fixes based on christinas review

docs/input.md

@@ -101,7 +101,7 @@ In case you start from a `biom` file, you may get a `.tsv` file using the
 ```bash 
 biom convert -i otu_table.biom -o otu_table.csv --to-tsv --header-key taxonomy
 ```
-Make sure you have the `biom` tools installed; if not, you may follow the instructions you can find [here](https://biom-format.org/index.html) to get them.
+Make sure you have the `biom` tools installed; if not, you may follow the instructions you can find [here](https://biom-format.org/index.html) how to get them.
 <!-- https://www.metagenomics.wiki/tools/16s/qiime/otu-biom-table -->
 
 

docs/mgg_tutorials/abd_and_metadata.md

@@ -15,7 +15,7 @@ description: "tutorial using an abundance table and a metadata file as input"
 {: .important-title}
 > INPUT FILES USED IN THIS TUTORIAL
 >
-> We will also do the same but this time considering also metadata ([`metadata.tsv`][4]) accompanying a shorter version of our abundance table ([`testAbundMeta`][5])
+> We will also do the same but this time considering also metadata ([`metadata.tsv`][4]) accompanying a shorter version of our abundance table ([`testAbundMeta.tsv`][5])
 
 
 In this case, you follow the exact steps as in the previous scenario and once you have loaded your abundance table, you import also the one with your metadata. 

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/mgg_tutorials/mgg_totorials.md

@@ -69,7 +69,7 @@ You need first to feed the app with your abundance table and, if available, your
 In both cases though, the **abundance table** will be **required**. 
 
 Please, make sure your taxonomy fits the criteria for `microbetag` to run. 
-You may find more on that issue on the [*Input files*](./input.md#input-files) section.
+You may find more on that issue on the [*Input files*](../input.md#input-files) section.
 
 Then, as you will see in the following two cases, you will have to set the values to a set of parameters to describe your input data but also what annotation steps you would like `microbetag` to perform.
 

docs/mgg_tutorials/roaming.md

@@ -87,7 +87,7 @@ Here is an example:
 ![kegg_seed_map](../../assets/images/app/seedComplPanel.png)
 
 
-[*Seed scores*](../modules/modules.md#seed-scores-based-on-genome-scale-draft-reconstructions-gems) between the two genomes are also shown here. 
+[*Seed scores*](../modules/modules.md#seed-scores-and-complements-based-on-genome-scale-draft-reconstructions-gems) between the two genomes are also shown here. 
 Remember that like the edge under study, seed scores have also *directionality*; seed score for competition between $genome_A$ and $genome_B$ is not necessarily the same with the one between $genome_B$ and $genome_A$.
 Those scores are only indicative, and they should not be considered as fact of observed cooperation/competition. 
 Seed complements are then recorded in the same way as pathway complementarities.

docs/modules/modules.md

@@ -125,7 +125,7 @@ microbetag annotates all **edges** where both nodes represent species/strain lev
 ## Seed scores and complements based on genome-scale draft reconstructions (GEMs)
 
 Based on Borenstein *et al.* (2008) [5] a metabolic network's “seed set” is the set of compounds that, based on the network topology, are exogenously acquired".
-Here is an example (based on the [Borensteil lab webpage](http://borensteinlab.com/software_netseed_examples.html)):
+Here is an example (based on the [Borenstein lab webpage](https://borensteinlab.sites.tau.ac.il/items-1/netseed)):
 
 ![seed concept](../../assets/images/seed_concept_example.png)
 

docs/modules/phen-traits.md

@@ -6,6 +6,9 @@ nav_order: 2
 ---
 
 
+# Phen models supported
+
+
 Phen traits have been annotated to all representative GTDB genomes. 
 Here are the corresponding abbreviations' descriptions.
 Each function is referring to the species under study (node).
@@ -45,7 +48,7 @@ Each function is referring to the species under study (node).
 |`thermophilic`   | species has a thermophilic lifestyle |
 
 
-* not returned by a Phen model
+\* not returned by a Phen model
 
 
 

docs/tutorials/load.md

@@ -30,7 +30,7 @@ description: "an example case of how to load a previously microbetag-annotated n
 ## Load an edge list to be used with `microbetag`
 
 In this case, you need to first load your network on Cytoscape and then import it on the MGG. 
-We show how to do that in the [Cytoscape App Tutorial](https://hariszaf.github.io/microbetag/docs/cytoApp/#-starting-from-a-co-occurrence-network). 
+We show how to do that in the [Cytoscape App Tutorial](../mgg_tutorials/from_net.md). 
 
 {: .note}
 Remember, you need always to call the column to be used as weight of the network as `microbetag::weight`.

docs/tutorials/local.md

@@ -14,7 +14,7 @@ v1.0.2
 {: .label .label-green }
 
 
-[Docker image](https://hub.docker.com/repository/docker/hariszaf/microbetag/general){: .btn .btn-green .fs-5 .mb-4 .mb-md-0 }
+[Docker image](https://hub.docker.com/r/hariszaf/microbetag){: .btn .btn-green .fs-5 .mb-4 .mb-md-0 }
 [Tutorial files](https://github.com/hariszaf/microbetag/tree/user-bins/tests/dev_io_microbetag){: .btn .btn-purple .fs-5 .mb-4 .mb-md-0 }
 [GitHub release](https://github.com/hariszaf/microbetag/releases/tag/v1.0.2){: .btn .btn-blue .fs-5 .mb-4 .mb-md-0 }
 
@@ -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).
@@ -63,7 +63,7 @@ In our experience, memory can hard be an issue, and `microbetag` is more often t
 > 
 > In the initial run, there are only 3 input files:
 > - the [`config.yml`][1] file; allows you to set all the relative parameters for `microbetag` to run
-> - an **abundance table** (following the format of the Cytoscape app tutorial) called [`thirty_Samples`][2], and
+> - an **abundance table** (following the format of the Cytoscape app tutorial) called [`thirty_Samples.tsv`][2], and
 > - its corresponding edge list ([`edgelist.csv`][3])
 >
 > **Remember!**
@@ -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/prep.md

@@ -8,7 +8,7 @@ nav_order: 1
 description: "an example case of how to run the microbetag prep step"
 ---
 
-# `microrbetag` preparation steps
+# `microbetag` preparation steps
 {: .no_toc }
 
 
@@ -168,8 +168,10 @@ More complex scenarios can be the case, however they are all based on the princi
 > Having an already optimal co-occurrence network to annotate is essential from a biological point-of-view.
 > Thus, we strongly suggest you first build your co-occurrence network using FlashWeave or any inference tool 
 > on your own, in order to address the idiosyncrasy of your data the best you can. 
-> In the framework of microbetag, you can do that by running the pre-processing Docker image we provide and by editing the
-> `flashweave.jl` script (see the [preparation step](../../input.md#the-preparation) as well as the [FlashWeave documentation](https://githubhelp.com/meringlab/FlashWeave.jl) for more).
+>
+> In the framework of microbetag, you can do that by running the pre-processing Docker image we provide. 
+> You may check the [FAQs](../faq.md#when-to-enable-the-sensitive-and-heterogeneous-arguments) for FlashWeave's most essential parameters you can set through the `config.yml` file of the preprocessing image.
+> In addition, you can edit the `flashweave.jl` script to adjust it to your needs; you may check for more information the [FlashWeave documentation](https://github.com/meringlab/flashweave.jl) direclty.
 
 
 
@@ -243,7 +245,7 @@ No matter how you edit the file, you need to make sure the following:
 
 
 In case you have asked for building a network, then you also need to consider setting the two related parameters.
-You may check this [FAQ](../../faq.md#what-is-sensitive-and-heterogeneous-in-flashweave) and of course advise the FlashWeave GitHub and paper for that. 
+You may check this [FAQ](../faq.md#when-to-enable-the-sensitive-and-heterogeneous-arguments) and of course advise the FlashWeave GitHub and paper for that. 
 In our case, we set `flashweave_sensitive` as `True` and `flashweave_heterogeneous` as `False`.
 
 
@@ -252,7 +254,7 @@ In our case, we set `flashweave_sensitive` as `True` and `flashweave_heterogeneo
 
 Now, based on your container technology you are ready to run the preparation image.
 
-An example of a directory to mount can be seen [here](https://github.com/hariszaf/microbetag/tree/preprocess/preprocess/test). 
+An example of a directory to mount can be seen [here](https://github.com/hariszaf/microbetag/tree/preprocess/test).
 The mandatory abundance table file can be provided as a `.tsv` or a `.csv` file and needs to be specified in the `config.yml` file accordingly.
 
 
@@ -265,7 +267,7 @@ docker run --rm -v ./my_microbetag_prep/:/media hariszaf/microbetag_prep
 Make sure you are in the parent folder of the `my_microbetag_prep` directory. 
 
 If you would like to edit the FlashWeave script and add extra argument on it, you can 
-fire a Docker container as explained [here](../input.md#io-folder) and edit the script as you wish.
+fire a Docker container as explained [above](./prep.md#docker) and edit the script as you wish.
 
 
 Once the preprocessing is completed (based on your input this could take up to hours)
@@ -286,5 +288,5 @@ you will find two output files,
 
 
 [1]:{{ site.url }}/microbetag/download/prep/seq_ab_tab.tsv
-[2]:{{ site.url }}/microbetag/download/prep/config.tsv
+[2]:{{ site.url }}/microbetag/download/prep/config.yml
 

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](../mgg_tutorials/mgg_totorials.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
@@ -28,5 +28,7 @@ Here, we provide three tutorials:
 * [run `microbetag` locally](./local.md)
 * [load previously `microbetag`-annotated network on Cytoscape](./load.md)
 
-For any issues, bugs, questions, feel free to contact us on [Matrix](https://matrix.to/#/#microbetagcommunity:matrix.org) or just open an issue on our [GitHub repo](https://github.com/msysbio/microbetagApp/issues).
+For any issues, bugs, questions, feel free to 
+contact us on [Matrix](https://matrix.to/#/#microbetagcommunity:matrix.org) or 
+just open an issue on our [GitHub repo](https://github.com/msysbio/microbetagApp-public/issues/new).
 

index.md

@@ -105,7 +105,7 @@ where `tagname` is the name of the specific version.
 
 For hints on how to use microbetag, ideas for new features and bug reports find us on out [Matrix space](https://matrix.to/#/#microbetagcommunity:matrix.org).
 If you do not have a Matrix account, it's only two clicks away! 
-For more, you may check [here](https://matrix.org/docs/chat_basics/matrix-for-im/).
+For more information, you may check [here](https://matrix.org/docs/chat_basics/matrix-for-im/).
 
 
 ## Cite us
@@ -115,7 +115,7 @@ In prep.
 ## Funding
 
 This project was funded by an [EMBO Scientific Exchange Grants](https://www.embo.org/funding/fellowships-grants-and-career-support/scientific-exchange-grants/) 
-and the [3D’omics](https://www.3domics.eu) Horizon project (101000309).
+and the [3D’omics](https://www.3domics.eu) Horizon 2020 project (101000309).
 
 <!-- https://www.embo.org/documents/news/facts_figures/EMBO_facts_figures_2021.pdf -->