crc-rna-seq-salmon

A modular tool to aggregate results from bioinformatics analyses across many samples into a single report.

This report has been generated by the nf-core/rnaseq analysis pipeline. For information about how to interpret these results, please see the documentation.

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Report generated on 2025-11-29, 15:06 CET based on data in: /tmp/nxf.HASZSFzIcl

General Statistics

Showing ¹³²/₁₃₂ rows and ⁹/₁₈ columns.

Sample Name	% Aligned	M Aligned	Library types	CFR	M Bias	Dups	GC	Avg len	Median len	Failed	Seqs	Trimmed bases	Dups	GC	Avg len	Median len	Failed	Seqs
srr27320655	91.5%	18.0M	ISR	100.0%	0.0
srr27320655_1						46.7%	48.0%	59bp	59bp	18%	19.7M	1.2%	46.7%	47.0%	58bp	58bp	18%	19.7M
srr27320655_2						42.7%	49.0%	59bp	59bp	18%	19.7M	1.5%	42.7%	48.0%	58bp	58bp	18%	19.7M
srr27320656	93.8%	27.2M	ISR	100.0%	0.0
srr27320656_1						51.4%	47.0%	59bp	59bp	27%	29.0M	1.2%	51.3%	47.0%	58bp	58bp	27%	29.0M
srr27320656_2						45.0%	48.0%	59bp	59bp	18%	29.0M	1.4%	45.0%	48.0%	58bp	58bp	18%	29.0M
srr27320657	91.9%	37.2M	ISR	100.0%	0.0
srr27320657_1						62.8%	47.0%	59bp	59bp	27%	40.5M	1.1%	62.8%	47.0%	58bp	58bp	27%	40.4M
srr27320657_2						57.2%	48.0%	59bp	59bp	27%	40.5M	1.5%	57.2%	48.0%	58bp	58bp	27%	40.4M
srr27320658	92.2%	24.1M	ISR	100.0%	0.0
srr27320658_1						49.3%	49.0%	59bp	59bp	18%	26.1M	1.2%	49.3%	48.0%	58bp	58bp	18%	26.1M
srr27320658_2						44.2%	50.0%	59bp	59bp	18%	26.1M	1.4%	44.2%	50.0%	58bp	58bp	18%	26.1M
srr27320659	91.6%	23.1M	ISR	100.0%	0.0
srr27320659_1						53.7%	48.0%	59bp	59bp	27%	25.2M	1.2%	53.6%	47.0%	58bp	58bp	27%	25.2M
srr27320659_2						49.2%	49.0%	59bp	59bp	18%	25.2M	1.5%	49.2%	49.0%	58bp	58bp	18%	25.2M
srr27320660	92.2%	17.9M	ISR	100.0%	0.0
srr27320660_1						47.2%	48.0%	59bp	59bp	18%	19.4M	1.2%	47.1%	47.0%	58bp	58bp	18%	19.4M
srr27320660_2						42.5%	49.0%	59bp	59bp	18%	19.4M	1.4%	42.5%	49.0%	58bp	58bp	18%	19.4M
srr27320661	91.4%	28.0M	ISR	100.0%	0.0
srr27320661_1						56.2%	47.0%	59bp	59bp	27%	30.7M	1.2%	56.2%	47.0%	58bp	58bp	27%	30.7M
srr27320661_2						53.4%	48.0%	59bp	59bp	27%	30.7M	1.6%	53.4%	48.0%	58bp	58bp	27%	30.7M
srr27320662	92.1%	32.5M	ISR	100.0%	0.0
srr27320662_1						53.2%	47.0%	59bp	59bp	27%	35.4M	1.3%	53.1%	47.0%	58bp	58bp	27%	35.3M
srr27320662_2						51.9%	48.0%	59bp	59bp	27%	35.4M	1.5%	51.8%	47.0%	58bp	58bp	27%	35.3M
srr27320663	93.7%	37.2M	ISR	100.0%	0.0
srr27320663_1						59.8%	47.0%	59bp	59bp	27%	39.8M	1.4%	59.7%	47.0%	58bp	58bp	27%	39.7M
srr27320663_2						56.2%	48.0%	59bp	59bp	27%	39.8M	1.6%	56.2%	48.0%	58bp	58bp	27%	39.7M
srr27320664	91.7%	37.3M	ISR	100.0%	0.0
srr27320664_1						58.9%	47.0%	59bp	59bp	27%	40.8M	1.3%	58.8%	47.0%	58bp	58bp	27%	40.7M
srr27320664_2						52.5%	48.0%	59bp	59bp	27%	40.8M	1.7%	52.4%	48.0%	58bp	58bp	27%	40.7M
srr27320665	92.8%	28.0M	ISR	100.0%	0.0
srr27320665_1						55.2%	47.0%	59bp	59bp	27%	30.2M	1.2%	55.2%	47.0%	58bp	58bp	27%	30.1M
srr27320665_2						49.5%	48.0%	59bp	59bp	18%	30.2M	1.5%	49.4%	48.0%	58bp	58bp	18%	30.1M
srr27320666	88.7%	35.6M	ISR	100.0%	0.0
srr27320666_1						51.2%	47.0%	59bp	59bp	27%	40.1M	1.3%	51.1%	47.0%	58bp	58bp	27%	40.1M
srr27320666_2						45.2%	48.0%	59bp	59bp	18%	40.1M	1.5%	45.2%	48.0%	58bp	58bp	18%	40.1M
srr27320667	93.3%	20.1M	ISR	100.0%	0.0
srr27320667_1						49.9%	48.0%	59bp	59bp	9%	21.6M	1.2%	49.9%	48.0%	58bp	58bp	9%	21.5M
srr27320667_2						48.2%	48.0%	59bp	59bp	18%	21.6M	1.5%	48.2%	48.0%	58bp	58bp	18%	21.5M
srr27320668	93.6%	21.4M	ISR	100.0%	0.0
srr27320668_1						48.1%	47.0%	59bp	59bp	18%	22.9M	1.2%	48.0%	47.0%	58bp	58bp	18%	22.9M
srr27320668_2						43.9%	48.0%	59bp	59bp	18%	22.9M	1.4%	43.9%	48.0%	58bp	58bp	18%	22.9M
srr27320669	93.4%	20.6M	ISR	100.0%	0.0
srr27320669_1						49.7%	48.0%	59bp	59bp	18%	22.1M	1.2%	49.7%	47.0%	58bp	58bp	18%	22.0M
srr27320669_2						47.3%	49.0%	59bp	59bp	18%	22.1M	1.4%	47.3%	48.0%	58bp	58bp	18%	22.0M
srr27320670	91.6%	21.7M	ISR	100.0%	0.0
srr27320670_1						45.5%	46.0%	59bp	59bp	18%	23.8M	1.2%	45.5%	46.0%	58bp	58bp	18%	23.7M
srr27320670_2						40.9%	47.0%	59bp	59bp	18%	23.8M	1.4%	40.9%	47.0%	58bp	58bp	18%	23.7M
srr27320671	93.3%	27.6M	ISR	100.0%	0.0
srr27320671_1						56.9%	47.0%	59bp	59bp	27%	29.6M	1.1%	56.8%	47.0%	58bp	58bp	27%	29.6M
srr27320671_2						51.4%	48.0%	59bp	59bp	27%	29.6M	1.5%	51.3%	48.0%	58bp	58bp	27%	29.6M
srr27320672	92.6%	28.6M	ISR	100.0%	0.0
srr27320672_1						50.7%	47.0%	59bp	59bp	27%	31.0M	1.2%	50.7%	46.0%	58bp	58bp	27%	30.9M
srr27320672_2						46.5%	48.0%	59bp	59bp	18%	31.0M	1.5%	46.4%	47.0%	58bp	58bp	18%	30.9M
srr27320673	93.9%	27.4M	ISR	100.0%	0.0
srr27320673_1						59.2%	47.0%	59bp	59bp	27%	29.3M	1.2%	59.2%	47.0%	58bp	58bp	27%	29.2M
srr27320673_2						54.0%	49.0%	59bp	59bp	27%	29.3M	1.6%	54.0%	48.0%	58bp	58bp	27%	29.2M
srr27320674	93.3%	27.7M	ISR	100.0%	0.0
srr27320674_1						62.8%	46.0%	59bp	59bp	27%	29.9M	1.5%	62.6%	46.0%	58bp	58bp	27%	29.7M
srr27320674_2						55.6%	48.0%	59bp	59bp	27%	29.9M	1.8%	55.5%	47.0%	58bp	58bp	27%	29.7M
srr27320675	94.7%	29.8M	ISR	100.0%	0.0
srr27320675_1						58.7%	47.0%	59bp	59bp	27%	31.5M	1.1%	58.7%	47.0%	58bp	58bp	27%	31.4M
srr27320675_2						54.4%	48.0%	59bp	59bp	27%	31.5M	1.4%	54.4%	48.0%	58bp	58bp	27%	31.4M
srr27320676	94.0%	32.2M	ISR	100.0%	0.0
srr27320676_1						70.8%	47.0%	59bp	59bp	27%	34.3M	1.1%	70.8%	47.0%	58bp	58bp	27%	34.3M
srr27320676_2						67.1%	49.0%	59bp	59bp	27%	34.3M	1.4%	67.1%	49.0%	58bp	58bp	27%	34.3M
srr27320677	93.9%	20.0M	ISR	100.0%	0.0
srr27320677_1						49.1%	47.0%	59bp	59bp	18%	21.3M	1.2%	49.1%	47.0%	58bp	58bp	18%	21.3M
srr27320677_2						47.4%	48.0%	59bp	59bp	18%	21.3M	1.4%	47.4%	48.0%	58bp	58bp	18%	21.3M
srr27320678	94.0%	31.0M	ISR	100.0%	0.0
srr27320678_1						59.4%	46.0%	59bp	59bp	27%	33.0M	1.2%	59.4%	45.0%	58bp	58bp	27%	33.0M
srr27320678_2						55.0%	47.0%	59bp	59bp	27%	33.0M	1.3%	55.0%	47.0%	58bp	58bp	27%	33.0M
srr27320679	94.0%	21.0M	ISR	100.0%	0.0
srr27320679_1						52.2%	48.0%	59bp	59bp	27%	22.3M	1.1%	52.1%	47.0%	58bp	58bp	27%	22.3M
srr27320679_2						51.1%	48.0%	59bp	59bp	27%	22.3M	1.5%	51.1%	48.0%	58bp	58bp	27%	22.3M
srr27320680	93.3%	18.1M	ISR	100.0%	0.0
srr27320680_1						47.3%	48.0%	59bp	59bp	9%	19.5M	1.2%	47.3%	48.0%	58bp	58bp	9%	19.5M
srr27320680_2						45.7%	48.0%	59bp	59bp	18%	19.5M	1.4%	45.7%	48.0%	58bp	58bp	18%	19.5M
srr27320681	93.9%	26.8M	ISR	100.0%	0.0
srr27320681_1						56.8%	47.0%	59bp	59bp	27%	28.6M	1.1%	56.7%	47.0%	58bp	58bp	27%	28.6M
srr27320681_2						51.4%	48.0%	59bp	59bp	27%	28.6M	1.5%	51.4%	48.0%	58bp	58bp	27%	28.6M
srr27320682	94.1%	34.2M	ISR	100.0%	0.0
srr27320682_1						61.9%	46.0%	59bp	59bp	18%	36.4M	1.2%	61.8%	46.0%	58bp	58bp	18%	36.4M
srr27320682_2						59.1%	47.0%	59bp	59bp	18%	36.4M	1.4%	59.1%	47.0%	58bp	58bp	18%	36.4M
srr27320683	91.6%	26.9M	ISR	100.0%	0.0
srr27320683_1						52.9%	48.0%	59bp	59bp	27%	29.5M	1.3%	52.8%	48.0%	58bp	58bp	27%	29.4M
srr27320683_2						49.6%	49.0%	59bp	59bp	18%	29.5M	1.5%	49.5%	48.0%	58bp	58bp	18%	29.4M
srr27320684	92.7%	26.4M	ISR	100.0%	0.0
srr27320684_1						48.3%	47.0%	59bp	59bp	18%	28.5M	1.2%	48.3%	47.0%	58bp	58bp	18%	28.4M
srr27320684_2						42.6%	49.0%	59bp	59bp	18%	28.5M	1.4%	42.6%	48.0%	58bp	58bp	18%	28.4M
srr27320685	94.6%	32.3M	ISR	100.0%	0.0
srr27320685_1						61.8%	48.0%	59bp	59bp	27%	34.3M	1.4%	61.6%	48.0%	58bp	58bp	27%	34.2M
srr27320685_2						58.5%	49.0%	59bp	59bp	27%	34.3M	1.7%	58.4%	48.0%	58bp	58bp	27%	34.2M
srr27320686	94.7%	25.2M	ISR	100.0%	0.0
srr27320686_1						52.7%	47.0%	59bp	59bp	27%	26.7M	1.2%	52.6%	47.0%	58bp	58bp	27%	26.6M
srr27320686_2						46.6%	48.0%	59bp	59bp	18%	26.7M	1.4%	46.5%	48.0%	58bp	58bp	18%	26.6M
srr27320687	94.8%	33.8M	ISR	100.0%	0.0
srr27320687_1						57.9%	48.0%	59bp	59bp	27%	35.7M	1.2%	57.8%	48.0%	58bp	58bp	27%	35.6M
srr27320687_2						55.4%	48.0%	59bp	59bp	27%	35.7M	1.4%	55.4%	48.0%	58bp	58bp	27%	35.6M
srr27320688	93.9%	25.0M	ISR	100.0%	0.0
srr27320688_1						50.1%	48.0%	59bp	59bp	27%	26.7M	1.2%	50.0%	47.0%	58bp	58bp	27%	26.7M
srr27320688_2						46.2%	49.0%	59bp	59bp	18%	26.7M	1.5%	46.1%	48.0%	58bp	58bp	18%	26.7M
srr27320689	93.5%	26.0M	ISR	100.0%	0.0
srr27320689_1						51.8%	48.0%	59bp	59bp	27%	27.9M	1.2%	51.7%	47.0%	58bp	58bp	27%	27.9M
srr27320689_2						47.8%	49.0%	59bp	59bp	18%	27.9M	1.5%	47.8%	49.0%	58bp	58bp	18%	27.9M
srr27320690	88.8%	34.6M	ISR	100.0%	0.0
srr27320690_1						58.8%	47.0%	59bp	59bp	27%	39.0M	1.2%	58.8%	46.0%	58bp	58bp	27%	38.9M
srr27320690_2						52.6%	48.0%	59bp	59bp	27%	39.0M	1.6%	52.6%	47.0%	58bp	58bp	27%	38.9M
srr27320691	94.2%	28.9M	ISR	100.0%	0.0
srr27320691_1						56.8%	47.0%	59bp	59bp	27%	30.7M	1.1%	56.7%	47.0%	58bp	58bp	27%	30.7M
srr27320691_2						52.8%	48.0%	59bp	59bp	27%	30.7M	1.5%	52.8%	48.0%	58bp	58bp	27%	30.7M
srr27320692	94.4%	21.0M	ISR	100.0%	0.0
srr27320692_1						46.8%	48.0%	59bp	59bp	18%	22.3M	1.2%	46.7%	47.0%	58bp	58bp	18%	22.2M
srr27320692_2						42.4%	49.0%	59bp	59bp	18%	22.3M	1.5%	42.3%	49.0%	58bp	58bp	18%	22.2M
srr27320693	94.1%	34.7M	ISR	100.0%	0.0
srr27320693_1						67.9%	48.0%	59bp	59bp	27%	37.0M	1.2%	67.9%	47.0%	58bp	58bp	27%	36.9M
srr27320693_2						64.9%	49.0%	59bp	59bp	27%	37.0M	1.5%	64.9%	48.0%	58bp	58bp	27%	36.9M
srr27320694	88.5%	24.5M	ISR	100.0%	0.0
srr27320694_1						40.9%	48.0%	59bp	59bp	18%	27.7M	1.2%	40.9%	48.0%	58bp	58bp	18%	27.6M
srr27320694_2						37.9%	49.0%	59bp	59bp	18%	27.7M	1.4%	37.9%	49.0%	58bp	58bp	18%	27.6M
srr27320695	93.7%	26.4M	ISR	100.0%	0.0
srr27320695_1						58.1%	48.0%	59bp	59bp	27%	28.2M	1.2%	58.1%	47.0%	58bp	58bp	27%	28.1M
srr27320695_2						52.1%	49.0%	59bp	59bp	27%	28.2M	1.5%	52.1%	48.0%	58bp	58bp	27%	28.1M
srr27320696	93.5%	29.0M	ISR	100.0%	0.0
srr27320696_1						60.3%	47.0%	59bp	59bp	27%	31.0M	1.1%	60.3%	47.0%	58bp	58bp	27%	31.0M
srr27320696_2						55.9%	48.0%	59bp	59bp	27%	31.0M	1.6%	55.9%	48.0%	58bp	58bp	27%	31.0M
srr27320697	92.5%	34.5M	ISR	100.0%	0.0
srr27320697_1						58.3%	47.0%	59bp	59bp	27%	37.4M	1.3%	58.2%	47.0%	58bp	58bp	27%	37.3M
srr27320697_2						51.7%	48.0%	59bp	59bp	27%	37.4M	1.6%	51.7%	48.0%	58bp	58bp	27%	37.3M
srr27320698	93.1%	24.3M	ISR	100.0%	0.0
srr27320698_1						49.1%	47.0%	59bp	59bp	18%	26.2M	1.4%	49.0%	46.0%	58bp	58bp	18%	26.1M
srr27320698_2						41.9%	48.0%	59bp	59bp	18%	26.2M	1.6%	41.8%	48.0%	58bp	58bp	18%	26.1M

Uncheck the tick box to hide columns. Click and drag the handle on the left to change order. Table ID: general_stats_table_table

Sort	Group	Column	Description	ID	Scale
\|\|	Salmon	% Aligned	% Mapped reads	`salmon-percent_mapped`
\|\|	Salmon	M Aligned	Mapped reads (millions)	`salmon-num_mapped`	read_count
\|\|	Salmon	Library types	Library types	`salmon-library_types`
\|\|	Salmon	CFR	Compatible fragment ratio	`salmon-compatible_fragment_ratio`
\|\|	Salmon	M Bias	Strand mapping bias	`salmon-strand_mapping_bias`
\|\|	FastQC (raw)	Dups	% duplicate reads	`fastqc_raw-percent_duplicates`
\|\|	FastQC (raw)	GC	Average % GC content	`fastqc_raw-percent_gc`
\|\|	FastQC (raw)	Avg len	Average read length	`fastqc_raw-avg_sequence_length`
\|\|	FastQC (raw)	Median len	Median read length	`fastqc_raw-median_sequence_length`
\|\|	FastQC (raw)	Failed	Percentage of modules failed in FastQC report (includes those not plotted here)	`fastqc_raw-percent_fails`
\|\|	FastQC (raw)	Seqs	Total sequences (millions)	`fastqc_raw-total_sequences`	read_count
\|\|	Cutadapt	Trimmed bases	% total base pairs trimmed	`cutadapt-percent_trimmed`
\|\|	FastQC (trimmed)	Dups	% duplicate reads	`fastqc_trimmed-percent_duplicates`
\|\|	FastQC (trimmed)	GC	Average % GC content	`fastqc_trimmed-percent_gc`
\|\|	FastQC (trimmed)	Avg len	Average read length	`fastqc_trimmed-avg_sequence_length`
\|\|	FastQC (trimmed)	Median len	Median read length	`fastqc_trimmed-median_sequence_length`
\|\|	FastQC (trimmed)	Failed	Percentage of modules failed in FastQC report (includes those not plotted here)	`fastqc_trimmed-percent_fails`
\|\|	FastQC (trimmed)	Seqs	Total sequences (millions)	`fastqc_trimmed-total_sequences`	read_count

FastQC (raw)

This section of the report shows FastQC results before adapter trimming.URL: http://www.bioinformatics.babraham.ac.uk/projects/fastqc

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.

Created with MultiQC

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.

Created with MultiQC

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.

Created with MultiQC

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 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.

Created with MultiQC

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.

Created with MultiQC

Sequence Length Distribution

The distribution of fragment sizes (read lengths) found. See the FastQC help

Created with MultiQC

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 (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.

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.

Created with MultiQC

Overrepresented sequences by sample

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 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 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 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.

88 samples had less than 1% of reads made up of overrepresented sequences

Top overrepresented sequences

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.

Showing ⁴/₄ rows and ³/₃ columns.

Overrepresented sequence	Reports	Occurrences	% of all reads
ACTGTCTCTTATACACATCTGACGCTGCCGACGACCTTCGTGATGTGTAG	1	62800	0.0024%
ACTGTCTCTTATACACATCTCCGAGCCCACGAGACGGTTGCGAGGATCTC	1	41302	0.0016%
ACTGTCTCTTATACACATCTGACGCTGCCGACGATTGCTCTATTGTGTAG	1	38013	0.0015%
TCTGTCTCTTATACACATCTCCGAGCCCACGAGACGGTTGCGAGGATCTC	1	30501	0.0012%

Uncheck the tick box to hide columns. Click and drag the handle on the left to change order. Table ID: fastqc_raw_top_overrepresented_sequences_table_table

Sort	Column	Description	ID
\|\|	Reports	Number of FastQC reports where this sequence is founds as overrepresented	`fastqc_raw-samples`
\|\|	Occurrences	Total number of occurrences of the sequence (among the samples where the sequence is overrepresented)	`fastqc_raw-total_count`
\|\|	% of all reads	Total number of occurrences as the percentage of all reads (among samples where the sequence is overrepresented)	`fastqc_raw-total_percent`

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.

Created with MultiQC

Status Checks

Status for each FastQC section showing whether results seem entirely normal (green), slightly abnormal (orange) or very unusual (red).

FastQC assigns a status for each section of the report. These give a quick evaluation of whether the results of the analysis seem entirely normal (green), slightly abnormal (orange) or very unusual (red).

It is important to stress that although the analysis results appear to give a pass/fail result, these evaluations must be taken in the context of what you expect from your library. A 'normal' sample as far as FastQC is concerned is random and diverse. Some experiments may be expected to produce libraries which are biased in particular ways. You should treat the summary evaluations therefore as pointers to where you should concentrate your attention and understand why your library may not look random and diverse.

Specific guidance on how to interpret the output of each module can be found in the relevant report section, or in the FastQC help.

In this heatmap, we summarise all of these into a single heatmap for a quick overview. Note that not all FastQC sections have plots in MultiQC reports, but all status checks are shown in this heatmap.

Created with MultiQC

Cutadapt

Finds and removes adapter sequences, primers, poly-A tails, and other types of unwanted sequences.URL: https://cutadapt.readthedocs.ioDOI: 10.14806/ej.17.1.200

Filtered Reads

This plot shows the number of reads (SE) / pairs (PE) removed by Cutadapt.

Created with MultiQC

Trimmed Sequence Lengths (3')

This plot shows the number of reads with certain lengths of adapter trimmed for the 3' end.

Obs/Exp shows the raw counts divided by the number expected due to sequencing errors. A defined peak may be related to adapter length.

See the cutadapt documentation for more information on how these numbers are generated.

Created with MultiQC

FastQC (trimmed)

This section of the report shows FastQC results after adapter trimming.URL: http://www.bioinformatics.babraham.ac.uk/projects/fastqc

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:

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.

Created with MultiQC

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:

Created with MultiQC

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:

Created with MultiQC

Per Base Sequence Content

The proportion of each base position for which each of the four normal DNA bases has been called.

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.

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.

Created with MultiQC

Per Base N Content

The percentage of base calls at each position for which an N was called.

From the FastQC help:

Created with MultiQC

Sequence Length Distribution

The distribution of fragment sizes (read lengths) found. See the FastQC help

Created with MultiQC

Sequence Duplication Levels

The relative level of duplication found for every sequence.

From the FastQC Help:

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.

Created with MultiQC

Overrepresented sequences by sample

The total amount of overrepresented sequences found in each library.

From the FastQC Help:

88 samples had less than 1% of reads made up of overrepresented sequences

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:

Created with MultiQC

Status Checks

Status for each FastQC section showing whether results seem entirely normal (green), slightly abnormal (orange) or very unusual (red).

Specific guidance on how to interpret the output of each module can be found in the relevant report section, or in the FastQC help.

Created with MultiQC

Salmon

Quantifies expression of transcripts using RNA-seq data.URL: https://combine-lab.github.io/salmonDOI: 10.1038/nmeth.4197

Created with MultiQC

Sample relationships

Plots interrogating sample relationships, based on final count matrices.

SALMON DESeq2 sample similarity

is generated from clustering by Euclidean distances between DESeq2 rlog values for each sample in the deseq2_qc.r script.

Created with MultiQC

SALMON DESeq2 PCA plot

PCA plot between samples in the experiment. These values are calculated using DESeq2 in the deseq2_qc.r script.

Created with MultiQC

Software Versions

Software Versions lists versions of software tools extracted from file contents.

Group	Software	Version
CUSTOM_GETCHROMSIZES	getchromsizes	`1.21`
CUSTOM_TX2GENE	python	`3.10.4`
DESEQ2_QC_PSEUDO	bioconductor-deseq2	`1.28.0`
	r-base	`4.0.3`
FASTQC	fastqc	`0.12.1`
FQ_LINT	fq	`0.12.0 (2024-07-08)`
FQ_SUBSAMPLE	fq	`0.12.0 (2024-07-08)`
GTF2BED	perl	`5.26.2`
GTF_FILTER	python	`3.9.5`
GUNZIP_FASTA	gunzip	`1.13`
GUNZIP_GTF	gunzip	`1.13`
MAKE_TRANSCRIPTS_FASTA	rsem	`1.3.1`
	star	`2.7.10a`
SALMON_QUANT	salmon	`1.10.3`
SE_GENE_UNIFIED	bioconductor-summarizedexperiment	`1.32.0`
SE_TRANSCRIPT_UNIFIED	bioconductor-summarizedexperiment	`1.32.0`
TRIMGALORE	cutadapt	`4.9`
	pigz	`2.8`
	trimgalore	`0.6.10`
TXIMETA_TXIMPORT	bioconductor-tximeta	`1.20.1`
Workflow	Nextflow	`25.04.6`
	nf-core/rnaseq	`v3.21.0-g9738a2d`

nf-core/rnaseq Methods Description

Suggested text and references to use when describing pipeline usage within the methods section of a publication.URL: https://github.com/nf-core/rnaseq

Methods

Data was processed using nf-core/rnaseq v3.21.0 (doi: 10.5281/zenodo.1400710) of the nf-core collection of workflows (Ewels et al., 2020), utilising reproducible software environments from the Bioconda (Grüning et al., 2018) and Biocontainers (da Veiga Leprevost et al., 2017) projects.

The pipeline was executed with Nextflow v25.04.6 (Di Tommaso et al., 2017) with the following command:

nextflow run nf-core/rnaseq -profile unavcluster -params-file params_rnaseq.yaml -c hpc.config

References

Di Tommaso, P., Chatzou, M., Floden, E. W., Barja, P. P., Palumbo, E., & Notredame, C. (2017). Nextflow enables reproducible computational workflows. Nature Biotechnology, 35(4), 316-319. doi: 10.1038/nbt.3820
Ewels, P. A., Peltzer, A., Fillinger, S., Patel, H., Alneberg, J., Wilm, A., Garcia, M. U., Di Tommaso, P., & Nahnsen, S. (2020). The nf-core framework for community-curated bioinformatics pipelines. Nature Biotechnology, 38(3), 276-278. doi: 10.1038/s41587-020-0439-x
Grüning, B., Dale, R., Sjödin, A., Chapman, B. A., Rowe, J., Tomkins-Tinch, C. H., Valieris, R., Köster, J., & Bioconda Team. (2018). Bioconda: sustainable and comprehensive software distribution for the life sciences. Nature Methods, 15(7), 475–476. doi: 10.1038/s41592-018-0046-7
da Veiga Leprevost, F., Grüning, B. A., Alves Aflitos, S., Röst, H. L., Uszkoreit, J., Barsnes, H., Vaudel, M., Moreno, P., Gatto, L., Weber, J., Bai, M., Jimenez, R. C., Sachsenberg, T., Pfeuffer, J., Vera Alvarez, R., Griss, J., Nesvizhskii, A. I., & Perez-Riverol, Y. (2017). BioContainers: an open-source and community-driven framework for software standardization. Bioinformatics (Oxford, England), 33(16), 2580–2582. doi: 10.1093/bioinformatics/btx192

Notes:

The command above does not include parameters contained in any configs or profiles that may have been used. Ensure the config file is also uploaded with your publication!
You should also cite all software used within this run. Check the "Software Versions" of this report to get version information.

nf-core/rnaseq Workflow Summary

this information is collected when the pipeline is started.URL: https://github.com/nf-core/rnaseq

Input/output options

input: samplesheet_locrc.csv
multiqc_title: crc-rna-seq-salmon
outdir: results

Reference genome options

fasta: data/genomes/GRCh38/GRCh38.primary_assembly.genome.fa.gz
gencode: true
gtf: data/genomes/GRCh38/gencode.v49.primary_assembly.annotation.gtf.gz
igenomes_ignore: true
salmon_index: data/genomes/GRCh38/genome_gencode/index/salmon

UMI options

umi_discard_read: 0

Alignment options

pseudo_aligner: salmon

Process skipping options

skip_alignment: true

Generic options

trace_report_suffix: 2025-11-29_13-36-58

Core Nextflow options

configFiles: /home/samarquez/.nextflow/assets/nf-core/rnaseq/nextflow.config, /beegfs/home/samarquez/tfm-rnaseq/master-bioinformatics/nextflow-pipelines/nf-core-rnaseq/nf-core-rnaseq-salmon-pe/hpc.config
containerEngine: singularity
launchDir: /beegfs/home/samarquez/tfm-rnaseq/master-bioinformatics/nextflow-pipelines/nf-core-rnaseq/nf-core-rnaseq-salmon-pe
profile: unavcluster
projectDir: /home/samarquez/.nextflow/assets/nf-core/rnaseq
revision: master
runName: cheeky_kalam
userName: samarquez
workDir: /beegfs/home/samarquez/tfm-rnaseq/master-bioinformatics/nextflow-pipelines/nf-core-rnaseq/nf-core-rnaseq-salmon-pe/work

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crc-rna-seq-salmon

General Statistics

General Statistics: Columns

FastQC (raw)

Sequence Counts Help

Sequence Quality Histograms Help

Per Sequence Quality Scores Help

Per Base Sequence Content Help

Rollover for sample name

Per Sequence GC Content Help

Per Base N Content Help

Sequence Length Distribution

Sequence Duplication Levels Help

Overrepresented sequences by sample Help

Top overrepresented sequences

FastQC: Top overrepresented sequences: Columns

Adapter Content Help

Status Checks Help

Cutadapt

Filtered Reads

Trimmed Sequence Lengths (3') Help

FastQC (trimmed)

Sequence Counts Help

Sequence Quality Histograms Help

Per Sequence Quality Scores Help

Per Base Sequence Content Help

Rollover for sample name

Per Sequence GC Content Help

Per Base N Content Help

Sequence Length Distribution

Sequence Duplication Levels Help

Overrepresented sequences by sample Help

Adapter Content Help

Status Checks Help

Salmon

Sample relationships

SALMON DESeq2 sample similarity

SALMON DESeq2 PCA plot

Software Versions

nf-core/rnaseq Methods Description

Methods

References

Notes:

nf-core/rnaseq Workflow Summary

v1.31

Highlight Samples

Rename Samples

Show / Hide Samples

Sequence Counts

Sequence Quality Histograms

Per Sequence Quality Scores

Per Base Sequence Content

Per Sequence GC Content

Per Base N Content

Sequence Duplication Levels

Overrepresented sequences by sample

Adapter Content

Status Checks

Trimmed Sequence Lengths (3')

Sequence Counts

Sequence Quality Histograms

Per Sequence Quality Scores

Per Base Sequence Content

Per Sequence GC Content

Per Base N Content

Sequence Duplication Levels

Overrepresented sequences by sample

Adapter Content

Status Checks