Software
Orthogonal sequence design tool
This is a tool that utilizes Nupack and a simple evolutionary algorithm to generate sequences with minimal interactions other than with their reverse complements. The algorithm enables users to select Na+ and Mg++ concentrations, temperature, and a target DNA melting temerature according to the Wallace model. https://github.com/Nordic-DNA-Microscopy-Projects/ortho-seq-design
NGS Data Characterization Tool
This is a tool meant to characterize the composition of an NGS dataset. It uses a limited sample and performs clustering analysis to identify commonly occuring domains/motifs. The tool also enables the user to use reference sequences and probe their extent of representation in the sample. https://github.com/Intertangler/NGS_tool
Text Complexity Ranker and Frequency-Ranked Vocabulary List Maker
The purpose of this package of
tools is to 1. create a repository of sample text representative of a language e.g. Gutenberg.
2. Rank the frequency of words in the language.
2. Analyze a particular piece of text, e.g. a book or an article, and assess the text for its
difficulty based on the distribution of words (e.g. figures to the left) given their relative frequencies in the repository.
The idea is for it to be a kind of learning tool...one often has trouble deciding whether a piece of
foreign language text is going to be painful to read with constant dictionary lookups or engaging.
With this tool, it should be possible to assess that. The scripts are available for download here.
Another related script generates a ranking of words for vocabulary-learning purposes. This way, if you are learning
a foreign language, you can study only the most frequent words earliest. It works by pasting in a
large body of text representative of the language (or subfield) that you want to learn. The script
is available here.
Bayesian Experimental Design Tool
Experimental design in the face of uncertainty can be tricky. At times it is easy to be biased,
favoring conclusions that support what you think is "supposed" to happen. One can even
shoot oneself in the foot by proceeding down a dead-end when the data suggest otherwise.
This Bayesian experimental design tool is meant to help clarify one's hypotheses and distinguish
which experiments are likely to be useful in distinguishing branching explanations of reality.
The tool is entirely user-input driven, so it will not reveal anything that you technically do not
have access to knowing already. But the process of filling in the form and the report it generates at
the end may shed light on your inner thought process. The tool is not necessarily restricted to science either,
rather it could be used in any situation where experimentation and observation are needed to make decisions
under uncertainty. The output report is formatted in wiki markup to be recorded in a lab notebook.
Download the script here.