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.
Report
generated on 2020-05-31, 21:33
based on data in:
nf-core/viralrecon/work/fd/3c8e92b3222a535ed19d3834f6d258
Variant calling metrics
generated by the nf-core/viralrecon pipeline
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 | 2371846 | 2216597 | 2371846 | 523 | 215 | 1096 | 479 | 1 | 6 | 0 | 6 | 0 | 6 | 0 | 2 | 2 | 6 | 224 | 167 | 224 |
sample2 | 2139958 | 1913910 | 99 | 1890837 | 1816623 | 1890837 | 480 | 177 | 499 | 312 | 1 | 6 | 0 | 7 | 0 | 7 | 0 | 4 | 5 | 5 | 338 | 292 | 338 |
De novo assembly metrics
generated by the nf-core/viralrecon pipeline
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 | 6 | 0 | 3 | 0 | 8 | 0 | 344 | 2 | 1 | 4 |
sample2 | 2139958 | 1913910 | 99 | 800 | 5513 | 86 | 1070 | 17 | 29919 | 100 | 29919 | 30 | 2400 | 83 | 1478 | 29 | 18084 | 99 | 16188 | 88 | 26 | 7 | 0 | 7 | 1 | 6 | 0 | 64 | 5 | 5 | 5 |
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.
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
The distribution of fragment sizes (read lengths) found. See the FastQC help
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.
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.
Duplication Rates
Duplication rates of sampled reads.
Insert Sizes
Insert size estimation of sampled reads.
Sequence Quality
Average sequencing quality over each base of all reads.
GC Content
Average GC content over each base of all reads.
N content
Average N content over each base of all reads.
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.
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
The distribution of fragment sizes (read lengths) found. See the FastQC help
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.
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.
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.
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).
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.
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.
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.
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).
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.
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.
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).
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.
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.
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.
Base Distribution
Plot shows the distribution of bases by cycle.
Insert Size
Plot shows the number of reads at a given insert size. Reads with different orientations are summed.
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
Mean Base Quality by Cycle
Plot shows the mean base quality by cycle.
This metric gives an overall snapshot of sequencing machine performance. For most types of sequencing data, the output is expected to show a slight reduction in overall base quality scores towards the end of each read.
Spikes in quality within reads are not expected and may indicate that technical problems occurred during sequencing.
Base Quality Distribution
Plot shows the count of each base quality score.
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.
WGS Filtered Bases
For more information about the filtered categories, see the Picard documentation.
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.
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
Variant Quality
Indel Distribution
Variant depths
Read depth support distribution for called variants
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.
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
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.
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.
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
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% |
Number of Contigs
This plot shows the number of contigs found for each assembly, broken down by length.
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
Variant Quality
Indel Distribution
Variant depths
Read depth support distribution for called variants
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.
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
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.
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.
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
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% |
Number of Contigs
This plot shows the number of contigs found for each assembly, broken down by length.
VARIANTS: BCFTools (BCFTools)
Bcftools This section of the report shows BCFTools stats results for variants called by BCFTools.
Variant Substitution Types
Variant Quality
Indel Distribution
Variant depths
Read depth support distribution for called variants
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.
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
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.
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.
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.
VARIANTS: QUAST (BCFTools)
VARIANTS: QUAST (BCFTools) This section of the report shows QUAST results for consensus sequence generated from BCFTools variants.
Assembly Statistics
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% |
Number of Contigs
This plot shows the number of contigs found for each assembly, broken down by length.
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.
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.
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
The distribution of fragment sizes (read lengths) found. See the FastQC help
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.
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.
ASSEMBLY: QUAST (SPAdes)
ASSEMBLY: QUAST (SPAdes) This section of the report shows QUAST results from SPAdes de novo assembly.
Assembly Statistics
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% |
Number of Contigs
This plot shows the number of contigs found for each assembly, broken down by length.
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
Variant Quality
Indel Distribution
Variant depths
Read depth support distribution for called variants
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.
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
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.
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.
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.
ASSEMBLY: QUAST (MetaSPAdes)
ASSEMBLY: QUAST (MetaSPAdes) This section of the report shows QUAST results from MetaSPAdes de novo assembly.
ASSEMBLY: BCFTools (MetaSPAdes)
Bcftools This section of the report shows BCFTools stats results for variants called in the MetaSPAdes assembly relative to the reference.
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.
ASSEMBLY: QUAST (Unicycler)
ASSEMBLY: QUAST (Unicycler) This section of the report shows QUAST results from Unicycler de novo assembly.
Assembly Statistics
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% |
Number of Contigs
This plot shows the number of contigs found for each assembly, broken down by length.
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
Variant Quality
Indel Distribution
Variant depths
Read depth support distribution for called variants
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.
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
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.
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.
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.
ASSEMBLY: QUAST (minia)
ASSEMBLY: QUAST (minia) This section of the report shows QUAST results from minia de novo assembly.
Assembly Statistics
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% |
Number of Contigs
This plot shows the number of contigs found for each assembly, broken down by length.
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
Variant Quality
Indel Distribution
Variant depths
Read depth support distribution for called variants
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.
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
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.
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.
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.
nf-core/viralrecon Software Versions
are collected at run time from the software output.
- nf-core/viralrecon
- v1.0dev
- Nextflow
- v20.01.0
- parallel-fastq-dump
- v0.6.6
- FastQC
- v0.11.9
- fastp
- v0.20.1
- Bowtie 2
- v2.3.5.1
- Samtools
- v1.9
- BEDTools
- v2.29.2
- Picard
- v2.22.8
- iVar
- v1.2.2
- VarScan 2
- v2.4.4
- SnpEff
- v4.5covid19
- SnpSift
- v4.3t
- BCFTools
- v1.9
- Cutadapt
- v2.10
- Kraken2
- v2.0.9-beta
- SPAdes
- v3.14.0
- Unicycler
- v0.4.7
- minia
- v3.2.3
- Minimap2
- v2.17-r941
- vg
- v1.24.0
- BLAST
- v2.9.0+
- ABACAS
- v1.3.1
- QUAST
- v5.0.2
- Bandage
- v0.8.1
- R
- v3.6.2
- MultiQC
- v1.9