Skip to content

Latest commit

 

History

History
72 lines (57 loc) · 2.58 KB

readme.md

File metadata and controls

72 lines (57 loc) · 2.58 KB
Raw

######################################################

Spatially varying cis-regulatory divergence in

Drosophila embryos elucidates cis-regulatory logic

###################################################### Peter A. Combs and Hunter B. Fraser

Department of Biology, Stanford University

This repository contains the analysis code for Combs and Fraser 2017 (bioRxiv preprint; currently submitted for review). Raw and processed data files available from the Gene Expression Omnibus.

Very briefly, the data analyses in these scripts do two things:

  1. Process RNA-seq data from cryosliced D. melanogaster x simulans hybrid embryos. We are looking for genes with spatially varying allele-specific expression (svASE) is different in one part of the embryo compared to the other.

  2. Perform modeling of the cis-regulatory input functions of genes using a modeling approach inspired in large part by Ilsley, et al (2013). Using genome alignments and motif searches, we can then make inferences about which cis-regulatory changes actually produced the spatially varying ASE that we observed in part 1.

Almost all of the code is written in either Python 3 or Snakemake. Known dependencies include:

  • SRA Tools
  • Snakemake
  • Bowtie2 (Reference gDNA alignment)
  • STAR (RNA-seq alignment)
  • Cufflinks (RNA-seq quantification)
  • Bedtools
  • Samtools
  • ASEr (https:/thefraserlab/aser)
  • Hornet (https:/thefraserlab/hornet)
  • Various python modules
    • pysam
    • progressbar
    • numpy/scipy/pandas/matplotlib
    • svgwrite
    • pyemd
    • BioPython
    • statsmodels

RNA-seq and finding svASE

You should be able to go from raw reads to summary data tables by doing:

snakemake

Though you probably want to run this on a pretty high-powered machine---or, ideally, a compute cluster. The basic steps for a single sample are:

  1. Download reads from SRA
  2. Map reads to the D. melanogaster genome (the resulting file is called assigned_dmelR.bam for various historical reasons)
  3. Calculate absolute abundances using Cufflinks
  4. Filter out potentially ambiguous/mismapped reads using Hornet to implement the WASP pipeline.
  5. Count allele-specific reads for each gene using ASEr

Then, all of the expresion and ASE data are combined into summary tables.

We found that fitting either a logistic or gaussian function to the data found all of the genes whose allele-specific expression had spatial patterns.