×

nf-core/diann @ 0.0.0-6c4ed3a

Generic DIA-NN module for running any DIA-NN operation including in-silico library generation, preliminary analysis, empirical library assembly, individual analysis, and final quantification

Latest version: 0.0.0-6c4ed3a
Total downloads: 8
Source: nf-core/modules
Authors: @pinin4fjords
Maintainers: @pinin4fjords

Summary

Generic DIA-NN module for running any DIA-NN operation including in-silico library generation, preliminary analysis, empirical library assembly, individual analysis, and final quantification

Get started

Add the following snippet to your workflow script to include this module.

include { DIANN } from 'nf-core/diann'

License

MIT License

Process
Name DIANN
Input 1 channel
#1 tuple
meta map

Groovy Map containing sample information e.g. [ id:'sample1' ]

ms_files file

MS data file(s) in mzML or Bruker .d format (can be single file or list). Thermo RAW files are only supported on Linux with DIA-NN 2.0+; older versions require conversion to mzML or .d format first. For preliminary/assembly/individual analysis, these are actual file paths. For final quantification with --use-quant, this should be an empty list.

*.{mzML,raw,d}
ms_file_names string

MS file basenames (not paths) as strings (can be single name or list). Used for final quantification step with --use-quant where only filenames are needed. For other analysis steps, this should be an empty list. Example: ['sample1.mzML', 'sample2.mzML'] or []

fasta file

FASTA database file for peptide searches. Use a placeholder file (e.g., 'NO_FASTA_FILE') if not needed for the specific analysis step.

*.{fasta,fa}
library file

Spectral library file in .speclib or .tsv format. Use a placeholder file (e.g., 'NO_LIB_FILE') if not needed for the specific analysis step.

*.{speclib,tsv}
quant directory

Directory containing .quant files from previous DIA-NN analysis. When provided, enables --use-quant mode to reuse cached quantification results, improving performance for empirical library assembly and final quantification. Pass empty list [] if not needed. Files are staged as 'quant/*' in the work directory.

Output 15 channels
#1 log tuple
meta map

Groovy Map containing sample information e.g. [ id:'sample1' ]

*.log.txt file

DIA-NN log file containing run information and recommended settings

*.log.txt
#2 gg_matrix tuple
meta map

Groovy Map containing sample information e.g. [ id:'sample1' ]

${prefix}.gg_matrix.tsv file

Gene group-level quantification matrix. Filename is determined by the prefix (task.ext.prefix or meta.id).

*.gg_matrix.tsv
#3 pg_matrix tuple
meta map

Groovy Map containing sample information e.g. [ id:'sample1' ]

${prefix}.pg_matrix.tsv file

Protein group-level quantification matrix. Filename is determined by the prefix (task.ext.prefix or meta.id).

*.pg_matrix.tsv
#4 pr_matrix tuple
meta map

Groovy Map containing sample information e.g. [ id:'sample1' ]

${prefix}.pr_matrix.tsv file

Precursor-level quantification matrix (peptides across runs). Filename is determined by the prefix (task.ext.prefix or meta.id).

*.pr_matrix.tsv
#5 diann_quant tuple
meta map

Groovy Map containing sample information e.g. [ id:'sample1' ]

*.quant file

Quantification results in .quant format (intermediate output for empirical library assembly and final quantification)

*.quant
#6 main_report tuple
meta map

Groovy Map containing sample information e.g. [ id:'sample1' ]

${prefix}.tsv file

Main DIA-NN report in TSV format containing peptide and protein quantification. Filename is determined by the prefix (task.ext.prefix or meta.id).

*.tsv
#7 report_stats tuple
meta map

Groovy Map containing sample information e.g. [ id:'sample1' ]

${prefix}.stats.tsv file

Report statistics including identification and quantification metrics. Filename is determined by the prefix (task.ext.prefix or meta.id).

*.stats.tsv
#8 final_speclib tuple
meta map

Groovy Map containing sample information e.g. [ id:'sample1' ]

${prefix}.speclib file

Empirical spectral library refined from experimental data. Produced by the library assembly step, which combines predicted library information with actual MS measurements to improve search accuracy.

*.speclib
#9 report_parquet tuple
meta map