BOL: Related items

DendroPy: a Python library for phylogenetic computing

Seema Singh — Mon, 23 Apr 2018 05:49:50 -0500

DendroPy is a Python library for phylogenetic computing. It provides classes and functions for the simulation, processing, and manipulation of phylogenetic trees and character matrices, and supports the reading and writing of phylogenetic data in a range of formats, such as NEXUS, NEWICK, NeXML, Phylip, FASTA, etc. Application scripts for performing some useful phylogenetic operations, such as data conversion and tree posterior distribution summarization, are also distributed and installed as part of the libary. DendroPy can thus function as a stand-alone library for phylogenetics, a component of more complex multi-library phyloinformatic pipelines, or as a scripting “glue” that assembles and drives such pipelines.

The primary home page for DendroPy, with detailed tutorials and documentation, is at:

http://dendropy.org/

DendroPy is also hosted in the official Python repository:

http://packages.python.org/DendroPy/

Requirements and Installation

DendroPy 4.x runs under Python 3 (all versions > 3.1) and Python 2 (Python 2.7 only).

You can install DendroPy by running:

More information is available here:

http://dendropy.org/downloading.html

Documentation

Full documentation is available here:

http://dendropy.org/

This includes:

A comprehensive “getting started” primer .

API documentation .

Descriptions of data formats supported for reading/writing .

and more.

Address of the bookmark: https://pypi.org/project/DendroPy/

Setting python version as default on Linux

Rahul Nayak — Tue, 04 Sep 2018 10:15:47 -0500

If you have a later version than 2.6 you'll need to set 2.6 as the default Python. Later versions would be 2.7 and 3.1; see what you have by typing

python -V

at the terminal. For purposes of this example we'll assume you have 3.1 installed. You'll next need to execute the following commands:

sudo apt-get install python2.6 idle-python2.6
sudo update-alternatives --install /usr/bin/python python /usr/bin/python3.1 1
sudo update-alternatives --install /usr/bin/python python /usr/bin/python2.6 10
sudo update-alternatives --config python

This last command will allow you to choose which version of python to use by default. If you have done everything above correctly, python2.6 should already be set as the default. If it is not, choose it to be the default. From now on, running python should start version 2.6.

Undoing These Changes

In some cases (e.g., installing or updating certain packages), you'll get an error message if you've run the commands above. To update these packages, you'll have to temporarily undo these changes. Here's how to do that:

sudo update-alternatives --remove-all python
sudo ln -s python3.1 /usr/bin/python

Once you're done updating these packages, execute the commands at the top to set python2.6 as the default again.

JCVI:Python utility libraries on genome assembly, annotation and comparative genomics

Jit — Tue, 17 Mar 2020 06:19:06 -0500

Collection of Python libraries to parse bioinformatics files, or perform computation related to assembly, annotation, and comparative genomics.

https://github.com/tanghaibao/jcvi

More at https://github.com/tanghaibao/jcvi/wiki

Address of the bookmark: https://github.com/tanghaibao/jcvi

Subprocess pkg

Rahul Agarwal — Sun, 17 Aug 2014 07:59:32 -0500

Subprocess is one of simplest way of running linux command from within python code

Example:

if you want to run fastqc for QC of fastq file:

from subprocess import Popen,PIPE,call

p=Popen(["fastqc","-f","fastq","-o", "/home/name/result/","/dev/stdin"],stdin=fopen("read.fastq","r") ,stdout=PIPE,stderr=PIPE)

print p.stderr

p.stdout.close()

http://pymotw.com/2/subprocess/

CONTIGuator !

BioStar — Fri, 04 Oct 2019 01:27:58 -0500

CONTIGuator is a Python script for Linux environments whose purpose is to speed-up the bacterial genome assembly process and to obtain a first insight of the genome structure using the well-known artemis comparison tool (ACT).

Address of the bookmark: https://sourceforge.net/projects/contiguator/

MitoHiFi: a python pipeline for mitochondrial genome assembly from PacBio high fidelity reads

Abhi — Tue, 05 Sep 2023 07:31:35 -0500

MitoHiFi v3.2 is a python pipeline distributed under MIT License !

MitoHiFi was first developed to assemble the mitogenomes for a wide range of species in the Darwin Tree of Life Project (DToL)

https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-023-05385-y

Address of the bookmark: https://github.com/marcelauliano/MitoHiFi

R package for visualising GO enrichment

Jit — Mon, 22 Jul 2013 12:25:09 -0500

An R package that visualizes the GO enrichment results as word clouds and arranges them together with figures of experimental data. This allows us to draw informative summary plots for analyses such as differential expression or clustering, where for each gene list we display its behaviour in the experiment alongside with its GO annotations.

Links @ http://raivokolde.github.io/GOsummaries/

Lab @ http://biit.cs.ut.ee/about/main

Surrogate Variable Analysis (SVA)

Jit — Thu, 30 Oct 2014 08:01:58 -0500

The sva package contains functions for removing batch effects and other unwanted variation in high-throughput experiment. Specifically, the sva package contains functions for the identifying and building surrogate variables for high-dimensional data sets. Surrogate variables are covariates constructed directly from high-dimensional data (like gene expression/RNA sequencing/methylation/brain imaging data) that can be used in subsequent analyses to adjust for unknown, unmodeled, or latent sources of noise. The sva package can be used to remove artifacts in three ways:

(1) identifying and estimating surrogate variables for unknown sources of variation in high-throughput experiments (Leek and Storey 2007 PLoS Genetics,2008 PNAS),

(2) directly removing known batch effects using ComBat (Johnson et al. 2007 Biostatistics) and

(3) removing batch effects with known control probes (Leek 2014 biorXiv).

Removing batch effects and using surrogate variables in differential expression analysis have been shown to reduce dependence, stabilize error rate estimates, and improve reproducibility, see (Leek and Storey 2007 PLoS Genetics, 2008 PNAS or Leek et al. 2011 Nat. Reviews Genetics).

More at http://www.bioconductor.org/packages/release/bioc/html/sva.html

Circular Visualization in R

Jit — Wed, 05 Jul 2017 04:11:30 -0500

This is the documentation of the circlize package. Examples in the book are generated under version 0.4.1.

If you use circlize in your publications, I would be appreciated if you can cite:

Gu, Z. (2014) circlize implements and enhances circular visualization in R. Bioinformatics. DOI: 10.1093/bioinformatics/btu393

Address of the bookmark: http://zuguang.de/circlize_book/book/

IONiseR: tools for the quality assessment of data produced by Oxford Nanopore’s MinION sequencer

Jit — Thu, 23 Nov 2017 10:24:19 -0600

This package is intended to provide tools for the quality assessment of data produced by Oxford Nanopore’s MinION sequencer. It includes a functions to generate a number plots for examining the statistics that we think will be useful for this task.

However, nanopore sequencing is an emerging and rapidly developing technology. It is not clear what will be most informative. We hope that IONiseR will provide a framework for visualisation of metrics that we haven’t thought of, and welcome feedback at mike.smith@embl.de.

If you’re not interested in the quality assement of the raw or event level data, and want to jump straight to the getting FASTQ format files from fast5 files you can go straight to the final section of this document.

Address of the bookmark: https://www.bioconductor.org/packages/devel/bioc/vignettes/IONiseR/inst/doc/IONiseR.html