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5 studies, 19 datasets. |
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Achilles Phase 2 (296.8MB)
Phase 2 of the Achilles pooled RNAi study:
'Systematic investigation of genetic vulnerabilities across cancer cell lines reveals lineage-specific dependencies in ovarian cancer', PNAS 2011 Jul 26;108(30):12372-7.
Also available at www.broadinstitute.org/achilles
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Achilles 2 Annotation files (9.8MB) Sample annotation
(Published)
Annotations for the cell lines and shRNAs
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Achilles 2 RNAi Enrichment - 3/03/2011 (236.0MB) RNAi enrichment
(Published)
Achilles RNAi dataset from the publication:
'Systematic investigation of genetic vulnerabilities across cancer cell lines reveals lineage-specific dependencies in ovarian cancer', PNAS 2011 Jul 26;108(30):12372-7.
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| 20110303_achilles2_PMAD_adjFC.rnai.gct
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97.4MB
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12-Sep-2012 02:08:37
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File of normalized, shRNA adjusted log fold change values per cell line. Click 'Analyses' button to open directly in the GENE-E software. Or download to perform a class comparison using the GP modules 'MakeSubsetGctandCls' and 'ScoreByClassComp'.
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| 20110303_achilles2.gct
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122.3MB
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12-Sep-2012 02:08:34
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dCHIP array normalized file of shRNA values per replicate.
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| GENE-E_format.Ovarian.rnai.gct
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1.6MB
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12-Sep-2012 02:08:35
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shRNA differential essentiality scores for a comparison of Ovarian cell lines vs all other lineages. Compatible with GENE-E software.
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| GENE-E_format.Colon.rnai.gct
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1.6MB
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12-Sep-2012 02:08:34
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shRNA differential essentiality scores for a comparison of Colon cell lines vs all other lineages. Compatible with GENE-E software.
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| GENE-E_format.Pancreas.rnai.gct
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1.6MB
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12-Sep-2012 02:08:37
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shRNA differential essentiality scores for a comparison of Pancreas cell lines vs all other lineages. Compatible with GENE-E software.
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| GENE-E_format.NSCLC.rnai.gct
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1.6MB
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12-Sep-2012 02:08:34
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shRNA differential essentiality scores for a comparison of NSCLC cell lines vs all other lineages. Compatible with GENE-E software.
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| GENE-E_format.EsophagealSq.rnai.gct
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1.6MB
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12-Sep-2012 02:08:35
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shRNA differential essentiality scores for a comparison of Esophageal Squamous cell lines vs all other lineages. Compatible with GENE-E software.
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| GENE-E_format.GBM.rnai.gct
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1.6MB
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12-Sep-2012 02:08:34
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shRNA differential essentiality scores for a comparison of GBM cell lines vs all other lineages. Compatible with GENE-E software.
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| GENE-E_format.BRAF_Colon.rnai.gct
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1.6MB
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12-Sep-2012 02:08:35
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shRNA differential essentiality scores for a comparison of BRAF mutant Colon cell lines vs wildtype BRAF Colon lines. Compatible with GENE-E software.
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| GENE-E_format.KRAS.rnai.gct
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1.6MB
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12-Sep-2012 02:08:37
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shRNA differential essentiality scores for a comparison of KRAS mutant cell lines vs wildtype KRAS lines. Compatible with GENE-E software.
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| GENE-E_format.BRAF.rnai.gct
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1.6MB
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12-Sep-2012 02:08:34
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shRNA differential essentiality scores for a comparison of BRAF mutant cell lines vs wildtype BRAF lines. Compatible with GENE-E software.
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| GENE-E_format.PIK3CA.rnai.gct
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1.6MB
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12-Sep-2012 02:08:35
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shRNA differential essentiality scores for a comparison of PIK3CA mutant cell lines vs wildtype PIK3CA lines. Compatible with GENE-E software
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| Paper arrays.xls
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18.0KB
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12-Sep-2012 02:08:34
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List of arrays processed by dCHIP
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Achilles 2 GenePattern modules (32.7MB) Undefined
(Published)
GP modules for Achilles 2 data analysis
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| RNAiGCTConverter.zip
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1.7KB
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12-Sep-2012 02:08:30
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Converts file from dCHIP (array normalization steps) into a .gct file format for downstream analysis.
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| MakeArrayInfo.zip
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44.6KB
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12-Sep-2012 02:08:31
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Makes an information file for each replicate array, using the cell line information (sample information file). The output is used to collapse replicate arrays to a single cell line in downstream steps and can be used as an sample annotation file in GENE-E when looking at data from individual replicates.
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| MvAplots.zip
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10.5MB
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12-Sep-2012 02:08:31
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QC step; makes MvA plots of cell line replicate array data to look for replicate outliers.
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| shRNAscores.zip
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10.6MB
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12-Sep-2012 02:08:31
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Collapses replicate sample data per cell line, calculates a fold change score compared to the reference (initial DNA plasmid pool).
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| MakeSubsetGctAndCls.zip
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62.0KB
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12-Sep-2012 02:08:31
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Makes a .cls file and corresponding .gct dataset file for a class comparison of interest.
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| NormalizeCellLines.zip
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795.3KB
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12-Sep-2012 02:08:30
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Normalizes cell line data by PMAD (or ZMAD).
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| ScorebyClassComp.zip
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10.6MB
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12-Sep-2012 02:08:30
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Scores a class comparison of interest, results are compatible with GENE-E software.
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| Analysis_paper_schematic.pdf
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46.6KB
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12-Sep-2012 02:08:31
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Figure showing analysis pipeline.
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Achilles 2 tutorials (56.5KB) Undefined
(In process)
Help for getting started with Achilles 2 pooled shRNA data.
Also check: http://www.broadinstitute.org/cancer/software/GENE-E/
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ATARiS (18.2MB) RNAi enrichment
(Published)
ATARiS analysis of the Achilles RNAi dataset (102 cell lines from the PNAS 2011 publication).
See "ATARiS: Computational quantification of gene suppression phenotypes from multi-sample RNAi screens", Genome Research 2012 Dec 26 (Epub) and http://broadinstitute.org/ataris for details.
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Achilles Phase I (1.1MB)
Broad project Achilles phase 1 RNAi data
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Cell Line Data (1.1MB) RNAi
(Published)
Raw cell viability data for each shRNA was generated 6 days post-infection in 384-well plates using an ATP-based luminescence assay (Cell-Titer Glo, Promega), with the exception of A549 cells, for which cell number was quantified using high throughput imaging. All wells were tested in quadruplicate, including duplicate samples with or without puromycin to assess infection efficiency. Data for shRNAs that yielded low infection efficiency (ie puromycin positive to puromycin negative ratio < 0.25) were excluded ('NT'). Data normalization was performed using the B-score metric, a variant of the Z-score that uses the median absolute deviation to account for plate-to-plate variability, as well as a two-way median polish to minimize row/column effects that accompany RNAi screens. The average of B-scores for all replicates (puromycin positive and negative) was calculated for each individual shRNA and aligned for the 5002 shRNAs (rows) tested across all 19 cell lines (columns).
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Modeling genomic diversity and tumor dependency in malignant melanoma (20.4GB)
Data from the 2008 paper "Modeling genomic diversity and tumor dependency in malignant melanoma", William M. Lin et al, Cancer Research, 2007.
This dataset was previously available on the Broad Melanoma Portal.
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Sample Information Files (152.0KB) Sample annotation
(Published)
Sample information files for all the SNP array and gene expression samples included. Please note that the following files contain data from patient samples. The data should be treated confidentially and used purely for research purposes.
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| lin et al. sample information file for 129 samples.xls
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62.0KB
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07-Nov-2012 11:38:22
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Sample information file for all melanoma samples described in Lin et al.: Sample Name, Type, Platform (250K, 50K, HT-HGU133A), Provider, Well Location on GE plate, Array Name, Mutation status (BRAF, NRAS, PTEN, Other), CI-1040 GI50.
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| sampleinformationfile_874_forgistic.txt
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90.0KB
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07-Nov-2012 11:38:38
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Sample information file for all the melanoma samples run on SNP arrays in addition to a collection of normal samples used for determination of copy number.
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Tables and Figures (490.4KB) Undefined
(Published)
The most recent versions of the tables and figures, reflecting updated versions of the genome build (hg17) and the latest analytical processing algorithms.
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| Linetal.Figure2.pdf
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408.5KB
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07-Nov-2012 11:40:07
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Figure 2. Clustering analysis of significant melanoma genomic alterations. Hierarchical clustering of GISTIC lesions (discretized smoothed copy number; see Materials and Methods) by the Euclidean distance metric and complete linkage.
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| linetal.table1and2.pdf
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81.9KB
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07-Nov-2012 11:40:24
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Significant regions of chromosomal copy number alterations
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Gene Expression Data (262.2MB) Gene expression
(Published)
Affymetrix HT-HGU133A gene expression data from melanoma short term cultures, melanoma cell lines, and normal samples. NOTE: The following data is derived from patient samples and should be treated confidentially and used only for research purposes.
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| HTHGU133A_CELFILES.zip
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239.3MB
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07-Nov-2012 11:44:23
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88 melanoma samples 5 normal melanocytes 1 breast cancer sample control
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| 88MelanomaGeneExpression.txt
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22.8MB
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07-Nov-2012 11:45:16
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88 RMA-normalized melanoma samples used in differential expression analyses
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SNP array data (20.1GB) Affy SNP
(Published)
Affymetrix 250K StyI and XbaI SNP array data from melanoma short term cultures, melanoma cell lines and normal samples. NOTE: The following data is derived from patient samples and should be treated confidentially and used only for research purposes
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| CopyNumber_101MelanomaSamples_250KStyIand50KXbaI_hg17_2.1.08.snp
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301.0MB
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07-Nov-2012 11:49:49
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Copy number data for 101 melanoma samples processed as described in Lin et al. Basic steps: Batch correction, copy number calculation, per sample quality control, smoothing of copy number, combination of 250K and 50K data.
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| 50KXbaSamples.tar
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827.9MB
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07-Nov-2012 12:18:33
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Melanoma Short Term Culture and Cell Line and Normal samples (100 CEL files).
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| 773CelFiles_a.zip
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3.8GB
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07-Nov-2012 13:46:59
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Melanoma Short Term Culture and Normal samples (773 CEL files split across 5 zip file archives)
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| 773CelFiles_b.zip
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3.8GB
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07-Nov-2012 13:52:43
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Melanoma Short Term Culture and Normal samples (773 CEL files split across 5 zip file archives)
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| 773CelFiles_c.zip
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3.8GB
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07-Nov-2012 14:02:18
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Melanoma Short Term Culture and Normal samples (773 CEL files split across 5 zip file archives)
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| 773CelFiles_d.zip
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3.7GB
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07-Nov-2012 14:05:36
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Melanoma Short Term Culture and Normal samples (773 CEL files split across 5 zip file archives)
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| 773CelFiles_e.zip
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3.8GB
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07-Nov-2012 14:10:56
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Melanoma Short Term Culture and Normal samples (773 CEL files split across 5 zip file archives)
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TCGA GBM and Ovarian (226.8MB)
GBM and Ovarian data from the TCGA project.
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Gene Expression (177.9MB) Gene expression
(Published)
Gene Expression data from Affy U133aplus2
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Copy Number(Segmented) (35.3MB) Agilent aCGH
(Published)
Segmented copy number data
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Copy Number (level 4) (11.4MB) Agilent aCGH
(In process)
TCGA level 4 processed and summarized copy number results including gistic and RAE results
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Methylation (1.9MB) Undefined
(Undefined)
IGV Formatted methylation data
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Mutation (111.8KB) Mutation
(In process)
Mutation data (TCGA MAF format) summarized across multiple centers
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Sample Annotation (281.3KB) Sample annotation
(Unprocessed)
Sample annotation and linking identifiers for TCGA samples across the multiple data sets
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The Nova Splicing-Regulatory Network and Its Combinatorial Controls (229.6MB)
The control of RNA alternative splicing is critical for generating biological diversity. Despite emerging genome-wide technologies to study RNA complexity, reliable and comprehensive RNA-regulatory networks have not been defined. Here, we used Bayesian networks to probabilistically model diverse data sets and predict the target networks of specific regulators. We applied this strategy to identify ~700 alternative splicing events directly regulated by the neuron-specific factor Nova in the mouse brain, integrating RNA-binding data, splicing microarray data, Nova-binding motifs, and evolutionary signatures. The resulting integrative network revealed combinatorial regulation by Nova and the neuronal splicing factor Fox, interplay between phosphorylation and splicing, and potential links to neurologic disease.
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Dataset S1 (211.3MB) Resequencing
(Published)
Unique Nova CLIP tags. The coordinate of each tag mapped to the mouse genome (mm9) is shown in the BED format.
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Dataset S2 (18.3MB) Resequencing
(Published)
Nova CLIP tag clusters. The coordinate of each cluster in the mouse genome (mm9) is shown in the BED format. The peak height (PH) of each cluster is indicated in the score (5th) column.
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Dataset S3 (38.5KB) Resequencing
(Published)
325 non-redundant Nova target cassette exons. The coordinates of the cassette exon and flanking exons in the mouse genome (mm9) are shown in the BED format. Exons with Nova-dependent inclusion and exclusion are indicated by 1 and -1 in the score (5th) column, respectively.
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