Biology subjects

Freedman, A. H.

Publications and source records attributed to Freedman, A. H..

4 recordsLinked to original sources

bioRxiv2024

Environmentally-mediated selection parallels population divergence across a chimpanzee subspecies contact zone

Species evolve from populations with ancestor-descendant relationships in a bifurcating process shaped by geography, gene flow, genetic drift, and natural selection leading to local adaptation to prevailing environmental and ecological conditions. Building on this foundational understanding, we explored local adaptation in chimpanzees (Pan troglodytes) at a key geographical intersection in Cameroon where the two main chimpanzee phylogenetic lineages converge. The Nigeria-Cameroon chimpanzee (P. t. ellioti) and central chimpanzee (P. t. troglodytes) last shared a common ancestor about 500 thousand years ago, with occasional gene flow between them. The evolutionary processes driving their prolonged separation are not fully understood, but neutral evolutionary mechanisms alone cannot account for the observed divergence pattern. Cameroon is often referred to as Africa in miniature because the Gulf of Guinea Forest, Congo Basin Forest, and savanna converge there, forming an ecotone. Thus, this contact zone between subspecies in Cameroon provides a unique natural laboratory that enabled us to investigate how environmental variation and natural selection shape divergence in chimpanzees. We developed a genome-wide panel of single-nucleotide polymorphisms (SNPs) in 112 wild chimpanzees sampled in multiple habitats across this contact zone. We augmented SNP discovery by sequencing eight new chimpanzee genomes from Cameroon and analyzing them with previously published chimpanzee genomes. We found that P. t. ellioti and P. t. troglodytes diverged from one another around 478,000 years ago and occasionally exchange migrants. We identified 1,690 unique SNPs across 905 genes associated with 31 environmental variables that describe the habitat. These genes are involved in essential biological processes, including immune response, neurological development, behavior, and dietary adaptations. This study highlights the importance of understanding the geographical context of natural selection, paving the way for future studies to interpret evidence for genetic variation with phenotypic traits and deepening our understanding of how populations diverge in response to environmental pressures. Author SummaryWe investigated how local adaptation contributes to shaping the diversification of chimpanzee subspecies at the geographical convergence point for the two major branches of the chimpanzee phylogenetic tree. We analyzed genome-wide SNP genotypes of 112 chimpanzees sampled from natural communities located in this understudied area. We used tiered methods that identified 905 genes subject to selection, each associated with one or more of 31 environmental predictors describing the habitat. We found strong signals of selection in immune response genes that separate P. t. troglodytes from P. t. ellioti, highlighting the important role of different pathogen histories in their evolution. We also found evidence of selection in genes associated with neurological development, behavior, and diet, that separate both the subspecies and populations of P. t. ellioti that occupy different niches. These findings suggest that ecological and cultural factors may also contribute to shaping the diversification of chimpanzees across the contact zone.

genomics↗

bioRxiv2024

Building better genome annotations across the tree of life

Recent technological advances in long read DNA sequencing accompanied by dramatic reduction in costs have made the production of genome assemblies financially achievable and computationally feasible, such that genome assembly no longer represents the major hurdle to evolutionary analysis for most non-model organisms. Now, the more difficult challenge is to properly annotate a draft genome assembly once it has been constructed. The primary challenge to annotations is how to select from the myriad gene prediction tools that are currently available, determine what kinds of data are necessary to generate high quality annotations, and evaluate the quality of the annotation. To determine which methods perform the best and determine whether the inclusion of RNA-seq data is necessary to obtain a high-quality annotation, we generated annotations with 10 different methods for 21 different species spanning vertebrates, plants, and insects. We found that the RNA-seq assembler Stringtie and the annotation transfer method TOGA were consistently top performers across a variety of metrics including BUSCO recovery, CDS length, and false positive rate, with the exception that TOGA performed less in plants with larger genomes. RNA-seq alignment rate was best with RNA-seq assemblers. HMM-based methods such as BRAKER, MAKER, and multi-genome AUGUSTUS mostly underperformed relative to Stringtie and TOGA. In general, inclusion of RNA-seq data will lead to substantial improvements to genome annotations, and there may be cases where complementarity among methods may motivate combining annotations from multiple sources.

bioinformatics↗

bioRxiv2019

Short paired-end reads trump long single-end reads for expression analysis

BackgroundTypical experimental design advice for expression analyses using RNA-seq generally assumes that single-end reads provide robust gene-level expression estimates in a cost-effective manner, and that the additional benefits obtained from paired-end sequencing are not worth the additional cost. However, in many cases (e.g., with Illumina NextSeq and NovaSeq instruments), shorter paired-end reads and longer single-end reads can be generated for the same cost, and it is not obvious which strategy should be preferred. Using publicly available data, we test whether short-paired end reads can achieve more robust expression estimates and differential expression results than single-end reads of approximately the same total number of sequenced bases. ResultsAt both the transcript and gene levels, 2x40 paired-end reads unequivocally provide expression estimates that are more highly correlated with 2x125 than 1x75 reads; in nearly all cases, those correlations are also greater than for 1x125, despite the greater total number of sequenced bases for the latter. Across an array of metrics, differential expression tests based upon 2x40 consistently outperform those using 1x75. ConclusionResearchers seeking a cost-effective approach for gene-level expression analysis should prefer short paired-end reads over a longer single-end strategy. Short paired-end reads will also give reasonably robust expression estimates and differential expression results at the isoform level.

bioinformatics↗

bioRxiv2019

Error, noise and bias in de novo transcriptome assemblies

De novo transcriptome assembly is a powerful tool, widely used over the last decade for making evolutionary inferences. However, it relies on two implicit assumptions: that the assembled transcriptome is an unbiased representation of the underlying expressed transcriptome, and that expression estimates from the assembly are good, if noisy approximations of the relative abundance of expressed transcripts. Using publicly available data for model organisms, we demonstrate that, across assembly algorithms and data sets, these assumptions are consistently violated. Bias exists at the nucleotide level, with genotyping error rates ranging from 30-83%. As a result, diversity is underestimated in transcriptome assemblies, with consistent under-estimation of heterozygosity in all but the most inbred samples. Even at the gene level, expression estimates show wide deviations from map-to-reference estimates, and positive bias at lower expression levels. Standard filtering of transcriptome assemblies improves the robustness of gene expression estimates but leads to the loss of a meaningful number of protein-coding genes, including many that are highly expressed. We demonstrate a computational method, length-rescaled CPM, to partly alleviate noise and bias in expression estimates. Researchers should consider ways to minimize the impact of bias in transcriptome assemblies.

bioinformatics↗