1. Garbage In, Garbage Out: Quality control on sequence data

2. Key concepts of session
The quality of the data limits what you can confidently say about the data and how you can subsequently use it.
An important component to quality control is visualization: you must actually LOOK at your data.

3. So you have reads off a sequencer … where do you start?
The fastQ format:
More on the file format and quality encoding:

4. Expectation

5. But the reality may be very different

6. So what? Why does QC matter?
You are going to spend a LOT of time (and $) on this dataset.
Downstream analysis software assumes pretty well behaved data!!

7. How to assess a bag of reads
Pre-mapping: FastQC
GC content
read quality (Phred score)
Post-mapping:
read coverage (which regions, how much)
complexity (# unique samples)

8. Protocol matters – how the experiment influences your QC
Mistakes in protocol can result in abnormal distributions
Poor read quality = poor mapping = poor coverage

9. WHY doesn’t it look like I wanted?
Cell clustering – over-amplification
Low library complexity
Problems with amplification or size selection
Problem with adapters
See also:

10. But one person’s garbage is another’s treasure.

11. You can still obtain information
Even low coverage samples can give you information:
Which genes are being actively transcribed
Differentially expressed genes (depending on depth and coverage)

12. Running FastQC – Pre-Trim
Determine which adapters are present if you are unsure of the protocol
Assess whether sequencing/protocol providing the results expected
Refine trimming options

13. In this script, we will:
Flip reads (reverse complement) – protocol dependent
Run FastQC
To run (after adjusting parameters in green box):
$ bash fastqc_pretrim.sh

14. Open up our fastqc .html report

15. Trimming Many different trimming programs available
We will use “bbduk” – quick runtime, lots of trim options
$ vi trim.sh

16. In this script, we will:
Trim for adapters (followed by length)
Trim for quality
To run (after adjusting rootname/project):
$ bash trim.sh

17. View trim stats $ cd /home/user/hackcon/trimmed $ ls
$ vim sample.stats
What can we learn from this report?

18. Running FastQC – Post-Trim
Determine which adapters are present if you are unsure of the protocol
Assess whether sequencing/protocol providing the results expected
Refine trimming options

19. In this script, we will:
Assess our trimming parameters
Determine if we need to re-trim or move forward with mapping
To run (after adjusting rootname/project):
$ bash fastqc_postrim.sh

20. Open up our fastqc .html report