🧬 Sarcoma RNA-Seq DEG Run

Experimental Overview 🧪

RNA-Seq analysis was performed to identify differentially expressed genes (DEGs) between and

Wetlab Phase 💧

1. RNA Extraction

Total RNA was extracted using QIAGEN's RNeasy Fibrous Tissue Kit from tumoral tissue preserved in RNAlater. After extraction, Invitrogen Qubit RNA High Sensitivity Kit was used to assess the RNA concentration. Yield: X ng/µL, RIN: X.X.

2. Library Preparation

mRNA enrichment via poly(A) selection, fragmentation, cDNA synthesis, and adapter ligation using [kit name]. Libraries were QC'd with Bioanalyzer.

3. Sequencing

Paired-end (2 × 150 bp) sequencing on an Illumina [platform], generating ~X million reads per sample.

Drylab Time 🧬

1. Quality Control & Trimming 🚀

QC and adapter trimming were done using FastQC and Trimmomatic.

fastqc *.fastq.gz
trimmomatic PE -threads 8 input_R1.fastq.gz input_R2.fastq.gz \
  output_R1_paired.fastq.gz output_R1_unpaired.fastq.gz \
  output_R2_paired.fastq.gz output_R2_unpaired.fastq.gz \
  ILLUMINACLIP:adapters.fa:2:30:10 SLIDINGWINDOW:4:20 MINLEN:50

2. Alignment 📌

Reads were aligned to the reference genome ([Genome version]) using HISAT2.

hisat2 -p 8 -x genome_index -1 output_R1_paired.fastq.gz -2 output_R2_paired.fastq.gz \
  -S aligned_reads.sam

SAM files were converted to sorted BAM files using SAMtools:

samtools view -bS aligned_reads.sam | samtools sort -o aligned_reads_sorted.bam
samtools index aligned_reads_sorted.bam

3. Gene Quantification 📊

FeatureCounts was used to assign reads to genes:

featureCounts -T 8 -a annotation.gtf -o counts.txt aligned_reads_sorted.bam

4. Differential Expression Analysis 🔬

DESeq2 was used for identifying DEGs.

library(DESeq2)
counts <- read.table("counts.txt", header=TRUE, row.names=1)
coldata <- data.frame(condition=c("A", "A", "B", "B"))
dds <- DESeqDataSetFromMatrix(countData=counts, colData=coldata, design=~condition)
dds <- DESeq(dds)
res <- results(dds)
write.csv(res, file="DEGs.csv")

5. Functional Enrichment Analysis 🔎

Gene Ontology (GO) and KEGG pathway enrichment were performed using g:Profiler.

library(clusterProfiler)
library(org.Hs.eg.db)
enriched <- enrichGO(gene=rownames(res[res$padj < 0.05, ]), OrgDb=org.Hs.eg.db, ont="BP", pAdjustMethod="BH")

Results & Insights 📌

• X genes were differentially expressed (padj < 0.05).
• Upregulated pathways: [Pathway X, Pathway Y]
• Downregulated pathways: [Pathway Z]
• [Biological interpretation and next steps]

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Next up: Validation and integration with other omics data! 🚀