A modular tool to aggregate results from bioinformatics analyses across many samples into a single report.
This report has been generated by the Arcadia-Science/seqqc analysis pipeline. The purpose of this pipeline is to rapidly assess the quality of new sequencing data so that you can feel confident depositing it into an INSDC database like the European Nucleotide Archive rapidly after data generation. In typical academic settings, data would be deposited in a an INSDC database at time of publication, so any quality issues with the data itself would be caught during the analysis. The seqqc pipeline was designed to perform minimum quality control reporting so that data could be posted more closely to time of generation, but also so that quality problems would still be caught prior to deposition. The pipeline is designed to report technical sequencing issues viaFastQC, sample similarity withsourmash compare, and potential contamination withsourmash gather. Because this pipeline is designed to run on any type of raw sequencing data, it is purely descriptive and you will need to rely on a combination of your domain specific expertise, the section descriptions, and the results to determine whether there is a quality issue with your sequencing data or not. For information about how to interpret these results, please consult each section of this document. Some section documentation has been taken directly from the tool documentation itself. Note that while you can run this pipeline on any type of data, the visualizations will be best scaled if you run it on only short reads or only long reads for any given single pipeline run. Note also that documentation in many sections states that for a given problem, you may be able to contact the responsible sequencing facility (if the facility was external to Arcadia) to discuss if resequencing is appropriate. If you are an Arcadian and think this may be the case for your data, reach out to Taylor to figure our the best next steps before contacting any facility.
Report generated on 2023-01-19, 17:04 UTC
General Statistics
Showing 1/1 rows and 3/5 columns.| Sample Name | % Dups | % GC | M Seqs |
|---|---|---|---|
| SRR19070014_T1 | 15.8% | 48% | 0.3 |
FastQC
FastQC is a quality control tool for high throughput sequence data, written by Simon Andrews at the Babraham Institute in Cambridge.
Sequence Counts
Sequence counts for each sample. Duplicate read counts are an estimate only.
This module counts the number of reads present in each sample and highlights sequence duplication levels (see Sequence Duplication Levels for a discussion of how to interpret sequence duplication levels plot). Most of the time, sequences from the same experiment will have a similar sequencing depth and if sequencing is outsourced, you will have communicated your desired sequencing depth to the sequencing facility. Make sure the sequencing depth matches expectation within ~15% of your requested depth; if the sequencing depth is dramatically lower than you requested, you may be able to contact the sequencing facility to resequence to achieve the agreed upon depth.
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.
Sequence Quality Histograms
The mean quality value across each base position in the read.
This module shows the average quality at each read position in each library. The Y axis is the Phred score, a quality metric for sequencing data. Phred scores are logarithmically linked to the error probabilities, so a Phred score of 10 corresponds to a 1 in 10 probability of an incorrect base call while a Phred score of 20 corresponds to a 1 in 100 probability of an incorrect base call. The X axis corresponds to the base pair position in the read. Phred score profiles are usually worse at the beginning and end of reads, but quality typically drops smoothly toward the end of the read (as opposed to suddenly at one position). It's common for newer Illumina chemistries to maintain high (>30 or 35) Phred scores across the entire read. Nanopore reads typically have lower Phred scores. Across all samples in a given experiment, one would expect to see roughly similar quality scores.
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.
Per Sequence Quality Scores
The number of reads with average quality scores. Shows if a subset of reads has poor quality.
This module shows an alternate visualization of sequence quality scores, counting the number of reads that have specific average quality scores. This module allows you to see if a subset of your sequences have universally low quality values. Across all samples in a given experiment, one would expect to see roughly consistent results for this module.
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.
Per Base Sequence Content
The proportion of each base position for which each of the four normal DNA bases has been called.
This module reports the proportion of each base (A, T, C, or G) at each position in a read, averaged over all reads. It is common for this module to fail on raw data because sequences at the beginning of reads may (should) contain barcodes or adapters that make the sequences non-random. For randomly generated sequence libraries from diverse samples (RNA-seq, metagenomes, genomes, etc), we expect no biases for the middle portion of reads that fall between adapter sequences. For sequence libraries generated from less diverse samples (amplicon data), we expect biases throughout the reads.
To enable multiple samples to be shown in a single plot, the base composition data is shown as a heatmap. The colours represent the balance between the four bases: an even distribution should give an even muddy brown colour. Hover over the plot to see the percentage of the four bases under the cursor.
To see the data as a line plot, as in the original FastQC graph, click on a sample track.
From the FastQC help:
Per Base Sequence Content plots out the proportion of each base position in a file for which each of the four normal DNA bases has been called.
In a random library you would expect that there would be little to no difference between the different bases of a sequence run, so the lines in this plot should run parallel with each other. The relative amount of each base should reflect the overall amount of these bases in your genome, but in any case they should not be hugely imbalanced from each other.
It's worth noting that some types of library will always produce biased sequence composition, normally at the start of the read. Libraries produced by priming using random hexamers (including nearly all RNA-Seq libraries) and those which were fragmented using transposases inherit an intrinsic bias in the positions at which reads start. This bias does not concern an absolute sequence, but instead provides enrichement of a number of different K-mers at the 5' end of the reads. Whilst this is a true technical bias, it isn't something which can be corrected by trimming and in most cases doesn't seem to adversely affect the downstream analysis.
Rollover for sample name
Per Sequence GC Content
The average GC content of reads. Normal random library typically have a roughly normal distribution of GC content.
This module reports the average GC content of reads in a library. It is common for this module to fail for metagenomes or metatranscriptomes as we would expect a non-normal distribution of GC content owing to the different GC content of the genomes of different community members. For sequencing libraries generated from a single organism, it's typical to have a roughly normal distribution.
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.
Per Base N Content
The percentage of base calls at each position for which an N was called.
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 module plots out the percentage of base calls at each position for which an N was called. A low percentage of Ns biased toward the beginnig or end of a read is normal. If many reads have Ns at a specific position, especially in the middle of the read, this could have been caused by a bubble obscuring the camera during sequencing. Excessive Ns caused by technical reasons like that may warrant re-sequencing by the sequencing facility. However if a sample was sequenced deeply enough, it may not be necessary to re-sequence to get quality results from your analysis.
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.
Sequence Length Distribution
The distribution of fragment sizes (read lengths) found. See the FastQC help
This module reports on the sequence length of samples in your library. For raw Illumina sequences, all samples should have the same length. If your sequences don't all have the same length, they may have been sequenced at different times or may be trimmed already. Long reads will have variable sequence lengths.
Sequence Duplication Levels
The relative level of duplication found for every sequence.
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).
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.
Overrepresented sequences
The total amount of overrepresented sequences found in each library.
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. This module lists all of the sequence 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.
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.
Adapter Content
The cumulative percentage count of the proportion of your library which has seen each of the adapter sequences at each position.
This module reports the presence of adapters and the position in which the adapter was detected in the read. It is normal for raw reads to fail this module. Adapters are typically removed by during read trimming.
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.
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.
sourmash
sourmash Quickly search, compare, and analyze genomic and metagenomic data sets.DOI: 10.21105/joss.00027.
compare: Sample Similarity
Input: comp.
Heatmap of similarity values from the output of sourmash compare
sourmash compareestimates sample similarity using angular similarity. Angular similarity takes both shared sequence content and sequence abundance information into account when estimating similarity. Shared sequence content is measured using the intersection over the union of k-mers (k = 21) in each sample. About 1/1000th of all distinct k-mers are used to estimate similarity. Sequence similarity scores range from 0 to 1 and samples that are more similar will have higher value. Note that because this module runs on raw sequencing data, sequencing errors will falsely deflate similarity estimates but should still provide relatively good estimates. This module is designed to highlight mislabelled samples or to catch technical artifacts that might lead to outlying replicate samples. Replicates or biologically similar samples should have the highest similarity scores. Samples are alphabetically ordered on the X and Y axis of the heatmap, and each square represents the similarity score between the intersecting samples. You can run your mouse over each heatmap square to highlight the similarity value.
Sourmash compare outputs a similarity score between two samples. A higher score indicates a higher degree of similarity, up to a maximum of 1. Samples are clustered by similarity on each axis, and specific IDs can be found in the graph with the Highlight tab.
Arcadia-Science/seqqc Methods Description
Suggested text and references to use when describing pipeline usage within the methods section of a publication.
Methods
Data was processed using Arcadia-Science/seqqc v1.0dev of the nf-core collection of workflows (Ewels et al., 2020).
The pipeline was executed with Nextflow v22.10.5 (Di Tommaso et al., 2017) with the following command:
nextflow run 'https://github.com/Arcadia-Science/seqqc' -name full_isoseq_redo_2 -params-file 'https://api.tower.nf/ephemeral/bW7EhuXoLsyj9a0ABFqKzQ.json' -with-tower -r 85e68350143675667e33b90e8c604ca102b545a2 -profile docker -resume ac281124-00c4-4749-a087-87afb81f75a4 -latestReferences
- Di Tommaso, P., Chatzou, M., Floden, E. W., Barja, P. P., Palumbo, E., & Notredame, C. (2017). Nextflow enables reproducible computational workflows. Nature Biotechnology, 35(4), 316-319. https://doi.org/10.1038/nbt.3820
- Ewels, P. A., Peltzer, A., Fillinger, S., Patel, H., Alneberg, J., Wilm, A., Garcia, M. U., Di Tommaso, P., & Nahnsen, S. (2020). The nf-core framework for community-curated bioinformatics pipelines. Nature Biotechnology, 38(3), 276-278. https://doi.org/10.1038/s41587-020-0439-x
Notes:
- If available, make sure to update the text to include the Zenodo DOI of version of the pipeline used.
- The command above does not include parameters contained in any configs or profiles that may have been used. Ensure the config file is also uploaded with your publication!
- You should also cite all software used within this run. Check the "Software Versions" of this report to get version information.
Arcadia-Science/seqqc Software Versions
are collected at run time from the software output.
| Process Name | Software | Version |
|---|---|---|
| CUSTOM_DUMPSOFTWAREVERSIONS | python | 3.10.6 |
| yaml | 6.0 | |
| FASTQC | fastqc | 0.11.9 |
| SAMPLESHEET_CHECK | python | 3.8.3 |
| Workflow | Arcadia-Science/seqqc | 1.0dev |
| Nextflow | 22.10.5 |
Arcadia-Science/seqqc Workflow Summary
- this information is collected when the pipeline is started.
Core Nextflow options
- runName
- full_isoseq_redo_2
- containerEngine
- docker
- launchDir
- /
- workDir
- /arcadia-seqqc/scratch/3q0IOloBnLohWg
- projectDir
- /.nextflow/assets/Arcadia-Science/seqqc
- userName
- root
- profile
- docker
- configFiles
- /.nextflow/assets/Arcadia-Science/seqqc/nextflow.config, /nextflow.config
Input/output options
- input
- https://raw.githubusercontent.com/Arcadia-Science/test-datasets/ter/seqqc-full/seqqc/samplesheet_isoseq.csv
- outdir
- s3://arcadia-seqqc/outdir/full/isoseq/
- [email protected]
- multiqc_title
- is this required
