CodonLogo: a sequence logo-based viewer for codon patterns

Molecular Characterization and Expression of unc-13d in the Sex Reversal of Monopterus albus

Monopterus albus is a protogynous hermaphroditic fish that changes from female to male, but the underlying sex change mechanism remains as-yet unknown. In this study, we firstly cloned and characterized the sequence and protein structure of unc-13d of M. albus. We found that the genomic structure of unc-13d was different from other species. Expression was detected in the developing gonad by applying qRT-PCR and in situ hybridization. We found that the expression of unc-13d in the ovotestis was higher than in the ovary and testes. A strong signal of unc-13d was detected in oocytes and granulosa cells in the ovary and spermatogonia and primary spermatocytes in the testes. We found that the promoter methylation of unc-13d was negatively correlated with gene expression in developing gonads, especially at site 114. A dual-luciferase assay was designed and revealed that dmrt1 regulates promoter activity opposite to foxl2. In summary, during sex reversal, DNA methylation affects the binding of the transcription factor dmrt1 and foxl2 in the promoter region through methylation and demethylation interactions to regulate the expression of unc-13d during gonadal development.

plotnineSeqSuite: a Python package for visualizing sequence data using ggplot2 style

BACKGROUND

The visual sequence logo has been a hot area in the development of bioinformatics tools. ggseqlogo written in R language has been the most popular API since it was published. With the popularity of artificial intelligence and deep learning, Python is currently the most popular programming language. The programming language used by bioinformaticians began to shift to Python. Providing APIs in Python that are similar to those in R can reduce the learning cost of relearning a programming language. And compared to ggplot2 in R, drawing framework is not as easy to use in Python. The appearance of plotnine (ggplot2 in Python version) makes it possible to unify the programming methods of bioinformatics visualization tools between R and Python.

RESULTS

Here, we introduce plotnineSeqSuite, a new plotnine-based Python package provides a ggseqlogo-like API for programmatic drawing of sequence logos, sequence alignment diagrams and sequence histograms. To be more precise, it supports custom letters, color themes, and fonts. Moreover, the class for drawing layers is based on object-oriented design so that users can easily encapsulate and extend it.

CONCLUSIONS

plotnineSeqSuite is the first ggplot2-style package to implement visualization of sequence -related graphs in Python. It enhances the uniformity of programmatic plotting between R and Python. Compared with tools appeared already, the categories supported by plotnineSeqSuite are much more complete. The source code of plotnineSeqSuite can be obtained on GitHub ( https://github.com/caotianze/plotnineseqsuite ) and PyPI ( https://pypi.org/project/plotnineseqsuite ), and the documentation homepage is freely available on GitHub at ( https://caotianze.github.io/plotnineseqsuite/ ).

Genome-wide identification of GRF gene family and their contribution to abiotic stress response in pitaya (Hylocereus polyrhizus)

Growth-regulating factors (GRFs) are plant-specific transcription factors identified in many land plants. Recently, their indispensable roles in stress response are highlighted. In present work, 11 HpGRFs were cloned in pitaya. Segmental duplication is considered essential for the expansion of HpGRFs. A phylogenetic tree suggested that GRFs could be divided into eight categories, among which G-I was a Caryophyllales-specific one. The categorization was further evidenced by differences in the gene structure, collinearity, protein domain of HpGRFs. Five miR396 hairpins giving rise to two types of matured miR396s were identified in pitaya via sRNA-Seq in combination with bioinformatic analysis. Parallel analysis of RNA ends proved that HpGRFs except HpGRF5 were degraded by miR396-directed cleavages at the regions which code the conserved WRC motifs of HpGRFs. Multiple cis-regulatory elements were discovered in the promoters of HpGRFs. Among the elements, most are involved in stress and phytohormone response as well as plant growth, indicating a crosstalk between them. Expression analysis showed the responsive patterns of the miR396-GRF module under abiotic stresses. To conclude, our work systematically identified the miR396-targeted HpGRFs in pitaya and confirmed their involvement in stress response, providing novel insights into the comprehensive understanding of the stress resistance of pitaya.

CoCoView - A Codon Conservation Viewer via Sequence Logos

Sequence logos are a simple way to display a set of aligned sequences, and they are useful to identify conserved patterns. Since their introduction, several tools have been developed for generating these representations at the single residue level (amino acids or nucleotides). We have developed a tool to build sequence logos of protein-coding sequences at the codon level, allowing more accurate analysis of coding-sequences as they represent synonymous and non-synonymous changes instead of showing only changes that imply on amino acid substitutions. We built CoCoView on top of the Logomaker Python API. It creates codon sequence logos from a multiple sequence alignment of protein-coding sequences. Some properties of the data and the generated logos can be controlled by the end-users, such as data redundancy, plot type and alphabet color.

• Split aligned sequences into codon positions;

• For each position compute codon frequency and information content;

• Use the computed information to plot the graphic.

Logomaker: beautiful sequence logos in Python

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 native matplotlib objects that are easy to stylize and incorporate into multi-panel figures.

Availability and implementation

Logomaker can be installed using the pip package manager and is compatible with both Python 2.7 and Python 3.6. Documentation is provided at http://logomaker.readthedocs.io; source code is available at http://github.com/jbkinney/logomaker.

Examination of the Glycine Betaine-Dependent Methylotrophic Methanogenesis Pathway: Insights Into Anaerobic Quaternary Amine Methylotrophy

Recent studies indicate that environmentally abundant quaternary amines (QAs) are a primary source for methanogenesis, yet the catabolic enzymes are unknown. We hypothesized that the methanogenic archaeon Methanolobus vulcani B1d metabolizes glycine betaine (GB) through a corrinoid-dependent GB:coenzyme M (CoM) methyl transfer pathway. The draft genome sequence of M. vulcani B1d revealed a gene encoding a predicted non-pyrrolysine MttB homolog (MV8460) with high sequence similarity to the GB methyltransferase encoded by Desulfitobacterium hafniense Y51. MV8460 catalyzes GB-dependent methylation of free cob(I)alamin indicating it is an authentic MtgB enzyme. Proteomic analysis revealed that MV8460 and a corrinoid binding protein (MV8465) were highly abundant when M. vulcani B1d was grown on GB relative to growth on trimethylamine. The abundance of a corrinoid reductive activation enzyme (MV10335) and a methylcorrinoid:CoM methyltransferase (MV10360) were significantly higher in GB-grown B1d lysates compared to other homologs. The GB:CoM pathway was fully reconstituted in vitro using recombinant MV8460, MV8465, MV10335, and MV10360. Demonstration of the complete GB:CoM pathway expands the knowledge of direct QA-dependent methylotrophy and establishes a model to identify additional ecologically relevant anaerobic quaternary amine pathways.

Sequence Bundles: a novel method for visualising, discovering and exploring sequence motifs

Background

We introduce Sequence Bundles--a novel data visualisation method for representing multiple sequence alignments (MSAs). We identify and address key limitations of the existing bioinformatics data visualisation methods (i.e. the Sequence Logo) by enabling Sequence Bundles to give salient visual expression to sequence motifs and other data features, which would otherwise remain hidden.

Methods

For the development of Sequence Bundles we employed research-led information design methodologies. Sequences are encoded as uninterrupted, semi-opaque lines plotted on a 2-dimensional reconfigurable grid. Each line represents a single sequence. The thickness and opacity of the stack at each residue in each position indicates the level of conservation and the lines' curved paths expose patterns in correlation and functionality. Several MSAs can be visualised in a composite image. The Sequence Bundles method is designed to favour a tangible, continuous and intuitive display of information.

Results

We have developed a software demonstration application for generating a Sequence Bundles visualisation of MSAs provided for the BioVis 2013 redesign contest. A subsequent exploration of the visualised line patterns allowed for the discovery of a number of interesting features in the dataset. Reported features include the extreme conservation of sequences displaying a specific residue and bifurcations of the consensus sequence.

Conclusions

Sequence Bundles is a novel method for visualisation of MSAs and the discovery of sequence motifs. It can aid in generating new insight and hypothesis making. Sequence Bundles is well disposed for future implementation as an interactive visual analytics software, which can complement existing visualisation tools.

ProfileGrids: a sequence alignment visualization paradigm that avoids the limitations of Sequence Logos

Background

The 2013 BioVis Contest provided an opportunity to evaluate different paradigms for visualizing protein multiple sequence alignments. Such data sets are becoming extremely large and thus taxing current visualization paradigms. Sequence Logos represent consensus sequences but have limitations for protein alignments. As an alternative, ProfileGrids are a new protein sequence alignment visualization paradigm that represents an alignment as a color-coded matrix of the residue frequency occurring at every homologous position in the aligned protein family.

Results

The JProfileGrid software program was used to analyze the BioVis contest data sets to generate figures for comparison with the Sequence Logo reference images.

Conclusions

The ProfileGrid representation allows for the clear and effective analysis of protein multiple sequence alignments. This includes both a general overview of the conservation and diversity sequence patterns as well as the interactive ability to query the details of the protein residue distributions in the alignment. The JProfileGrid software is free and available from http://www.ProfileGrid.org.

BlockLogo: visualization of peptide and sequence motif conservation

BlockLogo is a web-server application for the visualization of protein and nucleotide fragments, continuous protein sequence motifs, and discontinuous sequence motifs using calculation of block entropy from multiple sequence alignments. The user input consists of a multiple sequence alignment, selection of motif positions, type of sequence, and output format definition. The output has BlockLogo along with the sequence logo, and a table of motif frequencies. We deployed BlockLogo as an online application and have demonstrated its utility through examples that show visualization of T-cell epitopes and B-cell epitopes (both continuous and discontinuous). Our additional example shows a visualization and analysis of structural motifs that determine the specificity of peptide binding to HLA-DR molecules. The BlockLogo server also employs selected experimentally validated prediction algorithms to enable on-the-fly prediction of MHC binding affinity to 15 common HLA class I and class II alleles as well as visual analysis of discontinuous epitopes from multiple sequence alignments. It enables the visualization and analysis of structural and functional motifs that are usually described as regular expressions. It provides a compact view of discontinuous motifs composed of distant positions within biological sequences. BlockLogo is available at: http://research4.dfci.harvard.edu/cvc/blocklogo/ and http://met-hilab.bu.edu/blocklogo/.