fixes based on christinas review

docs/mgg_tutorials/abd_only.md

@@ -59,7 +59,7 @@ Once clicking on that, a parameter-setting box will pop up, asking for values on
 
 
 Please make sure you set the input type as `abundance_table` and you select the correct [taxonomy scheme](../input.md#input-files).
-It is crucial to also set the [FlashWeave related parameters](../faq.md#what-is-sensitive-and-heterogeneous-in-flashweave) in a way they address your abundance table idiosyncrasy.
+It is crucial to also set the [FlashWeave related parameters](../faq.md#when-to-enable-the-sensitive-and-heterogeneous-arguments) in a way they address your abundance table idiosyncrasy.
 
 
 {: .important}

docs/modules/phen-traits.md

@@ -6,7 +6,8 @@ nav_order: 2
 ---
 
 
-## Genome-oriented models for phenotypic trains prediction
+# Phen models supported
+
 
 Phen traits have been annotated to all representative GTDB genomes. 
 Here are the corresponding abbreviations' descriptions.

docs/tutorials/local.md

@@ -34,7 +34,7 @@ v1.0.2
 <!-- > You may find the input files we are using for this tutorial in the `user-bins` branch of `microbetag`s GitHub repo, under the [`tests/dev_io_microbetag` folder](https://github.com/hariszaf/microbetag/tree/user-bins/tests/dev_io_microbetag).
 >  -->
 
-In the [Cytoscape App tutorial](../cytoApp.md), our sequences were already taxonomically assigned before running `microbetag` and their taxonomies were mapped to representative GTDB genomes.
+In the [Cytoscape App tutorial](../mgg_tutorials/abd_only.md), our sequences were already taxonomically assigned before running `microbetag` and their taxonomies were mapped to representative GTDB genomes.
 `microbetag` then used these genomes for the annotation steps.
 
 However, in case of shotgun metagenomics one may end up with their own bins while further refinement of the latter can lead to Metagenome-Assembled Genomes (MAGs).
@@ -149,7 +149,7 @@ For example, the `aerobic_nitrite_oxidation.txt` looks like:
 
 `microbetag` invokes `prodigal` to extract Open Reading Frames (ORFs). 
 It creates a folder called `ORFs` in the `output_directory` and for each genome/bin it returns 3 files: 
-- `.gbk`: Genbank-like format (for more check [here](https://www.insdc.org/submitting-standards/feature-table/))
+- `.gbk`: Genbank-like format (for more information check [here](https://www.insdc.org/submitting-standards/feature-table/))
 - `.faa`: the reading frames as aminoacid sequences
 - `.ffn`: the reading frames as nucleic acid sequences
 

docs/tutorials/tutorials.md

@@ -16,7 +16,7 @@ To enable this and at the same time to support the taxonomy annotation of 16S rR
 
 
 The simplest case is you are about to use the first approach where your sequences are less than 1000 (up limit for the server to build a network).
-In this case, you may follow directly the instructions described in the [Cytoscape App](../cytoApp.md) tab. 
+In this case, you may follow directly the instructions described in the [Cytoscape tutorials](../cytoApp.md). 
 If you have a more complex data set, then you need to:
 * either perform the `microbetag_prep` step, where you get a network again using FlashWeave and/or a GTDB-based taxonomy assignment of your amplicon sequences (if that is your case), or
 * perform `microbetag` locally, in case you have your own genomes to use instead of the GTDB representative ones