materials
(https://galaxyproject.github.io/training-material/
topics/sequence-analysis/tutorials/quality-control/tutorial.
html) [24].
1. Upload the read data as .FASTQ or compressed fastq.gz to
Galaxy—http://usegalaxy.org (don’t forget to log in). If your
file names are not informative or your samples fall into groups,
it may be helpful to rename the read files or group them into a
collection.
2. Upload the .GTF file containing the gene annotations.
3. Upload the .FASTA file of the complete genome.
4. Run FastQC to assess data quality. FastQC can be accessed on
Galaxy Main under Genomic File Manipulation ! FastQ
Quality Control ! FastQC:
(a) The FastQC tool will provide two outputs after execution
(see Note 29):
l
A Webpage that shows a graphical summary of
readouts.
l
The RawData file containing this information.
(b) If you have only a few samples, you can run them all
through FastQC and look at the summary .html report
for all of them individually.
(c) If you have multiple samples, we recommend aggregating
the RawData using MultiQC.
5. Run MultiQC to summarize multiple QC reports. MultiQC
can be accessed on Galaxy Main under Genomic File Manipulation ! FastQ Quality Control ! MultiQC:
(a) After you have run FastQC on all of your samples, select
the RawData files as input for MultiQC to get a single
report summarizing all of your data.
6. If the quality score of your reads drops significantly near the
start or the end of your reads, you should consider trimming
the reads. To run Trimmomatic to trim sequencing reads
before mapping:
(a) Trimmomatic can be accessed on Galaxy Main under
Genomic File Manipulation ! FastQ Quality Control
! Trimmomatic.
(b) The tool defaults to sliding window trimming to an average quality score of 20 across 4 bases, this will be fine in
most cases.
(c) After executing, the Trimmomatic output can be run
through FastQC and summarized with MultiQC as
above to compare with untrimmed reads.
7. After you are confident in the quality of your reads, advance to
the alignment section.
RNAseq Workflow
505
(https://galaxyproject.github.io/training-material/
topics/sequence-analysis/tutorials/quality-control/tutorial.
html) [24].
1. Upload the read data as .FASTQ or compressed fastq.gz to
Galaxy—http://usegalaxy.org (don’t forget to log in). If your
file names are not informative or your samples fall into groups,
it may be helpful to rename the read files or group them into a
collection.
2. Upload the .GTF file containing the gene annotations.
3. Upload the .FASTA file of the complete genome.
4. Run FastQC to assess data quality. FastQC can be accessed on
Galaxy Main under Genomic File Manipulation ! FastQ
Quality Control ! FastQC:
(a) The FastQC tool will provide two outputs after execution
(see Note 29):
l
A Webpage that shows a graphical summary of
readouts.
l
The RawData file containing this information.
(b) If you have only a few samples, you can run them all
through FastQC and look at the summary .html report
for all of them individually.
(c) If you have multiple samples, we recommend aggregating
the RawData using MultiQC.
5. Run MultiQC to summarize multiple QC reports. MultiQC
can be accessed on Galaxy Main under Genomic File Manipulation ! FastQ Quality Control ! MultiQC:
(a) After you have run FastQC on all of your samples, select
the RawData files as input for MultiQC to get a single
report summarizing all of your data.
6. If the quality score of your reads drops significantly near the
start or the end of your reads, you should consider trimming
the reads. To run Trimmomatic to trim sequencing reads
before mapping:
(a) Trimmomatic can be accessed on Galaxy Main under
Genomic File Manipulation ! FastQ Quality Control
! Trimmomatic.
(b) The tool defaults to sliding window trimming to an average quality score of 20 across 4 bases, this will be fine in
most cases.
(c) After executing, the Trimmomatic output can be run
through FastQC and summarized with MultiQC as
above to compare with untrimmed reads.
7. After you are confident in the quality of your reads, advance to
the alignment section.
RNAseq Workflow
505