Biology subjects

Siepel, A.

Publications and source records attributed to Siepel, A..

35 records · Page 2Linked to original sources

bioRxiv2021

A multiple genome alignment workflow shows the impact of repeat masking and parameter tuning on alignment of functional regions in plants

Alignments of multiple genomes are a cornerstone of comparative genomics, but generating these alignments remains technically challenging and often impractical. We developed the msa_pipeline workflow (https://bitbucket.org/bucklerlab/msa_pipeline) based on the LAST aligner to allow practical and sensitive multiple alignment of diverged plant genomes with minimal user inputs. Our workflow only requires a set of genomes in FASTA format as input. The workflow outputs multiple alignments in MAF format, and includes utilities to help calculate genome-wide conservation scores. As high repeat content and genomic divergence are substantial challenges in plant genome alignment, we also explored the impact of different masking approaches and alignment parameters using genome assemblies of 33 grass species. Compared to conventional masking with RepeatMasker, a k-mer masking approach increased the alignment rate of CDS and non-coding functional regions by 25% and 14% respectively. We further found that default alignment parameters generally perform well, but parameter tuning can increase the alignment rate for non-coding functional regions by over 52% compared to default LAST settings. Finally, by increasing alignment sensitivity from the default baseline, parameter tuning can increase the number of non-coding sites that can be scored for conservation by over 76%.

genomics↗

bioRxiv2021

Deconvolution of Expression for Nascent RNA Sequencing Data (DENR) Highlights Pre-RNA Isoform Diversity in Human Cells

Quantification of mature-RNA isoform abundance from RNA-seq data has been extensively studied, but much less attention has been devoted to quantifying the abundance of distinct precursor RNAs based on nascent RNA sequencing data. Here we address this problem with a new computational method called Deconvolution of Expression for Nascent RNA sequencing data (DENR). DENR models the nascent RNA read counts at each locus as a mixture of user-provided isoforms. The performance of the baseline algorithm is enhanced by the use of machine-learning predictions of transcription start sites (TSSs) and an adjustment for the typical "shape profile" of read counts along a transcription unit. We show using simulated data that DENR clearly outperforms simple read-count-based methods for estimating the abundances of both whole genes and isoforms. By applying DENR to previously published PRO-seq data from K562 and CD4+ T cells, we find that transcription of multiple isoforms per gene is widespread, and the dominant isoform frequently makes use of an internal TSS. We also identify > 200 genes whose dominant isoforms make use of different TSSs in these two cell types. Finally, we apply DENR and StringTie to newly generated PRO-seq and RNA-seq data, respectively, for human CD4+ T cells and CD14+ monocytes, and show that entropy at the pre-RNA level makes a disproportionate contribution to overall isoform diversity, especially across cell types. Altogether, DENR is the first computational tool to enable abundance quantification of pre-RNA isoforms based on nascent RNA sequencing data, and it reveals high levels of pre-RNA isoform diversity in human cells.

genomics↗

bioRxiv2021

A Unified Probabilistic Modeling Framework for Eukaryotic Transcription Based on Nascent RNA Sequencing Data

Nascent RNA sequencing protocols, such as PRO-seq and NET-seq, are now widely used in the study of eukaryotic transcription, and these experimental techniques have given rise to a variety of statistical and machine-learning methods for data analysis. These computational methods, however, are generally designed to address specialized signal-processing or prediction tasks, rather than directly describing the dynamics of RNA polymerases as they move along the DNA template. Here, I introduce a general probabilistic model that describes the kinetics of transcription initiation, elongation, pause release, and termination, as well as the generation of sequencing read counts. I show that this generative model enables estimation of separate pause-release rates, termination rates, and the initiation/elongation rate ratio up to a proportionality constant. Furthermore, if applied to time-course data in a nonequilibrium setting, the model can be used to estimate elongation rates. This model leads naturally to likelihood ratio tests for differences between genes, conditions, or species in various rates of interest. If read counts are assumed to be Poisson-distributed, convenient, closed-form solutions are available for both parameter estimates and likelihood-ratio-test statistics. Straightforward extensions of the model accommodate variability in the pause site and steric hindrance of initiation by paused polymerases. Additional extensions address Bayesian inference under the Poisson model and a generalized linear model that can be used to discover genomic features associated with rates of elongation. Finally, I address technicalities concerning estimation of library size, normalization and sequencing replicates. Altogether, this modeling framework enables a unified treatment of many common tasks in the analysis of nascent RNA sequencing data.

bioinformatics↗

bioRxiv2020

Unraveling molecular mechanisms of immunity and cancer-resistance using the genomes of the Neotropical bats Artibeus jamaicensis and Pteronotus mesoamericanus

Bats are exceptional among mammals for their powered flight, extended lifespans, and robust immune systems. To investigate the genomic underpinnings of unique bat adaptations, we sequenced the genomes of the Jamaican fruit bat (Artibeus jamaicensis) and the Mesoamerican mustached bat (Pteronotus mesoamericanus) and compared them to a diverse collection of 13 additional bat species together with other mammals. We used the Oxford Nanopore Technologies long-read platform to generate highly complete assemblies (N50: 28-29Mb) and facilitate analysis of complex genomic regions containing duplicated genes. Using gene family size analysis, we found that the type I interferon locus was contracted by eight genes in the most recent common ancestor (MRCA) of bats, shifting the proportion of interferon-{omega} to interferon- and making interferon-{omega} the most common type I interferon in bats. Antiviral genes stimulated by type I interferons were also rapidly evolving, with interferon-induced transmembrane genes experiencing a lineage-specific duplication and strong positive selection in the gene IFIT2. Moreover, the lineage of phyllostomid bats showed an unprecedented expansion of PRDM9, a recombination-related gene also involved in infection responses, raising the possibility that this gene contributes to bat antiviral defenses. These modifications in the bat innate immune system may be important adaptations allowing them to harbor viruses asymptomatically. We additionally found evidence of positive selection on the branch leading to the MRCA of bats acting on 33 tumor suppressors and six DNA repair genes, which may contribute to the low cancer rates and longevity observed across bats. These new genomic resources enable insights into the extraordinary adaptations of bats, with implications for mammalian evolutionary studies and public health.

genomics↗

bioRxiv2020

Genomic islands of differentiation in a rapid avian radiation have been driven by recent selective sweeps

Numerous studies of emerging species have identified genomic "islands" of elevated differentiation against a background of relative homogeneity. The causes of these islands remain unclear, however, with some signs pointing toward "speciation genes" that locally restrict gene flow and others suggesting selective sweeps that have occurred within nascent species after speciation. Here, we examine this question through the lens of genome sequence data for five species of southern capuchino seedeaters, finch-like birds from South America that have undergone a species radiation during the last [~]50,000 generations. By applying newly developed statistical methods for ancestral recombination graph inference and machine-learning methods for the prediction of selective sweeps, we show that previously identified islands of differentiation in these birds appear to be generally associated with relatively recent, species-specific selective sweeps, most of which are predicted to be "soft" sweeps acting on standing genetic variation. Many of these sweeps coincide with genes associated with melanin-based variation in plumage, suggesting a prominent role for sexual selection. At the same time, a few loci also exhibit indications of possible selection against gene flow. These observations shed new light on the complex manner in which natural selection shapes genome sequences during speciation. Significance StatementGenome-wide scans can identify differentiated loci between species that may have promoted speciation. So-called "islands of differentiation" have generally been identified and characterized using standard population genetic summary statistics (e.g., FST and dxy), which are limited in distinguishing among possible causes of differentiation, such as selection against gene flow and selective sweeps. We introduce a powerful strategy for analyzing such islands, combining new methods for inferring the full Ancestral Recombination Graph and machine learning methods for identifying selective sweeps. We applied our methods to genomic sequences from closely related southern capuchino seedeaters (Neotropical tanagers) and found signatures of recent selective sweeps around pigmentation genes, including many "soft" sweeps that acted on standing variation.

evolutionary biology↗

bioRxiv2019

A community-maintained standard library of population genetic models

The explosion in population genomic data demands ever more complex modes of analysis, and increasingly these analyses depend on sophisticated simulations. Recent advances in population genetic simulation have made it possible to simulate large and complex models, but specifying such models for a particular simulation engine remains a difficult and error-prone task. Computational genetics researchers currently re-implement simulation models independently, leading to inconsistency and duplication of effort. This situation presents a major barrier to empirical researchers seeking to use simulations for power analyses of upcoming studies or sanity checks on existing genomic data. Population genetics, as a field, also lacks standard benchmarks by which new tools for inference might be measured. Here we describe a new resource, stdpopsim, that attempts to rectify this situation. Stdpopsim is a community-driven open source project, which provides easy access to a growing catalog of published simulation models from a range of organisms and supports multiple simulation engine backends. This resource is available as a well-documented python library with a simple command-line interface. We share some examples demonstrating how stdpopsim can be used to systematically compare demographic inference methods, and we encourage a broader community of developers to contribute to this growing resource.

evolutionary biology↗

bioRxiv2019

ACE: A Probabilistic Model for Characterizing Gene-Level Essentiality in CRISPR Screens

High-throughput knockout screens based on CRISPR-Cas9 are widely used to evaluate the essentiality of genes across a range of cell types. Here we introduce a probabilistic modeling framework, Analysis of CRISPR-based Essentiality (ACE), that enables new statistical tests for essentiality based on the raw sequence read counts from such screens. ACE estimates the essentiality of each gene using a flexible likelihood framework that accounts for multiple sources of variation in the CRISPR-Cas9 experimental process. In addition, the method can identify genes that differ in their degree of essentiality across samples using a likelihood ratio test. We show using simulations that ACE is competitive with the best available methods in predicting essentiality, and is especially useful for the identification of differential essentiality. Furthermore, by applying ACE to publicly available CRISPR-screen data, we are able to identify both known and previously overlooked candidates for genotype-specific essentiality, including RNA m6-A methyltransferases that exhibit enhanced essentiality in the presence of inactivating TP53 mutations. In summary, ACE provides improved quantification of essentiality specific to cancer subtypes, and a robust probabilistic framework for identifying genes responsive to therapeutic targeting.

bioinformatics↗

bioRxiv2019

Phylogenetic modeling of regulatory element turnover based on epigenomic data

Evolutionary changes in gene expression are often driven by gains and losses of cis-regulatory elements (CREs). The dynamics of CRE evolution can be examined using multi-species epigenomic data, but so far such analyses have generally been descriptive and model-free. Here, we introduce a probabilistic modeling framework for the evolution of CREs that operates directly on raw chromatin immunoprecipitation and sequencing (ChIP-seq) data and fully considers the phylogenetic relationships among species. Our framework includes a phylogenetic hidden Markov model, called epiPhyloHMM, for identifying the locations of multiply aligned CREs, and a combined phylogenetic and generalized linear model, called phyloGLM, for accounting for the influence of a rich set of genomic features in describing their evolutionary dynamics. We apply these methods to previously published ChIP-seq data for the H3K4me3 and H3K27ac histone modifications in liver tissue from nine mammals. We find that enhancers are gained and lost during mammalian evolution at about twice the rate of promoters, and that turnover rates are negatively correlated with DNA sequence conservation, expression level, and tissue breadth, and positively correlated with distance from the transcription start site, consistent with previous findings. In addition, we find that the predicted dosage sensitivity of target genes positively correlates with DNA sequence constraint in CREs but not with turnover rates, perhaps owing to differences in the effect sizes of the relevant mutations. Altogether, our probabilistic modeling framework enables a variety of powerful new analyses.

evolutionary biology↗

bioRxiv2019

Characterizing RNA stability genome-wide through combined analysis of PRO-seq and RNA-seq data

The rate at which RNA molecules decay is a key determinant of cellular RNA concentrations, yet current approaches for measuring RNA half-lives are generally labor-intensive, limited in sensitivity, and/or disruptive to normal cellular processes. Here we introduce a simple method for estimating relative RNA half-lives that is based on two standard and widely available high-throughput assays: Precision Run-On and sequencing (PRO-seq) and RNA sequencing (RNA-seq). Our method treats PRO-seq as a measure of transcription rate and RNA-seq as a measure of RNA concentration, and estimates the rate of RNA decay required for a steady-state equilibrium. We show that this approach can be used to assay relative RNA half-lives genome-wide, with good accuracy and sensitivity for both coding and noncoding transcription units. Using a structural equation model (SEM), we test several features of transcription units, nearby DNA sequences, and nearby epigenomic marks for associations with RNA stability after controlling for their effects on transcription. We find that RNA splicing-related features are positively correlated with RNA stability, whereas features related to miRNA binding, DNA methylation, and G+C-richness are negatively correlated with RNA stability. Furthermore, we find that a measure based on U1-binding and polyadenylation sites distinguishes between unstable noncoding and stable coding transcripts but is not predictive of relative stability within the mRNA or lincRNA classes. We also identify several histone modifications that are associated with RNA stability. Together, our estimation method and systematic analysis shed light on the pervasive impacts of RNA stability on cellular RNA concentrations.

genomics↗

bioRxiv2019

Mapping gene flow between ancient hominins through demography-aware inference of the ancestral recombination graph

The sequencing of Neanderthal and Denisovan genomes has yielded many new insights about interbreeding events between extinct hominins and the ancestors of modern humans. While much attention has been paid to the relatively recent gene flow from Neanderthals and Denisovans into modern humans, other instances of introgression leave more subtle genomic evidence and have received less attention. Here, we present an extended version of the ARGweaver algorithm, ARGweaver-D, which can infer local genetic relationships under a user-defined demographic model that includes population splits and migration events. This Bayesian algorithm probabilistically samples ancestral recombination graphs (ARGs) that specify not only tree topology and branch lengths along the genome, but also indicate migrant lineages. The sampled ARGs can therefore be parsed to produce probabilities of introgression along the genome. We show that this method is well powered to detect the archaic migration into modern humans, even with only a few samples. We then show that the method can also detect introgressed regions stemming from older migration events, or from unsampled populations. We apply it to human, Neanderthal, and Denisovan genomes, looking for signatures of older proposed migration events, including ancient humans into Neanderthal, and unknown archaic hominins into Denisovans. We identify 3% of the Neanderthal genome that is putatively introgressed from ancient humans, and estimate that the gene flow occurred between 200-300kya. We find no convincing evidence that negative selection acted against these regions. We also identify 1% of the Denisovan genome which was likely introgressed from an unsequenced hominin ancestor, and note that 15% of these regions have been passed on to modern humans through subsequent gene flow.

evolutionary biology↗

bioRxiv2018

Estimation of allele-specific fitness effects across human protein-coding sequences and implications for disease

A central challenge in human genomics is to understand the cellular, evolutionary, and clinical significance of genetic variants. Here we introduce a unified population-genetic and machine-learning model, called Linear Allele-Specific Selection InferencE (LASSIE), for estimating the fitness effects of all potential single-nucleotide variants, based on polymorphism data and predictive genomic features. We applied LASSIE to 51 high-coverage genome sequences annotated with 33 genomic features, and constructed a map of allele-specific selection coefficients across all protein-coding sequences in the human genome. We show that this map is informative about both human evolution and disease.

genomics↗

bioRxiv2018

phastWeb: a web interface for evolutionary conservation scoring of multiple sequence alignments using phastCons and phyloP

The Phylogenetic Analysis with Space/Time models (PHAST) package is a widely used software package for comparative genomics that has been freely available for download since 2002. Here we introduce a web interface (phastWeb) that makes it possible to use two of the most popular programs in PHAST, phastCons and phyloP, without downloading and installing the PHAST software. This interface allows users to upload a sequence alignment and either upload a corresponding phylogeny or have one estimated from the alignment. After processing, users can visualize alignments and conservation scores as genome browser tracks, and download estimated tree models and raw scores for further analysis. Altogether, this resource makes key features of the PHAST package conveniently available to a broad audience.\n\nAVAILABILITYphastWeb is freely available on the web at http://compgen.cshl.edu/phastweb/. The website provides instructions as well as examples.\n\nCONTACTphasthelp@cshl.edu

evolutionary biology↗

bioRxiv2018

How Much Information is Provided by Human Epigenomic Data? An Evolutionary View

Here, we ask the question, \"How much information do available epigenomic data sets provide about human genomic function, individually or in combination?\" We consider nine epigenomic and annotation features across 115 cell types and measure genomic function by using signatures of natural selection as a proxy. We measure information as the reduction in entropy under a probabilistic evolutionary model that describes genetic variation across [~]50 diverse humans and several nonhuman primates. We find that several genomic features yield more information in combination than they do individually, with DNase-seq displaying particularly strong synergy. Most of the entropy in human genetic variation, by far, reflects mutation and neutral drift; the genome-wide reduction in entropy due to selection is equivalent to only a small fraction of the storage requirements of a single human genome. Based on this framework, we produce cell-type-specific maps of the probability that a mutation at each nucleotide will have fitness consequences (FitCons scores). These scores are predictive of known functional elements and disease-associated variants, they reveal relationships among cell types, and they suggest that [~]8% of nucleotide sites are constrained by natural selection.

genomics↗

bioRxiv2017

Scikit-ribo: Accurate estimation and robust modeling of translation dynamics at codon resolution

Ribosome profiling (Riboseq) is a powerful technique for measuring protein translation, however, sampling errors and biological biases are prevalent and poorly understand. Addressing these issues, we present Scikit-ribo (https://github.com/hanfang/scikit-ribo), the first open-source software for accurate genome-wide A-site prediction and translation efficiency (TE) estimation from Riboseq and RNAseq data. Scikit-ribo accurately identifies A-site locations and reproduces codon elongation rates using several digestion protocols (r = 0.99). Next we show commonly used RPKM-derived TE estimation is prone to biases, especially for low-abundance genes. Scikit-ribo introduces a codon-level generalized linear model with ridge penalty that correctly estimates TE while accommodating variable codon elongation rates and mRNA secondary structure. This corrects the TE errors for over 2000 genes in S. cerevisiae, which we validate using mass spectrometry of protein abundances (r = 0.81) and allows us to determine the Kozak-like sequence directly from Riboseq. We conclude with an analysis of coverage requirements needed for robust codon-level analysis, and quantify the artifacts that can occur from cycloheximide treatment.

bioinformatics↗

bioRxiv2017

Deep experimental profiling of microRNA diversity, deployment, and evolution across the Drosophila genus

Comparative genomic analyses of microRNAs (miRNAs) have yielded myriad insights into their biogenesis and regulatory activity. While miRNAs have been deeply annotated in a small cohort of model organisms, evolutionary assessments of miRNA flux are clouded by the functional uncertainty of orthologs in related species, and insufficient data regarding the extent of species-specific miRNAs. We address this by generating a comparative small RNA (sRNA) catalog of unprecedented breadth and depth across the Drosophila genus, extending our extant deep analyses of D. melanogaster with sRNA data from multiple tissues of 11 other fly species. Aggregate analysis of several billion sRNA reads permits curation of accurate and holistic compendia of miRNAs across this genus, providing abundant opportunities to identify species- and clade-specific variation in miRNA identity, abundance, and processing. Amongst well-conserved miRNAs, we observe unexpected cases of clade-specific variation in 5' end precision, occasional antisense loci, and some putatively non-canonical loci. We also employ strict criteria to identify a massive set (649) of novel, evolutionarily-restricted miRNAs. Amongst the bulk collection of species-restricted miRNAs, two notable subpopulations of rapidly-evolving miRNAs are splicing-derived mirtrons and testis-restricted, clustered (TRC) canonical miRNAs. We quantify rates of miRNA birth and death using our annotation and a phylogenetic model for estimating rates of miRNA turnover in the presence of annotation uncertainty. We show striking differences in birth and death rates across miRNA classes defined by biogenesis pathway, genomic clustering, and tissue restriction, and even identify variation heterogeneity amongst Drosophila clades. In particular, distinct molecular rationales underlie the distinct evolutionary behavior of different miRNA classes. We broaden observations made from D. melanogaster as Drosophilid-wide principles for opposing evolutionary viewpoints for miRNA maintenance. Mirtrons are associated with a high rate of 3' untemplated addition, a mechanism that impedes their biogenesis, whereas TRC miRNAs appear to evolve under positive selection. Altogether, these data reveal miRNA diversity amongst Drosophila species and permit future discoveries in understanding their emergence and evolution.

genomics↗

bioRxiv2017

Nascent RNA sequencing reveals a dynamic global transcriptional response at genes and enhancers to the natural medicinal compound celastrol

Most studies of responses to transcriptional stimuli measure changes in cellular mRNA concentrations. By sequencing nascent RNA instead, it is possible to detect changes in transcription in minutes rather than hours, and thereby distinguish primary from secondary responses to regulatory signals. Here, we describe the use of PRO-seq to characterize the immediate transcriptional response in human cells to celastrol, a compound derived from traditional Chinese medicine that has potent anti-inflammatory, tumor-inhibitory and obesity-controlling effects. Our analysis of PRO-seq data for K562 cells reveals dramatic transcriptional effects soon after celastrol treatment at a broad collection of both coding and noncoding transcription units. This transcriptional response occurred in two major waves, one within 10 minutes, and a second 40-60 minutes after treatment. Transcriptional activity was generally repressed by celastrol, but one distinct group of genes, enriched for roles in the heat shock response, displayed strong activation. Using a regression approach, we identified key transcription factors that appear to drive these transcriptional responses, including members of the E2F and RFX families. We also found sequence-based evidence that particular TFs drive the activation of enhancers. We observed increased polymerase pausing at both genes and enhancers, suggesting that pause release may be widely inhibited during the celastrol response. Our study demonstrates that a careful analysis of PRO-seq time course data can disentangle key aspects of a complex transcriptional response, and it provides new insights into the activity of a powerful pharmacological agent.

genomics↗

bioRxiv2016

Natural Selection has Shaped Coding and Non-coding Transcription in Primate CD4+ T-cells

Transcriptional regulatory changes have been shown to contribute to phenotypic differences between species, but many questions remain about how gene expression evolves. Here we report the first comparative study of nascent transcription in primates. We used PRO-seq to map actively transcribing RNA polymerases in resting and activated CD4+ T-cells in multiple human, chimpanzee, and rhesus macaque individuals, with rodents as outgroups. This approach allowed us to measure transcription separately from post-transcriptional processes. We observed general conservation in coding and non-coding transcription, punctuated by numerous differences between species, particularly at distal enhancers and non-coding RNAs. We found evidence that transcription factor binding sites are a primary determinant of transcriptional differences between species, that stabilizing selection maintains gene expression levels despite frequent changes at distal enhancers, and that adaptive substitutions have driven lineage-specific transcription. Finally, we found strong correlations between evolutionary rates and long-range chromatin interactions. These observations clarify the role of primary transcription in regulatory evolution.

genomics↗