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Raw read counts and phased SNP counts for every single cell in the sequencing datasets of the breast cancer patient S1 from "Characterizing allele- and haplotype-specific copy numbers in single cells with CHISEL"

This dataset contains the raw read counts and phased SNP counts for every single cell in the sequencing datasets of breast cancer patient S1 from “Characterizing allele- and haplotype-specific copy numbers in single cells with CHISEL” [Zaccaria & Raphael, 2020]. These data enable the full reproduction of all the results in the related manuscript for breast cancer patient S1. Specifically, the data are provided in two files for every dataset DAT of patient S1 with the following format: <ol> <li>DAT.raw_read_counts.bed.gz is a multi-cell BED file containing the raw read counts in the following fields: <ul> <li>CHROMOSOME: the name of a human chromosome</li> <li>START: the starting genomic position of a genomic bin in the chromosome</li> <li>END: the ending genomic position of the genomic bin in the chromosome</li> <li>CELL: the cell barcode that uniquely identifies a cell</li> <li>NORMAL: the raw read count for the specified bin from a matched-normal sample</li> <li>COUNT: the raw read count for the specified bin in the specified cell</li> <li>RDR: the estimated read-depth ratio for the specified bin in the specified cell</li> </ul> </li> <li>DAT.phased_snps_counts.pos.gz is a multi-cell POS file containing the phased SNP counts in the following fields: <ul> <li>CHROMOSOME: the name of a human chromosome</li> <li>POS: the genomic position in the chromosome of a germline SNP</li> <li>CELL: the cell barcode that uniquely identifies a cell</li> <li>COUNT_HAPLOTYPE_A: the count of reads that cover the SNP and that belong to haplotype A in the specified cell</li> <li>COUNT_HAPLOTYPE_B: the count of reads that cover the SNP and that belong to haplotype B in the specified cell</li> </ul> </li> </ol> All the files have been compressed using standard gzip.

Raw read counts and phased SNP counts for every single cell in the sequencing datasets of the breast cancer patient S0 from "Characterizing allele- and haplotype-specific copy numbers in single cells with CHISEL"

This dataset contains the raw read counts and phased SNP counts for every single cell in the sequencing datasets of breast cancer patient S0 from “Characterizing allele- and haplotype-specific copy numbers in single cells with CHISEL” [Zaccaria & Raphael, 2020]. These data enable the full reproduction of all the results in the related manuscript for breast cancer patient S0. Specifically, the data are provided in two files for every dataset DAT of patient S0 with the following format: <ol> <li>DAT.raw_read_counts.bed.gz is a multi-cell BED file containing the raw read counts in the following fields: <ul> <li>CHROMOSOME: the name of a human chromosome</li> <li>START: the starting genomic position of a genomic bin in the chromosome</li> <li>END: the ending genomic position of the genomic bin in the chromosome</li> <li>CELL: the cell barcode that uniquely identifies a cell</li> <li>NORMAL: the raw read count for the specified bin from a matched-normal sample</li> <li>COUNT: the raw read count for the specified bin in the specified cell</li> <li>RDR: the estimated read-depth ratio for the specified bin in the specified cell</li> </ul> </li> <li>DAT.phased_snps_counts.pos.gz is a multi-cell POS file containing the phased SNP counts in the following fields: <ul> <li>CHROMOSOME: the name of a human chromosome</li> <li>POS: the genomic position in the chromosome of a germline SNP</li> <li>CELL: the cell barcode that uniquely identifies a cell</li> <li>COUNT_HAPLOTYPE_A: the count of reads that cover the SNP and that belong to haplotype A in the specified cell</li> <li>COUNT_HAPLOTYPE_B: the count of reads that cover the SNP and that belong to haplotype B in the specified cell</li> </ul> </li> </ol> All the files have been compressed using standard gzip.

Operation of a three terminal solar cell for children

The video describes in a simple manner the operation of a three terminal solar cell. Blue arrows represent the high energy photons. Red arrows represent the low energy photons, that are absorbed deeper in the cell. Each colored square represents a semiconductor layer. White balls, represent electrons. Black ball represent holes. For a more detailed description of a particular kind of three terminal solar cell, see for example:  A. Martí and A. Luque, “Three-terminal heterojunction bipolar transistor solar cell for high-efficiency photovoltaic conversion,” Nat. Commun., vol. 6, pp. 6902–6902, Apr. 2015.  (<a href="https://doi.org/10.1038/ncomms7902">https://doi.org/10.1038/ncomms7902</a>) The video has been created in Blender 2.82. (<a href="https://www.blender.org/">https://www.blender.org/</a>). Both the video and the source file with which we created it are attached

opencc-by-4.0Apr 2020View details →

Patient-derived and artificial ascites have minor effects on MeT-5A mesothelial cells and do not facilitate ovarian cancer cell adhesion

Raw data of "Patient-derived and artificial ascites have minor effects on MeT-5A mesothelial cells and do not facilitate ovarian cancer cell adhesion".

Longitudinal high-throughput TCR repertoire profiling reveals the dynamics of T cell memory formation after mild COVID-19 infection

Processed TCRbeta and TCRalpha repertoires after mild COVID-19 (Version 2.0: day 85 timepoints added) infection, see preprint: <a href="https://www.biorxiv.org/content/10.1101/2020.05.18.100545v3">https://www.biorxiv.org/content/10.1101/2020.05.18.100545v3</a> and GitHub repository: <a href="https://github.com/pogorely/Minervina_COVID">https://github.com/pogorely/Minervina_COVID</a> Two donors (M and W), two biological replicates of PBMC (F1 and F2), CD4+, CD8+, and Memory subpopulations for each post-infection time points (day 15, 30, 37, 45, 85 post-infection), and pre-infection PBMC repertoires sampled in 2019 and 2018.

AGO2 localises to cytokinetic protrusions in a p38 dependent manner and is needed for accurate cell division.

Argonaute 2 (AGO2) is an indispensable component of the RNA-induced silencing complex, operating at the transcriptional or posttranscriptional level. It is compartmentalized into structures such as GW- and P-bodies, stress granules and adherens junctions as well as the midbody.  Here we show using immunofluorescence, image- and bioinformatic analysis and cytogenetics that AGO2 also resides in membrane protrusions such as open- and close-ended tubes. The latter are cytokinetic bridges where AGO2 colocalizes at the midbody-arms with cytoskeletal components such as α-Τubulin and Aurora B and various kinases. AGO2, phosphorylated on serine 387 is located together with Dicer at the midbody ring in a manner dependent on p38 MAPK activity.  We further show that AGO2 is stress sensitive and important to ensure the proper chromosome segregation and cytokinetic fidelity. We suggest that AGO2 is part of a regulatory mechanism triggered by cytokinetic stress to generate the appropriate micro-environment for local transcript homeostasis. Statement: AGO2 resides in open-ended tunneling nanotubes and close-ended cytokinetic bridges. At the latter location AGO2 colocalises with cell division components and the authors show that AGO2 deregulation impairs cell division fidelity.

Numerical simulations of AZO/ZnGeO/Cu2O solar cells: Impact of the germanium composition of the buffer layer and the use of low cost fabrication on the photovoltaic performances

The dataset contains the results of the numerical simulations of AZO/ZnGeO/Cu2O solar cell models. The physical parameters of the model are chosen with special care to match literature experimental measurements or are interpolated using the values from binary metal oxides in the case of the new ZnGeO compound. The solar cell structure includes an interface and a defective layer at the ZnGeO/Cu2O heterojunction. The AZO/ZnGeO/Cu2O model results reproduce the photovoltaic characteristics of experimental devices presented by Minami et al. (Applied Physics Express 9, 052301 (2016) DOI:10.7567/APEX.9.052301) The dataset also includes results using models with different germanium compositions for the ZnGeO buffer layer. Other solar cell simulation results are presented to model the impact of low cost fabrication processes, such as spray pyrolysis, by varying the thickness, doping concentration, carrier mobilities and defect concentration of the Cu2O absorber layer as well as the germanium composition of the buffer layer.

opencc-by-4.0Dec 2020View details →

Three-dimensional magnetic reconnection in particle-in-cell simulations of anisotropic plasma turbulence (Simulation Data)

This folder contains the output of the following simulation:  We use the explicit Plasma Simulation Code (PSC, Germaschewski et al.2016) to simulate eight anisotropic counter-propagating Alfvén waves in an ion-electron plasma. The anisotropy of the initial fluctuation is set up according to the theory of critical balance by Sridhar & Goldreich (1994) and Goldreich & Sridhar (1995) at the small scale end of the inertial range: $k_{\parallel} d_{i} = C (|k_{\perp}|d_{i})^{2/3}$, where $C= 10^{-4/3}$. The normalization parameters are the speed of light $c = 1$, the vacuum permittivity $\epsilon_{0} = 1$, the magnetic permeability $\mu_{0} = 1$, the Boltzmann constant $k_{b}=1$, the elementary charge $q=1$, the ion mass $m_{i}=1$, the density of ions and electrons $n_{i}=n_{e}=1$ and the ion inertial length $d_{i}=c/\omega_{pi}$ where $\omega_{pi}=\sqrt{n_{i}q^{2}/m_{i}\epsilon_{0}}$ is the ion plasma frequency. We set $\beta_{s,\parallel}=1$ and $T_{s,\parallel}/T_{s,\perp}=1$, where $\beta_{s,\parallel}=2 n_s \mu_{0} k_{B}T_{s,\parallel}/B_{0}^{2}$ is the ratio between the plasma pressure parallel to the background magnetic field $\mathbf{B}_{0}$ and the magnetic pressure and $T_{s,\parallel}$ is the parallel temperature. The magnetic field is normalised to $B_{0}=V_{A}/c$,  where $V_{A}=B_{0} / \sqrt{\mu_{0}n_{i}m_{i}}$ is the ion Alfvén speed. We use 100 particles per cell (100 ions and 100 electrons), a mass ratio of $m_{i}/m_{e} = 100$ so that $d_e = 0.1 d_{i}$ where $m_{e}$ is the electron mass and $d_{e}$ is the electron inertial length. The simulation box size is $L_{x} \times L_{y} \times L_{z} = 24d_{i}\times24d_{i}\times125d_{i}$ and the spatial resolution is $\Delta x =\Delta y = \Delta z = 0.06d_{i}$. We use a time step $\Delta t =0.06/ \omega_{pi}$. In our normalisation, the Debye length $\lambda_{D}=d_{i}\sqrt{\beta_{i}/2}V_{A}/c$ defines the minimum spatial distance that needs to be resolve in the simulation and $\lambda_D=0.07d_i$. This output corresponds to $t=120 \omega_{pi}$.  These data were produced using the Data Intensive at Leicester (DIaL) facility provided by the DiRAC project dp126 "Identifying and Quantifying the Role of Magnetic Reconnection in Space Plasma Turbulence".

Supplementary files for Molecular Differences Between Squamous Cell Carcinoma and Adenocarcinoma Cervical Cancer Subtypes: Potential Prognostic Biomarkers

Supplementary files for Molecular Differences Between Squamous Cell Carcinoma and Adenocarcinoma Cervical Cancer Subtypes: Potential Prognostic Biomarkers

Data set for "Cell-type-specific nicotinic input disinhibits mouse barrel cortex during active sensing"

Data set for: Gasselin C, Hohl B, Vernet A, Crochet C, Petersen CCH (2021) Cell-type-specific nicotinic input disinhibits mouse barrel cortex during active sensing. Neuron doi: 10.1016/j.neuron.2020.12.018 There are 2 files in this upload: 1. The file named "2021_Gasselin_Neuron.pdf" is the Open Access pdf of the online publication in Neuron. 2. The file named "Gasselin_data_code.zip" (~9 GB) is a zipped version of a folder "Gasselin_data_code" (~13 GB), which contains the data analysed in the study along with the Matlab code used to generate the published figures. To access the data and the code, first unzip the file. Then add the folder with subfolders to the Matlab path and run the different codes. The current folder must be the main folder (‘Gasselin_data_code’). Each code computes and plots the results used in the corresponding figure. Figures and Tables are saved in the subfolder ‘Figures’. The subfolder ‘Functions’ contains functions called by the main codes. The main folder contains the following codes: Gasselin_Figure1: computes and plots the results for the panels D, E and F of figure 1. Gasselin_Figure2: computes and plots the results for the panels B and C of figure 2. Gasselin_Figure3: computes and plots the results for the panels B, C and D of figure 3. Gasselin_Figure4: computes and plots the results for the panels A, B and C of figure 4. Gasselin_FigureS1: computes and plots the results for the panels A, B and C of figure S1. Gasselin_FigureS2: computes and plots the results for the panels A and B of figure S2.   The subfolder ‘Data’ contains the data structures used for the different figures: data_figure1.mat data_figure2.mat data_figure3.mat data_figure4_MECA.mat data_figure4_Activation.mat data_figure4_Inactivation.mat data_figureS2_Activation.mat data_figureS2_Inactivation.mat data_Axon.mat   The data structures contain the following fields: Mouse_Name : name of the mouse. Mouse_DateOfBirth: date of birth of the mouse (YMD). Mouse_Sex: sex of the mouse (F or M). Mouse_Genotype: genotype of the mouse. Mouse_Drug: experimental condition of the recording (control = ‘No Drug’; blockade of glutamatergic transmission = ‘CNQX_DAPV’; blockade of glutamatergic transmission and nicotinic receptors = ‘CNQX_DAPV_MECA’; blockade of nicotinic receptors only = ‘MECA’). Mouse_Virus: virus injected if any. Cell_Counter; cell recorded in a given mouse. Cell_Type: type of the recorded cell based on 2P imaging. (EXC, VIP, PV, SST, 5HT3aR_non_VIP). Cell_Depth: depth of the recorded cell relative to pia (µm). Cell_TargetedBrainArea: cortical area targeted (C2 column of the barrel cortex = C2). Cell_Fluorescence: expression of the genetically encoded fluorophore (FALSE or TRUE). A neuron recorded in a VIP_IRES_Cre x LSL_tdTomato (cf Mouse_Genotype) with Cell_Fluorescence=TRUE is considered as a VIP neuron (cf Cell_Type). Sweep_Counter: number of the sweep recorded for a given neuron (data were acquired across successive continuous sweeps of 30-60 s). Sweep_Type: experimental condition during that sweep (Only spontaneous whisking onset = ‘Onset’; Whisking onset and whisker stimulus = ‘Onset_Whisker_Stim’ ; Optogenetic stimulation = ‘Opto_Stim’;  Optogenetic activation = ‘Opto_Activation’; Optogenetic inactivation = ‘Opto_Inactivation’;  ). Sweep_Start_Time: time at the beginning of the sweep recording (YMDHms). Sweep_WhiskerAngle: C2 whisker angular position extracted from simultaneous high-speed video filming (deg). Sweep_WhiskerAngle_SamplingRate: sampling rate of the whisker angle trace. Sweep_WhiskingOnset_Time: time of identified whisking onset - excluding any whisker stimulus shortly before or after (s). Sweep_MembranePotential: membrane potential recording (mV) after cutting of the APs. Sweep_MembranePotential_SamplingRate: sampling rate of the membrane potential signal (pt.s-1). Sweep_CurrentInjected: current injected into the cell (pA). Sweep_CurrentInjected_SamplingRate: sampling rate of current signal (pt.s-1). Sweep_WhiskerStim_Name: whisker to which the magnetic stimulus was applied to (C2 or B2&C2). Sweep_WhiskerStim_Time: onset times of the whisker stimulus (s). Sweep_OptoStim_Power: light power applied for optogenetic manipulations (% of the max power). Sweep_OptoStim_Time: onset times of the light pulses for optogenetic manipulations (s). Cell_ID: unique cell identifier (= Mouse_Name+Cell_Counter). SpikeThreshold: spike threshold used to detect APs (mV). Trial_WhiskingOnset: data structure containing the cut signals used to compute averaged responses around whisking onset times. Trial_WhiskerStim: data structure containing the cut signals used to compute averaged responses around whisker stimulus onset times. Trial_WhiskerStim_QuietTrials: data structure containing the cut signals used to compute averaged responses around whisker stimulus onset times for trials without whisker movements. Trial_WhiskerStim_WhiskingTrials: data structure containing the cut signals used to compute averaged responses around whisker stimulus onset times for trials with whisker movements. Trial_Opto: data structure containing the cut signals used to compute averaged responses around optogenetic stimulus onset times. Trial_OptoAndWhisker_QuietTrials: data structure containing the cut signals used to compute averaged responses around whisker stimulus onset times for trials with optogenetic manipulation and no whisker movements. Trial_OptoAndWhisker_WhiskingTrials: data structure containing the cut signals used to compute averaged responses around whisker stimulus onset times for trials with optogenetic manipulation and with whisker movements. Trial_OnlyWhisker_QuietTrials: data structure containing the cut signals used to compute averaged responses around whisker stimulus onset times for trials without optogenetic manipulation and without whisker movements. Trial_OnlyWhisker_WhiskingTrials: data structure containing the cut signals used to compute averaged responses around whisker stimulus onset times for trials without optogenetic manipulation and with whisker movements. Trial_OnlyOpto: data structure containing the cut signals used to compute averaged responses around optogenetic stimulus onset times in trials without whisker stimulus.

opencc-by-3.0Apr 2021View details →

Combinatorial and machine learning approaches for the analysis of Cu2ZnGeSe4: influence of the off-stoichiometry on defect formation and solar cell performance

Dataset of the results published in the <a href="https://zenodo.org/record/4742379#.YMzExOgzYmJ">J. Mater. Chem. A, 2021, 9, 10466</a>. The files represent: i) the measured compositional and optoelectronic data of each solar cell, as well as the data generated from the Raman spectra analysis; ii) Raman spectra of the representative cells; iii) Machine Learning discriminants. The elemental composition of the different cells of the combinatorial sample was determined by X-ray fluorescence (XRF) using a Fischerscope XDV system with a 1 mm spot diameter, a 50 kV acceleration voltage, a Ni10 lter and a 45 s acquisition time. Raman analysis with blue (442 nm) and green (532 nm) excitation wavelengths were performed on the bare absorber, while measurements with NIR (785 nm) were performed in complete devices using Horiba Jobin Yvon FHR640 and iHR320 monochromators coupled with CCD detectors. The first monochromator is optimized for the UV and visible spectral ranges and was used with 442 nm (He–Cd gas laser) and 532 nm (solid state laser) excitation wavelengths. The second monochromator is optimized for the NIR range and was used with a 785 nm (solid state laser) excitation wavelength. The power density of the lasers was kept below 150 W cm2 and the spot size was ~70 $\mu$m. The measurements were performed in a backscattering configuration through a specific probe designed at IREC. The J–V characteristics of the devices were obtained under simulated AM1.5 illumination (1000 W m2 intensity at room temperature) using a pre-calibrated Class AAA solar simulator (Abet Technologies Sun 3000).

opencc-by-4.0Oct 2014View details →

Processed NOMe-seq data for four human cell lines

Raw NOME-seq data (Gene Expression Omnibus accession GSE57498) for the human cell lines HMEC, MCF7, PrEC, PC3 were aligned to hg19 using bwa-meth. Methylation and occupancy calls were made at WCG and GCH sites respectively using bwa-meth, BisSNP and bespoke, tailor made scripts (https://github.com/astatham/NOMe-seq-analysis).

opencc-by-4.0Apr 2016View details →

Fluorescent Confocal Laser Scanning Microscopy of White Blood Cells, Cancer Cell Line MCF7, and Mixtures of these Cells: A Model System for Circulating Tumor Cell Biomarker Evaluation V.1

This is a confocal laser scanning microscopy data set of white blood cells (leukocytes), the cancer cell line MCF7, and mixtures of these cells acquired on a Zeiss LSM 780 microscope in the University of Colorado Anschutz Medical Campus Advanced Light Microscopy Core. Cells are fluorescently labeled for DNA with DAPI (Sigma D9542), lipids with Bodipy 495/503 (Thermo Fisher D3922), the filament protein cytokeratin (CK) with pan-cytokertain-alexa555 antibodies (Cell Signaling Technologies 3478S) and the surface membrane antigen CD45 with CD45-alexa647 antibodies (Biolegend 304020). Bodipy was excited with a continuous wave (CW) 488 nm laser, alexa555 was excited with CW 561 nm laser, and alexa647 was excited with a CW 633 nm laser. The acquiring instrument does not have a CW 405 nm source so DAPI was excited by two photon process using a Coherent Cameleon ultrafast pulsed laser tuned to 765 nm. The objective used was a Zeiss Plan-Apochromat 20x, 0.8 NA, air. The data consists of 4 channel 8x8 mosaic z-stacks. The Zeiss software performed stitching of the mosaics. These stitched data images are included and marked with _Stitched at the end. Those interested in performing the stitching themselves can do this with the raw data files (without the _Stitched). The jpeg images are processed from the stitched LSM images. The LSM files contain additional meta data on the experiment including power levels and acquisition settings. The _Stiched .lsm files will load in ImageJ (tested with V.1.49) as 4 channel 3 stack images. This data is a model system for evaluating the DNA/Lipids/CK/CD45 biomarker panel to identify circulating tumor cells (CTCs). The D- population of the model is the WBCs and the D+ population is the MCF7 cancer cell line. The amount of separation the biomarker panel plus analysis algorithm can produce between these populations (D+/D-) is an estimate the sensitivity and specificity of the biomarker panel plus algorithm to CTCs. Experiments generating the data were performed over the course of 15 days. Peripheral blood samples were collected from the Gynecological Tissue and Fluid Bank (COMIRB 07-0935 / COMIRB 05-1081) from consenting patients undergoing surgery at the University of Colorado Hospital. Blood samples were used the same day they were collected. Blood samples were collected from 3 patients with benign conditions, labeled WBBN#, and 3 patients with ovarian cancer, labeled WBCA#. We do not expect there to be any difference in the isolated white blood cells samples prepared from the cancer and benign patients. Samples were stored at room temperature until white blood cells were isolated. Mixed samples were prepared by passaging a MCF7 flask and mixing it with isolated white blood cells before fixation. A schedule showing the time duration between collection, processing and imaging is included as “experimental schedule.gif”. The MCF7 cancer cell line was a kind gift from Dr. Heide Ford. Genomic DNA was isolated from the MCF7 cell line after the experiment and sent for cell line authentication. The gDNA was a match to MCF7. The authentication report and data are included in this submission. CD45 antibodies were exhausted on day 7. New antibody was purchased and received on day 8. The day 7 images only has labels for DAPI and Bodipy. The samples prepared with the old antibodies on days 4 and 7 were relabeled and imaged with the new antibodies on days 14 and 15. This labeling was also done to confirm the pan-CK antibodies remained good since they are dim in the MCF7 cells imaged on days 12 and 13. The pan-CK on days 14 and 15 looks the same as it did on days 5 and 7 confirming the antibodies are good. Four of the filters containing cells were not sufficiently flat to be acquired with a 3 slice z-stack so a 5 slice z-stack was used. These files have been zipped to compress them under the 2 GB limit permitted by zenodo.org Further information on how these samples were prepared, processed, and analyzed can be found in our associated 2016 SPIE Photonics West BIOS conference proceeding titled, “Quantitative image cytometry measurements of lipids, DNA, CD45 and cytokeratin for circulating tumor cell identification in a model system”, http://dx.doi.org/10.1117/12.2222317. This work was supported by funding provided to the University of Colorado Cancer Center by the American Cancer Society and awarded as Institutional Research Grant Number 57-001-53, by funding provided by the Defense Advanced Research Projects Agency under grant number N66001-10-4035, and by funding provided by NIH/NCATS Colorado CTSI Grant Number TL1 TR001081. The University of Colorado Anschutz Medical Campus Advanced Light Microscopy Core is also supported in part by NIH/NCATS Colorado CTSI Grant Number UL1 TR001082. The funders had no role in the study design, data collection, analysis, or decision to publish.

opencc-by-4.0Oct 2024View details →

Developing Digital Image Processing methods to quantify internal and interfacial convection in the Hele-Shaw cell, with applications to the laboratory ice-ocean boundary layer

This dataset provides the video and image files obtained from Schlieren optical experiment 3 performed in the Laboratoire de Glaciologie (GLACIOL) at the Universite de libre Bruxelles. A document detailing the visual data and supporting figures is presented (DataOverview.pdf).

opencc-zeroApr 2024View details →

SeMRA Cell and Cell Line Mappings Database

Originally a reproduction of the EFO/Cellosaurus/DepMap/CCLE scenario posed in the Biomappings paper, this configuration imports several different cell and cell line resources and identifies mappings between them. See instructions for reproduction and usage in the attached README.md.

opencc-by-4.0Mar 2024View details →

IrCytoToxDB: a dataset of iridium(III) complexes cytotoxicities against various cell lines

<h1>If you use this dataset, please cite our paper: <a href="https://doi.org/10.1038/s41597-024-03735-w">https://doi.org/10.1038/s41597-024-03735-w</a></h1> IrCytoToxDB contains 4546 experimentally measured cytotoxicity values of 1295 unique iridium(III) complexes against 177 different cell lines reported in the 389 literature papers from 2008 to 2025. The 15 columns of this dataset are explained as follows: <ol> <li>L1 — SMILES representation of the L1 ligand attached to the iridium ion</li> <li>L2 — SMILES representation of the L2 ligand attached to the iridium ion</li> <li>L3 — SMILES representation of the L3 ligand attached to the iridium ion</li> <li>L4 — SMILES representation of the L4 ligand attached to the iridium ion</li> <li>Counterion — SMILES representation of the counterion (if the complex molecule is charged)</li> <li>Abbreviation_in_the_article — the original abbreviation depicting the complex in the article</li> <li>IC50Dark(M*10^-6) — value of IC50 originally presented in the article</li> <li>IC50Dark_standard_error(M*10^-6) — standard error of IC50 originally presented in the article</li> <li>IC50Light(M*10^-6) — value of IC50 under irradiation originally presented in the article</li> <li>IC50Light_standard_error(M*10^-6) — standard error of IC50 under irradiation originally presented in the article</li> <li>Excitation_Wavelength(nm) — excitation wavelength related to IC50Light values</li> <li>Irradiation_Time(minutes) — irradiation time related to IC50Light values</li> <li>Irradiation_Power(W*m^-2) — power of light source related to IC50Light values</li> <li>Cell_line — cell line (HeLa, A549, etc.)</li> <li>Time(h) — time of exposure of the complexes to the cell line</li> <li>DOI — doi of a data source for given values</li> <li>Year — year of a data source for given values </li> <li>Comments — additional comments regarding the data</li> </ol> Additional remarks: <ul> <li>The array of iridium(III) complexes could be formally mainly in two parts – bis-cyclometalated Ir(III) complexes and half-sandwich Ir(III) complexes. The former usually contain two cyclometalated ligands and one or two ancillary (or third cyclometalated) ligand; for these L1 and L2 correspond to the cyclometalated ligands and L3 (or L3 and L4) corresponds to the ancillary ligand. The latter usually contain one cyclopentadiene-(Cp-)-based ligand, one bidentate ligand and one monodentate ligand; for these L1 corresponds to the Cp--based ligand, L2 corresponds to the bidentate ligand and L3 corresponds to the monodentate ligand.</li> <li>Some ligands make formally covalent bonds with the Ir(III) ion. For these a negatively charged bond-forming atom is drawn in the SMILES of corresponding ligand.</li> </ul>

opencc-by-4.0Sep 2024View details →

Type III interferons may suppress viral infections by triggering cell death -- Imaging Dataset

This dataset accompanies the article "Type III interferons may suppress viral infections by triggering cell death". Earlier version is available as a preprint, <a href="https://doi.org/10.1101/2024.09.09.612051" target="_blank" rel="noopener">https://doi.org/10.1101/2024.09.09.612051</a>. The updated dataset includes quantifications for Figure 7C and Figure 7D.

opencc-by-4.0Oct 2023View details →

Suplementary data, results and scripts: "Reconstruction of Cell-specific Models Capturing the Influence of Metabolism on DNA methylation in Cancer"

This repository contains supplementary data, models and scripts associated with "Reconstruction of Cell-specific Models Capturing the Influence of Metabolism on DNA methylation in Cancer".Folders content:'data_results_matlabscripts': data, result files and scripts (original python scripts and adapted MATLAB scripts)'supplementary_figures': supplementary figures'supplementary_tables': supplementary tables

opencc-by-nc-sa-4.0Jun 2023View details →

Data archive: CICT for single cell RNA-seq network inference

This archive contains benchmarking input data and results for using single cell gene expression data to infer gene regulatory networks (GRN) by the Causal Inference with Composition of Transactions (CICT) method and a selected set of published methods. This accompanies the manuscript "Robust discovery of gene regulatory networks from single-cell gene expression data by Causal Inference Using Composition of Transactions" (Shojaee and Huang, Brief in Bioinform 2023. DOI: 10.1093/bib/bbad370). The CICT code is available at the GitHub repo (https://github.com/hlab1/scRNAseqWithCICT/).The original CICT algorithm was described in Shojaee et al. (arXiv:1608.02658, 2016). The benchmarked methods were included in the BEELINE benchmarking pipeline (Pratapa et al., Nat Methods 2020), to which we added DEEPDRIM (Chen et al., Brief Bioinform 2021), SCENIC (Aibar et al., Nat Methods 2017), Inferelator 3.0 (Gibbs et al., Bioinformatics 2022), and CellOracle (Kamimoto et al., Nature 2023). The output directory names are (subdirectories within each dataset):* CICT_ewMIshrink_RFmaxdepth10_RFntrees20/: CICT for simulated data * CICT_v2/: CICT for experimental data * CELLORACLEDB/: CellOracle for experimental data * DEEPDRIM72_ewMIshrink_RFmaxdepth10_RFntrees20/: DEEPDRIM for simulated data * DEEPDRIM72_v2/: DEEPDRIM for experimental data * INFERELATOR38_ewMIshrink_RFmaxdepth10_RFntrees20/: Inferelator-Prior for simulated data * INFERELATOR38_v2/: Inferelator-Prior for experimental data * INFERELATOR34_ewMIshrink_RFmaxdepth10_RFntrees20/: Inferelator-NoPrior for experimental data * INFERELATOR34_v2/: Inferelator-NoPrior for experimental data * GENIE3/: GENIE3 * GRNBOOST2/: GRNBOST2 * LEAP/: LEAP * PIDC/: PIDC * PPCOR/: PPCOR * SCENICDB/: SCENIC for experimental data * SCNS/: SCNS * SCODE/: SCODE * SCRIBE/: SCRIBE * SINCERITIES/: SINCERITIES * SINGE/: SINGE * RANDOM/: RANDOMThe methods were benchmarked against two kinds of scRNA-seq datasets: * Simulated datasets produced by the SERGIO simulator from a synthetic network (Dibaeinia et al., Cell Systems 2020), including complete datasets and datasets with dropouts with shape parameter k=6.5 and rate parameter q=10, 30, 50, 70, 80.  * Experimental datasets compiled by the BEELINE pipeline, evaluated at three different levels L0, L1 and L2, with three types of ground truth networks.     * Evaluation levels:         * L0: 500 highly varying genes plus TFs         * L1: 1000 highly varying genes plus TFs         * L2: 500 highly varying genes, TFs and 500 genes randomly selected that excluded the 1000 highly varying genes from L1.     * Types of ground truths:         * Cell-type-specific ChIP-seq ground truth (L0, L1, L2)         * Non-specific ChIP-seq ground truth (L0_ns, L1_ns, L2_ns)         * Loss-of-function/gain-of-function ground truth (L0_lofgof, L1_lofgof, L2_lofgof)The directory structure is organized in accordance with the BEELINE benchmarking pipeline. For complete details please please see the BEELINE documentation (https://murali-group.github.io/Beeline/) and Github repo (https://github.com/Murali-group/Beeline).