GSE34995 Processing Pipeline
RNA-Seq
code_examples
2 steps
Publication
Integrative genome-wide analysis reveals cooperative regulation of alternative splicing by hnRNP proteins.Cell reports (2012) — PMID 22574288
Dataset
GSE34995Integrative genome-wide analysis reveals cooperative regulation of alternative splicing by hnRNP proteins (RNA-Seq)
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
Strand-specific RNA-seq reads from control siRNA treatment, and each hnRNP depletion experiment were processed as previously described (Polymenidou et al., 2011).
RNA-seq v2.7.10a (Inferred with models/gemini-2.5-flash)$ Bash example
# Install STAR (if not already installed) # conda install -c bioconda star # Define variables # Placeholder for mouse GRCm39 genome index, as the original paper used mouse mm9 and the latest assembly is preferred. GENOME_DIR="/path/to/STAR_genome_index/GRCm39" READ1="control_siRNA_R1.fastq.gz" # Example input for control siRNA treatment READ2="control_siRNA_R2.fastq.gz" # Example input for control siRNA treatment (paired-end assumed) OUTPUT_PREFIX="control_siRNA_aligned" THREADS=8 # Number of CPU threads to use # Create genome index (run once for a given genome, uncomment and adjust paths if needed) # STAR --runMode genomeGenerate \ # --genomeDir ${GENOME_DIR} \ # --genomeFastaFiles /path/to/GRCm39.primary_assembly.fasta \ # --sjdbGTFfile /path/to/gencode.vM25.annotation.gtf \ # --runThreadN ${THREADS} # Align strand-specific RNA-seq reads using STAR # Assuming dUTP-based library prep, which is typically reverse-stranded, # indicated by --outSAMstrandField reverse for the XS tag. STAR --genomeDir ${GENOME_DIR} \ --readFilesIn ${READ1} ${READ2} \ --runThreadN ${THREADS} \ --outFileNamePrefix ${OUTPUT_PREFIX}_ \ --outSAMtype BAM SortedByCoordinate \ --outSAMstrandField reverse \ --outFilterMultimapNmax 20 \ --outFilterMismatchNmax 999 \ --outFilterMismatchNoverLmax 0.1 \ --alignIntronMin 20 \ --alignIntronMax 1000000 \ --alignMatesGapMax 1000000 \ --readFilesCommand zcat -
2
An average of 70% of reads mapped uniquely to our gene structure database, using Bowtie (version 0.12.2, with parameters âl 20 âm 5 âk 5 ââbest ââun ââ max âq).
$ Bash example
# Install Bowtie 0.12.2 # conda install -c bioconda bowtie=0.12.2 # Note: 'gene_structure_index' refers to a pre-built Bowtie index for the gene structure database. # 'input_reads.fastq' is the input FASTQ file containing the reads. # 'aligned_reads.sam' will contain the aligned reads in SAM format. # 'unaligned_reads.fastq' will contain reads that did not align. # The parameter '--max' from the description is not a valid Bowtie 0.12.2 flag and has been omitted. bowtie -q \ -l 20 \ -m 5 \ -k 5 \ --best \ --un unaligned_reads.fastq \ gene_structure_index \ input_reads.fastq \ -S aligned_reads.sam
Tools Used
Raw Source Text
Strand-specific RNA-seq reads from control siRNA treatment, and each hnRNP depletion experiment were processed as previously described (Polymenidou et al., 2011). An average of 70% of reads mapped uniquely to our gene structure database, using Bowtie (version 0.12.2, with parameters âl 20 âm 5 âk 5 ââbest ââun ââ max âq).