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Abstract Advances in genome sequencing and annotation have eased the difficulty of identifying new gene sequences. Predicting the functions of these newly identified genes remains challenging. Genes descended from a common ancestral sequence are likely to have common functions. As a result, homology is widely used for gene function prediction. This means functional annotation errors also propagate from one species to another. Several approaches based on machine learning classification algorithms were evaluated for their ability to accurately predict gene function from non‐homology gene features. Among the eight supervised classification algorithms evaluated, random‐forest‐based prediction consistently provided the most accurate gene function prediction. Non‐homology‐based functional annotation provides complementary strengths to homology‐based annotation, with higher average performance in Biological Process GO terms, the domain where homology‐based functional annotation performs the worst, and weaker performance in Molecular Function GO terms, the domain where the accuracy of homology‐based functional annotation is highest. GO prediction models trained with homology‐based annotations were able to successfully predict annotations from a manually curated “gold standard” GO annotation set. Non‐homology‐based functional annotation based on machine learning may ultimately prove useful both as a method to assign predicted functions to orphan genes which lack functionally characterized homologs, and to identify and correct functional annotation errors which were propagated through homology‐based functional annotations.
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Origins of lineage‐specific elements via gene duplication, relocation, and regional rearrangement in Neurospora crassa
Abstract The origin of new genes has long been a central interest of evolutionary biologists. However, their novelty means that they evade reconstruction by the classical tools of evolutionary modelling. This evasion of deep ancestral investigation necessitates intensive study of model species within well‐sampled, recently diversified, clades. One such clade is the model genusNeurospora, members of which lack recent gene duplications. SeveralNeurosporaspecies are comprehensively characterized organisms apt for studying the evolution of lineage‐specific genes (LSGs). Using gene synteny, we documented that 78% ofNeurosporaLSG clusters are located adjacent to the telomeres featuring extensive tracts of non‐coding DNA and duplicated genes. Here, we report several instances of LSGs that are likely from regional rearrangements and potentially from gene rebirth. To broadly investigate the functions of LSGs, we assembled transcriptomics data from 68 experimental data points and identified co‐regulatory modules using Weighted Gene Correlation Network Analysis, revealing that LSGs are widely but peripherally involved in known regulatory machinery for diverse functions. The ancestral status of the LSGmas‐1, a gene with roles in cell‐wall integrity and cellular sensitivity to antifungal toxins, was investigated in detail alongside its genomic neighbours, indicating that it arose from an ancient lysophospholipase precursor that is ubiquitous in lineages of the Sordariomycetes. Our discoveries illuminate a “rummage region” in theN. crassagenome that enables the formation of new genes and functions to arise via gene duplication and relocation, followed by fast mutation and recombination facilitated by sequence repeats and unconstrained non‐coding sequences.
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- PAR ID:
- 10559425
- Publisher / Repository:
- Wiley-Blackwell
- Date Published:
- Journal Name:
- Molecular Ecology
- Volume:
- 33
- Issue:
- 24
- ISSN:
- 0962-1083
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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