Frequent translation of small open reading frames in evolutionary conserved lncRNA regions

Ruiz-Orera, Jorge; Albà, M. Mar

doi:10.1101/348326

Cited by 1 publication

(3 citation statements)

References 106 publications

(126 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Each scenario that we tested predicted genes that are not annotated in Araport11. Compelling RNA-seq evidence from wide range of phyla, ranging from bacteria, to yeast, to humans, shows that many sequences that are not annotated as genes are highly transcribed and translated 1,55,56,62,[86][87][88][89][90] . For example, in a study of RNA-Seq data from over 3000 diverse yeast…”

Section: Discussionmentioning

confidence: 99%

“…samples, about half of the transcripts are not annotated as genes by Saccharomyces Genome Database (SGD) 55 . A large portion of these unannotated transcripts might be "transcriptional noise" 51 or, as suggested by 62 , fodder for de novo gene evolution. Other sequences may be on a continuum from protogene to gene to pseudogene and/or to oblivion 1,91 .…”

Section: /14mentioning

confidence: 99%

“…It has been suggested that canonical signatures are less well-defined in young genes, and therefore presumed that ab initio methods would be less likely to detect them 1,55,[58][59][60][61][62][63][64] . However, the ability of ab initio approaches to detect genes of recent origin had not been directly evaluated.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Foster thy young: Enhanced prediction of orphan genes in assembled genomes

Singh

Bhandary

et al. 2019

Preprint

View full text Add to dashboard Cite

The evolutionary rapid emergence of new genes gives rise to "orphan genes" that share no sequence homology to genes in closely related genomes. These genes provide organisms with a reservoir of genetic elements to quickly respond to changing selection pressures. Gene annotation pipelines that combine ab initio machine-learning with sequence homology-based searches are efficient in identifying basal genes with a long evolutionary history. However, their ability to identify orphan genes and other young genes has not been systematically evaluated. Here, we classify the phylostrata of curated Arabidopsis thaliana genes and use these to assess the ability of two of the most prevalent annotation pipelines, MAKER and BRAKER, to predict orphans and other young genes. MAKER predictions are highly dependent on the RNA-Seq evidence, predicting between 11% and 60% of the orphan-genes and 95% to 98% of basal-genes in the annotated genome of Arabidopsis. In contrast, BRAKER consistently predicts 33% of orphan-genes and 98% of basal-genes. A less used method to identify genes is by directly aligning RNA-Seq data to the genome sequence. We present a Findable, Accessible, Interoperable and Reusable (FAIR) approach, called BIND, that mitigates the under-prediction of orphan genes. BIND combines BRAKER predictions with direct evidence-based inference of transcripts based on RNA-Seq alignments to the genome. BIND increases the number and accuracy of orphan gene predictions, identifying 68% of Araport11-annotated orphan genes and 99% of the conserved genes.

show abstract