update docs

Author: pinin4fjords

docs/usage.md

@@ -116,6 +116,10 @@ If you would like to reduce the number of reads used in the analysis, for exampl
 
 ## Alignment options
 
+:::note
+Not currently available using ARM architecture ('-profile arm')
+:::
+
 By default, the pipeline uses [STAR](https://github.com/alexdobin/STAR) (i.e. `--aligner star_salmon`) to map the raw FastQ reads to the reference genome, project the alignments onto the transcriptome and to perform the downstream BAM-level quantification with [Salmon](https://salmon.readthedocs.io/en/latest/salmon.html). STAR is fast but requires a lot of memory to run, typically around 38GB for the Human GRCh37 reference genome. Since the [RSEM](https://github.com/deweylab/RSEM) (i.e. `--aligner star_rsem`) workflow in the pipeline also uses STAR you should use the [HISAT2](https://ccb.jhu.edu/software/hisat2/index.shtml) aligner (i.e. `--aligner hisat2`) if you have memory limitations.
 
 You also have the option to pseudoalign and quantify your data directly with [Salmon](https://salmon.readthedocs.io/en/latest/salmon.html) or [Kallisto](https://pachterlab.github.io/kallisto/) by specifying `salmon` or `kallisto` to the `--pseudo_aligner` parameter. The selected pseudoaligner will then be run in addition to the standard alignment workflow defined by `--aligner`, mainly because it allows you to obtain QC metrics with respect to the genomic alignments. However, you can provide the `--skip_alignment` parameter if you would like to run Salmon or Kallisto in isolation. By default, the pipeline will use the genome fasta and gtf file to generate the transcripts fasta file, and then to build the Salmon index. You can override these parameters using the `--transcript_fasta` and `--salmon_index` parameters, respectively.
@@ -298,6 +302,10 @@ By default, the input GTF file will be filtered to ensure that sequence names co
 
 ## Contamination screening options
 
+:::note
+Not currently available using ARM architecture ('-profile arm')
+:::
+
 The pipeline provides the option to scan unaligned reads for contamination from other species using [Kraken2](https://ccb.jhu.edu/software/kraken2/), with the possibility of applying corrections from [Bracken](https://ccb.jhu.edu/software/bracken/). Since running Bracken is not computationally expensive, we recommend always using it to refine the abundance estimates generated by Kraken2.
 
 It is important to note that the accuracy of Kraken2 is [highly dependent on the database](https://doi.org/10.1099/mgen.0.000949) used. Specifically, it is [crucial](https://doi.org/10.1128/mbio.01607-23) to ensure that the host genome is included in the database. If you are particularly concerned about certain contaminants, it may be beneficial to use a smaller, more focused database containing primarily those contaminants instead of the full standard database. Various pre-built databases [are available for download](https://benlangmead.github.io/aws-indexes/k2), and instructions for building a custom database can be found in the [Kraken2 documentation](https://github.com/DerrickWood/kraken2/blob/master/docs/MANUAL.markdown). Additionally, genomes of contaminants detected in previous sequencing experiments are available on the [OpenContami website](https://openlooper.hgc.jp/opencontami/help/help_oct.php).
@@ -420,6 +428,7 @@ If `-profile` is not specified, the pipeline will run locally and expect all sof
   - A generic configuration profile to enable [Wave](https://seqera.io/wave/) containers. Use together with one of the above (requires Nextflow ` 24.03.0-edge` or later).
 - `conda`
   - A generic configuration profile to be used with [Conda](https://conda.io/docs/). Please only use Conda as a last resort i.e. when it's not possible to run the pipeline with Docker, Singularity, Podman, Shifter, Charliecloud, or Apptainer.
+  - A configuration profile that will set `docker.runOptions` appropriately for ARM architectures, and apply overrides supplying ARM-compatible containers and Conda environments.
 
 ### `-resume`