Dataset Information

Improving Hox protein classification across the major model organisms.

ABSTRACT: The family of Hox-proteins has been a major focus of research for over 30 years. Hox-proteins are crucial to the correct development of bilateral organisms, however, some uncertainty remains as to which Hox-proteins are functionally equivalent across different species. Initial classification of Hox-proteins was based on phylogenetic analysis of the 60 amino acid homeodomain. This approach was successful in classifying Hox-proteins with differing homeodomains, but the relationships of Hox-proteins with nearly identical homeodomains, yet distinct biological functions, could not be resolved. Correspondingly, these 'problematic' proteins were classified into one large unresolved group. Other classifications used the relative location of the Hox-protein coding genes on the chromosome (synteny) to further resolve this group. Although widely used, this synteny-based classification is inconsistent with experimental evidence from functional equivalence studies. These inconsistencies led us to re-examine and derive a new classification for the Hox-protein family using all Hox-protein sequences available in the GenBank non-redundant protein database (NCBI-nr). We compare the use of the homeodomain, the homeodomain with conserved flanking regions (the YPWM and linker region), and full length Hox-protein sequences as a basis for classification of Hox-proteins. In contrast to previous attempts, our approach is able to resolve the relationships for the 'problematic' as well as ABD-B-like Hox-proteins. We highlight differences to previous classifications and clarify the relationships of Hox-proteins across the five major model organisms, Caenorhabditis elegans, Drosophila melanogaster, Branchiostoma floridae, Mus musculus and Danio rerio. Comparative and functional analysis of Hox-proteins, two fields crucial to understanding the development of bilateral organisms, have been hampered by difficulties in predicting functionally equivalent Hox-proteins across species. Our classification scheme offers a higher-resolution classification that is in accordance with phylogenetic as well as experimental data and, thereby, provides a novel basis for experiments, such as comparative and functional analyses of Hox-proteins.

SUBMITTER: Hueber SD

PROVIDER: S-EPMC2876039 | biostudies-literature | 2010

REPOSITORIES: biostudies-literature

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Improving Hox protein classification across the major model organisms.

Hueber Stefanie D SD Weiller Georg F GF Djordjevic Michael A MA Frickey Tancred T

PloS one 20100525 5

The family of Hox-proteins has been a major focus of research for over 30 years. Hox-proteins are crucial to the correct development of bilateral organisms, however, some uncertainty remains as to which Hox-proteins are functionally equivalent across different species. Initial classification of Hox-proteins was based on phylogenetic analysis of the 60 amino acid homeodomain. This approach was successful in classifying Hox-proteins with differing homeodomains, but the relationships of Hox-protein ...[more]

PMID: 20520839

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Project description:BackgroundThe advent of Next Generation Sequencing technologies and corresponding bioinformatics tools allows the definition of transcriptomes in non-model organisms. Non-model organisms are of great ecological and biotechnological significance, and consequently the understanding of their unique metabolic pathways is essential. Several methods that integrate de novo assembly with genome-based assembly have been proposed. Yet, there are many open challenges in defining genes, particularly where genomes are not available or incomplete. Despite the large numbers of transcriptome assemblies that have been performed, quality control of the transcript building process, particularly on the protein level, is rarely performed if ever. To test and improve the quality of the automated transcriptome reconstruction, we used manually defined and curated genes, several of them experimentally validated.ResultsSeveral approaches to transcript construction were utilized, based on the available data: a draft genome, high quality RNAseq reads, and ESTs. In order to maximize the contribution of the various data, we integrated methods including de novo and genome based assembly, as well as EST clustering. After each step a set of manually curated genes was used for quality assessment of the transcripts. The interplay between the automated pipeline and the quality control indicated which additional processes were required to improve the transcriptome reconstruction. We discovered that E. huxleyi has a very high percentage of non-canonical splice junctions, and relatively high rates of intron retention, which caused unique issues with the currently available tools. While individual tools missed genes and artificially joined overlapping transcripts, combining the results of several tools improved the completeness and quality considerably. The final collection, created from the integration of several quality control and improvement rounds, was compared to the manually defined set both on the DNA and protein levels, and resulted in an improvement of 20% versus any of the read-based approaches alone.ConclusionsTo the best of our knowledge, this is the first time that an automated transcript definition is subjected to quality control using manually defined and curated genes and thereafter the process is improved. We recommend using a set of manually curated genes to troubleshoot transcriptome reconstruction.

Project description:BACKGROUND: Protein-protein interactions play vital roles in nearly all cellular processes and are involved in the construction of biological pathways such as metabolic and signal transduction pathways. Although large-scale experiments have enabled the discovery of thousands of previously unknown linkages among proteins in many organisms, the high-throughput interaction data is often associated with high error rates. Since protein interaction networks have been utilized in numerous biological inferences, the inclusive experimental errors inevitably affect the quality of such prediction. Thus, it is essential to assess the quality of the protein interaction data. RESULTS: In this paper, a novel Bayesian network-based integrative framework is proposed to assess the reliability of protein-protein interactions. We develop a cross-species in silico model that assigns likelihood scores to individual protein pairs based on the information entirely extracted from model organisms. Our proposed approach integrates multiple microarray datasets and novel features derived from gene ontology. Furthermore, the confidence scores for cross-species protein mappings are explicitly incorporated into our model. Applying our model to predict protein interactions in the human genome, we are able to achieve 80% in sensitivity and 70% in specificity. Finally, we assess the overall quality of the experimentally determined yeast protein-protein interaction dataset. We observe that the more high-throughput experiments confirming an interaction, the higher the likelihood score, which confirms the effectiveness of our approach. CONCLUSION: This study demonstrates that model organisms certainly provide important information for protein-protein interaction inference and assessment. The proposed method is able to assess not only the overall quality of an interaction dataset, but also the quality of individual protein-protein interactions. We expect the method to continually improve as more high quality interaction data from more model organisms becomes available and is readily scalable to a genome-wide application.

Dataset Information

Improving Hox protein classification across the major model organisms.

Publications

Improving Hox protein classification across the major model organisms.

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