Genome 373: Mapping Short Sequence Reads III. Doug Fowler

Transcription

1 Genome 373: Mapping Short Sequence Reads III Doug Fowler

2 What is Galaxy? Galaxy is a free, open source web platform for running all sorts of computational analyses including pretty much all of the sequencing-related stuff we ve discussed! You can use it at usegalaxy.org

3 The Data We ll Be Playing With This is a FASTA file, which contains one sequence. This sequence, which corresponds to just one gene (MYC) will be our reference

4 The Data We ll Be Playing With

5 The Data We ll Be Playing With This is a FASTQ file, which is a standard high-throughput sequencing format. This file contains ~20,000 high-throughput sequencing reads which we will align to the MYC locus

6 The FASTQ format Why might we want to store Q scores as ASCII characters rather than numbers?

7

8 Short read mapping Go to User -> Register and register, if you haven t already if you have, login

9 Short read mapping Load data using the load your own data link

10 Short read mapping Upload MYClocus.fa (type = fasta) and cas9.fq (type = fastqsanger). Make sure to set the file type correctly, then click start

11 Short read mapping The files should appear in the history section on the right

12 First, let s do a quality analysis by clicking NGS: QC and manipulation and then FastQC

13 We see information and parameters for the FastQC tool make sure cas9.fq is selected and hit execute

14 We see that the job has been started and will produce two files, a RawData file and a webpage

15 Jobs are gray when waiting, yellow when running and green when done

16 You can click on the eye button to look at the data or the job name for more information

17 Once the FastQC job is done, click on the eye button for the Webpage output file to see the quality report looks pretty good!

18 Explore these results the first plot shows Q scores along the read

19 Next, let s align our reads (the.fq file) to our reference sequence (the.fa file) by clicking NGS: Mapping -> BWA-MEM

20 We need to tell BWA-MEM to use the MYClocus.fa reference by changing the load reference genome from local cache to history

21 We need to tell BWA-MEM to use only single read data (we don t have paired end reads) by changing to Single

22 Then, execute the alignment

23 Once the alignment is done, click the Visualize in Trackster button

24 We ll need to tell Galaxy we want to use our custom MYClocus.fa reference sequence so click Add a Custom Build

25 Name the custom build, add a key name and make sure the right file (MYClocus.fa) is selected then hit submit

26 Click the Back button on the browser to get back to the main Galaxy view and click on Visualize in Trackster again

27 This time, your MYClocus custom reference will be available select it and click create then wait while the visualization is prepared

28 Now you can see the read pileup from our alignment! Try zooming, clicking and dragging, etc

29 You can also play with the display mode to see the data in different ways

30 Save the visualization

31 Click the Analyze Data link to go back to the main Galaxy page

32 Now, lets call variants from our pileup using the Mpileup tool in NGS: SAMtools

33 We need to change one parameter for Mpileup. Select Perform INDEL calling and set advanced options and then enter 100,000 in the Skip INDEL calling if the average per-sample depth is above box. Then, execute Mpileup

34 You might be wondering why we would expect to see any variants in this sequencing data It s because we used a nuclease called Cas9 to cut the genome at the MYC locus

35 When these cuts are repaired, small insertions and deletions are created at the cut site. This is what we will be looking for in our pileup

36 Once our Mpileup job has finished, we can look at the results. Click on Visualization -> Saved Visualizations

37 Now, click on the visualization of the MYC locus read pileup we saved earlier

38 To add the results of Mpileup to the existing visualization as a new track, click the Add tracks button

39 Click the Histories -> Unnamed history button and select the Mpileup data. Note that you may need to click Data Libraries first and then back to Histories, as this was a bug that happened when I tried

40 When the visualization is finished, you can see the indel density by clicking Set display mode -> Coverage. You can see that the Cas9 treatment did, indeed, induce indels!

41 If you get done with this small tutorial with time to spare, consider playing with some of the data you can access through Shared Data