Logomaker

Logomaker generates sequence logos from numerical matrix representations of DNA, RNA, and protein sequences to visualize position-specific residue frequencies and information content.


Key Features:

  • Python API: Provides a Python API for programmatic generation of sequence logos.
  • Matrix input: Accepts any matrix-like array of numbers as input for logo construction.
  • Position-specific computations: Calculates position-specific letter (nucleotide/amino acid) frequencies and information content from input matrices.
  • Multiple-sequence alignment support: Includes methods to create logos from multiple-sequence alignments (MSAs).
  • Matplotlib integration: Uses matplotlib functions to style and render logos.
  • Vector graphics rendering: Renders logos as vector graphics for scalable output.

Scientific Applications:

  • Motif visualization: Visualizing sequence motifs and conserved regions in nucleotide and protein sequences.
  • Binding site analysis: Visualizing binding sites to assess residue conservation and specificity.
  • Regulatory element analysis: Visualizing regulatory elements within nucleotide sequences.
  • Functional domain analysis: Identifying and illustrating functional domains within protein sequences.
  • Comparative genomics and evolutionary studies: Visualizing conserved motifs across alignments to study evolutionary conservation.

Methodology:

Converts numerical matrices representing sequence information into sequence logos by calculating position-specific letter frequencies and transforming these values into a renderable format; renders logos via matplotlib as vector graphics; accepts matrix-like arrays and multiple-sequence alignments; compatible with Python 2.7 and Python 3.6.

Topics

Details

License:
MIT
Maturity:
Mature
Cost:
Free of charge
Tool Type:
library
Operating Systems:
Linux, Windows, Mac
Programming Languages:
Python
Added:
8/9/2019
Last Updated:
6/16/2020

Operations

Publications

Tareen A, Kinney JB. Logomaker: Beautiful sequence logos in python. Unknown Journal. 2019. doi:10.1101/635029.

Documentation

Downloads

Links