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Update usage.md
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@ -185,7 +185,7 @@ work # Directory containing the nextflow working files
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### Sequencing quality control
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nf-core taxprofiler offers [`falco`][https://github.com/smithlabcode/falco] as an alternative option to [`FastQC`](https://www.bioinformatics.babraham.ac.uk/projects/fastqc/).
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nf-core taxprofiler offers [`falco`](https://github.com/smithlabcode/falco) as an alternative option to [`FastQC`](https://www.bioinformatics.babraham.ac.uk/projects/fastqc/).
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### Preprocessing Steps
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@ -202,14 +202,14 @@ Raw sequencing read processing in the form of adapter clipping and paired-end re
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It is highly recommended to run this on raw reads to remove artifacts from sequencing that can cause false positive identification of taxa (e.g. contaminated reference genomes) and/or skews in taxonomic abundance profiles.
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There are currently two options for short-read preprocessing: `fastp` or `adapterremoval`.
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There are currently two options for short-read preprocessing: [`fastp`](https://github.com/OpenGene/fastp) or [`adapterremoval`](https://github.com/MikkelSchubert/adapterremoval).
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For adapter clipping, you can either rely on tool default adapter sequences, or supply your own adapters (`--shortread_qc_adapter1` and `--shortread_qc_adapter2`)
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By default, paired-end merging is not activated and paired-end profiling is performed where supported otherwise pairs will be independently profiled. If paired-end merging is activated you can also specify whether to include unmerged reads in the reads sent for profiling (`--shortread_qc_mergepairs` and `--shortread_qc_includeunmerged`).
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You can also turn off clipping and only perform paired-end merging, if requested. This can be useful when processing data downloaded from the ENA, SRA, or DDBJ (`--shortread_qc_skipadaptertrim`).
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Both tools support length filtering of reads and can be tuned with `--shortread_qc_minlength`. Performing length filtering can be useful to remove short (often low sequencing complexity) sequences that result in unspecific classification and therefore slow down runtime during profiling, with minimal gain.
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There is currently one option for long-read Oxford Nanopore processing: `porechop`.
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There is currently one option for long-read Oxford Nanopore processing: [`porechop`](https://github.com/rrwick/Porechop).
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For both short-read and long-read preprocessing, you can optionally save the resulting processed reads with `--save_preprocessed_reads`.
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@ -227,6 +227,8 @@ The tools offer different algorithms and parameters for removing low complexity
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You can optionally save the FASTQ output of the run merging with the `--save_complexityfiltered_reads`. If running with `fastp`, complexity filtering happens inclusively within the earlier shortread preprocessing step. Therefore there will not be an independent pipeline step for complexity filtering, and no independent FASTQ file (i.e. `--save_complexityfiltered_reads` will be ignored) - your complexity filtered reads will also be in the `fastp/` folder in the same file(s) as the preprocessed read.
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**We do not any read preprocessing or complexity filtering if you are using ONTs Guppy toolkit for basecalling and post-processing.**
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#### Host Removal
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Removal of possible-host reads from FASTQ files prior profiling can be activated with `--perform_shortread_hostremoval` or `--perform_longread_hostremoval`.
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@ -480,7 +482,7 @@ NXF_OPTS='-Xms1g -Xmx4g'
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Kraken2 allows the user to build custom databases. You can follow Kraken2 [tutorial](https://github.com/DerrickWood/kraken2/blob/master/docs/MANUAL.markdown#custom-databases).
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#### Centrifuge
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Centrifuge is similar to Kraken2 in that it enables to [build custom databases](https://ccb.jhu.edu/software/centrifuge/manual.shtml#custom-database).
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Centrifuge allows the user to [build custom databases](https://ccb.jhu.edu/software/centrifuge/manual.shtml#custom-database).
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#### Kaiju
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It is possible to [create custom databases](https://github.com/bioinformatics-centre/kaiju#custom-database) with Kaiju.
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@ -495,12 +497,29 @@ malt-build -i path/to/fasta/files/*.{fna,fa} -s DNA -d index -t 8 -st 4 -a2t meg
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```
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#### Bracken
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You can follow Bracken [tutorial](https://ccb.jhu.edu/software/bracken/index.shtml?t=manual) to build a custom database. Alternatively, you can use one of the indexes that can be found [here](https://benlangmead.github.io/aws-indexes/k2).
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#### KrakenUniq
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For KrakenUniq, we recommend using one of the available databases [here](https://benlangmead.github.io/aws-indexes/k2)
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#### DIAMOND
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To create a custom database for DIAMOND, the user should download and unzip the NCBI's taxonomy files. The `makedb` needs to be executed afterwards. A detailed description can be found [here](https://gensoft.pasteur.fr/docs/diamond/0.8.22/diamond_manual.pdf)
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```bash
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wget ftp://ftp.ncbi.nlm.nih.gov/pub/taxonomy/taxdmp.zip
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unzip taxdmp.zip
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## warning: large file!
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wget ftp://ftp.ncbi.nlm.nih.gov/pub/taxonomy/accession2taxid/prot.accession2taxid.FULL.gz
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## warning: takes a long time!
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cat ../raw/*.faa | diamond makedb -d testdb-diamond --taxonmap prot.accession2taxid.FULL.gz --taxonnodes nodes.dmp --taxonnames names.dmp
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rm *dmp *txt *gz *prt *zip
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```
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#### mOTUs
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A detailed description on how to download mOTUs database can be found [here](https://github.com/motu-tool/mOTUs)
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## Troubleshooting and FAQs
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