Methyl-MiniSeq Analysis Report for in0001: MultiQC Report

General Statistics

Showing ¹⁸/₃₆ rows and ⁶/₂₃ columns.

Sample Name	% Dups	% GC	Length	% Failed	Seqs	# Reads	% BP Trimmed	% Dups	% GC	Length	% Failed	Seqs	# Reads	Aligned	% Uniquely Aligned	% BS Conversion (Non-CpG)	Insert Size	Mean Insert Size	Aligned	% Uniquely Aligned	% BS Conversion (Spike-in)	Uniq. CpG	Avg. CpG Coverage
Sample_1003	78.1%	39%	151 bp	27%	43640743	43640743	27.4%	76.6%	31%	107 bp	18%	43539070	43539070	10698169	24.6%	99.2%	126 bp	135 bp	4579	0.0%	99.5%	7859191	7X
Sample_1003_R2	77.2%	39%	151 bp	36%	43640743		26.9%	75.9%	32%	108 bp	36%	43539070
Sample_1008	76.9%	40%	151 bp	27%	37695537	37695537	28.3%	75.2%	31%	106 bp	18%	37613923	37613923	9210792	24.5%	99.1%	126 bp	132 bp	4994	0.0%	99.4%	7588423	6X
Sample_1008_R2	76.2%	40%	151 bp	36%	37695537		27.9%	74.9%	33%	106 bp	36%	37613923
Sample_1009	77.5%	40%	151 bp	27%	34452060	34452060	29.8%	75.9%	31%	104 bp	18%	34387784	34387784	8096417	23.5%	99.2%	119 bp	125 bp	6628	0.0%	99.3%	7355841	6X
Sample_1009_R2	76.5%	40%	151 bp	36%	34452060		29.3%	75.3%	33%	104 bp	27%	34387784
Sample_1016	77.3%	43%	151 bp	27%	33431083	33431083	33.3%	75.8%	32%	97 bp	27%	33360795	33360795	7643324	22.9%	99.2%	98 bp	110 bp	8403	0.0%	99.5%	6952728	5X
Sample_1016_R2	75.2%	41%	151 bp	36%	33431083		33.2%	73.9%	33%	98 bp	27%	33360795
Sample_1021	78.5%	39%	151 bp	27%	42546621	42546621	30.3%	76.8%	31%	103 bp	18%	42461889	42461889	10475212	24.7%	99.2%	109 bp	123 bp	7806	0.0%	99.5%	7660322	7X
Sample_1021_R2	77.9%	39%	151 bp	45%	42546621		30.1%	76.6%	32%	103 bp	18%	42461889
Sample_1022	78.1%	44%	151 bp	36%	32785101	32785101	36.8%	76.6%	32%	93 bp	18%	32703048	32703048	7561325	23.1%	99.2%	90 bp	107 bp	6922	0.0%	99.5%	6928757	5X
Sample_1022_R2	77.2%	43%	151 bp	36%	32785101		36.5%	76.0%	33%	93 bp	27%	32703048
Sample_1028	78.6%	40%	151 bp	27%	42118209	42118209	31.8%	77.0%	32%	101 bp	18%	42031705	42031705	10224338	24.3%	99.1%	105 bp	122 bp	6630	0.0%	99.3%	7726313	7X
Sample_1028_R2	77.5%	40%	151 bp	36%	42118209		31.2%	76.2%	33%	101 bp	18%	42031705
Sample_1029	78.0%	43%	151 bp	27%	39839983	39839983	34.8%	76.5%	32%	96 bp	18%	39744810	39744810	9711333	24.4%	98.9%	95 bp	115 bp	7692	0.0%	99.3%	7579401	6X
Sample_1029_R2	77.0%	42%	151 bp	36%	39839983		34.3%	75.9%	33%	97 bp	18%	39744810
Sample_1034	80.5%	39%	151 bp	45%	49997653	49997653	26.1%	79.3%	31%	109 bp	18%	49901724	49901724	11712325	23.5%	99.2%	127 bp	134 bp	6158	0.0%	99.4%	7933065	8X
Sample_1034_R2	79.3%	38%	151 bp	45%	49997653		25.7%	78.3%	32%	109 bp	36%	49901724
Sample_1037	77.9%	41%	151 bp	27%	32728472	32728472	31.4%	76.2%	32%	101 bp	18%	32656613	32656613	7551048	23.1%	99.2%	109 bp	120 bp	8194	0.0%	99.5%	7009858	6X
Sample_1037_R2	77.3%	40%	151 bp	36%	32728472		30.9%	76.0%	33%	102 bp	27%	32656613
Sample_1038	78.5%	41%	151 bp	27%	38430316	38430316	33.0%	77.0%	32%	99 bp	18%	38333437	38333437	9236616	24.1%	99.1%	104 bp	120 bp	5872	0.0%	99.4%	7409637	6X
Sample_1038_R2	77.5%	42%	151 bp	36%	38430316		32.4%	76.4%	33%	99 bp	27%	38333437
Sample_1040	78.5%	41%	151 bp	27%	38476471	38476471	29.4%	77.0%	32%	104 bp	27%	38405171	38405171	9270235	24.1%	99.2%	122 bp	121 bp	13261	0.0%	99.4%	7249669	7X
Sample_1040_R2	77.8%	39%	151 bp	36%	38476471		29.0%	76.6%	33%	104 bp	36%	38405171
Sample_1041	79.7%	39%	151 bp	27%	46312207	46312207	28.5%	78.1%	31%	106 bp	18%	46225456	46225456	10952498	23.7%	99.2%	125 bp	128 bp	7840	0.0%	99.4%	7743854	7X
Sample_1041_R2	78.7%	40%	151 bp	36%	46312207		28.0%	77.5%	32%	106 bp	27%	46225456
Sample_1046	78.4%	43%	151 bp	27%	37264534	37264534	34.3%	77.0%	32%	97 bp	27%	37177372	37177372	8305553	22.3%	99.2%	99 bp	112 bp	8753	0.0%	99.4%	7055358	6X
Sample_1046_R2	77.7%	40%	151 bp	45%	37264534		33.7%	76.6%	33%	97 bp	36%	37177372
Sample_1051	79.5%	40%	151 bp	27%	45200077	45200077	30.9%	77.9%	32%	102 bp	18%	45108644	45108644	10884268	24.1%	99.2%	114 bp	123 bp	9069	0.0%	99.4%	7630596	7X
Sample_1051_R2	78.4%	40%	151 bp	36%	45200077		30.3%	77.1%	33%	102 bp	27%	45108644
Sample_1063	78.6%	43%	151 bp	27%	41269339	41269339	34.4%	77.3%	32%	97 bp	27%	41186042	41186042	10440901	25.4%	99.2%	100 bp	112 bp	13935	0.0%	99.4%	7585274	7X
Sample_1063_R2	77.7%	41%	151 bp	45%	41269339		33.8%	76.8%	33%	97 bp	36%	41186042
Sample_1067	78.6%	40%	151 bp	36%	38262178	38262178	28.2%	77.0%	31%	106 bp	27%	38177714	38177714	9672404	25.3%	99.2%	126 bp	134 bp	3140	0.0%	99.6%	7726648	7X
Sample_1067_R2	77.2%	39%	151 bp	36%	38262178		27.7%	76.0%	33%	106 bp	36%	38177714
Sample_1077	78.8%	39%	151 bp	36%	40554132	40554132	26.8%	77.3%	31%	107 bp	18%	40468455	40468455	9653585	23.9%	99.2%	126 bp	134 bp	4651	0.0%	99.4%	7560318	7X
Sample_1077_R2	77.4%	39%	151 bp	45%	40554132		26.7%	76.2%	32%	108 bp	36%	40468455

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Sort	Group	Column	Description	ID	Scale
\|\|	FastQC (raw)	% Dups	% Duplicate Reads	`percent_duplicates`	None
\|\|	FastQC (raw)	% GC	Average % GC Content	`percent_gc`	None
\|\|	FastQC (raw)	Length	Average Sequence Length (bp)	`avg_sequence_length`	None
\|\|	FastQC (raw)	% Failed	Percentage of modules failed in FastQC report (includes those not plotted here)	`percent_fails`	None
\|\|	FastQC (raw)	Seqs	Total Sequences (units)	`total_sequences`	read_count
\|\|	FastQC (raw)	# Reads	Total Reads (units)	`sequences_in_total`	read_count
\|\|	Cutadapt	% BP Trimmed	% Total Base Pairs trimmed	`percent_trimmed`	None
\|\|	FastQC (trimmed)	% Dups	% Duplicate Reads	`percent_duplicates`	None
\|\|	FastQC (trimmed)	% GC	Average % GC Content	`percent_gc`	None
\|\|	FastQC (trimmed)	Length	Average Sequence Length (bp)	`avg_sequence_length`	None
\|\|	FastQC (trimmed)	% Failed	Percentage of modules failed in FastQC report (includes those not plotted here)	`percent_fails`	None
\|\|	FastQC (trimmed)	Seqs	Total Sequences (units)	`total_sequences`	read_count
\|\|	FastQC (trimmed)	# Reads	Total Reads (units)	`sequences_in_total`	read_count
\|\|	Bismark	Aligned	Total Aligned Sequences (units)	`aligned_reads`	read_count
\|\|	Bismark	% Uniquely Aligned	Percent Aligned Sequences	`percent_aligned`	None
\|\|	Bismark	% BS Conversion (Non-CpG)	Percent Bisulfite Conversion Rate	`percent_bs_conversion`	None
\|\|	Insert Size	Insert Size	Median Insert Size, all read orientations (bp)	`summed_median`	None
\|\|	Insert Size	Mean Insert Size	Mean Insert Size, all read orientations (bp)	`summed_mean`	None
\|\|	Bismark (spike-in)	Aligned	Total Aligned Sequences (units)	`aligned_reads`	read_count
\|\|	Bismark (spike-in)	% Uniquely Aligned	Percent Aligned Sequences	`percent_aligned`	None
\|\|	Bismark (spike-in)	% BS Conversion (Spike-in)	Percent Bisulfite Conversion Rate	`percent_bs_conversion`	None
\|\|	MethylDackel	Uniq. CpG	Total Number Of Unique C's In CpG Context	`unique_cpg`	None
\|\|	MethylDackel	Avg. CpG Coverage	Average CpG Coverage	`average_cpg_coverage`	None

Cutadapt

Cutadapt is a tool to find and remove adapter sequences, primers, poly-Atails and other types of unwanted sequence from your high-throughput sequencing reads.

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.

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Filtered Reads

This plot shows the number of reads (single-end) or read pairs (paired-end) passing filters.

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FastQC (trimmed)

FastQC is a quality control tool for high throughput sequence data, written by Simon Andrews at the Babraham Institute in Cambridge.

This section of the report shows FastQC results after adapter and quality trimming.

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.

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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.

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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.

Click a sample row to see a line plot for that dataset.

Rollover for sample name

Position: -

%T: -

%C: -

%A: -

%G: -

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.

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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.

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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.

No samples found with any adapter contamination > 0.1%

Bismark

Bismark is a tool to map bisulfite converted sequence reads and determine cytosine methylation states.

Alignment Rates

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Strand Alignment

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M-Bias

This plot shows the average percentage methylation and coverage across reads. See the bismark user guide for more information on how these numbers are generated.

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Insert Size

This section of the report has a plot, generated by Picard, that shows the number of reads at a given insert size. Reads with different orientation are summed.

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CpG Coverage

CpG Coverage shows the distribution of the detected CpG coverage in each library.

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Genomic Region Coverage

This section presents the percentage of a certain type of genomic functional region that is covered in each RRBS library, categorized by coverage. The genomic functional region of interest includes CpG islands, gene promoters, and gene bodies. These regions are derived from public annotation files of the reference assemblies.

Promoter

Promoter Coverage shows the distribution of the number of individual CpG methylation measurements in promoters genome wide.

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Gene Body

Gene Body Coverage shows the distribution of the number of individual CpG methylation measurements in gene bodies genome wide.

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Promoter Profile

Accumulated view of the distribution of methylation percentages around annotated genes. Distances on the x-axis are relative to the nearest TSS. Promoter methylation values were generated using deepTools.

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Bismark (spike-in)

Bismark is a tool to map bisulfite converted sequence reads and determine cytosine methylation states.

This section of the report shows Bismark results for spike-in reads.

Strand Alignment

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Samtools (spike-in)

Samtools is a suite of programs for interacting with high-throughput sequencing data.

This section of the report shows Samtools results for spike-in reads.

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.

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MethylDackel (spike-in)

MethylDackel is a per-base methylation metrics extraction tool for BAM file with BS-seq alignments.

This section of the report shows the average methylation ratio per contig for spike-in reads.

Correlation Scatter Plot

This plot shows the correlation trend between observed and expected values.

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Correlation Table

This table shows the Pearson's Correlation Coefficient for each sample.

Showing ¹⁸/₁₈ rows.

Sample Name	Correlation Coefficient
Sample_1003	0.968
Sample_1008	0.981
Sample_1009	0.976
Sample_1016	0.974
Sample_1021	0.975
Sample_1022	0.977
Sample_1028	0.977
Sample_1029	0.973
Sample_1034	0.980
Sample_1037	0.984
Sample_1038	0.980
Sample_1040	0.975
Sample_1041	0.974
Sample_1046	0.973
Sample_1051	0.978
Sample_1063	0.975
Sample_1067	0.980
Sample_1077	0.975

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Sort	Visible	Group	Column	Description	ID	Scale
\|\|			Correlation Coefficient	Correlation Coefficient	`coefficient`	None

Differential Methylation

This section of the report presents identified differential methylation cytosines (DMCs) and regions (DMRs).

Workflow Description

A brief summary of the data workflow:

Data filtering: cytosines with read depth ≥ 5 in ≥ 2 samples in a group are kept, otherwise removed for that group before downstream analyses.
Detecting DMCs and DMRs: DMCs are detected with dss and DMRs are detected with dss. Significant DMCs and DMRs have FDR ≤ 0.05 (if P values are provided by statistical method) and absolute methylation difference ≥ 0.1.
Annotation of DMRs: each DMR is annotated by overlapping its genomic region with other functional regions, including genes, exons, introns, promoters, and CpG islands. The functional regions are derived from the ucsc database. The minimum size for an overlap is 1 bp.
Functional enrichment analyses of the overlapped genes: the overlapped genes identified in previous step are input into g:Profiler for functional enrichment analysis.
Plots: some plots such as heatmaps are generated to visualize the results.
Downloads: all result files are available for downloading at the Comparison Download section.

Sample Information

Here is a table of samples used for DMC/DMR analyses. The column group is used to group samples.

Showing ¹⁸/₁₈ rows and ²/₂ columns.

Sample Name	Comparision Group	Sample ID
line0	CON	Sample_1003
line1	CON	Sample_1016
line10	PNS	Sample_1009
line11	PNS	Sample_1021
line12	PNS	Sample_1028
line13	PNS	Sample_1029
line14	PNS	Sample_1038
line15	PNS	Sample_1051
line16	PNS	Sample_1063
line17	PNS	Sample_1067
line2	CON	Sample_1022
line3	CON	Sample_1034
line4	CON	Sample_1037
line5	CON	Sample_1040
line6	CON	Sample_1041
line7	CON	Sample_1046
line8	CON	Sample_1077
line9	PNS	Sample_1008

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Sort	Visible	Group	Column	Description	ID	Scale
\|\|			Comparision Group	The group name.	`group`	None
\|\|			Sample ID	The internal ID of the sample.	`sampleId`	None

Distribution of methylation values per sample

This figure displays the distribution of methylation values of all cytosines (or a sampled subset for the sake of performance) for each sample using violin plot.

Distribution of methylation values per group

This figure displays the distribution of methylation values averaged for each group using density plot. The number of cytosines (n) used is at the top.

Summary Table

The table below shows the number of DMCs identified for each comparison.

Showing ²/₂ rows and ⁹/₉ columns.

Sample Name	Type	Comparision Group	DMR Method	Input	StatOutput	Padj<=0.05	Padj<=0.05 & methDiff<=-0.1	Padj<=0.05 & methDiff>=0.1	File Name
line0_dmc	dmc	CON vs PNS	dss	973706	1082	1072	669	403	dms_dss.CON_vs_PNS.tsv.gz
line0_dmr	dmr	CON vs PNS	dss	973706	90	NA	50	40	dmr_dss.CON_vs_PNS.tsv.gz

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Sort	Column	Description	ID	Scale
\|\|	Type	Type of differential methylation (region or cytosine)	`type`	None
\|\|	Comparision Group	The groups under comparison, separated by comma.	`cmp`	None
\|\|	DMR Method	The statistical method used.	`method`	None
\|\|	Input	The total number of cytosines fed into the statistical method.	`input`	None
\|\|	StatOutput	The total number of cytosine sites output by the statistical method; in most cases, this number equals to the number of significant sites; if not, the statistical method may filter out some sites such as non-significant ones.	`statOutput`	None
\|\|	Padj<=0.05	The number of significant sites.	`padj<=0.05`	None
\|\|	Padj<=0.05 & methDiff<=-0.1	The number of significant hypomethylated sites (former group is less methylated than latter). This is not applicable for comparisons involved more than 2 groups.	`padj<=0.05 & methDiff<=-0.1`	None
\|\|	Padj<=0.05 & methDiff>=0.1	The number of significant hypermethylated sites. For comparisons with > 2 groups, methDiff is computed as the difference between the maximum and minimum methylation values of all compared groups.	`padj<=0.05 & methDiff>=0.1`	None
\|\|	File Name	The source file of the summary	`file`	None

DMC Heatmap

Here are tab views of DMC comparison heatmaps.

DMR Heatmap

Here are tab views of DMR comparison heatmaps.

Comparison Download

Showing ¹/₁ rows and ²/₂ columns.

Sample Name	DMCs	DMRs
CON_vs_PNS	Download	Download

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Sort	Visible	Group	Column	Description	ID	Scale
\|\|			DMCs	Identified DMCs.	`DMCs`	None
\|\|			DMRs	Identified DMRs.	`DMRs`	None

Instructions to download all files

Click here to obtain the code to download all the files. We assume it is in your Download folder.

Find and open Terminal (Mac/Linux) or Windows Powershell (Windows).

Type cd ~/Download and Enter. If the code is downloaded to a different location, change ~/Download to the path to that folder.

Type bash download_links.ps1 (Mac/Linux) or Powershell.exe -ExecutionPolicy Bypass -File .\download_links.ps1 (Windows) and Enter.

Download

This section contains links to download trimmed fastq files, data and/or images generated by various bioinformatics tools. To download individual files, click on the corresponding links. The links will expire in 90 days.

Showing ¹⁸/₁₈ rows and ⁶/₆ columns.

Sample Name	Trimmed Fastq R1	Trimmed Fastq R2	Alignment	DNA Methylation Calling	Browser Track	Methylation Distribution
Sample_1003	Download	Download	Download	Download	Download	Download
Sample_1008	Download	Download	Download	Download	Download	Download
Sample_1009	Download	Download	Download	Download	Download	Download
Sample_1016	Download	Download	Download	Download	Download	Download
Sample_1021	Download	Download	Download	Download	Download	Download
Sample_1022	Download	Download	Download	Download	Download	Download
Sample_1028	Download	Download	Download	Download	Download	Download
Sample_1029	Download	Download	Download	Download	Download	Download
Sample_1034	Download	Download	Download	Download	Download	Download
Sample_1037	Download	Download	Download	Download	Download	Download
Sample_1038	Download	Download	Download	Download	Download	Download
Sample_1040	Download	Download	Download	Download	Download	Download
Sample_1041	Download	Download	Download	Download	Download	Download
Sample_1046	Download	Download	Download	Download	Download	Download
Sample_1051	Download	Download	Download	Download	Download	Download
Sample_1063	Download	Download	Download	Download	Download	Download
Sample_1067	Download	Download	Download	Download	Download	Download
Sample_1077	Download	Download	Download	Download	Download	Download

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Sort	Column	Description	ID	Scale
\|\|	Trimmed Fastq R1	Trimmed fastq files.	`tr1`	None
\|\|	Trimmed Fastq R2	Trimmed fastq files.	`tr2`	None
\|\|	Alignment	Aligned sequence reads of individual samples in bam format.	`bam`	None
\|\|	DNA Methylation Calling	Methylation calling results in bedgraph format.	`bedgraph`	None
\|\|	Browser Track	Genome tracks for individual samples that includes methylation ratio and coverage in BigBed format.	`bigbed`	None
\|\|	Methylation Distribution	The overall methylation level in three genomic features of individual sample.	`violinplot`	None

Instructions to download all files

Click here to obtain the code to download all the files. We assume it is in your Download folder.

Find and open Terminal (Mac/Linux) or Windows Powershell (Windows).

Type cd ~/Download and Enter. If the code is downloaded to a different location, change ~/Download to the path to that folder.

Type bash download_links.ps1 (Mac/Linux) or Powershell.exe -ExecutionPolicy Bypass -File .\download_links.ps1 (Windows) and Enter.

Summary

Library Type: Methyl-MiniSeq
Directionality: non-directional
Genome: bosTau9
Trimming: 5'R1: 0bp / 5'R2: 0bp / Illumina adapter / RRBS: On

Software Versions

Versions are collected at run time from the software output.

fastqc: 0.11.9
trimgalore: 0.6.4_dev
bismark: 0.22.3
picard: 2.23.8
samtools: 1.9
methyldackel: 0.5.0
ucsc-bedgraphtobigwig: 377
deeptools: 3.5.1
DSS: 2.46.0 ( R version: 4.2.2 )
nextflow: 22.10.0
pipeline: null

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Methyl-MiniSeq Analysis Report for in0001

MultiQC Toolbox

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Apply Rename Samples

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Export Plots

Choose Plots

Save Settings

Load Settings

About MultiQC

Methyl-MiniSeq Analysis Report for in0001

General Statistics

General Statistics: Columns

Cutadapt

Trimmed Sequence Lengths Help

Filtered Reads

FastQC (trimmed)

Sequence Quality Histograms Help

Per Sequence Quality Scores Help

Per Base Sequence Content Help

Rollover for sample name

Per Sequence GC Content Help

Overrepresented sequences Help

Adapter Content Help

Bismark

Alignment Rates

Strand Alignment

M-Bias

Insert Size

CpG Coverage

Genomic Region Coverage

Promoter

Gene Body

Promoter Profile

Bismark (spike-in)

Strand Alignment

Samtools (spike-in)

Mapped reads per contig

MethylDackel (spike-in)

Correlation Scatter Plot

Correlation Table

Table Iqpf: Columns

Differential Methylation

Workflow Description

Sample Information

Table Oigw: Columns

Distribution of methylation values per sample

Distribution of methylation values per group

Summary Table

Table Pedt: Columns

DMC Heatmap

DMR Heatmap

Comparison Download

Table Twus: Columns

Instructions to download all files

Download

Table Mqlj: Columns

Instructions to download all files

Summary

Software Versions

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v1.9

Highlight Samples

Rename Samples

Show / Hide Samples

Trimmed Sequence Lengths

Sequence Quality Histograms

Per Sequence Quality Scores

Per Base Sequence Content

Per Sequence GC Content

Overrepresented sequences

Adapter Content