millironx/taxprofiler
1
0
Fork 0
mirror of https://github.com/MillironX/taxprofiler.git synced 2024-11-25 19:09:56 +00:00
Code Issues Releases Packages Activity

Apply suggestions from code review

Browse source 
Co-authored-by: Sofia Stamouli <91951607+sofstam@users.noreply.github.com>
This commit is contained in:
James A. Fellows Yates 2023-01-19 08:55:23 +01:00 committed by GitHub
parent 33d4a84237
commit 6e43695264
No known key found for this signature in database
GPG key ID: 4AEE18F83AFDEB23
1 changed files with 4 additions and 4 deletions
Show stats Download patch file Download diff file Expand all files Collapse all files

8
docs/usage.md
View file

@ -66,7 +66,7 @@ An [example samplesheet](../assets/samplesheet.csv) has been provided with the p
nf-core/taxprofiler supports multiple databases being classified/profiled against in parallel for each tool. nf-core/taxprofiler supports multiple databases being classified/profiled against in parallel for each tool.
Databases can be supplied either in the form of a compressed `.tar.gz` archive of a directory containing all relevant database files or the path to a directory on the filesystem. Databases can be supplied either in the form of a compressed `.tar.gz` archive of a directory containing all relevant database files or the path to a directory on the filesystem.
The pipeline takes the locations and specific classification/profiling parameters of the tool of these databases as input via a four column comma-separated sheet. The pipeline takes the paths and specific classification/profiling parameters of the tool of these databases as input via a four column comma-separated sheet.
> ⚠️ nf-core/taxprofiler does not provide any databases by default, nor does it currently generate them for you. This must be performed manually by the user. See below for more information of the expected database files. > ⚠️ nf-core/taxprofiler does not provide any databases by default, nor does it currently generate them for you. This must be performed manually by the user. See below for more information of the expected database files.
@ -155,7 +155,7 @@ There are currently two options for short-read preprocessing: [`fastp`](https://
For adapter clipping, you can either rely on the tool's default adapter sequences, or supply your own adapters (`--shortread_qc_adapter1` and `--shortread_qc_adapter2`) For adapter clipping, you can either rely on the tool's default adapter sequences, or supply your own adapters (`--shortread_qc_adapter1` and `--shortread_qc_adapter2`)
By default, paired-end merging is not activated. In this case paired-end 'alignment' against the reference databases is performed where supported, and if not, supported pairs will be independently classified/profiled. If paired-end merging is activated you can also specify whether to include unmerged reads in the reads sent for classification/profiling (`--shortread_qc_mergepairs` and `--shortread_qc_includeunmerged`). By default, paired-end merging is not activated. In this case paired-end 'alignment' against the reference databases is performed where supported, and if not, supported pairs will be independently classified/profiled. If paired-end merging is activated you can also specify whether to include unmerged reads in the reads sent for classification/profiling (`--shortread_qc_mergepairs` and `--shortread_qc_includeunmerged`).
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`). 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`).
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 classificationprofiling, with minimal gain. 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 classification/profiling, with minimal gain.
There is currently one option for long-read Oxford Nanopore processing: [`porechop`](https://github.com/rrwick/Porechop). There is currently one option for long-read Oxford Nanopore processing: [`porechop`](https://github.com/rrwick/Porechop).
@ -183,7 +183,7 @@ Removal of possible-host reads from FASTQ files prior classification/profiling c
Similarly to complexity filtering, host-removal can be useful for runtime optimisation and reduction in misclassified reads. It is not always necessary to report classification of reads from a host when you already know the host of the sample, therefore you can gain a run-time and computational advantage by removing these prior typically resource-heavy classification/profiling with more efficient methods. Furthermore, particularly with human samples, you can reduce the number of false positives during classification/profiling that occur due to host-sequence contamination in reference genomes on public databases. Similarly to complexity filtering, host-removal can be useful for runtime optimisation and reduction in misclassified reads. It is not always necessary to report classification of reads from a host when you already know the host of the sample, therefore you can gain a run-time and computational advantage by removing these prior typically resource-heavy classification/profiling with more efficient methods. Furthermore, particularly with human samples, you can reduce the number of false positives during classification/profiling that occur due to host-sequence contamination in reference genomes on public databases.
nf-core/taxprofiler currently offers host-removal via alignment against a reference genome with Bowtie2, and the use of the unaligned reads for downstream classification/profiling. nf-core/taxprofiler currently offers host-removal via alignment against a reference genome with Bowtie2 for short reads and minimap2 for long reads, and the use of the unaligned reads for downstream classification/profiling.
You can supply your reference genome in FASTA format with `--hostremoval_reference`. You can also optionally supply a directory containing pre-indexed Bowtie2 index files with `--shortread_hostremoval_index` or a minimap2 `.mmi` file for `--longread_hostremoval_index`, however nf-core/taxprofiler will generate these for you if necessary. Pre-supplying the index directory or files can greatly speed up the process, and these can be re-used. You can supply your reference genome in FASTA format with `--hostremoval_reference`. You can also optionally supply a directory containing pre-indexed Bowtie2 index files with `--shortread_hostremoval_index` or a minimap2 `.mmi` file for `--longread_hostremoval_index`, however nf-core/taxprofiler will generate these for you if necessary. Pre-supplying the index directory or files can greatly speed up the process, and these can be re-used.
@ -201,7 +201,7 @@ You can optionally save the FASTQ output of the run merging with the `--save_run
#### Classification and Profiling #### Classification and Profiling
The following suggestion gives you some tips and suggestions regarding running some of the different tools specifically _within the pipeline_. The following sections gives you some tips and suggestions regarding running some of the different tools specifically _within the pipeline_.
An important distinction between the different tools in included in the pipeline is classification versus profiling. Taxonomic _classification_ is concerned with simply detecting the presence of species in a given sample. Taxonomic _profiling_ involves additionally estimating the _abundance_ of each species. An important distinction between the different tools in included in the pipeline is classification versus profiling. Taxonomic _classification_ is concerned with simply detecting the presence of species in a given sample. Taxonomic _profiling_ involves additionally estimating the _abundance_ of each species.