README.md

modelAD_piplines

Pipelines for quantifying, analyzing, and submitting model-AD data in the Mortazavi lab

Table of Content

• Prepare metadata to upload the data into Synapse:

  • Fetching information on mice from CLIMB
  • prepare metadata table per each assay

• Bulk Short Read RNA_seq Analysis: Analysing bulk short read RNA_seq using RSEM output

  • Aggregating TPM and count of all mice in the same study
  • Filter metadata and genes (optional)
  • Differential Expression gene (DEG) analysis (EdgR and PyDESeq2)
  • Weighted correlation network analysis (PyWGCNA)

• Bulk Long Read RNA_seq Analysis: Analysing bulk long read RNA_seq using Kallisto

  • Quantify reads using Kallisto long read
  • Aggregating TPM and count of all mice in the same study
  • Differential Gene Expression (DGE) analysis (PyDESeq2)
  • Differential Expression Isoform (DIE) analysis (PyDESeq2)
  • SWAN report (SWAN_vis)

• Validating new mouse model using bulk long read RNA_seq: Validate new variant splicing through bulk long read RNA_seq using minimap and IGV

  • Prepare custom GTF and Fasta files if we have a chimeric gene or humanized gene
  • Map reads using minimap2
  • Visualize the coverage tracks using IGV