GSE198419 Processing Pipeline

RNA-Seq code_examples 7 steps

Publication

Remodeling oncogenic transcriptomes by small molecules targeting NONO.

Nature chemical biology (2023) — PMID 36864190

Dataset

GSE198419

Transcriptome changes in 22Rv1 and MCF7 cells after treatment with NONO ligands and controls

Warning: Pipeline descriptions and code snippets may be inferred or AI-generated. Use them only as a starting point to guide analysis, and validate before use.

Processing Steps

Generate Jupyter Notebook
  1. 1

    For differential expression:

  2. 2

    Transcript abundance was quantified using Salmon [v1.3.0] with GENCODE v37 annotation.

    Salmon v1.3.0
  3. 3

    Gene level quantification was performed using tximeta [v1.8.4].

  4. 4

    Differential gene expression was analyzed by DESeq2 [v1.30.1].

    DESeq2 v1.30.1
  5. 5

    For splicing analysis, 22Rv1 data only:

  6. 6

    RNA-seq reads were mapped to hg38 using STAR Aligner.

    STAR
  7. 7

    Alternative splicing (AS) analysis was completed using rMATs (v3.2.5).

    rMATS v3.2.5

Tools Used

DESeq2 STAR
Raw Source Text 
For differential expression:
Transcript abundance was quantified using Salmon [v1.3.0] with GENCODE v37 annotation.
Gene level quantification was performed using tximeta [v1.8.4].
Differential gene expression was analyzed by DESeq2 [v1.30.1].
For splicing analysis, 22Rv1 data only:
RNA-seq reads were mapped to hg38 using STAR Aligner.
Alternative splicing (AS) analysis was completed using rMATs (v3.2.5).
Assembly: hg19
Supplementary files format and content: *.csv: Gene counts were used for differential expression.
Supplementary files format and content: *.xlsx: rMATs table was used for splicing analysis.
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