anchor.diff:  To analyze a file with the container, we can use the `runWithMountedFile` function to work on a fasta file in the mounted directory. 
   The file can be either coming from outside or upstream analysis pipelines using BioFSharp and written to disk by `FastA.write`.
   
  -__Note: this function is available from version 2.0.0 onwards.__
  +_Note: this function is available from version 2.0.0 onwards._
   *)
   
   (***do-not-eval***)
   let results = 
       TargetP.runWithMountedFile
           imageContext 
  -        TargetP.TargetpParams.NonPlant
  -        @"C:\Users\kevin\Desktop\TargetPdocker\targetp-1.1b.Linux\targetp-1.1\test\one.fsa"
  +        TargetP.TargetpParams.NonPlant //Note that you can run custom commands using either NonPlantCustom or PlantCustom
  +        @"path/to/your/file"
       |> Seq.item 0
   
   //dont forget to get rid of the container after using it
  
positive.code: # SignalP install
  ADD ./signalp-4.1f.Linux.tar.gz /opt/
  ```
  *)
  
  (** 
  As always, we need to define the docker client endpoint, image, and container context to run:
  *)
  (***do-not-eval***)
  
negative.code: #r "BioFSharp.BioContainers.dll"
  
  (** 
  
  # TargetP BioContainer
  
  TargetP 1.1 is a widely used tool to predict subcellular localization of proteins by predicting N-terminal presequences.
  We can leverage the power of targetP from F# by using it in a docker container. To get academical access to the targetP software,
  please contact the friendly people at [DTU](https://services.healthtech.dtu.dk/software.php).
  
  
  ## The image
  
  After aquiring the software you can create a dockerfile that abides biocontainer conventions at the packages root and run 
  
  `docker build . -t nameOfYourContainer:yourTag` 
  
  to get the image needed. Here is an example of a possible dockerfile:
  
  ```(docker)
  ################## BASE IMAGE ######################
  