fastqc-rust 1.0.1

A Rust rewrite of FastQC - a quality control tool for high throughput sequence data
Documentation
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<title>Per Tile Sequence Quality</title>
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<h1>Per Tile Sequence Quality</h1>
<h2>Summary</h2>
<p>
This graph will only appear in your analysis results if you're using
an Illumina library which retains its original sequence identifiers.
Encoded in these is the flowcell tile from which each read came.  The
graph allows you to look at the quality scores from each tile across
all of your bases to see if there was a loss in quality associated
with only one part of the flowcell.
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<p>
The plot shows the deviation from the average quality for each tile.
The colours are on a cold to hot scale, with cold colours being 
positions where the quality was at or above the average for that
base in the run, and hotter colours indicate that a tile had worse
qualities than other tiles for that base.  In the example below you
can see that certain tiles show consistently poor quality.  A good
plot should be blue all over.
</p>
<p><img src="per_tile_quality.png" alt="Kmer profiles"></p>
<p>
Reasons for seeing warnings or errors on this plot could be transient
problems such as bubbles going through the flowcell, or they could
be more permanent problems such as smudges on the flowcell or debris
inside the flowcell lane.
</p>

<h2>Warning</h2>
<p>
This module will issue a warning if any tile shows a mean Phred
score more than 2 less than the mean for that base across all
tiles. 
</p>

<h2>Failure</h2>
<p>
This module will raise and error if any tile shows a mean Phred
score more than 5 less than the mean for that base across all
tiles. 
</p>

<h2>Common reasons for warnings</h2>
<p>
Whilst warnings in this module can be triggered by individual specific
events we have also observed that greater variation in the phred 
scores attributed to tiles can also appear when a flowcell is generally 
overloaded.  In this case events appear all over the flowcell rather 
than being confined to a specific area or range of cycles.  We would
generally ignore errors which mildly affected a small number of tiles for
only 1 or 2 cycles, but would pursue larger effects which showed high
deviation in scores, or which persisted for several cycles. 
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