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An introduction to Snakemake tutorial for beginners (CC248)




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Название :  An introduction to Snakemake tutorial for beginners (CC248)
Продолжительность :   30.12
Дата публикации :  
Просмотров :   9,6 rb


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ABHISHEK KUMAR
fantastic and straight to the point introduction to Snakemake Great Job! 🙌👏
Comment from : ABHISHEK KUMAR


Deniz Ural
Great tutorial Thank you very much 👏👏
Comment from : Deniz Ural


hilal su kanar
Great video, thanks!
Comment from : hilal su kanar


Daniel
Fantastic! Thank you very much Looking forward to watching this one
Comment from : Daniel


Ailton Pereira
Really cool video, definitely will help my research!
Comment from : Ailton Pereira


etterath
Do you use docker for reproducible research in R? When to choose snakemake and when docker to intialize functioning environment? Could both of them cooperate?
Comment from : etterath


Ricardo Rocha
Hi!! The videos are incredible Learning so much!! I would like to mention we need to install snakemake extension on VS code to run the scripts Thanks a lot!!
Comment from : Ricardo Rocha


Guillermo Parada
This is a good intro video! I will show this to a rotation student that seems to be interested in learning snakemake - Thanks a lotbrThe only thing I found a little bit confusing is that for running your script you required {paramsfile} instead of directly using {output} I guess this is because your script is just taking the name of the output file and automatically saving this in a folder called "data" Perhaps you could have explained this, as most of the time you don't need to specify output files as {params}
Comment from : Guillermo Parada


Patricio R
Please, Could You explain in a video, how to show in vs Code, something like environment panel in RStudio? Tnx
Comment from : Patricio R


musicspinner
Fantastic 👌🏽brThis is incredibly usefulbrbrRelated to this, have you worked with R `library(drake)` or its successor `library(targets)`? If so, any thoughts related those R-specific workflow management systems? I could see them being quite handy for improving Shiny app efficiency or just interactive R exploration of some computationally heavy analysis
Comment from : musicspinner



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