I am trying to make a python script that reads in a text file of pair end reads and then calls BWA mem on the pairs and outputs SAM files.
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You can ignore whomever told you that, he/she doesn't have a clue.
Python is more powerful, in that you can certainly do more with it easily, but doing something simple like stitching together a pipeline is often easier to do in a shell script (that's their intended purpose).
There are a lot of tools, learning which ones are most convenient for what situations are an important (and error ridden) part of the learning process.
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If you have to, use subprocess package. It should work fine.
I think shell script is straightforward, and should be used, if all it does is to run a simple command. But I prefer python when the pipeline is complex.
In my case, it is a little more complicated. In one of pipelines I created in python, I need to submit jobs to clusters. It is so much easier to read run configuration file, parse out sample name, create proper path, make meaningful job name in python (try it in bash script, you will know why I prefer python). However the submitted command errored out when it submitted from python, but pasted the print-out commands in terminal worked fine without any problem. I did not have time to find out a solution, so I let the python script generate a full set of shell commands with all the path, parameters, job dependencies, etc, nicely made, output to a xxxx.sh file, and run the shell script afterward. I probably did it backward, and need to do one extra step, but it did work well. I COULD have done all of it in shell script but then I am not so good at shell script, especially when it comes to text manipulation.
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by GATTACATLove this - good data definitely starts from good input, and poor input can only give relatively poor data. I particularly like the mention of Nanodrop/absorbance based methods for quantification. It's such a toss up if you'll get an accurate reading or what amounts to a randomly generated number, and a lot of library/sequencing related issues can be traced back to poor quant.
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by SEQadmin2
I’m not a sequencing expert. I’m a purification scientist who uses NGS to evaluate workflows my group develops. With this perspective, we think about the sample first and the NGS workflow second. The sequencer is an exceptionally honest reporter, but it can only report on what you give it, so whether you get clean, interpretable data from an NGS workflow is largely determined before you begin.
Here are nine questions we think about, in roughly the order they matter, before...-
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