GSE40652 Processing Pipeline
RNA-Seq
code_examples
1 step
Publication
Divergent roles of ALS-linked proteins FUS/TLS and TDP-43 intersect in processing long pre-mRNAs.Nature neuroscience (2012) — PMID 23023293
Dataset
GSE40652Divergent roles of ALS-linked proteins FUS/TLS and TDP-43 intersect in processing long pre-mRNAs (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
Sequenced reads were trimmed for adaptor sequence, and masked for low-complexity or low-quality sequence, then mapped to mm9 or hg18 whole genome using bowtie v0.12.2 with parameters -q -p 4 -e 100 -y -a -m 10 --best --strata
$ Bash example
# Install Bowtie v0.12.2 # conda create -n bowtie_0_12_2 bowtie=0.12.2 -c bioconda # conda activate bowtie_0_12_2 # Download reference genome (e.g., mm9 from UCSC) # wget https://hgdownload.soe.ucsc.edu/goldenPath/mm9/bigZips/mm9.fa.gz # gunzip mm9.fa.gz # Build Bowtie index for mm9 # bowtie-build mm9.fa mm9_index # Align pre-processed reads to the mm9 genome using Bowtie # Assuming 'trimmed_masked_reads.fastq' is the input file after trimming and masking, # and 'mm9_index' is the pre-built Bowtie index for the mm9 genome. bowtie -q -p 4 -e 100 -y -a -m 10 --best --strata mm9_index trimmed_masked_reads.fastq > mapped_reads.sam
Raw Source Text
Sequenced reads were trimmed for adaptor sequence, and masked for low-complexity or low-quality sequence, then mapped to mm9 or hg18 whole genome using bowtie v0.12.2 with parameters -q -p 4 -e 100 -y -a -m 10 --best --strata Genome_build: mm9 Supplementary_files_format_and_content: BED format of all reads