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/rnaseq analysis pipeline. For information about how to interpret these results, please see the documentation.
Report
generated on 2023-01-05, 23:47 UTC
based on data in:
/tmp/nxf.EPAompG8CC
General Statistics
Showing 24/24 rows and 25/29 columns.| Sample Name | M Reads Mapped | % rRNA | dupInt | % Dups | 5'-3' bias | M Aligned | % Alignable | % Proper Pairs | % Aligned | M Aligned | Error rate | M Non-Primary | M Reads Mapped | % Mapped | % Proper Pairs | M Total seqs | % Dups | % GC | Read Length | M Seqs | % BP Trimmed | % Dups | % GC | Read Length | M Seqs |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| GM12878_REP1 | 189.1 | 0.00% | 0.82% | 76.4% | 1.11 | 86.8 | 91.5% | 19.4% | 74.7% | 69.6 | 0.26% | 15.3 | 173.8 | 93.3% | 93.2% | 186.3 | |||||||||
| GM12878_REP1_1 | 83.1% | 50% | 101 bp | 93.6 | 2.0% | 82.4% | 50% | 98 bp | 93.1 | ||||||||||||||||
| GM12878_REP1_2 | 82.7% | 51% | 101 bp | 93.6 | 3.1% | 81.8% | 51% | 98 bp | 93.1 | ||||||||||||||||
| GM12878_REP2 | 192.1 | 0.00% | 0.79% | 76.8% | 1.08 | 88.3 | 88.6% | 18.6% | 71.1% | 69.1 | 0.30% | 15.3 | 176.8 | 91.1% | 90.9% | 194.1 | |||||||||
| GM12878_REP2_1 | 83.2% | 52% | 101 bp | 97.5 | 2.2% | 82.4% | 52% | 98 bp | 97.1 | ||||||||||||||||
| GM12878_REP2_2 | 82.9% | 53% | 101 bp | 97.5 | 3.4% | 82.0% | 52% | 97 bp | 97.1 | ||||||||||||||||
| H1_REP1 | 263.1 | 0.00% | 0.17% | 50.9% | 1.35 | 117.2 | 95.8% | 40.0% | 82.0% | 99.7 | 0.32% | 28.4 | 234.6 | 96.5% | 96.4% | 243.1 | |||||||||
| H1_REP1_1 | 5.5% | 51% | 76 bp | 125.4 | 6.0% | 4.9% | 51% | 72 bp | 121.5 | ||||||||||||||||
| H1_REP1_2 | 12.3% | 52% | 76 bp | 125.4 | 7.4% | 10.5% | 51% | 72 bp | 121.5 | ||||||||||||||||
| H1_REP2 | 216.5 | 0.00% | 0.19% | 53.1% | 1.41 | 97.3 | 91.6% | 37.5% | 78.3% | 81.8 | 0.26% | 21.6 | 194.9 | 93.3% | 93.2% | 208.9 | |||||||||
| H1_REP2_1 | 72.1% | 54% | 76 bp | 107.1 | 5.1% | 64.6% | 54% | 73 bp | 104.5 | ||||||||||||||||
| H1_REP2_2 | 69.7% | 55% | 76 bp | 107.1 | 6.4% | 63.5% | 55% | 72 bp | 104.5 | ||||||||||||||||
| K562_REP1 | 179.6 | 0.00% | 1.04% | 86.3% | 1.10 | 82.2 | 87.3% | 10.8% | 71.1% | 65.1 | 0.30% | 14.9 | 164.7 | 90.0% | 89.8% | 183.1 | |||||||||
| K562_REP1_1 | 88.2% | 50% | 101 bp | 92.2 | 2.6% | 86.9% | 50% | 98 bp | 91.5 | ||||||||||||||||
| K562_REP1_2 | 86.0% | 51% | 101 bp | 92.2 | 4.2% | 84.6% | 51% | 97 bp | 91.5 | ||||||||||||||||
| K562_REP2 | 223.6 | 0.00% | 0.88% | 83.1% | 1.13 | 102.1 | 88.5% | 13.4% | 71.6% | 80.6 | 0.28% | 18.8 | 204.7 | 91.0% | 90.7% | 225.1 | |||||||||
| K562_REP2_1 | 86.5% | 50% | 101 bp | 113.3 | 2.6% | 85.3% | 50% | 98 bp | 112.5 | ||||||||||||||||
| K562_REP2_2 | 84.3% | 51% | 101 bp | 113.3 | 4.2% | 82.7% | 51% | 97 bp | 112.5 | ||||||||||||||||
| MCF7_REP1 | 257.7 | 0.00% | 0.08% | 37.4% | 1.15 | 116.6 | 92.7% | 50.9% | 75.0% | 93.0 | 0.41% | 24.0 | 233.7 | 94.2% | 94.0% | 248.0 | |||||||||
| MCF7_REP1_1 | 61.4% | 46% | 76 bp | 128.2 | 10.8% | 51.7% | 45% | 68 bp | 124.0 | ||||||||||||||||
| MCF7_REP1_2 | 58.5% | 47% | 76 bp | 128.2 | 8.3% | 50.1% | 46% | 71 bp | 124.0 | ||||||||||||||||
| MCF7_REP2 | 262.5 | 0.00% | 0.14% | 43.7% | 1.20 | 119.3 | 91.1% | 45.4% | 75.0% | 96.5 | 0.22% | 23.5 | 239.0 | 92.9% | 92.8% | 257.2 | |||||||||
| MCF7_REP2_1 | 68.2% | 47% | 76 bp | 131.8 | 6.2% | 60.0% | 47% | 71 bp | 128.6 | ||||||||||||||||
| MCF7_REP2_2 | 65.6% | 48% | 76 bp | 131.8 | 7.0% | 56.9% | 47% | 72 bp | 128.6 |
Biotype Counts
shows reads overlapping genomic features of different biotypes, counted by featureCounts.
STAR_RSEM DESeq2 PCA plot
PCA plot between samples in the experiment. These values are calculated using DESeq2 in the deseq2_qc.r script.
STAR_RSEM DESeq2 sample similarity
is generated from clustering by Euclidean distances between DESeq2 rlog values for each sample in the deseq2_qc.r script.
SALMON DESeq2 PCA plot
PCA plot between samples in the experiment. These values are calculated using DESeq2 in the deseq2_qc.r script.
SALMON DESeq2 sample similarity
is generated from clustering by Euclidean distances between DESeq2 rlog values for each sample in the deseq2_qc.r script.
DupRadar
provides duplication rate quality control for RNA-Seq datasets. Highly expressed genes can be expected to have a lot of duplicate reads, but high numbers of duplicates at low read counts can indicate low library complexity with technical duplication. This plot shows the general linear models - a summary of the gene duplication distributions. .
Picard
Picard is a set of Java command line tools for manipulating high-throughput sequencing data.
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_DUPLICATESREADS_IN_UNIQUE_PAIRS = 2 * (READ_PAIRS_EXAMINED - READ_PAIR_DUPLICATES)READS_IN_UNIQUE_UNPAIRED = UNPAIRED_READS_EXAMINED - UNPAIRED_READ_DUPLICATESREADS_IN_DUPLICATE_PAIRS_OPTICAL = 2 * READ_PAIR_OPTICAL_DUPLICATESREADS_IN_DUPLICATE_PAIRS_NONOPTICAL = READS_IN_DUPLICATE_PAIRS - READS_IN_DUPLICATE_PAIRS_OPTICALREADS_IN_DUPLICATE_UNPAIRED = UNPAIRED_READ_DUPLICATESREADS_UNMAPPED = UNMAPPED_READS
QualiMap
QualiMap is a platform-independent application to facilitate the quality control of alignment sequencing data and its derivatives like feature counts.DOI: 10.1093/bioinformatics/btv566; 10.1093/bioinformatics/bts503.
Genomic origin of reads
Classification of mapped reads as originating in exonic, intronic or intergenic regions. These can be displayed as either the number or percentage of mapped reads.
There are currently three main approaches to map reads to transcripts in an RNA-seq experiment: mapping reads to a reference genome to identify expressed transcripts that are annotated (and discover those that are unknown), mapping reads to a reference transcriptome, and de novo assembly of transcript sequences (Conesa et al. 2016).
For RNA-seq QC analysis, QualiMap can be used to assess alignments produced by the first of these approaches. For input, it requires a GTF annotation file along with a reference genome, which can be used to reconstruct the exon structure of known transcripts. This allows mapped reads to be grouped by whether they originate in an exonic region (for QualiMap, this may include 5′ and 3′ UTR regions as well as protein-coding exons), an intron, or an intergenic region (see the Qualimap 2 documentation).
The inferred genomic origins of RNA-seq reads are presented here as a bar graph showing either the number or percentage of mapped reads in each read dataset that have been assigned to each type of genomic region. This graph can be used to assess the proportion of useful reads in an RNA-seq experiment. That proportion can be reduced by the presence of intron sequences, especially if depletion of ribosomal RNA was used during sample preparation (Sims et al. 2014). It can also be reduced by off-target transcripts, which are detected in greater numbers at the sequencing depths needed to detect poorly-expressed transcripts (Tarazona et al. 2011).
Gene Coverage Profile
Mean distribution of coverage depth across the length of all mapped transcripts.
There are currently three main approaches to map reads to transcripts in an RNA-seq experiment: mapping reads to a reference genome to identify expressed transcripts that are annotated (and discover those that are unknown), mapping reads to a reference transcriptome, and de novo assembly of transcript sequences (Conesa et al. 2016).
For RNA-seq QC analysis, QualiMap can be used to assess alignments produced by the first of these approaches. For input, it requires a GTF annotation file along with a reference genome, which can be used to reconstruct the exon structure of known transcripts. QualiMap uses this information to calculate the depth of coverage along the length of each annotated transcript. For a set of reads mapped to a transcript, the depth of coverage at a given base position is the number of high-quality reads that map to the transcript at that position (Sims et al. 2014).
QualiMap calculates coverage depth at every base position of each annotated transcript. To enable meaningful comparison between transcripts, base positions are rescaled to relative positions expressed as percentage distance along each transcript (0%, 1%, …, 99%). For the set of transcripts with at least one mapped read, QualiMap plots the cumulative mapped-read depth (y-axis) at each relative transcript position (x-axis). This plot shows the gene coverage profile across all mapped transcripts for each read dataset. It provides a visual way to assess positional biases, such as an accumulation of mapped reads at the 3′ end of transcripts, which may indicate poor RNA quality in the original sample (Conesa et al. 2016).
The Normalised plot is calculated by MultiQC to enable comparison of samples with varying sequencing depth. The cumulative mapped-read depth at each position across the averaged transcript position are divided by the total for that sample across the entire averaged transcript.
Rsem
Rsem RSEM (RNA-Seq by Expectation-Maximization) is a software package forestimating gene and isoform expression levels from RNA-Seq data.DOI: 10.1186/1471-2105-12-323.
Mapped Reads
A breakdown of how all reads were aligned for each sample.
Multimapping rates
A frequency histogram showing how many reads were aligned to n reference regions.
In an ideal world, every sequence reads would align uniquely to a single location in the reference. However, due to factors such as repeititve sequences, short reads and sequencing errors, reads can be align to the reference 0, 1 or more times. This plot shows the frequency of each factor of multimapping. Good samples should have the majority of reads aligning once.
RSeQC
RSeQC package provides a number of useful modules that can comprehensively evaluate high throughput RNA-seq data.DOI: 10.1093/bioinformatics/bts356.
Read Distribution
Read Distribution calculates how mapped reads are distributed over genome features.
Inner Distance
Inner Distance calculates the inner distance (or insert size) between two paired RNA reads. Note that this can be negative if fragments overlap.
Read Duplication
read_duplication.py calculates how many alignment positions have a certain number of exact duplicates. Note - plot truncated at 500 occurrences and binned.
Junction Annotation
Junction annotation compares detected splice junctions to a reference gene model. An RNA read can be spliced 2 or more times, each time is called a splicing event.
Junction Saturation
Junction Saturation counts the number of known splicing junctions that are observed in each dataset. If sequencing depth is sufficient, all (annotated) splice junctions should be rediscovered, resulting in a curve that reaches a plateau. Missing low abundance splice junctions can affect downstream analysis.
Infer experiment
Infer experiment counts the percentage of reads and read pairs that match the strandedness of overlapping transcripts. It can be used to infer whether RNA-seq library preps are stranded (sense or antisense).
Bam Stat
All numbers reported in millions.
Salmon
Salmon is a tool for quantifying the expression of transcripts using RNA-seq data.DOI: 10.1038/nmeth.4197.
Samtools
Samtools is a suite of programs for interacting with high-throughput sequencing data.DOI: 10.1093/bioinformatics/btp352.
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.
XY counts
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.
FastQC (raw)
FastQC (raw) This section of the report shows FastQC results 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
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.
Cutadapt
Cutadapt is a tool to find and remove adapter sequences, primers, poly-A tails and other types of unwanted sequence from your high-throughput sequencing reads.DOI: 10.14806/ej.17.1.200.
Filtered Reads
This plot shows the number of reads (SE) / pairs (PE) removed by Cutadapt.
Trimmed Sequence Lengths (3')
This plot shows the number of reads with certain lengths of adapter trimmed for the 3' end.
Obs/Exp shows the raw counts divided by the number expected due to sequencing errors. A defined peak may be related to adapter length.
See the cutadapt documentation for more information on how these numbers are generated.
FastQC (trimmed)
FastQC (trimmed) This section of the report shows FastQC results after 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.
nf-core/rnaseq Methods Description
Suggested text and references to use when describing pipeline usage within the methods section of a publication.
Methods
Data was processed using nf-core/rnaseq v3.10.1 (doi: https://doi.org/10.5281/zenodo.1400710) of the nf-core collection of workflows (Ewels et al., 2020).
The pipeline was executed with Nextflow v22.10.4 (Di Tommaso et al., 2017) with the following command:
nextflow run 'https://github.com/nf-core/rnaseq' -name prickly_cantor -params-file 'https://api.tower.nf/ephemeral/VKlGUT5Th1URd2eZ65qf_w.json' -with-tower -r 6e1e448f535ccf34d11cc691bb241cfd6e60a647 -profile test_full,aws_towerReferences
- 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:
- 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.
nf-core/rnaseq Software Versions
are collected at run time from the software output.
| Process Name | Software | Version |
|---|---|---|
| BEDTOOLS_GENOMECOV | bedtools | 2.30.0 |
| CUSTOM_DUMPSOFTWAREVERSIONS | python | 3.10.6 |
| yaml | 6.0 | |
| CUSTOM_GETCHROMSIZES | getchromsizes | 1.16.1 |
| DESEQ2_QC_RSEM | bioconductor-deseq2 | 1.28.0 |
| r-base | 4.0.3 | |
| DESEQ2_QC_SALMON | bioconductor-deseq2 | 1.28.0 |
| r-base | 4.0.3 | |
| DUPRADAR | bioconductor-dupradar | 1.28.0 |
| r-base | 4.2.1 | |
| FASTQC | fastqc | 0.11.9 |
| GTF_GENE_FILTER | python | 3.9.5 |
| MAKE_TRANSCRIPTS_FASTA | rsem | 1.3.1 |
| star | 2.7.10a | |
| MULTIQC_CUSTOM_BIOTYPE | python | 3.9.5 |
| PICARD_MARKDUPLICATES | picard | 2.27.4-SNAPSHOT |
| QUALIMAP_RNASEQ | qualimap | 2.2.2-dev |
| RSEM_CALCULATEEXPRESSION | rsem | 1.3.1 |
| star | 2.7.10a | |
| RSEM_MERGE_COUNTS | sed | 4.7 |
| RSEM_PREPAREREFERENCE_GENOME | rsem | 1.3.1 |
| star | 2.7.10a | |
| RSEQC_BAMSTAT | rseqc | 3.0.1 |
| RSEQC_INFEREXPERIMENT | rseqc | 3.0.1 |
| RSEQC_INNERDISTANCE | rseqc | 3.0.1 |
| RSEQC_JUNCTIONANNOTATION | rseqc | 3.0.1 |
| RSEQC_JUNCTIONSATURATION | rseqc | 3.0.1 |
| RSEQC_READDISTRIBUTION | rseqc | 3.0.1 |
| RSEQC_READDUPLICATION | rseqc | 3.0.1 |
| SALMON_INDEX | salmon | 1.9.0 |
| SALMON_QUANT | salmon | 1.9.0 |
| SALMON_SE_GENE | bioconductor-summarizedexperiment | 1.24.0 |
| r-base | 4.1.1 | |
| SALMON_TX2GENE | python | 3.9.5 |
| SALMON_TXIMPORT | bioconductor-tximeta | 1.12.0 |
| r-base | 4.1.1 | |
| SAMPLESHEET_CHECK | python | 3.9.5 |
| SAMTOOLS_FLAGSTAT | samtools | 1.16.1 |
| SAMTOOLS_IDXSTATS | samtools | 1.16.1 |
| SAMTOOLS_INDEX | samtools | 1.16.1 |
| SAMTOOLS_SORT | samtools | 1.16.1 |
| SAMTOOLS_STATS | samtools | 1.16.1 |
| STRINGTIE_STRINGTIE | stringtie | 2.2.1 |
| SUBREAD_FEATURECOUNTS | subread | 2.0.1 |
| TRIMGALORE | cutadapt | 3.4 |
| trimgalore | 0.6.7 | |
| UCSC_BEDCLIP | ucsc | 377 |
| UCSC_BEDGRAPHTOBIGWIG | ucsc | 377 |
| Workflow | Nextflow | 22.10.4 |
| nf-core/rnaseq | 3.10.1 |
nf-core/rnaseq Workflow Summary
- this information is collected when the pipeline is started.
Core Nextflow options
- revision
- 3.10.1
- runName
- prickly_cantor
- launchDir
- /
- workDir
- /fusion/s3/nf-core-awsmegatests/work/rnaseq/work-6e1e448f535ccf34d11cc691bb241cfd6e60a647
- projectDir
- /.nextflow/assets/nf-core/rnaseq
- userName
- root
- profile
- test_full,aws_tower
- configFiles
- /.nextflow/assets/nf-core/rnaseq/nextflow.config, /nextflow.config
Input/output options
- input
- https://raw.githubusercontent.com/nf-core/test-datasets/rnaseq/samplesheet/v3.10/samplesheet_full.csv
- outdir
- s3://nf-core-awsmegatests/rnaseq/results-6e1e448f535ccf34d11cc691bb241cfd6e60a647/aligner_star_rsem
Reference genome options
- genome
- GRCh37
- fasta
- s3://ngi-igenomes/igenomes/Homo_sapiens/Ensembl/GRCh37/Sequence/WholeGenomeFasta/genome.fa
- gtf
- s3://ngi-igenomes/igenomes/Homo_sapiens/Ensembl/GRCh37/Annotation/Genes/genes.gtf
- gene_bed
- s3://ngi-igenomes/igenomes/Homo_sapiens/Ensembl/GRCh37/Annotation/Genes/genes.bed
- star_index
- s3://ngi-igenomes/igenomes/Homo_sapiens/Ensembl/GRCh37/Sequence/STARIndex/
Alignment options
- aligner
- star_rsem
- pseudo_aligner
- salmon
Institutional config options
- config_profile_name
- Full test profile
- config_profile_description
- Full test dataset to check pipeline function
- config_profile_contact
- Gisela Gabernet (@ggabernet)
- config_profile_url
- https://aws.amazon.com/batch/
