Making Sense from Sequence#
cogent3 is a mature python library for analysis of biological sequence data. We endeavour to provide a first-class experience within Jupyter notebooks, but the algorithms also support parallel execution on compute systems with 1000’s of processors. It be used for…
cogent3 provides an extensive suite of capabilities for manipulating and analysing sequence data. For instance, the ability to read standard biological data formats, manipulate sequences by their annotations, to perform multiple sequence alignment (app docs) using any of our substitution models, phylogenetic reconstruction and tree manipulation, manipulation of tabular data, visualisation of phylogenies (image gallery) and much more.
🎬 Data wrangling with sequence annotations
Differences in the frequency of nucleotides between species are common. In such cases, non-reversible models of sequence evolution are required for robust estimation of important quantities such as branch lengths, or measuring natural selection [1, 2] (see using non-stationary models.). We have done more than just invent these new methods, we have established the most robust algorithms  for their implementation and their suitability for real data .
🎬 Testing a hypothesis involving a non-stationary nucleotide process
You don’t have to be an expert in structural programming languages (like Python) to use
cogent3! Interactive usage in Jupyter notebooks and a functional programming style interface lowers the barrier to entry. Individuals comfortable with R should find this interface less complex. (See the
🎬 Using cogent3 apps
Benjamin D Kaehler, Von Bing Yap, Rongli Zhang, and Gavin A Huttley. Genetic distance for a general non-stationary Markov substitution process. Systematic Biology, 64:281–93, 2015. URL: https://www.ncbi.nlm.nih.gov/pubmed/25503772.
Benjamin D Kaehler, Von Bing Yap, and Gavin A Huttley. Standard codon substitution models overestimate purifying selection for non-stationary data. Genome Biology and Evolution, 9:134–149, 2017. URL: https://www.ncbi.nlm.nih.gov/pubmed/28175284.
Harold W Schranz, Von Bing Yap, Simon Easteal, Rob Knight, and Gavin A Huttley. Pathological rate matrices: from primates to pathogens. BMC Bioinformatics, 9:550, 2008. URL: https://www.ncbi.nlm.nih.gov/pubmed/19099591.