A modular tool to aggregate results from bioinformatics analyses across many samples into a single report.
Report generated on 2022-07-07, 08:54 based on data in:
/vast/scratch/users/cmero.ma/projects/G000204/AGRF_CAGRF220410419_HFVGHDSX3/results/QC/fastQC/vast/scratch/users/cmero.ma/projects/G000204/AGRF_CAGRF220410419_HFVGHDSX3/results/QC/fastqScreen/vast/scratch/users/cmero.ma/projects/G000204/AGRF_CAGRF220410419_HFVGHDSX3/results/QC/qualimap/vast/scratch/users/cmero.ma/projects/G000204/AGRF_CAGRF220410419_HFVGHDSX3/results/QC/samtools
General Statistics
Showing 18/18 rows and 16/27 columns.| Sample Name | M Reads Mapped | % GC | Ins. size | ≥ 30X | Median cov | Mean cov | % Aligned | Error rate | M Non-Primary | M Reads Mapped | % Mapped | % Proper Pairs | M Total seqs | % Dups | % GC | M Seqs |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0-K12Rep1_HFVGHDSX3_CGGCTAAT-CTCGTTCT_L004 | 156.6 | 50% | 300 | 99.9% | 4746.0X | 4993.7X | 97.2% | 0.47% | 0.0 | 156.6 | 97.2% | 95.6% | 161.1 | 93.4% | 49% | 80.6 |
| 0-K12Rep1_HFVGHDSX3_CGGCTAAT-CTCGTTCT_L004_R2 | 90.4% | 49% | 80.6 | |||||||||||||
| 0-K12Rep2_HFVGHDSX3_ATCGATCG-TGGAAGCA_L004 | 113.2 | 50% | 255 | 99.9% | 3399.0X | 3603.0X | 96.8% | 0.38% | 0.0 | 113.1 | 96.8% | 95.9% | 116.9 | 94.2% | 49% | 58.4 |
| 0-K12Rep2_HFVGHDSX3_ATCGATCG-TGGAAGCA_L004_R2 | 92.9% | 49% | 58.4 | |||||||||||||
| 1-10-K12Rep1_HFVGHDSX3_CGCATGAT-AAGCCTGA_L004 | 3755.9 | 50% | 269 | 100.0% | 113949.0X | 119687.7X | 96.8% | 0.46% | 0.0 | 3755.0 | 96.8% | 95.3% | 3877.8 | 90.4% | 49% | 1938.9 |
| 1-10-K12Rep1_HFVGHDSX3_CGCATGAT-AAGCCTGA_L004_R2 | 90.0% | 49% | 1938.9 | |||||||||||||
| 1-K12Rep1_HFVGHDSX3_CTTGTCGA-CGATGTTC_L004 | 313.1 | 50% | 268 | 100.0% | 9592.0X | 9980.7X | 97.1% | 0.45% | 0.0 | 313.0 | 97.1% | 95.7% | 322.3 | 93.8% | 49% | 161.1 |
| 1-K12Rep1_HFVGHDSX3_CTTGTCGA-CGATGTTC_L004_R2 | 91.4% | 49% | 161.1 | |||||||||||||
| 1-K12Rep2_HFVGHDSX3_TTCCAAGG-CTACAAGG_L004 | 305.7 | 50% | 268 | 100.0% | 9300.0X | 9743.5X | 97.0% | 0.47% | 0.0 | 305.6 | 97.0% | 95.8% | 315.0 | 93.8% | 49% | 157.5 |
| 1-K12Rep2_HFVGHDSX3_TTCCAAGG-CTACAAGG_L004_R2 | 91.4% | 49% | 157.5 | |||||||||||||
| 10-K12Rep1_HFVGHDSX3_CTGATCGT-GCGCATAT_L004 | 43.7 | 50% | 261 | 100.0% | 1319.0X | 1388.9X | 95.3% | 0.55% | 0.0 | 43.6 | 95.3% | 94.0% | 45.8 | 93.7% | 49% | 22.9 |
| 10-K12Rep1_HFVGHDSX3_CTGATCGT-GCGCATAT_L004_R2 | 91.6% | 49% | 22.9 | |||||||||||||
| 10-K12Rep2_HFVGHDSX3_ACTCTCGA-CTGTACCA_L004 | 40.8 | 50% | 256 | 99.9% | 1224.0X | 1298.3X | 95.4% | 0.53% | 0.0 | 40.8 | 95.4% | 94.1% | 42.8 | 94.2% | 49% | 21.4 |
| 10-K12Rep2_HFVGHDSX3_ACTCTCGA-CTGTACCA_L004_R2 | 92.1% | 49% | 21.4 | |||||||||||||
| 5-K12Rep1_HFVGHDSX3_TGAGCTAG-GAACGGTT_L004 | 99.8 | 50% | 257 | 100.0% | 3037.0X | 3176.1X | 96.3% | 0.46% | 0.0 | 99.7 | 96.3% | 95.0% | 103.6 | 93.9% | 49% | 51.8 |
| 5-K12Rep1_HFVGHDSX3_TGAGCTAG-GAACGGTT_L004_R2 | 91.8% | 49% | 51.8 | |||||||||||||
| 5-K12Rep2_HFVGHDSX3_GAGACGAT-ACCGGTTA_L004 | 130.7 | 50% | 283 | 100.0% | 3963.0X | 4166.3X | 96.4% | 0.48% | 0.0 | 130.7 | 96.4% | 94.8% | 135.5 | 94.4% | 49% | 67.8 |
| 5-K12Rep2_HFVGHDSX3_GAGACGAT-ACCGGTTA_L004_R2 | 92.4% | 49% | 67.8 |
QualiMap
QualiMap is a platform-independent application to facilitate the quality control of alignment sequencing data and its derivatives like feature counts.
Coverage histogram
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 (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).
Cumulative genome coverage
Percentage of the reference genome with at least the 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.
Insert size histogram
Distribution of estimated insert sizes of mapped reads.
To overcome limitations in the length of DNA or RNA sequencing reads, many sequencing instruments can produce two or more shorter reads from one longer fragment in which the relative position of reads is approximately known, such as paired-end or mate-pair reads (Mardis 2013). Such techniques can extend the reach of sequencing technology, allowing for more accurate placement of reads (Reinert et al. 2015) and better resolution of repeat regions (Reinert et al. 2015), as well as detection of structural variation (Alkan et al. 2011) and chimeric transcripts (Maher et al. 2009).
All these methods assume that the approximate size of an insert is known. (Insert size can be defined as the length in bases of a sequenced DNA or RNA fragment, excluding technical sequences such as adapters, which are typically removed before alignment.) This plot allows for that assumption to be assessed. With the set of mapped fragments for a given sample, QualiMap groups the fragments by insert size, then plots the frequency of mapped fragments (y-axis) over a range of insert sizes (x-axis). In an ideal case, the distribution of fragment sizes for a sequencing library would culminate in a single peak indicating average insert size, with a narrow spread indicating highly consistent fragment lengths.
QualiMap calculates insert sizes as follows: for each fragment in which
every read mapped successfully to the same reference sequence, it
extracts the insert size from the TLEN field of the leftmost read
(see the Qualimap 2 documentation), where the TLEN (or
'observed Template LENgth') field contains 'the number of bases from the
leftmost mapped base to the rightmost mapped base'
(SAM
format specification). Note that because it is defined in terms of
alignment to a reference sequence, the value of the TLEN field may
differ from the insert size due to factors such as alignment clipping,
alignment errors, or structural variation or splicing in a gap between
reads from the same fragment.
GC content distribution
Each solid line represents the distribution of GC content of mapped reads for a given sample.
GC bias is the difference between the guanine-cytosine content (GC-content) of a set of sequencing reads and the GC-content of the DNA or RNA in the original sample. It is a well-known issue with sequencing systems, and may be introduced by PCR amplification, among other factors (Benjamini & Speed 2012; Ross et al. 2013).
QualiMap calculates the GC-content of individual mapped reads, then groups those reads by their GC-content (1%, 2%, …, 100%), and plots the frequency of mapped reads (y-axis) at each level of GC-content (x-axis). This plot shows the GC-content distribution of mapped reads for each read dataset, which should ideally resemble that of the original sample. It can be useful to display the GC-content distribution of an appropriate reference sequence for comparison, and QualiMap has an option to do this (see the Qualimap 2 documentation).
Samtools
Samtools is a suite of programs for interacting with high-throughput sequencing data.
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).
Alignment metrics
This module parses the output from samtools stats. All numbers in millions.
Samtools Flagstat
This module parses the output from samtools flagstat. All numbers in millions.
FastQ Screen
FastQ Screen allows you to screen a library of sequences in FastQ format against a set of sequence databases so you can see if the composition of the library matches with what you expect.
Mapped Reads
FastQC
FastQC is a quality control tool for high throughput sequence data, written by Simon Andrews at the Babraham Institute in Cambridge.
Sequence Counts
Sequence counts for each sample. Duplicate read counts are an estimate only.
This plot show the total number of reads, broken down into unique and duplicate if possible (only more recent versions of FastQC give duplicate info).
You can read more about duplicate calculation in the FastQC documentation. A small part has been copied here for convenience:
Only sequences which first appear in the first 100,000 sequences in each file are analysed. This should be enough to get a good impression for the duplication levels in the whole file. Each sequence is tracked to the end of the file to give a representative count of the overall duplication level.
The duplication detection requires an exact sequence match over the whole length of the sequence. Any reads over 75bp in length are truncated to 50bp for this analysis.
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.
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.
Per Base Sequence Content
The proportion of each base position for which each of the four normal DNA bases has been called.
To enable multiple samples to be shown in a single plot, the base composition data is shown as a heatmap. The colours represent the balance between the four bases: an even distribution should give an even muddy brown colour. Hover over the plot to see the percentage of the four bases under the cursor.
To see the data as a line plot, as in the original FastQC graph, click on a sample track.
From the FastQC help:
Per Base Sequence Content plots out the proportion of each base position in a file for which each of the four normal DNA bases has been called.
In a random library you would expect that there would be little to no difference between the different bases of a sequence run, so the lines in this plot should run parallel with each other. The relative amount of each base should reflect the overall amount of these bases in your genome, but in any case they should not be hugely imbalanced from each other.
It's worth noting that some types of library will always produce biased sequence composition, normally at the start of the read. Libraries produced by priming using random hexamers (including nearly all RNA-Seq libraries) and those which were fragmented using transposases inherit an intrinsic bias in the positions at which reads start. This bias does not concern an absolute sequence, but instead provides enrichement of a number of different K-mers at the 5' end of the reads. Whilst this is a true technical bias, it isn't something which can be corrected by trimming and in most cases doesn't seem to adversely affect the downstream analysis.
Rollover for sample name
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.
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.
Sequence Length Distribution
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.
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.
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.
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.
