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Microscope Image Analysis Course Sept 2109 -- Image Data siRNA Screen
<p>Contains 380 Fluorescence images of DAPI stained HeLa nuclei, acquired in 42 wells of a 384 well plate. individual wells were treated with siRNA according to the layout file. Images were acquired with an Olympus ScanR system at 10x magnification, maetadata is given in the experiment_descriptor.xml file.</p>
PROTAC virtual screening: A retrospective screening exercise
<p>My goal is to test whether retrospective virtual screening approaches can guide the design of PROTACs. As a first step, I showed that HADDOCK was the best protein-protein docking tool among those I tested to predict how E3 ligases interact with their protein substrates. Here, I ask whether docking virtual libraries of PROTAC candidates to these E3 ligase – substrate protein interfaces can be used to predict which PROTACs are active.</p>
Screen of A6,B7,1G4 TCRs against a library of HLA-A*02:01 MHC-I peptides from the human exome
<p>T2 cells expressing a library of off targets (derived from A6 and B7 binding motifs in Hausmann 1999) are co-cultured with A6, B7 or1G4 expressing T cells (from a non-A2 donor). Minigenes from surviving cells are amplified and sequenced.</p>
Buffer screen by DSF of C-HEAT and N-HEAT domains
<p>Raw data and summary of results of DSF data recorded for C-HEAT_2088-3144 (TOC019 C01), and constructs of the N-HEAT domain: N-HEAT_76-404 (TOC019 B02), <a href="https://zenodo.org/record/3462496#.XY4U1mYpA2x">N-HEAT_81-1643 (TOC019 B08)</a>, N-HEAT_76-1715 (TOC019 A08).</p>
Reporter CRISPR screens decipher cis- and trans-regulatory principles at the Xist locus [Extra tables + files]
<p>This zenodo contains extra files and tables necessary to run code specified in https://github.com/EddaSchulz/TFiScreen_Paper/.</p> <p>These files are associated with analysis in Schwämmle et al., 2025.</p>
Computational screening of the effects of mutations on protein-protein off-rates and dissociation mechanisms by τRAMD
<p>Set of data and scripts for the analysis of RAMD dissociation trajectories generated for BN-NS and BT/BCT-BPTI mutants, reported in the manuscript:</p> <p><br>"Computational screening of the effects of mutations on protein-protein off-rates and dissociation mechanisms by τRAMD"</p> <p>by Giulia D'Arrigo, Daria B. Kokh, Ariane Nunes-Alves and Rebecca C. Wade</p> <p> </p> <ol> <li>RAMD movies of WT Bn-Bs and D35Abn mutant dissociation pathway (WT-diss.mp4 and D35Abn-diss.mp4)</li> <li>Protein-Protein.zip contains:</li> </ol> <ul> <li>README - set of instructions to go through files and for the use of the scripts</li> <li>Jupyter notebooks: tauRAMD_PP_Residence_time.ipynb and tauRAMD_PP_Unbinding_pathways.ipynb</li> <li>Scripts used for analysis and postprocessing RAMD simulations - for the correct usage of the scripts check the corresponding file header</li> <li>System input files in BN-BS/ and BT_BCT-BPTI/</li> <li>Experimental data used</li> <li>2 RAMD movies (.mp4) showing dissociation of WT Bn-Bs and D35Abn mutant</li> </ul>
Primary in vivo screen for MAPK/ERK pathway inhibitors among 433 annotated oncology related compounds
<p>The aim was to test if a C. elegans model could identify known MEK inhibitors in a blinded screen of 433 annotated oncology related compounds. Primary hits were further validated for pathway specificity. Please see the paper cross-reference for more details.</p> <p>We bought a chemical library (SciLife lab, Stockholm, Sweden) with 433 oncology-related compounds (see table S1 for the full list), where compounds had been acoustically dispensed from 10 mM stocks into 96-well plates. DMSO concentrations were adjusted to 0.5 % in all wells, including negative controls. Trametinib was used as a positive control at 7 muM. All chemicals were kept at -20 degrees C until use. The C. elegans strain ST65 was used. We dispensed 50 muL of worm culture into each well and incubated animals for 4-5 days at 20 degrees C until DMSO control animals reached early adulthood. Animals were then sedated for 30 minutes by adding 4 muL 20 mM levamisole per well before scoring.<br><br>Primary hit selection: 1) Wells were excluded if they contained progeny or fluorescent specles from drugs or bacteria, indicated by too many objects classified as vulvae. The cutoff was set to vulvae >2x the number of adults in the positive control. 2) Wells were excluded they contained < 30 % adults compared to the positive control, indicative of severely reduced larval growth. 3) Remaining compounds with vulvae/adult scores < 0.4 were considered primary hits. Scoring of the Muv phenotype in adult lin-1(e1777) mutants was done manually from ImageXpress images. See the paper cross-reference for more details.</p>
"Toy Data Set" referenced in the article "A deep-learning based analysis framework for ultra-high throughput screening time-series data" (https://doi.org/10.1101/2024.08.22.609110)
<p>This data set, referenced as "toy data set" in the article "A deep-learning based analysis framework for ultra-high throughput screening time-series data" (<a href="Lint-to-article">https://doi.org/10.1101/2024.08.22.609110</a>), mimics a high-throughput screening data set. To demonstrate the application of our analysis framework described in the main article this toy data set was generated. It contains in total 1536000 individual transient signals, splitted in 5 batches of each 200 plates in 1536-well plate format. Five distinct signal classes were used to resemble typical shapes encountered in biological experiments. Fequency of occurrences for each class is reported in the main article.</p>
In‑vitro screening of compatible synbiotics and (introducing) "prophybiotics" as a tool to improve gut health
Open the record for dataset details and reuse information.
The raw data of the genome-wide CRISPR screens for VHL-WT and VHL-L188V Kelly cells
<p>The raw data including the conditions, reference, and PoolQ output files of the genome-wide CRISPR screens for VHL-WT and VHL-L188V Kelly cells were published in the paper "Total loss of VHL gene function impairs neuroendocrine cancer cell fitness due to excessive HIF2α activity".</p>
Impact of Interval Censoring on Data Accuracy and Machine Learning Performance in Biological High-Throughput Screening
<div> <h2>Overview</h2> <div>Data and Results used in the publication entitled "Impact of Interval Censoring on Data Accuracy and Machine Learning Performance in Biological High-Throughput Screening"</div> </div> <div> <h3><strong>Data</strong></h3> <div>This folder contains the raw data used during this work.</div> <div>`EvoEF.csv` contains information on the library used (sequences, number of mutations, etc.) and the fitness (energy) used as continuous mean values. `mut.csv` contains the information about the combinatorial scaling (N vs N_norm), the number of mutations (m) and the probability of each variant using different distributions (uniform and binomial) at different $p_{WT}$.</div> <div>For further details on how the fitness values were calculated and how the combinatorial scale works, please refer to our prevoius [Paper](https://arxiv.org/abs/2405.05167).</div> <div> </div> <h3><strong>Results</strong></h3> <div> <div>This folder contains the results (outputs) of all scripts used. Such results are included in the form of `.npy` and `.npz` files. To load such files with numpy you should include the option `allow_pickle=True`.</div> </div> </div>
Table 1 in Risk screening of non-native freshwater fishes in Yunnan Province, China
<p><b>Table 1.</b> Non-native freshwater fish (with taxonomy) screened for their risk of invasiveness in Yunnan Province with the Aquatic Species Invasiveness Screening Kit (AS-ISK)</p><table><tbody><tr><th>Order</th><th>Family</th><th>Taxon name</th><th>Common name</th><th>A priori categorisation</th><th>Native region</th></tr></tbody><tbody><tr><th>Anguilliformes</th><td>Anguillidae</td><td><i>Anguilla anguilla</i></td><td>European eel</td><td>Non-invasive</td><td>Europe</td></tr><tr><td><i>Anguilla japonica</i></td><td>Japanese eel</td><td>Non-invasive</td><td>Asia</td></tr><tr><th>Acipenseriformes</th><td>Acipenseridae</td><td><i>Acipenser baerii</i></td><td>Siberian sturgeon</td><td>Invasive</td><td>Europe, Asia</td></tr><tr><th>Beloniformes</th><td>Hemiramphidae</td><td><i>Hyporhamphus intermedius</i></td><td>Asian pencil halfbeak</td><td>Non-invasive</td><td>China, Japan</td></tr><tr><th>Centrarchiformes</th><td>Centrarchidae</td><td><i>Micropterus salmoides</i></td><td>Largemouth bass</td><td>Invasive</td><td>North America</td></tr><tr><th>Characiformes</th><td>Serrasalmidae</td><td><i>Piaractus brachypomus</i></td><td>Pirapitinga</td><td>Invasive</td><td>Brazil, Colombia</td></tr><tr><td>Prochilo-dontidae</td><td><i>Prochilodus lineatus</i></td><td>Streaked prochilod</td><td>Non-invasive</td><td>Brazil, Chile</td></tr><tr><td>Cyprinidae</td><td><i>Acheilognathus chankaensis</i></td><td>Xingkai bitterling</td><td>Non-invasive</td><td>China</td></tr><tr><td><i>Acheilognathus macropterus</i></td><td>Largefin bitterling</td><td>Non-invasive</td><td>China</td></tr><tr><td><i>Carassius cuvieri</i></td><td>Japanese white crucian carp</td><td>Invasive</td><td>Japan</td></tr><tr><td><i>Ctenopharyngodon idella</i></td><td>Grass carp</td><td>Invasive</td><td>China</td></tr><tr><td><i>Cultrichthys erythropterus</i></td><td>Redfin culter</td><td>Non-invasive</td><td>China</td></tr><tr><td><i>Hypophthalmichthys molitrix</i></td><td>Silver carp</td><td>Invasive</td><td>China</td></tr><tr><td><i>Hypophthalmichthys nobilis</i></td><td>Bighead carp</td><td>Invasive</td><td>China</td></tr><tr><td><i>Labeo rohita</i></td><td>Roho labeo</td><td>Non-invasive</td><td>India, Myanmar</td></tr><tr><td><i>Megalobrama amblycephala</i></td><td>Wuchang bream</td><td>Non-invasive</td><td>China</td></tr><tr><td><i>Mylopharyngodon piceus</i></td><td>Black carp</td><td>Invasive</td><td>China</td></tr><tr><td><i>Parabramis pekinensis</i></td><td>White Amur bream</td><td>Non-invasive</td><td>China</td></tr><tr><td>Cobitidae</td><td><i>Paramisgurnus dabryanus</i></td><td>Taiwaninmutakala</td><td>Non-invasive</td><td>China</td></tr><tr><td>Cyprinidae</td><td><i>Sarcocheilichthys nigripinnis</i></td><td>Rainbow gudgeon</td><td>Non-invasive</td><td>China</td></tr><tr><td><i>Tinca tinca</i></td><td>Tench</td><td>Invasive</td><td>Europe</td></tr><tr><td><i>Toxabramis swinhonis</i></td><td>Thin sharpbelly</td><td>Non-invasive</td><td>China</td></tr><tr><th>Cyprinodontiformes</th><td>Poeciliidae</td><td><i>Gambusia affinis</i></td><td>Western mosquitofish</td><td>Invasive</td><td>America</td></tr><tr><th>Osmeriformes</th><td>Osmeridae</td><td><i>Hypomesus olidus</i></td><td>Pond smelt</td><td>Non-invasive</td><td>China, Japan</td></tr><tr><td>Salangidae</td><td><i>Neosalanx taihuensis</i></td><td>Taihu icefish</td><td>Invasive</td><td>China</td></tr><tr><td><i>Protosalanx hyalocranius</i></td><td>Clearhead icefish</td><td>Non-invasive</td><td>China</td></tr><tr><th>Perciformes</th><td>Cichlidae</td><td><i>Oreochromis aureus</i></td><td>Blue tilapia</td><td>Invasive</td><td>Africa</td></tr><tr><td><i>Oreochromis mossambicus</i></td><td>Mozambique tilapia</td><td>Invasive</td><td>Africa</td></tr><tr><td><i>Oreochromis niloticus</i></td><td>Nile tilapia</td><td>Invasive</td><td>Africa</td></tr><tr><td>Gobiidae</td><td><i>Rhinogobius cliffordpopei</i></td><td>Goby</td><td>Invasive</td><td>China</td></tr><tr><td>Sinipercidae</td><td><i>Siniperca chuatsi</i></td><td>Chinese perch</td><td>Non-invasive</td><td>China</td></tr><tr><th>Salmoniformes</th><td>Salmonidae</td><td><i>Oncorhynchus mykiss</i></td><td>Rainbow trout</td><td>Invasive</td><td>North America</td></tr><tr><td><i>Salmo trutta</i></td><td>Brown trout</td><td>Invasive</td><td>Europe</td></tr><tr><th>Siluriformes</th><td>Ictaluridae</td><td><i>Ameiurus nebulosus</i></td><td>Brown bullhead</td><td>Invasive</td><td>Canada, America</td></tr><tr><td>Clariidae</td><td><i>Clarias gariepinus</i></td><td>North African catfish</td><td>Invasive</td><td>Africa</td></tr><tr><td>Loricariidae</td><td><i>Hypostomus plecostomus</i></td><td>Suckermouth catfish</td><td>Invasive</td><td>South America</td></tr><tr><td>Ictaluridae</td><td><i>Ictalurus punctatus</i></td><td>Channel catfish</td><td>Invasive</td><td>North American</td></tr></tbody></table>
Table 2 in Risk screening of non-native freshwater fishes in Yunnan Province, China
<p><b>Table 2.</b> Non-native freshwater fish species assessed with the Aquatic Species Invasiveness Screening Kit (AS-ISK) for Yunnan Province</p><table><tbody><tr><th>Species name</th><th>A priori categorisation</th><th>Screening component</th><th></th><th>Confidence</th></tr><tr><th>BRA</th><th>BRA+CCA</th><th></th><th>CL</th><th>CF</th></tr><tr><th>Score</th><th>Outcome</th><th>Score</th><th>Outcome</th><th>Delta</th><th>Total</th><th>BRA</th><th>CCA</th><th>Total</th><th>BRA</th><th>CCA</th></tr></tbody><tbody><tr><th><i>Acheilognathus chankaensis</i></th><td>N</td><td>10</td><td>Medium</td><td>8</td><td>Medium</td><td>−2</td><td>3.4</td><td>3.4</td><td>3.5</td><td>0.85</td><td>0.85</td><td>0.88</td></tr><tr><th><i>Acheilognathus macropterus</i></th><td>N</td><td>−2</td><td>Low</td><td>−4</td><td>Low</td><td>−2</td><td>3.3</td><td>3.3</td><td>3.2</td><td>0.81</td><td>0.82</td><td>0.79</td></tr><tr><th><i>Acipenser baerii</i></th><td>Y</td><td>26</td><td>High</td><td>32</td><td>High</td><td>6</td><td>3.0</td><td>3.0</td><td>2.8</td><td>0.74</td><td>0.74</td><td>0.71</td></tr><tr><th><i>Ameiurus nebulosus</i></th><td>Y</td><td>29</td><td>High</td><td>35</td><td>High</td><td>6</td><td>3.0</td><td>3.0</td><td>2.8</td><td>0.75</td><td>0.76</td><td>0.71</td></tr><tr><th><i>Anguilla anguilla</i></th><td>N</td><td>0</td><td>Low</td><td>−2</td><td>Low</td><td>−2</td><td>3.0</td><td>3.0</td><td>3.0</td><td>0.77</td><td>0.77</td><td>0.75</td></tr><tr><th><i>Anguilla japonica</i></th><td>N</td><td>−2</td><td>Low</td><td>−4</td><td>Low</td><td>−2</td><td>3.1</td><td>3.2</td><td>3.0</td><td>0.79</td><td>0.80</td><td>0.75</td></tr><tr><th><i>Carassius cuvieri</i></th><td>Y</td><td>24</td><td>High</td><td>28</td><td>High</td><td>4</td><td>3.1</td><td>3.1</td><td>2.8</td><td>0.78</td><td>0.79</td><td>0.71</td></tr><tr><th><i>Clarias gariepinus</i></th><td>Y</td><td>39.5</td><td>High</td><td>47.5</td><td>High</td><td>8</td><td>3.1</td><td>3.1</td><td>2.8</td><td>0.77</td><td>0.78</td><td>0.71</td></tr><tr><th><i>Ctenopharyngodon idella</i></th><td>Y</td><td>29.5</td><td>High</td><td>33.5</td><td>High</td><td>4</td><td>3.3</td><td>3.3</td><td>3.2</td><td>0.83</td><td>0.84</td><td>0.79</td></tr><tr><th><i>Cultrichthys erythropterus</i></th><td>N</td><td>22.5</td><td>High</td><td>24.5</td><td>High</td><td>2</td><td>3.2</td><td>3.3</td><td>3.0</td><td>0.81</td><td>0.82</td><td>0.75</td></tr><tr><th><i>Gambusia affinis</i></th><td>Y</td><td>39</td><td>High</td><td>41</td><td>High</td><td>2</td><td>3.1</td><td>3.1</td><td>3.0</td><td>0.78</td><td>0.78</td><td>0.75</td></tr><tr><th><i>Hypomesus olidus</i></th><td>N</td><td>16</td><td>Medium</td><td>16</td><td>Medium</td><td>0</td><td>3.2</td><td>3.2</td><td>3.0</td><td>0.79</td><td>0.80</td><td>0.75</td></tr><tr><th><i>Hypophthalmichthys molitrix</i></th><td>Y</td><td>24</td><td>High</td><td>30</td><td>High</td><td>6</td><td>3.3</td><td>3.3</td><td>3.0</td><td>0.83</td><td>0.84</td><td>0.75</td></tr><tr><th><i>Hypophthalmichthys nobilis</i></th><td>Y</td><td>26</td><td>High</td><td>32</td><td>High</td><td>6</td><td>3.3</td><td>3.4</td><td>3.0</td><td>0.84</td><td>0.85</td><td>0.75</td></tr><tr><th><i>Hyporhamphus intermedius</i></th><td>N</td><td>7</td><td>Medium</td><td>7</td><td>Medium</td><td>0</td><td>3.2</td><td>3.2</td><td>3.0</td><td>0.80</td><td>0.80</td><td>0.75</td></tr><tr><th><i>Hypostomus plecostomus</i></th><td>Y</td><td>51</td><td>High</td><td>59</td><td>High</td><td>8</td><td>3.0</td><td>3.0</td><td>3.0</td><td>0.75</td><td>0.74</td><td>0.75</td></tr><tr><th><i>Ictalurus punctatus</i></th><td>Y</td><td>36.5</td><td>High</td><td>42.5</td><td>High</td><td>6</td><td>3.0</td><td>3.0</td><td>2.7</td><td>0.75</td><td>0.76</td><td>0.67</td></tr><tr><th><i>Labeo rohita</i></th><td>N</td><td>10</td><td>Medium</td><td>14</td><td>Medium</td><td>4</td><td>3.1</td><td>3.2</td><td>3.0</td><td>0.79</td><td>0.79</td><td>0.75</td></tr><tr><th><i>Megalobrama amblycephala</i></th><td>N</td><td>10</td><td>Medium</td><td>10</td><td>Medium</td><td>0</td><td>3.4</td><td>3.4</td><td>3.3</td><td>0.84</td><td>0.84</td><td>0.83</td></tr><tr><th><i>Micropterus salmoides</i></th><td>Y</td><td>35</td><td>High</td><td>43</td><td>High</td><td>8</td><td>3.1</td><td>3.2</td><td>2.5</td><td>0.79</td><td>0.81</td><td>0.63</td></tr><tr><th><i>Mylopharyngodon piceus</i></th><td>Y</td><td>29</td><td>High</td><td>35</td><td>High</td><td>6</td><td>3.3</td><td>3.3</td><td>3.0</td><td>0.82</td><td>0.83</td><td>0.75</td></tr><tr><th><i>Neosalanx taihuensis</i></th><td>N</td><td>24.5</td><td>High</td><td>22.5</td><td>High</td><td>−2</td><td>3.4</td><td>3.4</td><td>3.0</td><td>0.85</td><td>0.86</td><td>0.75</td></tr><tr><th><i>Oncorhynchus mykiss</i></th><td>Y</td><td>29</td><td>High</td><td>37</td><td>High</td><td>8</td><td>3.0</td><td>3.0</td><td>2.8</td><td>0.75</td><td>0.75</td><td>0.71</td></tr><tr><th><i>Oreochromis aureus</i></th><td>Y</td><td>49</td><td>High</td><td>61</td><td>High</td><td>12</td><td>3.1</td><td>3.1</td><td>2.7</td><td>0.77</td><td>0.78</td><td>0.67</td></tr><tr><th><i>Oreochromis mossambicus</i></th><td>Y</td><td>44.5</td><td>High</td><td>56.5</td><td>High</td><td>12</td><td>2.9</td><td>3.0</td><td>2.8</td><td>0.75</td><td>0.75</td><td>0.71</td></tr><tr><th><i>Oreochromis niloticus</i></th><td>Y</td><td>47</td><td>High</td><td>59</td><td>High</td><td>12</td><td>2.9</td><td>3.0</td><td>2.5</td><td>0.74</td><td>0.75</td><td>0.63</td></tr><tr><th><i>Parabramis pekinensis</i></th><td>N</td><td>5.5</td><td>Medium</td><td>3.5</td><td>Medium</td><td>−2</td><td>3.3</td><td>3.3</td><td>3.0</td><td>0.81</td><td>0.82</td><td>0.75</td></tr><tr><th><i>Paramisgurnus dabryanus</i></th><td>N</td><td>13.5</td><td>Medium</td><td>17.5</td><td>High</td><td>4</td><td>3.2</td><td>3.2</td><td>3.0</td><td>0.80</td><td>0.80</td><td>0.75</td></tr><tr><th><i>Piaractus brachypomus</i></th><td>Y</td><td>8</td><td>Medium</td><td>12</td><td>Medium</td><td>4</td><td>3.1</td><td>3.1</td><td>3.0</td><td>0.77</td><td>0.77</td><td>0.75</td></tr><tr><th><i>Prochilodus lineatus</i></th><td>N</td><td>11</td><td>Medium</td><td>17</td><td>Medium</td><td>6</td><td>3.1</td><td>3.1</td><td>3.0</td><td>0.76</td><td>0.77</td><td>0.75</td></tr><tr><th><i>Protosalanx hyalocranius</i></th><td>N</td><td>14.5</td><td>Medium</td><td>12.5</td><td>Medium</td><td>−2</td><td>3.1</td><td>3.1</td><td>2.8</td><td>0.76</td><td>0.77</td><td>0.71</td></tr><tr><th><i>Rhinogobius cliffordpopei</i></th><td>Y</td><td>37</td><td>High</td><td>43</td><td>High</td><td>6</td><td>3.1</td><td>3.1</td><td>2.8</td><td>0.76</td><td>0.77</td><td>0.71</td></tr><tr><th><i>Salmo trutta</i></th><td>Y</td><td>18</td><td>High</td><td>12</td><td>Medium</td><td>−6</td><td>2.9</td><td>2.9</td><td>3.0</td><td>0.73</td><td>0.72</td><td>0.75</td></tr><tr><th><i>Sarcocheilichthys nigripinnis</i></th><td>N</td><td>3</td><td>Medium</td><td>3</td><td>Medium</td><td>0</td><td>3.5</td><td>3.5</td><td>3.2</td><td>0.86</td><td>0.87</td><td>0.79</td></tr><tr><th><i>Siniperca chuatsi</i></th><td>N</td><td>14.5</td><td>Medium</td><td>18.5</td><td>High</td><td>4</td><td>3.0</td><td>3.0</td><td>2.8</td><td>0.74</td><td>0.74</td><td>0.71</td></tr><tr><th><i>Tinca tinca</i></th><td>Y</td><td>21</td><td>High</td><td>27</td><td>High</td><td>6</td><td>3.1</td><td>3.1</td><td>3.0</td><td>0.77</td><td>0.77</td><td>0.75</td></tr><tr><th><i>Toxabramis swinhonis</i></th><td>N</td><td>16.5</td><td>Medium</td><td>16.5</td><td>Medium</td><td>0</td><td>3.3</td><td>3.4</td><td>3.2</td><td>0.84</td><td>0.84</td><td>0.79</td></tr><tr><th>A priori categorisation (N: non-invasive; Y: invasive); Screening component (BRA: Basic Risk Assessment, BRA+CCA: BRA+ Climate Change Assessment, Delta: BRA+CCA score minus BRA score); Confidence (CL: confidence level, CF: confidence factor); Outcome (Low: score <1, Medium: 1 ≤ score <17.25, High: score ≥ 17.25)</th></tr></tbody></table>
Screening of bacterial diversity by 16S rRNA metabarcoding in Orbicella faveolata healthy and with Black Band Disease
<p>Screening of bacterial diversity by 16S rRNA metabarcoding in Orbicella faveolata healthy and with Black Band Disease</p>
Transitive prediction of small molecule function through alignment of high-content screening resources
<p>This dataset supports the development of CLIP<sup>n</sup>, a contrastive-learning framework designed to align heterogeneous high-content screening (HCS) profile datasets.</p> <p><strong>GitHub link</strong>: https://github.com/AltschulerWu-Lab/CLIPn</p> <h2>Directory Structure</h2> <h3>Data Files</h3> <ul> <li>HCS_datasets.pkl: Contains 13 high-content screening (HCS) datasets from multiple studies across 20 years.</li> <li>Hypoxia.pkl: Contains 8 profile datasets using different assays and treated under diverse hypoxia durations.</li> <li>Expression.pkl: Contains 2 transcriptional profile datasets and 6 image profile datasets for multimodal analysis.</li> </ul> <h3>Folders</h3> <p><strong>raw_profiles</strong>:<br>HCS13/<br>- Contains raw data from 13 high-content screening (HCS) datasets. Each dataset includes meta and feature files. </p> <p>L1000/<br>- CDRP_feature_exp.csv: Raw L1000 expression data from the CDRP dataset.<br>- CDRP_meta_exp.csv: Metadata associated with the CDRP expression data.<br>- LINCS_feature_exp.csv: Raw L1000 expression data from the LINCS dataset.<br>- LINCS_meta_exp.csv: Metadata associated with the LINCS expression data.</p> <p>RxRx3/<br>- RxRx3_feature_final.csv: Profile data from the RxRx3 dataset.<br>- RxRx3_meta_final.csv: Metadata from the RxRx3 dataset.</p> <p>Uncharacterized_compounds/<br>- NCI_cpnData.csv: Feature data for uncharacterized compounds from the NCI dataset.<br>- NCI_cpnInfo.csv: Information about uncharacterized compounds in the NCI dataset.<br>- Prestwick_UTSW_cpnData.csv: Feature data for uncharacterized compounds from the Prestwick UTSW dataset.<br>- Prestwick_UTSW_cpnInfo.csv: Information about uncharacterized compounds from the Prestwick UTSW dataset.</p> <h2><br>Usage</h2> <p><br><br><code>import pickle</code><br><code>with open('data.pkl', 'rb') as f:</code><br><code> data = pickle.load(f)</code></p> <p><code>X = data['X']</code><br><code>y = data['y']</code><br><br></p> <h2>Data Reference</h2> <p><br>For raw datasets from 13 HCS database, data and analysis pipeline for dataset 1 was obtained from https://www.science.org/doi/suppl/10.1126/science.1100709/suppl_file/perlman.som.zip; for datasets 2-3, data were shared by authors; For datasets 4-5, analysis code was downloaded from https://static-content.springer.com/esm/art%3A10.1038%2Fnbt.3419/MediaObjects/41587_2016_BFnbt3419_MOESM21_ESM.zip and data were shared by authors; For datasets 6-7, processed dataset was downloaded from AWS following instructions from https://github.com/carpenter-singh-lab/2022_Haghighi_NatureMethods, and replicate_level_cp_normalized.csv.gz features were used. For project datasets 8-13, datasets and analysis results were downloaded from https://zenodo.org/records/7352487. For RxRx3, dataset was obtained from https://www.rxrx.ai/rxrx3. L1000 transcript datasets were downloaded using the same link as datasets 6-7 and the processed transcript data files (named “replicate_level_l1k.csv”) were used. </p>
Deliverable D1.4 - Supplementary material : Dataset on CS#2 Danube Suspect Screening Results Table : H2020 PROMISCES project
<p>Additional information on PROMISCES deliverable D1.4. Data set with list of substances identified in samples from the Danube catchment between Vienna and Budapest (CS#2) by suspect screening and database comparison and corresponding peak intensities. These data are non-target data, the measured intensities do not correspond to concentrations.</p>
Data from: Population-based screening for hepatitis C antibodies and active infection using a point-of-care test in a low prevalence area
<p><span><span><span><span><span><span><span><span><span><span><span><b>Background.</b> Data on the true prevalence of hepatitis C virus (HCV) infection in the </span></span></span></span></span></span></span></span></span></span></span><span><span><span><span><span><span><span><span><span><span><span>general population is essential to health policies. We evaluated a program implementing free universal HCV screening using a non-invasive point-of-care test (POCT) (OraQuick-HCV rapid test) in oral fluid in an urban area in Valencia, South-Eastern Spain. </span></span></span></span></span></span></span></span></span></span></span></p> <p><span><span><span><span><span><span><span><span><span><span><span><b>Methods.</b> A cross-sectional study was performed during 2015-2017. Free HCV screening was offered by regular mail to 11,500 individuals aged 18 and over, randomly selected from all census residents in the Health Department. All responding participants filled in a questionnaire about HCV infection risk factors and were tested in their tertiary Hospital. In those with a positive POCT, results were confirmed by enzyme-immunoassay and HCV-RNA.</span></span></span></span></span></span></span></span></span></span></span></p> <p><span><span><span><span><span><span><span><span><span><span><span><b>Results.</b> 1,206 persons agreed to participate (response rate: 11.16%). HCV antibodies were detected in 19 (1.60%) cases (age-sex standardized rate: 1.31%; 95%CI: 0.82-2.07), but only 8 showed positive HCV-RNA (age-sex standardized rate: 0.56%; 95%CI: 0.28-1.14). The majority (89%) of the cases were born before 1965 and 74% had at least one known risk factor for HCV infection. All anti-HCV positive individuals were already aware of their infection, and no undiagnosed cases were detected. The performance of the POCT was excellent for detecting active infection. </span></span></span></span></span></span></span></span></span></span></span></p> <p><span><span><span><span><span><span><span><span><span><span><span><b>Conclusions.</b> These preliminary data suggest that HCV population screening with a POCT is feasible but, in our setting, mailing recruiting is not effective (11% response rate). The low prevalence of HCV antibodies and active infection in the participant population (with no new diagnoses made) suggests that, in our setting, underdiagnosis may be uncommon.</span></span></span></span></span></span></span></span></span></span></span></p> <p><span><span><span><span><span><span><span><span><span><span><span><b>Files uploaded include the study database (Stata v.13) and the do.file of the study.</b></span></span></span></span></span></span></span></span></span></span></span></p>
Data from: Genome-wide CRISPR synthetic lethality screen identifies a role for the ADP-ribosyltransferase PARP14 in replication fork stability controlled by ATR
<p>The DNA damage response is essential to maintain genomic stability, suppress replication stress, and protect against carcinogenesis. The ATR-CHK1 pathway is an essential component of this response, which regulates cell cycle progression in the face of replication stress. PARP14 is an ADP-ribosyltransferase with multiple roles in transcription, signaling, and DNA repair. To understand the biological functions of PARP14, we catalogued the genetic components that impact cellular viability upon loss of PARP14 by performing an unbiased, comprehensive, genome-wide CRISPR knockout genetic screen in PARP14-deficient cells. We uncovered the ATR-CHK1 pathway as essential for viability of PARP14-deficient cells, and identified regulation of DNA replication dynamics as an important mechanistic contributor to the synthetic lethality observed. Our work shows that PARP14 is an important modulator of the response to ATR-CHK1 pathway inhibitors.</p>
Screening of tomato lines under combined stress in generative stage
<ol> <li>Identification of lines with superior performance under combined stress</li> <li>same as (i)</li> <li>Three individual trials, 2018 - 2019</li> <li><em>S. lycopersicum; S. pennellii; S. lycopersicum x S. pennellii</em></li> <li>Greenhouse @ University of Bonn (50.62 N 6.99 E)</li> <li>Growing plants in rockwool slabs with drip irrigation (as commercial practise); stressed plants received modified nutrient solution and only 50 % water</li> <li>General drop in fruit yield and quality under stress conditions; Performance of tested lines (eg water use efficiency) differ significantly.</li> <li>(viii) in progress</li> </ol> <p>(ix) to be discussed</p>
Screening of tomato lines under combined stress in generative stage
<ol> <li>Identification of lines with superior performance under combined stress</li> <li> same as (1)</li> <li>Three individual trials, 2018 - 2019</li> <li><em>S. lycopersicum; S. pennellii; S. lycopersicum x S. pennellii</em></li> <li>Greenhouse @ University of Bonn (50.62 N 6.99 E)</li> <li> Growing plants in rockwool slabs with drip irrigation (as commercial practise); stressed plants received modified nutrient solution and only 50 % water</li> <li> General drop in fruit yield and quality under stress conditions; Performance of tested lines (eg water use efficiency) differ significantly.</li> <li> in progress</li> <li>to be discussed</li> </ol>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.