dagLogo: an R/Bioconductor Package for Identifying and Visualizing Differential Amino Acid Group Usage in Proteomics data

Abstract

Sequence logos have been widely used as graphical representations of conserved nucleic acid and protein motifs. Due to the complexity of the amino acid (AA) alphabet, rich post-translational modification, and diverse subcellular localization of proteins, few versatile tools are available for effective identification and visualization of protein motifs. In addition, various reduced AA alphabets based on physicochemical, structural, or functional properties have been valuable in the study of protein alignment, folding, structure prediction, and evolution. However, there is lack of tools for applying reduced AA alphabets to the identification and visualization of statistically significant motifs. To fill this gap, we developed an R/Bioconductor package dagLogo, which has several advantages over existing tools. First, dagLogo allows various formats for input sets and provides comprehensive options to build optimal background models. It implements different reduced AA alphabets to group AAs of similar properties. Furthermore, dagLogo provides statistical and visual solutions for differential AA (or AA group) usage analysis of both large and small data sets. Case studies showed that dagLogo can better identify and visualize conserved protein sequence patterns from different types of inputs and can potentially reveal the biological patterns that could be missed by other logo generators.

The fitness cost of spurious phosphorylation

“…Supplementary figure 4: Kinase substrate motif enrichment analysis of selected phospho-tyrosine kinases. To test for kinase activity, kinase substrate motif enrichment analysis was performed on phospho-tyrosine containing peptides vs all other phospho-peptides using the R package dagLogo (Ou et al, 2020). By using only phosphorylated tyrosine peptides as the foreground, it could be assured that they must have been phosphorylated by the respective kinase.…”

“…P-values were multiple testing corrected using Benjamini Hochberg. Motif analysis was performed using the R package dagLogo (Ou et al, 2020). Enriched amino acids were filtered by p-value ≤ 0.05 and visualised using the R package ggseqlogo (Wagih, 2017).…”

A Brief History of Sequence Logos

“…k-mer probability logo (kpLogo) uses positional interdependencies of aligned sequences to visualize conserved patterns (motifs) [9]. dagLogo can visualize conserved amino acid sequence patterns in groups defined by charge, hydrophobicity etc [10]. Web servers pLogo and iceLogo use probability based methods to determine the stack heights [11,12].…”

Parameters Estimation of Weibull Distribution g Based on Fuzzy Data Using Neural Network

“…In many occasions due to unexpected errors, assignable causes or due to machine errors precise data can't be recorded. Pak et al [10] in their study considered the problem in which parameters of Weibull distribution are estimated based on the fact that collected data is not precise and are considered in the form of fuzzy numbers. Maximum likelihood estimates (MLEs) of the parameters were obtained by Newton-Raphson (NR) and Expectation Maximization (EM) algorithms.…”