GC content dependency of open reading frame prediction via stop codon frequencies

Pöhl, Martin; Theißen, Günter; Schuster, Stefan

doi:10.1016/j.gene.2012.09.031

Cited by 11 publications

(17 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In order to remove potential pseudogenes from the intergenic regions, we detected all ORFs with length greater or equal than a set length. The threshold was set to 30 codons, which corresponds to a false positive rate of 0.25 at the G + C content of 0.514 (median of the G + C content in our data set) ( Pohl et al 2012 ). We created a new data set in which oligonucleotide frequencies were calculated from the intergenic regions, after having excluded the parts which were identified as potential pseudogenes.…”

Section: Methodsmentioning

confidence: 99%

Global Shifts in Genome and Proteome Composition Are Very Tightly Coupled

Brbić¹,

Warnecke²,

Kriško³

et al. 2015

Genome Biology and Evolution

View full text Add to dashboard Cite

The amino acid composition (AAC) of proteomes differs greatly between microorganisms and is associated with the environmental niche they inhabit, suggesting that these changes may be adaptive. Similarly, the oligonucleotide composition of genomes varies and may confer advantages at the DNA/RNA level. These influences overlap in protein-coding sequences, making it difficult to gauge their relative contributions. We disentangle these effects by systematically evaluating the correspondence between intergenic nucleotide composition, where protein-level selection is absent, the AAC, and ecological parameters of 909 prokaryotes. We find that G + C content, the most frequently used measure of genomic composition, cannot capture diversity in AAC and across ecological contexts. However, di-/trinucleotide composition in intergenic DNA predicts amino acid frequencies of proteomes to the point where very little cross-species variability remains unexplained (91% of variance accounted for). Qualitatively similar results were obtained for 49 fungal genomes, where 80% of the variability in AAC could be explained by the composition of introns and intergenic regions. Upon factoring out oligonucleotide composition and phylogenetic inertia, the residual AAC is poorly predictive of the microbes’ ecological preferences, in stark contrast with the original AAC. Moreover, highly expressed genes do not exhibit more prominent environment-related AAC signatures than lowly expressed genes, despite contributing more to the effective proteome. Thus, evolutionary shifts in overall AAC appear to occur almost exclusively through factors shaping the global oligonucleotide content of the genome. We discuss these results in light of contravening evidence from biophysical data and further reading frame-specific analyses that suggest that adaptation takes place at the protein level.

show abstract

Section: Methodsmentioning

confidence: 99%

Global Shifts in Genome and Proteome Composition Are Very Tightly Coupled

Brbić¹,

Warnecke²,

Kriško³

et al. 2015

Genome Biology and Evolution

View full text Add to dashboard Cite

show abstract

“…If not functional or expressed, long ORFs (more than 600 bp in the present case) are expected to accumulate mutations [54]. Indeed, one of the clearest signals of pseudogenization in a putative ORF is the presence of multiple stop codons or frameshift mutations [55,56].…”

Section: Introductionmentioning

confidence: 99%

Computational Characterization of the mtORF of Pocilloporid Corals: Insights into Protein Structure and Function in Stylophora Lineages from Contrasting Environments

Banguera-Hinestroza

Ferrada

Sawall

et al. 2019

Genes

View full text Add to dashboard Cite

More than a decade ago, a new mitochondrial Open Reading Frame (mtORF) was discovered in corals of the family Pocilloporidae and has been used since then as an effective barcode for these corals. Recently, mtORF sequencing revealed the existence of two differentiated Stylophora lineages occurring in sympatry along the environmental gradient of the Red Sea (18.5°C to 33.9°C). In the endemic Red Sea lineage RS_LinB, the mtORF and the heat shock protein gene hsp70 uncovered similar phylogeographic patterns strongly correlated with environmental variations. This suggests that the mtORF too might be involved in thermal adaptation. Here, we used computational analyses to explore the features and putative function of this mtORF. In particular, we tested the likelihood that this gene encodes a functional protein and whether it may play a role in adaptation. Analyses of full mitogenomes showed that the mtORF originated in the common ancestor of Madracis and other pocilloporids, and that it encodes a transmembrane protein differing in length and domain architecture among genera. Homology-based annotation and the relative conservation of metal-binding sites revealed traces of an ancient hydrolase catalytic activity. Furthermore, signals of pervasive purifying selection, lack of stop codons in 1830 sequences analyzed, and a codon-usage bias similar to that of other mitochondrial genes indicate that the protein is functional, i.e., not a pseudogene. Other features, such as intrinsically disordered regions, tandem repeats, and signals of positive selection particularly in Stylophora RS_LinB populations, are consistent with a role of the mtORF in adaptive responses to environmental changes.

show abstract

“…Previous study shows that the length of coding ORFs is considerably longer than that of noncoding ORFs [ 47 ]. Thus, we can use the following feature vector X ORFC to represent metagenomic fragment by computing the length proportion of the ORF to the fragment:

where l is the ORF length and L is the length of fragment.…”

Section: Methodsmentioning

confidence: 99%

Gene Prediction in Metagenomic Fragments with Deep Learning

Zhang

Jin

Zhang

2017

BioMed Research International

View full text Add to dashboard Cite

Next generation sequencing technologies used in metagenomics yield numerous sequencing fragments which come from thousands of different species. Accurately identifying genes from metagenomics fragments is one of the most fundamental issues in metagenomics. In this article, by fusing multifeatures (i.e., monocodon usage, monoamino acid usage, ORF length coverage, and Z-curve features) and using deep stacking networks learning model, we present a novel method (called Meta-MFDL) to predict the metagenomic genes. The results with 10 CV and independent tests show that Meta-MFDL is a powerful tool for identifying genes from metagenomic fragments.

show abstract

GC content dependency of open reading frame prediction via stop codon frequencies

Cited by 11 publications

References 22 publications

Global Shifts in Genome and Proteome Composition Are Very Tightly Coupled

Global Shifts in Genome and Proteome Composition Are Very Tightly Coupled

Computational Characterization of the mtORF of Pocilloporid Corals: Insights into Protein Structure and Function in Stylophora Lineages from Contrasting Environments

Gene Prediction in Metagenomic Fragments with Deep Learning

Contact Info

Product

Resources

About