bioinformaticszen.com
Bioinformatics Zen - Why write good software
http://www.bioinformaticszen.com/post/why-write-good-software
Why write good software. Published Tue February 3 2009. Bioinformatics is far from commercial software development. A
bioinformatician's goal is developing novel scientific research or tools. A
software developer is judged on delivering software that people will pay to
use. A biologist, whether they use Perl, a pipette or both, is evaluated on
their publication record. X000A; automated building. The situation in bioinformatics is different; a hypothesis is made,
implemented, and tested. There ...Either e...
bioinformaticszen.com
Bioinformatics Zen - Bioinformatics software is a disgrace to our field.
http://www.bioinformaticszen.com/post/bioinformatics-software-is-a-disgrace
Bioinformatics software is a disgrace to our field. Published Thu April 3 2014. Every piece of software written for publication, which then can't be found
after publication is grant funding thrown away. Every piece of software that
only worked for the author during manuscript preparation is grant money thrown
away. Every piece of software reinvented solely for the purpose of adding a new
feature and publishing is grant money thrown away. Follow me on twitter @bioinformatics.
bioinformaticszen.com
Bioinformatics Zen - Bioinformatics - a wide set of transferable skills
http://www.bioinformaticszen.com/post/bioinformatics-wide-set-of-transferable-skills
Bioinformatics - a wide set of transferable skills. Published Wed July 27 2011. Working in academia feels like a lifestyle rather than simply a job. I feel
that being a researcher, in particular a bioinformatician, is part of my
identity. I carry my research around in my head outside of working
hours but at the same time I enjoy the autonomy and mastery. Of my
work. Therefore if you become unhappy in academia, as thoughtfully described
by Massimo Sandal. And if you already know one language. Furthermore ...
bioinformaticszen.com
Bioinformatics Zen - Scripting
http://www.bioinformaticszen.com/post/scripting
Published Fri February 20 2009. Scripts differentiate computational research from software production. A script
is a file of code with a specific purpose such as running a BLAST search on the
. Removing the dependencies between workflow steps is difficult. Build files such
as Rake. Or even that the entire analysis is repeated from scratch.
Capistrano. Keeping light and simple, and formalising dependencies makes script-based
projects easier to manage, maintain, and repeat.
bioinformaticszen.com
Bioinformatics Zen - Organising yourself as a dry lab scientist
http://www.bioinformaticszen.com/post/organising-yourself-as-a-dry-lab-scientist
Organising yourself as a dry lab scientist. Published Fri February 16 2007. I found this small section. On keeping organised
as a practising bioinformatician. In particular, I found these lines very
interesting:. Use text files/plain e-mail whenever possible. Give meaningful names to your files. Create separate folders/directories for each project with meaningful names. All three files contain a script fitting an ancova model, but all differ
slighty in focusing on different parts of the model&#...
bioinformaticszen.com
Bioinformatics Zen - The dark side of bioinformatics data mining
http://www.bioinformaticszen.com/post/the-dark-side-of-bioinformatics-data-mining
The dark side of bioinformatics data mining. Published Tue February 13 2007. I spend much of my day analysing yeast high throughput data, recently produced
in the laboratory. On one hand I'm very lucky to have access to fresh data at
many cellular levels. On the other hand, with all this information, I'm easily
swept away by the amount of variables I have access to - the dark side of data
mining. How
does it relate to previous findings? Lure of the dark side. Or bioinformatics
in general?
bioinformaticszen.com
Bioinformatics Zen - Organised bioinformatics experiments
http://www.bioinformaticszen.com/post/organised-bioinformatics-experiments
Published Sat May 24 2008. One of the things I've found in two years of doing bioinformatics is that
directories quickly fill up with files: data, scripts, results, and so forth.
Remembering the contents of each file is difficult as the only available
identifier is the file path. When there are many similarly named files this
makes remembering the purpose of each file even more difficult. A previous post I wrote. Use a database and object relational management system. Using this class, data can be manipu...
bioinformaticszen.com
Bioinformatics Zen - Using a database
http://www.bioinformaticszen.com/post/using-a-database
Published Fri February 20 2009. Using a database allows all data to be accessed in the same way, whether in a
script, at the command line, or through third-party database software.
Databases are fast and optimised for searching and joining datasets. Joins
between two sets of data that would be difficult when merging two files are
made much easier using database relationships. Follow me on twitter @bioinformatics.
bioinformaticszen.com
Bioinformatics Zen - What's the point of writing good scientific software?
http://www.bioinformaticszen.com/post/whats-the-point-of-writing-good-scientific-software
What's the point of writing good scientific software? Published Fri February 8 2013. Software written by academics has a reputation of being poorer quality than
that of software written by professional software developers. This poorer
quality can be quantified by a lack of documentation, a non-intuitive
interface, or bug-ridden with a tendency to crash. I assume, since you're reading a bioinformatics blog, that you've experienced
trying to use code that you can't download, won't compile, won't run...
bioinformaticszen.com
Bioinformatics Zen - Data analysis using R functions as objects
http://www.bioinformaticszen.com/post/data-analysis-using-r-functions-as-objects
Data analysis using R functions as objects. Published Fri October 2 2009. The R language is useful because of the available statistical and plotting functions in the base and addon packages. Before using any function though it's usually necessary to get your input data into the format that the function expects. Performing complicated data manipulations with R's standard methods for accessing and subsetting data can however quickly lead to complex and confusing R scripts. I have a function called. With(su...
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