lab-9.md

tags
ggg, ggg2021, ggg201b

Lab 9 outline - assembly collapse; RNAseq - GGG 201(b) lab, March 4th, 2022

(Permanent link on github)

Contents:

[toc]

Assembly collapse - some data exploration

I will use the data in: https://github.com/ctb/2022-ggg-201b-assembly-collapse

Final notebook from class: here

Exploring Titus's results

I cheated and made a Snakefile that did coverage directly :) -

rule all:
    input:
        "SRR2584857_quast",
        "SRR2584857_annot",
        expand("SRR2584857.{cover}C_quast", cover=range(1, 41)),
        expand("SRR2584857.{cover}C_annot", cover=range(1, 41))

rule subset_wc:
    input:
        r1 = "{sample}_1.fastq.gz",
        r2 = "{sample}_2.fastq.gz",
    output:
        r1 = "{sample}.{cover}C_1.fastq.gz",
        r2 = "{sample}.{cover}C_2.fastq.gz",
    params:
        n_lines = lambda w: int(float(w.cover) * 4.5e6 / 100 / 2) * 4
    shell: """
        gunzip -c {input.r1} | head -{params.n_lines} | gzip > {output.r1} || 
true
        gunzip -c {input.r2} | head -{params.n_lines} | gzip > {output.r2} || 
true
    """

RNAseq again

Amanda gave a great introduction to the statistics last week, and I wanted to take over where she left off with the binder.

Start by running the RNASeq binder:

Reminder, to run the analysis --

1. start the binder

2. at the command line, run

snakemake -j 4 --use-conda

3. open rnaseq-workflow.Rmd

4. knit to HTML

Check out the Snakefile

What's the output of the Snakefile?

Let's walk through the RMarkdown file

Statistical considerations and cutoffs

Appendix: FastQC and MultiQC

(may cover in lab 10)