GSE147849 RNA-seq Data Processing

RNA-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

Dataset

GSE147849

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

    We used Illumina CASAVA 1.8.2 software to generate fastq files.

  2. 2

    Reads were trimmed using cutadapt (Martin 2011) (parameters: -a ADAPTER1 -a “A{10}” -a “T{10}” -A “A{10}” -A “T{10}” –times 2 -q 20 -m 25).

    cutadapt
  3. 3

    Reads were mapped to genome mm10 using STAR (Dobin et al.

  4. 4

    2013) v2.4.2a (parameters: –alignEndsType EndToEnd, –outFilterMismatchNoverLmax 0.05, –twopassMode Basic).

  5. 5

    Sample counting was done using STAR, quantifying mm10 RefSeq annotated genes.

  6. 6

    Normalization of the counts and differential expression analysis was performed using DESeq2 (Love et al.

  7. 7

    2014) with the parameters: betaPrior=True, cooksCutoff=FALSE, independentFiltering=FALSE.

Tools Used

Raw Source Text
We used Illumina CASAVA 1.8.2 software to generate fastq files.
Reads were trimmed using cutadapt (Martin 2011) (parameters: -a ADAPTER1 -a “A{10}” -a “T{10}” -A “A{10}” -A “T{10}” –times 2 -q 20 -m 25).
Reads were mapped to genome mm10 using STAR (Dobin et al. 2013) v2.4.2a (parameters: –alignEndsType EndToEnd, –outFilterMismatchNoverLmax 0.05, –twopassMode Basic).
Sample counting was done using STAR, quantifying mm10 RefSeq annotated genes.
Normalization of the counts and differential expression analysis was performed using DESeq2 (Love et al. 2014) with the parameters: betaPrior=True, cooksCutoff=FALSE, independentFiltering=FALSE.
Genome_build: mm10
Supplementary_files_format_and_content: DESeq2 output
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