I want to extract differential expressed genes using LIMMA from RNA seq data for three cancer types viz breast, lung and prostate. These data should have tumor and normal samples. I have read some papers which have used data from TCGA. BUt now TCGA has linked to Genomics Data Commons and all data are not open access. All BAM files are under controlled access. Also, LIMMA requires raw read counts for analysis. I an new to RNA seq data and analysis. Can anyone help, where should I get these data and what format should it be, as I have read that TPM, FPKM normalized values cannot be input to LIMMA.
-
-
For reference cross-posted: https://www.biostars.org/p/250946/ -
I'm not sure what you'll have to do to get your data, but I can give you some advice on the RNA-seq part.
If you're starting with BAM files that means your reads are already aligned, if you start with reads (.fastq) you can map them yourself using any splice aware mapping tool. Assuming you have bam files though, you'll want to use a tool that extracts raw counts from the bam files. You'll need an annotation file (.gtf or gff format probably) and a counting tool. htseq-counts or featureCounts (in the RSubread package) are both good and widely used tools for extracting raw counts from BAM files, so start there and extract your counts.
Once you have gotten that far, then there are alot of tools like limma, deseq2, or edgeR that can use that data for analysis.Comment
-
CRISPR/Cas9 sparked the gene editing revolution for both research and therapeutics.1 But this system still showed severe issues that limited its applications. The most prominent were the heavy reliance on PAM sequences, delivery limitations, double-stranded breaks that prompt unintended edits and cell death, and editing inefficiency (both in targeting and in knock-in reliability).
Despite this, “CRISPR helped turn genome editing from a specialized technique into...07-31-2026, 11:01 AM -
Proteomics platforms are evolving rapidly, with advances in mass spectrometry and affinity-based approaches expanding what researchers can detect and at what scale. As the field moves toward deeper proteome coverage and clinical applications, scientists face an increasingly complex landscape of tools. This article will explore how researchers are navigating these choices to find the right platform for their work.
The systematic characterization of the human proteome has...07-20-2026, 11:48 AM
Comment