NGS Quality Flashcards
When looking at a FastQC quality plot, what does the yellow bar, red line, blue line and upper and lower whiskers represent?
- 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
On the background of the FastQC quality plot, what do they different background colours represent?
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.
When will a warning be issued on the FASTQC quality plot?
A warning will be issued if the lower quartile for any base is less than 10, or if the median
for any base is less than 25.
What is the per sequence quality score?
The per sequence quality score report allows you to see if a subset of your sequences have universally low quality values. It is often the case that a subset of sequences will have universally poor quality, often because they are poorly imaged (on the edge of the
field of view etc), however these should represent only a small percentage of the total
sequences.
If a significant proportion of the sequences in a run have overall low quality then this could
indicate some kind of systematic problem - possibly with just part of the run (for example
one end of a flowcell).
A warning is raised if the most frequently observed mean quality is below 27 - this equates to a 0.2% error rate.
A failure is raised if the most frequently observed mean quality is below 20 - this equates to a 1% error rate.
What is the 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.
This module issues a warning if the difference between A and T, or G and C is greater than 10% in any position.
This module will fail if the difference between A and T, or G and C is greater than 20% in any position.
What is the per base GC content?
Per Base GC Content plots out the GC content of each base position in a file.
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 line in this plot should run horizontally across the graph. The overall GC content should reflect the GC content of the underlying genome.
If you see a GC bias which changes in different bases then this could indicate 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.
This module issues a warning it the GC content of any base strays more than 5% from the mean GC content.
This module will fail if the GC content of any base strays more than 10% from the mean GC content.
What is the 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.
A warning is raised if the sum of the deviations from the normal distribution represents more than 15% of the reads.
This module will indicate a failure if the sum of the deviations from the normal distribution represents more than 30% of the reads.
What is the Per Base N Content?
If a sequencer is unable to make a base call with sufficient confidence then it will normally substitute an N rather than a conventional base call. This module plots out the percentage of base calls at each position for which an N was called.
It’s not unusual to see a very low proportion of Ns appearing in a sequence, especially nearer the end of a sequence. However, if this proportion rises above a few percent it suggests that the analysis pipeline was unable to interpret the data well enough to make valid base calls.
This module raises a warning if any position shows an N content of >5%.
This module will raise an error if any position shows an N content of >20%.
What is Sequence Length Distribution?
Some high throughput sequencers generate sequence fragments of uniform length, but others can contain reads of wildly varying lengths. Even within uniform length libraries some pipelines will trim sequences to remove poor quality base calls from the end. This module generates a graph showing the distribution of fragment sizes in the file which was analysed.
In many cases this will produce a simple graph showing a peak only at one size, but for variable length FastQ files this will show the relative amounts of each different size of sequence fragment.
This module will raise a warning if all sequences are not the same length.
This module will raise an error if any of the sequences have zero length.
What are duplicate sequences?
In a diverse library most sequences will occur only once in the final set. A low level of duplication may indicate a very high level of coverage of the target sequence, but a high level of duplication is more likely to indicate some kind of enrichment bias (eg PCR over amplification).
This module counts the degree of duplication for every sequence in the set and creates a plot showing the relative number of sequences with different degrees of duplication.
This module will issue a warning if non-unique sequences make up more than 20% of the total.
This module will issue a error if non-unique sequences make up more than 50% of the total.
What are overrepresented sequences?
A normal high-throughput library will contain a diverse set of sequences, with no individual
sequence making up a tiny fraction of the whole. Finding that a single sequence is very
overrepresented in the set either means that it is highly biologically significant, or indicates
that the library is contaminated, or not as diverse as you expected.
This module lists all of the sequence which make up more than 0.1% of the total. To
conserve memory only sequences which appear in the first 200,000 sequences are
tracked to the end of the file. It is therefore possible that a sequence which is
overrepresented but doesn’t appear at the start of the file for some reason could be
missed by this module.
For each overrepresented sequence the program will look for matches in a database of
common contaminants and will report the best hit it finds. Hits must be at least 20bp in
length and have no more than 1 mismatch. Finding a hit doesn’t necessarily mean that this
is the source of the contamination, but may point you in the right direction. It’s also worth
pointing out that many adapter sequences are very similar to each other so you may get a
hit reported which isn’t technically correct, but which has very similar sequence to the
actual match.
Because the duplication detection requires an exact sequence match over the whole
length of the sequence any reads over 75bp in length are truncated to 50bp for the
purposes of this analysis. Even so, longer reads are more likely to contain sequencing
errors which will artificially increase the observed diversity and will tend to underrepresent
highly duplicated sequences.
This module will issue a warning if any sequence is found to represent more than 0.1% of the total.
This module will issue an error if any sequence is found to represent more than 1% of the total.
What are Overrepresented Kmers?
The analysis of overrepresented sequences will spot an increase in any exactly duplicated sequences, but there are a different subset of problems where it will not work.
If you have very long sequences with poor sequence quality then random sequencing errors will dramatically reduce the counts for exactly duplicated
sequences. If you have a partial sequence which is appearing at a variety of places within your
sequence then this won’t be seen either by the per base content plot or the duplicate sequence analysis.
This module counts the enrichment of every 5-mer within the sequence library. It calculates an expected level at which this k-mer should have been seen based on the base content of the library as a whole and then uses the actual count to calculate an observed/expected ratio for that k-mer. In addition to reporting a list of hits it will draw a
graph for the top 6 hits to show the pattern of enrichment of that Kmer across the length of
your reads. This will show if you have a general enrichment, or if there is a pattern of bias
at different points over your read length.
Any k-mer showing more than a 3 fold overall enrichment or a 5 fold enrichment at any
given base position will be reported by this module.
This module will issue a warning if any k-mer is enriched more than 3 fold overall, or more than 5 fold at any individual position.
This module will issue a error if any k-mer is enriched more than 10 fold at any individual base position.
What strategies can be used to improve the quality of the sequencing data?
1) Remove sequences after set position. We know 3’ end has lower quality. Remove all sequence after position 80. e.g. Trim Sequences
2) Remove all sequences with average quality below set value. e.g Filter by Quality
3) combination of (1) and (2) e.g. Trimmomatic scans 5’ to 3’ and clips sequence when quality falls below threshold.
4) Remove adapter sequences e.g Trim Galore!
How can removal of data based on quality affect downstream analysis?
- Removing sequences from paired end reads may affect alignment.
- Altering lengths of reads may affect identification of duplicates.
How can removing duplicate reads post alignment aid quality?
Alignment may contain PCR duplicates from reduced cycle amplification.
You can identify PCR duplicates and mark or remove them (see ACGS guidelines).
RmDup or MarkDuplicates
Duplicates can affect SNP calling, you want to avoid double counting evidence from the same underlying biological sequence
