You are an expert in bioinformatics, sequencing technologies, genomics data analysis, and adjacent fields.

You are given findings from a MultiQC report, generated by a bioinformatics workflow.
MultiQC supports various bioinformatics tools that output QC metrics, and aggregates those metrics
into a single report. It outputs a "General Statistics" table with key metrics for each sample across
all tools. That table is followed by more detailed sections from specific tools, that can include tables,
as well as plots of different types (bar plot, line plot, scatter plot, heatmap, etc.)

You are given data from such a report. Your task is to analyse the data, and
give 1-2 bullet points of a very short and concise overall summary for the results.
Don't waste words: mention only the important QC issues. If there are no issues, just say so.
Just print one or two bullet points, nothing else.
Please do not add any extra headers to the response.

Use markdown to format your reponse for readability. Use directives with pre-defined classes
.text-green, .text-red, and .text-yellow to highlight severity, e.g. :span[39.2%]{.text-red}.
Highlight any mentioned sample names or sample named prefixes or suffixes with a sample directive,
and make sure to use the same color classes for severity, e.g. :sample[A1001.2003]{.text-yellow}
or :sample[A1001]{.text-yellow}. Do not put multiple sample names inside one directive.

You must use only multiples of 4 spaces to indent nested lists.



Two examples of short summaries:

- :span[11/13 samples]{.text-green} show consistent metrics within expected ranges.
- :sample[A1001.2003]{.text-red} and :sample[A1001.2004]{.text-red} exhibit extremely high percentage of :span[duplicates]{.text-red} (:span[65.54%]{.text-red} and :span[83.14%]{.text-red}, respectively).

- All samples show good quality metrics with :span[75.7-77.0%]{.text-green} CpG methylation and :span[76.3-86.0%]{.text-green} alignment rates
- :sample[2wk]{.text-yellow} samples show slightly higher duplication (:span[11-15%]{.text-yellow}) compared to :sample[1wk]{.text-green} samples (:span[6-9%]{.text-green})'



----------------------

Tools used in the report:

1. FastQC
Description: <p>Quality control tool for high throughput sequencing data.</p>
Links: http://www.bioinformatics.babraham.ac.uk/projects/fastqc/

----------------------

----------------------

Tool: FastQC
Section: Sequence Counts
Section description: Sequence counts for each sample. Duplicate read counts are an estimate only.
Section help text: 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
<a href="https://www.bioinformatics.babraham.ac.uk/projects/fastqc/Help/3%20Analysis%20Modules/8%20Duplicate%20Sequences.html">FastQC documentation</a>.
A small part has been copied here for convenience:
<em>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.</em>
<em>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.</em>
Title: FastQC: Sequence Counts

Plot type: bar plot
Values: Number of reads

|Sample|Unique Reads|Duplicate Reads|
|---|---|---|
|20190507.A-S6_diff_R1-subsample|99,291|709|
|20190507.A-S5_diff_R1-subsample|97,287|2,713|
|20190507.A-S4_diff_R1-subsample|99,382|618|
|20190507.A-S3_undiff_R1-subsample|99,454|546|
|20190507.A-S2_undiff_R1-subsample|98,624|1,376|
|20190507.A-S1a_undiff_R1-subsample|99,383|617|

----------------------

Tool: FastQC
Section: Sequence Quality Histograms
Section description: The mean quality value across each base position in the read.
Section help text: 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 <a href="http://www.bioinformatics.babraham.ac.uk/projects/fastqc/Help/3%20Analysis%20Modules/2%20Per%20Base%20Sequence%20Quality.html">FastQC help</a>:
<em>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.</em>
Title: FastQC: Mean Quality Scores

Plot type: x/y line
X axis: Position (bp)
Y axis: Phred Score

Samples: 20190507.A-S1a_undiff_R1-subsample, 20190507.A-S2_undiff_R1-subsample, 20190507.A-S3_undiff_R1-subsample, 20190507.A-S4_diff_R1-subsample, 20190507.A-S5_diff_R1-subsample, 20190507.A-S6_diff_R1-subsample

X values are in  bp

20190507.A-S1a_undiff_R1-subsample 1: 30, 2: 31, 3: 31, 4: 31, 5: 31, 6: 34, 7: 34, 8: 34, 9: 34, 10: 34, 15: 34, 20: 34, 25: 34, 30: 34, 35: 34, 40: 34, 45: 34, 50: 34, 55: 33, 60: 33, 65: 33, 70: 33, 75: 33, 80: 33, 85: 33, 90: 33, 95: 33, 100: 33, 105: 32, 110: 32, 115: 32, 120: 31, 125: 31, 130: 31, 135: 31, 140: 30, 145: 30, 150: 27

20190507.A-S2_undiff_R1-subsample 1: 30, 2: 31, 3: 31, 4: 31, 5: 31, 6: 34, 7: 34, 8: 34, 9: 34, 10: 34, 15: 34, 20: 34, 25: 34, 30: 34, 35: 34, 40: 34, 45: 34, 50: 33, 55: 32, 60: 32, 65: 31, 70: 31, 75: 31, 80: 31, 85: 31, 90: 30, 95: 31, 100: 30, 105: 29, 110: 28, 115: 27, 120: 26, 125: 25, 130: 24, 135: 24, 140: 24, 145: 24, 150: 25

20190507.A-S3_undiff_R1-subsample 1: 30, 2: 31, 3: 31, 4: 31, 5: 31, 6: 34, 7: 34, 8: 34, 9: 34, 10: 34, 15: 34, 20: 34, 25: 34, 30: 34, 35: 34, 40: 34, 45: 34, 50: 34, 55: 33, 60: 33, 65: 33, 70: 33, 75: 33, 80: 33, 85: 33, 90: 32, 95: 33, 100: 32, 105: 32, 110: 32, 115: 31, 120: 31, 125: 30, 130: 30, 135: 30, 140: 29, 145: 29, 150: 27

20190507.A-S4_diff_R1-subsample 1: 30, 2: 31, 3: 31, 4: 31, 5: 31, 6: 34, 7: 34, 8: 34, 9: 34, 10: 34, 15: 34, 20: 34, 25: 34, 30: 34, 35: 34, 40: 34, 45: 34, 50: 34, 55: 33, 60: 33, 65: 33, 70: 33, 75: 33, 80: 33, 85: 33, 90: 33, 95: 33, 100: 33, 105: 32, 110: 32, 115: 32, 120: 31, 125: 31, 130: 31, 135: 31, 140: 31, 145: 31, 150: 28

20190507.A-S5_diff_R1-subsample 1: 30, 2: 31, 3: 31, 4: 31, 5: 31, 6: 34, 7: 34, 8: 34, 9: 34, 10: 34, 15: 34, 20: 34, 25: 34, 30: 34, 35: 34, 40: 34, 45: 34, 50: 33, 55: 33, 60: 33, 65: 33, 70: 32, 75: 32, 80: 32, 85: 31, 90: 31, 95: 32, 100: 31, 105: 31, 110: 31, 115: 30, 120: 30, 125: 29, 130: 29, 135: 29, 140: 29, 145: 29, 150: 28

20190507.A-S6_diff_R1-subsample 1: 30, 2: 31, 3: 31, 4: 31, 5: 31, 6: 34, 7: 34, 8: 34, 9: 34, 10: 34, 15: 34, 20: 34, 25: 34, 30: 34, 35: 34, 40: 34, 45: 34, 50: 34, 55: 33, 60: 33, 65: 33, 70: 33, 75: 33, 80: 33, 85: 32, 90: 32, 95: 32, 100: 32, 105: 32, 110: 31, 115: 31, 120: 30, 125: 29, 130: 29, 135: 29, 140: 29, 145: 29, 150: 27

----------------------

Tool: FastQC
Section: Per Sequence Quality Scores
Section description: The number of reads with average quality scores. Shows if a subset of reads has poor quality.
Section help text: From the <a href="http://www.bioinformatics.babraham.ac.uk/projects/fastqc/Help/3%20Analysis%20Modules/3%20Per%20Sequence%20Quality%20Scores.html">FastQC help</a>:
<em>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.</em>
Title: FastQC: Per Sequence Quality Scores

Plot type: x/y line
X axis: Mean Sequence Quality (Phred Score)
Y axis: Count

Samples: 20190507.A-S1a_undiff_R1-subsample, 20190507.A-S2_undiff_R1-subsample, 20190507.A-S3_undiff_R1-subsample, 20190507.A-S4_diff_R1-subsample, 20190507.A-S5_diff_R1-subsample, 20190507.A-S6_diff_R1-subsample

20190507.A-S1a_undiff_R1-subsample 15: 1, 16: 1, 17: 0, 18: 8, 19: 12, 20: 76, 21: 200, 22: 306, 23: 433, 24: 590, 25: 968, 26: 1,370, 27: 1,902, 28: 2,539, 29: 3,417, 30: 4,482, 31: 6,284, 32: 9,007, 33: 14,965, 34: 30,249, 35: 23,190

20190507.A-S2_undiff_R1-subsample 17: 1, 18: 8, 19: 53, 20: 208, 21: 442, 22: 891, 23: 1,141, 24: 1,616, 25: 2,295, 26: 3,147, 27: 4,339, 28: 5,760, 29: 8,777, 30: 16,837, 31: 21,608, 32: 16,068, 33: 8,554, 34: 5,694, 35: 2,561

20190507.A-S3_undiff_R1-subsample 16: 1, 17: 3, 18: 8, 19: 36, 20: 105, 21: 236, 22: 379, 23: 570, 24: 835, 25: 1,156, 26: 1,632, 27: 2,162, 28: 2,975, 29: 3,910, 30: 5,546, 31: 8,178, 32: 11,201, 33: 15,233, 34: 26,136, 35: 19,698

20190507.A-S4_diff_R1-subsample 15: 1, 16: 0, 17: 7, 18: 2, 19: 22, 20: 101, 21: 205, 22: 319, 23: 421, 24: 565, 25: 899, 26: 1,250, 27: 1,822, 28: 2,461, 29: 3,217, 30: 4,145, 31: 5,471, 32: 8,133, 33: 13,594, 34: 30,341, 35: 27,024

20190507.A-S5_diff_R1-subsample 17: 1, 18: 5, 19: 26, 20: 171, 21: 477, 22: 731, 23: 870, 24: 1,047, 25: 1,498, 26: 2,041, 27: 2,781, 28: 3,458, 29: 4,578, 30: 6,052, 31: 8,765, 32: 13,301, 33: 19,511, 34: 24,755, 35: 9,932

20190507.A-S6_diff_R1-subsample 17: 1, 18: 2, 19: 18, 20: 99, 21: 295, 22: 426, 23: 552, 24: 811, 25: 1,291, 26: 1,737, 27: 2,366, 28: 3,325, 29: 4,291, 30: 6,111, 31: 9,195, 32: 13,066, 33: 16,209, 34: 23,789, 35: 16,416

----------------------

Tool: FastQC
Section: Per Base Sequence Content
Section description: The proportion of each base position for which each of the four normal DNA bases has been called.
Section help text: 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.
<strong>To see the data as a line plot, as in the original FastQC graph, click on a sample track.</strong>
From the <a href="http://www.bioinformatics.babraham.ac.uk/projects/fastqc/Help/3%20Analysis%20Modules/4%20Per%20Base%20Sequence%20Content.html">FastQC help</a>:
<em>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.</em>
<em>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.</em>
<em>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.</em>
<div id="fastqc_per_base_sequence_content_plot_div">
            <div class="alert alert-info">
               <span class="glyphicon glyphicon-hand-up"></span>
               Click a sample row to see a line plot for that dataset.
            </div>
            <h5><span class="s_name text-primary"><span class="glyphicon glyphicon-info-sign"></span> Rollover for sample name</span></h5>
            <div class="fastqc_seq_heatmap_key">
                Position: <span id="fastqc_seq_heatmap_key_pos">-</span>
                <div><span id="fastqc_seq_heatmap_key_t"> %T: <span>-</span></span></div>
                <div><span id="fastqc_seq_heatmap_key_c"> %C: <span>-</span></span></div>
                <div><span id="fastqc_seq_heatmap_key_a"> %A: <span>-</span></span></div>
                <div><span id="fastqc_seq_heatmap_key_g"> %G: <span>-</span></span></div>
            </div>
            <div id="fastqc_seq_heatmap_div" class="fastqc-overlay-plot">
                <div id="fastqc_per_base_sequence_content_plot" class="fastqc_per_base_sequence_content_plot hc-plot has-custom-export">
                    <canvas id="fastqc_seq_heatmap" height="100%" width="800px" style="width:100%;"></canvas>
                </div>
            </div>
            <div class="clearfix"></div>
        </div>
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"t": 21.36, "c": 9.55}, "6": {"base": 6.0, "g": 49.47, "a": 21.48, "t": 19.92, "c": 9.12}, "7": {"base": 7.0, "g": 47.87, "a": 20.67, "t": 19.66, "c": 11.81}, "8": {"base": 8.0, "g": 46.15, "a": 21.25, "t": 19.38, "c": 13.22}, "9": {"base": 9.0, "g": 29.94, "a": 14.8, "t": 37.95, "c": 17.32}, "10": {"base": "10-14", "g": 22.58, "a": 27.34, "t": 30.75, "c": 19.33}, "15": {"base": "15-19", "g": 21.42, "a": 29.68, "t": 29.11, "c": 19.78}, "20": {"base": "20-24", "g": 22.45, "a": 28.83, "t": 27.16, "c": 21.56}, "25": {"base": "25-29", "g": 22.22, "a": 28.94, "t": 26.35, "c": 22.5}, "30": {"base": "30-34", "g": 21.6, "a": 28.23, "t": 26.92, "c": 23.26}, "35": {"base": "35-39", "g": 22.0, "a": 28.3, "t": 27.04, "c": 22.66}, "40": {"base": "40-44", "g": 21.13, "a": 27.71, "t": 27.09, "c": 24.06}, "45": {"base": "45-49", "g": 21.97, "a": 27.79, "t": 26.82, "c": 23.43}, "50": {"base": "50-54", "g": 22.7, "a": 27.63, "t": 26.63, "c": 23.04}, "55": {"base": "55-59", "g": 21.95, "a": 27.55, "t": 27.3, "c": 23.19}, "60": {"base": "60-64", "g": 22.91, "a": 27.18, "t": 26.63, "c": 23.27}, "65": {"base": "65-69", "g": 23.82, "a": 26.51, "t": 26.08, "c": 23.58}, "70": {"base": "70-74", "g": 23.76, "a": 26.8, "t": 26.28, "c": 23.17}, "75": {"base": "75-79", "g": 24.75, "a": 26.86, "t": 25.76, "c": 22.63}, "80": {"base": "80-84", "g": 23.86, "a": 30.04, "t": 23.95, "c": 22.15}, "85": {"base": "85-89", "g": 24.08, "a": 30.71, "t": 23.31, "c": 21.89}, "90": {"base": "90-94", "g": 27.21, "a": 27.71, "t": 23.0, "c": 22.08}, "95": {"base": "95-99", "g": 28.19, "a": 27.03, "t": 22.73, "c": 22.05}, "100": {"base": "100-104", "g": 28.09, "a": 27.13, "t": 22.38, "c": 22.4}, "105": {"base": "105-109", "g": 27.99, "a": 27.05, "t": 22.34, "c": 22.62}, "110": {"base": "110-114", "g": 27.82, "a": 26.93, "t": 22.32, "c": 22.93}, "115": {"base": "115-119", "g": 27.83, "a": 26.79, "t": 22.26, "c": 23.12}, "120": {"base": "120-124", "g": 27.99, "a": 26.33, "t": 22.69, "c": 22.99}, "125": {"base": "125-129", "g": 28.04, "a": 26.07, "t": 22.78, "c": 23.11}, "130": {"base": "130-134", "g": 28.12, "a": 25.79, "t": 23.19, "c": 22.9}, "135": {"base": "135-139", "g": 28.23, "a": 25.56, "t": 23.47, "c": 22.75}, "140": {"base": "140-144", "g": 28.48, "a": 25.59, "t": 23.7, "c": 22.23}, "145": {"base": "145-149", "g": 29.3, "a": 25.69, "t": 23.51, "c": 21.51}, "150": {"base": "150-151", "g": 27.62, "a": 26.36, "t": 25.79, "c": 20.23}}, "20190507.A-S4_diff_R1-subsample": {"1": {"base": 1.0, "g": 30.32, "a": 27.0, "t": 22.09, "c": 20.59}, "2": {"base": 2.0, "g": 26.75, "a": 22.48, "t": 32.16, "c": 18.61}, "3": {"base": 3.0, "g": 28.39, "a": 24.65, "t": 30.5, "c": 16.46}, "4": {"base": 4.0, "g": 38.6, "a": 23.01, "t": 26.67, "c": 11.73}, "5": {"base": 5.0, "g": 48.96, "a": 20.63, "t": 21.74, "c": 8.67}, "6": {"base": 6.0, "g": 50.82, "a": 19.92, "t": 20.73, "c": 8.54}, "7": {"base": 7.0, "g": 48.49, "a": 19.74, "t": 20.82, "c": 10.96}, "8": {"base": 8.0, "g": 46.71, "a": 20.21, "t": 19.94, "c": 13.13}, "9": {"base": 9.0, "g": 30.02, "a": 14.35, "t": 38.31, "c": 17.32}, "10": {"base": "10-14", "g": 22.82, "a": 26.13, "t": 30.76, "c": 20.29}, "15": {"base": "15-19", "g": 23.01, "a": 27.33, "t": 28.91, "c": 20.74}, "20": {"base": "20-24", "g": 23.84, "a": 26.64, "t": 26.99, "c": 22.53}, "25": {"base": "25-29", "g": 23.61, "a": 26.64, "t": 26.5, "c": 23.25}, "30": {"base": "30-34", "g": 23.58, "a": 26.27, "t": 26.66, "c": 23.48}, "35": {"base": "35-39", "g": 23.73, "a": 26.33, "t": 26.55, "c": 23.4}, "40": {"base": "40-44", "g": 23.22, "a": 25.91, "t": 26.65, "c": 24.22}, "45": {"base": "45-49", "g": 23.61, "a": 26.08, "t": 26.23, "c": 24.08}, "50": {"base": "50-54", "g": 23.63, "a": 26.41, "t": 26.28, "c": 23.68}, "55": {"base": "55-59", "g": 23.86, "a": 25.94, "t": 26.32, "c": 23.88}, "60": {"base": "60-64", "g": 23.91, "a": 25.68, "t": 26.18, "c": 24.24}, "65": {"base": "65-69", "g": 24.49, "a": 25.15, "t": 25.96, "c": 24.4}, "70": {"base": "70-74", "g": 24.21, "a": 25.28, "t": 25.88, "c": 24.62}, "75": {"base": "75-79", "g": 25.09, "a": 25.21, "t": 25.53, "c": 24.16}, "80": {"base": "80-84", "g": 25.24, "a": 26.48, "t": 24.48, "c": 23.8}, "85": {"base": "85-89", "g": 25.78, "a": 26.68, "t": 23.97, "c": 23.57}, "90": {"base": "90-94", "g": 27.76, "a": 25.64, "t": 23.38, "c": 23.23}, "95": {"base": "95-99", "g": 28.22, "a": 25.94, "t": 22.85, "c": 22.99}, "100": {"base": "100-104", "g": 28.8, "a": 26.35, "t": 22.0, "c": 22.85}, "105": {"base": "105-109", "g": 28.58, "a": 26.69, "t": 21.59, "c": 23.14}, "110": {"base": "110-114", "g": 28.69, "a": 26.82, "t": 21.23, "c": 23.26}, "115": {"base": "115-119", "g": 28.64, "a": 27.17, "t": 20.71, "c": 23.48}, "120": {"base": "120-124", "g": 28.13, "a": 27.47, "t": 20.75, "c": 23.65}, "125": {"base": "125-129", "g": 27.9, "a": 27.68, "t": 20.66, "c": 23.76}, "130": {"base": "130-134", "g": 27.48, "a": 27.57, "t": 20.83, "c": 24.12}, "135": {"base": "135-139", "g": 27.22, "a": 27.22, "t": 21.33, "c": 24.23}, "140": {"base": "140-144", "g": 27.17, "a": 27.23, "t": 21.67, "c": 23.93}, "145": {"base": "145-149", "g": 27.12, "a": 27.06, "t": 22.12, "c": 23.7}, "150": {"base": "150-151", "g": 24.65, "a": 27.62, "t": 24.5, "c": 23.24}}, "20190507.A-S5_diff_R1-subsample": {"1": {"base": 1.0, "g": 30.68, "a": 27.36, "t": 22.16, "c": 19.79}, "2": {"base": 2.0, "g": 28.19, "a": 24.42, "t": 29.09, "c": 18.3}, "3": {"base": 3.0, "g": 28.87, "a": 25.15, "t": 29.0, "c": 16.99}, "4": {"base": 4.0, "g": 37.9, "a": 22.71, "t": 26.77, "c": 12.63}, "5": {"base": 5.0, "g": 46.78, "a": 20.49, "t": 23.0, "c": 9.74}, "6": {"base": 6.0, "g": 48.85, "a": 19.57, "t": 22.24, "c": 9.34}, "7": {"base": 7.0, "g": 49.44, "a": 18.96, "t": 20.38, "c": 11.23}, "8": {"base": 8.0, "g": 51.42, "a": 17.29, "t": 18.38, "c": 12.9}, "9": {"base": 9.0, "g": 37.92, "a": 13.05, "t": 32.67, "c": 16.36}, "10": {"base": "10-14", "g": 32.82, "a": 22.11, "t": 24.86, "c": 20.21}, "15": {"base": "15-19", "g": 28.4, "a": 26.38, "t": 24.66, "c": 20.55}, "20": {"base": "20-24", "g": 30.68, "a": 25.1, "t": 20.96, "c": 23.26}, "25": {"base": "25-29", "g": 30.79, "a": 25.61, "t": 19.53, "c": 24.08}, "30": {"base": "30-34", "g": 28.42, "a": 23.73, "t": 21.2, "c": 26.65}, "35": {"base": "35-39", "g": 28.95, "a": 24.74, "t": 22.08, "c": 24.23}, "40": {"base": "40-44", "g": 26.12, "a": 23.66, "t": 21.35, "c": 28.88}, "45": {"base": "45-49", "g": 28.28, "a": 24.21, "t": 20.89, "c": 26.62}, "50": {"base": "50-54", "g": 28.76, "a": 26.29, "t": 20.67, "c": 24.28}, "55": {"base": "55-59", "g": 26.74, "a": 24.41, "t": 24.06, "c": 24.79}, "60": {"base": "60-64", "g": 29.42, "a": 24.18, "t": 22.29, "c": 24.11}, "65": {"base": "65-69", "g": 31.72, "a": 22.39, "t": 19.58, "c": 26.32}, "70": {"base": "70-74", "g": 28.05, "a": 22.81, "t": 23.2, "c": 25.94}, "75": {"base": "75-79", "g": 30.93, "a": 23.9, "t": 21.42, "c": 23.74}, "80": {"base": "80-84", "g": 26.04, "a": 36.44, "t": 16.15, "c": 21.37}, "85": {"base": "85-89", "g": 24.67, "a": 37.55, "t": 15.97, "c": 21.81}, "90": {"base": "90-94", "g": 34.15, "a": 27.07, "t": 16.53, "c": 22.24}, "95": {"base": "95-99", "g": 34.54, "a": 25.36, "t": 17.25, "c": 22.85}, "100": {"base": "100-104", "g": 34.13, "a": 24.7, "t": 18.21, "c": 22.95}, "105": {"base": "105-109", "g": 34.29, "a": 23.75, "t": 19.11, "c": 22.85}, "110": {"base": "110-114", "g": 35.16, "a": 22.37, "t": 20.3, "c": 22.16}, "115": {"base": "115-119", "g": 36.5, "a": 21.32, "t": 21.13, "c": 21.05}, "120": {"base": "120-124", "g": 38.55, "a": 20.31, "t": 21.49, "c": 19.65}, "125": {"base": "125-129", "g": 41.18, "a": 19.71, "t": 21.02, "c": 18.08}, "130": {"base": "130-134", "g": 44.1, "a": 19.55, "t": 20.26, "c": 16.09}, "135": {"base": "135-139", "g": 47.6, "a": 19.91, "t": 18.78, "c": 13.71}, "140": {"base": "140-144", "g": 52.19, "a": 20.63, "t": 16.17, "c": 11.01}, "145": {"base": "145-149", "g": 57.87, "a": 20.56, "t": 13.23, "c": 8.35}, "150": {"base": "150-151", "g": 61.66, "a": 19.63, "t": 12.41, "c": 6.3}}, "20190507.A-S6_diff_R1-subsample": {"1": {"base": 1.0, "g": 31.46, "a": 27.28, "t": 20.89, "c": 20.37}, "2": {"base": 2.0, "g": 27.01, "a": 22.42, "t": 31.48, "c": 19.09}, "3": {"base": 3.0, "g": 29.22, "a": 24.4, "t": 29.42, "c": 16.96}, "4": {"base": 4.0, "g": 39.14, "a": 22.79, "t": 25.68, "c": 12.39}, "5": {"base": 5.0, "g": 48.13, "a": 20.9, "t": 21.45, "c": 9.52}, "6": {"base": 6.0, "g": 50.9, "a": 20.05, "t": 20.1, "c": 8.95}, "7": {"base": 7.0, "g": 49.17, "a": 19.77, "t": 19.72, "c": 11.35}, "8": {"base": 8.0, "g": 47.92, "a": 19.99, "t": 19.2, "c": 12.89}, "9": {"base": 9.0, "g": 32.29, "a": 14.2, "t": 36.64, "c": 16.87}, "10": {"base": "10-14", "g": 24.9, "a": 25.38, "t": 29.73, "c": 20.0}, "15": {"base": "15-19", "g": 23.88, "a": 27.57, "t": 28.04, "c": 20.51}, "20": {"base": "20-24", "g": 25.01, "a": 26.74, "t": 25.83, "c": 22.42}, "25": {"base": "25-29", "g": 25.02, "a": 26.84, "t": 25.02, "c": 23.12}, "30": {"base": "30-34", "g": 24.46, "a": 26.0, "t": 25.58, "c": 23.96}, "35": {"base": "35-39", "g": 24.64, "a": 26.29, "t": 25.75, "c": 23.32}, "40": {"base": "40-44", "g": 23.94, "a": 25.52, "t": 25.65, "c": 24.89}, "45": {"base": "45-49", "g": 24.76, "a": 25.87, "t": 25.15, "c": 24.22}, "50": {"base": "50-54", "g": 25.44, "a": 25.9, "t": 25.75, "c": 22.91}, "55": {"base": "55-59", "g": 24.84, "a": 25.7, "t": 26.01, "c": 23.44}, "60": {"base": "60-64", "g": 25.86, "a": 25.56, "t": 25.1, "c": 23.48}, "65": {"base": "65-69", "g": 27.0, "a": 24.73, "t": 24.09, "c": 24.18}, "70": {"base": "70-74", "g": 26.92, "a": 24.93, "t": 24.36, "c": 23.79}, "75": {"base": "75-79", "g": 28.3, "a": 25.21, "t": 23.43, "c": 23.06}, "80": {"base": "80-84", "g": 27.16, "a": 29.25, "t": 21.26, "c": 22.33}, "85": {"base": "85-89", "g": 27.29, "a": 29.74, "t": 20.62, "c": 22.35}, "90": {"base": "90-94", "g": 30.89, "a": 26.48, "t": 20.33, "c": 22.29}, "95": {"base": "95-99", "g": 31.04, "a": 26.11, "t": 20.24, "c": 22.61}, "100": {"base": "100-104", "g": 30.74, "a": 26.24, "t": 20.12, "c": 22.89}, "105": {"base": "105-109", "g": 30.24, "a": 25.97, "t": 20.63, "c": 23.16}, "110": {"base": "110-114", "g": 29.94, "a": 25.52, "t": 21.23, "c": 23.31}, "115": {"base": "115-119", "g": 29.91, "a": 25.09, "t": 21.98, "c": 23.02}, "120": {"base": "120-124", "g": 29.9, "a": 24.41, "t": 22.83, "c": 22.86}, "125": {"base": "125-129", "g": 30.21, "a": 24.11, "t": 23.41, "c": 22.27}, "130": {"base": "130-134", "g": 30.76, "a": 23.55, "t": 23.92, "c": 21.77}, "135": {"base": "135-139", "g": 31.28, "a": 23.57, "t": 24.15, "c": 21.0}, "140": {"base": "140-144", "g": 32.42, "a": 23.7, "t": 23.93, "c": 19.94}, "145": {"base": "145-149", "g": 34.46, "a": 23.95, "t": 22.96, "c": 18.62}, "150": {"base": "150-151", "g": 34.18, "a": 24.27, "t": 25.08, "c": 16.47}}}]</script>
        

----------------------

Tool: FastQC
Section: Per Sequence GC Content
Section description: The average GC content of reads. Normal random library typically have a
        roughly normal distribution of GC content.
Section help text: From the <a href="http://www.bioinformatics.babraham.ac.uk/projects/fastqc/Help/3%20Analysis%20Modules/5%20Per%20Sequence%20GC%20Content.html">FastQC help</a>:
<em>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.</em>
<em>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 GC content of the genome the
modal GC content is calculated from the observed data and used to build a
reference distribution.</em>
<em>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.</em>
Title: FastQC: Per Sequence GC Content

Plot type: x/y line
X axis: % GC
Y axis: Percentage

### Percentages

Samples: 20190507.A-S1a_undiff_R1-subsample, 20190507.A-S2_undiff_R1-subsample, 20190507.A-S3_undiff_R1-subsample, 20190507.A-S4_diff_R1-subsample, 20190507.A-S5_diff_R1-subsample, 20190507.A-S6_diff_R1-subsample

Y values are in %

X values are in %

20190507.A-S1a_undiff_R1-subsample 0: 0, 1: 0, 2: 0, 3: 0, 4: 0, 5: 0, 6: 0, 7: 0, 8: 0, 9: 0, 10: 0, 11: 0, 12: 0, 13: 0, 14: 0, 15: 0, 16: 0, 17: 0, 18: 0, 19: 0, 20: 0, 21: 0, 22: 0, 23: 0, 24: 0, 25: 0, 26: 0, 27: 0, 28: 0, 29: 0, 30: 0, 31: 1, 32: 1, 33: 1, 34: 1, 35: 1, 36: 2, 37: 2, 38: 2, 39: 3, 40: 3, 41: 3, 42: 3, 43: 3, 44: 4, 45: 4, 46: 3, 47: 3, 48: 3, 49: 3, 50: 3, 51: 3, 52: 3, 53: 3, 54: 3, 55: 3, 56: 2, 57: 2, 58: 2, 59: 2, 60: 1, 61: 1, 62: 1, 63: 0, 64: 0, 65: 0, 66: 0, 67: 0, 68: 0, 69: 0, 70: 0, 71: 0, 72: 0, 73: 0, 74: 0, 75: 0, 76: 0, 77: 0, 78: 0, 79: 0, 80: 0, 81: 0, 82: 0, 83: 0, 84: 0, 85: 0, 86: 0, 87: 0, 88: 0, 89: 0, 90: 0, 91: 0, 92: 0, 93: 0, 94: 0, 95: 0, 96: 0, 97: 0, 98: 0, 99: 0, 100: 0

20190507.A-S2_undiff_R1-subsample 0: 0, 1: 0, 2: 0, 3: 0, 4: 0, 5: 0, 6: 0, 7: 0, 8: 0, 9: 0, 10: 0, 11: 0, 12: 0, 13: 0, 14: 0, 15: 0, 16: 0, 17: 0, 18: 0, 19: 0, 20: 0, 21: 0, 22: 0, 23: 0, 24: 0, 25: 0, 26: 0, 27: 0, 28: 0, 29: 0, 30: 0, 31: 0, 32: 0, 33: 0, 34: 0, 35: 0, 36: 0, 37: 0, 38: 0, 39: 0, 40: 0, 41: 0, 42: 0, 43: 0, 44: 1, 45: 1, 46: 2, 47: 2, 48: 3, 49: 3, 50: 4, 51: 4, 52: 5, 53: 5, 54: 6, 55: 6, 56: 6, 57: 6, 58: 6, 59: 5, 60: 5, 61: 4, 62: 3, 63: 2, 64: 1, 65: 1, 66: 0, 67: 0, 68: 0, 69: 0, 70: 0, 71: 0, 72: 0, 73: 0, 74: 0, 75: 0, 76: 0, 77: 0, 78: 0, 79: 0, 80: 0, 81: 0, 82: 0, 83: 0, 84: 0, 85: 0, 86: 0, 87: 0, 88: 0, 89: 0, 90: 0, 91: 0, 92: 0, 93: 0, 94: 0, 95: 0, 96: 0, 97: 0, 98: 0, 99: 0, 100: 0

20190507.A-S3_undiff_R1-subsample 0: 0, 1: 0, 2: 0, 3: 0, 4: 0, 5: 0, 6: 0, 7: 0, 8: 0, 9: 0, 10: 0, 11: 0, 12: 0, 13: 0, 14: 0, 15: 0, 16: 0, 17: 0, 18: 0, 19: 0, 20: 0, 21: 0, 22: 0, 23: 0, 24: 0, 25: 0, 26: 0, 27: 0, 28: 0, 29: 0, 30: 1, 31: 1, 32: 1, 33: 1, 34: 1, 35: 2, 36: 2, 37: 2, 38: 2, 39: 2, 40: 3, 41: 3, 42: 3, 43: 3, 44: 3, 45: 3, 46: 3, 47: 3, 48: 3, 49: 3, 50: 3, 51: 3, 52: 3, 53: 3, 54: 3, 55: 3, 56: 3, 57: 2, 58: 2, 59: 2, 60: 1, 61: 1, 62: 1, 63: 1, 64: 0, 65: 0, 66: 0, 67: 0, 68: 0, 69: 0, 70: 0, 71: 0, 72: 0, 73: 0, 74: 0, 75: 0, 76: 0, 77: 0, 78: 0, 79: 0, 80: 0, 81: 0, 82: 0, 83: 0, 84: 0, 85: 0, 86: 0, 87: 0, 88: 0, 89: 0, 90: 0, 91: 0, 92: 0, 93: 0, 94: 0, 95: 0, 96: 0, 97: 0, 98: 0, 99: 0, 100: 0

20190507.A-S4_diff_R1-subsample 0: 0, 1: 0, 2: 0, 3: 0, 4: 0, 5: 0, 6: 0, 7: 0, 8: 0, 9: 0, 10: 0, 11: 0, 12: 0, 13: 0, 14: 0, 15: 0, 16: 0, 17: 0, 18: 0, 19: 0, 20: 0, 21: 0, 22: 0, 23: 0, 24: 0, 25: 0, 26: 0, 27: 0, 28: 0, 29: 0, 30: 0, 31: 0, 32: 0, 33: 1, 34: 1, 35: 1, 36: 1, 37: 1, 38: 2, 39: 2, 40: 2, 41: 2, 42: 3, 43: 3, 44: 3, 45: 3, 46: 3, 47: 4, 48: 4, 49: 4, 50: 4, 51: 4, 52: 4, 53: 4, 54: 3, 55: 3, 56: 3, 57: 3, 58: 2, 59: 2, 60: 2, 61: 1, 62: 1, 63: 1, 64: 1, 65: 0, 66: 0, 67: 0, 68: 0, 69: 0, 70: 0, 71: 0, 72: 0, 73: 0, 74: 0, 75: 0, 76: 0, 77: 0, 78: 0, 79: 0, 80: 0, 81: 0, 82: 0, 83: 0, 84: 0, 85: 0, 86: 0, 87: 0, 88: 0, 89: 0, 90: 0, 91: 0, 92: 0, 93: 0, 94: 0, 95: 0, 96: 0, 97: 0, 98: 0, 99: 0, 100: 0

20190507.A-S5_diff_R1-subsample 0: 0, 1: 0, 2: 0, 3: 0, 4: 0, 5: 0, 6: 0, 7: 0, 8: 0, 9: 0, 10: 0, 11: 0, 12: 0, 13: 0, 14: 0, 15: 0, 16: 0, 17: 0, 18: 0, 19: 0, 20: 0, 21: 0, 22: 0, 23: 0, 24: 0, 25: 0, 26: 0, 27: 0, 28: 0, 29: 0, 30: 0, 31: 0, 32: 0, 33: 0, 34: 0, 35: 0, 36: 0, 37: 0, 38: 0, 39: 0, 40: 0, 41: 0, 42: 0, 43: 0, 44: 0, 45: 1, 46: 2, 47: 2, 48: 3, 49: 4, 50: 5, 51: 6, 52: 7, 53: 8, 54: 8, 55: 7, 56: 6, 57: 5, 58: 4, 59: 4, 60: 3, 61: 3, 62: 2, 63: 2, 64: 1, 65: 1, 66: 0, 67: 0, 68: 0, 69: 0, 70: 0, 71: 0, 72: 0, 73: 0, 74: 0, 75: 0, 76: 0, 77: 0, 78: 0, 79: 0, 80: 0, 81: 0, 82: 0, 83: 0, 84: 0, 85: 0, 86: 0, 87: 0, 88: 0, 89: 0, 90: 0, 91: 0, 92: 0, 93: 0, 94: 0, 95: 0, 96: 0, 97: 0, 98: 0, 99: 0, 100: 0

20190507.A-S6_diff_R1-subsample 0: 0, 1: 0, 2: 0, 3: 0, 4: 0, 5: 0, 6: 0, 7: 0, 8: 0, 9: 0, 10: 0, 11: 0, 12: 0, 13: 0, 14: 0, 15: 0, 16: 0, 17: 0, 18: 0, 19: 0, 20: 0, 21: 0, 22: 0, 23: 0, 24: 0, 25: 0, 26: 0, 27: 0, 28: 0, 29: 0, 30: 0, 31: 0, 32: 0, 33: 0, 34: 0, 35: 0, 36: 1, 37: 1, 38: 1, 39: 1, 40: 2, 41: 2, 42: 2, 43: 2, 44: 3, 45: 3, 46: 3, 47: 4, 48: 4, 49: 4, 50: 4, 51: 4, 52: 5, 53: 5, 54: 4, 55: 4, 56: 4, 57: 3, 58: 3, 59: 2, 60: 2, 61: 2, 62: 1, 63: 1, 64: 1, 65: 0, 66: 0, 67: 0, 68: 0, 69: 0, 70: 0, 71: 0, 72: 0, 73: 0, 74: 0, 75: 0, 76: 0, 77: 0, 78: 0, 79: 0, 80: 0, 81: 0, 82: 0, 83: 0, 84: 0, 85: 0, 86: 0, 87: 0, 88: 0, 89: 0, 90: 0, 91: 0, 92: 0, 93: 0, 94: 0, 95: 0, 96: 0, 97: 0, 98: 0, 99: 0, 100: 0


### Counts

Samples: 20190507.A-S1a_undiff_R1-subsample, 20190507.A-S2_undiff_R1-subsample, 20190507.A-S3_undiff_R1-subsample, 20190507.A-S4_diff_R1-subsample, 20190507.A-S5_diff_R1-subsample, 20190507.A-S6_diff_R1-subsample

X values are in %

20190507.A-S1a_undiff_R1-subsample 0: 1, 1: 1, 2: 2, 3: 2, 4: 1, 5: 2, 6: 1, 7: 2, 8: 4, 9: 2, 10: 1, 11: 2, 12: 6, 13: 6, 14: 5, 15: 9, 16: 8, 17: 12, 18: 16, 19: 19, 20: 35, 21: 54, 22: 84, 23: 151, 24: 220, 25: 306, 26: 408, 27: 520, 28: 638, 29: 781, 30: 949, 31: 1,119, 32: 1,321, 33: 1,479, 34: 1,662, 35: 1,973, 36: 2,337, 37: 2,625, 38: 2,810, 39: 3,017, 40: 3,280, 41: 3,498, 42: 3,745, 43: 3,975, 44: 4,064, 45: 4,038, 46: 3,994, 47: 3,961, 48: 3,916, 49: 3,925, 50: 3,893, 51: 3,756, 52: 3,604, 53: 3,388, 54: 3,187, 55: 3,019, 56: 2,805, 57: 2,583, 58: 2,337, 59: 2,024, 60: 1,748, 61: 1,537, 62: 1,258, 63: 985, 64: 742, 65: 550, 66: 445, 67: 329, 68: 214, 69: 159, 70: 120, 71: 79, 72: 55, 73: 48, 74: 35, 75: 23, 76: 15, 77: 14, 78: 9, 79: 5, 80: 4, 81: 1, 82: 0, 83: 0, 84: 1, 85: 1, 86: 0, 87: 0, 88: 0, 89: 0, 90: 0, 91: 0, 92: 0, 93: 0, 94: 0, 95: 1, 96: 1, 97: 1, 98: 1, 99: 0, 100: 5

20190507.A-S2_undiff_R1-subsample 0: 0, 1: 1, 2: 1, 3: 0, 4: 0, 5: 0, 6: 1, 7: 1, 8: 0, 9: 0, 10: 0, 11: 0, 12: 3, 13: 6, 14: 4, 15: 5, 16: 7, 17: 6, 18: 9, 19: 14, 20: 16, 21: 18, 22: 26, 23: 38, 24: 47, 25: 45, 26: 46, 27: 54, 28: 60, 29: 59, 30: 66, 31: 76, 32: 75, 33: 74, 34: 82, 35: 82, 36: 97, 37: 142, 38: 203, 39: 283, 40: 378, 41: 494, 42: 681, 43: 941, 44: 1,220, 45: 1,634, 46: 2,121, 47: 2,639, 48: 3,217, 49: 3,769, 50: 4,246, 51: 4,747, 52: 5,317, 53: 5,901, 54: 6,412, 55: 6,673, 56: 6,675, 57: 6,602, 58: 6,474, 59: 5,952, 60: 5,219, 61: 4,456, 62: 3,572, 63: 2,718, 64: 1,973, 65: 1,402, 66: 972, 67: 654, 68: 437, 69: 295, 70: 211, 71: 128, 72: 80, 73: 55, 74: 24, 75: 15, 76: 10, 77: 4, 78: 2, 79: 2, 80: 2, 81: 0, 82: 0, 83: 0, 84: 0, 85: 0, 86: 0, 87: 0, 88: 0, 89: 0, 90: 0, 91: 0, 92: 0, 93: 0, 94: 0, 95: 0, 96: 0, 97: 0, 98: 0, 99: 1, 100: 5

20190507.A-S3_undiff_R1-subsample 0: 1, 1: 1, 2: 3, 3: 4, 4: 2, 5: 1, 6: 2, 7: 3, 8: 3, 9: 2, 10: 2, 11: 6, 12: 9, 13: 6, 14: 8, 15: 9, 16: 10, 17: 16, 18: 22, 19: 27, 20: 39, 21: 72, 22: 122, 23: 180, 24: 248, 25: 339, 26: 450, 27: 586, 28: 741, 29: 917, 30: 1,077, 31: 1,223, 32: 1,429, 33: 1,625, 34: 1,800, 35: 2,034, 36: 2,260, 37: 2,473, 38: 2,654, 39: 2,867, 40: 3,107, 41: 3,276, 42: 3,465, 43: 3,576, 44: 3,587, 45: 3,623, 46: 3,665, 47: 3,722, 48: 3,776, 49: 3,754, 50: 3,696, 51: 3,641, 52: 3,587, 53: 3,529, 54: 3,472, 55: 3,374, 56: 3,187, 57: 2,915, 58: 2,629, 59: 2,319, 60: 1,945, 61: 1,621, 62: 1,335, 63: 1,018, 64: 788, 65: 604, 66: 418, 67: 292, 68: 209, 69: 153, 70: 110, 71: 82, 72: 60, 73: 45, 74: 33, 75: 19, 76: 12, 77: 10, 78: 6, 79: 5, 80: 3, 81: 2, 82: 1, 83: 2, 84: 2, 85: 0, 86: 0, 87: 0, 88: 0, 89: 1, 90: 1, 91: 1, 92: 0, 93: 0, 94: 0, 95: 0, 96: 1, 97: 1, 98: 1, 99: 3, 100: 8

20190507.A-S4_diff_R1-subsample 0: 1, 1: 1, 2: 1, 3: 1, 4: 1, 5: 0, 6: 0, 7: 0, 8: 0, 9: 0, 10: 1, 11: 1, 12: 1, 13: 3, 14: 5, 15: 5, 16: 5, 17: 8, 18: 18, 19: 22, 20: 24, 21: 44, 22: 66, 23: 99, 24: 143, 25: 189, 26: 242, 27: 312, 28: 405, 29: 507, 30: 602, 31: 723, 32: 861, 33: 1,011, 34: 1,188, 35: 1,389, 36: 1,649, 37: 1,912, 38: 2,138, 39: 2,392, 40: 2,650, 41: 2,860, 42: 3,085, 43: 3,341, 44: 3,523, 45: 3,722, 46: 3,952, 47: 4,041, 48: 4,110, 49: 4,276, 50: 4,329, 51: 4,334, 52: 4,358, 53: 4,179, 54: 3,970, 55: 3,743, 56: 3,416, 57: 3,129, 58: 2,776, 59: 2,416, 60: 2,211, 61: 1,915, 62: 1,588, 63: 1,364, 64: 1,142, 65: 937, 66: 719, 67: 513, 68: 382, 69: 286, 70: 195, 71: 143, 72: 104, 73: 82, 74: 71, 75: 45, 76: 25, 77: 17, 78: 11, 79: 6, 80: 3, 81: 4, 82: 3, 83: 1, 84: 1, 85: 1, 86: 0, 87: 0, 88: 0, 89: 0, 90: 1, 91: 0, 92: 0, 93: 0, 94: 0, 95: 2, 96: 2, 97: 1, 98: 1, 99: 2, 100: 9

20190507.A-S5_diff_R1-subsample 0: 0, 1: 0, 2: 1, 3: 1, 4: 1, 5: 0, 6: 0, 7: 0, 8: 2, 9: 2, 10: 1, 11: 2, 12: 3, 13: 2, 14: 3, 15: 6, 16: 12, 17: 15, 18: 17, 19: 22, 20: 26, 21: 30, 22: 37, 23: 50, 24: 59, 25: 66, 26: 71, 27: 69, 28: 78, 29: 84, 30: 92, 31: 100, 32: 94, 33: 90, 34: 86, 35: 91, 36: 97, 37: 110, 38: 128, 39: 142, 40: 188, 41: 273, 42: 432, 43: 658, 44: 972, 45: 1,413, 46: 2,023, 47: 2,825, 48: 3,627, 49: 4,393, 50: 5,401, 51: 6,514, 52: 7,518, 53: 8,211, 54: 8,183, 55: 7,396, 56: 6,385, 57: 5,503, 58: 4,842, 59: 4,410, 60: 3,906, 61: 3,404, 62: 2,807, 63: 2,120, 64: 1,568, 65: 1,083, 66: 718, 67: 489, 68: 337, 69: 224, 70: 147, 71: 110, 72: 78, 73: 47, 74: 23, 75: 16, 76: 12, 77: 8, 78: 5, 79: 2, 80: 2, 81: 1, 82: 0, 83: 0, 84: 0, 85: 0, 86: 0, 87: 0, 88: 0, 89: 0, 90: 0, 91: 0, 92: 0, 93: 0, 94: 0, 95: 0, 96: 0, 97: 1, 98: 1, 99: 1, 100: 5

20190507.A-S6_diff_R1-subsample 0: 0, 1: 0, 2: 0, 3: 1, 4: 1, 5: 0, 6: 1, 7: 1, 8: 1, 9: 1, 10: 2, 11: 2, 12: 2, 13: 1, 14: 3, 15: 6, 16: 7, 17: 12, 18: 14, 19: 12, 20: 22, 21: 33, 22: 44, 23: 53, 24: 77, 25: 105, 26: 132, 27: 176, 28: 223, 29: 291, 30: 345, 31: 414, 32: 532, 33: 627, 34: 724, 35: 897, 36: 1,092, 37: 1,287, 38: 1,545, 39: 1,811, 40: 2,091, 41: 2,352, 42: 2,602, 43: 2,919, 44: 3,260, 45: 3,574, 46: 3,885, 47: 4,207, 48: 4,476, 49: 4,707, 50: 4,852, 51: 4,974, 52: 5,111, 53: 5,139, 54: 4,999, 55: 4,697, 56: 4,227, 57: 3,708, 58: 3,242, 59: 2,737, 60: 2,384, 61: 2,078, 62: 1,698, 63: 1,392, 64: 1,094, 65: 821, 66: 633, 67: 470, 68: 326, 69: 230, 70: 158, 71: 125, 72: 92, 73: 63, 74: 52, 75: 34, 76: 21, 77: 14, 78: 12, 79: 8, 80: 2, 81: 2, 82: 2, 83: 0, 84: 1, 85: 1, 86: 0, 87: 0, 88: 0, 89: 0, 90: 0, 91: 0, 92: 0, 93: 0, 94: 0, 95: 0, 96: 1, 97: 1, 98: 0, 99: 1, 100: 3



----------------------

Tool: FastQC
Section: Per Base N Content
Section description: The percentage of base calls at each position for which an <code>N</code> was called.
Section help text: From the <a href="http://www.bioinformatics.babraham.ac.uk/projects/fastqc/Help/3%20Analysis%20Modules/6%20Per%20Base%20N%20Content.html">FastQC help</a>:
<em>If a sequencer is unable to make a base call with sufficient confidence then it will
normally substitute an <code>N</code> rather than a conventional base call. This graph shows the
percentage of base calls at each position for which an <code>N</code> was called.</em>
<em>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.</em>
Title: FastQC: Per Base N Content

Plot type: x/y line
X axis: Position in Read (bp)
Y axis: Percentage N-Count

Samples: 20190507.A-S1a_undiff_R1-subsample, 20190507.A-S2_undiff_R1-subsample, 20190507.A-S3_undiff_R1-subsample, 20190507.A-S4_diff_R1-subsample, 20190507.A-S5_diff_R1-subsample, 20190507.A-S6_diff_R1-subsample

Y values are in %

X values are in  bp

20190507.A-S1a_undiff_R1-subsample 1: 0, 2: 0, 3: 0, 4: 0, 5: 0, 6: 0, 7: 0, 8: 0, 9: 0, 10: 0, 15: 0, 20: 0, 25: 0, 30: 0, 35: 0, 40: 0, 45: 0, 50: 0, 55: 0, 60: 0, 65: 0, 70: 0, 75: 0, 80: 0, 85: 0, 90: 0, 95: 0, 100: 0, 105: 0, 110: 0, 115: 0, 120: 0, 125: 0, 130: 0, 135: 0, 140: 0, 145: 0, 150: 0

20190507.A-S2_undiff_R1-subsample 1: 0, 2: 0, 3: 0, 4: 0, 5: 0, 6: 0, 7: 0, 8: 0, 9: 0, 10: 0, 15: 0, 20: 0, 25: 0, 30: 0, 35: 0, 40: 0, 45: 0, 50: 0, 55: 0, 60: 0, 65: 0, 70: 0, 75: 0, 80: 0, 85: 0, 90: 0, 95: 0, 100: 0, 105: 0, 110: 0, 115: 0, 120: 0, 125: 0, 130: 0, 135: 0, 140: 0, 145: 0, 150: 0

20190507.A-S3_undiff_R1-subsample 1: 0, 2: 0, 3: 0, 4: 0, 5: 0, 6: 0, 7: 0, 8: 0, 9: 0, 10: 0, 15: 0, 20: 0, 25: 0, 30: 0, 35: 0, 40: 0, 45: 0, 50: 0, 55: 0, 60: 0, 65: 0, 70: 0, 75: 0, 80: 0, 85: 0, 90: 0, 95: 0, 100: 0, 105: 0, 110: 0, 115: 0, 120: 0, 125: 0, 130: 0, 135: 0, 140: 0, 145: 0, 150: 0

20190507.A-S4_diff_R1-subsample 1: 0, 2: 0, 3: 0, 4: 0, 5: 0, 6: 0, 7: 0, 8: 0, 9: 0, 10: 0, 15: 0, 20: 0, 25: 0, 30: 0, 35: 0, 40: 0, 45: 0, 50: 0, 55: 0, 60: 0, 65: 0, 70: 0, 75: 0, 80: 0, 85: 0, 90: 0, 95: 0, 100: 0, 105: 0, 110: 0, 115: 0, 120: 0, 125: 0, 130: 0, 135: 0, 140: 0, 145: 0, 150: 0

20190507.A-S5_diff_R1-subsample 1: 0, 2: 0, 3: 0, 4: 0, 5: 0, 6: 0, 7: 0, 8: 0, 9: 0, 10: 0, 15: 0, 20: 0, 25: 0, 30: 0, 35: 0, 40: 0, 45: 0, 50: 0, 55: 0, 60: 0, 65: 0, 70: 0, 75: 0, 80: 0, 85: 0, 90: 0, 95: 0, 100: 0, 105: 0, 110: 0, 115: 0, 120: 0, 125: 0, 130: 0, 135: 0, 140: 0, 145: 0, 150: 0

20190507.A-S6_diff_R1-subsample 1: 0, 2: 0, 3: 0, 4: 0, 5: 0, 6: 0, 7: 0, 8: 0, 9: 0, 10: 0, 15: 0, 20: 0, 25: 0, 30: 0, 35: 0, 40: 0, 45: 0, 50: 0, 55: 0, 60: 0, 65: 0, 70: 0, 75: 0, 80: 0, 85: 0, 90: 0, 95: 0, 100: 0, 105: 0, 110: 0, 115: 0, 120: 0, 125: 0, 130: 0, 135: 0, 140: 0, 145: 0, 150: 0

----------------------

Tool: FastQC
Section: Sequence Length Distribution
<div class="alert alert-info">All samples have sequences of a single length (151bp)</div>

----------------------

Tool: FastQC
Section: Sequence Duplication Levels
Section description: The relative level of duplication found for every sequence.
Section help text: From the <a href="http://www.bioinformatics.babraham.ac.uk/projects/fastqc/Help/3%20Analysis%20Modules/8%20Duplicate%20Sequences.html">FastQC Help</a>:
<em>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 (e.g. 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.</em>
<em>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.</em>
<em>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.</em>
<em>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.</em>
Title: FastQC: Sequence Duplication Levels

Plot type: x/y line
X axis: Sequence Duplication Level
Y axis: % of Library

Samples: 20190507.A-S1a_undiff_R1-subsample, 20190507.A-S2_undiff_R1-subsample, 20190507.A-S3_undiff_R1-subsample, 20190507.A-S4_diff_R1-subsample, 20190507.A-S5_diff_R1-subsample, 20190507.A-S6_diff_R1-subsample

Y values are in %

20190507.A-S1a_undiff_R1-subsample 1: 98, 2: 0, 3: 0, 4: 0, 5: 0, 6: 0, 7: 0, 8: 0, 9: 0, >10: 0, >50: 0, >100: 0, >500: 0, >1k: 0, >5k: 0, >10k+: 0

20190507.A-S2_undiff_R1-subsample 1: 97, 2: 2, 3: 0, 4: 0, 5: 0, 6: 0, 7: 0, 8: 0, 9: 0, >10: 0, >50: 0, >100: 0, >500: 0, >1k: 0, >5k: 0, >10k+: 0

20190507.A-S3_undiff_R1-subsample 1: 98, 2: 0, 3: 0, 4: 0, 5: 0, 6: 0, 7: 0, 8: 0, 9: 0, >10: 0, >50: 0, >100: 0, >500: 0, >1k: 0, >5k: 0, >10k+: 0

20190507.A-S4_diff_R1-subsample 1: 98, 2: 0, 3: 0, 4: 0, 5: 0, 6: 0, 7: 0, 8: 0, 9: 0, >10: 0, >50: 0, >100: 0, >500: 0, >1k: 0, >5k: 0, >10k+: 0

20190507.A-S5_diff_R1-subsample 1: 95, 2: 3, 3: 0, 4: 0, 5: 0, 6: 0, 7: 0, 8: 0, 9: 0, >10: 0, >50: 0, >100: 0, >500: 0, >1k: 0, >5k: 0, >10k+: 0

20190507.A-S6_diff_R1-subsample 1: 98, 2: 1, 3: 0, 4: 0, 5: 0, 6: 0, 7: 0, 8: 0, 9: 0, >10: 0, >50: 0, >100: 0, >500: 0, >1k: 0, >5k: 0, >10k+: 0

----------------------

Tool: FastQC
Section: Overrepresented sequences by sample
Section description: The total amount of overrepresented sequences found in each library.
Section help text: 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 <em>number of sequences</em> categorized as overrepresented.
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 <a href="http://www.bioinformatics.babraham.ac.uk/projects/fastqc/Help/3%20Analysis%20Modules/9%20Overrepresented%20Sequences.html">FastQC Help</a>:
<em>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.</em>
<em>FastQC lists all 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.</em>
<div class="alert alert-info">6 samples had less than 1% of reads made up of overrepresented sequences</div>

----------------------

Tool: FastQC
Section: Top overrepresented sequences
Section description: Top overrepresented sequences across all samples. The table shows 20
most overrepresented sequences across all samples, ranked by the number of samples they occur in.

----------------------

Tool: FastQC
Section: Adapter Content
Section description: The cumulative percentage count of the proportion of your
            library which has seen each of the adapter sequences at each position.
Section help text: 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 <a href="http://www.bioinformatics.babraham.ac.uk/projects/fastqc/Help/3%20Analysis%20Modules/10%20Adapter%20Content.html">FastQC Help</a>:
<em>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.</em>
Title: FastQC: Adapter Content

Plot type: x/y line
X axis: Position (bp)
Y axis: % of Sequences

Samples: 20190507.A-S1a_undiff_R1-subsample - illumina_universal_adapter, 20190507.A-S1a_undiff_R1-subsample - poly-a, 20190507.A-S1a_undiff_R1-subsample - poly-g, 20190507.A-S1a_undiff_R1-subsample - poly-t, 20190507.A-S2_undiff_R1-subsample - illumina_universal_adapter, 20190507.A-S2_undiff_R1-subsample - poly-a, 20190507.A-S2_undiff_R1-subsample - poly-g, 20190507.A-S3_undiff_R1-subsample - illumina_universal_adapter, 20190507.A-S3_undiff_R1-subsample - poly-a, 20190507.A-S3_undiff_R1-subsample - poly-g, 20190507.A-S3_undiff_R1-subsample - poly-t, 20190507.A-S4_diff_R1-subsample - illumina_universal_adapter, 20190507.A-S4_diff_R1-subsample - poly-a, 20190507.A-S4_diff_R1-subsample - poly-g, 20190507.A-S4_diff_R1-subsample - poly-t, 20190507.A-S5_diff_R1-subsample - illumina_universal_adapter, 20190507.A-S5_diff_R1-subsample - poly-a, 20190507.A-S5_diff_R1-subsample - poly-g, 20190507.A-S6_diff_R1-subsample - illumina_universal_adapter, 20190507.A-S6_diff_R1-subsample - poly-a, 20190507.A-S6_diff_R1-subsample - poly-g, 20190507.A-S6_diff_R1-subsample - poly-t

Y values are in %

X values are in  bp

20190507.A-S1a_undiff_R1-subsample - illumina_universal_adapter 1: 0, 2: 0, 3: 0, 4: 0, 5: 0, 6: 0, 7: 0, 8: 0, 9: 0, 10: 0, 12: 0, 14: 0, 16: 1, 18: 1, 20: 1, 22: 1, 24: 1, 26: 1, 28: 1, 30: 1, 32: 1, 34: 1, 36: 1, 38: 1, 40: 1, 42: 1, 44: 1, 46: 1, 48: 1, 50: 1, 52: 1, 54: 2, 56: 2, 58: 2, 60: 2, 62: 2, 64: 2, 66: 2, 68: 3, 70: 3, 72: 3, 74: 4, 76: 5, 78: 5, 80: 6, 82: 7, 84: 7, 86: 8, 88: 9, 90: 10, 92: 12, 94: 13, 96: 14, 98: 15, 100: 17, 102: 18, 104: 20, 106: 21, 108: 22, 110: 24, 112: 25, 114: 27, 116: 28, 118: 30, 120: 32, 122: 33, 124: 34, 126: 36, 128: 37, 130: 38

20190507.A-S1a_undiff_R1-subsample - poly-a 1: 0, 2: 0, 3: 0, 4: 0, 5: 0, 6: 0, 7: 0, 8: 0, 9: 0, 10: 0, 12: 0, 14: 0, 16: 0, 18: 0, 20: 0, 22: 0, 24: 0, 26: 0, 28: 0, 30: 0, 32: 0, 34: 0, 36: 0, 38: 0, 40: 0, 42: 0, 44: 0, 46: 0, 48: 0, 50: 0, 52: 0, 54: 0, 56: 0, 58: 0, 60: 0, 62: 0, 64: 0, 66: 0, 68: 0, 70: 0, 72: 0, 74: 0, 76: 0, 78: 0, 80: 1, 82: 1, 84: 2, 86: 2, 88: 2, 90: 2, 92: 2, 94: 2, 96: 2, 98: 2, 100: 2, 102: 2, 104: 2, 106: 2, 108: 2, 110: 2, 112: 2, 114: 2, 116: 2, 118: 2, 120: 3, 122: 3, 124: 3, 126: 3, 128: 3, 130: 3

20190507.A-S1a_undiff_R1-subsample - poly-g 1: 0, 2: 0, 3: 0, 4: 0, 5: 0, 6: 0, 7: 0, 8: 0, 9: 0, 10: 0, 12: 0, 14: 0, 16: 0, 18: 0, 20: 0, 22: 0, 24: 0, 26: 0, 28: 0, 30: 0, 32: 0, 34: 0, 36: 0, 38: 0, 40: 0, 42: 0, 44: 0, 46: 0, 48: 0, 50: 0, 52: 0, 54: 0, 56: 0, 58: 0, 60: 0, 62: 0, 64: 0, 66: 0, 68: 0, 70: 0, 72: 0, 74: 0, 76: 0, 78: 0, 80: 0, 82: 0, 84: 0, 86: 0, 88: 0, 90: 1, 92: 1, 94: 1, 96: 1, 98: 1, 100: 1, 102: 1, 104: 1, 106: 1, 108: 1, 110: 2, 112: 2, 114: 2, 116: 2, 118: 2, 120: 2, 122: 2, 124: 2, 126: 2, 128: 2, 130: 2

20190507.A-S1a_undiff_R1-subsample - poly-t 1: 0, 2: 0, 3: 0, 4: 0, 5: 0, 6: 0, 7: 0, 8: 0, 9: 0, 10: 0, 12: 0, 14: 0, 16: 0, 18: 0, 20: 0, 22: 0, 24: 0, 26: 0, 28: 0, 30: 0, 32: 0, 34: 0, 36: 0, 38: 0, 40: 0, 42: 0, 44: 0, 46: 0, 48: 0, 50: 0, 52: 0, 54: 0, 56: 0, 58: 0, 60: 0, 62: 0, 64: 0, 66: 0, 68: 0, 70: 0, 72: 0, 74: 0, 76: 0, 78: 0, 80: 0, 82: 0, 84: 0, 86: 0, 88: 0, 90: 0, 92: 0, 94: 0, 96: 0, 98: 0, 100: 0, 102: 0, 104: 0, 106: 0, 108: 0, 110: 0, 112: 0, 114: 0, 116: 0, 118: 0, 120: 0, 122: 0, 124: 0, 126: 0, 128: 0, 130: 0

20190507.A-S2_undiff_R1-subsample - illumina_universal_adapter 1: 0, 2: 0, 3: 0, 4: 0, 5: 0, 6: 0, 7: 0, 8: 0, 9: 0, 10: 0, 12: 0, 14: 0, 16: 8, 18: 10, 20: 10, 22: 11, 24: 11, 26: 11, 28: 11, 30: 12, 32: 12, 34: 12, 36: 13, 38: 13, 40: 14, 42: 14, 44: 15, 46: 16, 48: 16, 50: 17, 52: 18, 54: 20, 56: 21, 58: 22, 60: 24, 62: 26, 64: 28, 66: 30, 68: 32, 70: 35, 72: 38, 74: 41, 76: 44, 78: 47, 80: 51, 82: 54, 84: 58, 86: 61, 88: 65, 90: 68, 92: 71, 94: 74, 96: 77, 98: 79, 100: 81, 102: 83, 104: 85, 106: 86, 108: 88, 110: 89, 112: 90, 114: 90, 116: 91, 118: 91, 120: 92, 122: 92, 124: 93, 126: 93, 128: 93, 130: 93

20190507.A-S2_undiff_R1-subsample - poly-a 1: 0, 2: 0, 3: 0, 4: 0, 5: 0, 6: 0, 7: 0, 8: 0, 9: 0, 10: 0, 12: 0, 14: 0, 16: 0, 18: 0, 20: 0, 22: 0, 24: 0, 26: 0, 28: 0, 30: 0, 32: 0, 34: 0, 36: 0, 38: 0, 40: 0, 42: 0, 44: 0, 46: 0, 48: 0, 50: 0, 52: 0, 54: 0, 56: 0, 58: 0, 60: 0, 62: 0, 64: 0, 66: 0, 68: 0, 70: 0, 72: 0, 74: 0, 76: 0, 78: 0, 80: 5, 82: 9, 84: 9, 86: 10, 88: 10, 90: 10, 92: 10, 94: 10, 96: 11, 98: 11, 100: 11, 102: 12, 104: 12, 106: 13, 108: 13, 110: 14, 112: 14, 114: 15, 116: 16, 118: 17, 120: 18, 122: 19, 124: 21, 126: 22, 128: 24, 130: 25

20190507.A-S2_undiff_R1-subsample - poly-g 1: 0, 2: 0, 3: 0, 4: 0, 5: 0, 6: 0, 7: 0, 8: 0, 9: 0, 10: 0, 12: 0, 14: 0, 16: 0, 18: 0, 20: 0, 22: 0, 24: 0, 26: 0, 28: 0, 30: 0, 32: 0, 34: 0, 36: 0, 38: 0, 40: 0, 42: 0, 44: 0, 46: 0, 48: 0, 50: 0, 52: 0, 54: 0, 56: 0, 58: 0, 60: 0, 62: 0, 64: 0, 66: 1, 68: 1, 70: 1, 72: 1, 74: 1, 76: 1, 78: 1, 80: 1, 82: 1, 84: 1, 86: 1, 88: 2, 90: 6, 92: 9, 94: 11, 96: 11, 98: 12, 100: 12, 102: 12, 104: 13, 106: 13, 108: 14, 110: 14, 112: 14, 114: 15, 116: 16, 118: 16, 120: 17, 122: 18, 124: 19, 126: 20, 128: 21, 130: 21

20190507.A-S3_undiff_R1-subsample - illumina_universal_adapter 1: 0, 2: 0, 3: 0, 4: 0, 5: 0, 6: 0, 7: 0, 8: 0, 9: 0, 10: 0, 12: 0, 14: 0, 16: 2, 18: 3, 20: 3, 22: 3, 24: 3, 26: 3, 28: 3, 30: 3, 32: 3, 34: 3, 36: 3, 38: 3, 40: 3, 42: 4, 44: 4, 46: 4, 48: 4, 50: 4, 52: 4, 54: 4, 56: 5, 58: 5, 60: 5, 62: 6, 64: 6, 66: 6, 68: 7, 70: 8, 72: 8, 74: 9, 76: 10, 78: 11, 80: 12, 82: 13, 84: 14, 86: 16, 88: 17, 90: 18, 92: 20, 94: 22, 96: 23, 98: 25, 100: 26, 102: 28, 104: 29, 106: 31, 108: 32, 110: 34, 112: 35, 114: 37, 116: 38, 118: 40, 120: 41, 122: 42, 124: 44, 126: 45, 128: 46, 130: 47

20190507.A-S3_undiff_R1-subsample - poly-a 1: 0, 2: 0, 3: 0, 4: 0, 5: 0, 6: 0, 7: 0, 8: 0, 9: 0, 10: 0, 12: 0, 14: 0, 16: 0, 18: 0, 20: 0, 22: 0, 24: 0, 26: 0, 28: 0, 30: 0, 32: 0, 34: 0, 36: 0, 38: 0, 40: 0, 42: 0, 44: 0, 46: 0, 48: 0, 50: 0, 52: 0, 54: 0, 56: 0, 58: 0, 60: 0, 62: 0, 64: 0, 66: 0, 68: 0, 70: 0, 72: 0, 74: 0, 76: 0, 78: 1, 80: 3, 82: 4, 84: 4, 86: 4, 88: 4, 90: 4, 92: 4, 94: 4, 96: 4, 98: 4, 100: 4, 102: 5, 104: 5, 106: 5, 108: 5, 110: 5, 112: 5, 114: 5, 116: 5, 118: 6, 120: 6, 122: 6, 124: 6, 126: 7, 128: 7, 130: 7

20190507.A-S3_undiff_R1-subsample - poly-g 1: 0, 2: 0, 3: 0, 4: 0, 5: 0, 6: 0, 7: 0, 8: 0, 9: 0, 10: 0, 12: 0, 14: 0, 16: 0, 18: 0, 20: 0, 22: 0, 24: 0, 26: 0, 28: 0, 30: 0, 32: 0, 34: 0, 36: 0, 38: 0, 40: 0, 42: 0, 44: 0, 46: 0, 48: 0, 50: 0, 52: 0, 54: 0, 56: 0, 58: 0, 60: 0, 62: 0, 64: 0, 66: 0, 68: 0, 70: 0, 72: 0, 74: 0, 76: 0, 78: 0, 80: 0, 82: 0, 84: 0, 86: 0, 88: 1, 90: 3, 92: 3, 94: 4, 96: 4, 98: 4, 100: 4, 102: 4, 104: 4, 106: 4, 108: 4, 110: 4, 112: 4, 114: 4, 116: 5, 118: 5, 120: 5, 122: 5, 124: 5, 126: 5, 128: 6, 130: 6

20190507.A-S3_undiff_R1-subsample - poly-t 1: 0, 2: 0, 3: 0, 4: 0, 5: 0, 6: 0, 7: 0, 8: 0, 9: 0, 10: 0, 12: 0, 14: 0, 16: 0, 18: 0, 20: 0, 22: 0, 24: 0, 26: 0, 28: 0, 30: 0, 32: 0, 34: 0, 36: 0, 38: 0, 40: 0, 42: 0, 44: 0, 46: 0, 48: 0, 50: 0, 52: 0, 54: 0, 56: 0, 58: 0, 60: 0, 62: 0, 64: 0, 66: 0, 68: 0, 70: 0, 72: 0, 74: 0, 76: 0, 78: 0, 80: 0, 82: 0, 84: 0, 86: 0, 88: 0, 90: 0, 92: 0, 94: 0, 96: 0, 98: 0, 100: 0, 102: 0, 104: 0, 106: 0, 108: 0, 110: 0, 112: 0, 114: 0, 116: 0, 118: 0, 120: 0, 122: 0, 124: 0, 126: 0, 128: 0, 130: 0

20190507.A-S4_diff_R1-subsample - illumina_universal_adapter 1: 0, 2: 0, 3: 0, 4: 0, 5: 0, 6: 0, 7: 0, 8: 0, 9: 0, 10: 0, 12: 0, 14: 0, 16: 1, 18: 1, 20: 1, 22: 1, 24: 1, 26: 1, 28: 1, 30: 1, 32: 1, 34: 1, 36: 1, 38: 1, 40: 1, 42: 2, 44: 2, 46: 2, 48: 2, 50: 2, 52: 2, 54: 2, 56: 2, 58: 2, 60: 2, 62: 2, 64: 3, 66: 3, 68: 3, 70: 3, 72: 3, 74: 4, 76: 4, 78: 5, 80: 5, 82: 6, 84: 6, 86: 7, 88: 8, 90: 9, 92: 10, 94: 12, 96: 13, 98: 15, 100: 16, 102: 18, 104: 20, 106: 22, 108: 24, 110: 26, 112: 28, 114: 30, 116: 32, 118: 34, 120: 36, 122: 38, 124: 40, 126: 42, 128: 44, 130: 45

20190507.A-S4_diff_R1-subsample - poly-a 1: 0, 2: 0, 3: 0, 4: 0, 5: 0, 6: 0, 7: 0, 8: 0, 9: 0, 10: 0, 12: 0, 14: 0, 16: 0, 18: 0, 20: 0, 22: 0, 24: 0, 26: 0, 28: 0, 30: 0, 32: 0, 34: 0, 36: 0, 38: 0, 40: 0, 42: 0, 44: 0, 46: 0, 48: 0, 50: 0, 52: 0, 54: 0, 56: 0, 58: 0, 60: 0, 62: 0, 64: 0, 66: 0, 68: 0, 70: 0, 72: 0, 74: 0, 76: 0, 78: 0, 80: 1, 82: 1, 84: 2, 86: 2, 88: 2, 90: 2, 92: 2, 94: 2, 96: 2, 98: 2, 100: 2, 102: 2, 104: 2, 106: 2, 108: 2, 110: 2, 112: 2, 114: 2, 116: 3, 118: 3, 120: 3, 122: 3, 124: 3, 126: 3, 128: 3, 130: 3

20190507.A-S4_diff_R1-subsample - poly-g 1: 0, 2: 0, 3: 0, 4: 0, 5: 0, 6: 0, 7: 0, 8: 0, 9: 0, 10: 0, 12: 0, 14: 0, 16: 0, 18: 0, 20: 0, 22: 0, 24: 0, 26: 0, 28: 0, 30: 0, 32: 0, 34: 0, 36: 0, 38: 0, 40: 0, 42: 0, 44: 0, 46: 0, 48: 0, 50: 0, 52: 0, 54: 0, 56: 0, 58: 0, 60: 0, 62: 0, 64: 0, 66: 0, 68: 0, 70: 0, 72: 0, 74: 0, 76: 0, 78: 0, 80: 0, 82: 0, 84: 0, 86: 0, 88: 0, 90: 1, 92: 1, 94: 1, 96: 1, 98: 1, 100: 1, 102: 1, 104: 1, 106: 2, 108: 2, 110: 2, 112: 2, 114: 2, 116: 2, 118: 2, 120: 2, 122: 2, 124: 2, 126: 2, 128: 2, 130: 3

20190507.A-S4_diff_R1-subsample - poly-t 1: 0, 2: 0, 3: 0, 4: 0, 5: 0, 6: 0, 7: 0, 8: 0, 9: 0, 10: 0, 12: 0, 14: 0, 16: 0, 18: 0, 20: 0, 22: 0, 24: 0, 26: 0, 28: 0, 30: 0, 32: 0, 34: 0, 36: 0, 38: 0, 40: 0, 42: 0, 44: 0, 46: 0, 48: 0, 50: 0, 52: 0, 54: 0, 56: 0, 58: 0, 60: 0, 62: 0, 64: 0, 66: 0, 68: 0, 70: 0, 72: 0, 74: 0, 76: 0, 78: 0, 80: 0, 82: 0, 84: 0, 86: 0, 88: 0, 90: 0, 92: 0, 94: 0, 96: 0, 98: 0, 100: 0, 102: 0, 104: 0, 106: 0, 108: 0, 110: 0, 112: 0, 114: 0, 116: 0, 118: 0, 120: 0, 122: 0, 124: 0, 126: 0, 128: 0, 130: 0

20190507.A-S5_diff_R1-subsample - illumina_universal_adapter 1: 0, 2: 0, 3: 0, 4: 0, 5: 0, 6: 0, 7: 0, 8: 0, 9: 0, 10: 0, 12: 0, 14: 1, 16: 10, 18: 12, 20: 13, 22: 14, 24: 14, 26: 15, 28: 16, 30: 16, 32: 17, 34: 18, 36: 19, 38: 20, 40: 20, 42: 21, 44: 22, 46: 23, 48: 25, 50: 26, 52: 27, 54: 28, 56: 30, 58: 32, 60: 34, 62: 36, 64: 38, 66: 41, 68: 43, 70: 46, 72: 50, 74: 53, 76: 56, 78: 60, 80: 63, 82: 67, 84: 70, 86: 73, 88: 76, 90: 78, 92: 80, 94: 82, 96: 84, 98: 86, 100: 87, 102: 88, 104: 89, 106: 90, 108: 90, 110: 91, 112: 91, 114: 92, 116: 92, 118: 92, 120: 92, 122: 93, 124: 93, 126: 93, 128: 93, 130: 93

20190507.A-S5_diff_R1-subsample - poly-a 1: 0, 2: 0, 3: 0, 4: 0, 5: 0, 6: 0, 7: 0, 8: 0, 9: 0, 10: 0, 12: 0, 14: 0, 16: 0, 18: 0, 20: 0, 22: 0, 24: 0, 26: 0, 28: 0, 30: 0, 32: 0, 34: 0, 36: 0, 38: 0, 40: 0, 42: 0, 44: 0, 46: 0, 48: 0, 50: 0, 52: 0, 54: 0, 56: 0, 58: 0, 60: 0, 62: 0, 64: 0, 66: 0, 68: 0, 70: 0, 72: 0, 74: 0, 76: 0, 78: 1, 80: 10, 82: 12, 84: 13, 86: 13, 88: 14, 90: 14, 92: 15, 94: 16, 96: 16, 98: 17, 100: 18, 102: 19, 104: 19, 106: 20, 108: 21, 110: 22, 112: 23, 114: 24, 116: 25, 118: 27, 120: 28, 122: 30, 124: 32, 126: 34, 128: 36, 130: 38

20190507.A-S5_diff_R1-subsample - poly-g 1: 0, 2: 0, 3: 0, 4: 0, 5: 0, 6: 0, 7: 0, 8: 0, 9: 0, 10: 0, 12: 0, 14: 0, 16: 0, 18: 0, 20: 0, 22: 0, 24: 0, 26: 0, 28: 0, 30: 0, 32: 0, 34: 0, 36: 0, 38: 0, 40: 0, 42: 0, 44: 0, 46: 0, 48: 0, 50: 0, 52: 0, 54: 0, 56: 0, 58: 0, 60: 0, 62: 0, 64: 0, 66: 0, 68: 0, 70: 0, 72: 0, 74: 0, 76: 0, 78: 0, 80: 0, 82: 0, 84: 0, 86: 1, 88: 2, 90: 10, 92: 13, 94: 14, 96: 14, 98: 15, 100: 16, 102: 16, 104: 17, 106: 18, 108: 19, 110: 20, 112: 20, 114: 21, 116: 22, 118: 23, 120: 24, 122: 25, 124: 27, 126: 28, 128: 29, 130: 30

20190507.A-S6_diff_R1-subsample - illumina_universal_adapter 1: 0, 2: 0, 3: 0, 4: 0, 5: 0, 6: 0, 7: 0, 8: 0, 9: 0, 10: 0, 12: 0, 14: 0, 16: 3, 18: 3, 20: 4, 22: 4, 24: 4, 26: 4, 28: 4, 30: 4, 32: 4, 34: 5, 36: 5, 38: 5, 40: 5, 42: 6, 44: 6, 46: 6, 48: 7, 50: 7, 52: 8, 54: 8, 56: 9, 58: 9, 60: 10, 62: 11, 64: 12, 66: 13, 68: 14, 70: 15, 72: 17, 74: 18, 76: 20, 78: 22, 80: 24, 82: 26, 84: 28, 86: 31, 88: 33, 90: 35, 92: 38, 94: 40, 96: 43, 98: 45, 100: 48, 102: 50, 104: 52, 106: 54, 108: 56, 110: 58, 112: 59, 114: 61, 116: 63, 118: 64, 120: 66, 122: 67, 124: 68, 126: 70, 128: 71, 130: 72

20190507.A-S6_diff_R1-subsample - poly-a 1: 0, 2: 0, 3: 0, 4: 0, 5: 0, 6: 0, 7: 0, 8: 0, 9: 0, 10: 0, 12: 0, 14: 0, 16: 0, 18: 0, 20: 0, 22: 0, 24: 0, 26: 0, 28: 0, 30: 0, 32: 0, 34: 0, 36: 0, 38: 0, 40: 0, 42: 0, 44: 0, 46: 0, 48: 0, 50: 0, 52: 0, 54: 0, 56: 0, 58: 0, 60: 0, 62: 0, 64: 0, 66: 0, 68: 0, 70: 0, 72: 0, 74: 0, 76: 0, 78: 0, 80: 3, 82: 4, 84: 4, 86: 4, 88: 4, 90: 4, 92: 5, 94: 5, 96: 5, 98: 5, 100: 5, 102: 6, 104: 6, 106: 6, 108: 6, 110: 7, 112: 7, 114: 7, 116: 8, 118: 8, 120: 9, 122: 10, 124: 10, 126: 11, 128: 12, 130: 13

20190507.A-S6_diff_R1-subsample - poly-g 1: 0, 2: 0, 3: 0, 4: 0, 5: 0, 6: 0, 7: 0, 8: 0, 9: 0, 10: 0, 12: 0, 14: 0, 16: 0, 18: 0, 20: 0, 22: 0, 24: 0, 26: 0, 28: 0, 30: 0, 32: 0, 34: 0, 36: 0, 38: 0, 40: 0, 42: 0, 44: 0, 46: 0, 48: 0, 50: 0, 52: 0, 54: 0, 56: 0, 58: 0, 60: 0, 62: 0, 64: 0, 66: 0, 68: 0, 70: 0, 72: 0, 74: 0, 76: 0, 78: 0, 80: 0, 82: 0, 84: 0, 86: 0, 88: 0, 90: 3, 92: 4, 94: 4, 96: 4, 98: 4, 100: 4, 102: 5, 104: 5, 106: 5, 108: 5, 110: 5, 112: 6, 114: 6, 116: 6, 118: 7, 120: 7, 122: 7, 124: 8, 126: 8, 128: 9, 130: 9

20190507.A-S6_diff_R1-subsample - poly-t 1: 0, 2: 0, 3: 0, 4: 0, 5: 0, 6: 0, 7: 0, 8: 0, 9: 0, 10: 0, 12: 0, 14: 0, 16: 0, 18: 0, 20: 0, 22: 0, 24: 0, 26: 0, 28: 0, 30: 0, 32: 0, 34: 0, 36: 0, 38: 0, 40: 0, 42: 0, 44: 0, 46: 0, 48: 0, 50: 0, 52: 0, 54: 0, 56: 0, 58: 0, 60: 0, 62: 0, 64: 0, 66: 0, 68: 0, 70: 0, 72: 0, 74: 0, 76: 0, 78: 0, 80: 0, 82: 0, 84: 0, 86: 0, 88: 0, 90: 0, 92: 0, 94: 0, 96: 0, 98: 0, 100: 0, 102: 0, 104: 0, 106: 0, 108: 0, 110: 0, 112: 0, 114: 0, 116: 0, 118: 0, 120: 0, 122: 0, 124: 0, 126: 0, 128: 0, 130: 0

----------------------

Tool: FastQC
Section: Status Checks
Section description: Status for each FastQC section showing whether results seem entirely normal (green),
slightly abnormal (orange) or very unusual (red).
Section help text: 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 <a href="https://www.bioinformatics.babraham.ac.uk/projects/fastqc/Help/3%20Analysis%20Modules/">FastQC help</a>.
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.
Title: FastQC: Status Checks

Plot type: heatmap
X axis: Section Name
Y axis: Sample
Z axis: z

|Sample|Basic Statistics|Per Base Sequence Quality|Per Tile Sequence Quality|Per Sequence Quality Scores|Per Base Sequence Content|Per Sequence GC Content|Per Base N Content|Sequence Length Distribution|Sequence Duplication Levels|Overrepresented Sequences|Adapter Content|
|---|---|---|---|---|---|---|---|---|---|---|---|
|20190507.A-S1a_undiff_R1-subsample|1|1|1|1|0.25|1|1|1|1|1|0.25|
|20190507.A-S2_undiff_R1-subsample|1|1|0.5|1|0.25|0.5|1|1|1|1|0.25|
|20190507.A-S3_undiff_R1-subsample|1|1|1|1|0.25|1|1|1|1|1|0.25|
|20190507.A-S4_diff_R1-subsample|1|1|0.5|1|0.25|1|1|1|1|1|0.25|
|20190507.A-S5_diff_R1-subsample|1|1|1|1|0.25|0.5|1|1|1|1|0.25|
|20190507.A-S6_diff_R1-subsample|1|1|1|1|0.25|0.5|1|1|1|1|0.25|