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-- A modular tool to aggregate results from bioinformatics analyses across many samples into a single report. -
- - - - - - - - - - -Report generated on 2020-03-18, 10:46 based on data in:
- /bi/home/fkrueger/VersionControl/nf-core-modules/tools/multiqc/test/work/52/07836c4fe43e822e375798bf42c0e4
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General Statistics
- - - - - - - - - - Showing 5/5 rows and 5/7 columns. - -| Sample Name | % Aligned | % Trimmed | % Dups | % GC | M Seqs |
|---|---|---|---|---|---|
| test_GRCm38_bowtie2_stats | 88.6% | ||||
| test_R1 | 1.5% | 8.3% | 44% | 0.0 | |
| test_R1_val_1 | 7.0% | 44% | 0.0 | ||
| test_R2 | 1.9% | 8.4% | 44% | 0.0 | |
| test_R2_val_2 | 7.2% | 44% | 0.0 |
Bowtie 2
-Bowtie 2 is an ultrafast and memory-efficient tool for aligning sequencing reads to long reference sequences.
- - - - - - -This plot shows the number of reads aligning to the reference in different ways.
Please note that single mate alignment counts are halved to tally with pair counts properly.
There are 6 possible types of alignment: -* PE mapped uniquely: Pair has only one occurence in the reference genome. -* PE mapped discordantly uniquely: Pair has only one occurence but not in proper pair. -* PE one mate mapped uniquely: One read of a pair has one occurence. -* PE multimapped: Pair has multiple occurence. -* PE one mate multimapped: One read of a pair has multiple occurence. -* PE neither mate aligned: Pair has no occurence.
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Cutadapt
-Cutadapt is a tool to find and remove adapter sequences, primers, poly-Atails and other types of unwanted sequence from your high-throughput sequencing reads.
- - - - -This plot shows the number of reads with certain lengths of adapter trimmed. - Obs/Exp shows the raw counts divided by the number expected due to sequencing errors. A defined peak - may be related to adapter length. See the - cutadapt documentation - for more information on how these numbers are generated.
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FastQ Screen
-FastQ Screen allows you to screen a library of sequences in FastQ format against a set of sequence databases so you can see if the composition of the library matches with what you expect.
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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 - -
- -The distribution of fragment sizes (read lengths) found. - See the FastQC help
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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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