Title: Predicting gene structure changes resulting from genetic variants via exon definition features
AbstractMotivation
Genetic variation that disrupts gene function by altering gene splicing between individuals can substantially influence traits and disease. In those cases, accurately predicting the effects of genetic variation on splicing can be highly valuable for investigating the mechanisms underlying those traits and diseases. While methods have been developed to generate high quality computational predictions of gene structures in reference genomes, the same methods perform poorly when used to predict the potentially deleterious effects of genetic changes that alter gene splicing between individuals. Underlying that discrepancy in predictive ability are the common assumptions by reference gene finding algorithms that genes are conserved, well-formed and produce functional proteins.
Results
We describe a probabilistic approach for predicting recent changes to gene structure that may or may not conserve function. The model is applicable to both coding and non-coding genes, and can be trained on existing gene annotations without requiring curated examples of aberrant splicing. We apply this model to the problem of predicting altered splicing patterns in the genomes of individual humans, and we demonstrate that performing gene-structure prediction without relying on conserved coding features is feasible. The model predicts an unexpected abundance of variants that create de novo splice sites, an observation supported by both simulations and empirical data from RNA-seq experiments. While these de novo splice variants are commonly misinterpreted by other tools as coding or non-coding variants of little or no effect, we find that in some cases they can have large effects on splicing activity and protein products and we propose that they may commonly act as cryptic factors in disease.
Availability and implementation
The software is available from geneprediction.org/SGRF.
Supplementary information
Supplementary information is available at Bioinformatics online.
The Aldabra giant tortoise (Aldabrachelys gigantea) is one of only two giant tortoise species left in the world. The species is endemic to Aldabra Atoll in Seychelles and is listed as Vulnerable on the International Union for Conservation of Nature Red List (v2.3) due to its limited distribution and threats posed by climate change. Genomic resources for A. gigantea are lacking, hampering conservation efforts for both wild and ex situpopulations. A high-quality genome would also open avenues to investigate the genetic basis of the species’ exceptionally long life span.
Findings
We produced the first chromosome-level de novo genome assembly of A. gigantea using PacBio High-Fidelity sequencing and high-throughput chromosome conformation capture. We produced a 2.37-Gbp assembly with a scaffold N50 of 148.6 Mbp and a resolution into 26 chromosomes. RNA sequencing–assisted gene model prediction identified 23,953 protein-coding genes and 1.1 Gbp of repetitive sequences. Synteny analyses among turtle genomes revealed high levels of chromosomal collinearity even among distantly related taxa. To assess the utility of the high-quality assembly for species conservation, we performed a low-coverage resequencing of 30 individuals from wild populations and two zoo individuals. Our genome-wide population structure analyses detected genetic population structure in the wild and identified the most likely origin of the zoo-housed individuals. We further identified putatively deleterious mutations to be monitored.
Conclusions
We establish a high-quality chromosome-level reference genome for A. gigantea and one of the most complete turtle genomes available. We show that low-coverage whole-genome resequencing, for which alignment to the reference genome is a necessity, is a powerful tool to assess the population structure of the wild population and reveal the geographic origins of ex situ individuals relevant for genetic diversity management and rewilding efforts.
Kaplow, Irene M.; Lawler, Alyssa J.; Schäffer, Daniel E.; Srinivasan, Chaitanya; Sestili, Heather H.; Wirthlin, Morgan E.; Phan, BaDoi N.; Prasad, Kavya; Brown, Ashley R.; Zhang, Xiaomeng; et al(
, Science)
INTRODUCTION Diverse phenotypes, including large brains relative to body size, group living, and vocal learning ability, have evolved multiple times throughout mammalian history. These shared phenotypes may have arisen repeatedly by means of common mechanisms discernible through genome comparisons. RATIONALE Protein-coding sequence differences have failed to fully explain the evolution of multiple mammalian phenotypes. This suggests that these phenotypes have evolved at least in part through changes in gene expression, meaning that their differences across species may be caused by differences in genome sequence at enhancer regions that control gene expression in specific tissues and cell types. Yet the enhancers involved in phenotype evolution are largely unknown. Sequence conservation–based approaches for identifying such enhancers are limited because enhancer activity can be conserved even when the individual nucleotides within the sequence are poorly conserved. This is due to an overwhelming number of cases where nucleotides turn over at a high rate, but a similar combination of transcription factor binding sites and other sequence features can be maintained across millions of years of evolution, allowing the function of the enhancer to be conserved in a particular cell type or tissue. Experimentally measuring the function of orthologous enhancers across dozens of species is currently infeasible, but new machine learning methods make it possible to make reliable sequence-based predictions of enhancer function across species in specific tissues and cell types. RESULTS To overcome the limits of studying individual nucleotides, we developed the Tissue-Aware Conservation Inference Toolkit (TACIT). Rather than measuring the extent to which individual nucleotides are conserved across a region, TACIT uses machine learning to test whether the function of a given part of the genome is likely to be conserved. More specifically, convolutional neural networks learn the tissue- or cell type–specific regulatory code connecting genome sequence to enhancer activity using candidate enhancers identified from only a few species. This approach allows us to accurately associate differences between species in tissue or cell type–specific enhancer activity with genome sequence differences at enhancer orthologs. We then connect these predictions of enhancer function to phenotypes across hundreds of mammals in a way that accounts for species’ phylogenetic relatedness. We applied TACIT to identify candidate enhancers from motor cortex and parvalbumin neuron open chromatin data that are associated with brain size relative to body size, solitary living, and vocal learning across 222 mammals. Our results include the identification of multiple candidate enhancers associated with brain size relative to body size, several of which are located in linear or three-dimensional proximity to genes whose protein-coding mutations have been implicated in microcephaly or macrocephaly in humans. We also identified candidate enhancers associated with the evolution of solitary living near a gene implicated in separation anxiety and other enhancers associated with the evolution of vocal learning ability. We obtained distinct results for bulk motor cortex and parvalbumin neurons, demonstrating the value in applying TACIT to both bulk tissue and specific minority cell type populations. To facilitate future analyses of our results and applications of TACIT, we released predicted enhancer activity of >400,000 candidate enhancers in each of 222 mammals and their associations with the phenotypes we investigated. CONCLUSION TACIT leverages predicted enhancer activity conservation rather than nucleotide-level conservation to connect genetic sequence differences between species to phenotypes across large numbers of mammals. TACIT can be applied to any phenotype with enhancer activity data available from at least a few species in a relevant tissue or cell type and a whole-genome alignment available across dozens of species with substantial phenotypic variation. Although we developed TACIT for transcriptional enhancers, it could also be applied to genomic regions involved in other components of gene regulation, such as promoters and splicing enhancers and silencers. As the number of sequenced genomes grows, machine learning approaches such as TACIT have the potential to help make sense of how conservation of, or changes in, subtle genome patterns can help explain phenotype evolution. Tissue-Aware Conservation Inference Toolkit (TACIT) associates genetic differences between species with phenotypes. TACIT works by generating open chromatin data from a few species in a tissue related to a phenotype, using the sequences underlying open and closed chromatin regions to train a machine learning model for predicting tissue-specific open chromatin and associating open chromatin predictions across dozens of mammals with the phenotype. [Species silhouettes are from PhyloPic]
Whole‐exome sequencing (WES) studies have identified multiple genes enriched forde novomutations (DNMs) in congenital heart disease (CHD) probands. However, risk gene identification based on DNMs alone remains statistically challenging due to heterogenous etiology of CHD and low mutation rate in each gene.
Methods
In this manuscript, we introduce a hierarchical Bayesian framework for gene‐level association test which jointly analyzesde novoand rare transmitted variants. Through integrative modeling of multiple types of genetic variants, gene‐level annotations, and reference data from large population cohorts, our method accurately characterizes the expected frequencies of bothde novoand transmitted variants and shows improved statistical power compared to analyses based on DNMs only.
Results
Applied to WES data of 2,645 CHD proband‐parent trios, our method identified 15 significant genes, half of which are novel, leading to new insights into the genetic bases of CHD.
Conclusion
These results showcase the power of integrative analysis of transmitted andde novovariants for disease gene discovery.
Freese, Nowlan H.; Estrada, April R.; Blakley, Ivory C.; Duan, Jinjie; Loraine, Ann E.(
, Plant Direct)
Abstract
Alternatively spliced genes produce multiple spliced isoforms, called transcript variants. In differential alternative splicing, transcript variant abundance differs across sample types. Differential alternative splicing is common in animal systems and influences cellular development in many processes, but its extent and significance is not as well known in plants. To investigate differential alternative splicing in plants, we examined RNA‐Seq data from rice seedlings. The data included three biological replicates per sample type, approximately 30 million sequence alignments per replicate, and four sample types: roots and shoots treated with exogenous cytokinin delivered hydroponically or a mock treatment. Cytokinin treatment triggered expression changes in thousands of genes but had negligible effect on splicing patterns. However, many genes were differentially spliced between mock‐treated roots and shoots, indicating that our methods were sufficiently sensitive to detect differential splicing between data sets. Quantitative fragment analysis of reverse transcriptase‐PCR products made from newly prepared rice samples confirmed 9 of 10 differential splicing events between rice roots and shoots. Differential alternative splicing typically changed the relative abundance of splice variants that co‐occurred in a data set. Analysis of a similar (but less deeply sequenced) RNA‐Seq data set fromArabidopsisshowed the same pattern. In both theArabidopsisand rice RNA‐Seq data sets, most genes annotated as alternatively spliced had small minor variant frequencies. Of splicing choices with abundant support for minor forms, most alternative splicing events were located within the protein‐coding sequence and maintained the annotated reading frame. A tool for visualizing protein annotations in the context of genomic sequence (ProtAnnot) together with a genome browser (Integrated Genome Browser) were used to visualize and assess effects of differential splicing on gene function. In general, differentially spliced regions coincided with conserved protein domains, indicating that differential alternative splicing is likely to affect protein function between root and shoot tissue in rice.
Lofgren, Lotus A.; Ross, Brandon S.; Cramer, Robert A.; Stajich, Jason E.(
, PLOS Biology)
Sil, Anita
(Ed.)
Aspergillus fumigatus is a deadly agent of human fungal disease where virulence heterogeneity is thought to be at least partially structured by genetic variation between strains. While population genomic analyses based on reference genome alignments offer valuable insights into how gene variants are distributed across populations, these approaches fail to capture intraspecific variation in genes absent from the reference genome. Pan-genomic analyses based on de novo assemblies offer a promising alternative to reference-based genomics with the potential to address the full genetic repertoire of a species. Here, we evaluate 260 genome sequences of A . fumigatus including 62 newly sequenced strains, using a combination of population genomics, phylogenomics, and pan-genomics. Our results offer a high-resolution assessment of population structure and recombination frequency, phylogenetically structured gene presence–absence variation, evidence for metabolic specificity, and the distribution of putative antifungal resistance genes. Although A . fumigatus disperses primarily via asexual conidia, we identified extraordinarily high levels of recombination with the lowest linkage disequilibrium decay value reported for any fungal species to date. We provide evidence for 3 primary populations of A . fumigatus , with recombination occurring only rarely between populations and often within them. These 3 populations are structured by both gene variation and distinct patterns of gene presence–absence with unique suites of accessory genes present exclusively in each clade. Accessory genes displayed functional enrichment for nitrogen and carbohydrate metabolism suggesting that populations may be stratified by environmental niche specialization. Similarly, the distribution of antifungal resistance genes and resistance alleles were often structured by phylogeny. Altogether, the pan-genome of A . fumigatus represents one of the largest fungal pan-genomes reported to date including many genes unrepresented in the Af293 reference genome. These results highlight the inadequacy of relying on a single-reference genome-based approach for evaluating intraspecific variation and the power of combined genomic approaches to elucidate population structure, genetic diversity, and putative ecological drivers of clinically relevant fungi.
Majoros, William H., Holt, Carson, Campbell, Michael S., Ware, Doreen, Yandell, Mark, Reddy, Timothy E., and Birol, ed., Inanc.
"Predicting gene structure changes resulting from genetic variants via exon definition features". Bioinformatics 34 (21). Country unknown/Code not available: Oxford University Press. https://doi.org/10.1093/bioinformatics/bty324.https://par.nsf.gov/biblio/10393370.
@article{osti_10393370,
place = {Country unknown/Code not available},
title = {Predicting gene structure changes resulting from genetic variants via exon definition features},
url = {https://par.nsf.gov/biblio/10393370},
DOI = {10.1093/bioinformatics/bty324},
abstractNote = {Abstract MotivationGenetic variation that disrupts gene function by altering gene splicing between individuals can substantially influence traits and disease. In those cases, accurately predicting the effects of genetic variation on splicing can be highly valuable for investigating the mechanisms underlying those traits and diseases. While methods have been developed to generate high quality computational predictions of gene structures in reference genomes, the same methods perform poorly when used to predict the potentially deleterious effects of genetic changes that alter gene splicing between individuals. Underlying that discrepancy in predictive ability are the common assumptions by reference gene finding algorithms that genes are conserved, well-formed and produce functional proteins. ResultsWe describe a probabilistic approach for predicting recent changes to gene structure that may or may not conserve function. The model is applicable to both coding and non-coding genes, and can be trained on existing gene annotations without requiring curated examples of aberrant splicing. We apply this model to the problem of predicting altered splicing patterns in the genomes of individual humans, and we demonstrate that performing gene-structure prediction without relying on conserved coding features is feasible. The model predicts an unexpected abundance of variants that create de novo splice sites, an observation supported by both simulations and empirical data from RNA-seq experiments. While these de novo splice variants are commonly misinterpreted by other tools as coding or non-coding variants of little or no effect, we find that in some cases they can have large effects on splicing activity and protein products and we propose that they may commonly act as cryptic factors in disease. Availability and implementationThe software is available from geneprediction.org/SGRF. Supplementary informationSupplementary information is available at Bioinformatics online.},
journal = {Bioinformatics},
volume = {34},
number = {21},
publisher = {Oxford University Press},
author = {Majoros, William H. and Holt, Carson and Campbell, Michael S. and Ware, Doreen and Yandell, Mark and Reddy, Timothy E. and Birol, ed., Inanc},
}
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