cogent3 is a mature python library for analysis of genomic 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.
Third-party integration demo for ``iplotx``
The iplotx team have contributed a demonstration of their tool's ability to produce images from cogent3 tree objects. See the cogent3 image gallery to read it.
Major advances in our progress towards a fully plugin-based architecture!
We have implemented the infrastructure to support alternative sequence storage plugins. These provide the backend storage for the new type sequence collections. We have implemented a proof-of-principle plugin cogent3-h5seqs for sequence storage based on the HDF5 format. This allows efficient storage of very large sequence collections (aligned or unaligned). See the readme for that project on how to use it.
We have implemented the infrastructure to support third-party provision of every bioinformaticians favourite game -- parsing / writing the multitude of sequence file formats. All builtin format parsers / writers are implemented as plugins. We use third-party versions by default.
We have implemented the infrastructure to support hook-style plugins. We have definied a single hook now -- the new type Alignment.quick_tree() method checks for an external plugin for calculation. The developers of piqtree have made the rapid-NJ algorithm available for this hook! Once installed, it is used as aln.quick_tree(use_hook="piqtree").
Note For assistance in writing your own plugins, contact us via the cogent3 discussions page.
Now distributed with sample data!
We have added sample data sets for quick testing of different features. Check out cogent3.available_datasets() to see the available datasets. You can load one using cogent3.get_dataset(name).
cogent3 is unique in providing numerous non-stationary Markov models for modelling sequence evolution, including codon models. cogent3 also includes an extensive collection of time-reversible models (again including novel codon 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. Additionally, there are novel signal processing methods focussed on statistical estimation of integer period signals.
🎬 Demo non-reversible substitution model
cogent3-demo-composable.mp4
Beyond our novel methods, cogent3 provides an extensive suite of capabilities for manipulating and analysing sequence data. You can manipulate sequences by their annotations, e.g.
🎬 Demo sequences with annotations
cogent3-demo-new-ann.mp4
Plus, you can read standard tabular and biological data formats, perform multiple sequence alignment using any cogent3 substitution models, phylogenetic reconstruction and tree manipulation, manipulation of tabular data, visualisation of phylogenies and much more.
Our cogent3.app module provides a very different approach to using the library capabilities. Expertise in structural programming concepts is not essential!
🎬 Demo friendly coding
cogent3-demo-composable.mp4
For most users we recommend
$ pip install "cogent3[extra]"which installs support for data visualisation and jupyter notebooks.
If you're running on a high-performance computing system we recommend
$ pip install cogent3which skips the data visualisation and notebook support.
To install the development version directly from GitHub
$ pip install git+https://github.com/cogent3/cogent3.git@develop#egg=cogent3cogent3 is released under the BSD-3 license, documentation is at cogent3.org, while cogent3 code is on GitHub. If you would like to contribute (and we hope you do!), we have created a companion c3dev GitHub repo which provides details on how to contribute and some useful tools for doing so.
cogent3 is a descendant of PyCogent. While there is much in common with PyCogent, the amount of change has been substantial, motivating the name change to cogent3. This name has been chosen because cogent was always the import name (dating back to PyEvolve in 2004) and it's Python 3 only.
Given this history, we are grateful to the multitude of individuals who have made contributions over the years. Many of these contributors were also co-authors on the original PyEvolve and PyCogent publications. Individual contributions can be seen by using "view git blame" on individual lines of code on GitHub, through git log in the terminal, and more recently the changelog.
Cogent3 has received funding support from the Australian National University and an Essential Open Source Software for Science Grant from the Chan Zuckerberg Initiative.
