paper: minor tweaks incorporating peer review discussion

paper/paper.md

@@ -45,14 +45,14 @@ Acanthophis is available under the Mozilla Public Licence v2 at <https://github.
 # Statement of Need
 
 Understanding plant biology benefits from ecosystem-scale analysis of genetic variation, and increasingly demands the characterisation of not only plant genomes but also the genomes of their associated microbes.
-Such analyses are often data intensive, particularly at the scale required for quantitative analyses, i.e. thousands of host individuals [@regalado20_combining; @karasov22_drought].
+Such analyses are often data intensive, particularly at the scale required for quantitative analyses, i.e. hundreds to thousands of host individuals [@regalado20_combining].
 They demand computationally-efficient pipelines that perform both host genotyping and host-associated microbiome characterisation in a consistent, flexible, and reproducible fashion.
 
 Currently, no such unified pipelines exist. Previous pipelines perform only a subset of these tasks (e.g. Snakemake's variant calling pipeline; @koster21_snakemakeworkflows). In addition, most host-aware microbiome analysis pipelines do not allow for host genotyping and/or assume an animal host (e.g. Taxprofiler; @yates23_nfcore). Acanthophis has attracted many users, and has been referred to in peer-reviewed journal articles and preprints (e.g. @murray19_landscapedrivers; @ahrens21_genomicconstraints).
 
 # Components and Features
 
-Acanthophis is a pipeline for the analysis of plant population resequencing data. It expects short-read shotgun whole (meta-)genome sequencing data, typically of plants collected in the field. A typical dataset might be 10s-1000s of samples from one or multiple closely related species, sequenced with 2x150bp paired-end short read sequencing. In a plant-microbe interaction genomics study, these plants and therefore sequencing libraries can contain microbes (a "hologenome"), however datasets focusing only on host genome variation are also catered for. Acanthophis can be configured to do any of the following analyses: mapping reads to a reference, calling variants, annotating variant effects, estimating genetic distances *de novo*, and profiling and/or assembling metagenomes. While we developed Acanthophis to handle plant data, there is no reason why it cannot be applied to other taxa, however some parameters may need adjustment (see below).
+Acanthophis is a pipeline for the analysis of plant population resequencing data. It expects short-read shotgun whole (meta-)genome sequencing data, typically of plants collected in the field (nothing fundamentally prevents Acanthophis operating on long-read data, however additional tools would need to be incorporated, which will happen given sufficient user demand). A typical dataset might be 10s-1000s of samples from one or multiple closely related species, sequenced with 2x150bp paired-end short read sequencing. In a plant-microbe interaction genomics study, these plants and therefore sequencing libraries can contain microbes (a "hologenome"), however datasets focusing only on host genome variation are also catered for. Acanthophis can be configured to do any of the following analyses: mapping reads to a reference, calling variants, annotating variant effects, estimating genetic distances *de novo*, and profiling and/or assembling metagenomes. While we developed Acanthophis to handle plant data, there is no reason why it cannot be applied to other taxa, however some parameters may need adjustment (see below). Philosophically, Acanthophis aims to for maximum efficiency and flexibility, and therefore does not bake any particular biological question into its outputs. As such, each user should for example filter the resulting variant files as appropriate for their biological question(s), and likewise apply other post-processing as needed.
 
 Across the entire pipeline, Acanthophis operates on 'sample sets', named groups of one or more samples, and each sample can be in any number of sample sets. The pipeline is configured via a global `config.yaml` file, in which one can configure the pipeline per sample-set. This way, one can configure the analyses to be run (most can be disabled if not needed), as well as tool-specific settings or thresholds. We provide a documented template as well as a reproducible workflow to simulate test data, which can be used as a basis for customisation. While Acanthophis is cross-platform, most of the underlying tools are only packaged for and/or only operate on GNU/Linux operating systems. Therefore, Acanthophis is only actively supported for users on Linux systems.
 
@@ -68,12 +68,12 @@ To align reads to reference genomes, Acanthophis can use any of `BWA MEM` [@li13
 
 ## Stage 3: Variant Calling
 
-Acanthophis uses `bcftools mpileup` and/or `freebayes` to call raw variants, using priors and thresholds configurable for each sample set. It then normalises variants with `bcftools norm`, splits multi-allelic variants, filters each allele with per-sample set filters, and combines filter-passing alleles back into unique sites, merges region-level VCFs, indexes, and calculates statistics on these final VCF files. Acanthophis provides two alternative approaches to parallelize variant calling: either a static list of non-overlapping genome windows (supplied in a BED file), or genome bins with approximately equal amounts of data, which are automatically generated using mosdepth [@pedersen18_mosdepthquick].
+Acanthophis uses `bcftools mpileup` and/or `freebayes` to call raw variants, using priors and thresholds configurable for each sample set. It then normalises variants with `bcftools norm`, splits multi-allelic variants, filters each allele with per-sample set filters, and combines filter-passing bialelic sites back into single multi-allelic sites, merges region-level VCFs, indexes, and calculates statistics on these final VCF files. Acanthophis provides two alternative approaches to parallelize variant calling: either a static list of non-overlapping genome windows (supplied in a BED file), or genome bins with approximately equal amounts of data, which are automatically generated using mosdepth [@pedersen18_mosdepthquick].
 
 
 ## Stage 4: Taxon profiling
 
-Acanthophis can create taxonomic profiles of each sample with reference to either public sequence databases (e.g. NCBI's `nt` or `refseq`), or user-supplied databases. Acanthophis can utilise any of Kraken 2 [@wood19_improved], Bracken [@lu17_brackenestimating], Kaiju [@menzel16_fastsensitive], Centrifuge [@kim16_centrifugerapid], and Diamond [@buchfink15_fastsensitive] to create taxonomic profiles for each sample against any number of taxon identification databases; most tools supply pre-computed indices for public databases. Acanthophis can then optionally use taxpasta [@beber23_taxpastataxonomic] to combine multiple profiles into combined tables for easy downstream use.
+Acanthophis can create taxonomic profiles of each sample with reference to either public sequence databases (e.g. NCBI's `nt` or `refseq`), or user-supplied databases. Acanthophis can utilise any of Kraken 2 [@wood19_improved], Bracken [@lu17_brackenestimating], Kaiju [@menzel16_fastsensitive], Centrifuge [@kim16_centrifugerapid], and Diamond [@buchfink15_fastsensitive] to create taxonomic profiles for each sample against any number of taxon identification databases; most tools supply pre-computed indices for public databases. Acanthophis can then optionally use taxpasta [@beber23_taxpastataxonomic] to merge multiple profiles into a single combined table for easy downstream use.
 
 
 ## Stage 5: *De novo* Estimates of Genetic Dissimilarity
@@ -85,8 +85,9 @@ Acanthophis can use either `kWIP` [@murray17_kwipkmer] or Mash [@ondov16_mashfas
 
 Throughout all pipeline stages, various tools output summaries of their actions and/or outputs. We optionally combine these into unified reports by pipeline stage and sample set using MultiQC [@ewels16_multiqcsummarize], allowing plotting of raw sequence QC statistics, alignment QC statistics, variant QC statistics, and summarisation of taxonomic identification analyses.
 
+
 # Acknowledgements
 
-We thank Luisa Teasdale, Anne-Cecile Colin, Rose Andrew, Johannes Köster, and Scott Ferguson for comments or advice on Acanthophis and/or on this manuscript. KDM is supported by a Marie Skłodowska-Curie Actions fellowship.
+We thank Luisa Teasdale, Anne-Cecile Colin, Rose Andrew, Johannes Köster, and Scott Ferguson for comments or advice on Acanthophis and/or on this manuscript. KDM is supported by a Marie Skłodowska-Curie Actions fellowship. KDM and DW are supported by 
 
 # References