GSE148038 RNA-seq Data Processing

RNA-seq geo_data_processing 5 steps

Publication

Context-dependent functional compensation between Ythdf m<sup>6</sup>A reader proteins.

Genes & development (2020) — PMID 32943573

Dataset

GSE148038

Ythdf m6A readers compensate each other in a context dependent manner [3'UTR 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. 1

    We used Illumina CASAVA 1.8.2 software to generate fastq files.

  2. 2

    Reads were aligned to mouse genome version mm10 with Bowtie2 software using its default parameters.

    Bowtie2
  3. 3

    Gene expression levels were estimated using ESAT software (Derr et al.

  4. 4

    2016), with parameters -minMappingQuality 0 -singleEnd -maxIntoGene 1000, for mm10 RefSeq genes.

    RefSeq
  5. 5

    Gene expression levels were normalized by library size of each sample (FPM, fragments per million reads).

Tools Used

Bowtie2
Raw Source Text 
We used Illumina CASAVA 1.8.2 software to generate fastq files.
Reads were aligned to mouse genome version mm10 with Bowtie2 software using its default parameters.
Gene expression levels were estimated using ESAT software (Derr et al. 2016), with parameters  -minMappingQuality 0 -singleEnd -maxIntoGene 1000, for mm10 RefSeq genes.
Gene expression levels were normalized by library size of each sample (FPM, fragments per million reads).
Genome_build: mm10
Supplementary_files_format_and_content: ESAT output
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