RaacLogo: a new sequence logo generator by using reduced amino acid clusters

Abstract

Sequence logos give a fast and concise display in visualizing consensus sequence. Protein exhibits greater complexity and diversity than DNA, which usually affects the graphical representation of the logo. Reduced amino acids perform powerful ability for simplifying complexity of sequence alignment, which motivated us to establish RaacLogo. As a new sequence logo generator by using reduced amino acid alphabets, RaacLogo can easily generate many different simplified logos tailored to users by selecting various reduced amino acid alphabets that consisted of more than 40 clustering algorithms. This current web server provides 74 types of reduced amino acid alphabet, which were manually extracted to generate 673 reduced amino acid clusters (RAACs) for dealing with protein alignment. A two-dimensional selector was proposed for easily selecting desired RAACs with underlying biology knowledge. It is anticipated that the RaacLogo web server will play more high-potential roles for protein sequence alignment, topological estimation and protein design experiments. RaacLogo is freely available at http://bioinfor.imu.edu.cn/raaclogo.

dagLogo: An R/Bioconductor package for identifying and visualizing differential amino acid group usage in proteomics data

“…With reduced alphabets, several AAs of a similar property are lumped together and represented by one letter. Reduced AA alphabets have been shown to be valuable for protein pattern discovery [28][29][30] and in the study of protein alignment, folding, structure prediction, and evolution [25,[31][32][33][34][35][36][37][38]. For example, serine (Ser) and threonine (Thr) are highly interchangeable as phosphorylation sites of Ser/Thr kinase substrates [39], and thus the two AAs can be binned into the same degenerate group to reduce the complexity of resulting logos showing conserved patterns of Ser/Thr kinase (STK) substrates.…”

“…Thus, unsurprisingly, coloring AA residues based on their similarity of physicochemical properties has been a general practice of many logo generators long before [2] (S1 Table). However, applying reduced AA alphabets to motif identification and visualization had rarely been practiced until recent [28,29]. This limitation is more obvious for sequence patterns of proteins than sequence patterns of nucleic acids because protein sequences have a greater alphabet complexity and variability of the occurrences of individual residues.…”

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

“…With reduced alphabets, several AAs of a similar property are lumped together and represented by one letter. Reduced AA alphabets have been shown to be valuable for protein pattern discovery [28][29][30] and in the study of protein alignment, folding, structure prediction, and evolution [25,[31][32][33][34][35][36][37][38]. For example, serine (Ser) and threonine (Thr) are highly interchangeable as phosphorylation sites of Ser/Thr kinase substrates [39], and thus the two AAs can be binned into the same degenerate group to reduce the complexity of resulting logos showing conserved patterns of Ser/Thr kinase (STK) substrates.…”

“…However, applying reduced AA alphabets to motif identification and visualization had rarely been practiced until recent [28,29]. This limitation is more obvious for sequence patterns of proteins than sequence patterns of nucleic acids because protein sequences have a greater alphabet complexity and variability of the occurrences of individual residues.…”

“…This limitation is more obvious for sequence patterns of proteins than sequence patterns of nucleic acids because protein sequences have a greater alphabet complexity and variability of the occurrences of individual residues. Even though RaacLogo [28] and logoJS [29] allow degenerate representations of reduced AA alphabets [40,41], both were based on information theory without statistical significance tests, and most importantly, neither offers alternative background models ( Supplementary Table S1).…”