, number of cells = 100, 400, 9000, 1600, 2500 cells</p> </td> <td> <p>Single</p> </td> </tr> </tbody> </table> <p> </p> <h2>Data format</h2> <p>When the simulations are run with full data export, the following files are generated: </p> <ul> <li>data_cells_<settings>.dat: <ul> <li>mcs: time stamp in Monte Carlo Steps (MCS) </li> <li>cell.id: number of the cell </li> <li>cell.type: type of the Cells (active = 2, passive = 1) </li> <li>xCOM: x-coordinate of the center of mass </li> <li>yCOM: y-coordinate of the center of mass </li> <li>zCOM: z-coordinate of the center of mass (always equal to 0 in 2D) </li> <li>pol_angle: angle with respect to the x-axis of the active force orientation. </li> <li>cluster.id: number of the cluster to which the cells belongs.</li> </ul> </li> <li>data_clusters_<settings>.dat: <ul> <li>mcs: time stamp in Monte Carlo Steps (MCS)</li> <li>cluster_id: number of the cluster (corresponding to the number cluster.id in data_cells_<settings>.dat </li> <li>cluster_size: number of cells in the given cluster </li> </ul> </li> <li>metadata_<settings>.dat: <ul> <li>Information about the full set of simulation parameters</li> </ul> </li> </ul> <p>When the simulations are run with single data export, the following files are generated: </p> <ul> <li>single_export_<settings>.dat: <ul> <li>steadyS: steady state value of the mean cluster size (mean calculated after 60000 mcs)</li> <li>steadyS_std: standard deviation of the steady state value of the mean cluster size</li> <li>steadyP: steady state value of the polarity order parameter </li> <li>steadyP_std: standard deviation of the steady state value of the polarity order parameter </li> </ul> </li> <li>St-single-export_<settings>.dat: <ul> <li>mcs: time stamp in Monte Carlo Steps (MCS)</li> <li>St: mean cluster size at given time stamp</li> <li>St_std: standard deviation of the mean cluster size at a given time stamp </li> </ul> </li> <li>S-max-single-export_<settings>.dat <ul> <li>mcs: time stamp in Monte Carlo Steps (MCS)</li> <li>Smax: largest cluster detected at a given time stamp</li> </ul> </li> <li>metadata_<settings>.dat: <ul> <li>Information about the full set of simulation parameters</li> </ul> </li> </ul>
Alignment of mitochondrial genes of Potamometra species
<p>Alignment of mitochondrial genes of Potamometra species. Sequences of rRNAs and tRNAs are aligned using MAFFT 7.402 under the G-INS-i strategy and alignments of 13 PCGs are performed based on amino acid sequences using Muscle implemented in Mega 7.0. </p>
Stockholm sequence-structure alignments for 9 Bacteroides sRNAs
<p>Sequence alignment supporting the manuscript "Comparative genomics provides structural and functional insights into Bacteroides RNA biology".</p>
Aligned LSU dataset and phylogenetic relationships of Tylopilus glutinosus
<p><i>Tylopilus glutinosus</i> from Bangladesh is described and illustrated based on morphology along with molecular data. This species is characterized by its caespitose habitat, a purple pileus that becomes very viscid when wet, unchanging context, an ixotrichodermial pileipellis with the outer layer consisting of 3–4 cylindrical cells and the inner layer composed of filamentous hyphae, broadly fusoid to fusoid basidiospores, and putative association with <i>Shorea robusta</i>. Morphologically, this species resembles <i>T. plumbeoviolaceoides</i> and <i>T. plumbeoviolaceus</i>. However, some distinctive macro- and microscopic features along with geographical distribution and host preference can be used to separate them from each other. Furthermore, DNA sequence of the nuclear ribosomal large subunit (nrLSU) analysis undoubtedly placed them in different lineages or clades within <i>Tylopilus</i>. Detailed morphological descriptions, illustrations of the new species, and its comparison with the related taxa of <i>Tylopilus</i> are also provided with phylogenetic placement.</p>
Supplementary material 3 from: Rajter Ľ, Dunthorn M (2021) Ciliate SSU-rDNA reference alignments and trees for phylogenetic placements of metabarcoding data. Metabarcoding and Metagenomics 5: e69602. https://doi.org/10.3897/mbmg.5.69602
File S3
Supplementary material 9 from: Rajter Ľ, Dunthorn M (2021) Ciliate SSU-rDNA reference alignments and trees for phylogenetic placements of metabarcoding data. Metabarcoding and Metagenomics 5: e69602. https://doi.org/10.3897/mbmg.5.69602
File S9
Supplementary material 1 from: Rajter Ľ, Dunthorn M (2021) Ciliate SSU-rDNA reference alignments and trees for phylogenetic placements of metabarcoding data. Metabarcoding and Metagenomics 5: e69602. https://doi.org/10.3897/mbmg.5.69602
File S1
Supplementary material 7 from: Rajter Ľ, Dunthorn M (2021) Ciliate SSU-rDNA reference alignments and trees for phylogenetic placements of metabarcoding data. Metabarcoding and Metagenomics 5: e69602. https://doi.org/10.3897/mbmg.5.69602
File S7
Supplementary material 21 from: Rajter Ľ, Dunthorn M (2021) Ciliate SSU-rDNA reference alignments and trees for phylogenetic placements of metabarcoding data. Metabarcoding and Metagenomics 5: e69602. https://doi.org/10.3897/mbmg.5.69602
Table S2
Supplementary material 8 from: Rajter Ľ, Dunthorn M (2021) Ciliate SSU-rDNA reference alignments and trees for phylogenetic placements of metabarcoding data. Metabarcoding and Metagenomics 5: e69602. https://doi.org/10.3897/mbmg.5.69602
File S8
Supplementary material 19 from: Rajter Ľ, Dunthorn M (2021) Ciliate SSU-rDNA reference alignments and trees for phylogenetic placements of metabarcoding data. Metabarcoding and Metagenomics 5: e69602. https://doi.org/10.3897/mbmg.5.69602
Figure S4
Supplementary material 18 from: Rajter Ľ, Dunthorn M (2021) Ciliate SSU-rDNA reference alignments and trees for phylogenetic placements of metabarcoding data. Metabarcoding and Metagenomics 5: e69602. https://doi.org/10.3897/mbmg.5.69602
Figure S3
Supplementary material 17 from: Rajter Ľ, Dunthorn M (2021) Ciliate SSU-rDNA reference alignments and trees for phylogenetic placements of metabarcoding data. Metabarcoding and Metagenomics 5: e69602. https://doi.org/10.3897/mbmg.5.69602
Figure S2
Supplementary material 16 from: Rajter Ľ, Dunthorn M (2021) Ciliate SSU-rDNA reference alignments and trees for phylogenetic placements of metabarcoding data. Metabarcoding and Metagenomics 5: e69602. https://doi.org/10.3897/mbmg.5.69602
Figure S1
Supplementary material 11 from: Rajter Ľ, Dunthorn M (2021) Ciliate SSU-rDNA reference alignments and trees for phylogenetic placements of metabarcoding data. Metabarcoding and Metagenomics 5: e69602. https://doi.org/10.3897/mbmg.5.69602
File S11
Supplementary material 15 from: Rajter Ľ, Dunthorn M (2021) Ciliate SSU-rDNA reference alignments and trees for phylogenetic placements of metabarcoding data. Metabarcoding and Metagenomics 5: e69602. https://doi.org/10.3897/mbmg.5.69602
File S15
Supplementary material 4 from: Rajter Ľ, Dunthorn M (2021) Ciliate SSU-rDNA reference alignments and trees for phylogenetic placements of metabarcoding data. Metabarcoding and Metagenomics 5: e69602. https://doi.org/10.3897/mbmg.5.69602
File S4
Supplementary material 2 from: Rajter Ľ, Dunthorn M (2021) Ciliate SSU-rDNA reference alignments and trees for phylogenetic placements of metabarcoding data. Metabarcoding and Metagenomics 5: e69602. https://doi.org/10.3897/mbmg.5.69602
File S2
Supplementary material 10 from: Rajter Ľ, Dunthorn M (2021) Ciliate SSU-rDNA reference alignments and trees for phylogenetic placements of metabarcoding data. Metabarcoding and Metagenomics 5: e69602. https://doi.org/10.3897/mbmg.5.69602
File S10
Supplementary material 6 from: Rajter Ľ, Dunthorn M (2021) Ciliate SSU-rDNA reference alignments and trees for phylogenetic placements of metabarcoding data. Metabarcoding and Metagenomics 5: e69602. https://doi.org/10.3897/mbmg.5.69602
File S6
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