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

Abstract

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.

De novo assembly, annotation, and comparative analysis of 26 diverse maize genomes

“…The 26 NAM genomes were annotated using a hybrid evidence and ab initio based gene prediction pipeline (81). Evidence-based predictions were directly inferred from the assembled transcripts, which were generated using five different genome-guided transcript assembly RNA-seq reads from each library were mapped to their respective NAM genomes using STAR (v2.5.3a) (88) with an iterative 2-pass mapping approach in which splice junctions generated from the first round were used to refine alignments in the subsequent round.…”

Landscape of the Dark Transcriptome Revealed Through Re-mining Massive RNA-Seq Data

“…A systematic analysis of current computational methods for genome annotation indicates many orphan genes may be missed in annotation projects (Li et al, 2021). This is because genes are often identified from sequenced genomes by combining evidence based on homology with other species (Meyer and Durbin, 2004;Proux-Wéra et al, 2012) with ab initio machine-learning predictions by detecting canonical sequence motifs (e.g., splice junctions) (Cantarel et al, 2008;Hoff et al, 2016).…”

TheStreptochaetagenome and the evolution of the grasses

“…e ., species-specific genes). As ab initio predictions of young genes can be unreliable (Seetharam et al, 2019), these were excluded. Finally, redundant copies of genes between direct evidence and ab initio predictions were identified and removed using Mikado compare (2.0rc2; (Venturini et al, 2018)) and merging was performed locus by locus, incorporating additional isoforms when necessary.…”