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-- A modular tool to aggregate results from bioinformatics analyses across many samples into a single report. -
- - - - - - - - - - - -Report
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- generated on 2020-11-28, 16:22
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- based on data in:
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- /home/paolo/Projects/NEXTFLOWetude/nf-core-modules/work/cc/e69df4c4d3d33e79fbb670c7f14b52
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General Statistics
- - - - - - - - - - Showing 2/2 rows and 3/5 columns. - -| Sample Name | % Dups | % GC | M Seqs |
|---|---|---|---|
| test_1 | 8.3% | 44% | 0.0 |
| test_2 | 8.4% | 44% | 0.0 |
FastQC
-FastQC is a quality control tool for high throughput sequence data, written by Simon Andrews at the Babraham Institute in Cambridge.
- - - - -- Sequence Counts - - - -
- -Sequence counts for each sample. Duplicate read counts are an estimate only.
This plot show the total number of reads, broken down into unique and duplicate -if possible (only more recent versions of FastQC give duplicate info).
-You can read more about duplicate calculation in the -FastQC documentation. -A small part has been copied here for convenience:
-Only sequences which first appear in the first 100,000 sequences -in each file are analysed. This should be enough to get a good impression -for the duplication levels in the whole file. Each sequence is tracked to -the end of the file to give a representative count of the overall duplication level.
-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 this analysis.
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- Sequence Quality Histograms - - - -
- -The mean quality value across each base position in the read.
To enable multiple samples to be plotted on the same graph, only the mean quality -scores are plotted (unlike the box plots seen in FastQC reports).
-Taken from the FastQC help:
-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.
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- Per Sequence Quality Scores - - - -
- -The number of reads with average quality scores. Shows if a subset of reads has poor quality.
From the FastQC help:
-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, however these should -represent only a small percentage of the total sequences.
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- Per Base Sequence Content - - - -
- -The proportion of each base position for which each of the four normal DNA bases has been called.
To enable multiple samples to be shown in a single plot, the base composition data -is shown as a heatmap. The colours represent the balance between the four bases: -an even distribution should give an even muddy brown colour. Hover over the plot -to see the percentage of the four bases under the cursor.
-To see the data as a line plot, as in the original FastQC graph, click on a sample track.
-From the FastQC help:
-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.
-It's worth noting that some types of library will always produce biased sequence -composition, normally at the start of the read. Libraries produced by priming -using random hexamers (including nearly all RNA-Seq libraries) and those which -were fragmented using transposases inherit an intrinsic bias in the positions -at which reads start. This bias does not concern an absolute sequence, but instead -provides enrichement of a number of different K-mers at the 5' end of the reads. -Whilst this is a true technical bias, it isn't something which can be corrected -by trimming and in most cases doesn't seem to adversely affect the downstream -analysis.
Rollover for sample name
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- Per Sequence GC Content - - - -
- -The average GC content of reads. Normal random library typically have a - roughly normal distribution of GC content.
From the FastQC help:
-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.
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- Per Base N Content - - - -
- -The percentage of base calls at each position for which an N was called.
From the FastQC help:
-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 graph shows 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.
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- Sequence Length Distribution - -
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- Sequence Duplication Levels - - - -
- -The relative level of duplication found for every sequence.
From the FastQC Help:
-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 graph shows -the degree of duplication for every sequence in a library: the relative -number of sequences with different degrees of duplication.
-Only sequences which first appear in the first 100,000 sequences -in each file are analysed. This should be enough to get a good impression -for the duplication levels in the whole file. Each sequence is tracked to -the end of the file to give a representative count of the overall duplication level.
-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 this analysis.
-In a properly diverse library most sequences should fall into the far left of the -plot in both the red and blue lines. A general level of enrichment, indicating broad -oversequencing in the library will tend to flatten the lines, lowering the low end -and generally raising other categories. More specific enrichments of subsets, or -the presence of low complexity contaminants will tend to produce spikes towards the -right of the plot.
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- Overrepresented sequences - - - -
- -The total amount of overrepresented sequences found in each library.
FastQC calculates and lists overrepresented sequences in FastQ files. It would not be -possible to show this for all samples in a MultiQC report, so instead this plot shows -the number of sequences categorized as over represented.
-Sometimes, a single sequence may account for a large number of reads in a dataset. -To show this, the bars are split into two: the first shows the overrepresented reads -that come from the single most common sequence. The second shows the total count -from all remaining overrepresented sequences.
-From the FastQC Help:
-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.
-FastQC lists all of the sequences which make up more than 0.1% of the total. -To conserve memory only sequences which appear in the first 100,000 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.
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- Adapter Content - - - -
- -The cumulative percentage count of the proportion of your - library which has seen each of the adapter sequences at each position.
Note that only samples with ≥ 0.1% adapter contamination are shown.
-There may be several lines per sample, as one is shown for each adapter -detected in the file.
-From the FastQC Help:
-The plot shows a cumulative percentage count of the proportion -of your library which has seen each of the adapter sequences at each position. -Once a sequence has been seen in a read it is counted as being present -right through to the end of the read so the percentages you see will only -increase as the read length goes on.
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- Status Checks - - - -
- -Status for each FastQC section showing whether results seem entirely normal (green), -slightly abnormal (orange) or very unusual (red).
FastQC assigns a status for each section of the report. -These give a quick evaluation of whether the results of the analysis seem -entirely normal (green), slightly abnormal (orange) or very unusual (red).
-It is important to stress that although the analysis results appear to give a pass/fail result, -these evaluations must be taken in the context of what you expect from your library. -A 'normal' sample as far as FastQC is concerned is random and diverse. -Some experiments may be expected to produce libraries which are biased in particular ways. -You should treat the summary evaluations therefore as pointers to where you should concentrate -your attention and understand why your library may not look random and diverse.
-Specific guidance on how to interpret the output of each module can be found in the relevant -report section, or in the FastQC help.
-In this heatmap, we summarise all of these into a single heatmap for a quick overview. -Note that not all FastQC sections have plots in MultiQC reports, but all status checks -are shown in this heatmap.
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