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2,362 results for “k562”
Synthetic datasets reflecting the shRNA-seq knockdown ENCODE data for HepG2 and K562 with coresponding GRN
<p>Synthetic data correspond to the ENCODE data for cell lines HepG2 (https://www.encodeproject.org/biosamples/ENCBS282XVK/) and K562 (https://www.encodeproject.org/biosamples/ENCBS023XVB/). The data and networks were generated using GeneSPIDER (publicly available at https://bitbucket.org/sonnhammergrni/genespider/).</p> <p> </p> <p><strong>Table.1 </strong>Description of the files</p> <table> <tbody> <tr> <td>data_HepG2like_SNR_L=0.0054699_diff=1.6188e-05.txt</td> <td>Synthetic gene expression knockdown (shRNA-seq) data immitating the ENCODE data for HepG2 cell line. Data size: 232 RBPs vs 464 experiments (2 replicates). SNR_L is the value of signal to noise ratio. Difference (diff) value tells the difference between replicate correlation coefficients of real and synthetic ENCODE data. Columns represent experiments, rows represent genes.</td> </tr> <tr> <td>data_K562like_SNR_L=0.0028692_diff=0.00017339.txt</td> <td>Synthetic gene expression knockdown (shRNA-seq) data immitating the ENCODE data for K562 cell line. Data size: 232 RBPs vs 464 experiments (2 replicates). SNR_L is the value of signal to noise ratio. Difference (diff) value tells the difference between replicate correlation coefficients of real and synthetic ENCODE data. Columns represent experiments, rows represent genes.</td> </tr> <tr> <td>network_HEPG2like_sparsity4.txt</td> <td>Synthetic scale-free gene regulatory network compatibile with data_HepG2like_SNR_L=0.0054699_diff=1.6188e-05.txt. Sparsity (average node degree) is 4 including selfloops. Direction should be read from columns to rows.</td> </tr> <tr> <td>network_K562like_sparsity4.txt</td> <td>Synthetic scale-free gene regulatory network compatibile with data_K562like_SNR_L=0.0028692_diff=0.00017339.txt. Sparsity (average node degree) is 4 including selfloops. Direction should be read from columns to rows.</td> </tr> <tr> <td>perturbations_HepG2&K562_2replicates.txt</td> <td>Perturbation matrix including information about knockeddown RBPs. Data size: 232 RBPs vs 464 experiments (2 replicates).</td> </tr> </tbody> </table> <p> </p> <p>Created by Garbulowski et al. (2024) as a part of the work entitled "Comprehensive analysis of the RBP regulome reveals functional modules and drug candidates in liver cancer"</p>
VeloCycle-estimated cell cycle phases of single cells from a genome-scale perturb-seq performed in K562
<p>Continuous cell cycle phase position between 0 and 2π estimated by <em>VeloCycle </em>(Lederer et al., <em>Nature Methods</em> 2024) for single cells of the perturb-seq performed in the K562 CML cell line by Replogle et al., Cell 2022. </p> <p>Underlies the cell cycle imbalances inferred in Pulver & Forey et al., 2024.</p> <p>Method manuscript exerpt:</p> <p>"For analysis on the genome-wide perturb-seq dataset of K562 cells (Replogle <em>et al.</em>, 2022), a transfer learning approach was applied. Condition-independent estimation of the periodic Fourier series components would be especially challenging on Perturb-seq knockdown conditions containing either (1) very few cells or (2) cells belonging to just one phase of the cell cycle. To infer accurate cell cycle phases for these cells, we first performed manifold-learning for 5,000 training steps to estimate the gene harmonic coefficients (ν0, ν1sin, ν1cos) on a larger set of non-targeting control (NT) K562 cells (75,328 cells), which are more evenly distributed throughout the various phases of the cell cycle. Next, we ran manifold-learning again for 5,000 training steps, but on the entire perturb-seq dataset of 1,971,608 cells and 4,127 gene knockdown conditions (with at least 75 cells per condition). This time, we conditioned <em>VeloCycle</em> on the gene harmonic coefficients learned in the first step. This allowed cells belonging to each stratified gene knockdown condition to be assigned to a position on the cell cycle manifold, while restricting those assignments such that they were based on gene expression patterns earned on a larger and more informative dataset (allowing for batch effect expression differences)."</p>
Prediction of nucleosome dyads for the K562 cell line in the hg19 genome assembly
<p><strong><a href="https://andre-rendeiro.com/2015/05/12/predicting_dyads_from_mnase">Predicting dyads from MNase-seq data</a></strong></p> <p>I needed the location of nucleosomal dyads in the K562 cell line (ENCODE tier 1 line). Surprisingly, although plenty of MNase-seq data for that cell line is available, no nucleosome and dyad prediction exists.</p> <p><strong>Running NuMap</strong></p> <p>I found the <a href="http://www-hsc.usc.edu/~valouev/NuMap/NuMap.html">NuMap</a> software by Anton Valouev to do exactly what I intended.</p> <p>Since it is in a somewhat obscure page and this seemed to be the only place where this software was, I have <a href="https://github.com/afrendeiro/NuMap">uploaded it into a Github repository</a> for the sake of preservation (<a href="https://github.com/orphancode/NuMap">https://github.com/orphancode/NuMap</a>).</p> <p>Predicting dyads from MNase-seq data with NuMap seemed trivial: I downloaded the <a href="http://hgdownload.cse.ucsc.edu/goldenPath/hg19/encodeDCC/wgEncodeSydhNsome/">K562 MNase-seq data set</a> (11 replicates ~85Gb!!), combined all replicates and ran NuMap on the data(instructions on the Github README).</p> <p>From NuMap output there are <a href="https://www.dropbox.com/s/asmp7bi40lrvtjb/K562_dyads.bed?dl=0">dyad positions in bed format</a> and you can also produce several metrics to evaluate how good the prediction was.</p> <p><strong>Distograms & phasograms</strong></p> <p>Valouev describes two measurements of the frequencies of distances between MNase-seq reads. The frequency of distances between reads mapping to opposite strands can be used to build a “distogram”, which ilustrates the expected nucleosome length (147 bp) - this is consistent across most eukaryotic cells. The frequency of distances between reads mapping to the same strand gives a measurement of the distance between nucleosomes, as they’re separated by some linker DNA - (Valouev calls this plot a “phasogram”). This measurement, on the other hand tends to be species and cell-type specific.</p> <p><strong>K562 predictions:</strong></p> <p>The expected 147 bp nucleosome length in K562 cells.</p> <p>The average distance between dyads in K562 cells seems to be 185 bp.</p> <p><strong>References:</strong></p> <p>Valouev, A., Johnson, S. M., Boyd, S. D., Smith, C. L., Fire, A. Z., Sidow, A. (2011). Determinants of nucleosome organization in primary human cells. Nature, 474(7352), 516–520. <a href="http://doi.org/10.1038/nature10002">http://doi.org/10.1038/nature10002</a></p>
Mass spectrometry analysis of MSI2 interacting partners in K562 cells
<p>Mass spectrometry analysis of MSI2 interacting partners in K562 cells for manuscript "<strong>Functional screen of MSI2 interactors identifies an essential role for SYNCRIP in myeloid leukemia stem cells"</strong> by Vu et al. 2017. </p>
Multiplexed single-cell characterization of alternative polyadenylation regulators (HEK293FT & K562 Perturb-seq data)
<p>This site provides access to datasets from the CPA-Perturb-seq <a href="https://www.biorxiv.org/content/10.1101/2023.02.09.527751v1">manuscript</a> Kowalski*, Wessels*, Linder* et al., including processed Perturb-seq datasets from HEK293FT and K562. We release these data as Seurat objects, where each object contains single-cell quantifications of gene expression (RNA assay), and in addition, quantifications of polyA site usage (polyA site assay). To explore these data, please install the <a href="https://github.com/satijalab/PASTA">PASTA</a> (PolyA Site analysis using relative Transcript Abundance) package, which provides infrastructure and analytical tools to explore alternative polyadenylation at single-cell resolution. For each dataset, we also include a fragment file which enables visualization of read coverage plots across groups of cells. </p> <p>The files include:</p> <p>1. CPA_K562.Rds : Seurat object containing the K562 CPA-Perturb-seq dataset </p> <p>2. CPA_K562_fragments.tsv.gz : Fragment file for the K562 dataset </p> <p>3. CPA_K562_fragments.tsv.gz.tbi : Fragment file index for the K562 dataset </p> <p> </p> <p>R code below:</p> <pre><code>library(PASTA) k562 <- readRDS("CPA_K562.Rds") # Add fragments for plotting Fragments(k562) <- CreateFragmentObject(path = "download/CPA_K562_blocks.tsv.gz", cells = Cells(k562)) # visualize polyA site usage PolyACoveragePlot(k562, region ="chr7-26212195-26213351")</code></pre>
Gene expression profile at both bulk and single cell level for heterogeneous cell type mixtures of A375, HAP1, THP1, HEK239FT, K562, and A549
<p>We used poly-A bulk RNA sequencing and single-cell RNA sequencing using mixtures of well-characterized purified cell lines to better understand measurement noise between technologies. A375, HAP1, THP1, HEK239FT, K562, and A549 cells were mixed in various proportions to generate four in vivo cell mixtures that mimic tissues. Mixtures of 6, 4, 2, and 1 of the cell types are respectively denoted 6ct, 4ct, 2ct, and 1ct. In the included zip, directories are labeled using this notation, followed by a list of cell types in the mixture after an underscore. In each directory, bulk and single cell count data is included.</p>
HepG2 and K562 cCRE syntenic ages
<p>### DESCRIPTION ###</p> <p>HepG2 and K562 ENCODE 3 cCRE pELS, dELS, pELS + CTCF, dELS +CTCF.</p> <p>### METHOD ###</p> <p>To assign sequence ages enhancer cCRE .bed coordinates were intersected with hg38 100-way multiZ alignment syntenic blocks from UCSC genome browser.</p> <p> </p> <p>### Column names ###</p> <p>[#chr start_syn end_syn enh_id #chr_enh start_enh end_enh segment_number simple_0_complex_1 core_0_derived_1 mrca_age]</p> <p>### explanation of column names ###</p> <p>[#chr start_syn end_syn]: syntenic block .bed coordinates in hg38</p> <p>[ enh_id #chr_enh start_enh end_enh]: cCRE .bed coordinates in hg38</p> <p>[segment_number]: We count the number of syntenic block segments overlapping a single enhancer. </p> <p>[simple_0_complex_1]: Some syntenic blocks overlap multiple syntenic blocks, which we term "complex". Others overlap a single syntenic block, which we term "simple". Complex elements are annotated with 1 and simple elements are annotated with 0. </p> <p>[core_0_derived_1]: In complex enhancer sequences (i.e. enhancer sequences with multiple sequence ages). We refer to the oldest sequence in an enhancer is the "core" (annotated as 1) and the younger sequence age(s) as "derived" (annotated as 0). </p> <p>[mrca_age]: For each syntenic segment, we annotate the most recent common ancestor (MRCA) age. This corresponds to the estimated branch length between humans and the related MRCA. </p> <p>Taxon and summarized taxon ("Taxon2") for each MRCA age is annotated in the hg38_syn_taxon.bed file. </p>
InfoHiC models for cancer Hi-C prediction (K562, 1Mb window, 10kb resolution)
<p>InfoHiC models for cancer Hi-C prediction</p> <ul> <li>K562</li> <li>1Mb window</li> <li>10kb resolution</li> <li>CSCN encoding</li> </ul>
individual gene knock outs in K562 cells-1
<p>This data (individual gene knock outs in K562 cells,1-8) includes bulk RNA sequencing data from individual gene knockouts K562 cells for 5 selected genes (KCTD5, EIF2AK1, APAF1, CCNC, IGF1R, triplicate samples for each) in addition to control guideRNA transduced K562 cells (n=5 samples)</p>
GeCKO_v2_CRISPR_drug screens in K562 cells
<p>The data contain raw DNA deep sequencing files for GeCKO-v2 genome scale transduced K562 screens with selected drugs (imatinib, dasatinib, asciminib, tivozanib, AZD1775, mepacrine). For all drug except mepacrine the drug, day 0 and day 17 control samples' files are provided. For mepacrine which was screened separately, a triplicate drug, D15 control sample files are provided. The dates are indicated in the file names. </p>
individual gene knock outs in K562 cells-8
<p>This data (individual gene knock outs in K562 cells,1-8) includes bulk RNA sequencing data from individual gene knockouts K562 cells for 5 selected genes (KCTD5, EIF2AK1, APAF1, CCNC, IGF1R, triplicate samples for each) in addition to control guideRNA transduced K562 cells (n=5 samples)</p>
individual gene knock outs in K562 cells-3
<p>This data (individual gene knock outs in K562 cells,1-8) includes bulk RNA sequencing data from individual gene knockouts K562 cells for 5 selected genes (KCTD5, EIF2AK1, APAF1, CCNC, IGF1R, triplicate samples for each) in addition to control guideRNA transduced K562 cells (n=5 samples)</p>
individual gene knock outs in K562 cells-5
<p>This data (individual gene knock outs in K562 cells,1-8) includes bulk RNA sequencing data from individual gene knockouts K562 cells for 5 selected genes (KCTD5, EIF2AK1, APAF1, CCNC, IGF1R, triplicate samples for each) in addition to control guideRNA transduced K562 cells (n=5 samples)</p>
individual gene knock outs in K562 cells-4
<p>This data includes (individual gene knock outs in K562 cells,1-8) bulk RNA sequencing data from individual gene knockouts K562 cells for 5 selected genes (KCTD5, EIF2AK1, APAF1, CCNC, IGF1R, triplicate samples for each) in addition to control guideRNA transduced K562 cells (n=5 samples)</p>
individual gene knock outs in K562 cells-2
<p>This data (individual gene knock outs in K562 cells,1-8) includes bulk RNA sequencing data from individual gene knockouts K562 cells for 5 selected genes (KCTD5, EIF2AK1, APAF1, CCNC, IGF1R, triplicate samples for each) in addition to control guideRNA transduced K562 cells (n=5 samples)</p>
shRNA RNA-seq from K562 (ENCSR481YXD)
GEO Series GSE88302. Homo sapiens. 2 samples. Type: Expression profiling by high throughput sequencing.
CRISPR RNA-seq from K562 (ENCSR034FLW)
GEO Series GSE219367. Homo sapiens. 2 samples. Type: Expression profiling by high throughput sequencing.
Control eCLIP from K562 (ENCSR241ALC)
GEO Series GSE176820. Homo sapiens. 1 samples. Type: Other.
MPRA from K562 (ENCSR917SFD)
GEO Series GSE266790. Homo sapiens. 5 samples. Type: Other.
Identification of transcripts regulated by CEBPA in K562
GEO Series GSE58743. Homo sapiens. 8 samples. Type: Expression profiling by array.
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