1. NGS Analysis Using Galaxy
Sequences and Alignment Format
Galaxy overview and Interface
Getting Data in Galaxy
Analyzing Data in Galaxy
Quality Control
Mapping Data
History and workflow
Galaxy Exercises (

2. SNPSeq Analysis dataset
Data source: SRR sample from from experiment published by Kaufman et al (2012, GSE20176)
FastqFile:
TAIR10 Genome:
The API transcription factor is a key regulator of Arabidopsis flower development. To understand the target molecular mechanisms underlying AP1 function, target genes were identified during floral initiation using a combination of gene expression profiling and genome-wide binding studies. Many of its targets encode transcriptional regulators, including floral repressors.

3. SNP-Seq pipeline Sequence (fastq file) Reference genome (fasta file)
Upload data and QC
Sequence (fastq file)
Reference genome (fasta file)
Filter reads based on quality scores
Alignment with BWA
Align with BWA
SAM to BAM conversion
Variant calling
Mpileup
Bcftools view

4. Upload data
Go to ”Get Data”, click open ”Upload File from your computer”. Then specify the following list of URLs in URL/Text box
Next, we need to upload files from URL. Click on Upload file. Uploading file on galaxy can be done in a number of ways. You could simply upload the file from your local desktop/computer or you could upload the data from any URL or ftp website. Another option could be to upload the data from popular browsers or databases like UCSC Genome browser or BioMart. There are also a number of file formats that you could choose from here. Next, we will copy and paste the URLs given here in the URL/Text box and click on Execute. For this exercise, we are uploading a part of Arabidopsis genome and the

5. Convert Fastq file to sanger sequences
Select NGS: QC and manipulation and Fastq groomer
The FASTQ Groomer tool is used to verify and convert between the known FASTQ variants.
After grooming, the user is presented with a valid FASTQ format that is accepted by all downstream analysis tools.
Next, we need to convert these FASTQ files into sanger format. Select NGS: QC and manipulation and Fastq groomer
The FASTQ Groomer tool is used to verify and convert between the known FASTQ variants.
After grooming, the user is presented with a valid FASTQ format that is accepted by all downstream analysis tools. The output of the FASTQ Groomer is sanger formatted fastq reads, which is acceptable by all downstream, analysis tools.

6. FASTQ Summary Statistics
To understand the quality properties of the reads, one can run the FASTQ Summary Statistics tool from NGS: QC and manipulation.

7. FASTQ Quality control
To understand the quality properties of the reads, one can also run the FASTQC: Read QC reports from NGS: QC and manipulation.

8. Quality control output

9. Quality control reports
Per base sequence quality
Per base sequence content
Per base sequence quality
The central red line is the median value
The yellow box represents the inter-quartile range (25-75%)
The upper and lower whiskers represent the 10% and 90% points
The blue line represents the mean quality
The y-axis on the graph shows the quality scores. The higher the score the better the base call. The background of the graph divides the y axis into very good quality calls (green), calls of reasonable quality (orange), and calls of poor quality (red). The quality of calls on most platforms will degrade as the run progresses, so it is common to see base calls falling into the orange area towards the end of a read.
Per base sequence content
Per Base Sequence Content plots out the proportion of each base position in a file for which each of the four normal DNA bases has been called.
In a random library you would expect that there would be little to no difference between the different bases of a sequence run, so the lines in this plot should run parallel with each other. The relative amount of each base should reflect the overall amount of these bases in your genome, but in any case they should not be hugely imbalanced from each other.
If you see strong biases which change in different bases then this usually indicates an overrepresented sequence which is contaminating your library. A bias which is consistent across all bases either indicates that the original library was sequence biased, or that there was a systematic problem during the sequencing of the library.
Per sequence GC content
This module measures the GC content across the whole length of each sequence in a file and compares it to a modelled normal distribution of GC content.
In a normal random library you would expect to see a roughly normal distribution of GC content where the central peak corresponds to the overall GC content of the underlying genome. Since we don't know the the GC content of the genome the modal GC content is calculated from the observed data and used to build a reference distribution.
An unusually shaped distribution could indicate a contaminated library or some other kinds of biased subset. A normal distribution which is shifted indicates some systematic bias which is independent of base position. If there is a systematic bias which creates a shifted normal distribution then this won't be flagged as an error by the module since it doesn't know what your genome's GC content should be.
Per base GC content

10. Quality filter
This tool filters reads based on quality scores. NGS: QC and manipulation -> Generic FASTQ manipulation->Filter FASTQ reads by quality score and length

11. Alignment with BWA
BWA is a fast and accurate short read aligner that allows mismatches and indels
Go to ”NGS: Mapping” and click on ”Map with BWA”.

12. SAM to BAM format conversion
Produce an indexed BAM file based on a sorted input SAM file.
Go to ”NGS: SAM Tools”, then click open ”SAM-to-BAM”.

13. Variants calling with Mpileup
SNP and INDEL caller to Generate BCF (Binary Variant Format) for one or multiple BAM files
Go to ”NGS: SAM Tools”, then click open ”MPileup SNP and indel caller”.

14. Bcftools view Converts BCF format to VCF format.
Go to ”NGS: SAM Tools”, then click open ”bcftools view”

15. Exercise 2: RNASeq Analysis
Data source: RNA-seq experiment SRA023501
Four Samples:
Samples
Factors
Fastq
AP3_f14
AP3
T1_f14
TRL

16. RNASeq Analysis workflow
Input data
Upload the fastq files, reference genome and GTF file
Use fastq groomer to groom the fastq sequences
Alignment
TopHat for alignment
Results in insertions, deletions, splice junctions and accepted hits
Differential expression testing
Use Cuffdiff for differential expression testing
Finds significant changes in gene expression, splicing and promoter use

17. Upload Data Upload four fastq files with URL
Upload tair10chr.fa with URL (
Upload TAIR10.GTF with URL ,specify the format ”gtf” (

18. Fastq Groomer Select NGS: QC and manipulation and Fastq groomer
Run Fastq Groomer for all the 4 fastq sequences.

19. Alignment with TopHat
TopHat is a fast splice junction mapper for RNA-Seq reads, it can identify splice junctions between exons.
Go to ”NGS RNA analysis”, click open ”Tophat for illumina”
Similarly repeat this process for all the 4 fastq groomed sequences

20. Find Significant Changes
Cuffdiff find significant changes in transcript expression.
Go to ”NGS RNA analysis”, click open ”Cuffdiff”

21. Cuffdiff Output TSS... files report on Transcription Start Sites
splicing... report on splicing
CDS... track coding region expression
transcript... track transcripts
gene... rolls up the transcripts into their genes
gene/transcript FPKM tracking: gives information about the gene/transcript (length, nearest ref id, TSS, etc) and the confidence intervals for FPKM for each condition.
gene/transcript differential expression testing: gives the expression change between groups, a status of whether there was enough data for that value to be accurate (OK is good, FAIL and NOTEST are bad. LOWDATA is somewhere in between). Finally, it gives a p-value.
see more details… Link

22. Find Significant Changes
Cuffdiff find significant changes in transcript expression

23. Output File from Galaxy
SNPseq
Save Bam file BWA generated
Save .bai file (index of BAM) BWA generated
Save vcf file Samtools mpileup generated
Already saved them at
RNASeq
Save four Bam files and four .bai files

24. Downloading bam index file (.bai)

25. Outline What is Galaxy Galaxy for Bioinformaticians
Galaxy for Experimental Biologists
Using Galaxy for NGS Analysis
NGS Data Visualization and Exploration Using IGV

26. Why IGV IGV is an integrated visualization tool of large data types
View large dataset easily
Faster navigation on browsing
Run it locally on your computer
Easy to use interface

27. IGV Interface Tool bar Chromosome ideogram Ruler Track data Features
Track names
Attributes
See more details (Link)

28. IGV download

29. Load data
Select genome: Click the genome drop-down list in the toolbar and select the genome
Select chromosome: Click the chromosome drop-down box and select chromosome

30. Load data files
Load from URL, file, server, DAS (Distributed Annotation System): UCSC DAS Sources
Import genome

31. Toolbar Genome drop-down box: loads a genome
Chromosome drop-down box: zooms to a chromosome
Search box: Displays the chromosome location being shown. To scroll to a different location, enter the gene name, locus or track name and click Go.
Whole genome view: Zooms to whole genome view.
Define a region: Defines a region of interest on the chromosome.
Zoom slider: Zooms in and out on a chromosome.

32. Change Display Options
IGV offers several display options for tracks
Zoom in and Zoom out
Modify Track Height
Sort the Tracks
Filter the Tracks
Group the Tracks
Sort Tracks based on Region of Interest

33. Variants Visualization in IGV
Load TAIR10 genome to IGV
Load BAM file “SRR bam” to IGV with “Load from URL”
Load VCF file “var.raw.vcf” to IGV with “Load from URL”

34. Zoom In Screen
Zoom in to : Chr5:57,073-57,142

35. Zoom in position (chr5:6,435-6,475)

36. RNAseq Results Visualization
Load four BAM files to IGV
Load gene differential expression GFF3 file “expression_diff.gff3” to IGV

37. Exercise2: RNAseq Result Visualization
Zoom in to Chr1:41,351-51,208