Update README with TFK updates (proba + links)

README.md

@@ -14,7 +14,7 @@ ANNEXA works by using only three parameter files (a reference genome, a referenc
 2. Transcriptome reconstruction and quantification with [bambu](https://github.com/GoekeLab/bambu).
 3. Novel classification with [FEELnc](https://github.com/tderrien/FEELnc).
 4. Retrieve information from input annotation and format final gtf with 3 level structure: gene -> transcript -> exon.
-5. Filter novel transcripts based on [bambu](https://github.com/GoekeLab/bambu) and/or [TransforKmers](https://github.com/mlorthiois/transforkmers) Novel Discovery Rates.
+5. Filter novel transcripts based on [bambu NDR (Novel Discovery Rates)](https://github.com/GoekeLab/bambu) and/or [TransforKmers TSS validation](https://github.com/IGDRion/transforkmers) to assess fulllength transcripts.
 6. Perform a quality control of both the full and filtered extended annotations (see [example](https://github.com/igdrion/ANNEXA/blob/master/examples/results/qc_gtf.pdf)).
 7. Optional: Check gene body coverage with [RSeQC](http://rseqc.sourceforge.net/#genebody-coverage-py).
 
@@ -28,7 +28,7 @@ This pipeline has been tested with reference annotation from Ensembl and NCBI-Re
 
 ```sh
 nextflow run IGDRion/ANNEXA \
-    -profile test,conda
+    -profile test,singularity
 ```
 
 3. Run ANNEXA on your own data (change input, gtf, fa with path of your files).
@@ -69,7 +69,7 @@ Optional:
 --tfkmers_tokenizer : Path to TransforKmers tokenizer. Required if filter activated.
 --tfkmers_model     : Path to TransforKmers model. Required if filter activated.
 --bambu_threshold   : bambu NDR threshold below which new transcripts are retained.
---tfkmers_threshold : TransforKmers NDR threshold below which new transcripts are retained.
+--tfkmers_threshold : TransforKmers prediction threshold below which new transcripts are retained.
 --operation         : Operation to retained novel transcripts. "union" retain tx validated by either bambu or transforkmers, "intersection" retain tx validated by both.
 
 --withGeneCoverage  : Run RSeQC (can be long depending on annotation and bam sizes). False by default.
@@ -78,22 +78,22 @@ Optional:
 --maxMemory         : max memory used by ANNEXA. 40GB by default.
 ```
 
-> If the filter argument is set to `true`, TransforKmers model and tokenizer paths have to be given. They can be either downloaded from the [TransforKmers official repository](https://github.com/mlorthiois/TransforKmers) or trained in advance by yourself on your own data.
+> If the filter argument is set to `true`, TransforKmers model and tokenizer paths have to be given. They can be either downloaded from the [TransforKmers official repository](https://github.com/IGDRion/TransforKmers) or trained in advance by yourself on your own data.
 
 ### Filtering step
 
 By activating the filtering step (`--filter`), ANNEXA proposes to filter the generated extended annotation according to 2 methods:
 
-1. By using the NDR proposed by [bambu](https://github.com/GoekeLab/bambu). This threshold includes several information such as sequence profile, structure (mono-exonic, etc) and quantification (number of samples, expression). Each transcript with an NDR below the classification threshold will be retained by ANNEXA.
+1. By using the NDR proposed by [bambu](https://github.com/GoekeLab/bambu). This threshold includes several information such as sequence profile, structure (mono-exonic, etc) and quantification (number of samples, expression). Each transcript with an NDR below the classification threshold will be retained by ANNEXA (default: 0.2).
 
-2. By analysing the TSS of each new transcript using the [TransforKmers](https://github.com/mlorthiois/TransforKmers) (deep-learning) tool. Each TSS validated below a certain threshold will be retained. We already provide 2 trained models for filtering TSS with TransforKmers.
+2. By analysing the Transcription Start Sites (TSS) of each new transcripts using the [TransforKmers](https://github.com/IGDRion/TransforKmers) deep-learning based tool. Each TSS validated below a certain threshold will be retained (default: 0.2). We already provide 2 trained models for filtering TSS with TransforKmers.
 
 - A [human specific
   model](https://genostack-api-swift.genouest.org/v1/AUTH_07c8a078861e436ba41c4416a821e5d0/transforkmers/hsa_5prime_bert_6-512.zip?temp_url_sig=59e4bd439f42fc2bb8953e78eae82306466917d2&temp_url_expires=2661501621)
 - A [dog specific model](https://genostack-api-swift.genouest.org/v1/AUTH_07c8a078861e436ba41c4416a821e5d0/transforkmers/dog_5prime_bert_6-512.zip?temp_url_sig=a5378b6f2cc9ffc10b8f5d4fa6e535070d22f845&temp_url_expires=2661844043)
 
 To use them, extract the zip, and point `--tfkmers_model` and `--tfkmers_tokenizer` to the subdirectories.
 
-The filtered annotation can be the `union` of these 2 tools, _i.e._ all the transcripts validated by one or both of these tools; or the `intersection`, _i.e._ the transcripts validated by both tools.
+The filtered annotation can be the `union` of these 2 tools, _i.e._ all the transcripts validated by one or both of these tools; or the `intersection`, _i.e._ the transcripts validated by both tools (the latter being the default).
 
 At the end, the QC steps are performed both on the full and filtered extended annotations.