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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

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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

MultiQC is developed by Seqera.

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#

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

Loading report..
Report generated on 2025-12-09, 21:07 CET based on data in: `/Users/ewels/GitHub/seqera/web/services/website/public/examples/rna-seq`
Welcome! Not sure where to start? Watch a tutorial video (6:06)

##### Report AI Summary

- SRR3192398 and SRR3192399 show reduced assignment rates (50.87% and 52.32%) and lower duplication (57-59%) compared to other samples (66-73% assignment, 75-81% duplication).
- SRR3192400 and SRR3192401 exhibit lower alignment rates (82-83%) and shorter read lengths (94-95bp average) versus typical 92-98% alignment and 97-98bp in other samples.

Provider: Seqera AI, model: claude-sonnet-4-0 Chat with Seqera AI

## General Statistics

##### AI Summary

Provider: Seqera AI, model: claude-sonnet-4-0
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Created with MultiQC
Showing 8/8 rows and 7/15 columns.

Sample Name | Assigned | Assigned | Total reads | Aligned | Aligned | Uniq aligned | Uniq aligned | Multimapped | Trimmed bases | Dups | GC | Avg len | Median len | Failed | Seqs
--- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | ---
SRR3192396 | 71.9M | 67.5% | 104.4M | 101.5M | 97.2% | 97.8M | 93.7% | 3.7M | 3.2% | 75.9% | 50.5% | 98bp | 100bp | 41% | 208.8M
↳ SRR3192396 R1 |  |  |  |  |  |  |  |  | 2.5% | 72.8% | 50.0% | 98bp | 100bp | 36% | 104.4M
↳ SRR3192396 R2 |  |  |  |  |  |  |  |  | 4.0% | 78.9% | 51.0% | 97bp | 100bp | 45% | 104.4M
SRR3192397 | 63.0M | 66.6% | 92.0M | 90.2M | 98.1% | 87.1M | 94.7% | 3.1M | 2.8% | 74.7% | 48.5% | 98bp | 100bp | 36% | 183.9M
↳ SRR3192397 R1 |  |  |  |  |  |  |  |  | 2.1% | 72.2% | 48.0% | 99bp | 100bp | 36% | 92.0M
↳ SRR3192397 R2 |  |  |  |  |  |  |  |  | 3.5% | 77.2% | 49.0% | 98bp | 100bp | 36% | 92.0M
SRR3192398 | 36.5M | 50.9% | 66.6M | 63.8M | 95.9% | 58.7M | 88.2% | 5.2M | 5.0% | 57.6% | 47.0% | 97bp | 100bp | 32% | 133.1M
↳ SRR3192398 R1 |  |  |  |  |  |  |  |  | 4.9% | 60.0% | 47.0% | 97bp | 100bp | 36% | 66.6M
↳ SRR3192398 R2 |  |  |  |  |  |  |  |  | 5.0% | 55.3% | 47.0% | 97bp | 100bp | 27% | 66.6M
SRR3192399 | 42.3M | 52.3% | 74.3M | 71.6M | 96.3% | 65.6M | 88.2% | 6.0M | 5.0% | 59.3% | 47.0% | 97bp | 100bp | 36% | 148.7M
↳ SRR3192399 R1 |  |  |  |  |  |  |  |  | 4.9% | 61.2% | 47.0% | 97bp | 100bp | 45% | 74.3M
↳ SRR3192399 R2 |  |  |  |  |  |  |  |  | 5.0% | 57.4% | 47.0% | 97bp | 100bp | 27% | 74.3M
SRR3192400 | 63.4M | 70.3% | 94.9M | 78.9M | 83.1% | 73.4M | 77.3% | 5.5M | 6.2% | 75.1% | 45.0% | 94bp | 99bp | 27% | 189.9M
↳ SRR3192400 R1 |  |  |  |  |  |  |  |  | 5.3% | 76.0% | 45.0% | 95bp | 100bp | 27% | 94.9M
↳ SRR3192400 R2 |  |  |  |  |  |  |  |  | 7.2% | 74.1% | 45.0% | 94bp | 98bp | 27% | 94.9M
SRR3192401 | 63.8M | 71.2% | 95.2M | 78.3M | 82.2% | 72.8M | 76.4% | 5.5M | 6.2% | 75.9% | 45.0% | 94bp | 98bp | 27% | 190.5M
↳ SRR3192401 R1 |  |  |  |  |  |  |  |  | 6.2% | 75.4% | 45.0% | 94bp | 98bp | 27% | 95.2M
↳ SRR3192401 R2 |  |  |  |  |  |  |  |  | 6.3% | 76.3% | 45.0% | 94bp | 98bp | 27% | 95.2M
SRR3192657 | 67.1M | 73.1% | 93.1M | 87.7M | 94.1% | 85.0M | 91.2% | 2.7M | 2.5% | 81.4% | 50.5% | 98bp | 100bp | 27% | 186.3M
↳ SRR3192657 R1 |  |  |  |  |  |  |  |  | 2.0% | 80.7% | 50.0% | 99bp | 100bp | 27% | 93.1M
↳ SRR3192657 R2 |  |  |  |  |  |  |  |  | 3.1% | 82.2% | 51.0% | 98bp | 100bp | 27% | 93.1M
SRR3192658 | 66.9M | 71.2% | 97.1M | 89.8M | 92.5% | 87.1M | 89.7% | 2.7M | 2.8% | 81.4% | 52.0% | 98bp | 100bp | 27% | 194.1M
↳ SRR3192658 R1 |  |  |  |  |  |  |  |  | 2.2% | 80.5% | 52.0% | 99bp | 100bp | 27% | 97.1M
↳ SRR3192658 R2 |  |  |  |  |  |  |  |  | 3.4% | 82.3% | 52.0% | 98bp | 100bp | 27% | 97.1M

#### General Statistics: Columns

Uncheck the tick box to hide columns. Click and drag the handle on the left to change order. Table ID: `general_stats_table_table`

Sort | Visible | Group | Column | Description | ID | Scale
--- | --- | --- | --- | --- | --- | ---
|| |  | featureCounts | Assigned | Assigned reads (millions) | `featurecounts-Assigned` | read_count
|| |  | featureCounts | Assigned | % Assigned reads | `featurecounts-percent_assigned` |
|| |  | STAR | Total reads | Number of input reads | `star-total_reads` | read_count
|| |  | STAR | Aligned | Mapped reads | `star-mapped` | read_count
|| |  | STAR | Aligned | % Mapped reads | `star-mapped_percent` |
|| |  | STAR | Uniq aligned | Uniquely mapped reads | `star-uniquely_mapped` | read_count
|| |  | STAR | Uniq aligned | % Uniquely mapped reads | `star-uniquely_mapped_percent` |
|| |  | STAR | Multimapped | Multiple mapped reads | `star-multimapped` | read_count
|| |  | Cutadapt | Trimmed bases | % total base pairs trimmed (weighted average for grouped samples) | `cutadapt-percent_trimmed` |
|| |  | FastQC: trimmed | Dups | % duplicate reads (weighted average for grouped samples) | `fastqc_trimmed-percent_duplicates` |
|| |  | FastQC: trimmed | GC | Average % GC content (weighted average for grouped samples) | `fastqc_trimmed-percent_gc` |
|| |  | FastQC: trimmed | Avg len | Average read length (weighted average for grouped samples) | `fastqc_trimmed-avg_sequence_length` |
|| |  | FastQC: trimmed | Median len | Median read length (weighted average for grouped samples) | `fastqc_trimmed-median_sequence_length` |
|| |  | FastQC: trimmed | Failed | Percentage of modules failed in FastQC report (includes those not plotted here) | `fastqc_trimmed-percent_fails` |
|| |  | FastQC: trimmed | Seqs | Total sequences (millions) (summed for grouped samples) | `fastqc_trimmed-total_sequences` | read_count

## featureCounts

Counts mapped reads for genomic features such as genes, exons, promoter, gene bodies, genomic bins and chromosomal locations.http://subread.sourceforge.net DOI: 10.1093/bioinformatics/btt656

### Assignments

##### AI Summary

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## STAR

Universal RNA-seq aligner.https://github.com/alexdobin/STAR DOI: 10.1093/bioinformatics/bts635

### Summary Statistics

Summary statistics from the STAR alignment

##### AI Summary

Provider: Seqera AI, model: claude-sonnet-4-0
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Created with MultiQC
Showing 8/8 rows and 10/19 columns.

Sample Name | Total reads | Aligned | Aligned | Uniq aligned | Uniq aligned | Multimapped | Avg. read len | Avg. mapped len | Splices | Annotated splices | GT/AG splices | GC/AG splices | AT/AC splices | Non-canonical splices | Mismatch rate | Del rate | Del len | Ins rate | Ins len
--- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | ---
SRR3192396_1 | 104.4M | 101.5M | 97.2% | 97.8M | 93.7% | 3.7M | 196.0bp | 195.6bp | 40.7M | 40.1M | 40.2M | 0.3M | 0.0M | 0.1M | 0.2% | 0.0% | 1.6bp | 0.0% | 1.6bp
SRR3192397_1 | 92.0M | 90.2M | 98.1% | 87.1M | 94.7% | 3.1M | 196.0bp | 196.5bp | 35.8M | 35.3M | 35.3M | 0.3M | 0.0M | 0.1M | 0.2% | 0.0% | 1.6bp | 0.0% | 1.5bp
SRR3192398_1 | 66.6M | 63.8M | 95.9% | 58.7M | 88.2% | 5.2M | 194.0bp | 193.8bp | 24.7M | 24.4M | 24.4M | 0.2M | 0.0M | 0.1M | 0.2% | 0.0% | 1.5bp | 0.0% | 1.6bp
SRR3192399_1 | 74.3M | 71.6M | 96.3% | 65.6M | 88.2% | 6.0M | 194.0bp | 193.9bp | 29.5M | 29.1M | 29.1M | 0.2M | 0.0M | 0.1M | 0.2% | 0.0% | 1.4bp | 0.0% | 1.5bp
SRR3192400_1 | 94.9M | 78.9M | 83.1% | 73.4M | 77.3% | 5.5M | 188.0bp | 187.8bp | 53.2M | 52.4M | 52.4M | 0.4M | 0.0M | 0.4M | 0.3% | 0.0% | 1.6bp | 0.0% | 1.6bp
SRR3192401_1 | 95.2M | 78.3M | 82.2% | 72.8M | 76.4% | 5.5M | 188.0bp | 187.5bp | 54.1M | 53.3M | 53.3M | 0.4M | 0.0M | 0.4M | 0.3% | 0.0% | 1.6bp | 0.0% | 1.5bp
SRR3192657_1 | 93.1M | 87.7M | 94.1% | 85.0M | 91.2% | 2.7M | 197.0bp | 196.5bp | 58.7M | 58.1M | 58.2M | 0.4M | 0.1M | 0.1M | 0.3% | 0.0% | 1.8bp | 0.0% | 1.7bp
SRR3192658_1 | 97.1M | 89.8M | 92.5% | 87.1M | 89.7% | 2.7M | 196.0bp | 195.6bp | 58.2M | 57.5M | 57.6M | 0.4M | 0.1M | 0.1M | 0.3% | 0.0% | 1.8bp | 0.0% | 1.7bp

#### STAR: Summary Statistics: Columns

Uncheck the tick box to hide columns. Click and drag the handle on the left to change order. Table ID: `star_summary_table_table`

Sort | Visible | Group | Column | Description | ID | Scale
--- | --- | --- | --- | --- | --- | ---
|| |  |  | Total reads | Number of input reads | `star-total_reads` | read_count
|| |  |  | Aligned | Mapped reads | `star-mapped` | read_count
|| |  |  | Aligned | % Mapped reads | `star-mapped_percent` |
|| |  |  | Uniq aligned | Uniquely mapped reads | `star-uniquely_mapped` | read_count
|| |  |  | Uniq aligned | % Uniquely mapped reads | `star-uniquely_mapped_percent` |
|| |  |  | Multimapped | Multiple mapped reads | `star-multimapped` | read_count
|| |  |  | Avg. read len | Average input read length | `star-avg_input_read_length` |
|| |  |  | Avg. mapped len | Average mapped length | `star-avg_mapped_read_length` |
|| |  |  | Splices | Number of splices: Total | `star-num_splices` | read_count
|| |  |  | Annotated splices | Number of splices: Annotated (sjdb) | `star-num_annotated_splices` | read_count
|| |  |  | GT/AG splices | Number of splices: GT/AG | `star-num_GTAG_splices` | read_count
|| |  |  | GC/AG splices | Number of splices: GC/AG | `star-num_GCAG_splices` | read_count
|| |  |  | AT/AC splices | Number of splices: AT/AC | `star-num_ATAC_splices` | read_count
|| |  |  | Non-canonical splices | Number of splices: Non-canonical | `star-num_noncanonical_splices` | read_count
|| |  |  | Mismatch rate | Mismatch rate per base | `star-mismatch_rate` |
|| |  |  | Del rate | Deletion rate per base | `star-deletion_rate` |
|| |  |  | Del len | Deletion average length | `star-deletion_length` |
|| |  |  | Ins rate | Insertion rate per base | `star-insertion_rate` |
|| |  |  | Ins len | Insertion average length | `star-insertion_length` |

### Alignment Scores

##### AI Summary

Provider: Seqera AI, model: claude-sonnet-4-0
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## Cutadapt

Version: `1.9.1`

Finds and removes adapter sequences, primers, poly-A tails, and other types of unwanted sequences.https://cutadapt.readthedocs.io DOI: 10.14806/ej.17.1.200

### Filtered Reads

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

##### AI Summary

Provider: Seqera AI, model: claude-sonnet-4-0
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Created with MultiQC

### Trimmed Sequence Lengths (3')

This plot shows the number of reads with certain lengths of adapter trimmed for the 3' end.

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.

##### AI Summary

Provider: Seqera AI, model: claude-sonnet-4-0
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Created with MultiQC

## FastQC: trimmed

Version: `0.11.2`

Quality control tool for high throughput sequencing data.http://www.bioinformatics.babraham.ac.uk/projects/fastqc

### 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.

##### AI Summary

Provider: Seqera AI, model: claude-sonnet-4-0
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Created with MultiQC

### Sequence Quality Histograms
16

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.

##### AI Summary

Provider: Seqera AI, model: claude-sonnet-4-0
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Created with MultiQC

### Per Sequence Quality Scores
16

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.

##### AI Summary

Provider: Seqera AI, model: claude-sonnet-4-0
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Created with MultiQC

### 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.

##### AI Summary

Provider: Seqera AI, model: claude-sonnet-4-0
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$
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
4
7
5

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 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.

##### AI Summary

Provider: Seqera AI, model: claude-sonnet-4-0
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Created with MultiQC

### Per Base N Content
16

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.

##### AI Summary

Provider: Seqera AI, model: claude-sonnet-4-0
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Created with MultiQC

### Sequence Length Distribution
16

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

##### AI Summary

Provider: Seqera AI, model: claude-sonnet-4-0
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Created with MultiQC

### Sequence Duplication Levels
16

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 (e.g. 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.

##### AI Summary

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Created with MultiQC

### Overrepresented sequences by sample

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 overrepresented.

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 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.

##### AI Summary

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Created with MultiQC

### Top overrepresented sequences

Top overrepresented sequences across all samples. The table shows 20 most overrepresented sequences across all samples, ranked by the number of samples they occur in.

##### AI Summary

Provider: Seqera AI, model: claude-sonnet-4-0
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Created with MultiQC
Showing 20/20 rows and 3/3 columns.

Overrepresented sequence | Reports | Occurrences | % of all reads
--- | --- | --- | ---
CGGTGGCGCGTGCCTGTAGTCCCAGCTACTCGGGAGGCTGAGGCTGGAGG | 4 | 3921837 | 0.2732%
CGGTGGCGCGTGCCTGTAGTCCCAGCTACTCGGGAGGCTGAGGTGGGAGG | 4 | 2259179 | 0.1574%
GTGGCTATTCACAGGCGCGATCCCACTACTGATCAGCACGGGAGTTTTGA | 4 | 1472374 | 0.1026%
GGTGGCGCGTGCCTGTAGTCCCAGCTACTCGGGAGGCTGAGGCTGGAGGA | 4 | 1426750 | 0.0994%
CCCAGCTACTCGGGAGGCTGAGGCTGGAGGATCGCTTGAGTCCAGGAGTT | 4 | 1164172 | 0.0811%
GTGCAGTGGCTATTCACAGGCGCGATCCCACTACTGATCAGCACGGGAGT | 2 | 996369 | 0.0694%
GCTCCGTTTCCGACCTGGGCCGGTTCACCCCTCCTTAGGCAACCTGGTGG | 4 | 984719 | 0.0686%
GGTGGCGCGTGCCTGTAGTCCCAGCTACTCGGGAGGCTGAGGTGGGAGGA | 4 | 909135 | 0.0633%
TTTTGACCTGCTCCGTTTCCGACCTGGGCCGGTTCACCCCTCCTTAGGCA | 2 | 882103 | 0.0615%
CTCCGTTTCCGACCTGGGCCGGTTCACCCCTCCTTAGGCAACCTGGTGGT | 4 | 861941 | 0.0601%
GTTTTGACCTGCTCCGTTTCCGACCTGGGCCGGTTCACCCCTCCTTAGGC | 2 | 842778 | 0.0587%
GCAGTGGCTATTCACAGGCGCGATCCCACTACTGATCAGCACGGGAGTTT | 2 | 830530 | 0.0579%
GGGCGATCTGGCTGCGACATCTGTCACCCCATTGATCGCCAGGGTTGATT | 4 | 773808 | 0.0539%
CCCAGCTACTCGGGAGGCTGAGGTGGGAGGATCGCTTGAGCCCAGGAGTT | 4 | 745489 | 0.0519%
GTCCCAGCTACTCGGGAGGCTGAGGCTGGAGGATCGCTTGAGTCCAGGAG | 4 | 564336 | 0.0393%
GCCCAGGCTGGAGTGCAGTGGCTATTCACAGGCGCGATCCCACTACTGAT | 4 | 557506 | 0.0388%
GGCAACCTGGTGGTCCCCCGCTCCCGGGAGGTCACCATATTGATGCCGAA | 4 | 502127 | 0.0350%
GGCTGGAGTGCAGTGGCTATTCACAGGCGCGATCCCACTACTGATCAGCA | 4 | 471414 | 0.0328%
CACGGGAGTTTTGACCTGCTCCGTTTCCGACCTGGGCCGGTTCACCCCTC | 2 | 368470 | 0.0257%
CGCGTGCCTGTAGTCCCAGCTACTCGGGAGGCTGAGGCTGGAGGATCGCT | 3 | 327899 | 0.0228%

Expand table

#### FastQC: Top overrepresented sequences: Columns

Uncheck the tick box to hide columns. Click and drag the handle on the left to change order. Table ID: `fqc_trimmed_top_overrepresented_sequences_table_table`

Sort | Visible | Group | Column | Description | ID | Scale
--- | --- | --- | --- | --- | --- | ---
|| |  |  | Reports | Number of FastQC reports where this sequence is founds as overrepresented | `fastqc_trimmed-samples` |
|| |  |  | Occurrences | Total number of occurrences of the sequence (among the samples where the sequence is overrepresented) | `fastqc_trimmed-total_count` |
|| |  |  | % of all reads | Total number of occurrences as the percentage of all reads (among samples where the sequence is overrepresented) | `fastqc_trimmed-total_percent` |

### Adapter Content
16

The cumulative percentage count of the proportion of your library which has seen each of the adapter sequences at each position.

Note that only samples with ≥ 0.1% adapter contamination are shown.

There may be several lines per sample, as one is shown for each adapter detected in the file.

From the FastQC Help:

The plot shows a cumulative percentage count of the proportion of your library which has seen each of the adapter sequences at each position. Once a sequence has been seen in a read it is counted as being present right through to the end of the read so the percentages you see will only increase as the read length goes on.

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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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## FastQC: raw

Version: `0.11.2`

Quality control tool for high throughput sequencing data.http://www.bioinformatics.babraham.ac.uk/projects/fastqc

### 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
16

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
16

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.

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$
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
4
7
5

The dashed black line shows theoretical GC content: `Human Transcriptome (UCSC hg38)`

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 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
16

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
16

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All samples have sequences of a single length (100bp, 101bp) See the General Statistics Table.

### Sequence Duplication Levels
16

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 (e.g. 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 by sample

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 overrepresented.

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 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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### Top overrepresented sequences

Top overrepresented sequences across all samples. The table shows 20 most overrepresented sequences across all samples, ranked by the number of samples they occur in.

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Showing 20/20 rows and 3/3 columns.

Overrepresented sequence | Reports | Occurrences | % of all reads
--- | --- | --- | ---
CGGTGGCGCGTGCCTGTAGTCCCAGCTACTCGGGAGGCTGAGGCTGGAGG | 4 | 4085392 | 0.2814%
CGGTGGCGCGTGCCTGTAGTCCCAGCTACTCGGGAGGCTGAGGTGGGAGG | 4 | 2310948 | 0.1592%
GTGGCTATTCACAGGCGCGATCCCACTACTGATCAGCACGGGAGTTTTGA | 4 | 1504185 | 0.1036%
GGTGGCGCGTGCCTGTAGTCCCAGCTACTCGGGAGGCTGAGGCTGGAGGA | 4 | 1498991 | 0.1033%
CCCAGCTACTCGGGAGGCTGAGGCTGGAGGATCGCTTGAGTCCAGGAGTT | 4 | 1203129 | 0.0829%
GGGCGATCTGGCTGCGACATCTGTCACCCCATTGATCGCCAGGGTTGATT | 4 | 1016279 | 0.0700%
GCTCCGTTTCCGACCTGGGCCGGTTCACCCCTCCTTAGGCAACCTGGTGG | 4 | 1015647 | 0.0700%
GTGCAGTGGCTATTCACAGGCGCGATCCCACTACTGATCAGCACGGGAGT | 2 | 1014297 | 0.0699%
GGTGGCGCGTGCCTGTAGTCCCAGCTACTCGGGAGGCTGAGGTGGGAGGA | 4 | 931389 | 0.0642%
TTTTGACCTGCTCCGTTTCCGACCTGGGCCGGTTCACCCCTCCTTAGGCA | 2 | 916258 | 0.0631%
CTCCGTTTCCGACCTGGGCCGGTTCACCCCTCCTTAGGCAACCTGGTGGT | 4 | 887812 | 0.0612%
GTATCAACGCAGAGTACTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTT | 4 | 818614 | 0.0564%
CCCAGCTACTCGGGAGGCTGAGGTGGGAGGATCGCTTGAGCCCAGGAGTT | 4 | 762382 | 0.0525%
GGTATCAACGCAGAGTACTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTT | 4 | 714272 | 0.0492%
GTCCCAGCTACTCGGGAGGCTGAGGCTGGAGGATCGCTTGAGTCCAGGAG | 4 | 588688 | 0.0406%
GCCCAGGCTGGAGTGCAGTGGCTATTCACAGGCGCGATCCCACTACTGAT | 4 | 570404 | 0.0393%
GGCAACCTGGTGGTCCCCCGCTCCCGGGAGGTCACCATATTGATGCCGAA | 4 | 515408 | 0.0355%
TATCAACGCAGAGTACTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTT | 4 | 506705 | 0.0349%
GGCTGGAGTGCAGTGGCTATTCACAGGCGCGATCCCACTACTGATCAGCA | 4 | 483307 | 0.0333%
CGCGTGCCTGTAGTCCCAGCTACTCGGGAGGCTGAGGCTGGAGGATCGCT | 3 | 345589 | 0.0238%

Expand table

#### FastQC: Top overrepresented sequences: Columns

Uncheck the tick box to hide columns. Click and drag the handle on the left to change order. Table ID: `fqc_raw_top_overrepresented_sequences_table_table`

Sort | Visible | Group | Column | Description | ID | Scale
--- | --- | --- | --- | --- | --- | ---
|| |  |  | Reports | Number of FastQC reports where this sequence is founds as overrepresented | `fastqc_raw-samples` |
|| |  |  | Occurrences | Total number of occurrences of the sequence (among the samples where the sequence is overrepresented) | `fastqc_raw-total_count` |
|| |  |  | % of all reads | Total number of occurrences as the percentage of all reads (among samples where the sequence is overrepresented) | `fastqc_raw-total_percent` |

### Adapter Content
12
4

The cumulative percentage count of the proportion of your library which has seen each of the adapter sequences at each position.

Note that only samples with ≥ 0.1% adapter contamination are shown.

There may be several lines per sample, as one is shown for each adapter detected in the file.

From the FastQC Help:

The plot shows a cumulative percentage count of the proportion of your library which has seen each of the adapter sequences at each position. Once a sequence has been seen in a read it is counted as being present right through to the end of the read so the percentages you see will only increase as the read length goes on.

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### Status Checks

Status for each FastQC section showing whether results seem entirely normal (green), slightly abnormal (orange) or very unusual (red).

FastQC assigns a status for each section of the report. These give a quick evaluation of whether the results of the analysis seem entirely normal (green), slightly abnormal (orange) or very unusual (red).

It is important to stress that although the analysis results appear to give a pass/fail result, these evaluations must be taken in the context of what you expect from your library. A 'normal' sample as far as FastQC is concerned is random and diverse. Some experiments may be expected to produce libraries which are biased in particular ways. You should treat the summary evaluations therefore as pointers to where you should concentrate your attention and understand why your library may not look random and diverse.

Specific guidance on how to interpret the output of each module can be found in the relevant report section, or in the FastQC help.

In this heatmap, we summarise all of these into a single heatmap for a quick overview. Note that not all FastQC sections have plots in MultiQC reports, but all status checks are shown in this heatmap.

##### AI Summary

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## Software Versions

Software Versions lists versions of software tools extracted from file contents.

###

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Software | Version
--- | ---
Cutadapt | `1.9.1`
FastQC: raw | `0.11.2`
FastQC: trimmed | `0.11.2`

### 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.