Domain similarity based orthology detection

Domainoid: domain-oriented orthology inference

Background

Orthology inference is normally based on full-length protein sequences. However, most proteins contain independently folding and recurring regions, domains. The domain architecture of a protein is vital for its function, and recombination events mean individual domains can have different evolutionary histories. It has previously been shown that orthologous proteins may differ in domain architecture, creating challenges for orthology inference methods operating on full-length sequences. We have developed Domainoid, a new tool aiming to overcome these challenges faced by full-length orthology methods by inferring orthology on the domain level. It employs the InParanoid algorithm on single domains separately, to infer groups of orthologous domains.

Results

This domain-oriented approach allows detection of discordant domain orthologs, cases where different domains on the same protein have different evolutionary histories. In addition to domain level analysis, protein level orthology based on the fraction of domains that are orthologous can be inferred. Domainoid orthology assignments were compared to those yielded by the conventional full-length approach InParanoid, and were validated in a standard benchmark.

Conclusions

Our results show that domain-based orthology inference can reveal many orthologous relationships that are not found by full-length sequence approaches.

Availability

https://bitbucket.org/sonnhammergroup/domainoid/

Prediction of Protein-Protein Interactions Based on Domain

Protein-protein interactions (PPIs) play a crucial role in various biological processes. To better comprehend the pathogenesis and treatments of various diseases, it is necessary to learn the detail of these interactions. However, the current experimental method still has many false-positive and false-negative problems. Computational prediction of protein-protein interaction has become a more important prediction method which can overcome the obstacles of the experimental method. In this work, we proposed a novel computational domain-based method for PPI prediction, and an SVM model for the prediction was built based on the physicochemical property of the domain. The outcomes of SVM and the domain-domain score were used to construct the prediction model for protein-protein interaction. The predicted results demonstrated the domain-based research can enhance the ability to predict protein interactions.

Evolution of Protein Domain Architectures

This chapter reviews current research on how protein domain architectures evolve. We begin by summarizing work on the phylogenetic distribution of proteins, as this will directly impact which domain architectures can be formed in different species. Studies relating domain family size to occurrence have shown that they generally follow power law distributions, both within genomes and larger evolutionary groups. These findings were subsequently extended to multi-domain architectures. Genome evolution models that have been suggested to explain the shape of these distributions are reviewed, as well as evidence for selective pressure to expand certain domain families more than others. Each domain has an intrinsic combinatorial propensity, and the effects of this have been studied using measures of domain versatility or promiscuity. Next, we study the principles of protein domain architecture evolution and how these have been inferred from distributions of extant domain arrangements. Following this, we review inferences of ancestral domain architecture and the conclusions concerning domain architecture evolution mechanisms that can be drawn from these. Finally, we examine whether all known cases of a given domain architecture can be assumed to have a single common origin (monophyly) or have evolved convergently (polyphyly). We end by a discussion of some available tools for computational analysis or exploitation of protein domain architectures and their evolution.

Muscle differentiation induced up-regulation of calcium-related gene expression in quail myoblasts

Objective

In the poultry industry, the most important economic traits are meat quality and carcass yield. Thus, many studies were conducted to investigate the regulatory pathways during muscle differentiation. To gain insight of muscle differentiation mechanism during growth period, we identified and validated calcium-related genes which were highly expressed during muscle differentiation through mRNA sequencing analysis.

Methods

We conducted next-generation-sequencing (NGS) analysis of mRNA from undifferentiated QM7 cells and differentiated QM7 cells (day 1 to day 3 of differentiation periods). Subsequently, we obtained calcium related genes related to muscle differentiation process and examined the expression patterns by quantitative reverse-transcription polymerase chain reaction (qRT-PCR).

Results

Through RNA sequencing analysis, we found that the transcription levels of six genes (troponin C1, slow skeletal and cardiac type [TNNC1], myosin light chain 1 [MYL1], MYL3, phospholamban [PLN], caveolin 3 [CAV3], and calsequestrin 2 [CASQ2]) particularly related to calcium regulation were gradually increased according to days of myotube differentiation. Subsequently, we validated the expression patterns of calcium-related genes in quail myoblasts. These results indicated that TNNC1, MYL1, MYL3, PLN, CAV3, CASQ2 responded to differentiation and growth performance in quail muscle.

Conclusion

These results indicated that calcium regulation might play a critical role in muscle differentiation. Thus, these findings suggest that further studies would be warranted to investigate the role of calcium ion in muscle differentiation and could provide a useful biomarker for muscle differentiation and growth.

New Tools in Orthology Analysis: A Brief Review of Promising Perspectives

Nowadays defying homology relationships among sequences is essential for biological research. Within homology the analysis of orthologs sequences is of great importance for computational biology, annotation of genomes and for phylogenetic inference. Since 2007, with the increase in the number of new sequences being deposited in large biological databases, researchers have begun to analyse computerized methodologies and tools aimed at selecting the most promising ones in the prediction of orthologous groups. Literature in this field of research describes the problems that the majority of available tools show, such as those encountered in accuracy, time required for analysis (especially in light of the increasing volume of data being submitted, which require faster techniques) and the automatization of the process without requiring manual intervention. Conducting our search through BMC, Google Scholar, NCBI PubMed, and Expasy, we examined more than 600 articles pursuing the most recent techniques and tools developed to solve most the problems still existing in orthology detection. We listed the main computational tools created and developed between 2011 and 2017, taking into consideration the differences in the type of orthology analysis, outlining the main features of each tool and pointing to the problems that each one tries to address. We also observed that several tools still use as their main algorithm the BLAST "all-against-all" methodology, which entails some limitations, such as limited number of queries, computational cost, and high processing time to complete the analysis. However, new promising tools are being developed, like OrthoVenn (which uses the Venn diagram to show the relationship of ortholog groups generated by its algorithm); or proteinOrtho (which improves the accuracy of ortholog groups); or ReMark (tackling the integration of the pipeline to turn the entry process automatic); or OrthAgogue (using algorithms developed to minimize processing time); and proteinOrtho (developed for dealing with large amounts of biological data). We made a comparison among the main features of four tool and tested them using four for prokaryotic genomas. We hope that our review can be useful for researchers and will help them in selecting the most appropriate tool for their work in the field of orthology.

DOGMA: domain-based transcriptome and proteome quality assessment

MOTIVATION

Genome studies have become cheaper and easier than ever before, due to the decreased costs of high-throughput sequencing and the free availability of analysis software. However, the quality of genome or transcriptome assemblies can vary a lot. Therefore, quality assessment of assemblies and annotations are crucial aspects of genome analysis pipelines.

RESULTS

We developed DOGMA, a program for fast and easy quality assessment of transcriptome and proteome data based on conserved protein domains. DOGMA measures the completeness of a given transcriptome or proteome and provides information about domain content for further analysis. DOGMA provides a very fast way to do quality assessment within seconds.

AVAILABILITY AND IMPLEMENTATION

DOGMA is implemented in Python and published under GNU GPL v.3 license. The source code is available on https://ebbgit.uni-muenster.de/domainWorld/DOGMA/ CONTACTS: e.dohmen@wwu.de or c.kemena@wwu.de

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

PhyloPro2.0: a database for the dynamic exploration of phylogenetically conserved proteins and their domain architectures across the Eukarya

PhyloPro is a database and accompanying web-based application for the construction and exploration of phylogenetic profiles across the Eukarya. In this update article, we present six major new developments in PhyloPro: (i) integration of Pfam-A domain predictions for all proteins; (ii) new summary heatmaps and detailed level views of domain conservation; (iii) an interactive, network-based visualization tool for exploration of domain architectures and their conservation; (iv) ability to browse based on protein functional categories (GOSlim); (v) improvements to the web interface to enhance drill down capability from the heatmap view; and (vi) improved coverage including 164 eukaryotes and 12 reference species. In addition, we provide improved support for downloading data and images in a variety of formats. Among the existing tools available for phylogenetic profiles, PhyloPro provides several innovative domain-based features including a novel domain adjacency visualization tool. These are designed to allow the user to identify and compare proteins with similar domain architectures across species and thus develop hypotheses about the evolution of lineage-specific trajectories. Database URL: http://www.compsysbio.org/phylopro/.

Inferring Orthologs: Open Questions and Perspectives

With the increasing number of sequenced genomes and their comparisons, the detection of orthologs is crucial for reliable functional annotation and evolutionary analyses of genes and species. Yet, the dynamic remodeling of genome content through gain, loss, transfer of genes, and segmental and whole-genome duplication hinders reliable orthology detection. Moreover, the lack of direct functional evidence and the questionable quality of some available genome sequences and annotations present additional difficulties to assess orthology. This article reviews the existing computational methods and their potential accuracy in the high-throughput era of genome sequencing and anticipates open questions in terms of methodology, reliability, and computation. Appropriate taxon sampling together with combination of methods based on similarity, phylogeny, synteny, and evolutionary knowledge that may help detecting speciation events appears to be the most accurate strategy. This review also raises perspectives on the potential determination of orthology throughout the whole species phylogeny.