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.. remove right sidebar
:html_theme.sidebar_secondary.remove:
.. page style and classes
.. raw:: html
<style>
.bd-main .bd-content .bd-article-container {
max-width: 100%;
}
details > summary {
font-weight: bold;
}
.centered {
text-align: center;
}
.subtitle {
text-align: center;
font-size: 1.2em;
margin: 20px;
}
.heading {
text-align: center;
font-size: 1.5em;
font-weight: 900;
}
.no-top-pardding {
margin-top: 0;
padding-top: 0;
}
</style>
.. hidden page title
.. title:: scikit-bio: Bioinformatics in Python
.. light/dark logo image
.. image:: _static/img/logo.svg
:class: only-light
:width: 600 px
:align: center
.. image:: _static/img/logo_inv.svg
:class: only-dark
:width: 600 px
:align: center
.. brief description of the project
.. rst-class:: subtitle
A community-driven Python library for bioinformatics, providing versatile data structures, algorithms and educational resources.
.. toctree hidden from document but provides header links
.. toctree::
:hidden:
:maxdepth: 1
Install <install>
Learn <learn>
Documentation <https://scikit.bio/docs/latest/index.html>
Contribute <contribute>
Community <https://github.com/scikit-bio/scikit-bio/discussions>
Releases <https://github.com/scikit-bio/scikit-bio/blob/main/CHANGELOG.md>
About <about>
.. grid:: 1 1 1 3
.. grid-item-card::
:class-header: centered
**For Researchers** :octicon:`beaker;1.2em;sd-text-danger`
^^^
Robust, performant and scalable algorithms tailored for the vast landscape of biological data analysis spanning genomics, microbiomics, ecology, evolutionary biology and more. Built to unveil the insights hidden in complex, multi-omic data.
+++
.. dropdown example code, implemented using raw html details instead of sphinx-design's dropdown, because the latter has extra edges
.. raw:: html
<details>
<summary>Example</summary>
.. code-block:: python
from skbio.tree import TreeNode
from skbio.diversity import beta_diversity
from skbio.stats.ordination import pcoa
data = pd.read_table('data.tsv', index_col=0)
metadata = pd.read_table('metadata.tsv', index_col=0)
tree = TreeNode.read('tree.nwk')
bdiv = beta_diversity(
'weighted_unifrac', data, ids=data.index, otu_ids=data.columns, tree=tree
)
ordi = pcoa(bdiv, number_of_dimensions=3)
ordi.plot(metadata, column='bodysite')
.. image:: _static/img/hmp1_pcoa.png
:alt: PCoA plot
.. raw:: html
</details>
.. grid-item-card::
:class-header: centered
**For Educators** :octicon:`mortar-board;1.2em;sd-text-info`
^^^
Fundamental bioinformatics algorithms enriched by comprehensive documentation, examples and references, offering a rich resource for classroom and laboratory education (with proven `success <https://readiab.org/>`_). Designed to spark curiosity and foster innovation.
+++
.. raw:: html
<details>
<summary>Example</summary>
.. code-block:: python
from skbio.alignment import global_pairwise_align_protein
from skbio.sequence.distance import hamming
from skbio.stats.distance import DistanceMatrix
from skbio.tree import nj
def align_dist(seq1, seq2):
aln = global_pairwise_align_protein(seq1, seq2)[0]
return hamming(aln[0], aln[1])
dm = DistanceMatrix.from_iterable(
seqs, align_dist, keys=ids, validate=False
)
tree = nj(dm).root_at_midpoint()
print(tree.ascii_art())
::
/-chicken
|
-root----| /-rat
| /--------|
| | \-mouse
\--------|
| /-pig
| |
\--------| /-chimp
| /--------|
\--------| \-human
|
\-monkey
.. raw:: html
</details>
.. grid-item-card::
:class-header: centered
**For Developers** :octicon:`git-merge;1.2em;sd-text-warning`
^^^
Industry-standard, production-ready Python codebase featuring a stable, unit-tested API that streamlines development and integration. Licensed under the :repo:`3-Clause BSD <blob/main/LICENSE.txt>`, it provides an expansive platform for both academic research and commercial ventures.
+++
.. raw:: html
<details>
<summary>Example</summary>
.. code-block:: python
def centralize(mat):
r"""Center data around its geometric average.
Parameters
----------
mat : array_like of shape (n_compositions, n_components)
A matrix of proportions.
Returns
-------
ndarray of shape (n_compositions, n_components)
centered composition matrix.
Examples
--------
>>> import numpy as np
>>> from skbio.stats.composition import centralize
>>> X = np.array([[.1, .3, .4, .2], [.2, .2, .2, .4]])
>>> centralize(X)
array([[ 0.17445763, 0.30216948, 0.34891526, 0.17445763],
[ 0.32495488, 0.18761279, 0.16247744, 0.32495488]])
"""
mat = closure(mat)
cen = gmean(mat, axis=0)
return perturb_inv(mat, cen)
.. raw:: html
</details>
----
.. grid:: 1 1 1 2
.. grid-item::
:columns: 12 12 5 5
:padding: 0 0 4 4
.. rst-class:: heading
Install
.. tab-set::
:class: no-top-pardding
.. tab-item:: Conda
.. code-block:: bash
conda install -c conda-forge scikit-bio
.. tab-item:: PyPI
.. code-block:: bash
pip install scikit-bio
.. tab-item:: Dev
.. code-block:: bash
pip install git+https://github.com/scikit-bio/scikit-bio.git
.. tab-item:: More
See detailed :doc:`instructions <install>` on installing scikit-bio on various platforms.
.. grid-item::
:columns: 12 12 7 7
:padding: 0 0 4 4
.. rst-class:: heading
News
.. card-carousel:: 3
.. card::
Latest release (2025-01-13):
.. button-link:: https://github.com/scikit-bio/scikit-bio/releases/tag/0.6.3
:color: success
:shadow:
scikit-bio 0.6.3
.. card::
Thrilled with completion of `scikit-bio workshop <https://www.iscb.org/ismb2024/programme-schedule/tutorials#ip3>`_ at ISMB 2024. Materials are `publicly available <https://github.com/scikit-bio/scikit-bio-tutorials>`_.
.. card::
New `DOE award <https://genomicscience.energy.gov/compbioawards2023/#Expanding>`_ for scikit-bio development in multi-omics and complex modeling.
.. card::
New website: `scikit.bio <https://scikit.bio>`_ and organization: https://github.com/scikit-bio are online.
----
.. rst-class:: heading
Feature Highlights
.. grid:: 1 2 3 3
:padding: 0 0 4 4
:gutter: 3
.. grid-item-card::
:fa:`dna;fa-2x sd-text-success`
**Biological sequences**: Efficient data structure with a :docs:`flexible grammar <sequence.GrammaredSequence>` for easy manipulation, :docs:`annotation <sequence.GrammaredSequence.has_positional_metadata>`, :docs:`alignment <alignment.global_pairwise_align>`, and conversion into :docs:`motifs <sequence.GrammaredSequence.find_motifs>` or :docs:`k-mers <sequence.GrammaredSequence.iter_kmers>` for in-depth analysis.
.. grid-item-card::
:fa:`tree;fa-2x sd-text-success`
**Phylogenetic trees**: Scalable :docs:`tree structure <tree.TreeNode>` tailored for evolutionary biology, supporting diverse operations in :docs:`navigation <tree.TreeNode.lca>`, :docs:`manipulation <tree.TreeNode.root_at_midpoint>`, :docs:`comparison <tree.TreeNode.compare_rfd>`, and :docs:`construction <tree.nj>`.
.. grid-item-card::
:fa:`chart-column;fa-2x sd-text-success`
**Community diversity** analysis for ecological studies, with an extensive suite of metrics such as :docs:`UniFrac <diversity.beta.weighted_unifrac>` and :docs:`PD <diversity.alpha.faith_pd>`, optimized to handle large-scale community datasets.
.. grid-item-card::
:fa:`compass-drafting;fa-2x sd-text-success`
**Ordination methods**, such as :docs:`PCoA <stats.ordination.pcoa>`, :docs:`CA <stats.ordination.ca>`, and :docs:`RDA <stats.ordination.rda>`, to uncover patterns underlying high-dimensional data, facilitating insightful visualization.
.. grid-item-card::
:fa:`arrow-up-right-dots;fa-2x sd-text-success`
**Multivariate statistical tests**, such as :docs:`PERMANOVA <stats.distance.permanova>`, :docs:`BIOENV <stats.distance.bioenv>`, and :docs:`Mantel <stats.distance.mantel>`, to decode complex relationships across data matrices and sample properties.
.. grid-item-card::
:fa:`chart-pie;fa-2x sd-text-success`
**Compositional data** processing and analysis, such as :docs:`CLR <stats.composition.clr>` transform and :docs:`ANCOM <stats.composition.ancom>`, built for various omic data types from high-throughput experiments.
|
