ArticleNucleic acids research2022
Foster thy young: enhanced prediction of orphan genes in assembled genomes.
Article in Nucleic acids research, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.
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Who cites it
24 citing papers in PubMed.
- Identification and Classification of Expressed Orphan Genes, Spurious Orphan Genes, and Conserved Genes in the Human Gut Microbiome.Genome biology and evolution · 2026Article
- A whole-genome assembly of St. Augustinegrass and visualizing diversity within the species.The plant genome · 2026Article
- De Novo Gene Emergence: Summary, Classification, and Challenges of Current Methods.Genome biology and evolution · 2025Review
- Importance of De Novo Gene Evolution to Emerging Viral Threats: The ORF10 Strain-Restricted Orphan Gene of SARS-CoV-2 Promotes Pathogenesis.Molecular biology and evolution · 2025Article
- Roles of a Y-Linked iDmrt1 Paralogue and Insulin-like Androgenic Gland Hormone in Sexual Development in the Tropical Rock Lobster,International journal of molecular sciences · 2025Article
- Extensive genome evolution distinguishes maize within a stable tribe of grasses.bioRxiv : the preprint server for biology · 2025Article
- Construction and evaluation ofFrontiers in plant science · 2025Article
- Orphan geneHorticulture research · 2024Article
- High-quality chromosome scale genome assemblies of two important Sorghum inbred lines, Tx2783 and RTx436.NAR genomics and bioinformatics · 2024Article
- Harnessing the predicted maize pan-interactome for putative gene function prediction and prioritization of candidate genes for important traits.G3 (Bethesda, Md.) · 2024Article
- The Lost and Found: Unraveling the Functions of Orphan Genes.Journal of developmental biology · 2023Review
- A happy accident: a novel turfgrass reference genome.G3 (Bethesda, Md.) · 2023Article
- Single-cell genetic models to evaluate orphan gene function: The case of QQS regulating carbon and nitrogen allocation.Frontiers in plant science · 2023Article
- Origins, evolution, and physiological implications of de novo genes in yeast.Yeast (Chichester, England) · 2022Review
- Identification of novel PHD-finger genes in pepper by genomic re-annotation and comparative analyses.BMC plant biology · 2022Article
- Initiation of genomics-assisted breeding in Virginia-type peanuts through the generation of aFrontiers in plant science · 2022Article
- Research Advances and Prospects of Orphan Genes in Plants.Frontiers in plant science · 2022Review
- TSEBRA: transcript selector for BRAKER.BMC bioinformatics · 2021Article
- orfipy: a fast and flexible tool for extracting ORFs.Bioinformatics (Oxford, England) · 2021Article
- De novo assembly, annotation, and comparative analysis of 26 diverse maize genomes.Science (New York, N.Y.) · 2021Article
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Authors and funding
7 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Proteins encoded by newly-emerged genes ('orphan genes') share no sequence similarity with proteins in any other species. They provide organisms with a reservoir of genetic elements to quickly respond to changing selection pressures. Here, we systematically assess the ability of five gene prediction pipelines to accurately predict genes in genomes according to phylostratal origin. BRAKER and MAKER are existing, popular ab initio tools that infer gene structures by machine learning. Direct Inference is an evidence-based pipeline we developed to predict gene structures from alignments of RNA-Seq data. The BIND pipeline integrates ab initio predictions of BRAKER and Direct inference; MIND combines Direct Inference and MAKER predictions. We use highly-curated Arabidopsis and yeast annotations as gold-standard benchmarks, and cross-validate in rice. Each pipeline under-predicts orphan genes (as few as 11 percent, under one prediction scenario). Increasing RNA-Seq diversity greatly improves prediction efficacy. The combined methods (BIND and MIND) yield best predictions overall, BIND identifying 68% of annotated orphan genes, 99% of ancient genes, and give the highest sensitivity score regardless dataset in Arabidopsis. We provide a light weight, flexible, reproducible, and well-documented solution to improve gene prediction.
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