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[…] d 22Rv1 and LNCaP cells treated with independent siRNAs targeting SWI/SNF protein SNF5 (SMARCB1) as well as control non-targeting siRNAs. These profiling experiments were run as biological duplicates for a total of 4 arrays (2 cell lines × 2 independent siRNAs × 1 protein). Finally, we profiled of RWPE cells expressing two different SChLAP1 isoforms as well as the control LacZ gene. These profiling experiments were run in technical duplicate for a total of 4 arrays (2 from RWPE-SChLAP1 isoform #1 and 2 from RWPE-SChLAP1 isoform #2)., All of the microarray data were represented as base-2 log fold-change between targeting versus control siRNAs. We used the CollapseDataset tool provided by the GSEA package to convert from Agilent Probe IDs to gene symbols. Genes measured by multiple probes were consolidated using the median of probes. We then ran one-class SAM analysis from the Multi-Experiment Viewer application and ranked all genes by the difference between observed versus expected statistics. These ranked gene lists was imported to GSEA version 2.07., For the 22Rv1 and LNCaP SChLAP1 knockdown experiments we ran the GseaPreRanked tool to discover enriched gene sets in the Molecular Signatures Database (MSigDB) version 3.0. Lists of positively and negatively enriched concepts were interpreted manually., For each SNF5 protein knockdown we nominated genes that were altered by an average of at least 2-fold. These signatures of putative SNF5 target genes were then used to assess enrichment of SChLAP1-regulated genes using the GseaPreRanked tool. Additionally, we nominated genes that changed by an average 2-fold or greater across SNF5 knockdown experiments and quantified the enrichment for SChLAP1 target genes using GSEA., The RWPE-SChLAP1 versus RWPE-LacZ expression profiles were ranked using SAM analysis as described above. A total of 1,245 genes were significantly over- or under-expressed and are shown in . A q-value of 0.0 in this SAM analysis signifies that no permutation generated a more significant difference between observed and expected gene expression ratios. The ranked gene expression list was used as input to the GseaPreRanked tool and compared against SNF5 ChIP-Seq promoter peaks that decreased by >2-fold in RWPE-SChLAP1 cells. Of the 389 genes in the ChIP-Seq gene set, 250 were profiled by the Agilent HumanGenome microarray chip and present in the GSEA gene symbol database. An expression profile across these 250 genes is in ., We assembled an RNA-Seq cohort from prostate cancer tissues sequenced at multiple institutions. We included data 12 primary tumors and 5 benign tissues published in GEO as GSE22260, 16 primary tumors and 3 benign tissues released in dbGAP as study phs000310.v1.p1, and 17 benign, 57 primary, 14 metastatic tumors sequenced by our own institution and released as dbGAP study phs000443.v1.p1. shows sample information, and shows sequencing library information., Sequencing data were aligned using Tophat version 1.3.1 against the Ensembl GRCh37 human genome build. Known introns (Ensembl release 63) were provided to Tophat. Gene expression across the Ensembl version 63 genes and the SChLAP1 transcript was quantified by HT-Seq version 0.5.3p3 using the script htseq-count (www-huber.embl.de/users/anders/HTSeq/). Reads were counted without respect to strand to avoid bias between unstranded and strand-specific library preparation methods. This bias results from the inability to resolve reads in regions where two genes on opposite strands overlap in the genome., Differential expression analysis was performed using R package DESeq version 1.6.1. Read counts were normalized using the estimateSizeFactors function and variance was modeled by the estimateDispersions function. Differentially expression statistics were computed by the nbinomTest function. We called differentially expressed genes by imposing adjusted p-value cutoffs for cancer versus benign (padj < 0.05), metastasis versus primary (padj < 0.05), and gleason 8+ versus 6 (padj < 0.10). Heatmap visualizations for these analyses are presented as ., Read count data were normalized using functions from the R package DESeq version 1.6.1. Adjustments for library size were made using the estimateSizeFactors function and variance was modeled using the estimateDispersions function using the parameters “method=blind” and “sharingMode=fit-only”. Next, the raw read count data was converted to pseudo-counts using the getVarianceStabilizedData function. Gene expression levels were then mean-centered and standardized using the scale function in R. Pearson correlation coefficients were computed between each gene of interest and all other genes. Statistical significance of Pearson correlations was determined by comparison to correlation coefficients achieved by 1,000 random permutations of the expression data. We co […]

Pipeline specifications

Software tools GSEA, TopHat, HTSeq, DESeq
Databases dbGaP MSigDB
Diseases Neoplasm Metastasis, Neoplastic Processes, Neoplasms, Urogenital Neoplasms, Genital Neoplasms, Male, Prostatic Diseases