cogent3

To help you get started, we've collected the most common ways that cogent3 is being used within popular public projects.

cogent3 / cogent3 / src / cogent3 / core / sequence.py View on Github

data = self._data

        not_array = isinstance(data, str)

        if motif_length == 1:
            counts = CategoryCounter(data)
        else:
            if len(data) % motif_length != 0:
                warnings.warn(
                    "%s length not divisible by %s, truncating"
                    % (self.name, motif_length)
                )
            limit = (len(data) // motif_length) * motif_length
            data = data[:limit]
            if not_array:
                counts = CategoryCounter(
                    data[i : i + motif_length] for i in range(0, limit, motif_length)
                )
            else:
                counts = CategoryCounter(
                    tuple(v) for v in data.reshape(limit // motif_length, motif_length)
                )
        if not not_array:
            for key in list(counts):
                indices = [key] if motif_length == 1 else key
                motif = self.alphabet.to_chars(indices).astype(str)
                motif = "".join(motif)
                counts[motif] = counts.pop(key)

        exclude = []
        if not include_ambiguity or not allow_gap:
            is_degen = self.moltype.is_degenerate

View more ways to use cogent3

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.

Who is it for?

Anyone who wants to analyse sequence divergence using robust statistical models

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.

Anyone who wants to undertake exploratory genomic data analysis

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.

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.

Beginner friendly approach to genomic data analysis

Our cogent3.app module provides a very different approach to using the library capabilities. Expertise in structural programming concepts is not essential!

Installation?

$ pip install cogent3

Install extra -- adds visualisation support

The extra group includes python libraries required for visualisation, i.e. plotly, kaleido, psutil and pandas.

$ pip install "cogent3[extra]"

Install dev -- adds cogent3 development related libraries

The dev group includes python libraries required for development of cogent3.

$ pip install "cogent3[dev]"

Install the development version

$ pip install git+https://github.com/cogent3/cogent3.git@develop#egg=cogent3

Project Information

cogent3 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.

Project History

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.

Compared to PyCogent version 1.9, there has been a massive amount of changes. These include integration of many of the new developments on algorithms and modelling published by the Huttley lab over the last decade. We have also modernised our dependencies. For example, we now use plotly for visualisation, tqdm for progress bar display, concurrent.futures and mpi4py.futures for parallel process execution, nox and pytest for unit testing.

Funding

Cogent3 has received funding support from the Australian National University and an Essential Open Source Software for Science Grant from the Chan Zuckerberg Initiative.