Annotated Protein-coding Genes Research Articles

Analysis of equine protein-coding gene structure and expression by RNA-sequencing

Background RNA-sequencing (RNA-seq) data from eight equine tissue samples (34-day whole embryo, full term placental villous, adult testes, adult cerebellum, adult articular cartilage, adult LPS-stimulated articular cartilage, adult synovial membrane, and adult LPS-stimulated synovial membrane) were used to refine the structural annotation of protein-coding genes in the horse and for a preliminary assessment of tissue-specific expression patterns.

Reannotation of protein‐coding genes based on an improved graphical representation of DNA sequence

Over annotation of protein coding genes is common phenomenon in microbial genomes, the genome of Amsacta moorei entomopoxvirus (AmEPV) is a typical case, because more than 63% of its annotated ORFs are hypothetical. In this article, we propose an improved graphical representation titled I-TN (improved curve based on trinucleotides) curve, which allows direct inspection of composition and distribution of codons and asymmetric gene structure. This improved graphical representation can also provide convenient tools for genome analysis. From this presentation, 18 variables are exploited as numerical descriptors to represent the specific features of protein coding genes quantitatively, with which we reannotate the protein coding genes in several viral genomes. Using the parameters trained on the experimentally validated genes, all of the 30 experimentally validated genes and 63 putative genes in AmEPV genome are recognized correctly as protein coding, the accuracies of the present method for self-test and cross-validation are 100%, respectively. Twenty-eight annotated hypothetical genes are predicted as noncoding, and then the number of reannotated protein coding genes in AmEPV should be 266 instead of 294 reported in the original annotations. Extending the present method trained in AmEPV to other entomopoxvirus genomes directly, such as Melanoplus sanguinipes entomopoxvirus (MsEPV), all of the 123 annotated function-known and putative genes are recognized correctly as protein coding, and 17 hypothetical genes are recognized as noncoding. The present method could also be extended to other genomes with or without adaptation of training sets with high accuracy.

Bacillus anthracis genome organization in light of whole transcriptome sequencing

Emerging knowledge of whole prokaryotic transcriptomes could validate a number of theoretical concepts introduced in the early days of genomics. What are the rules connecting gene expression levels with sequence determinants such as quantitative scores of promoters and terminators? Are translation efficiency measures, e.g. codon adaptation index and RBS score related to gene expression? We used the whole transcriptome shotgun sequencing of a bacterial pathogen Bacillus anthracis to assess correlation of gene expression level with promoter, terminator and RBS scores, codon adaptation index, as well as with a new measure of gene translational efficiency, average translation speed. We compared computational predictions of operon topologies with the transcript borders inferred from RNA-Seq reads. Transcriptome mapping may also improve existing gene annotation. Upon assessment of accuracy of current annotation of protein-coding genes in the B. anthracis genome we have shown that the transcriptome data indicate existence of more than a hundred genes missing in the annotation though predicted by an ab initio gene finder. Interestingly, we observed that many pseudogenes possess not only a sequence with detectable coding potential but also promoters that maintain transcriptional activity.

Non-Coding RNA Prediction and Verification in Saccharomyces cerevisiae

Non-coding RNA (ncRNA) play an important and varied role in cellular function. A significant amount of research has been devoted to computational prediction of these genes from genomic sequence, but the ability to do so has remained elusive due to a lack of apparent genomic features. In this work, thermodynamic stability of ncRNA structural elements, as summarized in a Z-score, is used to predict ncRNA in the yeast Saccharomyces cerevisiae. This analysis was coupled with comparative genomics to search for ncRNA genes on chromosome six of S. cerevisiae and S. bayanus. Sets of positive and negative control genes were evaluated to determine the efficacy of thermodynamic stability for discriminating ncRNA from background sequence. The effect of window sizes and step sizes on the sensitivity of ncRNA identification was also explored. Non-coding RNA gene candidates, common to both S. cerevisiae and S. bayanus, were verified using northern blot analysis, rapid amplification of cDNA ends (RACE), and publicly available cDNA library data. Four ncRNA transcripts are well supported by experimental data (RUF10, RUF11, RUF12, RUF13), while one additional putative ncRNA transcript is well supported but the data are not entirely conclusive. Six candidates appear to be structural elements in 5′ or 3′ untranslated regions of annotated protein-coding genes. This work shows that thermodynamic stability, coupled with comparative genomics, can be used to predict ncRNA with significant structural elements.

The 2008 update of the Aspergillus nidulans genome annotation: A community effort

The identification and annotation of protein-coding genes is one of the primary goals of whole-genome sequencing projects, and the accuracy of predicting the primary protein products of gene expression is vital to the interpretation of the available data and the design of downstream functional applications. Nevertheless, the comprehensive annotation of eukaryotic genomes remains a considerable challenge. Many genomes submitted to public databases, including those of major model organisms, contain significant numbers of wrong and incomplete gene predictions. We present a community-based reannotation of the Aspergillus nidulans genome with the primary goal of increasing the number and quality of protein functional assignments through the careful review of experts in the field of fungal biology.

Using native and syntenically mapped cDNA alignments to improve de novo gene finding

Computational annotation of protein coding genes in genomic DNA is a widely used and essential tool for analyzing newly sequenced genomes. However, current methods suffer from inaccuracy and do poorly with certain types of genes. Including additional sources of evidence of the existence and structure of genes can improve the quality of gene predictions. For many eukaryotic genomes, expressed sequence tags (ESTs) are available as evidence for genes. Related genomes that have been sequenced, annotated, and aligned to the target genome provide evidence of existence and structure of genes. We incorporate several different evidence sources into the gene finder AUGUSTUS. The sources of evidence are gene and transcript annotations from related species syntenically mapped to the target genome using TransMap, evolutionary conservation of DNA, mRNA and ESTs of the target species, and retroposed genes. The predictions include alternative splice variants where evidence supports it. Using only ESTs we were able to correctly predict at least one splice form exactly correct in 57% of human genes. Also using evidence from other species and human mRNAs, this number rises to 77%. Syntenic mapping is well-suited to annotate genomes closely related to genomes that are already annotated or for which extensive transcript evidence is available. Native cDNA evidence is most helpful when the alignments are used as compound information rather than independent positionwise information. AUGUSTUS is open source and available at http://augustus.gobics.de. The gene predictions for human can be browsed and downloaded at the UCSC Genome Browser (http://genome.ucsc.edu).

LeARN: a platform for detecting, clustering and annotating non-coding RNAs

BackgroundIn the last decade, sequencing projects have led to the development of a number of annotation systems dedicated to the structural and functional annotation of protein-coding genes. These annotation systems manage the annotation of the non-protein coding genes (ncRNAs) in a very crude way, allowing neither the edition of the secondary structures nor the clustering of ncRNA genes into families which are crucial for appropriate annotation of these molecules.ResultsLeARN is a flexible software package which handles the complete process of ncRNA annotation by integrating the layers of automatic detection and human curation.ConclusionThis software provides the infrastructure to deal properly with ncRNAs in the framework of any annotation project. It fills the gap between existing prediction software, that detect independent ncRNA occurrences, and public ncRNA repositories, that do not offer the flexibility and interactivity required for annotation projects. The software is freely available from the download section of the website

Overcoming function annotation errors in the Gram-positive pathogen Streptococcus suis by a proteomics-driven approach

BackgroundAnnotation of protein-coding genes is a key step in sequencing projects. Protein functions are mainly assigned on the basis of the amino acid sequence alone by searching of homologous proteins. However, fully automated annotation processes often lead to wrong prediction of protein functions, and therefore time-intensive manual curation is often essential. Here we describe a fast and reliable way to correct function annotation in sequencing projects, focusing on surface proteomes. We use a proteomics approach, previously proven to be very powerful for identifying new vaccine candidates against Gram-positive pathogens. It consists of shaving the surface of intact cells with two proteases, the specific cleavage-site trypsin and the unspecific proteinase K, followed by LC/MS/MS analysis of the resulting peptides. The identified proteins are contrasted by computational analysis and their sequences are inspected to correct possible errors in function prediction.ResultsWhen applied to the zoonotic pathogen Streptococcus suis, of which two strains have been recently sequenced and annotated, we identified a set of surface proteins without cytoplasmic contamination: all the proteins identified had exporting or retention signals towards the outside and/or the cell surface, and viability of protease-treated cells was not affected. The combination of both experimental evidences and computational methods allowed us to determine that two of these proteins are putative extracellular new adhesins that had been previously attributed a wrong cytoplasmic function. One of them is a putative component of the pilus of this bacterium.ConclusionWe illustrate the complementary nature of laboratory-based and computational methods to examine in concert the localization of a set of proteins in the cell, and demonstrate the utility of this proteomics-based strategy to experimentally correct function annotation errors in sequencing projects. This approach also contributes to provide strong experimental evidences that can be used to annotate those proteins for which a Gene Ontology (GO) term has not been assigned so far. Function annotation correction would then improve the identification of surface-associated proteins in bacterial pathogens, thus accelerating the discovery of new vaccines in infectious disease research.

Systematic analyses of the cancer genome: lessons learned from sequencing most of the annotated human protein-coding genes

The availability of a reference human genome sequence has enabled unbiased mutational analyses of tumor genomes to identify the mutated genes that cause cancer. This review discusses recent insights from such analyses of protein-coding genes in breast and colorectal cancers. Mutational analyses of approximately 18,000 human protein-coding genes in breast and colorectal cancers have identified 280 candidate cancer genes. These include known cancer genes, but most had not previously been linked to cancer. There are few frequently mutated cancer genes among hundreds of less frequently mutated candidate cancer genes, and the compendium of mutated genes differs among tumors of the same tissue origin. Recent work has shown the feasibility of coding cancer genome sequencing, and new technologies promise to facilitate these mutational analyses. Whereas cancer genetics can identify candidate genes in a rapid and scalable fashion, careful functional studies of mutated genes are required for ultimate proof of cancer gene status and translation into clinical utility. The rapid progress of cancer genetics has yielded novel diagnostic and therapeutic modalities, and cancer genome sequencing will accelerate this development to the benefit of cancer patients.

Improving gene annotation using peptide mass spectrometry.

Annotation of protein-coding genes is a key goal of genome sequencing projects. In spite of tremendous recent advances in computational gene finding, comprehensive annotation remains a challenge. Peptide mass spectrometry is a powerful tool for researching the dynamic proteome and suggests an attractive approach to discover and validate protein-coding genes. We present algorithms to construct and efficiently search spectra against a genomic database, with no prior knowledge of encoded proteins. By searching a corpus of 18.5 million tandem mass spectra (MS/MS) from human proteomic samples, we validate 39,000 exons and 11,000 introns at the level of translation. We present translation-level evidence for novel or extended exons in 16 genes, confirm translation of 224 hypothetical proteins, and discover or confirm over 40 alternative splicing events. Polymorphisms are efficiently encoded in our database, allowing us to observe variant alleles for 308 coding SNPs. Finally, we demonstrate the use of mass spectrometry to improve automated gene prediction, adding 800 correct exons to our predictions using a simple rescoring strategy. Our results demonstrate that proteomic profiling should play a role in any genome sequencing project.

Genomic and Genetic Characterization of RiceCen3Reveals Extensive Transcription and Evolutionary Implications of a Complex Centromere

The centromere is the chromosomal site for assembly of the kinetochore where spindle fibers attach during cell division. In most multicellular eukaryotes, centromeres are composed of long tracts of satellite repeats that are recalcitrant to sequencing and fine-scale genetic mapping. Here, we report the genomic and genetic characterization of the complete centromere of rice (Oryza sativa) chromosome 3. Using a DNA fiber-fluorescence in situ hybridization approach, we demonstrated that the centromere of chromosome 3 (Cen3) contains approximately 441 kb of the centromeric satellite repeat CentO. Cen3 includes an approximately 1,881-kb domain associated with the centromeric histone CENH3. This CENH3-associated chromatin domain is embedded within a 3,113-kb region that lacks genetic recombination. Extensive transcription was detected within the CENH3 binding domain based on comprehensive annotation of protein-coding genes coupled with empirical measurements of mRNA levels using RT-PCR and massively parallel signature sequencing. Genes <10 kb from the CentO satellite array were expressed in several rice tissues and displayed histone modification patterns consistent with euchromatin, suggesting that rice centromeric chromatin accommodates normal gene expression. These results support the hypothesis that centromeres can evolve from gene-containing genomic regions.

Plant receptor-like kinase gene family: diversity, function, and signaling.

Plant receptor-like kinases (RLKs) are transmembrane proteins with putative amino-terminal extracellular domains and carboxyl-terminal intracellular kinase domains, with striking resemblance in domain organization to the animal receptor tyrosine kinases such as epidermal growth factor receptor. The recently sequenced Arabidopsis genome contains more than 600 RLK homologs, representing nearly 2.5% of the annotated protein-coding genes in Arabidopsis. Although only a handful of these genes have known functions and fewer still have identified ligands or downstream targets, the studies of several RLKs such as CLAVATA1, Brassinosteroid Insensitive 1, Flagellin Insensitive 2, and S-locus receptor kinase provide much-needed information on the functions mediated by members of this large gene family. RLKs control a wide range of processes, including development, disease resistance, hormone perception, and self-incompatibility. Combined with the expression studies and biochemical analysis of other RLKs, more details of RLK function and signaling are emerging.