Designating eukaryotic orthology via processed transcription units

Identification of gene-oriented exon orthology between human and mouse

BACKGROUND

Gene orthology has been well studied in the evolutionary area and is thought to be an important implication to functional genome annotations. As the accumulation of transcriptomic data, alternative splicing is taken into account in the assignments of gene orthologs and the orthology is suggested to be further considered at transcript level. Whether gene or transcript orthology, exons are the basic units that represent the whole gene structure; however, there is no any reported study on how to build exon level orthology in a whole genome scale. Therefore, it is essential to establish a gene-oriented exon orthology dataset.

RESULTS

Using a customized pipeline, we first build exon orthologous relationships from assigned gene orthologs pairs in two well-annotated genomes: human and mouse. More than 92% of non-overlapping exons have at least one ortholog between human and mouse and only a small portion of them own more than one ortholog. The exons located in the coding region are more conserved in terms of finding their ortholog counterparts. Within the untranslated region, the 5' UTR seems to have more diversity than the 3' UTR according to exon orthology designations. Interestingly, most exons located in the coding region are also conserved in length but this conservation phenomenon dramatically drops down in untranslated regions. In addition, we allowed multiple assignments in exon orthologs and a subset of exons with possible fusion/split events were defined here after a thorough analysis procedure.

CONCLUSIONS

Identification of orthologs at the exon level is essential to provide a detailed way to interrogate gene orthology and splicing analysis. It could be used to extend the genome annotation as well. Besides examining the one-to-one orthologous relationship, we manage the one-to-multi exon pairs to represent complicated exon generation behavior. Our results can be further applied in many research fields studying intron-exon structure and alternative/constitutive exons in functional genomic areas.

Controversies in modern evolutionary biology: the imperative for error detection and quality control

Background

The data from high throughput genomics technologies provide unique opportunities for studies of complex biological systems, but also pose many new challenges. The shift to the genome scale in evolutionary biology, for example, has led to many interesting, but often controversial studies. It has been suggested that part of the conflict may be due to errors in the initial sequences. Most gene sequences are predicted by bioinformatics programs and a number of quality issues have been raised, concerning DNA sequencing errors or badly predicted coding regions, particularly in eukaryotes.

Results

We investigated the impact of these errors on evolutionary studies and specifically on the identification of important genetic events. We focused on the detection of asymmetric evolution after duplication, which has been the subject of controversy recently. Using the human genome as a reference, we established a reliable set of 688 duplicated genes in 13 complete vertebrate genomes, where significantly different evolutionary rates are observed. We estimated the rates at which protein sequence errors occur and are accumulated in the higher-level analyses. We showed that the majority of the detected events (57%) are in fact artifacts due to the putative erroneous sequences and that these artifacts are sufficient to mask the true functional significance of the events.

Conclusions

Initial errors are accumulated throughout the evolutionary analysis, generating artificially high rates of event predictions and leading to substantial uncertainty in the conclusions. This study emphasizes the urgent need for error detection and quality control strategies in order to efficiently extract knowledge from the new genome data.

Refining orthologue groups at the transcript level

Background

Orthologues are genes in different species that are related through divergent evolution from a common ancestor and are expected to have similar functions. Many databases have been created to describe orthologous genes based on existing sequence data. However, alternative splicing (in eukaryotes) is usually disregarded in the determination of orthologue groups and the functional consequences of alternative splicing have not been considered. Most multi-exon genes can encode multiple protein isoforms which often have different functions and can be disease-related. Extending the definition of orthologue groups to take account of alternate splicing and the functional differences it causes requires further examination.

Results

A subset of the orthologous gene groups between human and mouse was selected from the InParanoid database for this study. Each orthologue group was divided into sub-clusters, at the transcript level, using a method based on the sequence similarity of the isoforms. Transcript based sub-clusters were verified by functional signatures of the cluster members in the InterPro database. Functional similarity was higher within than between transcript-based sub-clusters of a defined orthologous group. In certain cases, cancer-related isoforms of a gene could be distinguished from other isoforms of the gene. Predictions of intrinsic disorder in protein regions were also correlated with the isoform sub-clusters within an orthologue group.

Conclusions

Sub-clustering of orthologue groups at the transcript level is an important step to more accurately define functionally equivalent orthologue groups. This work appears to be the first effort to refine orthologous groupings of genes based on the consequences of alternative splicing on function. Further investigation and refinement of the methodology to classify and verify isoform sub-clusters is needed, particularly to extend the technique to more distantly related species.

Gene-oriented ortholog database: a functional comparison platform for orthologous loci

The accumulation of complete genomic sequences enhances the need for functional annotation. Associating existing functional annotation of orthologs can speed up the annotation process and even examine the existing annotation. However, current protein sequence-based ortholog databases provide ambiguous and incomplete orthology in eukaryotes. It is because that isoforms, derived by alternative splicing (AS), often share higher sequence similarity to interfere the sequence-based identification. Gene-Oriented Ortholog Database (GOOD) employs genomic locations of transcripts to cluster AS-derived isoforms prior to ortholog delineation to eliminate the interference from AS. From the gene-oriented presentation, isoforms can be clearly associated to their genes to provide comprehensive ortholog information and further be discriminated from paralogs. Aside from, displaying clusters of isoforms between orthologous genes can present the evolution variation at the transcription level. Based on orthology, GOOD additionally comprises functional annotation from the Gene Ontology (GO) database. However, there exist redundant annotations, both parent and child terms assigned to the same gene, in the GO database. It is difficult to precisely draw the numerical comparison of term counts between orthologous genes annotated with redundant terms. Instead of the description only, GOOD further provides the GO graphs to reveal hierarchical-like relationships among divergent functionalities. Therefore, the redundancy of GO terms can be examined, and the context among compared terms is more comprehensive. In sum, GOOD can improve the interpretation in the molecular function from experiments in the model organism and provide clear comparative genomic annotation across organisms. Database URL: http://goods.ibms.sinica.edu.tw/goods/