+
+
+ 
+
+
+
+
+
++ A modular tool to aggregate results from bioinformatics analyses across many samples into a single report. +
+ + + +This report has been generated by CCBR/CHAMPAGNE. For information about how to interpret these results, please see the documentation. ++ + + + + + + + + +
Report
+
+ generated on 2023-09-06, 17:28 EDT
+
+
+ based on data in:
+
+ /lscratch/7526425/nxf.FBQXPcOVYD
+ + + + + + + + +
General Statistics
+ + + + + + + + + + + Showing 32/32 rows and 7/12 columns. + +| Sample Name | % Dups | % GC | M Seqs | M Reads Mapped | Frag Length | NSC | RSC |
|---|---|---|---|---|---|---|---|
| CTCF_ChIP_MEF_p20_1.aligned.filtered.dedup.bam.flagstat | 32.9 | ||||||
| CTCF_ChIP_MEF_p20_1.spp.out | 135 | 1.73 | 2.84 | ||||
| CTCF_ChIP_MEF_p20_1.trimmed.fastq.gz | 19.1% | 44% | 48.5 | ||||
| CTCF_ChIP_MEF_p20_2.aligned.filtered.dedup.bam.flagstat | 13.6 | ||||||
| CTCF_ChIP_MEF_p20_2.spp.out | 110 | 3.39 | 2.45 | ||||
| CTCF_ChIP_MEF_p20_2.trimmed.fastq.gz | 23.5% | 51% | 36.6 | ||||
| CTCF_ChIP_macrophage_p20_1.aligned.filtered.dedup.bam.flagstat | 38.1 | ||||||
| CTCF_ChIP_macrophage_p20_1.spp.out | 130 | 1.51 | 2.66 | ||||
| CTCF_ChIP_macrophage_p20_1.trimmed.fastq.gz | 19.3% | 45% | 59.8 | ||||
| CTCF_ChIP_macrophage_p20_2.aligned.filtered.dedup.bam.flagstat | 10.6 | ||||||
| CTCF_ChIP_macrophage_p20_2.spp.out | 115 | 2.08 | 2.12 | ||||
| CTCF_ChIP_macrophage_p20_2.trimmed.fastq.gz | 9.3% | 45% | 22.3 | ||||
| CTCF_ChIP_macrophage_p3_1.aligned.filtered.dedup.bam.flagstat | 31.1 | ||||||
| CTCF_ChIP_macrophage_p3_1.spp.out | 135 | 1.53 | 2.65 | ||||
| CTCF_ChIP_macrophage_p3_1.trimmed.fastq.gz | 17.2% | 43% | 50.2 | ||||
| CTCF_ChIP_macrophage_p3_2.aligned.filtered.dedup.bam.flagstat | 16.0 | ||||||
| CTCF_ChIP_macrophage_p3_2.spp.out | 105 | 1.93 | 2.57 | ||||
| CTCF_ChIP_macrophage_p3_2.trimmed.fastq.gz | 26.0% | 51% | 44.4 | ||||
| SRR3081748_1.fastq.gz | 20.2% | 45% | 60.4 | ||||
| SRR3081749_1.fastq.gz | 10.0% | 45% | 22.7 | ||||
| SRR3081750_1.fastq.gz | 17.6% | 44% | 50.5 | ||||
| SRR3081751_1.fastq.gz | 36.2% | 51% | 51.4 | ||||
| SRR3081752_1.fastq.gz | 19.4% | 44% | 48.7 | ||||
| SRR3081753_1.fastq.gz | 24.5% | 51% | 36.9 | ||||
| SRR3081772_1.fastq.gz | 14.0% | 39% | 27.6 | ||||
| SRR3081773_1.fastq.gz | 16.9% | 39% | 38.2 | ||||
| WCE_p20.aligned.filtered.dedup.bam.flagstat | 27.6 | ||||||
| WCE_p20.spp.out | 0 | 1.01 | 1.00 | ||||
| WCE_p20.trimmed.fastq.gz | 16.5% | 39% | 38.0 | ||||
| WCE_p3.aligned.filtered.dedup.bam.flagstat | 20.9 | ||||||
| WCE_p3.spp.out | 145 | 1.01 | 1.10 | ||||
| WCE_p3.trimmed.fastq.gz | 13.6% | 39% | 27.5 |
ChIP-specific QC metrics
+Encode3 standards
+ +Usable fragments (A fragment corresponds to a read mapping to one location in the genome.) +If single-end, one read is considered a fragment. If paired-end, one pair is considered a fragment.+Usable fragments for TF/narrow histones: concerning <10M, acceptable 10-20M, ideal >20M.
+Usable fragments for broad histones: concerning <20M, acceptable 20-40M, ideal >40M.
+
-
+
- NRF: Number of distinct mapping reads after removing duplicates/total number of reads. +
- PBC1: Number of genomic locations where exactly one read maps uniquely/number of distinct genomic locations to which one read maps uniquely. +
- PBC2: Number of genomic locations where only one read maps uniquely/number of genomic locations where two reads map uniquely +
- Concerning(severe bottlenecking): NRF < 0.5, Acceptable: 0.5 < NRF < 0.8, Ideal (no bottlenecking): NRF > 0.9 +
+
phantompeakqualtools standards
+-
+
- NSC: cross-correlation value/minimum cross-correlation
+ Optimal NSC: > 1.1, Okay NSC: 1.05 - 1.1. Low NSC means low signal to noise or very few peaks (biological or technical).
+ - RSC: (cross-correlation value - minimum cross-correlation) / (correlation at phantom peak - minimum cross-correlation)
+ Optimal RSC: > 1, Okay RSC: 0.8-1. Low RSC means low signal to noise caused by: poor quality ChIP, + low read sequence quality, shallow sequencing depth, or few peaks.
QualityTag: -2: very low, 2: very high
+
| SampleName | NUniqMappedReads | NRF | PBC1 | PBC2 | FragmentLength | NSC | RSC | Qtag |
|---|---|---|---|---|---|---|---|---|
| CTCF_ChIP_MEF_p20_2 | 31099296 | 0.8 | 0.9 | 7.0 | 200.0 | 1.93 | 2.6 | 2.0 |
| CTCF_ChIP_MEF_p20_1 | 15983324 | 0.8 | 0.8 | 4.4 | 200.0 | 2.08 | 2.1 | 2.0 |
| CTCF_ChIP_macrophage_p20_2 | 38105036 | 0.9 | 0.9 | 12.9 | 200.0 | 1.53 | 2.7 | 2.0 |
| CTCF_ChIP_macrophage_p20_1 | 32917124 | 0.8 | 0.9 | 7.9 | 200.0 | 3.39 | 2.4 | 2.0 |
| CTCF_ChIP_macrophage_p3_1 | 10554895 | 0.9 | 0.9 | 8.2 | 200.0 | 1.73 | 2.8 | 2.0 |
| WCE_p20 | 13607337 | 0.9 | 0.9 | 18.0 | 200.0 | 1.51 | 2.7 | 2.0 |
| CTCF_ChIP_macrophage_p3_2 | 27568678 | 0.7 | 0.8 | 4.2 | 200.0 | 1.01 | 1.0 | 1.0 |
| WCE_p3 | 20856684 | NA | NA | NA | 200.0 | 1.01 | 1.1 | 1.0 |
+ + + +
FastQC
+FastQC is a quality control tool for high throughput sequence data, written by Simon Andrews at the Babraham Institute in Cambridge.
+ + + + ++ Sequence Counts + + + +
+ +Sequence counts for each sample. Duplicate read counts are an estimate only.
This plot show the total number of reads, broken down into unique and duplicate +if possible (only more recent versions of FastQC give duplicate info).
+You can read more about duplicate calculation in the +FastQC documentation. +A small part has been copied here for convenience:
+Only sequences which first appear in the first 100,000 sequences +in each file are analysed. This should be enough to get a good impression +for the duplication levels in the whole file. Each sequence is tracked to +the end of the file to give a representative count of the overall duplication level.
+The duplication detection requires an exact sequence match over the whole length of +the sequence. Any reads over 75bp in length are truncated to 50bp for this analysis.
+
+ Sequence Quality Histograms + + + +
+ +The mean quality value across each base position in the read.
To enable multiple samples to be plotted on the same graph, only the mean quality +scores are plotted (unlike the box plots seen in FastQC reports).
+Taken from the FastQC help:
+The y-axis on the graph shows the quality scores. The higher the score, the better +the base call. The background of the graph divides the y axis into very good quality +calls (green), calls of reasonable quality (orange), and calls of poor quality (red). +The quality of calls on most platforms will degrade as the run progresses, so it is +common to see base calls falling into the orange area towards the end of a read.
+
+ Per Sequence Quality Scores + + + +
+ +The number of reads with average quality scores. Shows if a subset of reads has poor quality.
From the FastQC help:
+The per sequence quality score report allows you to see if a subset of your +sequences have universally low quality values. It is often the case that a +subset of sequences will have universally poor quality, however these should +represent only a small percentage of the total sequences.
+
+ Per Base Sequence Content + + + +
+ +The proportion of each base position for which each of the four normal DNA bases has been called.
To enable multiple samples to be shown in a single plot, the base composition data +is shown as a heatmap. The colours represent the balance between the four bases: +an even distribution should give an even muddy brown colour. Hover over the plot +to see the percentage of the four bases under the cursor.
+To see the data as a line plot, as in the original FastQC graph, click on a sample track.
+From the FastQC help:
+Per Base Sequence Content plots out the proportion of each base position in a +file for which each of the four normal DNA bases has been called.
+In a random library you would expect that there would be little to no difference +between the different bases of a sequence run, so the lines in this plot should +run parallel with each other. The relative amount of each base should reflect +the overall amount of these bases in your genome, but in any case they should +not be hugely imbalanced from each other.
+It's worth noting that some types of library will always produce biased sequence +composition, normally at the start of the read. Libraries produced by priming +using random hexamers (including nearly all RNA-Seq libraries) and those which +were fragmented using transposases inherit an intrinsic bias in the positions +at which reads start. This bias does not concern an absolute sequence, but instead +provides enrichement of a number of different K-mers at the 5' end of the reads. +Whilst this is a true technical bias, it isn't something which can be corrected +by trimming and in most cases doesn't seem to adversely affect the downstream +analysis.
Rollover for sample name
+ ++
+ Per Sequence GC Content + + + +
+ +The average GC content of reads. Normal random library typically have a + roughly normal distribution of GC content.
From the FastQC help:
+This module measures the GC content across the whole length of each sequence +in a file and compares it to a modelled normal distribution of GC content.
+In a normal random library you would expect to see a roughly normal distribution +of GC content where the central peak corresponds to the overall GC content of +the underlying genome. Since we don't know the the GC content of the genome the +modal GC content is calculated from the observed data and used to build a +reference distribution.
+An unusually shaped distribution could indicate a contaminated library or +some other kinds of biased subset. A normal distribution which is shifted +indicates some systematic bias which is independent of base position. If there +is a systematic bias which creates a shifted normal distribution then this won't +be flagged as an error by the module since it doesn't know what your genome's +GC content should be.
+
+ Per Base N Content + + + +
+ +The percentage of base calls at each position for which an N was called.
From the FastQC help:
+If a sequencer is unable to make a base call with sufficient confidence then it will
+normally substitute an N rather than a conventional base call. This graph shows the
+percentage of base calls at each position for which an N was called.
It's not unusual to see a very low proportion of Ns appearing in a sequence, especially +nearer the end of a sequence. However, if this proportion rises above a few percent +it suggests that the analysis pipeline was unable to interpret the data well enough to +make valid base calls.
+
+ Sequence Length Distribution + +
+ +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.
+ + + +
Samtools
+Samtools is a suite of programs for interacting with high-throughput sequencing data.DOI: 10.1093/bioinformatics/btp352.
+ + + + ++ 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.
+ + + +
deepTools
+deepTools is a suite of tools to process and analyze deep sequencing data.DOI: 10.1093/nar/gkw257.
+ + + + ++ Correlation heatmap + +
+ +Pairwise correlations of samples based on distribution of sequence reads
+
+ PCA plot + +
+ +PCA plot with the top two principal components calculated based on genome-wide distribution of sequence reads
+
+ Fingerprint plot + +
+ +Signal fingerprint according to plotFingerprint
+
+ Fingerprint quality metrics + +
+ +Various quality metrics returned by plotFingerprint
+
+ Read Distribution Profile after Annotation + +
+ +Accumulated view of the distribution of sequence reads related to the closest annotated gene. +All annotated genes have been normalized to the same size.
+ + + + + +
+