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## Overview

Teaching: 30 min
Exercises: 20 min
Questions
• How can I describe the quality of my data?

Objectives
• Explain how a FASTQ file encodes per-base quality scores.

• Interpret a FastQC plot summarizing per-base quality across all reads.

• Use for loops to automate operations on multiple files.

## Bioinformatic workflows

When working with high-throughput sequencing data, the raw reads you get off of the sequencer will need to pass through a number of different tools in order to generate your final desired output. The execution of this set of tools in a specified order is commonly referred to as a workflow or a pipeline.

An example of the workflow we will be using for our analysis is provided below with a brief description of each step.

1. Quality control - Assessing quality using FastQC and Trimming and/or filtering reads (if necessary)
2. Assembly of metagenome
3. Binning
4. Taxonomic assignation

These workflows in bioinformatics adopt a plug-and-play approach in that the output of one tool can be easily used as input to another tool without any extensive configuration. Having standards for data formats is what makes this feasible. Standards ensure that data is stored in a way that is generally accepted and agreed upon within the community. The tools that are used to analyze data at different stages of the workflow are therefore built under the assumption that the data will be provided in a specific format.

## Quality control

We will now assess the quality of the sequence reads contained in our FASTQ files.

### Details on the FASTQ format

Although it looks complicated (and it is), we can understand the FASTQ format with a little decoding. Some rules about the format include…

Line Description
1 Always begins with ‘@’ followed by the information about the read
2 The actual DNA sequence
3 Always begins with a ‘+’ and sometimes contains the same info as in line 1
4 Has a string of characters which represent the quality scores; must have same number of characters as line 2

We can view the first complete read in one of the files from our dataset by using head to look at the first four lines. But we have to decompress one of the files first.

$cd /dc_workshop/data/untrimmed_fastq/$ gunzip JP4D_R1.fastq.gz

$head -n 4 JP4D_R1.fastq  @MISEQ-LAB244-W7:156:000000000-A80CV:1:1101:12622:2006 1:N:0:CTCAGA CCCGTTCCTCGGGCGTGCAGTCGGGCTTGCGGTCTGCCATGTCGTGTTCGGCGTCGGTGGTGCCGATCAGGGTGAAATCCGTCTCGTAGGGGATCGCGAAGATGATCCGCCCGTCCGTGCCCTGAAAGAAATAGCACTTGTCAGATCGGAAGAGCACACGTCTGAACTCCAGTCACCTCAGAATCTCGTATGCCGTCTTCTGCTTGAAAAAAAAAAAAGCAAACCTCTCACTCCCTCTACTCTACTCCCTT + A>>1AFC>DD111A0E0001BGEC0AEGCCGEGGFHGHHGHGHHGGHHHGGGGGGGGGGGGGHHGEGGGHHHHGHHGHHHGGHHHHGGGGGGGGGGGGGGGGHHHHHHHGGGGGGGGHGGHHHHHHHHGFHHFFGHHHHHGGGGGGGGGGGGGGGGGGGGGGGGGGGGFFFFFFFFFFFFFFFFFFFFFBFFFF@F@FFFFFFFFFFBBFF?@;@####################################  Line 4 shows the quality for each nucleotide in the read. Quality is interpreted as the probability of an incorrect base call (e.g. 1 in 10) or, equivalently, the base call accuracy (e.g. 90%). To make it possible to line up each individual nucleotide with its quality score, the numerical score is converted into a code where each individual character represents the numerical quality score for an individual nucleotide. For example, in the line above, the quality score line is: A>>1AFC>DD111A0E0001BGEC0AEGCCGEGGFHGHHGHGHHGGHHHGGGGGGGGGGGGGHHGEGGGHHHHGHHGHHHGGHHHHGGGGGGGGGGGGGGGGHHHHHHHGGGGGGGGHGGHHHHHHHHGFHHFFGHHHHHGGGGGGGGGGGGGGGGGGGGGGGGGGGGFFFFFFFFFFFFFFFFFFFFFBFFFF@F@FFFFFFFFFFBBFF?@;@####################################  The numerical value assigned to each of these characters depends on the sequencing platform that generated the reads. The sequencing machine used to generate our data uses the standard Sanger quality PHRED score encoding, using Illumina version 1.8 onwards. Each character is assigned a quality score between 0 and 41 as shown in the chart below. Quality encoding: !"#$%&'()*+,-./0123456789:;<=>?@ABCDEFGHIJ
|         |         |         |         |
Quality score:    01........11........21........31........41


Each quality score represents the probability that the corresponding nucleotide call is incorrect. This quality score is logarithmically based, so a quality score of 10 reflects a base call accuracy of 90%, but a quality score of 20 reflects a base call accuracy of 99%. These probability values are the results from the base calling algorithm and depend on how much signal was captured for the base incorporation. In this link you can find more information about quality scores.

@MISEQ-LAB244-W7:156:000000000-A80CV:1:1101:12622:2006 1:N:0:CTCAGA
CCCGTTCCTCGGGCGTGCAGTCGGGCTTGCGGTCTGCCATGTCGTGTTCGGCGTCGGTGGTGCCGATCAGGGTGAAATCCGTCTCGTAGGGGATCGCGAAGATGATCCGCCCGTCCGTGCCCTGAAAGAAATAGCACTTGTCAGATCGGAAGAGCACACGTCTGAACTCCAGTCACCTCAGAATCTCGTATGCCGTCTTCTGCTTGAAAAAAAAAAAAGCAAACCTCTCACTCCCTCTACTCTACTCCCTT
+
A>>1AFC>DD111A0E0001BGEC0AEGCCGEGGFHGHHGHGHHGGHHHGGGGGGGGGGGGGHHGEGGGHHHHGHHGHHHGGHHHHGGGGGGGGGGGGGGGGHHHHHHHGGGGGGGGHGGHHHHHHHHGFHHFFGHHHHHGGGGGGGGGGGGGGGGGGGGGGGGGGGGFFFFFFFFFFFFFFFFFFFFFBFFFF@F@FFFFFFFFFFBBFF?@;@####################################


We can now see that there is a range of quality scores, but that the end of the sequence is very poor (# = a quality score of 2).

## Exercise 1: Looking at specific reads

How would you show in the terminal the ID and quality of the last read in JP4D_R1.fastq ?
a) tail JP4D_R1.fastq
b) head -n 4 JP4D_R1.fastq
c) more JP4D_R1.fastq
d) tail -n4 JP4D_R1.fastq
e) tail -n4 JP4D_R1.fastq | head -n2

Do you trust the sequence in this read?

## Solution


a) It does show the ID and quality of the last read but also show unnecesary lines from previous reads.
b) No. It shows the first read's info.
c) It shows the text of the entire file.
d) This option is the best answer as it only shows info for the last read.
e) It does show the ID of the last read but not the quality.


@MISEQ-LAB244-W7:156:000000000-A80CV:1:2114:17866:28868 1:N:0:CTCAGA

CCCGTTCTCCACCTCGGCGCGCGCCAGCTGCGGCTCGTCCTTCCACAGGAACTTCCACGTCGCCGTCAGCCGCGACACGTTCTCCCCCCTCGCATGCTCGTCCTGTCTCTCGTGCTTGGCCGACGCCTGCGCCTCGCACTGCGCCCGCTCGGTGTCGTTCATGTTGATCTTCACCGTGGCGTGCATGAAGCGGTTCCCGGCCTCGTCGCCACCCACGCCATCCGCGTCGGCCAGCCACTCTCACTGCTCGC

+

AA11AC1>3@DC1F1111000A0/A///BB#############################################################################################################################################################################################################################


This read has more consistent quality at its first than at the end but still has a range of quality scores, most of them low. We will look at variations in position-based quality in just a moment.

At this point, lets validate that all the relevant tools are installed. If you are using the AWS AMI then these should be preinstalled.

$fastqc -h   FastQC - A high throughput sequence QC analysis tool SYNOPSIS fastqc seqfile1 seqfile2 .. seqfileN fastqc [-o output dir] [--(no)extract] [-f fastq|bam|sam] [-c contaminant file] seqfile1 .. seqfileN DESCRIPTION FastQC reads a set of sequence files and produces from each one a quality control report consisting of a number of different modules, each one of which will help to identify a different potential type of problem in your data. . . .  If FastQC is not installed then you would expect to see an error like $ fastqc -h

The program 'fastqc' is currently not installed. You can install it by typing:
sudo apt-get install fastqc


If this happens check with your instructor before trying to install it.

## Assessing quality using FastQC

In real life, you won’t be assessing the quality of your reads by visually inspecting your FASTQ files. Rather, you’ll be using a software program to assess read quality and filter out poor quality reads. We’ll first use a program called FastQC to visualize the quality of our reads. Later in our workflow, we’ll use another program to filter out poor quality reads.

FastQC has a number of features which can give you a quick impression of any problems your data may have, so you can take these issues into consideration before moving forward with your analyses. Rather than looking at quality scores for each individual read, FastQC looks at quality collectively across all reads within a sample. The image below shows one FastQC-generated plot that indicates a very high quality sample:

The x-axis displays the base position in the read, and the y-axis shows quality scores. In this example, the sample contains reads that are 40 bp long. This is much shorter than the reads we are working with in our workflow. For each position, there is a box-and-whisker plot showing the distribution of quality scores for all reads at that position. The horizontal red line indicates the median quality score and the yellow box shows the 1st to 3rd quartile range. This means that 50% of reads have a quality score that falls within the range of the yellow box at that position. The whiskers show the absolute range, which covers the lowest (0th quartile) to highest (4th quartile) values.

For each position in this sample, the quality values do not drop much lower than 32. This is a high quality score. The plot background is also color-coded to identify good (green), acceptable (yellow), and bad (red) quality scores.

Now let’s take a look at a quality plot on the other end of the spectrum.

Here, we see positions within the read in which the boxes span a much wider range. Also, quality scores drop quite low into the “bad” range, particularly on the tail end of the reads. The FastQC tool produces several other diagnostic plots to assess sample quality, in addition to the one plotted above.

## Running FastQC

We will now assess the quality of the reads that we downloaded. First, make sure you’re still in the untrimmed_fastq directory

$cd ~/dc_workshop/data/untrimmed_fastq/  ## Exercise 2: Looking at files metadata How would you see the size of the files in the untrimmed_fastq\ directory? (Hint: Look at the options for the ls command to see how to show file sizes.) a) ls -a b) ls -S c) ls -l d) ls -lh e) ls -ahlS ## Solution  a) No. The flag -a shows all of the contents, including hidden files and directories. b) No. The flag -S shows the content Sorted by size starting with the largest file. c) Yes. The flag -l shows the contents with metadata including file size. d) Yes. The flag -lh shows the content with metadata in a human readable manner. e) Yes. The combination of all of the flags shows all of the contents with metadata including hidden files, sorted by size.  -rw-r--r-- 1 dcuser dcuser 24M Nov 26 21:34 JC1A_R1.fastq.gz -rw-r--r-- 1 dcuser dcuser 24M Nov 26 21:34 JC1A_R2.fastq.gz -rw-r--r-- 1 dcuser dcuser 616M Nov 26 21:34 JP4D_R1.fastq -rw-r--r-- 1 dcuser dcuser 203M Nov 26 21:35 JP4D_R2.fastq.gz  There are four FASTQ files ranging from 24M (24MB) to 616M. FastQC can accept multiple file names as input, and on both zipped and unzipped files, so we can use the \*.fastq*wildcard to run FastQC on all of the FASTQ files in this directory. $ fastqc *.fastq*


You will see an automatically updating output message telling you the progress of the analysis. It will start like this:

Started analysis of JC1A_R1.fastq.gz
Approx 5% complete for JC1A_R1.fastq.gz
Approx 10% complete for JC1A_R1.fastq.gz
Approx 15% complete for JC1A_R1.fastq.gz
Approx 20% complete for JC1A_R1.fastq.gz
Approx 25% complete for JC1A_R1.fastq.gz
Approx 30% complete for JC1A_R1.fastq.gz
Approx 35% complete for JC1A_R1.fastq.gz


In total, it should take about five minutes for FastQC to run on all four of our FASTQ files. When the analysis completes, your prompt will return. So your screen will look something like this:

Approx 80% complete for JP4D_R2.fastq.gz
Approx 85% complete for JP4D_R2.fastq.gz
Approx 90% complete for JP4D_R2.fastq.gz
Approx 95% complete for JP4D_R2.fastq.gz
Analysis complete for JP4D_R2.fastq.gz
$ The FastQC program has created several new files within our data/untrimmed_fastq/ directory. $ ls

JC1A_R1_fastqc.html             JP4D_R1.fastq
JC1A_R1_fastqc.zip              JP4D_R1_fastqc.html
JC1A_R1.fastq.gz                JP4D_R1_fastqc.zip
JC1A_R2_fastqc.html             JP4D_R2_fastqc.html
JC1A_R2_fastqc.zip              JP4D_R2_fastqc.zip
JC1A_R2.fastq.gz                JP4D_R2.fastq.gz


For each input FASTQ file, FastQC has created a .zip file and a .html file. The .zip file extension indicates that this is actually a compressed set of multiple output files. We’ll be working with these output files soon. The .html file is a stable webpage displaying the summary report for each of our samples.

We want to keep our data files and our results files separate, so we will move these output files into a new directory within our results/ directory.

$mkdir -p ~/dc_workshop/results/fastqc_untrimmed_reads$ mv *.zip ~/dc_workshop/results/fastqc_untrimmed_reads/
$mv *.html ~/dc_workshop/results/fastqc_untrimmed_reads/  Now we can navigate into this results directory and do some closer inspection of our output files. $ cd ~/dc_workshop/results/fastqc_untrimmed_reads/


## Viewing the FastQC results

If we were working on our local computers, we’d be able to look at each of these HTML files by opening them in a web browser. However, these files are currently sitting on our remote AWS instance, where our local computer can’t see them. Since we are only logging into the AWS instance via the command line our remote computer it doesn’t have any web browser setup to display these files either. So, the easiest way to look at these webpage summary reports is to transfer them to our local computers (i.e. your laptop). To copy a file from a remote server to our own machines, we will use scp, which we learned yesterday in the Introduction to the Command Line lesson.

First, open a new terminal in you local computer, we will make a new directory on our computer to store the HTML files we’re transferring. Let’s put it on our desktop for now. Open a new tab in your terminal program (you can use the pull down menu at the top of your screen or the Cmd+t keyboard shortcut) and type:

$mkdir -p ~/Desktop/fastqc_html  Now we can transfer our HTML files to our local computer using scp. $ scp dcuser@ec2-34-238-162-94.compute-1.amazonaws.com:~/dc_workshop/results/fastqc_untrimmed_reads/*.html ~/Desktop/fastqc_html


As a reminder, the first part of the command dcuser@ec2-34-238-162-94.compute-1.amazonaws.com is the address for your remote computer. Make sure you replace everything after dcuser@ with your instance number (the one you used to log in).

The second part starts with a : and then gives the absolute path of the files you want to transfer from your remote computer. Don’t forget the :. We used a wildcard (*.html) to indicate that we want all of the HTML files.

The third part of the command gives the absolute path of the location you want to put the files in. This is on your local computer and is the directory we just created ~/Desktop/fastqc_html.

You should see a status output like this:

JC1A_R1_fastqc.html     100%  253KB 320.0KB/s   00:00
JC1A_R2_fastqc.html     100%  262KB 390.1KB/s   00:00
JP4D_R1_fastqc.html     100%  237KB 360.8KB/s   00:00
JP4D_R2_fastqc.html     100%  244KB 385.2KB/s   00:00


Now we can go to our new directory and open the 4 HTML files.

Depending on your system, you should be able to select and open them all at once via a right click menu in your file browser.

## Exercise 3: Discuss the quality of sequencing files

Discuss your results with a neighbor. Which sample(s) looks the best in terms of per base sequence quality? Which sample(s) look the worst?

## Solution

All of the reads contain usable data, but the quality decreases toward the end of the reads. File JC1A_R2_fastqc shows the lowest quality.

## Decoding the other FastQC outputs

We’ve now looked at quite a few “Per base sequence quality” FastQC graphs, but there are nine other graphs that we haven’t talked about! Below we have provided a brief overview of interpretations for each of these plots. For more information, please see the FastQC documentation here

• Per tile sequence quality: the machines that perform sequencing are divided into tiles. This plot displays patterns in base quality along these tiles. Consistently low scores are often found around the edges, but hot spots can also occur in the middle if an air bubble was introduced at some point during the run.
• Per sequence quality scores: a density plot of quality for all reads at all positions. This plot shows what quality scores are most common.
• Per base sequence content: plots the proportion of each base position over all of the reads. Typically, we expect to see each base roughly 25% of the time at each position, but this often fails at the beginning or end of the read due to quality or adapter content.
• Per sequence GC content: a density plot of average GC content in each of the reads.
• Per base N content: the percent of times that ‘N’ occurs at a position in all reads. If there is an increase at a particular position, this might indicate that something went wrong during sequencing.
• Sequence Length Distribution: the distribution of sequence lengths of all reads in the file. If the data is raw, there is often on sharp peak, however if the reads have been trimmed, there may be a distribution of shorter lengths.
• Sequence Duplication Levels: a distribution of duplicated sequences. In sequencing, we expect most reads to only occur once. If some sequences are occurring more than once, it might indicate enrichment bias (e.g. from PCR). If the samples are high coverage (or RNA-seq or amplicon), this might not be true.
• Overrepresented sequences: a list of sequences that occur more frequently than would be expected by chance.
• K-mer Content: a graph showing any sequences which may show a positional bias within the reads.

## Working with the FastQC text output

Now that we’ve looked at our HTML reports to get a feel for the data, let’s look more closely at the other output files. Go back to the tab in your terminal program that is connected to your AWS instance (the tab label will start with dcuser@ip) and make sure you’re in our results subdirectory.

$cd ~/dc_workshop/results/fastqc_untrimmed_reads/$ ls

JC1A_R1_fastqc.html           JP4D_R1_fastqc.html
JC1A_R1_fastqc.zip            JP4D_R1_fastqc.zip
JC1A_R2_fastqc.html           JP4D_R2_fastqc.html
JC1A_R2_fastqc.zip            JP4D_R2_fastqc.zip


Our .zip files are compressed files. They each contain multiple different types of output files for a single input FASTQ file. To view the contents of a .zip file, we can use the program unzip to decompress these files. Let’s try doing them all at once using a wildcard.

$unzip *.zip  Archive: JC1A_R1_fastqc.zip caution: filename not matched: JC1A_R2_fastqc.zip caution: filename not matched: JP4D_R1_fastqc.zip caution: filename not matched: JP4D_R2_fastqc.zip  This didn’t work. It identified the first file and then got a warning message for each of the other .zip files. This is because unzip expects to get only one zip file as input. We could go through and unzip each file one at a time, but this is very time consuming and error-prone. Someday you may have 500 files to unzip! A more efficient way is to use a for loop like we learned in the Command Line lesson to iterate through all of our .zip files. Let’s see what that looks like and then we’ll discuss what we’re doing with each line of our loop. $ for filename in *.zip
> do
> unzip $filename > done  In this example, the input is the four filenames (one filename for each of our .zip files). Each time the loop iterates, it will assign a file name to the variable filename and run the unzip command. The first time through the loop, $filename is JC1A_R1_fastqc.zip. The interpreter runs the command unzip on JC1A_R1_fastqc.zip. For the second iteration, $filename becomes JC1A_R2_fastqc.zip. This time, the shell runs unzip on JC1A_R2_fastqc.zip. It then repeats this process for the other .zip files in our directory. When we run our for loop, you will see output that starts like this: Archive: JC1A_R1_fastqc.zip creating: JC1A_R1_fastqc/ creating: JC1A_R1_fastqc/Icons/ creating: JC1A_R1_fastqc/Images/ inflating: JC1A_R1_fastqc/Icons/fastqc_icon.png inflating: JC1A_R1_fastqc/Icons/warning.png inflating: JC1A_R1_fastqc/Icons/error.png inflating: JC1A_R1_fastqc/Icons/tick.png inflating: JC1A_R1_fastqc/summary.txt inflating: JC1A_R1_fastqc/Images/per_base_quality.png inflating: JC1A_R1_fastqc/Images/per_tile_quality.png inflating: JC1A_R1_fastqc/Images/per_sequence_quality.png inflating: JC1A_R1_fastqc/Images/per_base_sequence_content.png inflating: JC1A_R1_fastqc/Images/per_sequence_gc_content.png inflating: JC1A_R1_fastqc/Images/per_base_n_content.png inflating: JC1A_R1_fastqc/Images/sequence_length_distribution.png inflating: JC1A_R1_fastqc/Images/duplication_levels.png inflating: JC1A_R1_fastqc/Images/adapter_content.png inflating: JC1A_R1_fastqc/fastqc_report.html inflating: JC1A_R1_fastqc/fastqc_data.txt inflating: JC1A_R1_fastqc/fastqc.fo  The unzip program is decompressing the .zip files and creating a new directory (with subdirectories) for each of our samples, to store all of the different output that is produced by FastQC. There are a lot of files here. The one we’re going to focus on is the summary.txt file. If you list the files in our directory now you will see: $ ls

JC1A_R1_fastqc                  JP4D_R1_fastqc
JC1A_R1_fastqc.html             JP4D_R1_fastqc.html
JC1A_R1_fastqc.zip              JP4D_R1_fastqc.zip
JC1A_R2_fastqc                  JP4D_R2_fastqc
JC1A_R2_fastqc.html             JP4D_R2_fastqc.html
JC1A_R2_fastqc.zip              JP4D_R2_fastqc.zip


The .html files and the uncompressed .zip files are still present, but now we also have a new directory for each of our samples. We can see for sure that it’s a directory if we use the -F flag for ls.

$ls -F  JC1A_R1_fastqc/ JP4D_R1_fastqc/ JC1A_R1_fastqc.html JP4D_R1_fastqc.html JC1A_R1_fastqc.zip JP4D_R1_fastqc.zip JC1A_R2_fastqc/ JP4D_R2_fastqc/ JC1A_R2_fastqc.html JP4D_R2_fastqc.html JC1A_R2_fastqc.zip JP4D_R2_fastqc.zip  Let’s see what files are present within one of these output directories. $ ls -F JC1A_R1_fastqc/

fastqc_data.txt  fastqc.fo  fastqc_report.html	Icons/	Images/  summary.txt


Use less to preview the summary.txt file for this sample.

$grep FAIL fastqc_summaries.txt  FAIL Per base sequence quality JC1A_R1.fastq.gz FAIL Per sequence GC content JC1A_R1.fastq.gz FAIL Sequence Duplication Levels JC1A_R1.fastq.gz FAIL Adapter Content JC1A_R1.fastq.gz FAIL Per base sequence quality JC1A_R2.fastq.gz FAIL Per sequence GC content JC1A_R2.fastq.gz FAIL Sequence Duplication Levels JC1A_R2.fastq.gz FAIL Adapter Content JC1A_R2.fastq.gz FAIL Per base sequence content JP4D_R1.fastq FAIL Adapter Content JP4D_R1.fastq FAIL Per base sequence quality JP4D_R2.fastq.gz FAIL Per base sequence content JP4D_R2.fastq.gz FAIL Adapter Content JP4D_R2.fastq.gz  ## Quality Encodings Vary Although we’ve used a particular quality encoding system to demonstrate interpretation of read quality, different sequencing machines use different encoding systems. This means that, depending on which sequencer you use to generate your data, a # may not be an indicator of a poor quality base call. This mainly relates to older Solexa/Illumina data, but it’s essential that you know which sequencing platform was used to generate your data, so that you can tell your quality control program which encoding to use. If you choose the wrong encoding, you run the risk of throwing away good reads or (even worse) not throwing away bad reads! ## Bonus Exercise: Automating a quality control workflow If you loose your FastQC analyses results. How would you do it again but faster than the first time? As we have seen in a previous lesson, making scripts for repetitive tasks is a very efficient practice during bioinformatic pipelines. ## Solution Make a new script with nano nano quality_control.sh  Paste inside the commands that we used along with echo commands that shows you how the script is running. set -e # This will ensure that our script will exit if an error occurs cd ~/dc_workshop/data/untrimmed_fastq/ echo "Running FastQC ..." fastqc *.fastq* mkdir -p ~/dc_workshop/results/fastqc_untrimmed_reads echo "Saving FastQC results..." mv *.zip ~/dc_workshop/results/fastqc_untrimmed_reads/ mv *.html ~/dc_workshop/results/fastqc_untrimmed_reads/ cd ~/dc_workshop/results/fastqc_untrimmed_reads/ echo "Unzipping..." for filename in *.zip do unzip$filename
done

echo "Saving summary..."
mkdir -p ~/dc_workshop/docs
cat */summary.txt > ~/dc_workshop/docs/fastqc_summaries.txt


If we were to run this script it would ask us for confirmation to redo several steps because we already did all of them. If you want to, you can run it to check that it works, but it is not necessary if you already completed every step of the previous episode.

## Key Points

• Quality encodings vary across sequencing platforms.

• for loops let you perform the same set of operations on multiple files with a single command.

• It is important to know the quality of our data to be able to make decisions in the subsequent steps.