seqlogo 0.0.3

Python port of the R Bioconductor `seqLogo` package

seqLogo

Python port of Bioconductor's seqLogo served by WebLogo

Overview

In the field of bioinformatics, a common task is to look for sequence motifs at different sites along the genome or within a protein sequence. One aspect of this analysis involves creating a Position Weight Matrix (PWM). The formal format for a PWM file can be found here.

In short, a PWM file has M number of rows by N number of columns, where the number of rows is the number of positions within the target sequence, and the number of columns is the number of possible letters that can be found in the sequence.

Each item within the PWM is the probability of that given letter being seen at that given position. This is often generated in a frequentist fashion. If a pipeline tallies all observed letters at each position, this is called a Position Frequency Matrix (PFM).

• seqLogo can use both PWMs and PFMs as entry points for analysis (from a file or in array formats) and, subsequently, plot the sequence logos.

• seqLogo was written to support BIOINF 529 :Bioinformatics Concepts and Algorithms at the University of Michigan in the Department of Computational Medicine & Bioinformatics.

• seqLogo attempts to blend the user-friendly api of Bioconductor's seqLogo and the rendering power of the WebLogoPython API.

• seqLogo can handle numerous alphabets (e.g. DNA, RNA, Amino Acid), all of which can be extended, reduced, or ambiguous.

• seqLogo can also render sequence logos in a number of formats:

  • svg (default)
  • eps
  • pdf
  • jpeg
  • bmp
  • png

• All plots can be rendered in 4 different sizes:

  • small: 3.54" wide
  • medium: 5" wide
  • large: 7.25" wide
  • xlarge: 10.25" wide

Notes:

• For best results, implement seqLogo within a IPython/Jupyter environment (for inline plotting purposes).
• Initially written for Python 3.7. No other runtime has been tested.

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Installation

Minimal Requirements:

1. numpy
2. pandas
3. weblogo

Note: it is strongly encouraged that jupyter is installed as well.

conda environment:

To produce the ideal virtual environment that will run seqLogo on a conda-based build, clone the repo or download the environment.yml within the repo. Then run the following command:

$ conda env create -f environment.yml

To install using pip:

$ pip install seqLogo

Or clone the repo:

$ git clone https://github.com/betteridiot/seqLogo.git
$ python setup.py install

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Quickstart

Importing

import numpy as np
import pandas as pd
import seqLogo

Generate some PWM data (without frequency data)

For many demonstrations that speak to PWMs, they are often started with PWM data. Many packages preclude sequence logo generation from this entry point. However, seqLogo can handle it just fine. One point to make though is that if no count data is provided, seqLogo just generates pseudo-count data by multiplying the probabilities by 100. This is only for weblogolib comparability.

# Setting seed for demonstration purposes
>>> np.random.seed(42)

# Making a fake PWM
>>> random_pwm = np.random.dirichlet(np.ones(4), size=6)
>>> pwm = seqLogo.Pwm(random_pwm)
>>> pwm
          A         C         G         T
0  0.082197  0.527252  0.230641  0.159911
1  0.070375  0.070363  0.024826  0.834435
2  0.161962  0.216972  0.003665  0.617401
3  0.735638  0.098290  0.082638  0.083434
4  0.179898  0.368931  0.280463  0.170708
5  0.498510  0.079138  0.182004  0.240349

Generate some frequency data and convert to PWM

Sometimes the user has frequency data instead of PWM. To construct a Pwm instance that automatically computes Information Content and PWM values, the user can use the seqLogo.pfm2pwm() function.

# Setting seed for demonstration purposes
>>> np.random.seed(42)

# Making some fake Position Frequency Data (PFM)
>>> pfm = pd.DataFrame(np.random.randint(0, 36, size=(8, 4)))

# Convert to Position Weight Matrix (PWM)
>>> pwm = seqLogo.pfm2pwm(pfm)
>>> pwm
          A         C         G         T
0  0.405797  0.202899  0.101449  0.289855
1  0.300000  0.366667  0.166667  0.166667
2  0.277108  0.421687  0.277108  0.024096
3  0.283784  0.013514  0.310811  0.391892
4  0.015625  0.312500  0.500000  0.171875
5  0.214286  0.244898  0.265306  0.275510
6  0.405405  0.378378  0.054054  0.162162
7  0.416667  0.166667  0.354167  0.062500

Plot the sequence logo with information content scaling

# Setting seed for demonstration purposes
>>> np.random.seed(42)

# Making a fake PWM
>>> random_pwm = np.random.dirichlet(np.ones(4), size=6)
>>> pwm = seqLogo.Pwm(random_pwm)
>>> seqLogo.seqLogo(pwm, ic_scale = False, format = 'svg', size = 'medium')

The above code will produce:

Plot the sequence logo with no information content scaling

# Setting seed for demonstration purposes
>>> np.random.seed(42)

# Making a fake PWM
>>> random_pwm = np.random.dirichlet(np.ones(4), size=6)
>>> pwm = seqLogo.Pwm(random_pwm)
>>> seqLogo.seqLogo(pwm, ic_scale = False, format = 'svg', size = 'medium')

The above code will produce:

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Contributing

Please see our contribution guidelines here

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Acknowledgments

1. Bembom O (2018). seqLogo: Sequence logos for DNA sequence alignments. R package version 1.48.0.
2. Crooks GE, Hon G, Chandonia JM, Brenner SE WebLogo: A sequence logo generator, Genome Research, 14:1188-1190, (2004).