Based on: https://docs.microsoft.com/en-us/azure/databox/data-box-deploy-ordered?tabs=portal and observations during setup. Also : https://docs.microsoft.com/en-us/azure/databox/data-box-deploy-picked-up?tabs=in-us-canada-europe
Requires: i) 1x Azure Databox
| *bcftools filter | |
| *Filter variants per region (in this example, print out only variants mapped to chr1 and chr2) | |
| qbcftools filter -r1,2 ALL.chip.omni_broad_sanger_combined.20140818.snps.genotypes.hg38.vcf.gz | |
| *printing out info for only 2 samples: | |
| bcftools view -s NA20818,NA20819 filename.vcf.gz | |
| *printing stats only for variants passing the filter: | |
| bcftools view -f PASS filename.vcf.gz |
Total BUSCO groups searched: 843
Phase Two
*** Training Augustus using Single-Copy Complete BUSCOs ***
*** Re-running Augustus with the new metaparameters, number targets: 273 ***
=> 10.0% of predictions performed
=> 25.0% of predictions performed
=> 50.0% of predictions performed
=> 75.0% of predictions performed
=> 100%% of predictions performed
Whether you're trying to give back to the open source community or collaborating on your own projects, knowing how to properly fork and generate pull requests is essential. Unfortunately, it's quite easy to make mistakes or not know what you should do when you're initially learning the process. I know that I certainly had considerable initial trouble with it, and I found a lot of the information on GitHub and around the internet to be rather piecemeal and incomplete - part of the process described here, another there, common hangups in a different place, and so on.
In an attempt to coallate this information for myself and others, this short tutorial is what I've found to be fairly standard procedure for creating a fork, doing your work, issuing a pull request, and merging that pull request back into the original project.
Just head over to the GitHub page and click the "Fork" button. It's just that simple. Once you've done that, you can use your favorite git client to clone your repo or j
This is a tutorial I have presented for the class Genomics and Systems Biology at the University of Chicago. In this course the students learn about study design, normalization, and statistical testing for genomic studies. This is meant to introduce them to how these ideas are implemented in practice. The specific example is a differential expression analysis with edgeR starting with a table of counts and ending with a list of differentially expressed genes.
Past versions:
SAM and BAM filtering one-liners
@author: David Fredman, david.fredmanAAAAAA@gmail.com (sans poly-A tail)
@dependencies: http://sourceforge.net/projects/bamtools/ and http://samtools.sourceforge.net/
Please extend with additional/faster/better solutions via a pull request!
BWA mapping (using piping for minimal disk I/O)