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  1. The ability to have a lab wide conda environment file or share a conda environment with other people in your lab to make sure that everyone is working from the same starting point and with tools that should work. I can tell you a sizable portion of the time I spend in lab helping other members is devoted to dealing with accessing programs. I look forward Being able to just being able to share a conda environment file with them significantly improves this.
  2. In the section dealing with selecting tools, I talk a bit about publishing, and how some papers struggle to define exactly what they did computationally with enough accuracy detail to be able to make use of the analysis they did in looking at my own work. Conda environments are increasingly being shared as part of supplemental information not just in/for publications about computational based programs, but for any paper involving command line analysis. I hope that this trend continues, and that you see the value in doing such things and contribute to it.
  3. As mentioned early in class, your laptop is not going to be capable of performing the majority of the analysis you are going to want to do. That being said, sometimes we have to work with what we have, or it can be useful to look at or build things on your local machine before transferring the analysis to TACC. Downstream analysis often requires working with python/R/etc to generate figures for publication. Putting required visualization packages into a single environment (or given number of environments) can aid in publication reproducabilityreproducibility/interaction as mentioned above.Other reasons! I haven't been working with conda in my own work enough yet to have a complete feel for all the ways it helps things (and much of this is based around this ability to transfer environments. I would not be surprised to hear from you that you have thought of another way it can be helpful in your own work.
Code Block
languagebash
titleCommand to get the environments that you have used in class ready to export to your local machine for storage, or use elsewhere
cdh 
for pathway in $(for conda_env in ~/miniconda3/envs/*; do env`conda env list`;do echo $conda_env|grep "^/.*miniconda3\/envs";done); do env_name=$(echo $conda_env$pathway|sed 's/.*\///');echo $env_name;conda activate $env_naamename;conda list;done > list_of_envrionments_used_in_GVA2021GVA2022_and_packages_installed.txt
#
for pathway in $(for conda_env in ~/miniconda3/envs/* `conda env list`;do echo $conda_env|grep "^/.*miniconda3\/envs";done); do env_name=$(echo $conda_env$pathway|sed 's/.*\///');echo $env_name;conda activate $env_naamename;conda env export > $env_name.yml;done
#
mkdir GVA2021GVA2022_conda_files
mv *.yml GVA2021GVA2022_conda_files
mv list_of_envrionments_used_in_GVA2021GVA2022_and_packages_installed.txt GVA2021GVA2022_conda_files
tar -czvf GVA2021GVA2022_conda_files.tar.gz GVA2021GVA2022_conda_files/

Using the scp tutorial you can now transfer the GVA2021the GVA2022_conda_files.tar.gz file back to your computer

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  • When you type a command, only locations that are in your PATH variable are searched for an executable command matching that name.
  • When the command is found in your PATH, the computer immediately substitutes the location it was found for the short command name you entered, and stops searching.
  • This means that things that are early in your path are always searched first. In some extreme circumstances if you add a bunch of locations with lots of files to the front of your PATH, you can actually slow down your entire computer, so try to limit the path variable to only look in directories containing executable files.
  • You can manually manipulate your $PATH variable, usually in your .bashrc file in your home direcotrydirectory.

  • Warning
    titleOne of the most important lessons you can ever learn

    Anytime you manipulate your PATH variable you always want to make sure that you include $PATH on the right side of the equation somewhere separated by : either before it, after it, or on both sides of it if you want it in the middle of 2 different locations. As we are explaining right now, there are reasons and times to put it in different relative places, but if you fail to include it (or include a typo in it by calling it say $PTAH) you can actually remove access to all existing commands including the most basic things like "ls" "mkdir" "cd".


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  1. It is not widely used/downloaded.
    1. Even I consider myself a novice at best when it comes to computational analysis. I rely on others who are more versed in the computer science, biostatistics, etc to evaluate  tools.
    2. If you try to publish using a tool that is not common to the field or worse that none of the reviewers have ever heard of, you will deal with it in the review process, including potentially having to redo analysis with better accepted/common tools as a condition of publication
  2. Difficult to install
  3. Lack example command on command line to model your work after
  4. Lack command line help detailing what options are expected/available
  5. Present in a paper but no github/website/forum.
    1. Broadly this suggests not actively being developed, and increasing liklihood likelihood you will have difficulties getting answers to questions or help if there are problems.

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  1. Any version that works to get you closer to answering the biological question that does not have a newer version that "fixes bugs/errors associated with" the version you want to use for the options you are selecting.
    1. Most new versions deal with expanding features, or some kind of speed/memory improvement. Very few new versions result in producing different results.
    2. Generally the bigger the value change, the bigger the change to the program. Changing from version 1 to version 2 is a very big change. version 1.1 to 1.2 less big 1.15 1.5 to 1.1.16 6 smaller still etc. There is a lot of wiggle room in this rule.
  2. Version similar to that which others are publishing about.
    1. If field is publishing using gatk version 4.0 and you have to cite gatk version 2.0 those are very different.
  3. A single version for all analysis in a single paper, or at least section of the methods.
    1. Personally I am incredibly frustrated with papers that say something like "versions x to y of toy Z were used". This is going to make it very hard to figure out how to mimic their analysis, repeat their analysis, evaluate their analysis.
      1. Not listing a version of a computational program is even worse.
      2. Not citing a paper as tools request is worse still.
    2. The bad thigns things listed above are also associated with papers that do not detail what actual commands were used in analysis, again making it harder to mimic their analysis.
    3. If you do decide to update the version of a program, repeat the analysis you have already done.
      1. You should be conducting your analysis in such a way that you can repeat the analysis for exactly this reason.

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  • Results you have don't make sense, can't be validated, or are inconsistent.
  • A major bug effecting analysis is repaired.
    • can Can be difficult to determine what the effect a version change will have. If the documentation calls it a "major bug" or describes something that impacts your analysis.
    • forums Forums of some of the better programs will break down what change changes a little better and most will respond to any inquiries of "do i I need to update".
    • If looking for help on an error or problem with the program and lots of answers deal with mentioning specific versions being wrong/bad/the source of the problem and you are using that version (even if you don't think you are having problems).
  • An existing pipeline no longer works with new sample/data/improvement.
  • Has new feature you want to try.
    • Probably a good idea to test this in separate environment.
  • Staring work on a new project/paper or new analysis in an on going project/paper that uses the same tool.



Transferring files

The majority of the files we have worked with have been in our $SCRATCH space. Recall that files on $SCRATCH can be deleted after a period of inactivity. Below is a list of things that you SHOULD copy to your $HOME or $WORK2 $WORK space

Collecting information via job submission

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Code Block
languagebash
titleSample commands that can be pasted into nano
linenumberstrue
echo "My name is _____ and todays date is:" > GVA2021GVA2022.output.txt
date >> GVA2021GVA2022.output.txt
echo "I have just demonstrated that I know how to redirect output to a new file, and to append things to an already created file. Or at least thats what I think I did" >> GVA2021GVA2022.output.txt
echo "i'm going to test this by counting the number of lines in the file that I am writing to. So if the next line reads 4 I remember I'm on the right track" >> GVA2021GVA2022.output.txt
wc -l GVA2021GVA2022.output.txt >> GVA2021GVA2022.output.txt
echo "I know that normally i would be typing commands on each line of this file, that would be executed on a compute node instead of the head node so that my programs run faster, in parallel, and do not slow down others or risk my tacc account being locked out" >> GVA2021GVA2022.output.txt
echo "i'm currently in my scratch directory on stampede2. there are 2 main ways of getting here: cds and cd $SCRATCH" >>GVA2021>>GVA2022.output.txt
pwd >> GVA2021GVA2022.output.txt
echo "over the last week I've conducted multiple different types of analysis on a variety of sample types and under different conditions. Each of the exercises was taken from the website https://wikis.utexas.edu/display/bioiteam/Genome+Variant+Analysis+Course+20212022" >> GVA2021GVA2022.output.txt
echo "using the ls command i'm now going to try to remind you (my future self) of what tutorials I did" >> GVA2021GVA2022.output.txt
ls -1 >> GVA2021GVA2022.output.txt
echo "the contents of those directories (representing the data i downloaded and the work i did) are as follows: ">> GVA2021GVA2022.output.txt
find . >> GVA2021GVA2022.output.txt
echo "the commands that i have run on the headnode are: " >> GVA2021GVA2022.output.txt
history >> GVA2021GVA2022.output.txt
echo "the contents of this, my commands file are: ">>GVA2021>>GVA2022.output.txt
cat commands >> GVA2021GVA2022.output.txt
echo "I will next creatcreate a what_i_did_at_GVA2021GVA2022.slurm file that will run for 15 minutes" >> GVA2021GVA2022.output.txt
echo "and i will send this job to the queue using the the command: sbatch what_i_did_at_GVA2021GVA2022.slurm" >> GVA2021GVA2022.output.txt


Code Block
languagebash
titleModify your slurm fileUse wc -l command to verify the number of lines in your commands file.
wc -l commands

If you get a number larger than 19 edit your commands file with nano so each command is a single line as they appear above. Several of the lines are likely long enough that they will wrap when you paste them in nano and cause problems

Code Block
languagebash
titleModify your slurm file
cp /corral-repl/utexas/BioITeam/gva_course/GVA2021GVA2022.launcher.slurm what_i_did_at_GVA2021GVA2022.slurm
nano what_i_did_at_GVA2021GVA2022.slurm

As stated above things we want to change are:

Line numberAs isTo be
16

#SBATCH -J jobName

#SBATCH -J end_of_class
21

#SBATCH -t 12:00:00

#SBATCH -t 0:10:00

27

conda activate GVA2021

conda activate GVA2021

Line 27 could be deleted if you prefer

Again use ctl-o and ctl-x to save the file and exit.

Code Block
languagebash
titlesubmit job
sbatch what_i_did_at_GVA2021.slurm

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the

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wc -l commands

If you get a number larger than 19 edit your commands file with nano so each command is a single line as they appear above. Several of the lines are likely long enough that they will wrap when you paste them in nano and cause problemsfile and exit.


Code Block
languagebash
titlesubmit job
sbatch what_i_did_at_GVA2022.slurm


Evaluating your job submission

Based on our example you may have expected 1 new file to have been created during the job submission (GVA2021GVA2022.output.txt), but instead you will find 2 extra files as follows: what_i_did_at_GVA2020GVA2022.e(job-ID), and what_i_did_at_GVA2020GVA2022.o(job-ID). When things have worked well, these files are typically ignored. When your job fails, these files offer insight into the why so you can fix things and resubmit. 

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Code Block
languagebash
titlemake a single final file using the cat command and copy to a useful work directory
collapsetrue
# remember that things after the # sign are ignored by bash
cat GVA2021GVA2022.output.txt > end_of_class_job_submission.final.output
mkdir $WORK2$WORK/GVA2021GVA2022
mkdir $WORK2$WORK/GVA2021/end_of_course_summary/  # each directory must be made in order to avoid getting a no such file or directory error
cp end_of_class_job_submission.final.output $WORK2$WORK/GVA2021GVA2022/end_of_course_summary/
cp what_i_did* $WORK2$WORK/GVA2021GVA2022/end_of_course_summary/  # note this grabs the 2 output files generated by tacc about your job run as well as the .slurm file you created to tell it how to run your commands file
 
cp commands $WORK2$WORK/GVA2021GVA2022/end_of_course_summary/

Helpful cheat sheets

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Return to GVA2021 to work on any additional tutorials you are interested in.