MultiQC Report

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/smrnaseq analysis pipeline. For information about how to interpret these results, please see the documentation.

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Report generated on 2025-04-23, 14:59 CEST based on data in: /global/home/TRI.LAN/benat.villanueva/Documents/smrnaseq_dre_dark/work/77/6f2c9ad743b0758cc42245b98d402d

General Statistics

Showing ⁰/₅₆ rows and ¹²/₁₇ columns.

Sample Name	M Ref miRNA reads	IsomiR %	M IsomiR reads	M Reads	Error rate	Non-primary	Reads mapped	% Mapped	% Proper pairs	% MapQ 0 reads	Total seqs	Dups	GC	Avg len	Median len	Failed	Seqs
V350307986_1	0.0M	0.0%	0.0M	0.0M	9.23%	0.0M	0.0M	0.0%	0.0%	0.0%	11.5M	52.3%	47.0%	40bp	47bp	20%	24.5M
V350307986_10	0.0M	0.0%	0.0M	0.0M	2.32%	0.0M	0.0M	0.1%	0.0%	0.0%	11.5M	52.8%	48.0%	38bp	44bp	20%	24.7M
V350307986_11	0.0M	0.0%	0.0M	0.0M	9.65%	0.0M	0.0M	0.0%	0.0%	0.0%	10.8M	53.6%	48.0%	39bp	47bp	20%	22.8M
V350307986_12	0.0M	0.0%	0.0M	0.0M	2.26%	0.0M	0.0M	0.1%	0.0%	0.0%	11.4M	60.3%	51.0%	38bp	44bp	20%	23.3M
V350307986_13	0.0M	0.0%	0.0M	0.0M	9.73%	0.0M	0.0M	0.0%	0.0%	0.0%	12.1M	56.7%	48.0%	38bp	47bp	20%	27.1M
V350307986_14	0.0M	0.0%	0.0M	0.0M	9.79%	0.0M	0.0M	0.0%	0.0%	0.0%	7.4M	52.5%	47.0%	39bp	47bp	20%	19.0M
V350307986_15	0.0M	0.0%	0.0M	0.0M	1.91%	0.0M	0.0M	0.2%	0.0%	0.0%	9.8M	62.5%	50.0%	38bp	44bp	20%	21.8M
V350307986_16	0.0M	0.0%	0.0M	0.0M	2.11%	0.0M	0.0M	0.2%	0.0%	0.0%	9.8M	70.5%	51.0%	38bp	44bp	20%	22.2M
V350307986_17	0.0M	0.0%	0.0M	0.0M	1.81%	0.0M	0.0M	0.2%	0.0%	0.0%	7.2M	60.7%	49.0%	39bp	47bp	20%	16.5M
V350307986_18	0.0M	0.0%	0.0M	0.0M	9.58%	0.0M	0.0M	0.0%	0.0%	0.0%	7.3M	58.4%	48.0%	39bp	47bp	30%	17.5M
V350307986_19	0.0M	0.0%	0.0M	0.0M	1.75%	0.0M	0.0M	0.3%	0.0%	0.0%	9.6M	58.8%	48.0%	39bp	47bp	10%	23.4M
V350307986_2	0.0M	0.0%	0.0M	0.0M	1.69%	0.0M	0.0M	0.2%	0.0%	0.0%	10.5M	62.6%	51.0%	40bp	47bp	20%	22.0M
V350307986_20	0.0M	0.0%	0.0M	0.0M	9.21%	0.0M	0.0M	0.0%	0.0%	0.0%	8.0M	56.3%	47.0%	39bp	47bp	20%	19.3M
V350307986_21	0.0M	0.0%	0.0M	0.0M	1.70%	0.0M	0.0M	0.2%	0.0%	0.0%	6.3M	57.5%	47.0%	39bp	47bp	30%	15.8M
V350307986_22	0.0M	0.0%	0.0M	0.0M	9.15%	0.0M	0.0M	0.0%	0.0%	0.0%	20.6M	76.7%	52.0%	39bp	47bp	30%	43.9M
V350307986_23	0.0M	0.0%	0.0M	0.0M	9.58%	0.0M	0.0M	0.1%	0.0%	0.0%	11.9M	63.0%	49.0%	39bp	47bp	10%	28.3M
V350307986_24	0.0M	0.0%	0.0M	0.0M	1.71%	0.0M	0.1M	0.3%	0.0%	0.0%	19.5M	63.6%	50.0%	41bp	47bp	10%	40.4M
V350307986_25	0.0M	0.0%	0.0M	0.0M	2.43%	0.0M	0.0M	0.1%	0.0%	0.0%	15.7M	67.1%	52.0%	42bp	47bp	20%	33.4M
V350307986_26	0.0M	0.0%	0.0M	0.0M	1.86%	0.0M	0.0M	0.2%	0.0%	0.0%	15.8M	68.0%	52.0%	41bp	47bp	20%	32.6M
V350307986_27	0.0M	0.0%	0.0M	0.0M	8.30%	0.0M	0.0M	0.0%	0.0%	0.0%	17.0M	66.6%	51.0%	42bp	47bp	20%	37.2M
V350307986_28	0.0M	0.0%	0.0M	0.0M	9.11%	0.0M	0.0M	0.0%	0.0%	0.0%	13.2M	65.0%	52.0%	42bp	47bp	20%	28.4M
V350307986_29	0.0M	0.0%	0.0M	0.0M	1.85%	0.0M	0.0M	0.2%	0.0%	0.0%	13.4M	67.5%	51.0%	42bp	47bp	20%	30.7M
V350307986_3	0.0M	0.0%	0.0M	0.0M	1.95%	0.0M	0.0M	0.2%	0.0%	0.0%	11.6M	51.8%	48.0%	39bp	47bp	10%	24.4M
V350307986_30	0.0M	0.0%	0.0M	0.0M	1.91%	0.0M	0.0M	0.2%	0.0%	0.0%	14.5M	65.5%	51.0%	42bp	47bp	20%	32.7M
V350307986_31	0.0M	0.0%	0.0M	0.0M	1.75%	0.0M	0.0M	0.3%	0.0%	0.0%	15.4M	65.9%	50.0%	43bp	47bp	20%	35.4M
V350307986_32	0.0M	0.0%	0.0M	0.0M	8.40%	0.0M	0.0M	0.0%	0.0%	0.0%	16.2M	65.6%	50.0%	43bp	47bp	20%	37.1M
V350307986_33	0.0M	0.0%	0.0M	0.0M	7.98%	0.0M	0.0M	0.0%	0.0%	0.0%	9.8M	68.1%	50.0%	43bp	47bp	10%	24.8M
V350307986_34	0.0M	0.0%	0.0M	0.0M	1.83%	0.0M	0.0M	0.2%	0.0%	0.0%	10.9M	65.9%	51.0%	43bp	47bp	20%	24.3M
V350307986_35	0.0M	0.0%	0.0M	0.0M	1.94%	0.0M	0.0M	0.2%	0.0%	0.0%	13.4M	65.5%	50.0%	42bp	47bp	10%	31.0M
V350307986_36	0.0M	0.0%	0.0M	0.0M	2.20%	0.0M	0.0M	0.1%	0.0%	0.0%	17.2M	71.8%	53.0%	40bp	47bp	10%	36.9M
V350307986_37	0.0M	0.0%	0.0M	0.0M	1.99%	0.0M	0.0M	0.2%	0.0%	0.0%	12.5M	69.3%	52.0%	42bp	47bp	20%	27.8M
V350307986_38	0.0M	0.0%	0.0M	0.0M	1.87%	0.0M	0.0M	0.1%	0.0%	0.0%	8.1M	69.2%	52.0%	41bp	47bp	10%	17.8M
V350307986_39	0.0M	0.0%	0.0M	0.0M	8.91%	0.0M	0.0M	0.0%	0.0%	0.0%	13.6M	70.8%	53.0%	42bp	47bp	20%	29.2M
V350307986_4	0.0M	0.0%	0.0M	0.0M	9.61%	0.0M	0.0M	0.0%	0.0%	0.0%	12.2M	65.1%	52.0%	39bp	47bp	20%	25.0M
V350307986_40	0.0M	0.0%	0.0M	0.0M	9.36%	0.0M	0.0M	0.0%	0.0%	0.0%	16.9M	66.9%	52.0%	42bp	47bp	20%	36.2M
V350307986_41	0.0M	0.0%	0.0M	0.0M	9.37%	0.0M	0.0M	0.0%	0.0%	0.0%	16.1M	76.2%	54.0%	42bp	47bp	20%	33.9M
V350307986_42	0.0M	0.0%	0.0M	0.0M	9.03%	0.0M	0.0M	0.0%	0.0%	0.0%	17.1M	64.8%	50.0%	42bp	47bp	10%	39.4M
V350307986_43	0.0M	0.0%	0.0M	0.0M	8.94%	0.0M	0.0M	0.0%	0.0%	0.0%	15.6M	72.7%	54.0%	42bp	47bp	20%	31.9M
V350307986_44	0.0M	0.0%	0.0M	0.0M	1.91%	0.0M	0.0M	0.3%	0.0%	0.0%	13.0M	62.7%	50.0%	42bp	47bp	20%	30.5M
V350307986_45	0.0M	0.0%	0.0M	0.0M	1.62%	0.0M	0.1M	0.3%	0.0%	0.0%	16.6M	63.5%	50.0%	42bp	47bp	20%	37.4M
V350307986_46	0.0M	0.0%	0.0M	0.0M	2.75%	0.0M	0.0M	0.1%	0.0%	0.0%	16.0M	77.4%	55.0%	42bp	47bp	20%	35.4M
V350307986_47	0.0M	0.0%	0.0M	0.0M	2.89%	0.0M	0.0M	0.2%	0.0%	0.0%	12.7M	63.7%	51.0%	43bp	47bp	20%	29.6M
V350307986_48	0.0M	0.0%	0.0M	0.0M	2.38%	0.0M	0.0M	0.1%	0.0%	0.0%	14.2M	66.0%	51.0%	39bp	47bp	20%	32.6M
V350307986_49	0.0M	0.0%	0.0M	0.0M	8.91%	0.0M	0.0M	0.0%	0.0%	0.0%	11.0M	70.9%	53.0%	40bp	47bp	20%	25.1M
V350307986_5	0.0M	0.0%	0.0M	0.0M	2.20%	0.0M	0.0M	0.2%	0.0%	0.0%	6.0M	44.0%	47.0%	40bp	47bp	0%	13.0M
V350307986_50	0.0M	0.0%	0.0M	0.0M	1.84%	0.0M	0.0M	0.2%	0.0%	0.0%	9.4M	66.4%	50.0%	39bp	47bp	30%	23.2M
V350307986_51	0.0M	0.0%	0.0M	0.0M	9.67%	0.0M	0.0M	0.0%	0.0%	0.0%	8.8M	61.1%	50.0%	40bp	47bp	20%	22.5M
V350307986_52	0.0M	0.0%	0.0M	0.0M	2.55%	0.0M	0.0M	0.1%	0.0%	0.0%	14.3M	64.7%	50.0%	38bp	47bp	20%	34.8M
V350307986_53	0.0M	0.0%	0.0M	0.0M	9.25%	0.0M	0.0M	0.0%	0.0%	0.0%	10.8M	59.5%	49.0%	40bp	47bp	30%	25.6M
V350307986_54	0.0M	0.0%	0.0M	0.0M	9.24%	0.0M	0.0M	0.0%	0.0%	0.0%	15.3M	78.5%	55.0%	38bp	47bp	20%	33.7M
V350307986_55	0.0M	0.0%	0.0M	0.0M	9.47%	0.0M	0.0M	0.0%	0.0%	0.0%	10.6M	57.7%	49.0%	40bp	47bp	30%	24.2M
V350307986_56	0.0M	0.0%	0.0M	0.0M	1.87%	0.0M	0.0M	0.1%	0.0%	0.0%	10.0M	62.2%	49.0%	38bp	47bp	20%	24.6M
V350307986_6	0.0M	0.0%	0.0M	0.0M	9.85%	0.0M	0.0M	0.0%	0.0%	0.0%	10.3M	50.9%	47.0%	40bp	47bp	10%	22.5M
V350307986_7		0.0%	0.0M	0.0M	9.63%	0.0M	0.0M	0.1%	0.0%	0.0%	2.7M	19.7%	48.0%	38bp	47bp	20%	5.7M
V350307986_8	0.0M	0.0%	0.0M	0.0M	9.51%	0.0M	0.0M	0.0%	0.0%	0.0%	11.1M	55.7%	50.0%	39bp	47bp	20%	23.1M
V350307986_9	0.0M	0.0%	0.0M	0.0M	9.84%	0.0M	0.0M	0.0%	0.0%	0.0%	10.7M	53.4%	48.0%	39bp	47bp	20%	23.5M

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
\|\|	mirtop	M Ref miRNA reads	Read counts summed over all reference miRNAs (millions)	`mirtop-ref_miRNA_sum`	read_count
\|\|	mirtop	IsomiR %	% of total read counts corresponding to isomiRs	`mirtop-isomiR_perc`
\|\|	mirtop	M IsomiR reads	Read counts summed over all isomiRs (millions)	`mirtop-isomiR_sum`	read_count
\|\|	mirtop	M Reads	Total read counts - both isomiRs and reference miRNA (millions)	`mirtop-read_count`	read_count
\|\|	Samtools: stats	Error rate	Error rate: mismatches (NM) / bases mapped (CIGAR)	`samtools_stats-error_rate`
\|\|	Samtools: stats	Non-primary	Non-primary alignments (millions)	`samtools_stats-non_primary_alignments`	read_count
\|\|	Samtools: stats	Reads mapped	Reads mapped in the bam file (millions)	`samtools_stats-reads_mapped`	read_count
\|\|	Samtools: stats	% Mapped	% Mapped reads	`samtools_stats-reads_mapped_percent`
\|\|	Samtools: stats	% Proper pairs	% Properly paired reads	`samtools_stats-reads_properly_paired_percent`
\|\|	Samtools: stats	% MapQ 0 reads	% of reads that are ambiguously placed (MapQ=0)	`samtools_stats-reads_MQ0_percent`
\|\|	Samtools: stats	Total seqs	Total sequences in the bam file (millions)	`samtools_stats-raw_total_sequences`	read_count
\|\|	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`

Contamination Filtering

This plot shows the amount of reads filtered by contaminant type.

Created with MultiQC

mirtop

Annotates miRNAs and isomiRs and compute general statistics in mirGFF3 format.URL: https://github.com/miRTop/mirtopDOI: 10.5281/zenodo.45385

This tool is dedicated to the creation and management of miRNA alignment output using the standardized GFF3 format (see [miRTop/mirGFF3](https://github.com/miRTop/mirGFF3)). A unified miRNA alignment format allows to easily compare the output of different alignment tools. Currently, mirtop can convert into mirGFF3 the outputs of commonly used pipelines, such as seqbuster, isomiR-SEA, sRNAbench, Prost! as well as BAM files.

IsomiR read counts

Total counts of reads aligned for each isomiR type, over all detected miRNAs.

The total counts of reads detected as reference miRNA sequences is also shown.

Since a read can belong to 2 (or more) different isomiRs types (e.g iso_3p and iso_5p), the cumulative read counts shown in this plot for a sample can be higher than its total read count shown in the general statistics.

For each sample, the mean counts of each type of isomiRs over all detected miRNAs is displayed in a different color.

The different isomiR types are:

iso_3p: a sequence with a 3' end difference because of trimming or templated tailing
iso_5p: a sequence with a 5' end difference because of trimming or templated tailing
iso_add3p: a sequence with non templated tailing in the 3' end
iso_add5p: a sequence with non templated tailing in the 5' end
iso_snv: a sequence with a single nucleotide variant

The ref_miRNA label corresponds to the reference miRNA (canonical sequence).

Created with MultiQC

IsomiR unique read counts

The number of distinct sequences detected for each isomiR type, over all miRNAs.

The number of reference miRNA sequences detected is also shown.

For each sample, the number of miRNAs with each type of isomiRs, is displayed in a different color.

The different isomiR types are:

iso_3p: a sequence with a 3' end difference because of trimming or templated tailing
iso_5p: a sequence with a 5' end difference because of trimming or templated tailing
iso_add3p: a sequence with non templated tailing in the 3' end
iso_add5p: a sequence with non templated tailing in the 5' end
iso_snv: a sequence with a single nucleotide variant

The ref_miRNA label corresponds to the reference miRNA (canonical sequence).

Created with MultiQC

Mean isomiR read counts

Mean counts for each isomiR type, over all detected miRNAs.

The mean counts of reads detected as reference miRNA sequences is also shown.

For each sample, the mean counts of each type of isomiRs over all detected miRNAs is displayed in a different color.

The different isomiR types are:

iso_3p: a sequence with a 3' end difference because of trimming or templated tailing
iso_5p: a sequence with a 5' end difference because of trimming or templated tailing
iso_add3p: a sequence with non templated tailing in the 3' end
iso_add5p: a sequence with non templated tailing in the 5' end
iso_snv: a sequence with a single nucleotide variant

The ref_miRNA label corresponds to the reference miRNA (canonical sequence).

Created with MultiQC

miRTrace

Quality control for small RNA sequencing data.URL: https://github.com/friedlanderlab/mirtraceDOI: 10.1186/s13059-018-1588-9

miRTrace performs adapter trimming and discards the reads that fail to pass the QC filters. miRTrace specifically addresses sequencing quality, read length, sequencing depth and miRNA complexity and also identifies the presence of both miRNAs and undesirable sequences derived from tRNAs, rRNAs, or Illumina artifact sequences. miRTrace also profiles clade-specific miRNAs based on a comprehensive catalog of clade-specific miRNA families identified previously. With this information, miRTrace can detect exogenous miRNAs, which could be contamination derived, e.g. index mis-assignment on sample demultiplexing, or biologically derived, e.g. parasitic RNAs.

QC Plot

Created with MultiQC

RNA Categories

Created with MultiQC

Read Length Distribution

Created with MultiQC

Contamination Check

Created with MultiQC

miRNA Complexity

Created with MultiQC

Samtools

Toolkit for interacting with BAM/CRAM files.URL: http://www.htslib.orgDOI: 10.1093/bioinformatics/btp352

Percent mapped

Alignment metrics from samtools stats; mapped vs. unmapped reads vs. reads mapped with MQ0.

For a set of samples that have come from the same multiplexed library, similar numbers of reads for each sample are expected. Large differences in numbers might indicate issues during the library preparation process. Whilst large differences in read numbers may be controlled for in downstream processings (e.g. read count normalisation), you may wish to consider whether the read depths achieved have fallen below recommended levels depending on the applications.

Low alignment rates could indicate contamination of samples (e.g. adapter sequences), low sequencing quality or other artefacts. These can be further investigated in the sequence level QC (e.g. from FastQC).

Reads mapped with MQ0 often indicate that the reads are ambiguously mapped to multiple locations in the reference sequence. This can be due to repetitive regions in the genome, the presence of alternative contigs in the reference, or due to reads that are too short to be uniquely mapped. These reads are often filtered out in downstream analyses.

Created with MultiQC

Alignment stats

This module parses the output from samtools stats. All numbers in millions.

Created with MultiQC

Showing ⁰/₅₆ rows and ¹³/₁₃ columns.

Sample Name	Total sequences	Mapped & paired	Properly paired	Duplicated	QC Failed	Reads MQ0	Mapped bases (CIGAR)	Bases Trimmed	Duplicated bases	Diff chromosomes	Other orientation	Inward pairs	Outward pairs
V350307986_1	11.5M	0.0M	0.0M	0.0M	0.0M	0.0M	0.1Mb	0.0Mb	0.0Mb	0.0M	0.0M	0.0M	0.0M
V350307986_10	11.5M	0.0M	0.0M	0.0M	0.0M	0.0M	0.3Mb	0.0Mb	0.0Mb	0.0M	0.0M	0.0M	0.0M
V350307986_11	10.8M	0.0M	0.0M	0.0M	0.0M	0.0M	0.1Mb	0.0Mb	0.0Mb	0.0M	0.0M	0.0M	0.0M
V350307986_12	11.4M	0.0M	0.0M	0.0M	0.0M	0.0M	0.2Mb	0.0Mb	0.0Mb	0.0M	0.0M	0.0M	0.0M
V350307986_13	12.1M	0.0M	0.0M	0.0M	0.0M	0.0M	0.1Mb	0.0Mb	0.0Mb	0.0M	0.0M	0.0M	0.0M
V350307986_14	7.4M	0.0M	0.0M	0.0M	0.0M	0.0M	0.1Mb	0.0Mb	0.0Mb	0.0M	0.0M	0.0M	0.0M
V350307986_15	9.8M	0.0M	0.0M	0.0M	0.0M	0.0M	0.3Mb	0.0Mb	0.0Mb	0.0M	0.0M	0.0M	0.0M
V350307986_16	9.8M	0.0M	0.0M	0.0M	0.0M	0.0M	0.3Mb	0.0Mb	0.0Mb	0.0M	0.0M	0.0M	0.0M
V350307986_17	7.2M	0.0M	0.0M	0.0M	0.0M	0.0M	0.3Mb	0.0Mb	0.0Mb	0.0M	0.0M	0.0M	0.0M
V350307986_18	7.3M	0.0M	0.0M	0.0M	0.0M	0.0M	0.1Mb	0.0Mb	0.0Mb	0.0M	0.0M	0.0M	0.0M
V350307986_19	9.6M	0.0M	0.0M	0.0M	0.0M	0.0M	0.5Mb	0.0Mb	0.0Mb	0.0M	0.0M	0.0M	0.0M
V350307986_2	10.5M	0.0M	0.0M	0.0M	0.0M	0.0M	0.3Mb	0.0Mb	0.0Mb	0.0M	0.0M	0.0M	0.0M
V350307986_20	8.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.1Mb	0.0Mb	0.0Mb	0.0M	0.0M	0.0M	0.0M
V350307986_21	6.3M	0.0M	0.0M	0.0M	0.0M	0.0M	0.2Mb	0.0Mb	0.0Mb	0.0M	0.0M	0.0M	0.0M
V350307986_22	20.6M	0.0M	0.0M	0.0M	0.0M	0.0M	0.1Mb	0.0Mb	0.0Mb	0.0M	0.0M	0.0M	0.0M
V350307986_23	11.9M	0.0M	0.0M	0.0M	0.0M	0.0M	0.1Mb	0.0Mb	0.0Mb	0.0M	0.0M	0.0M	0.0M
V350307986_24	19.5M	0.0M	0.0M	0.0M	0.0M	0.0M	1.2Mb	0.0Mb	0.0Mb	0.0M	0.0M	0.0M	0.0M
V350307986_25	15.7M	0.0M	0.0M	0.0M	0.0M	0.0M	0.4Mb	0.0Mb	0.0Mb	0.0M	0.0M	0.0M	0.0M
V350307986_26	15.8M	0.0M	0.0M	0.0M	0.0M	0.0M	0.5Mb	0.0Mb	0.0Mb	0.0M	0.0M	0.0M	0.0M
V350307986_27	17.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.1Mb	0.0Mb	0.0Mb	0.0M	0.0M	0.0M	0.0M
V350307986_28	13.2M	0.0M	0.0M	0.0M	0.0M	0.0M	0.1Mb	0.0Mb	0.0Mb	0.0M	0.0M	0.0M	0.0M
V350307986_29	13.4M	0.0M	0.0M	0.0M	0.0M	0.0M	0.5Mb	0.0Mb	0.0Mb	0.0M	0.0M	0.0M	0.0M
V350307986_3	11.6M	0.0M	0.0M	0.0M	0.0M	0.0M	0.4Mb	0.0Mb	0.0Mb	0.0M	0.0M	0.0M	0.0M
V350307986_30	14.5M	0.0M	0.0M	0.0M	0.0M	0.0M	0.5Mb	0.0Mb	0.0Mb	0.0M	0.0M	0.0M	0.0M
V350307986_31	15.4M	0.0M	0.0M	0.0M	0.0M	0.0M	0.9Mb	0.0Mb	0.0Mb	0.0M	0.0M	0.0M	0.0M
V350307986_32	16.2M	0.0M	0.0M	0.0M	0.0M	0.0M	0.1Mb	0.0Mb	0.0Mb	0.0M	0.0M	0.0M	0.0M
V350307986_33	9.8M	0.0M	0.0M	0.0M	0.0M	0.0M	0.1Mb	0.0Mb	0.0Mb	0.0M	0.0M	0.0M	0.0M
V350307986_34	10.9M	0.0M	0.0M	0.0M	0.0M	0.0M	0.3Mb	0.0Mb	0.0Mb	0.0M	0.0M	0.0M	0.0M
V350307986_35	13.4M	0.0M	0.0M	0.0M	0.0M	0.0M	0.4Mb	0.0Mb	0.0Mb	0.0M	0.0M	0.0M	0.0M
V350307986_36	17.2M	0.0M	0.0M	0.0M	0.0M	0.0M	0.4Mb	0.0Mb	0.0Mb	0.0M	0.0M	0.0M	0.0M
V350307986_37	12.5M	0.0M	0.0M	0.0M	0.0M	0.0M	0.4Mb	0.0Mb	0.0Mb	0.0M	0.0M	0.0M	0.0M
V350307986_38	8.1M	0.0M	0.0M	0.0M	0.0M	0.0M	0.2Mb	0.0Mb	0.0Mb	0.0M	0.0M	0.0M	0.0M
V350307986_39	13.6M	0.0M	0.0M	0.0M	0.0M	0.0M	0.1Mb	0.0Mb	0.0Mb	0.0M	0.0M	0.0M	0.0M
V350307986_4	12.2M	0.0M	0.0M	0.0M	0.0M	0.0M	0.1Mb	0.0Mb	0.0Mb	0.0M	0.0M	0.0M	0.0M
V350307986_40	16.9M	0.0M	0.0M	0.0M	0.0M	0.0M	0.1Mb	0.0Mb	0.0Mb	0.0M	0.0M	0.0M	0.0M
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V350307986_43	15.6M	0.0M	0.0M	0.0M	0.0M	0.0M	0.1Mb	0.0Mb	0.0Mb	0.0M	0.0M	0.0M	0.0M
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V350307986_45	16.6M	0.0M	0.0M	0.0M	0.0M	0.0M	0.9Mb	0.0Mb	0.0Mb	0.0M	0.0M	0.0M	0.0M
V350307986_46	16.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.3Mb	0.0Mb	0.0Mb	0.0M	0.0M	0.0M	0.0M
V350307986_47	12.7M	0.0M	0.0M	0.0M	0.0M	0.0M	0.4Mb	0.0Mb	0.0Mb	0.0M	0.0M	0.0M	0.0M
V350307986_48	14.2M	0.0M	0.0M	0.0M	0.0M	0.0M	0.3Mb	0.0Mb	0.0Mb	0.0M	0.0M	0.0M	0.0M
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V350307986_50	9.4M	0.0M	0.0M	0.0M	0.0M	0.0M	0.4Mb	0.0Mb	0.0Mb	0.0M	0.0M	0.0M	0.0M
V350307986_51	8.8M	0.0M	0.0M	0.0M	0.0M	0.0M	0.1Mb	0.0Mb	0.0Mb	0.0M	0.0M	0.0M	0.0M
V350307986_52	14.3M	0.0M	0.0M	0.0M	0.0M	0.0M	0.4Mb	0.0Mb	0.0Mb	0.0M	0.0M	0.0M	0.0M
V350307986_53	10.8M	0.0M	0.0M	0.0M	0.0M	0.0M	0.1Mb	0.0Mb	0.0Mb	0.0M	0.0M	0.0M	0.0M
V350307986_54	15.3M	0.0M	0.0M	0.0M	0.0M	0.0M	0.1Mb	0.0Mb	0.0Mb	0.0M	0.0M	0.0M	0.0M
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V350307986_56	10.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.2Mb	0.0Mb	0.0Mb	0.0M	0.0M	0.0M	0.0M
V350307986_6	10.3M	0.0M	0.0M	0.0M	0.0M	0.0M	0.1Mb	0.0Mb	0.0Mb	0.0M	0.0M	0.0M	0.0M
V350307986_7	2.7M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0Mb	0.0Mb	0.0Mb	0.0M	0.0M	0.0M	0.0M
V350307986_8	11.1M	0.0M	0.0M	0.0M	0.0M	0.0M	0.1Mb	0.0Mb	0.0Mb	0.0M	0.0M	0.0M	0.0M
V350307986_9	10.7M	0.0M	0.0M	0.0M	0.0M	0.0M	0.1Mb	0.0Mb	0.0Mb	0.0M	0.0M	0.0M	0.0M

Uncheck the tick box to hide columns. Click and drag the handle on the left to change order. Table ID: samtools-stats-dp_table-1

Sort	Column	Description	ID	Scale
\|\|	Total sequences	Total sequences	`raw_total_sequences`	read_count
\|\|	Mapped & paired	Paired-end technology bit set + both mates mapped	`reads_mapped_and_paired`	read_count
\|\|	Properly paired	Proper-pair bit set	`reads_properly_paired`	read_count
\|\|	Duplicated	PCR or optical duplicate bit set	`reads_duplicated`	read_count
\|\|	QC Failed	QC Failed	`reads_QC_failed`	read_count
\|\|	Reads MQ0	Reads mapped and MQ=0	`reads_MQ0`	read_count
\|\|	Mapped bases (CIGAR)	Mapped bases (CIGAR)	`bases_mapped__cigar`	base_count
\|\|	Bases Trimmed	Bases Trimmed	`bases_trimmed`	base_count
\|\|	Duplicated bases	Duplicated bases	`bases_duplicated`	base_count
\|\|	Diff chromosomes	Pairs on different chromosomes	`pairs_on_different_chromosomes`	read_count
\|\|	Other orientation	Pairs with other orientation	`pairs_with_other_orientation`	read_count
\|\|	Inward pairs	Inward oriented pairs	`inward_oriented_pairs`	read_count
\|\|	Outward pairs	Outward oriented pairs	`outward_oriented_pairs`	read_count

FastQC (raw)

This section of the report shows FastQC results on the input files.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.

Created with MultiQC

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 ⁰/₂₀ rows and ³/₃ columns.

Overrepresented sequence	Reports	Occurrences	% of all reads
CGCGACCTCAGATCAGACGTGGCGACCCGCTGAATTTAAGCATATTA	55	15938515	1.0422%
GGCCGGGCGCGGTGGCTCACGCCTGTAATCCCAGCTCTCAGGGAGGC	55	8057330	0.5269%
CGACTCTTAGCGGTGGATCACTCGGCTCGTGCGTCGATGAAGAACGC	55	9246140	0.6046%
GACTCTTAGCGGTGGATCACTCGGCTCGTGCGTCGATGAAGAACGCA	47	3430054	0.2243%
GGGTCGGGGTTTCGTACGTAGCAGAGCAGCTCCCTCGCTGCGATCTA	42	1671852	0.1093%
GTAGACGACCTGCTTCTGGGTCGGGGTTTCGTACGTAGCAGAGCAGC	37	1239801	0.0811%
ATTGTGAAGCAGAATTCACCAAGCGTTGGATTGTTCACCCACTAATA	34	2809290	0.1837%
AGAGCGAAAGCATTTGCCAAGAATGTTTTCATTAATC	33	3749607	0.2452%
AGAGCGAAAGCATTTGCCAAGAATGTTTTCATTAATCAAGAACG	32	1253627	0.0820%
AAGAATGTTTTCATTAATCAAG	31	1466606	0.0959%
AAGAATGTTTTCATTAATC	31	1500045	0.0981%
GATGTCCGGGGCTGCACGCGCGCTACACTGACTGGCTCAGCGTGTGC	29	1430199	0.0935%
AGAGCGAAAGCATTTGCCAAGAATGTTTTCATT	26	1024842	0.0670%
CAGAGCGAAAGCATTTGCCAAGAATGTTTTCATTAATC	26	979393	0.0640%
ATGATTAAGAGGGACGGCCGGGGGCATTCGTATTGCGCCGCTAGAGG	25	816406	0.0534%
CGCGCGGCGCCTCGCCTCGGCCGGCGCCTAGCAGCCGACTTAGAACT	25	1538406	0.1006%
GATTAAGTCCCTGCCCTTTGTACACACCGCCCGTCGCTACTACCGAT	22	996987	0.0652%
GTCTACGGCCATACCACCCTGAACGCGCCCGATCTCGTCTGATCTCG	21	1111864	0.0727%
GACCCGCTGAATTTAAGCATATTAGTCAGCGGAGG	19	1575013	0.1030%
CAAAGCACCCAACTTACACTTAGGAGATTTCAACTTAACTTGACCGC	19	656501	0.0429%

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

No samples found with any adapter contamination > 0.1%

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.

Min:

Max:

Created with MultiQC

Software Versions

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

Group	Software	Version
BLAT_CDNA	blat	`36`
BLAT_NCRNA	blat	`36`
BOWTIE2_ALIGN_CDNA	bowtie2	`2.5.2`
	pigz	`2.6`
	samtools	`1.18`
BOWTIE2_ALIGN_NCRNA	bowtie2	`2.5.2`
	pigz	`2.6`
	samtools	`1.18`
BOWTIE_MAP_GENOME	bowtie	`1.3.0`
	samtools	`1.16.1`
BOWTIE_MAP_HAIRPIN	bowtie	`1.3.0`
	samtools	`1.16.1`
BOWTIE_MAP_MATURE	bowtie	`1.3.0`
	samtools	`1.16.1`
BOWTIE_MAP_SEQCLUSTER	bowtie	`1.3.0`
	samtools	`1.16.1`
CSVTK_JOIN	csvtk	`0.30.0`
DATATABLE_MERGE	r-base	`3.6.2`
FASTQC_RAW	fastqc	`0.12.1`
FILTER_STATS	BusyBox	`1.32.1`
FORMAT_HAIRPIN	fastx_toolkit	`0.0.14`
FORMAT_MATURE	fastx_toolkit	`0.0.14`
GAWK_CDNA	gawk	`5.3.0`
GAWK_NCRNA	gawk	`5.3.0`
INDEX_CDNA	bowtie2	`2.5.2`
INDEX_HAIRPIN	bowtie	`1.3.0`
INDEX_MATURE	bowtie	`1.3.0`
INDEX_NCRNA	bowtie2	`2.5.2`
MIRDEEP2_MAPPER	mirdeep2	`2.0.1`
MIRDEEP2_MIRDEEP2	mirdeep2	`2.0.1`
MIRTOP_COUNTS	mirtop	`0.4.28`
MIRTOP_EXPORT	mirtop	`0.4.28`
MIRTOP_GFF	mirtop	`0.4.28`
MIRTOP_STATS	mirtop	`0.4.28`
MIRTRACE_QC	mirtrace	`1.0.1`
PARSE_HAIRPIN	seqkit	`2.6.1`
PARSE_MATURE	seqkit	`2.6.1`
SAMTOOLS_FLAGSTAT	samtools	`1.21`
SAMTOOLS_IDXSTATS	samtools	`1.21`
SAMTOOLS_INDEX	samtools	`1.21`
SAMTOOLS_SORT	samtools	`1.21`
SAMTOOLS_STATS	samtools	`1.21`
SEQCLUSTER_COLLAPSE	seqcluster	`1.2.9`
SEQKIT_FQ2FA	seqkit	`2.8.0`
SEQKIT_GREP_CDNA	seqkit	`2.8.0`
SEQKIT_GREP_NCRNA	seqkit	`2.8.0`
SEQKIT_REPLACE	seqkit	`2.8.0`
STATS_GAWK_CDNA	gawk	`5.3.0`
STATS_GAWK_NCRNA	gawk	`5.3.0`
Workflow	Nextflow	`24.04.3`
	nf-core/smrnaseq	`v2.4.0-g72c4c4c`

nf-core/smrnaseq Workflow Summary

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

Input/output options

input: samplesheet.csv
outdir: dir_out_smrnaseq

Reference genome options

bowtie_index: /global/home/TRI.LAN/benat.villanueva/dir_db/reference_genomes/GRCz11_BOWTIE/NCBI
fasta: /global/home/TRI.LAN/benat.villanueva/dir_db/reference_genomes/GRCz11_BOWTIE/NCBI/GCF_000002035.6_GRCz11_genomic.fna
hairpin: /global/home/TRI.LAN/benat.villanueva/dir_db/miRBase/hairpin.fa
mature: /global/home/TRI.LAN/benat.villanueva/dir_db/miRBase/mature.fa
mirgenedb_gff: /global/home/TRI.LAN/benat.villanueva/dir_db/MirGeneDB_3.0/ALL.gff
mirgenedb_hairpin: /global/home/TRI.LAN/benat.villanueva/dir_db/MirGeneDB_3.0/ALL-pre.fas
mirgenedb_mature: /global/home/TRI.LAN/benat.villanueva/dir_db/MirGeneDB_3.0/ALL-mature.fas
mirgenedb_species: dre
mirna_gtf: /global/home/TRI.LAN/benat.villanueva/dir_db/miRBase/dre.gff3
mirtrace_species: dre
save_reference: true

Trimming options

clip_r1: 28
three_prime_adapter: AAAAAAAGTCGGAGGCCAAGCGGTCT
three_prime_clip_r1: 5
trim_fastq: false

Contamination filter options

cdna: /global/home/TRI.LAN/benat.villanueva/dir_db/RNA_contamination_filtering/cDNA/Homo_sapiens.GRCh38.cdna.all.fa.gz
filter_contamination: true
ncrna: /global/home/TRI.LAN/benat.villanueva/dir_db/RNA_contamination_filtering/ncRNA/Homo_sapiens.GRCh38.ncrna.fa.gz

Skipping pipeline steps

skip_fastp: true

Core Nextflow options

configFiles: N/A
containerEngine: singularity
launchDir: /global/home/TRI.LAN/benat.villanueva/Documents/smrnaseq_dre_dark
profile: singularity
projectDir: /global/home/TRI.LAN/benat.villanueva/.nextflow/assets/nf-core/smrnaseq
revision: 2.4.0
runName: distracted_lamarck
userName: benat.villanueva@TRI.LAN
workDir: /global/home/TRI.LAN/benat.villanueva/Documents/smrnaseq_dre_dark/work

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About MultiQC

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Contamination Filtering

mirtop

IsomiR read counts Help

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miRTrace

QC Plot

RNA Categories

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Samtools

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Top overrepresented sequences

FastQC: Top overrepresented sequences: Columns

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Software Versions

nf-core/smrnaseq Workflow Summary

v1.25.1

Highlight Samples

Rename Samples

Show / Hide Samples

IsomiR read counts

IsomiR unique read counts

Mean isomiR read counts

Percent mapped

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