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general:computerenvironment:tasksetbash [2025/04/15 14:13] – freyageneral:computerenvironment:tasksetbash [2026/01/13 10:33] (current) – felix
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 +<WRAP tabs>
 +  * [[beh_molevol:start|EcoEvo main]]
 +  * [[beh_molevol:mee:workplan|EcoEvo workplan]]
 +  * [[mbw_bioinf:start|MolBio main]]
 +  * [[mbw_bioinf:mastermbw:2022:workpackages|MolBio workplan]]
 +  * [[pbioc_basics:start|PBioC main]]
 +  * [[pbioc_basics:workplan|PBioC workplan]]
 +  * [[physaliacg:|Back to Physalia course]]
 +</WRAP>
 +
 +
 ====== Working with the command line ====== ====== Working with the command line ======
  
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 We have compiled a set of tasks for you that will deepen your knowledge about working with the BASH shell and will introduce some principles and dataformats which are common to bioinformatics. We have compiled a set of tasks for you that will deepen your knowledge about working with the BASH shell and will introduce some principles and dataformats which are common to bioinformatics.
  
-These exercises will come in the format of [[https://jupyter.org/|Jupyter notebooks]] which are a great way of making analyses reproducible and easy to share. If you don't have a working version of jupyter notebook on your computer system you can install it via [[https://applbio.biologie.uni-frankfurt.de/teaching/wiki/doku.php?id=general:computerenvironment:conda&s[]=anaconda|Anaconda]]. Please set up  Anaconda with the [[https://applbio.biologie.uni-frankfurt.de/teaching/wiki/doku.php?id=general:computerenvironment:conda&s[]=anaconda|tutorial in our wiki]]. Now you can install Jupyter notebook by typing:+These exercises will come in the format of [[https://jupyter.org/|Jupyter notebooks]] which are a great way of making analyses reproducible and easy to share. If you don't have a working version of jupyter notebook on your computer system you can install it via [[https://applbio.biologie.uni-frankfurt.de/teaching/wiki/doku.php?id=general:computerenvironment:conda&s[]=anaconda|Anaconda]]. 
  
-<code>conda install -c anaconda jupyter</code> +  - Set up  Anaconda with the [[https://applbio.biologie.uni-frankfurt.de/teaching/wiki/doku.php?id=general:computerenvironment:conda&s[]=anaconda|tutorial in our wiki]].  
-Go ahead and download our exercises from GitHub via this [[https://github.com/BIONF/digital_competence|LINK]]. The easiest way to start the download is to click on the green "Code button" in the top right corner and select "Download ZIP" (Figure {{ref>git}}). Don't forget to unpack the directory with a [[https://en.wikipedia.org/wiki/ZIP_%28file_format%29|ZIP]] file manager of your choice. +  - Now you can create a new conda environment and install Jupyter notebook by typing:<WRAP> 
 +<code>mamba create -n jupyter jupyter</code></WRAP> 
 +  - Activate your new environment:<WRAP> 
 +<code>mamba activate jupyter</code></WRAP>
  
 +=== 2.2 Exercises ===
 +
 +  - To start, [[https://github.com/BIONF/digital_competence|click this link to download our exercises from GitHub]]. <WRAP>
 +<hidden Hint>The easiest way to start the download is to click on the green "Code button" in the top right corner and select "Download ZIP" (Figure {{ref>git}}).
 <figure git> <figure git>
-{{ :studentarea:felix:git.png?700 |}}+{{:general:computerenvironment:download_digital_competence.png?700|}}
 <caption> <caption>
 **Starting download of the ZIP file.** **Starting download of the ZIP file.**
 </caption> </caption>
 </figure> </figure>
- +</hidden></WRAP> 
-=== 2.2 Exercises === +  - Unpack the directory with a [[https://en.wikipedia.org/wiki/ZIP_%28file_format%29|ZIP]] file manager of your choice, or the ''unzip'' command in the terminal 
-[[general:computerenvironment:openterminal|Open a terminal]] on your system and navigate to the directory you have just downloaded and extracted. Now, you can start a Jupyter notebook by simply typing: +  - [[general:computerenvironment:openterminal|Open a terminal]] on your system and use ''cd'' to navigate to the //digital_competence// directory that you have just downloaded and extracted.  
-<code>jupyter notebook</code> +  - Now, make sure that the your ''(jupyter)'' Anaconda environment is activated in your current terminal session. Then start the Jupyter notebook server with:<WRAP> 
- +<code>jupyter notebook</code></WRAP> 
-This will open a window in your browser with which you can navigate to the `.ipynb` files of each exercise. The notebooks contain a set of instructions and some tasks. They also contain code cells in which you should document the command which solve the task. +  - This will open a window in your browser with which you can navigate to the `.ipynb` files of each exercise. The notebooks contain a set of instructions and some tasks. They also contain code cells in which you should document the command which solve the task. You can also use the code cells to experiment and find your solution, but we recommend to **find out the solution to each task in your local terminal**. 
- +
-You can also use the code cells to experiment and find your solution, but we encourage you to try out all commands in your local terminal as well.  +
- +
-==== 2.3. The final boss ==== +
- +
-Professor Ebersberger prepared a heartwarming message, but the tutor decided to corrupt it: +
- +
-<file hidden message.txt> +
-ajtxdfsdfbrxKKmbbbbbbbnjyfvyilxdm +
-aliushsjwnKlpqk$jjw4iutjsmrwxwprt +
-u$dkslncwdikbmjlznm4$xjwhhuws$zsg +
-vpdlrdK$scfztmzmmin1ihhjhkuccjhyu +
-gywolhzkdjchKj$vdbs_wqjufqkdkhfKs +
-gzkppuzcrnwugnzunKj9opsfikwzmc$tv +
-cgbuwuvychKkynuwm$$vtnrqvlh$fixdl +
-mcubfbmvrozsxuytkcp4mzyzsfglqbftv +
-qiyghrtyrnbsmplftdf1yjhcyzqbcruw$ +
-rKmxzijztdbsfyqnaaaaaaapcjgksbrvl +
-cutxtszqgiuKhjcqKgl2ikipinzKinmnq +
-mopnjprmyrhrwgkptxmw$ykkqgtKlwcKs +
-nmrhj$hyniqmdksgszl7fqKumtzinykvt +
-kwcyjfokfhsmsvdrKlr0xvgtqjv$btghs +
-lubdtrlqkumhoxmmosl7ntiwkv$jytjoK +
-lydvjKzyfpw$uwtkzwk_nruofvtthk$ug +
-bixcokiyngorzjlgKhw$xofKuzkvrpjnh +
-glKqdudq$ypipnydbbd8gzdc$ogkoxrtK +
-nrzjh$qly$uirKcmgcg6Kuzujhjwfmzql +
-ntKycbgzxvhK$$qknjqKhnfdxhdvl$$ks +
-nviqqzsKtwhryqkjbzlKmqkotfghfdycn +
-nwbhtfdtqokgooKdwgx3uzuhKn$zwKwsc +
-d$xgKm$nguicrxftptuKmdvlivq$ktniy +
-ehlfmxdqpbxlvkbmqcm_pwzyqtyonroki +
-fnzxbglvgxjtfjyptqh$plthdtlbibhjp +
-otzvubvbrmhbtKKprdhkcsymygpxrvvsu +
-pbobvqvlyswgdbwhqkuKqflquqwpwfdiK +
-qcyhmzjdbncqnxcccccccKrKqqmbfuwwq +
-gmxmcnlsfxpjiqjwiwu6Kshyzysbwfnud +
-tliryqipzmrrurhjypj5dhxgzvwfjhwdw +
-txyvnvfjrvmziyjiuhK2v$cuxtKjgvutg +
-tzcxpskunxhoxhyjnjf2uqzowqzyksblK +
-h$gyhbmsckritgddddddd$oqblpdmppqy +
-i$dcwssvps$qirwfymy0o$xgzpxuibcpm +
-idxgdqrk$jjxkotxccliKKut$rpjlylxb +
-jbbkrjgKfvu$voggbtccojkwhwdydkzc$ +
-kqrbkgfcuucmfglzwutKhnyucpduKwsKq +
-vwsfnmssyzucKwtxvfb5tzx$qmzpgqKyy +
-vykspgktfgKqlufqtlr2hqdioqsfdppfo +
-wdqnfq$orlbongwsrlz1vmhzcpdvjtKsy +
-wmlrylbmnfwzvohyokl3ctmbvvppysndu +
-wpfgkgscKpvifmiqpmp9j$cmmwtocxlci +
-xojuilwkykvh$hjjjig8fpzthxyKdhykp +
-xovyzjukwdsycjxkwqr3gbsywdrjrvufy +
-yKprfKyydsgnvzwqfnp5tsrKqiqyzKjuw +
-xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx +
-</file> +
- +
-<file hidden codex.txt> +
-aaaaaaa rkydfrp +
-bbbbbbb vgkkhbw +
-ccccccc lskhrnt +
-ddddddd hmKlcnd +
-xxxxxxx xxxxxxx +
-</file> +
- +
-In order to decode the secret message, the following will be needed: +
-  * Files will be downloaded to your "~/Downloads". +
-  * Use the codex to restore the words from the first column, into the second one. (Make a backup. Try a combination of while-read + sed -i "")<WRAP> +
-<hidden hint><code>cat codex.txt | while read n k; do sed -i "s/???/???/" ???? ; done </code></hidden></WRAP> +
-  * Get rid of the lines with numbers +
-  * Instead of capital letter <fc #9400d3>K</fc>, we need the letter <fc #ff0000>e</fc> +
-  * Instead of <fc #800080>$</fc>, we need the letter <fc #ff0000>a</fc> +
-  * Sort the lines +
-  * The message should be in the 20th column. +
-  * Read them in one line +
- +
 ==== 3. Using a computer cluster ==== ==== 3. Using a computer cluster ====
 In the previous exercises you have learned to write commands and pipelines in the BASH shell. Now we want to look at how we can expand our analyses to large-scale analyses or datasets. For such resource heavy jobs we have a computer cluster available which is managed by the SLURM architecture. Please read through the [[https://applbio.biologie.uni-frankfurt.de/teaching/wiki/doku.php?id=general:computerenvironment:slurm|information about SLURM]] and then solve the task below. In the previous exercises you have learned to write commands and pipelines in the BASH shell. Now we want to look at how we can expand our analyses to large-scale analyses or datasets. For such resource heavy jobs we have a computer cluster available which is managed by the SLURM architecture. Please read through the [[https://applbio.biologie.uni-frankfurt.de/teaching/wiki/doku.php?id=general:computerenvironment:slurm|information about SLURM]] and then solve the task below.
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 <WRAP tabs> <WRAP tabs>
-  * [[ecoevo_molevol:course_introduction|EcoEvo main]] +  * [[beh_molevol:start|EcoEvo main]] 
-  * [[ecoevo_molevol:mee:workplan|EcoEvo workplan]] +  * [[beh_molevol:mee:workplan|EcoEvo workplan]] 
-  * [[mbw_bioinf:mastermbw|MolBio main]]+  * [[mbw_bioinf:start|MolBio main]]
   * [[mbw_bioinf:mastermbw:2022:workpackages|MolBio workplan]]   * [[mbw_bioinf:mastermbw:2022:workpackages|MolBio workplan]]
   * [[pbioc_basics:start|PBioC main]]   * [[pbioc_basics:start|PBioC main]]
   * [[pbioc_basics:workplan|PBioC workplan]]   * [[pbioc_basics:workplan|PBioC workplan]]
-  * [[:physaliacg:|Back to Physalia course]]+  * [[physaliacg:|Back to Physalia course]]
 </WRAP> </WRAP>