GSE148536 Ribo-seq Data Processing
Ribo-seq
geo_data_processing
7 steps
Publication
Context-dependent functional compensation between Ythdf m<sup>6</sup>A reader proteins.Genes & development (2020) — PMID 32943573
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-
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Library strategy: Ribo-seq
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We used Illumina CASAVA 1.8.2 software to generate fastq files.
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Reads were pre-processed by trimming their linker (sequence CTGTAGGCACCATCAAT) and polyA removal with cutadapt.
cutadapt -
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Reads were aligned to mouse genome version mm10 with Bowtie aligner (parameters -v -m 16 -p 8 --max), where only uniquely aligned reads where used for further analyses.
Bowtie -
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Per gene, for translation calculation, reads were counted in the coding region excluding 15 and 6 nucleotides from the beginning and end of each coding sequence (CDS), respectively (Ingolia et al.
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2009; McGlincy and Ingolia 2017).
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For each gene and sample, Ribo-seq RPKM values were calculated
Raw Source Text
Library strategy: Ribo-seq We used Illumina CASAVA 1.8.2 software to generate fastq files. Reads were pre-processed by trimming their linker (sequence CTGTAGGCACCATCAAT) and polyA removal with cutadapt. Reads were aligned to mouse genome version mm10 with Bowtie aligner (parameters -v -m 16 -p 8 --max), where only uniquely aligned reads where used for further analyses. Per gene, for translation calculation, reads were counted in the coding region excluding 15 and 6 nucleotides from the beginning and end of each coding sequence (CDS), respectively (Ingolia et al. 2009; McGlincy and Ingolia 2017). For each gene and sample, Ribo-seq RPKM values were calculated Genome_build: mm10 Supplementary_files_format_and_content: Ribo-seq RPKM