2019
DOI: 10.1093/bioinformatics/btz921
|View full text |Cite
|
Sign up to set email alerts
|

Logomaker: beautiful sequence logos in Python

Abstract: Summary Sequence logos are visually compelling ways of illustrating the biological properties of DNA, RNA and protein sequences, yet it is currently difficult to generate and customize such logos within the Python programming environment. Here we introduce Logomaker, a Python API for creating publication-quality sequence logos. Logomaker can produce both standard and highly customized logos from either a matrix-like array of numbers or a multiple-sequence alignment. Logos are rendered as nati… Show more

Help me understand this report
View preprint versions

Search citation statements

Order By: Relevance

Paper Sections

Select...
1
1
1
1

Citation Types

3
270
0
2

Year Published

2020
2020
2024
2024

Publication Types

Select...
6
4

Relationship

1
9

Authors

Journals

citations
Cited by 342 publications
(301 citation statements)
references
References 36 publications
3
270
0
2
Order By: Relevance
“…Analysis(MEGA)(44) using the Maximum Likelihood method. The sequence logo was plotted from aligned sequences by logomaker (45).…”
Section: Contributionsmentioning
confidence: 99%
“…Analysis(MEGA)(44) using the Maximum Likelihood method. The sequence logo was plotted from aligned sequences by logomaker (45).…”
Section: Contributionsmentioning
confidence: 99%
“…The tree is provided in Supplementary Figure 59 . The logo for the catalytic site was generated with logomaker v0.8 in python v2.7.15 106 .…”
Section: Methodsmentioning
confidence: 99%
“…To test whether published RIN motifs are enriched in genes co-immunoprecipitated with RIN in our analysis, we used in vitro-determined Drosophila RIN and human G3BP2 motifs (Cook et al, 2011;Ray et al, 2013) (see http://cisbp-rna.ccbr.utoronto.ca/), and in vivo motifs of human G3BP1 and G3BP2 (Edupuganti et al, 2017). Position frequency matrices (PFMs) of the in vivo motifs were estimated from the height of base logos and produced using ggseqlogo (Wagih, 2017) and Logomaker (Tareen and Kinney, 2019). To eliminate the effect of sequence length on motif enrichment, we randomly selected corresponding transcript regions from length-paired co-expressed unbound genes as negative set for each transcript region of genes in the RIP-enriched gene sets (positive set).…”
Section: Enrichment Test Of Published Rin Motifsmentioning
confidence: 99%