Biology subjects

Lloyd, J. P.

Publications and source records attributed to Lloyd, J. P..

3 recordsLinked to original sources

bioRxiv2018

Evolutionary characteristics of intergenic transcribed regions indicate widespread noisy transcription in the Poaceae

Extensive transcriptional activity occurring in unannotated, intergenic regions of genomes has raised the question whether intergenic transcription represents the activity of novel genes or noisy expression. To address this, we evaluated cross-species and post-duplication sequence and expression conservation of intergenic transcribed regions (ITRs) in four Poaceae species. Most ITR sequences are species-specific. Those found across species tend to be more divergent in expression and have more recent duplicates compared to annotated genes. To assess if ITRs are functional (under selection), machine learning models were established in Oryza sativa (rice) that could distinguish between benchmark functional (phenotype genes) and nonfunctional (pseudogenes) sequences with high accuracy based on 44 evolutionary and biochemical features. Based on the prediction models, 584 rice ITRs (8%) are classified as likely functional that tend to have conserved expression and ancient retained duplicates. However, most ITRs do not exhibit sequence or expression conservation across species or following duplication, consistent with computational predictions that suggest 61% ITRs are not under selection. We outline key evolutionary characteristics that are tightly associated with likely-functional ITRs and provide a framework to identify novel genes to improve genome annotation and move toward connecting genotype to phenotype in crop and model systems.

genomics↗

bioRxiv2018

Robust predictions of specialized metabolism genes through machine learning

Plant specialized metabolism (SM) enzymes produce lineage-specific metabolites with important ecological, evolutionary, and biotechnological implications. Using Arabidopsis thaliana as a model, we identified distinguishing characteristics of SM and GM (general metabolism, traditionally referred to as primary metabolism) genes through a detailed study of features including duplication pattern, sequence conservation, transcription, protein domain content, and gene network properties. Analysis of multiple sets of benchmark genes revealed that SM genes tend to be tandemly duplicated, co-expressed with their paralogs, narrowly expressed at lower levels, less conserved, and less well connected in gene networks relative to GM genes. Although the values of each of these features significantly differed between SM and GM genes, any single feature was ineffective at predicting SM from GM genes. Using machine learning methods to integrate all features, a well performing prediction model was established with a true positive rate of 0.87 and a true negative rate of 0.71. In addition, 86% of known SM genes not used to create the machine learning model were predicted as SM genes, further demonstrating its accuracy. We also demonstrated that the model could be further improved when we distinguished between SM, GM, and junction genes responsible for reactions shared by SM and GM pathways. Application of the prediction model led to the identification of 1,217 A. thaliana genes with previously unknown functions, providing a global, high-confidence estimate of SM gene content in a plant genome.\n\nSignificanceSpecialized metabolites are critical for plant-environment interactions, e.g., attracting pollinators or defending against herbivores, and are important sources of plant-based pharmaceuticals. However, it is unclear what proportion of enzyme-encoding genes play roles in specialized metabolism (SM) as opposed to general metabolism (GM) in any plant species. This is because of the diversity of specialized metabolites and the considerable number of incompletely characterized pathways responsible for their production. In addition, SM gene ancestors frequently played roles in GM. We evaluate features distinguishing SM and GM genes and build a computational model that accurately predicts SM genes. Our predictions provide candidates for experimental studies, and our modeling approach can be applied to other species that produce medicinally or industrially useful compounds.

plant biology↗

bioRxiv2017

Defining functional intergenic transcribed regions based on heterogeneous features of phenotype genes and pseudogenes

With advances in transcript profiling, the presence of transcriptional activities in intergenic regions has been well established in multiple model systems. However, whether intergenic expression reflects transcriptional noise or the activity of novel genes remains unclear. We identified intergenic transcribed regions (ITRs) in 15 diverse flowering plant species and found that the amount of intergenic expression correlates with genome size, a pattern that could be expected if intergenic expression is largely non-functional. To further assess the functionality of ITRs, we first built machine learning classifiers using Arabidopsis thaliana as a model that can accurately distinguish functional sequences (phenotype genes) and non-functional ones (pseudogenes and random unexpressed intergenic regions) by integrating 93 biochemical, evolutionary, and sequence-structure features. Next, by applying the models to ITRs, we found that 2,453 (21%) had features significantly similar to phenotype genes and thus were likely parts of functional genes, while an additional 17% resembled benchmark RNA genes. However, [~]60% of ITRs were more similar to nonfunctional sequences and should be considered transcriptional noise unless falsified with experiments. The predictive framework establish here provides not only a comprehensive look at how functional, genic sequences are distinct from likely non-functional ones, but also a new way to differentiate novel genes from genomic regions with noisy transcriptional activities.

genomics↗