DrugMap: A quantitative pan-cancer analysis of cysteine ligandability
ChIP-seq / ATAC-seq (genome binding and open chromatin) · human · 71 samples
- What it is
- ChIP-seq / ATAC-seq (genome binding and open chromatin)Where proteins bind the genome, or which regions are open and active.
- Organism
- Homo sapiens (human)
- Samples
- 71 samples: 21 conditions, mostly in triplicateGroups are read from the sample names, not from the paper. Check the list below.
- Data you can get
- A processed table (series matrix) and supplementary files from NCBI; the raw sequencing reads from ENA. Which of these hold counts depends on the study, so check the file list.
- Paper
- DrugMap: A quantitative pan-cancer analysis of cysteine ligandabilityCell · 2024
Direct links to NCBI and ENA, no account and no request form. Pick by what you want to do: take the small processed table to look, the supplementary files to re-analyse, or the original raw data to start over.
Processed table
The series matrix: one table of values per sample as the submitters processed it. The smallest download, and it opens in Excel or R. Start here to see what the data look like.
Supplementary files
The count tables and other files the authors uploaded. Take only the samples you need, or the whole study as GSE256467_RAW.tar (it can run to tens of gigabytes).
Raw sequencing reads
The original FASTQ files, straight from the sequencer, held at ENA. Large, and you align them yourself, so only choose this if you need to re-process the study from scratch.
What the files usually look like: Look for peak files (.bed, .narrowPeak) and signal tracks (.bigWig) among the supplementary files; the raw reads are on ENA.
Also filed as BioProject PRJNA1079738 and SRA study SRP491387. Searching any of these in the dataset finder brings you back here.
Supports a between-group comparison across 60 samples.
How we read this: 21 replicated groups read from 71 sample titles; they account for 60 of them. These groups are guessed from the sample names, not taken from the paper, so check them against the sample list below before relying on them.
- GSM8098884 SK-MEL5-wt-H3K27ac-201, ChIP
- GSM8098885 SK-MEL5-wt-SOX10, ChIP
- GSM8098898 HMCB_siControl_F10
- GSM8098899 HMCB_siControl_G10
- GSM8098900 HMCB_siControl_H10
- GSM8098901 HMCB_siSOX10_C10
- GSM8098902 HMCB_siSOX10_D10
- GSM8098903 HMCB_siSOX10_E10
- GSM8098904 Mewo_siControl_A01 Mewo_siControl_A
- GSM8098905 Mewo_siControl_A03 Mewo_siControl_A
- GSM8098906 Mewo_siSOX10_A05 Mewo_siSOX10_A
- GSM8098907 Mewo_siSOX10_A06 Mewo_siSOX10_A
- GSM8098908 SK_MEL2_siControl_B01 SK_MEL2_siControl_B
- GSM8098909 SK_MEL2_siControl_B03 SK_MEL2_siControl_B
- GSM8098910 SK_MEL2_siSOX10_B04 SK_MEL2_siSOX10_B
- GSM8098911 SK_MEL2_siSOX10_B05 SK_MEL2_siSOX10_B
- GSM8098912 SK_MEL2_siSOX10_B06 SK_MEL2_siSOX10_B
- GSM8098913 SK_MEL5_siControl_D01 SK_MEL5_siControl_D
- GSM8098914 SK_MEL5_siControl_D03 SK_MEL5_siControl_D
- GSM8098915 SK_MEL5_siSOX10_D04 SK_MEL5_siSOX10_D
- GSM8098916 SK_MEL5_siSOX10_D05 SK_MEL5_siSOX10_D
- GSM8098917 SK_MEL28_siControl_C01 SK_MEL28_siControl_C
- GSM8098918 SK_MEL28_siControl_C03 SK_MEL28_siControl_C
- GSM8098919 SK_MEL28_siSOX10_C04 SK_MEL28_siSOX10_C
- GSM8098920 SK_MEL28_siSOX10_C05 SK_MEL28_siSOX10_C
- GSM8098921 SK_MEL28_siSOX10_C06 SK_MEL28_siSOX10_C
- GSM8098922 U62_siControl_H01 U62_siControl_H
- GSM8098923 U62_siControl_H03 U62_siControl_H
- GSM8098924 U62_siSOX10_H04 U62_siSOX10_H
- GSM8098925 U62_siSOX10_H06 U62_siSOX10_H
- GSM8098926 U257_siControl_E01 U257_siControl_E
- GSM8098927 U257_siControl_E03 U257_siControl_E
- GSM8098928 U257_siSOX10_E04 U257_siSOX10_E
- GSM8098929 U257_siSOX10_E05 U257_siSOX10_E
- GSM8098930 U257_siSOX10_E06 U257_siSOX10_E
- GSM8098931 C_DMSO_10_1
- GSM8098932 C_DMSO_11_1
- GSM8098933 C_DMSO_12_1
- GSM8098934 C_13_F10_16 C_13_F10
- GSM8098935 C_13_F10_17 C_13_F10
+ 31 more — browse all 71 samples with per-sample file links →
- GSE256466 DrugMap: A quantitative pan-cancer analysis of cysteine ligandability (RNA-Seq) 69 samples shares ligandability, quantitative, pan-cancer, cysteine, drugmap
- GSE311521 Single-cell multimodal profiling of pan-cancer cell lines uncovers gene regulatory principles underlying intrinsic cell states and environmental features 15 samples shares pan-cancer
- GSE15745 Abundant Quantitative Trait Loci for CpG Methylation and Expression Across Human Brain Tissues 1658 samples shares quantitative
- GSE40279 Genome-wide Methylation Profiles Reveal Quantitative Views of Human Aging Rates 656 samples shares quantitative
- GSE264684 Genetic effects on chromatin accessibility uncover mechanisms of liver gene regulation and quantitative traits 477 samples shares quantitative
- GSE318861 Quantitative analysis of Tat-dependent and host-driven HIV transcription by ChIP-seq and RNA-seq 52 samples shares quantitative
- GSE157198 Next Generation Sequencing Facilitates Quantitative Analysis of HIV Elite controller, HIV ART, HIV ART-naive and healthy control Transcriptomes 44 samples shares quantitative
- GSE68086 RNA-seq of tumor-educated platelets enables blood-based pan-cancer, multiclass and molecular pathway cancer diagnostics 285 samples shares pan-cancer
Metadata from NCBI GEO, cached and refreshed periodically — the NCBI page above is authoritative. Downloads link straight to NCBI/ENA; nothing is proxied through BioTransfer.