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### MultiQC Toolbox

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If you use plots from MultiQC in a publication or presentation, please cite:

> MultiQC: Summarize analysis results for multiple tools and samples in a single report
> Philip Ewels, Måns Magnusson, Sverker Lundin and Max Käller
> Bioinformatics (2016)
> doi: 10.1093/bioinformatics/btw354
> PMID: 27312411

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#### About MultiQC

This report was generated using MultiQC, version 1.9

You can see a YouTube video describing how to use MultiQC reports here: https://youtu.be/qPbIlO_KWN0

For more information about MultiQC, including other videos and extensive documentation, please visit http://multiqc.info

You can report bugs, suggest improvements and find the source code for MultiQC on GitHub: https://github.com/ewels/MultiQC

MultiQC is published in Bioinformatics:

> MultiQC: Summarize analysis results for multiple tools and samples in a single report
> Philip Ewels, Måns Magnusson, Sverker Lundin and Max Käller
> Bioinformatics (2016)
> doi: 10.1093/bioinformatics/btw354
> PMID: 27312411

#

A modular tool to aggregate results from bioinformatics analyses across many samples into a single report.

> This report has been generated by the nf-core/viralrecon analysis pipeline. For information about how to interpret these results, please see the documentation.

Loading report..

Report generated on 2020-06-22, 18:47 based on data in: `nfcore/viralrecon/test_full/work/fb/bfe7e69a95d108076e42773b526fd3`

Welcome! Not sure where to start? Watch a tutorial video (6:06)

## Variant calling metrics

generated by the nf-core/viralrecon pipeline

Showing 2/2 rows and 23/23 columns.

Sample | # Input reads | # Trimmed reads (fastp) | % Mapped reads (viral) | # Trimmed reads (iVar) | # Duplicate reads | # Reads after MarkDuplicates | Insert size mean | Insert size std dev | Coverage mean | Coverage std dev | % Coverage > 10x | # High conf SNPs (VarScan 2) | # High conf INDELs (VarScan 2) | # High conf SNPs (iVar) | # High conf INDELs (iVar) | # High conf SNPs (BCFTools) | # High conf INDELs (BCFTools) | # Missense variants (VarScan 2) | # Missense variants (iVar) | # Missense variants (BCFTools) | # Ns per 100kb consensus (VarScan 2) | # Ns per 100kb consensus (iVar) | # Ns per 100kb consensus (BCFTools)
--- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | ---
sample1 | 2755026 | 2384570 | 100 | 2372162 | 2216894 | 2372162 | 523 | 216 | 1095 | 479 | 1 | 6 | 0 | 6 | 0 | 6 | 0 | 2 | 2 | 2 | 224 | 164 | 224
sample2 | 2139958 | 1913910 | 99 | 1891311 | 1816848 | 1891311 | 478 | 179 | 498 | 311 | 1 | 7 | 0 | 7 | 0 | 6 | 0 | 5 | 5 | 5 | 338 | 288 | 338

#### Variant calling metrics: Columns

Uncheck the tick box to hide columns. Click and drag the handle on the left to change order.

Sort | Visible | Group | Column | Description | ID | Scale
--- | --- | --- | --- | --- | --- | ---
|| |  | Variant calling metrics | # Input reads | # Input reads | `# Input reads` | None
|| |  | Variant calling metrics | # Trimmed reads (fastp) | # Trimmed reads (fastp) | `# Trimmed reads (fastp)` | None
|| |  | Variant calling metrics | % Mapped reads (viral) | % Mapped reads (viral) | `% Mapped reads (viral)` | None
|| |  | Variant calling metrics | # Trimmed reads (iVar) | # Trimmed reads (iVar) | `# Trimmed reads (iVar)` | None
|| |  | Variant calling metrics | # Duplicate reads | # Duplicate reads | `# Duplicate reads` | None
|| |  | Variant calling metrics | # Reads after MarkDuplicates | # Reads after MarkDuplicates | `# Reads after MarkDuplicates` | None
|| |  | Variant calling metrics | Insert size mean | Insert size mean | `Insert size mean` | None
|| |  | Variant calling metrics | Insert size std dev | Insert size std dev | `Insert size std dev` | None
|| |  | Variant calling metrics | Coverage mean | Coverage mean | `Coverage mean` | None
|| |  | Variant calling metrics | Coverage std dev | Coverage std dev | `Coverage std dev` | None
|| |  | Variant calling metrics | % Coverage > 10x | % Coverage > 10x | `% Coverage > 10x` | None
|| |  | Variant calling metrics | # High conf SNPs (VarScan 2) | # High conf SNPs (VarScan 2) | `# High conf SNPs (VarScan 2)` | None
|| |  | Variant calling metrics | # High conf INDELs (VarScan 2) | # High conf INDELs (VarScan 2) | `# High conf INDELs (VarScan 2)` | None
|| |  | Variant calling metrics | # High conf SNPs (iVar) | # High conf SNPs (iVar) | `# High conf SNPs (iVar)` | None
|| |  | Variant calling metrics | # High conf INDELs (iVar) | # High conf INDELs (iVar) | `# High conf INDELs (iVar)` | None
|| |  | Variant calling metrics | # High conf SNPs (BCFTools) | # High conf SNPs (BCFTools) | `# High conf SNPs (BCFTools)` | None
|| |  | Variant calling metrics | # High conf INDELs (BCFTools) | # High conf INDELs (BCFTools) | `# High conf INDELs (BCFTools)` | None
|| |  | Variant calling metrics | # Missense variants (VarScan 2) | # Missense variants (VarScan 2) | `# Missense variants (VarScan 2)` | None
|| |  | Variant calling metrics | # Missense variants (iVar) | # Missense variants (iVar) | `# Missense variants (iVar)` | None
|| |  | Variant calling metrics | # Missense variants (BCFTools) | # Missense variants (BCFTools) | `# Missense variants (BCFTools)` | None
|| |  | Variant calling metrics | # Ns per 100kb consensus (VarScan 2) | # Ns per 100kb consensus (VarScan 2) | `# Ns per 100kb consensus (VarScan 2)` | None
|| |  | Variant calling metrics | # Ns per 100kb consensus (iVar) | # Ns per 100kb consensus (iVar) | `# Ns per 100kb consensus (iVar)` | None
|| |  | Variant calling metrics | # Ns per 100kb consensus (BCFTools) | # Ns per 100kb consensus (BCFTools) | `# Ns per 100kb consensus (BCFTools)` | None

## De novo assembly metrics

generated by the nf-core/viralrecon pipeline

Showing 2/2 rows and 31/31 columns.

Sample | # Input reads | # Trimmed reads (Cutadapt) | % Non-host reads (Kraken 2) | # Contigs (SPAdes) | Largest contig (SPAdes) | % Genome fraction (SPAdes) | N50 (SPAdes) | # Contigs (metaSPAdes) | Largest contig (metaSPAdes) | % Genome fraction (metaSPAdes) | N50 (metaSPAdes) | # Contigs (Unicycler) | Largest contig (Unicycler) | % Genome fraction (Unicycler) | N50 (Unicycler) | # Contigs (minia) | Largest contig (minia) | % Genome fraction (minia) | N50 (minia) | # SNPs (SPAdes) | # INDELs (SPAdes) | # SNPs (metaSPAdes) | # INDELs (metaSPAdes) | # SNPs (Unicycler) | # INDELs (Unicycler) | # SNPs (minia) | # INDELs (minia) | # Missense variants (SPAdes) | # Missense variants (metaSPAdes) | # Missense variants (Unicycler) | # Missense variants (minia)
--- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | ---
sample1 | 2755026 | 2384570 | 100 | 3576 | 5513 | 40 | 774 | 16 | 29962 | 100 | 29962 | 166 | 5386 | 38 | 916 | 29 | 16188 | 99 | 15793 | 435 | 174 | 2 | 0 | 3 | 0 | 8 | 0 | 344 | 1 | 1 | 4
sample2 | 2139958 | 1913910 | 99 | 800 | 5513 | 86 | 1070 | 17 | 29919 | 100 | 29919 | 30 | 2400 | 83 | 1478 | 29 | 18084 | 99 | 16188 | 88 | 26 | 4 | 0 | 7 | 1 | 6 | 0 | 64 | 3 | 5 | 5

#### De novo assembly metrics: Columns

Uncheck the tick box to hide columns. Click and drag the handle on the left to change order.

Sort | Visible | Group | Column | Description | ID | Scale
--- | --- | --- | --- | --- | --- | ---
|| |  | De novo assembly metrics | # Input reads | # Input reads | `# Input reads` | None
|| |  | De novo assembly metrics | # Trimmed reads (Cutadapt) | # Trimmed reads (Cutadapt) | `# Trimmed reads (Cutadapt)` | None
|| |  | De novo assembly metrics | % Non-host reads (Kraken 2) | % Non-host reads (Kraken 2) | `% Non-host reads (Kraken 2)` | None
|| |  | De novo assembly metrics | # Contigs (SPAdes) | # Contigs (SPAdes) | `# Contigs (SPAdes)` | None
|| |  | De novo assembly metrics | Largest contig (SPAdes) | Largest contig (SPAdes) | `Largest contig (SPAdes)` | None
|| |  | De novo assembly metrics | % Genome fraction (SPAdes) | % Genome fraction (SPAdes) | `% Genome fraction (SPAdes)` | None
|| |  | De novo assembly metrics | N50 (SPAdes) | N50 (SPAdes) | `N50 (SPAdes)` | None
|| |  | De novo assembly metrics | # Contigs (metaSPAdes) | # Contigs (metaSPAdes) | `# Contigs (metaSPAdes)` | None
|| |  | De novo assembly metrics | Largest contig (metaSPAdes) | Largest contig (metaSPAdes) | `Largest contig (metaSPAdes)` | None
|| |  | De novo assembly metrics | % Genome fraction (metaSPAdes) | % Genome fraction (metaSPAdes) | `% Genome fraction (metaSPAdes)` | None
|| |  | De novo assembly metrics | N50 (metaSPAdes) | N50 (metaSPAdes) | `N50 (metaSPAdes)` | None
|| |  | De novo assembly metrics | # Contigs (Unicycler) | # Contigs (Unicycler) | `# Contigs (Unicycler)` | None
|| |  | De novo assembly metrics | Largest contig (Unicycler) | Largest contig (Unicycler) | `Largest contig (Unicycler)` | None
|| |  | De novo assembly metrics | % Genome fraction (Unicycler) | % Genome fraction (Unicycler) | `% Genome fraction (Unicycler)` | None
|| |  | De novo assembly metrics | N50 (Unicycler) | N50 (Unicycler) | `N50 (Unicycler)` | None
|| |  | De novo assembly metrics | # Contigs (minia) | # Contigs (minia) | `# Contigs (minia)` | None
|| |  | De novo assembly metrics | Largest contig (minia) | Largest contig (minia) | `Largest contig (minia)` | None
|| |  | De novo assembly metrics | % Genome fraction (minia) | % Genome fraction (minia) | `% Genome fraction (minia)` | None
|| |  | De novo assembly metrics | N50 (minia) | N50 (minia) | `N50 (minia)` | None
|| |  | De novo assembly metrics | # SNPs (SPAdes) | # SNPs (SPAdes) | `# SNPs (SPAdes)` | None
|| |  | De novo assembly metrics | # INDELs (SPAdes) | # INDELs (SPAdes) | `# INDELs (SPAdes)` | None
|| |  | De novo assembly metrics | # SNPs (metaSPAdes) | # SNPs (metaSPAdes) | `# SNPs (metaSPAdes)` | None
|| |  | De novo assembly metrics | # INDELs (metaSPAdes) | # INDELs (metaSPAdes) | `# INDELs (metaSPAdes)` | None
|| |  | De novo assembly metrics | # SNPs (Unicycler) | # SNPs (Unicycler) | `# SNPs (Unicycler)` | None
|| |  | De novo assembly metrics | # INDELs (Unicycler) | # INDELs (Unicycler) | `# INDELs (Unicycler)` | None
|| |  | De novo assembly metrics | # SNPs (minia) | # SNPs (minia) | `# SNPs (minia)` | None
|| |  | De novo assembly metrics | # INDELs (minia) | # INDELs (minia) | `# INDELs (minia)` | None
|| |  | De novo assembly metrics | # Missense variants (SPAdes) | # Missense variants (SPAdes) | `# Missense variants (SPAdes)` | None
|| |  | De novo assembly metrics | # Missense variants (metaSPAdes) | # Missense variants (metaSPAdes) | `# Missense variants (metaSPAdes)` | None
|| |  | De novo assembly metrics | # Missense variants (Unicycler) | # Missense variants (Unicycler) | `# Missense variants (Unicycler)` | None
|| |  | De novo assembly metrics | # Missense variants (minia) | # Missense variants (minia) | `# Missense variants (minia)` | None

## PREPROCESS: FastQC (raw reads)

PREPROCESS: FastQC (raw reads) This section of the report shows FastQC results for the raw reads before adapter trimming.

### 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.

loading..

### 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.

loading..

### 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.

loading..

### 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.

Click a sample row to see a line plot for that dataset.

##### Rollover for sample name

Position: -
%T: -
%C: -
%A: -
%G: -

### 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.

loading..

### 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.

loading..

### Sequence Length Distribution

The distribution of fragment sizes (read lengths) found. See the FastQC help

loading..

### 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.

loading..

### 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.

loading..

### 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.

No samples found with any adapter contamination > 0.1%

### 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.

loading..

## PREPROCESS: fastp (adapter trimming)

PREPROCESS: fastp (adapter trimming) This section of the report shows fastp results for reads after adapter and quality trimming.

### Filtered Reads

Filtering statistics of sampled reads.

loading..

### Duplication Rates

Duplication rates of sampled reads.

loading..

### Insert Sizes

Insert size estimation of sampled reads.

loading..

### Sequence Quality

Average sequencing quality over each base of all reads.

loading..

### GC Content

Average GC content over each base of all reads.

loading..

### N content

Average N content over each base of all reads.

loading..

## PREPROCESS: FastQC (adapter trimming)

PREPROCESS: FastQC (adapter trimming) This section of the report shows FastQC results for reads after adapter and quality trimming.

### 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.

loading..

### 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.

loading..

### 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.

loading..

### 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.

Click a sample row to see a line plot for that dataset.

##### Rollover for sample name

Position: -
%T: -
%C: -
%A: -
%G: -

### 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.

loading..

### 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.

loading..

### Sequence Length Distribution

The distribution of fragment sizes (read lengths) found. See the FastQC help

loading..

### 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.

loading..

### 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.

No samples found with any adapter contamination > 0.1%

### 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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## VARIANTS: Bowtie 2

This section of the report shows Bowtie 2 mapping results for reads after adapter trimming and quality trimming.

### Paired-end alignments

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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## VARIANTS: SAMTools (raw)

Samtools This section of the report shows SAMTools counts/statistics after mapping with Bowtie 2.

### Percent Mapped

Alignment metrics from `samtools stats`; mapped vs. unmapped reads.

For a set of samples that have come from the same multiplexed library, similar numbers of reads for each sample are expected. Large differences in numbers might indicate issues during the library preparation process. Whilst large differences in read numbers may be controlled for in downstream processings (e.g. read count normalisation), you may wish to consider whether the read depths achieved have fallen below recommended levels depending on the applications.

Low alignment rates could indicate contamination of samples (e.g. adapter sequences), low sequencing quality or other artefacts. These can be further investigated in the sequence level QC (e.g. from FastQC).

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### Alignment metrics

This module parses the output from `samtools stats`. All numbers in millions.

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### Samtools Flagstat

This module parses the output from `samtools flagstat`. All numbers in millions.

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### Mapped reads per contig

The `samtools idxstats` tool counts the number of mapped reads per chromosome / contig. Chromosomes with < 0.1% of the total aligned reads are omitted from this plot.

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## VARIANTS: iVar trim

VARIANTS: iVar trim This section of the report shows counts observed for each amplicon primer per sample as detected by iVar trim.

### iVar Primer Counts

Counts observed for each primer per sample.

This lists the number of times a specific primer was found in the respective sample.

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## VARIANTS: SAMTools (iVar)

Samtools This section of the report shows SAMTools counts/statistics after primer sequence removal with iVar.

### Percent Mapped

Alignment metrics from `samtools stats`; mapped vs. unmapped reads.

For a set of samples that have come from the same multiplexed library, similar numbers of reads for each sample are expected. Large differences in numbers might indicate issues during the library preparation process. Whilst large differences in read numbers may be controlled for in downstream processings (e.g. read count normalisation), you may wish to consider whether the read depths achieved have fallen below recommended levels depending on the applications.

Low alignment rates could indicate contamination of samples (e.g. adapter sequences), low sequencing quality or other artefacts. These can be further investigated in the sequence level QC (e.g. from FastQC).

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### Alignment metrics

This module parses the output from `samtools stats`. All numbers in millions.

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### Samtools Flagstat

This module parses the output from `samtools flagstat`. All numbers in millions.

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### Mapped reads per contig

The `samtools idxstats` tool counts the number of mapped reads per chromosome / contig. Chromosomes with < 0.1% of the total aligned reads are omitted from this plot.

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## VARIANTS: SAMTools (MarkDuplicates)

Samtools This section of the report shows SAMTools counts/statistics after duplicate removal with picard MarkDuplicates.

### Percent Mapped

Alignment metrics from `samtools stats`; mapped vs. unmapped reads.

For a set of samples that have come from the same multiplexed library, similar numbers of reads for each sample are expected. Large differences in numbers might indicate issues during the library preparation process. Whilst large differences in read numbers may be controlled for in downstream processings (e.g. read count normalisation), you may wish to consider whether the read depths achieved have fallen below recommended levels depending on the applications.

Low alignment rates could indicate contamination of samples (e.g. adapter sequences), low sequencing quality or other artefacts. These can be further investigated in the sequence level QC (e.g. from FastQC).

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### Alignment metrics

This module parses the output from `samtools stats`. All numbers in millions.

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### Samtools Flagstat

This module parses the output from `samtools flagstat`. All numbers in millions.

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### Mapped reads per contig

The `samtools idxstats` tool counts the number of mapped reads per chromosome / contig. Chromosomes with < 0.1% of the total aligned reads are omitted from this plot.

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## VARIANTS: Picard Metrics

VARIANTS: Picard Metrics This section of the report shows picard CollectMultipleMetrics and MarkDuplicates results after mapping (if "--protocol amplicon" this will be after primer sequence removal with iVar).

### Alignment Summary

Please note that Picard's read counts are divided by two for paired-end data.

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### Insert Size

Plot shows the number of reads at a given insert size. Reads with different orientations are summed.

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### Mark Duplicates

Number of reads, categorised by duplication state. Pair counts are doubled - see help text for details.

The table in the Picard metrics file contains some columns referring read pairs and some referring to single reads.

To make the numbers in this plot sum correctly, values referring to pairs are doubled according to the scheme below:

- `READS_IN_DUPLICATE_PAIRS = 2 * READ_PAIR_DUPLICATES`
- `READS_IN_UNIQUE_PAIRS = 2 * (READ_PAIRS_EXAMINED - READ_PAIR_DUPLICATES)`
- `READS_IN_UNIQUE_UNPAIRED = UNPAIRED_READS_EXAMINED - UNPAIRED_READ_DUPLICATES`
- `READS_IN_DUPLICATE_PAIRS_OPTICAL = 2 * READ_PAIR_OPTICAL_DUPLICATES`
- `READS_IN_DUPLICATE_PAIRS_NONOPTICAL = READS_IN_DUPLICATE_PAIRS - READS_IN_DUPLICATE_PAIRS_OPTICAL`
- `READS_IN_DUPLICATE_UNPAIRED = UNPAIRED_READ_DUPLICATES`
- `READS_UNMAPPED = UNMAPPED_READS`

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### WGS Coverage

The number of bases in the genome territory for each fold coverage. Note that final 1% of data is hidden to prevent very long tails.

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### WGS Filtered Bases

For more information about the filtered categories, see the Picard documentation.

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## VARIANTS: mosdepth

VARIANTS: mosdepth This section of the report shows genome-wide coverage metrics generated by mosdepth.

### Coverage distribution

Distribution of the number of locations in the reference genome with a given depth of coverage

For a set of DNA or RNA reads mapped to a reference sequence, such as a genome or transcriptome, the depth of coverage at a given base position is the number of high-quality reads that map to the reference at that position, while the breadth of coverage is the fraction of the reference sequence to which reads have been mapped with at least a given depth of coverage (Sims et al. 2014).

Defining coverage breadth in terms of coverage depth is useful, because sequencing experiments typically require a specific minimum depth of coverage over the region of interest (Sims et al. 2014), so the extent of the reference sequence that is amenable to analysis is constrained to lie within regions that have sufficient depth. With inadequate sequencing breadth, it can be difficult to distinguish the absence of a biological feature (such as a gene) from a lack of data (Green 2007).

For increasing coverage depths (1×, 2×, …, N×), coverage breadth is calculated as the percentage of the reference sequence that is covered by at least that number of reads, then plots coverage breadth (y-axis) against coverage depth (x-axis). This plot shows the relationship between sequencing depth and breadth for each read dataset, which can be used to gauge, for example, the likely effect of a minimum depth filter on the fraction of a genome available for analysis.

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### Coverage plot

Number of locations in the reference genome with a given depth of coverage

For a set of DNA or RNA reads mapped to a reference sequence, such as a genome or transcriptome, the depth of coverage at a given base position is the number of high-quality reads that map to the reference at that position (Sims et al. 2014).

Bases of a reference sequence (y-axis) are groupped by their depth of coverage (0×, 1×, …, N×) (x-axis). This plot shows the frequency of coverage depths relative to the reference sequence for each read dataset, which provides an indirect measure of the level and variation of coverage depth in the corresponding sequenced sample.

If reads are randomly distributed across the reference sequence, this plot should resemble a Poisson distribution (Lander & Waterman 1988), with a peak indicating approximate depth of coverage, and more uniform coverage depth being reflected in a narrower spread. The optimal level of coverage depth depends on the aims of the experiment, though it should at minimum be sufficiently high to adequately address the biological question; greater uniformity of coverage is generally desirable, because it increases breadth of coverage for a given depth of coverage, allowing equivalent results to be achieved at a lower sequencing depth (Sampson et al. 2011; Sims et al. 2014). However, it is difficult to achieve uniform coverage depth in practice, due to biases introduced during sample preparation (van Dijk et al. 2014), sequencing (Ross et al. 2013) and read mapping (Sims et al. 2014).

This plot may include a small peak for regions of the reference sequence with zero depth of coverage. Such regions may be absent from the given sample (due to a deletion or structural rearrangement), present in the sample but not successfully sequenced (due to bias in sequencing or preparation), or sequenced but not successfully mapped to the reference (due to the choice of mapping algorithm, the presence of repeat sequences, or mismatches caused by variants or sequencing errors). Related factors cause most datasets to contain some unmapped reads (Sims et al. 2014).

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### Average coverage per contig

Average coverage per contig or chromosome

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## VARIANTS: VarScan 2

VARIANTS: VarScan 2 This section of the report shows total number of variants called by VarScan 2 broken down by those that were reported or not.

### Variants detected

This plot shows the total number of variant positions, broken down by those that were reported or not.

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## VARIANTS: BCFTools (VarScan 2; high freq)

Bcftools This section of the report shows BCFTools stats results for high frequency variants called by VarScan 2. The allele frequency filtering threshold can be set by the --max_allele_freq parameter (Default: 0.8).

### Variant Substitution Types

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### Variant Quality

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### Indel Distribution

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### Variant depths

Read depth support distribution for called variants

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## VARIANTS: SnpEff (VarScan 2; high freq)

VARIANTS: SnpEff (VarScan 2; high freq) This section of the report shows SnpEff results for high frequency variants called by VarScan 2. The allele frequency filtering threshold can be set by the --max_allele_freq parameter (Default: 0.8).

### Variants by Genomic Region

The stacked bar plot shows locations of detected variants in the genome and the number of variants for each location.

The upstream and downstream interval size to detect these genomic regions is 5000bp by default.

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### Variant Effects by Impact

The stacked bar plot shows the putative impact of detected variants and the number of variants for each impact.

There are four levels of impacts predicted by SnpEff:

- High: High impact (like stop codon)
- Moderate: Middle impact (like same type of amino acid substitution)
- Low: Low impact (ie silence mutation)
- Modifier: No impact

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### Variants by Effect Types

The stacked bar plot shows the effect of variants at protein level and the number of variants for each effect type.

This plot shows the effect of variants with respect to the mRNA.

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### Variants by Functional Class

The stacked bar plot shows the effect of variants and the number of variants for each effect type.

This plot shows the effect of variants on the translation of the mRNA as protein. There are three possible cases:

- Silent: The amino acid does not change.
- Missense: The amino acid is different.
- Nonsense: The variant generates a stop codon.

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## VARIANTS: QUAST (VarScan 2; high freq)

VARIANTS: QUAST (VarScan 2; high freq) This section of the report shows QUAST results for consensus sequences generated from high frequency variants with VarScan 2. The allele frequency filtering threshold can be set by the --max_allele_freq parameter (Default: 0.8).

### Assembly Statistics

Showing 2/2 rows and 10/10 columns.

Sample Name | N50 (Kbp) | N75 (Kbp) | L50 (K) | L75 (K) | Largest contig (Kbp) | Length (Mbp) | Misassemblies | Mismatches/100kbp | Indels/100kbp | Genome Fraction
--- | --- | --- | --- | --- | --- | --- | --- | --- | --- | ---
sample1 | 29.9Kbp | 29.9Kbp | 0.0K | 1.0K | 29.9Kbp | 0.0Mbp | 0.0 | 20.11 | 0.00 | 99.8%
sample2 | 29.9Kbp | 29.9Kbp | 0.0K | 1.0K | 29.9Kbp | 0.0Mbp | 0.0 | 23.49 | 0.00 | 99.7%

#### Quast Table: Columns

Uncheck the tick box to hide columns. Click and drag the handle on the left to change order.

Sort | Visible | Group | Column | Description | ID | Scale
--- | --- | --- | --- | --- | --- | ---
|| |  | QUAST | N50 (Kbp) | N50 is the contig length such that using longer or equal length contigs produces half (50%) of the bases of the assembly. | `N50` | None
|| |  | QUAST | N75 (Kbp) | N75 is the contig length such that using longer or equal length contigs produces 75% of the bases of the assembly | `N75` | None
|| |  | QUAST | L50 (K) | L50 is the number of contigs larger than N50, i.e. the minimum number of contigs comprising 50% of the total assembly length. | `L50` | None
|| |  | QUAST | L75 (K) | L75 is the number of contigs larger than N75, i.e. the minimum number of contigs comprising 75% of the total assembly length. | `L75` | None
|| |  | QUAST | Largest contig (Kbp) | The size of the largest contig of the assembly | `Largest contig` | None
|| |  | QUAST | Length (Mbp) | The total number of bases in the assembly. | `Total length` | None
|| |  | QUAST | Misassemblies | The number of positions in the assembled contigs where the left flanking sequence aligns over 1 kbp away from the right flanking sequence on the reference (relocation) or they overlap on more than 1 kbp (relocation) or flanking sequences align on different strands (inversion) or different chromosomes (translocation). | `# misassemblies` | None
|| |  | QUAST | Mismatches/100kbp | The number of mismatches per 100 kbp | `# mismatches per 100 kbp` | None
|| |  | QUAST | Indels/100kbp | The number of indels per 100 kbp | `# indels per 100 kbp` | None
|| |  | QUAST | Genome Fraction | The total number of aligned bases in the reference, divided by the genome size. | `Genome fraction (%)` | None

### Number of Contigs

This plot shows the number of contigs found for each assembly, broken down by length.

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## VARIANTS: iVar variant counts

is calculated from the total number of variants called by iVar.

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## VARIANTS: BCFTools (iVar; high freq)

Bcftools This section of the report shows BCFTools stats results for high frequency variants called by iVar. The allele frequency filtering threshold can be set by the --max_allele_freq parameter (Default: 0.8).

### Variant Substitution Types

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### Variant Quality

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### Indel Distribution

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### Variant depths

Read depth support distribution for called variants

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## VARIANTS: SnpEff (iVar; high freq)

VARIANTS: SnpEff (iVar; high freq) This section of the report shows SnpEff results for high frequency variants called by iVar. The allele frequency filtering threshold can be set by the --max_allele_freq parameter (Default: 0.8).

### Variants by Genomic Region

The stacked bar plot shows locations of detected variants in the genome and the number of variants for each location.

The upstream and downstream interval size to detect these genomic regions is 5000bp by default.

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### Variant Effects by Impact

The stacked bar plot shows the putative impact of detected variants and the number of variants for each impact.

There are four levels of impacts predicted by SnpEff:

- High: High impact (like stop codon)
- Moderate: Middle impact (like same type of amino acid substitution)
- Low: Low impact (ie silence mutation)
- Modifier: No impact

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### Variants by Effect Types

The stacked bar plot shows the effect of variants at protein level and the number of variants for each effect type.

This plot shows the effect of variants with respect to the mRNA.

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### Variants by Functional Class

The stacked bar plot shows the effect of variants and the number of variants for each effect type.

This plot shows the effect of variants on the translation of the mRNA as protein. There are three possible cases:

- Silent: The amino acid does not change.
- Missense: The amino acid is different.
- Nonsense: The variant generates a stop codon.

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## VARIANTS: QUAST (iVar; high freq)

VARIANTS: QUAST (iVar; high freq) This section of the report shows QUAST results for consensus sequences generated from high frequency variants with iVar. The allele frequency filtering threshold can be set by the --max_allele_freq parameter (Default: 0.8).

### Assembly Statistics

Showing 2/2 rows and 10/10 columns.

Sample Name | N50 (Kbp) | N75 (Kbp) | L50 (K) | L75 (K) | Largest contig (Kbp) | Length (Mbp) | Misassemblies | Mismatches/100kbp | Indels/100kbp | Genome Fraction
--- | --- | --- | --- | --- | --- | --- | --- | --- | --- | ---
sample1 | 29.9Kbp | 29.9Kbp | 0.0K | 1.0K | 29.9Kbp | 0.0Mbp | 0.0 | 20.11 | 0.00 | 99.8%
sample2 | 29.8Kbp | 29.8Kbp | 0.0K | 1.0K | 29.8Kbp | 0.0Mbp | 0.0 | 23.53 | 0.00 | 99.5%

#### Quast Table-1: Columns

Uncheck the tick box to hide columns. Click and drag the handle on the left to change order.

Sort | Visible | Group | Column | Description | ID | Scale
--- | --- | --- | --- | --- | --- | ---
|| |  | QUAST | N50 (Kbp) | N50 is the contig length such that using longer or equal length contigs produces half (50%) of the bases of the assembly. | `N50` | None
|| |  | QUAST | N75 (Kbp) | N75 is the contig length such that using longer or equal length contigs produces 75% of the bases of the assembly | `N75` | None
|| |  | QUAST | L50 (K) | L50 is the number of contigs larger than N50, i.e. the minimum number of contigs comprising 50% of the total assembly length. | `L50` | None
|| |  | QUAST | L75 (K) | L75 is the number of contigs larger than N75, i.e. the minimum number of contigs comprising 75% of the total assembly length. | `L75` | None
|| |  | QUAST | Largest contig (Kbp) | The size of the largest contig of the assembly | `Largest contig` | None
|| |  | QUAST | Length (Mbp) | The total number of bases in the assembly. | `Total length` | None
|| |  | QUAST | Misassemblies | The number of positions in the assembled contigs where the left flanking sequence aligns over 1 kbp away from the right flanking sequence on the reference (relocation) or they overlap on more than 1 kbp (relocation) or flanking sequences align on different strands (inversion) or different chromosomes (translocation). | `# misassemblies` | None
|| |  | QUAST | Mismatches/100kbp | The number of mismatches per 100 kbp | `# mismatches per 100 kbp` | None
|| |  | QUAST | Indels/100kbp | The number of indels per 100 kbp | `# indels per 100 kbp` | None
|| |  | QUAST | Genome Fraction | The total number of aligned bases in the reference, divided by the genome size. | `Genome fraction (%)` | None

### Number of Contigs

This plot shows the number of contigs found for each assembly, broken down by length.

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## VARIANTS: BCFTools (BCFTools)

Bcftools This section of the report shows BCFTools stats results for variants called by BCFTools.

### Variant Substitution Types

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### Variant Quality

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### Indel Distribution

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### Variant depths

Read depth support distribution for called variants

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## VARIANTS: SnpEff (BCFTools)

VARIANTS: SnpEff (BCFTools) This section of the report shows SnpEff results for variants called by BCFTools.

### Variants by Genomic Region

The stacked bar plot shows locations of detected variants in the genome and the number of variants for each location.

The upstream and downstream interval size to detect these genomic regions is 5000bp by default.

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### Variant Effects by Impact

The stacked bar plot shows the putative impact of detected variants and the number of variants for each impact.

There are four levels of impacts predicted by SnpEff:

- High: High impact (like stop codon)
- Moderate: Middle impact (like same type of amino acid substitution)
- Low: Low impact (ie silence mutation)
- Modifier: No impact

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### Variants by Effect Types

The stacked bar plot shows the effect of variants at protein level and the number of variants for each effect type.

This plot shows the effect of variants with respect to the mRNA.

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### Variants by Functional Class

The stacked bar plot shows the effect of variants and the number of variants for each effect type.

This plot shows the effect of variants on the translation of the mRNA as protein. There are three possible cases:

- Silent: The amino acid does not change.
- Missense: The amino acid is different.
- Nonsense: The variant generates a stop codon.

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### Variant Qualities

The line plot shows the quantity as function of the variant quality score.

The quality score corresponds to the QUAL column of the VCF file. This score is set by the variant caller.

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## VARIANTS: QUAST (BCFTools)

VARIANTS: QUAST (BCFTools) This section of the report shows QUAST results for consensus sequence generated from BCFTools variants.

### Assembly Statistics

Showing 2/2 rows and 10/10 columns.

Sample Name | N50 (Kbp) | N75 (Kbp) | L50 (K) | L75 (K) | Largest contig (Kbp) | Length (Mbp) | Misassemblies | Mismatches/100kbp | Indels/100kbp | Genome Fraction
--- | --- | --- | --- | --- | --- | --- | --- | --- | --- | ---
sample1 | 29.9Kbp | 29.9Kbp | 0.0K | 1.0K | 29.9Kbp | 0.0Mbp | 0.0 | 20.11 | 0.00 | 99.8%
sample2 | 29.9Kbp | 29.9Kbp | 0.0K | 1.0K | 29.9Kbp | 0.0Mbp | 0.0 | 20.13 | 0.00 | 99.7%

#### Quast Table-2: Columns

Uncheck the tick box to hide columns. Click and drag the handle on the left to change order.

Sort | Visible | Group | Column | Description | ID | Scale
--- | --- | --- | --- | --- | --- | ---
|| |  | QUAST | N50 (Kbp) | N50 is the contig length such that using longer or equal length contigs produces half (50%) of the bases of the assembly. | `N50` | None
|| |  | QUAST | N75 (Kbp) | N75 is the contig length such that using longer or equal length contigs produces 75% of the bases of the assembly | `N75` | None
|| |  | QUAST | L50 (K) | L50 is the number of contigs larger than N50, i.e. the minimum number of contigs comprising 50% of the total assembly length. | `L50` | None
|| |  | QUAST | L75 (K) | L75 is the number of contigs larger than N75, i.e. the minimum number of contigs comprising 75% of the total assembly length. | `L75` | None
|| |  | QUAST | Largest contig (Kbp) | The size of the largest contig of the assembly | `Largest contig` | None
|| |  | QUAST | Length (Mbp) | The total number of bases in the assembly. | `Total length` | None
|| |  | QUAST | Misassemblies | The number of positions in the assembled contigs where the left flanking sequence aligns over 1 kbp away from the right flanking sequence on the reference (relocation) or they overlap on more than 1 kbp (relocation) or flanking sequences align on different strands (inversion) or different chromosomes (translocation). | `# misassemblies` | None
|| |  | QUAST | Mismatches/100kbp | The number of mismatches per 100 kbp | `# mismatches per 100 kbp` | None
|| |  | QUAST | Indels/100kbp | The number of indels per 100 kbp | `# indels per 100 kbp` | None
|| |  | QUAST | Genome Fraction | The total number of aligned bases in the reference, divided by the genome size. | `Genome fraction (%)` | None

### Number of Contigs

This plot shows the number of contigs found for each assembly, broken down by length.

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## ASSEMBLY: Cutadapt (primer trimming)

ASSEMBLY: Cutadapt (primer trimming) This section of the report shows Cutadapt results for reads after primer sequence trimming.

### Filtered Reads

This plot shows the number of reads (SE) / pairs (PE) removed by Cutadapt.

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### Trimmed Sequence Lengths

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.

Flat image plot. Toolbox functions such as highlighting / hiding samples will not work (see the docs).

## ASSEMBLY: FastQC (primer trimming)

ASSEMBLY: FastQC (primer trimming) This section of the report shows FastQC results for reads after primer sequence trimming with Cutadapt.

### 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.

Click a sample row to see a line plot for that dataset.

##### Rollover for sample name

Position: -
%T: -
%C: -
%A: -
%G: -

### 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.

No samples found with any adapter contamination > 0.1%

### 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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## ASSEMBLY: Kraken 2

ASSEMBLY: Kraken 2 This section of the report shows Kraken 2 classification results for reads after primer sequence trimming with Cutadapt.

### Top taxa

The number of reads falling into the top 5 taxa across different ranks.

To make this plot, the percentage of each sample assigned to a given taxa is summed across all samples. The counts for these top five taxa are then plotted for each of the 9 different taxa ranks. The unclassified count is always shown across all taxa ranks.

The total number of reads is approximated by dividing the number of `unclassified` reads by the percentage of the library that they account for. Note that this is only an approximation, and that kraken percentages don't always add to exactly 100%.

The category "Other" shows the difference between the above total read count and the sum of the read counts in the top 5 taxa shown + unclassified. This should cover all taxa not in the top 5, +/- any rounding errors.

Note that any taxon that does not exactly fit a taxon rank (eg. `-` or `G2`) is ignored.

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## ASSEMBLY: QUAST (SPAdes)

ASSEMBLY: QUAST (SPAdes) This section of the report shows QUAST results from SPAdes de novo assembly.

### Assembly Statistics

Showing 2/2 rows and 10/10 columns.

Sample Name | N50 (Kbp) | N75 (Kbp) | L50 (K) | L75 (K) | Largest contig (Kbp) | Length (Mbp) | Misassemblies | Mismatches/100kbp | Indels/100kbp | Genome Fraction
--- | --- | --- | --- | --- | --- | --- | --- | --- | --- | ---
sample1 | 0.8Kbp | 0.6Kbp | 0.0K | 11.0K | 5.5Kbp | 0.0Mbp | 3.0 | 50.60 | 0.00 | 39.7%
sample2 | 1.1Kbp | 0.9Kbp | 0.0K | 17.0K | 5.5Kbp | 0.0Mbp | 2.0 | 39.10 | 0.00 | 85.5%

#### Quast Table-3: Columns

Uncheck the tick box to hide columns. Click and drag the handle on the left to change order.

Sort | Visible | Group | Column | Description | ID | Scale
--- | --- | --- | --- | --- | --- | ---
|| |  | QUAST | N50 (Kbp) | N50 is the contig length such that using longer or equal length contigs produces half (50%) of the bases of the assembly. | `N50` | None
|| |  | QUAST | N75 (Kbp) | N75 is the contig length such that using longer or equal length contigs produces 75% of the bases of the assembly | `N75` | None
|| |  | QUAST | L50 (K) | L50 is the number of contigs larger than N50, i.e. the minimum number of contigs comprising 50% of the total assembly length. | `L50` | None
|| |  | QUAST | L75 (K) | L75 is the number of contigs larger than N75, i.e. the minimum number of contigs comprising 75% of the total assembly length. | `L75` | None
|| |  | QUAST | Largest contig (Kbp) | The size of the largest contig of the assembly | `Largest contig` | None
|| |  | QUAST | Length (Mbp) | The total number of bases in the assembly. | `Total length` | None
|| |  | QUAST | Misassemblies | The number of positions in the assembled contigs where the left flanking sequence aligns over 1 kbp away from the right flanking sequence on the reference (relocation) or they overlap on more than 1 kbp (relocation) or flanking sequences align on different strands (inversion) or different chromosomes (translocation). | `# misassemblies` | None
|| |  | QUAST | Mismatches/100kbp | The number of mismatches per 100 kbp | `# mismatches per 100 kbp` | None
|| |  | QUAST | Indels/100kbp | The number of indels per 100 kbp | `# indels per 100 kbp` | None
|| |  | QUAST | Genome Fraction | The total number of aligned bases in the reference, divided by the genome size. | `Genome fraction (%)` | None

### Number of Contigs

This plot shows the number of contigs found for each assembly, broken down by length.

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## ASSEMBLY: BCFTools (SPAdes)

Bcftools This section of the report shows BCFTools stats results for variants called in the SPAdes assembly relative to the reference.

### Variant Substitution Types

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### Variant Quality

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### Indel Distribution

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### Variant depths

Read depth support distribution for called variants

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## ASSEMBLY: SnpEff (SPAdes)

ASSEMBLY: SnpEff (SPAdes) This section of the report shows SnpEff results for variants called in the SPAdes assembly relative to the reference.

### Variants by Genomic Region

The stacked bar plot shows locations of detected variants in the genome and the number of variants for each location.

The upstream and downstream interval size to detect these genomic regions is 5000bp by default.

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### Variant Effects by Impact

The stacked bar plot shows the putative impact of detected variants and the number of variants for each impact.

There are four levels of impacts predicted by SnpEff:

- High: High impact (like stop codon)
- Moderate: Middle impact (like same type of amino acid substitution)
- Low: Low impact (ie silence mutation)
- Modifier: No impact

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### Variants by Effect Types

The stacked bar plot shows the effect of variants at protein level and the number of variants for each effect type.

This plot shows the effect of variants with respect to the mRNA.

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### Variants by Functional Class

The stacked bar plot shows the effect of variants and the number of variants for each effect type.

This plot shows the effect of variants on the translation of the mRNA as protein. There are three possible cases:

- Silent: The amino acid does not change.
- Missense: The amino acid is different.
- Nonsense: The variant generates a stop codon.

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### Variant Qualities

The line plot shows the quantity as function of the variant quality score.

The quality score corresponds to the QUAL column of the VCF file. This score is set by the variant caller.

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## ASSEMBLY: QUAST (MetaSPAdes)

ASSEMBLY: QUAST (MetaSPAdes) This section of the report shows QUAST results from MetaSPAdes de novo assembly.

### Assembly Statistics

Showing 2/2 rows and 10/10 columns.

Sample Name | N50 (Kbp) | N75 (Kbp) | L50 (K) | L75 (K) | Largest contig (Kbp) | Length (Mbp) | Misassemblies | Mismatches/100kbp | Indels/100kbp | Genome Fraction
--- | --- | --- | --- | --- | --- | --- | --- | --- | --- | ---
sample1 | 30.0Kbp | 30.0Kbp | 0.0K | 1.0K | 30.0Kbp | 0.0Mbp | 0.0 | 20.09 | 0.00 | 99.9%
sample2 | 29.9Kbp | 29.9Kbp | 0.0K | 1.0K | 29.9Kbp | 0.0Mbp | 0.0 | 23.48 | 0.00 | 99.7%

#### Quast Table-4: Columns

Uncheck the tick box to hide columns. Click and drag the handle on the left to change order.

Sort | Visible | Group | Column | Description | ID | Scale
--- | --- | --- | --- | --- | --- | ---
|| |  | QUAST | N50 (Kbp) | N50 is the contig length such that using longer or equal length contigs produces half (50%) of the bases of the assembly. | `N50` | None
|| |  | QUAST | N75 (Kbp) | N75 is the contig length such that using longer or equal length contigs produces 75% of the bases of the assembly | `N75` | None
|| |  | QUAST | L50 (K) | L50 is the number of contigs larger than N50, i.e. the minimum number of contigs comprising 50% of the total assembly length. | `L50` | None
|| |  | QUAST | L75 (K) | L75 is the number of contigs larger than N75, i.e. the minimum number of contigs comprising 75% of the total assembly length. | `L75` | None
|| |  | QUAST | Largest contig (Kbp) | The size of the largest contig of the assembly | `Largest contig` | None
|| |  | QUAST | Length (Mbp) | The total number of bases in the assembly. | `Total length` | None
|| |  | QUAST | Misassemblies | The number of positions in the assembled contigs where the left flanking sequence aligns over 1 kbp away from the right flanking sequence on the reference (relocation) or they overlap on more than 1 kbp (relocation) or flanking sequences align on different strands (inversion) or different chromosomes (translocation). | `# misassemblies` | None
|| |  | QUAST | Mismatches/100kbp | The number of mismatches per 100 kbp | `# mismatches per 100 kbp` | None
|| |  | QUAST | Indels/100kbp | The number of indels per 100 kbp | `# indels per 100 kbp` | None
|| |  | QUAST | Genome Fraction | The total number of aligned bases in the reference, divided by the genome size. | `Genome fraction (%)` | None

### Number of Contigs

This plot shows the number of contigs found for each assembly, broken down by length.

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## ASSEMBLY: BCFTools (MetaSPAdes)

Bcftools This section of the report shows BCFTools stats results for variants called in the MetaSPAdes assembly relative to the reference.

### Variant Substitution Types

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### Variant Quality

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### Indel Distribution

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### Variant depths

Read depth support distribution for called variants

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## ASSEMBLY: SnpEff (MetaSPAdes)

ASSEMBLY: SnpEff (MetaSPAdes) This section of the report shows SnpEff results for variants called in the MetaSPAdes assembly relative to the reference.

### Variants by Genomic Region

The stacked bar plot shows locations of detected variants in the genome and the number of variants for each location.

The upstream and downstream interval size to detect these genomic regions is 5000bp by default.

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### Variant Effects by Impact

The stacked bar plot shows the putative impact of detected variants and the number of variants for each impact.

There are four levels of impacts predicted by SnpEff:

- High: High impact (like stop codon)
- Moderate: Middle impact (like same type of amino acid substitution)
- Low: Low impact (ie silence mutation)
- Modifier: No impact

loading..

### Variants by Effect Types

The stacked bar plot shows the effect of variants at protein level and the number of variants for each effect type.

This plot shows the effect of variants with respect to the mRNA.

loading..

### Variants by Functional Class

The stacked bar plot shows the effect of variants and the number of variants for each effect type.

This plot shows the effect of variants on the translation of the mRNA as protein. There are three possible cases:

- Silent: The amino acid does not change.
- Missense: The amino acid is different.
- Nonsense: The variant generates a stop codon.

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### Variant Qualities

The line plot shows the quantity as function of the variant quality score.

The quality score corresponds to the QUAL column of the VCF file. This score is set by the variant caller.

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## ASSEMBLY: QUAST (Unicycler)

ASSEMBLY: QUAST (Unicycler) This section of the report shows QUAST results from Unicycler de novo assembly.

### Assembly Statistics

Showing 2/2 rows and 10/10 columns.

Sample Name | N50 (Kbp) | N75 (Kbp) | L50 (K) | L75 (K) | Largest contig (Kbp) | Length (Mbp) | Misassemblies | Mismatches/100kbp | Indels/100kbp | Genome Fraction
--- | --- | --- | --- | --- | --- | --- | --- | --- | --- | ---
sample1 | 0.9Kbp | 0.7Kbp | 0.0K | 9.0K | 5.4Kbp | 0.0Mbp | 0.0 | 17.69 | 0.00 | 37.8%
sample2 | 1.5Kbp | 1.0Kbp | 0.0K | 12.0K | 2.4Kbp | 0.0Mbp | 0.0 | 28.32 | 0.00 | 82.7%

#### Quast Table-5: Columns

Uncheck the tick box to hide columns. Click and drag the handle on the left to change order.

Sort | Visible | Group | Column | Description | ID | Scale
--- | --- | --- | --- | --- | --- | ---
|| |  | QUAST | N50 (Kbp) | N50 is the contig length such that using longer or equal length contigs produces half (50%) of the bases of the assembly. | `N50` | None
|| |  | QUAST | N75 (Kbp) | N75 is the contig length such that using longer or equal length contigs produces 75% of the bases of the assembly | `N75` | None
|| |  | QUAST | L50 (K) | L50 is the number of contigs larger than N50, i.e. the minimum number of contigs comprising 50% of the total assembly length. | `L50` | None
|| |  | QUAST | L75 (K) | L75 is the number of contigs larger than N75, i.e. the minimum number of contigs comprising 75% of the total assembly length. | `L75` | None
|| |  | QUAST | Largest contig (Kbp) | The size of the largest contig of the assembly | `Largest contig` | None
|| |  | QUAST | Length (Mbp) | The total number of bases in the assembly. | `Total length` | None
|| |  | QUAST | Misassemblies | The number of positions in the assembled contigs where the left flanking sequence aligns over 1 kbp away from the right flanking sequence on the reference (relocation) or they overlap on more than 1 kbp (relocation) or flanking sequences align on different strands (inversion) or different chromosomes (translocation). | `# misassemblies` | None
|| |  | QUAST | Mismatches/100kbp | The number of mismatches per 100 kbp | `# mismatches per 100 kbp` | None
|| |  | QUAST | Indels/100kbp | The number of indels per 100 kbp | `# indels per 100 kbp` | None
|| |  | QUAST | Genome Fraction | The total number of aligned bases in the reference, divided by the genome size. | `Genome fraction (%)` | None

### Number of Contigs

This plot shows the number of contigs found for each assembly, broken down by length.

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## ASSEMBLY: BCFTools (Unicycler)

Bcftools This section of the report shows BCFTools stats results for variants called in the Unicycler assembly relative to the reference.

### Variant Substitution Types

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### Variant Quality

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### Indel Distribution

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### Variant depths

Read depth support distribution for called variants

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## ASSEMBLY: SnpEff (Unicycler)

ASSEMBLY: SnpEff (Unicycler) This section of the report shows SnpEff results for variants called in the Unicycler assembly relative to the reference.

### Variants by Genomic Region

The stacked bar plot shows locations of detected variants in the genome and the number of variants for each location.

The upstream and downstream interval size to detect these genomic regions is 5000bp by default.

loading..

### Variant Effects by Impact

The stacked bar plot shows the putative impact of detected variants and the number of variants for each impact.

There are four levels of impacts predicted by SnpEff:

- High: High impact (like stop codon)
- Moderate: Middle impact (like same type of amino acid substitution)
- Low: Low impact (ie silence mutation)
- Modifier: No impact

loading..

### Variants by Effect Types

The stacked bar plot shows the effect of variants at protein level and the number of variants for each effect type.

This plot shows the effect of variants with respect to the mRNA.

loading..

### Variants by Functional Class

The stacked bar plot shows the effect of variants and the number of variants for each effect type.

This plot shows the effect of variants on the translation of the mRNA as protein. There are three possible cases:

- Silent: The amino acid does not change.
- Missense: The amino acid is different.
- Nonsense: The variant generates a stop codon.

loading..

### Variant Qualities

The line plot shows the quantity as function of the variant quality score.

The quality score corresponds to the QUAL column of the VCF file. This score is set by the variant caller.

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## ASSEMBLY: QUAST (minia)

ASSEMBLY: QUAST (minia) This section of the report shows QUAST results from minia de novo assembly.

### Assembly Statistics

Showing 2/2 rows and 10/10 columns.

Sample Name | N50 (Kbp) | N75 (Kbp) | L50 (K) | L75 (K) | Largest contig (Kbp) | Length (Mbp) | Misassemblies | Mismatches/100kbp | Indels/100kbp | Genome Fraction
--- | --- | --- | --- | --- | --- | --- | --- | --- | --- | ---
sample1 | 15.8Kbp | 6.1Kbp | 0.0K | 3.0K | 16.2Kbp | 0.0Mbp | 0.0 | 20.21 | 0.00 | 99.3%
sample2 | 16.2Kbp | 7.6Kbp | 0.0K | 3.0K | 18.1Kbp | 0.0Mbp | 0.0 | 20.31 | 0.00 | 98.8%

#### Quast Table-6: Columns

Uncheck the tick box to hide columns. Click and drag the handle on the left to change order.

Sort | Visible | Group | Column | Description | ID | Scale
--- | --- | --- | --- | --- | --- | ---
|| |  | QUAST | N50 (Kbp) | N50 is the contig length such that using longer or equal length contigs produces half (50%) of the bases of the assembly. | `N50` | None
|| |  | QUAST | N75 (Kbp) | N75 is the contig length such that using longer or equal length contigs produces 75% of the bases of the assembly | `N75` | None
|| |  | QUAST | L50 (K) | L50 is the number of contigs larger than N50, i.e. the minimum number of contigs comprising 50% of the total assembly length. | `L50` | None
|| |  | QUAST | L75 (K) | L75 is the number of contigs larger than N75, i.e. the minimum number of contigs comprising 75% of the total assembly length. | `L75` | None
|| |  | QUAST | Largest contig (Kbp) | The size of the largest contig of the assembly | `Largest contig` | None
|| |  | QUAST | Length (Mbp) | The total number of bases in the assembly. | `Total length` | None
|| |  | QUAST | Misassemblies | The number of positions in the assembled contigs where the left flanking sequence aligns over 1 kbp away from the right flanking sequence on the reference (relocation) or they overlap on more than 1 kbp (relocation) or flanking sequences align on different strands (inversion) or different chromosomes (translocation). | `# misassemblies` | None
|| |  | QUAST | Mismatches/100kbp | The number of mismatches per 100 kbp | `# mismatches per 100 kbp` | None
|| |  | QUAST | Indels/100kbp | The number of indels per 100 kbp | `# indels per 100 kbp` | None
|| |  | QUAST | Genome Fraction | The total number of aligned bases in the reference, divided by the genome size. | `Genome fraction (%)` | None

### Number of Contigs

This plot shows the number of contigs found for each assembly, broken down by length.

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## ASSEMBLY: BCFTools (minia)

Bcftools This section of the report shows BCFTools stats results for variants called in the minia assembly relative to the reference.

### Variant Substitution Types

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### Variant Quality

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### Indel Distribution

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### Variant depths

Read depth support distribution for called variants

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## ASSEMBLY: SnpEff (minia)

ASSEMBLY: SnpEff (minia) This section of the report shows SnpEff results for variants called in the minia assembly relative to the reference.

### Variants by Genomic Region

The stacked bar plot shows locations of detected variants in the genome and the number of variants for each location.

The upstream and downstream interval size to detect these genomic regions is 5000bp by default.

loading..

### Variant Effects by Impact

The stacked bar plot shows the putative impact of detected variants and the number of variants for each impact.

There are four levels of impacts predicted by SnpEff:

- High: High impact (like stop codon)
- Moderate: Middle impact (like same type of amino acid substitution)
- Low: Low impact (ie silence mutation)
- Modifier: No impact

loading..

### Variants by Effect Types

The stacked bar plot shows the effect of variants at protein level and the number of variants for each effect type.

This plot shows the effect of variants with respect to the mRNA.

loading..

### Variants by Functional Class

The stacked bar plot shows the effect of variants and the number of variants for each effect type.

This plot shows the effect of variants on the translation of the mRNA as protein. There are three possible cases:

- Silent: The amino acid does not change.
- Missense: The amino acid is different.
- Nonsense: The variant generates a stop codon.

loading..

### Variant Qualities

The line plot shows the quantity as function of the variant quality score.

The quality score corresponds to the QUAL column of the VCF file. This score is set by the variant caller.

loading..

## nf-core/viralrecon Software Versions

are collected at run time from the software output.

nf-core/viralrecon
  `v1.1.0`
Nextflow
  `v20.01.0`
parallel-fastq-dump
  `v0.6.6`
SRA-Tools
  `v2.10.7`
FastQC
  `v0.11.9`
fastp
  `v0.20.1`
Bowtie 2
  `N/A`
Samtools
  `v1.9`
BEDTools
  `v2.29.2`
Mosdepth
  `v0.2.6`
Picard
  `v2.23.0`
iVar
  `v1.2.2`
VarScan 2
  `v2.4.4`
BCFTools
  `v1.9`
SnpEff
  `v4.5covid19`
SnpSift
  `v4.3t`
QUAST
  `v5.0.2`
Cutadapt
  `v2.10`
Kraken2
  `v2.0.9-beta`
SPAdes
  `v3.14.0`
Unicycler
  `v0.4.7`
minia
  `v3.2.4`
BLAST
  `v2.9.0+`
ABACAS
  `v1.3.1`
plasmidID
  `v1.6.3`
Bandage
  `v0.8.1`
Minimap2
  `v2.17-r941`
vg
  `v1.24.0`
R
  `v3.6.2`
MultiQC
  `v1.9`

## nf-core/viralrecon Workflow Summary

- this information is collected when the pipeline is started.

Run Name
  `distracted_yalow`
Samplesheet
  `https://raw.githubusercontent.com/nf-core/test-datasets/viralrecon/samplesheet/samplesheet_full_amplicon.csv`
Protocol
  `amplicon`
Amplicon Fasta File
  `https://raw.githubusercontent.com/nf-core/test-datasets/viralrecon/genome/NC_045512.2/amplicon/nCoV-2019.artic.V1.primer.fasta`
Amplicon BED File
  `https://raw.githubusercontent.com/nf-core/test-datasets/viralrecon/genome/NC_045512.2/amplicon/nCoV-2019.artic.V1.bed`
Amplicon Left Suffix
  `_LEFT`
Amplicon Right Suffix
  `_RIGHT`
Viral Genome
  `NC_045512.2`
Viral Fasta File
  `https://raw.githubusercontent.com/nf-core/test-datasets/viralrecon/genome/NC_045512.2/GCF_009858895.2_ASM985889v3_genomic.200409.fna.gz`
Viral GFF
  `https://raw.githubusercontent.com/nf-core/test-datasets/viralrecon/genome/NC_045512.2/GCF_009858895.2_ASM985889v3_genomic.200409.gff.gz`
Fastp Mean Qual
  `30`
Fastp Qual Phred
  `30`
Fastp Unqual % Limit
  `10`
Fastp Min Trim Length
  `50`
Variant Calling Tools
  `varscan2,ivar,bcftools`
Min Mapped Reads
  `1000`
iVar Trim Min Len
  `20`
iVar Trim Min Qual
  `20`
iVar Trim Window
  `4`
Mpileup Depth
  `N/A`
Min Base Quality
  `20`
Min Read Depth
  `10`
Min Allele Freq
  `0.25`
Max Allele Freq
  `0.75`
Varscan2 Strand Filter
  `Yes`
Host Kraken2 DB
  `https://zenodo.org/record/3738199/files/kraken2_human.tar.gz`
Host Kraken2 Name
  `human`
Assembly Tools
  `spades,metaspades,unicycler,minia`
Minia Kmer Size
  `31`
Max Resources
  `224 GB memory, 32 cpus, 3d time per job`
Container
  `singularity - nfcore-viralrecon-1.1.0.img`
Output dir
  `./results`
Publish dir mode
  `copy`
Launch dir
  `nfcore/viralrecon/test_full`
Working dir
  `nfcore/viralrecon/test_full/work`
Script dir
  `nfcore/viralrecon`
User
  `patelh`
Config Profile
  `test_full,crick`
Config Description
  `Full test dataset to check pipeline function`
Config Contact
  `Harshil Patel (@drpatelh)`
Config URL
  `https://www.crick.ac.uk/research/platforms-and-facilities/scientific-computing/technologies`
E-mail Address
  `harshil.patel@crick.ac.uk`
E-mail on failure
  `N/A`
MultiQC maxsize
  `25 MB`

### Plot Table Data

Please select two table columns.

### Regex Help

Toolbox search strings can behave as regular expressions (regexes). Click a button below to see an example of it in action. Try modifying them yourself in the text box.

    samp_1
    samp_1_edited
    samp_2
    samp_2_edited
    samp_3
    samp_3_edited
    prepended_samp_1
    tmp_samp_1_edited
    tmpp_samp_1_edited
    tmppp_samp_1_edited
    #samp_1_edited.tmp
    samp_11
    samp_11111

See regex101.com for a more heavy duty testing suite.