Reference-free population genomics from next-generation transcriptome data and the vertebrate-invertebrate gap

Gene duplication, population genomics, and species-level differentiation within a tropical mountain shrub

Gene duplication leads to paralogy, which complicates the de novo assembly of genotyping-by-sequencing (GBS) data. The issue of paralogous genes is exacerbated in plants, because they are particularly prone to gene duplication events. Paralogs are normally filtered from GBS data before undertaking population genomics or phylogenetic analyses. However, gene duplication plays an important role in the functional diversification of genes and it can also lead to the formation of postzygotic barriers. Using populations and closely related species of a tropical mountain shrub, we examine 1) the genomic differentiation produced by putative orthologs, and 2) the distribution of recent gene duplication among lineages and geography. We find high differentiation among populations from isolated mountain peaks and species-level differentiation within what is morphologically described as a single species. The inferred distribution of paralogs among populations is congruent with taxonomy and shows that GBS could be used to examine recent gene duplication as a source of genomic differentiation of nonmodel species.

The Anadara trapezia transcriptome: a resource for molluscan physiological genomics

In this study we undertook deep sequencing of the blood cockle, Anadara trapezia, transcriptome to generate genomic resources for future functional genomics analyses. Over 27 million high quality paired end reads were assembled into 75024 contigs. Of these contigs, 29013 (38.7%) received significant BLASTx hits and gene ontology (GO) terms were assigned to 13718 of these sequences. This resource will facilitate physiological genomic studies to test the gene expression response of A. trapezia to various environmental stresses.

Effects of new mutations on fitness: insights from models and data

The rates and properties of new mutations affecting fitness have implications for a number of outstanding questions in evolutionary biology. Obtaining estimates of mutation rates and effects has historically been challenging, and little theory has been available for predicting the distribution of fitness effects (DFE); however, there have been recent advances on both fronts. Extreme-value theory predicts the DFE of beneficial mutations in well-adapted populations, while phenotypic fitness landscape models make predictions for the DFE of all mutations as a function of the initial level of adaptation and the strength of stabilizing selection on traits underlying fitness. Direct experimental evidence confirms predictions on the DFE of beneficial mutations and favors distributions that are roughly exponential but bounded on the right. A growing number of studies infer the DFE using genomic patterns of polymorphism and divergence, recovering a wide range of DFE. Future work should be aimed at identifying factors driving the observed variation in the DFE. We emphasize the need for further theory explicitly incorporating the effects of partial pleiotropy and heterogeneity in the environment on the expected DFE.

Transcriptome population genomics reveals severe bottleneck and domestication cost in the African rice (Oryza glaberrima)

The African cultivated rice (Oryza glaberrima) was domesticated in West Africa 3000 years ago. Although less cultivated than the Asian rice (O. sativa), O. glaberrima landraces often display interesting adaptation to rustic environment (e.g. drought). Here, using RNA-seq technology, we were able to compare more than 12,000 transcripts between 9 O. glaberrima, 10 wild O. barthii and one O. meridionalis individuals. With a synonymous nucleotide diversity πs = 0.0006 per site, O. glaberrima appears as the least genetically diverse crop grass ever documented. Using approximate Bayesian computation, we estimated that O. glaberrima experienced a severe bottleneck during domestication. This demographic scenario almost fully accounts for the pattern of genetic diversity across O. glaberrima genome as we detected very few outliers regions where positive selection may have further impacted genetic diversity. Moreover, the large excess of derived nonsynonymous substitution that we detected suggests that the O. glaberrima population suffered from the 'cost of domestication'. In addition, we used this genome-scale data set to demonstrate that (i) O. barthii genetic diversity is positively correlated with recombination rate and negatively with gene density, (ii) expression level is negatively correlated with evolutionary constraint, and (iii) one region on chromosome 5 (position 4-6 Mb) exhibits a clear signature of introgression with a yet unidentified Oryza species. This work represents the first genome-wide survey of the African rice genetic diversity and paves the way for further comparison between the African and the Asian rice, notably regarding the genetics underlying domestication traits.

Selecting optimal partitioning schemes for phylogenomic datasets

BACKGROUND

Partitioning involves estimating independent models of molecular evolution for different subsets of sites in a sequence alignment, and has been shown to improve phylogenetic inference. Current methods for estimating best-fit partitioning schemes, however, are only computationally feasible with datasets of fewer than 100 loci. This is a problem because datasets with thousands of loci are increasingly common in phylogenetics.

METHODS

We develop two novel methods for estimating best-fit partitioning schemes on large phylogenomic datasets: strict and relaxed hierarchical clustering. These methods use information from the underlying data to cluster together similar subsets of sites in an alignment, and build on clustering approaches that have been proposed elsewhere.

RESULTS

We compare the performance of our methods to each other, and to existing methods for selecting partitioning schemes. We demonstrate that while strict hierarchical clustering has the best computational efficiency on very large datasets, relaxed hierarchical clustering provides scalable efficiency and returns dramatically better partitioning schemes as assessed by common criteria such as AICc and BIC scores.

CONCLUSIONS

These two methods provide the best current approaches to inferring partitioning schemes for very large datasets. We provide free open-source implementations of the methods in the PartitionFinder software. We hope that the use of these methods will help to improve the inferences made from large phylogenomic datasets.

Selecting optimal partitioning schemes for phylogenomic datasets

BACKGROUND

Partitioning involves estimating independent models of molecular evolution for different subsets of sites in a sequence alignment, and has been shown to improve phylogenetic inference. Current methods for estimating best-fit partitioning schemes, however, are only computationally feasible with datasets of fewer than 100 loci. This is a problem because datasets with thousands of loci are increasingly common in phylogenetics.

METHODS

We develop two novel methods for estimating best-fit partitioning schemes on large phylogenomic datasets: strict and relaxed hierarchical clustering. These methods use information from the underlying data to cluster together similar subsets of sites in an alignment, and build on clustering approaches that have been proposed elsewhere.

RESULTS

We compare the performance of our methods to each other, and to existing methods for selecting partitioning schemes. We demonstrate that while strict hierarchical clustering has the best computational efficiency on very large datasets, relaxed hierarchical clustering provides scalable efficiency and returns dramatically better partitioning schemes as assessed by common criteria such as AICc and BIC scores.

CONCLUSIONS

These two methods provide the best current approaches to inferring partitioning schemes for very large datasets. We provide free open-source implementations of the methods in the PartitionFinder software. We hope that the use of these methods will help to improve the inferences made from large phylogenomic datasets.

Selecting optimal partitioning schemes for phylogenomic datasets

BACKGROUND

Partitioning involves estimating independent models of molecular evolution for different subsets of sites in a sequence alignment, and has been shown to improve phylogenetic inference. Current methods for estimating best-fit partitioning schemes, however, are only computationally feasible with datasets of fewer than 100 loci. This is a problem because datasets with thousands of loci are increasingly common in phylogenetics.

METHODS

We develop two novel methods for estimating best-fit partitioning schemes on large phylogenomic datasets: strict and relaxed hierarchical clustering. These methods use information from the underlying data to cluster together similar subsets of sites in an alignment, and build on clustering approaches that have been proposed elsewhere.

RESULTS

We compare the performance of our methods to each other, and to existing methods for selecting partitioning schemes. We demonstrate that while strict hierarchical clustering has the best computational efficiency on very large datasets, relaxed hierarchical clustering provides scalable efficiency and returns dramatically better partitioning schemes as assessed by common criteria such as AICc and BIC scores.

CONCLUSIONS

These two methods provide the best current approaches to inferring partitioning schemes for very large datasets. We provide free open-source implementations of the methods in the PartitionFinder software. We hope that the use of these methods will help to improve the inferences made from large phylogenomic datasets.

Evolution of transcription factor binding in metazoans - mechanisms and functional implications

Differences in transcription factor binding can contribute to organismal evolution by altering downstream gene expression programmes. Genome-wide studies in Drosophila melanogaster and mammals have revealed common quantitative and combinatorial properties of in vivo DNA binding, as well as marked differences in the rate and mechanisms of evolution of transcription factor binding in metazoans. Here, we review the recently discovered rapid 're-wiring' of in vivo transcription factor binding between related metazoan species and summarize general principles underlying the observed patterns of evolution. We then consider what might explain the differences in genome evolution between metazoan phyla and outline the conceptual and technological challenges facing this research field.

Correcting for differential transcript coverage reveals a strong relationship between alternative splicing and organism complexity

What at the genomic level underlies organism complexity? Although several genomic features have been associated with organism complexity, in the case of alternative splicing, which has long been proposed to explain the variation in complexity, no such link has been established. Here, we analyzed over 39 million expressed sequence tags available for 47 eukaryotic species with fully sequenced genomes to obtain a comparable index of alternative splicing estimates, which corrects for the distorting effect of a variable number of transcripts per species—an important obstacle for comparative studies of alternative splicing. We find that alternative splicing has steadily increased over the last 1,400 My of eukaryotic evolution and is strongly associated with organism complexity, assayed as the number of cell types. Importantly, this association is not explained as a by-product of covariance between alternative splicing with other variables previously linked to complexity including gene content, protein length, proteome disorder, and protein interactivity. In addition, we found no evidence to suggest that the relationship of alternative splicing to cell type number is explained by drift due to reduced N_e in more complex species. Taken together, our results firmly establish alternative splicing as a significant predictor of organism complexity and are, in principle, consistent with an important role of transcript diversification through alternative splicing as a means of determining a genome’s functional information capacity.

De novo assembly and characterization of Sophora japonica transcriptome using RNA-seq

Sophora japonica Linn (Chinese Scholar Tree) is a shrub species belonging to the subfamily Faboideae of the pea family Fabaceae. In this study, RNA sequencing of S. japonica transcriptome was performed to produce large expression datasets for functional genomic analysis. Approximate 86.1 million high-quality clean reads were generated and assembled de novo into 143010 unique transcripts and 57614 unigenes. The average length of unigenes was 901 bps with an N50 of 545 bps. Four public databases, including the NCBI nonredundant protein (NR), Swiss-Prot, Kyoto Encyclopedia of Genes and Genomes (KEGG), and the Cluster of Orthologous Groups (COG), were used to annotate unigenes through NCBI BLAST procedure. A total of 27541 of 57614 unigenes (47.8%) were annotated for gene descriptions, conserved protein domains, or gene ontology. Moreover, an interaction network of unigenes in S. japonica was predicted based on known protein-protein interactions of putative orthologs of well-studied plant genomes. The transcriptome data of S. japonica reported here represents first genome-scale investigation of gene expressions in Faboideae plants. We expect that our study will provide a useful resource for further studies on gene expression, genomics, functional genomics, and protein-protein interaction in S. japonica.

Speciation in fungal and oomycete plant pathogens

The process of speciation, by definition, involves evolution of one or more reproductive isolating mechanisms that split a single species into two that can no longer interbreed. Determination of which processes are responsible for speciation is important yet challenging. Several studies have proposed that speciation in pathogens is heavily influenced by host-pathogen dynamics and that traits that mediate such interactions (e.g., host mobility, reproductive mode of the pathogen, complexity of the life cycle, and host specificity) must lead to reproductive isolation and ultimately affect speciation rates. In this review, we summarize the main evolutionary processes that lead to speciation of fungal and oomycete plant pathogens and provide an outline of how speciation can be studied rigorously, including novel genetic/genomic developments.

Population genomics of the endangered giant Galápagos tortoise

BACKGROUND

The giant Galápagos tortoise, Chelonoidis nigra, is a large-sized terrestrial chelonian of high patrimonial interest. The species recently colonized a small continental archipelago, the Galápagos Islands, where it has been facing novel environmental conditions and limited resource availability. To explore the genomic consequences of this ecological shift, we analyze the transcriptomic variability of five individuals of C. nigra, and compare it to similar data obtained from several continental species of turtles.

RESULTS

Having clarified the timing of divergence in the Chelonoidis genus, we report in C. nigra a very low level of genetic polymorphism, signatures of a weakened efficacy of purifying selection, and an elevated mutation load in coding and regulatory sequences. These results are consistent with the hypothesis of an extremely low long-term effective population size in this insular species. Functional evolutionary analyses reveal a reduced diversity of immunity genes in C. nigra, in line with the hypothesis of attenuated pathogen diversity in islands, and an increased selective pressure on genes involved in response to stress, potentially related to the climatic instability of its environment and its elongated lifespan. Finally, we detect no population structure or homozygosity excess in our five-individual sample.

CONCLUSIONS

These results enlighten the molecular evolution of an endangered taxon in a stressful environment and point to island endemic species as a promising model for the study of the deleterious effects on genome evolution of a reduced long-term population size.

Ecological genomics of local adaptation

It is increasingly important to improve our understanding of the genetic basis of local adaptation because of its relevance to climate change, crop and animal production, and conservation of genetic resources. Phenotypic patterns that are generated by spatially varying selection have long been observed, and both genetic mapping and field experiments provided initial insights into the genetic architecture of adaptive traits. Genomic tools are now allowing genome-wide studies, and recent theoretical advances can help to design research strategies that combine genomics and field experiments to examine the genetics of local adaptation. These advances are also allowing research in non-model species, the adaptation patterns of which may differ from those of traditional model species.

Genomic inference accurately predicts the timing and severity of a recent bottleneck in a nonmodel insect population

The analysis of molecular data from natural populations has allowed researchers to answer diverse ecological questions that were previously intractable. In particular, ecologists are often interested in the demographic history of populations, information that is rarely available from historical records. Methods have been developed to infer demographic parameters from genomic data, but it is not well understood how inferred parameters compare to true population history or depend on aspects of experimental design. Here, we present and evaluate a method of SNP discovery using RNA sequencing and demographic inference using the program δaδi, which uses a diffusion approximation to the allele frequency spectrum to fit demographic models. We test these methods in a population of the checkerspot butterfly Euphydryas gillettii. This population was intentionally introduced to Gothic, Colorado in 1977 and has as experienced extreme fluctuations including bottlenecks of fewer than 25 adults, as documented by nearly annual field surveys. Using RNA sequencing of eight individuals from Colorado and eight individuals from a native population in Wyoming, we generate the first genomic resources for this system. While demographic inference is commonly used to examine ancient demography, our study demonstrates that our inexpensive, all-in-one approach to marker discovery and genotyping provides sufficient data to accurately infer the timing of a recent bottleneck. This demographic scenario is relevant for many species of conservation concern, few of which have sequenced genomes. Our results are remarkably insensitive to sample size or number of genomic markers, which has important implications for applying this method to other nonmodel systems.

Population size and the rate of evolution

Does evolution proceed faster in larger or smaller populations? The relationship between effective population size (Ne) and the rate of evolution has consequences for our ability to understand and interpret genomic variation, and is central to many aspects of evolution and ecology. Many factors affect the relationship between Ne and the rate of evolution, and recent theoretical and empirical studies have shown some surprising and sometimes counterintuitive results. Some mechanisms tend to make the relationship positive, others negative, and they can act simultaneously. The relationship also depends on whether one is interested in the rate of neutral, adaptive, or deleterious evolution. Here, we synthesize theoretical and empirical approaches to understanding the relationship and highlight areas that remain poorly understood.

Disentangling homeologous contigs in allo-tetraploid assembly: application to durum wheat

BACKGROUND

Using Next Generation Sequencing, SNP discovery is relatively easy on diploid species and still hampered in polyploid species by the confusion due to homeology. We develop HomeoSplitter; a fast and effective solution to split original contigs obtained by RNAseq into two homeologous sequences. It uses the differential expression of the two homeologous genes in the RNA. We verify that the new sequences are closer to the diploid progenitors of the allopolyploid species than the original contig. By remapping original reads on these new sequences, we also verify that the number of valuable detected SNPs has significantly increased.

RESULTS

HomeoSplitter is a fast and effective solution to disentangle homeologous sequences based on a maximum likelihood optimization. On a benchmark set of 2,505 clusters containing homologous sequences of urartu, speltoides and durum, HomeoSplitter was efficient to build sequences closer to the diploid references and increased the number of valuable SNPs from 188 out of 1,360 SNPs detected when mapping the reads on the de novo durum assembly to 762 out of 1,620 SNPs when mapping on HomeoSplitter contigs.

CONCLUSIONS

The HomeoSplitter program is freely available at http://bioweb.supagro.inra.fr/homeoSplitter/. This work provides a practical solution to the complex problem of disentangling homeologous transcripts in allo-tetraploids, which further allows an improved SNP detection.