Nils Homer received his Bachelors degree in Mathematics and Computer Science in 2006 from Colgate University. He followed that with a M. Sc. in 2009 and a Ph. D. in 2010 both in Computer Science from University of California Los Angeles (UCLA). After graduating from UCLA he joined Life Technologies where he worked on developing algorithms and analysis software for the Ion Torrent sequencing platform: for example TMAP (Torrent Mapping Alignment Program).
Currently Dr. Homer works at the Broad Institute as a software manager where he is responsible for the Picard software suite.
What hardware do you use?
I use 15 inch macbook pro, kinesis advantage pro keyboard, magic mouse, dual 27" displays, and a large coffee cup.
I typically work at places that have access to linux compute farm or a cloud compute environment.
What is your text editor?
What software do you use for your work?
I use Unix, IntelliJ, Office, Google Applications & Drive, Jira/Confluence, Chrome, HipChat, as well as all the software we and others help develop, both internal and public.
What do you use to create plots and charts?
Pyplot or R, depending on the format of my input data. Whiteboards and a camera phone in a pinch.
What do you consider the best language to do bioinformatics with?
The language that allows you to answer your question the fastest and most correctly. I choose my language based on the problem. I currently gravitate towards in Scala, Java, C, Python, and Bash.
What bioinformatics tools/software do not get enough recognition?
Firstly, all of the tools in unix: sed, awk, cut, paste, sort, uniq, xargs, piping, wait, and the list goes on. I once took a course from Paul Eggert (UCLA) who asked us a simple multi-step text transformation problem in the first day of class. I wrote a 50-line perl script and felt quite proud. He wrote it in one line with 5-6 unix commands piped together. I knew I had to learn the various Unix tools at that point.
Secondly, all of the workflow and execution service tools that we all have written time and time again. They really are the workhorses running our vast production and development bioinformatic workflows.
Thirdly, git has really revolutionized how we share and contribute code.
Finally, having joined the Broad Institute recently, I have a greater appreciation for Picard tools and all the hard work making that software work quickly and correctly, especially as it has so many useful utilities.
See all post in this series https://www.biostars.org/t/uses-this/
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