add review suggestions

Author: Darcy220606

docs/output.md

@@ -443,7 +443,9 @@ Note that filtered FASTA is only used for BGC workflow for run-time optimisation
 
 [GECCO](https://gecco.embl.de) is a fast and scalable method for identifying putative novel Biosynthetic Gene Clusters (BGCs) in genomic and metagenomic data using Conditional Random Fields (CRFs).
 
-### Summary too](#ampcombi), [hAMRonization](#hamronization), [comBGC](#combgc), [MultiQC](#multiqc), [pipeline information](#pipeline-information), [argNorm](#argnorm).
+### Summary tools
+
+[AMPcombi](#ampcombi), [hAMRonization](#hamronization), [comBGC](#combgc), [MultiQC](#multiqc), [pipeline information](#pipeline-information), [argNorm](#argnorm).
 
 #### AMPcombi
 
@@ -508,7 +510,7 @@ Note that filtered FASTA is only used for BGC workflow for run-time optimisation
 
 </details>
 
-[AMPcombi](https://github.com/Darcy220606/AMPcombi) summarizes the results of **antimicrobial peptide (AMP)** prediction tools (ampir, AMPlify, Macrel, and other non-nf-core tools) into a single table and aligns the hits against a reference AMP database for functional, structural and taxonomic classification using [MMseqs2](https://github.com/soedinglab/MMseqs2). It further assigns the physiochemical properties (e.g. hydrophobicity, molecular weight) using the [Biopython toolkit](https://github.com/biopython/biopython) and clusters the resulting AMP hits from all samples using [MMseqs2](https://github.com/soedinglab/MMseqs2). To further filter the recovered AMPs using the presence of signaling peptides, the output file `Ampcombi_summary_cluster.tsv` or `ampcombi_complete_summary_taxonomy.tsv.gz` can be used downstream as detailed [here](https://ampcombi.readthedocs.io/en/main/usage.html#signal-peptide). The final tables generated may also be visualized and explored using an interactive [user interface](https://ampcombi.readthedocs.io/en/main/visualization.html).
+[AMPcombi](https://github.com/Darcy220606/AMPcombi) summarizes the results of **antimicrobial peptide (AMP)** prediction tools (ampir, AMPlify, Macrel, and other non-nf-core supported tools) into a single table and aligns the hits against a reference AMP database for functional, structural and taxonomic classification using [MMseqs2](https://github.com/soedinglab/MMseqs2). It further assigns the physiochemical properties (e.g. hydrophobicity, molecular weight) using the [Biopython toolkit](https://github.com/biopython/biopython) and clusters the resulting AMP hits from all samples using [MMseqs2](https://github.com/soedinglab/MMseqs2). To further filter the recovered AMPs using the presence of signaling peptides, the output file `Ampcombi_summary_cluster.tsv` or `ampcombi_complete_summary_taxonomy.tsv.gz` can be used downstream as detailed [here](https://ampcombi.readthedocs.io/en/main/usage.html#signal-peptide). The final tables generated may also be visualized and explored using an interactive [user interface](https://ampcombi.readthedocs.io/en/main/visualization.html).
 
 <img src="https://raw.githubusercontent.com/Darcy220606/AMPcombi/main/docs/ampcombi_interface_screenshot2.png" alt="AMPcombi interface" width="650" height="300">
 

docs/usage.md

@@ -224,7 +224,7 @@ wget https://github.com/nf-core/funcscan/raw/<PIPELINE_VERSION>/bin/ampcombi_dow
 python3 ampcombi_download.py
 ```
 
-IN addition to [DRAMP](http://dramp.cpu-bioinfor.org/), two more reference databases can be used to classify the recovered AMPs in the AMP workflow; [APD](https://aps.unmc.edu/) and [UniRef100](https://academic.oup.com/bioinformatics/article/23/10/1282/197795). Only one database can be used at a time using `--amp_ampcombi_db database_name`.
+In addition to [DRAMP](http://dramp.cpu-bioinfor.org/), two more reference databases can be used to classify the recovered AMPs in the AMP workflow; [APD](https://aps.unmc.edu/) and [UniRef100](https://academic.oup.com/bioinformatics/article/23/10/1282/197795). Only one database can be used at a time using `--amp_ampcombi_db database_name`.
 
 However, the user can also supply their own custom AMP database by following the guidelines in [AMPcombi](https://ampcombi.readthedocs.io/en/main/).
 This can then be passed to the pipeline with:
@@ -250,7 +250,10 @@ amp_DRAMP_database/
     └── ref_DB.source
 ```
 
-🗒️ **Note**: For both [DRAMP](http://dramp.cpu-bioinfor.org/) and [APD](https://aps.unmc.edu/), AMPcombi removes entries that contains any non amino acid residues by default.
+:::note{.fa-whale}
+For both [DRAMP](http://dramp.cpu-bioinfor.org/) and [APD](https://aps.unmc.edu/), AMPcombi removes entries that contains any non amino acid residues by default.
+:::
+
 
 :::warning
 The pipeline will automatically run Pyrodigal instead of Prodigal if the parameters `--run_annotation_tool prodigal --run_amp_screening` are both provided.

nextflow_schema.json

@@ -626,15 +626,15 @@
             "description": "Antimicrobial peptides parsing, filtering, and annotating submodule of AMPcombi2. More info: https://github.com/Darcy220606/AMPcombi",
             "default": "",
             "properties": {
-                "amp_ampcombi_db": {
+                "amp_ampcombi_db_id": {
                     "type": "string",
                     "description": "The name of the database used to classify the AMPs.",
                     "help_text": "AMPcombi can use three different AMP databases to classify the recovered AMPS. These can either be: \n\n- [DRAMP database](http://dramp.cpu-bioinfor.org/downloads/): Only general AMPs are downloaded and filtered to remove any entry that has an instance of non amino acid residues in their sequence.\n\n- [APD](https://aps.unmc.edu/): Only experimentally validated AMPs are present.\n\n- [UniRef100](https://academic.oup.com/bioinformatics/article/23/10/1282/197795): Combines a more general protein dataset including curated and non curated AMPs. Helpful for identifying the clusters to remove any potential false positives. Beware: If the thresholds are for ampcombi are not strict enough, alignment with this database can take a long time. \n\nBy default this is set to 'DRAMP'. Other valid options include 'APD' or 'UniRef100'.\n\nFor more information check the AMPcombi [documentation](https://ampcombi.readthedocs.io/en/main/usage.html#parse-tables).",
                     "fa_icon": "fas fa-address-book",
                     "default": "DRAMP",
                     "enum": ["DRAMP", "APD", "UniRef100"]
                 },
-                "amp_ampcombi_db_dir_path": {
+                "amp_ampcombi_db": {
                     "type": "string",
                     "description": "The path to the folder containing the reference database files.",
                     "help_text": "The path to the folder containing the reference database files (`*.fasta` and `*.tsv`); a fasta file and the corresponding table with structural, functional and if reported taxonomic classifications. AMPcombi will then generate the corresponding `mmseqs2` directory, in which all binary files are prepared for the downstream alignment of teh recovered AMPs with [MMseqs2](https://github.com/soedinglab/MMseqs2). These can also be provided by the user by setting up an mmseqs2 compatible database using `mmseqs createdb *.fasta` in a directory called `mmseqs2`.\n\nExample file structure for the reference database supplied by the user:\n\n```bash\namp_DRAMP_database/\n\u251c\u2500\u2500 general_amps_2024_11_13.fasta\n\u251c\u2500\u2500 general_amps_2024_11_13.txt\n\u2514\u2500\u2500 mmseqs2\n    \u251c\u2500\u2500 ref_DB\n    \u251c\u2500\u2500 ref_DB.dbtype\n    \u251c\u2500\u2500 ref_DB_h\n    \u251c\u2500\u2500 ref_DB_h.dbtype\n    \u251c\u2500\u2500 ref_DB_h.index\n    \u251c\u2500\u2500 ref_DB.index\n    \u251c\u2500\u2500 ref_DB.lookup\n    \u2514\u2500\u2500 ref_DB.source\n\nFor more information check the AMPcombi [documentation](https://ampcombi.readthedocs.io/en/main/usage.html#parse-tables)."

subworkflows/local/amp.nf

@@ -110,14 +110,14 @@ workflow AMP {
             gbk: it[3]
         }
 
-    if ( params.amp_ampcombi_db_dir_path != null ) {
-        ch_ampcombi_input_db = Channel.of( file(params.amp_ampcombi_db_dir_path) )
+    if ( params.amp_ampcombi_db != null ) {
+        ch_ampcombi_input_db = Channel.of( file(params.amp_ampcombi_db) )
     } else {
-        AMP_DATABASE_DOWNLOAD( params.amp_ampcombi_db )
+        AMP_DATABASE_DOWNLOAD( params.amp_ampcombi_db_id )
         ch_versions = ch_versions.mix( AMP_DATABASE_DOWNLOAD.out.versions )
         ch_ampcombi_input_db = AMP_DATABASE_DOWNLOAD.out.db
     }
-    AMPCOMBI2_PARSETABLES ( ch_input_for_ampcombi.input, ch_input_for_ampcombi.faa, ch_input_for_ampcombi.gbk, params.amp_ampcombi_db, ch_ampcombi_input_db )
+    AMPCOMBI2_PARSETABLES ( ch_input_for_ampcombi.input, ch_input_for_ampcombi.faa, ch_input_for_ampcombi.gbk, params.amp_ampcombi_db_id, ch_ampcombi_input_db )
     ch_versions = ch_versions.mix( AMPCOMBI2_PARSETABLES.out.versions )
 
     ch_ampcombi_summaries = AMPCOMBI2_PARSETABLES.out.tsv.map{ it[1] }.collect()