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/chipseq analysis pipeline. For information about how to interpret these results, please see the documentation.
Report generated on 2020-03-18, 04:17 based on data in:
/home/agalicina/DANIO/CHIPSEQ/chipseq/work/46/4bdec7237f8a2311b95879906d7755
General Statistics
Showing 24/24 rows and 38/44 columns.| Sample Name | M Reads Mapped | M Reads Mapped | M Reads Mapped | Frag Length | NSC | RSC | % Dups | % GC | Length | M Seqs | % Trimmed | % Dups | % GC | Length | M Seqs | Error rate | M Non-Primary | M Reads Mapped | % Mapped | % Proper Pairs | M Total seqs | Error rate | M Non-Primary | M Reads Mapped | % Mapped | % Proper Pairs | M Total seqs | Error rate | M Non-Primary | M Reads Mapped | % Mapped | % Proper Pairs | M Total seqs | % Aligned | Insert Size | % Dups | % Assigned | M Assigned |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| SPT5_INPUT_R1 | 0.2 | 0.2 | 150 | 1.15 | 8.03 | 0.06% | 0.0 | 0.2 | 94.3% | 94.2% | 0.2 | 0.05% | 0.0 | 0.2 | 100.0% | 99.9% | 0.2 | 100% | 155 bp | |||||||||||||||||||
| SPT5_INPUT_R1_T1 | 0.2 | 0.06% | 0.0 | 0.2 | 94.3% | 94.2% | 0.2 | 0.0% | ||||||||||||||||||||||||||||||
| SPT5_INPUT_R1_T1_1 | 2.6% | 40% | 50 bp | 0.1 | 1.1% | 2.5% | 40% | 49 bp | 0.1 | |||||||||||||||||||||||||||||
| SPT5_INPUT_R1_T1_2 | 2.5% | 40% | 50 bp | 0.1 | 1.6% | 2.5% | 40% | 49 bp | 0.1 | |||||||||||||||||||||||||||||
| SPT5_INPUT_R2 | 0.2 | 0.2 | 155 | 1.14 | 7.83 | 0.06% | 0.0 | 0.2 | 96.5% | 96.3% | 0.2 | 0.06% | 0.0 | 0.2 | 100.0% | 99.8% | 0.2 | 100% | 160 bp | |||||||||||||||||||
| SPT5_INPUT_R2_T1 | 0.2 | 0.06% | 0.0 | 0.2 | 96.5% | 96.3% | 0.2 | 0.0% | ||||||||||||||||||||||||||||||
| SPT5_INPUT_R2_T1_1 | 1.7% | 39% | 50 bp | 0.1 | 1.1% | 1.6% | 39% | 49 bp | 0.1 | |||||||||||||||||||||||||||||
| SPT5_INPUT_R2_T1_2 | 1.6% | 39% | 50 bp | 0.1 | 1.6% | 1.6% | 39% | 49 bp | 0.1 | |||||||||||||||||||||||||||||
| SPT5_T0_R1 | 0.2 | 0.1 | 95 | 1.47 | 0.91 | 0.26% | 0.0 | 0.2 | 95.4% | 95.0% | 0.2 | 0.16% | 0.0 | 0.1 | 100.0% | 99.8% | 0.1 | 100% | 118 bp | 51.8% | 0.0 | |||||||||||||||||
| SPT5_T0_R1_T1 | 0.2 | 0.26% | 0.0 | 0.2 | 95.4% | 95.0% | 0.2 | 4.8% | ||||||||||||||||||||||||||||||
| SPT5_T0_R1_T1_1 | 29.6% | 43% | 76 bp | 0.1 | 10.4% | 21.7% | 43% | 68 bp | 0.1 | |||||||||||||||||||||||||||||
| SPT5_T0_R1_T1_2 | 29.9% | 43% | 76 bp | 0.1 | 10.6% | 21.7% | 43% | 68 bp | 0.1 | |||||||||||||||||||||||||||||
| SPT5_T0_R2 | 0.2 | 0.1 | 95 | 1.56 | 0.92 | 0.26% | 0.0 | 0.2 | 93.0% | 92.6% | 0.2 | 0.18% | 0.0 | 0.1 | 100.0% | 99.9% | 0.1 | 100% | 144 bp | 52.8% | 0.0 | |||||||||||||||||
| SPT5_T0_R2_T1 | 0.2 | 0.26% | 0.0 | 0.2 | 93.0% | 92.6% | 0.2 | 5.7% | ||||||||||||||||||||||||||||||
| SPT5_T0_R2_T1_1 | 37.1% | 43% | 76 bp | 0.1 | 9.1% | 28.7% | 43% | 69 bp | 0.1 | |||||||||||||||||||||||||||||
| SPT5_T0_R2_T1_2 | 37.3% | 43% | 76 bp | 0.1 | 9.3% | 28.6% | 43% | 69 bp | 0.1 | |||||||||||||||||||||||||||||
| SPT5_T15_R1 | 0.2 | 0.1 | 105 | 1.36 | 0.93 | 0.23% | 0.0 | 0.2 | 94.6% | 94.3% | 0.2 | 0.14% | 0.0 | 0.1 | 100.0% | 99.9% | 0.1 | 100% | 164 bp | 47.3% | 0.0 | |||||||||||||||||
| SPT5_T15_R1_T1 | 0.2 | 0.23% | 0.0 | 0.2 | 94.6% | 94.3% | 0.2 | 3.6% | ||||||||||||||||||||||||||||||
| SPT5_T15_R1_T1_1 | 26.6% | 43% | 76 bp | 0.1 | 5.5% | 21.7% | 43% | 72 bp | 0.1 | |||||||||||||||||||||||||||||
| SPT5_T15_R1_T1_2 | 26.6% | 43% | 76 bp | 0.1 | 5.6% | 21.6% | 43% | 72 bp | 0.1 | |||||||||||||||||||||||||||||
| SPT5_T15_R2 | 0.2 | 0.1 | 95 | 1.35 | 0.89 | 0.26% | 0.0 | 0.2 | 94.5% | 94.1% | 0.2 | 0.16% | 0.0 | 0.1 | 100.0% | 99.8% | 0.1 | 100% | 124 bp | 46.8% | 0.0 | |||||||||||||||||
| SPT5_T15_R2_T1 | 0.2 | 0.26% | 0.0 | 0.2 | 94.5% | 94.1% | 0.2 | 3.2% | ||||||||||||||||||||||||||||||
| SPT5_T15_R2_T1_1 | 23.5% | 42% | 76 bp | 0.1 | 8.8% | 17.8% | 42% | 69 bp | 0.1 | |||||||||||||||||||||||||||||
| SPT5_T15_R2_T1_2 | 23.5% | 42% | 76 bp | 0.1 | 9.0% | 17.8% | 42% | 69 bp | 0.1 |
FastQC (library; raw)
FastQC (library; 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.
cutadapt (library; trimmed)
cutadapt (library; trimmed) This section of the report shows the length of trimmed reads by cutadapt.
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.
FastQC (library; trimmed)
FastQC (library; 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.
SAMTools (library)
Samtools This section of the report shows SAMTools results for individual libraries.
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.
SAMTools (merged library; unfiltered)
Samtools This section of the report shows SAMTools results after merging libraries and before filtering.
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.
Preseq (merged library; unfiltered)
Preseq (merged library; unfiltered) This section of the report shows Preseq results after merging libraries and before filtering.
Complexity curve
Note that the x axis is trimmed at the point where all the datasets show 80% of their maximum y-value, to avoid ridiculous scales.
SAMTools (merged library; filtered)
Samtools This section of the report shows SAMTools results after merging libraries and after filtering.
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.
Picard (merged library; filtered)
Picard (merged library; filtered) This section of the report shows picard results after merging libraries and after filtering.
Alignment Summary
Plase 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
deepTools
deepTools This section of the report shows ChIP-seq QC plots generated by deepTools.
Fingerprint plot
Signal fingerprint according to 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. Green: -3.0Kb upstream of gene to TSS; Yellow: TSS to TES; Pink: TES to 3.0Kb downstream of gene
featureCounts
Subread featureCounts This section of the report shows featureCounts results for the number of reads assigned to merged library consensus peaks.
MACS2: Peak FRiP score
MACS2: Peak FRiP score is generated by calculating the fraction of all mapped reads that fall into the MACS2 called peak regions. A read must overlap a peak by at least 20% to be counted. See FRiP score.
spp: Strand-shift correlation plot
spp: Strand-shift correlation plot generated using run_spp.R script from phantompeakqualtools.
spp: NSC coefficient
spp: NSC coefficient generated using run_spp.R script from phantompeakqualtools.
spp: RSC coefficient
spp: RSC coefficient generated using run_spp.R script from phantompeakqualtools.
HOMER: Peak annotation
HOMER: Peak annotation is generated by calculating the proportion of peaks assigned to genomic features by HOMER annotatePeaks.pl.
SPT5 DESeq2: PCA plot
SPT5 DESeq2: PCA plot between samples in the experiment. These values are calculated using DESeq2 in the featurecounts_deseq2.r script.
SPT5 DESeq2: Sample similarity
SPT5 DESeq2: Sample similarity matrix is generated from clustering by Euclidean distances between DESeq2 rlog values for each sample (see featurecounts_deseq2.r script).
nf-core/chipseq Software Versions
nf-core/chipseq Software Versions are collected at run time from the software output.
- nf-core/chipseq
- v1.1.0
- Nextflow
- v19.10.0
- FastQC
- v0.11.8
- Trim Galore!
- v0.5.0
- BWA
- v0.7.17-r1188
- Samtools
- v1.9
- BEDTools
- v2.27.1
- BamTools
- v2.5.1
- deepTools
- v3.2.1
- Picard
- v2.19.0
- R
- v3.4.1
- Pysam
- v0.15.2
- MACS2
- v2.1.2
- featureCounts
- v1.6.4
- Preseq
- v2.0.3
- MultiQC
- v1.7
nf-core/chipseq Workflow Summary
nf-core/chipseq Workflow Summary - this information is collected when the pipeline is started.
- Run Name
- pensive_mcclintock
- Data Type
- Paired-End
- Design File
- https://raw.githubusercontent.com/nf-core/test-datasets/chipseq/design.csv
- Genome
- Not supplied
- Fasta File
- https://raw.githubusercontent.com/nf-core/test-datasets/atacseq/reference/genome.fa
- GTF File
- https://raw.githubusercontent.com/nf-core/test-datasets/atacseq/reference/genes.gtf
- MACS2 Genome Size
- 1.2E+7
- Min Consensus Reps
- 1
- MACS2 Narrow Peaks
- No
- MACS2 Broad Cutoff
- 0.1
- Trim R1
- 0 bp
- Trim R2
- 0 bp
- Trim 3' R1
- 0 bp
- Trim 3' R2
- 0 bp
- NextSeq Trim
- 0 bp
- Fingerprint Bins
- 100
- Save Genome Index
- No
- Max Resources
- 6 GB memory, 2 cpus, 12h time per job
- Output Dir
- ./results
- Launch Dir
- /home/agalicina/DANIO/CHIPSEQ/chipseq
- Working Dir
- /home/agalicina/DANIO/CHIPSEQ/chipseq/work
- Script Dir
- /home/agalicina/.nextflow/assets/nf-core/chipseq
- User
- agalicina
- Config Profile
- test
- Config Description
- Minimal test dataset to check pipeline function
