The computer software used for genomic analysis has become a crucial component of the infrastructure for life sciences. However, genomic software is still typically developed in an ad hoc manner, ...with inadequate funding, and by academic researchers not trained in software development, at substantial costs to the research community. I examine the roots of the incongruity between the importance of and the degree of investment in genomic software, and I suggest several potential remedies for current problems. As genomics continues to grow, new strategies for funding and developing the software that powers the field will become increasingly essential.
Many genetic variants that influence phenotypes of interest are located outside of protein-coding genes, yet existing methods for identifying such variants have poor predictive power. Here we ...introduce a new computational method, called LINSIGHT, that substantially improves the prediction of noncoding nucleotide sites at which mutations are likely to have deleterious fitness consequences, and which, therefore, are likely to be phenotypically important. LINSIGHT combines a generalized linear model for functional genomic data with a probabilistic model of molecular evolution. The method is fast and highly scalable, enabling it to exploit the 'big data' available in modern genomics. We show that LINSIGHT outperforms the best available methods in identifying human noncoding variants associated with inherited diseases. In addition, we apply LINSIGHT to an atlas of human enhancers and show that the fitness consequences at enhancers depend on cell type, tissue specificity, and constraints at associated promoters.
Investigators have recently introduced powerful methods for population genetic inference that rely on supervised machine learning from simulated data. Despite their performance advantages, these ...methods can fail when the simulated training data does not adequately resemble data from the real world. Here, we show that this "simulation mis-specification" problem can be framed as a "domain adaptation" problem, where a model learned from one data distribution is applied to a dataset drawn from a different distribution. By applying an established domain-adaptation technique based on a gradient reversal layer (GRL), originally introduced for image classification, we show that the effects of simulation mis-specification can be substantially mitigated. We focus our analysis on two state-of-the-art deep-learning population genetic methods-SIA, which infers positive selection from features of the ancestral recombination graph (ARG), and ReLERNN, which infers recombination rates from genotype matrices. In the case of SIA, the domain adaptive framework also compensates for ARG inference error. Using the domain-adaptive SIA (dadaSIA) model, we estimate improved selection coefficients at selected loci in the 1000 Genomes CEU population. We anticipate that domain adaptation will prove to be widely applicable in the growing use of supervised machine learning in population genetics.
We describe a new computational method for estimating the probability that a point mutation at each position in a genome will influence fitness. These 'fitness consequence' (fitCons) scores serve as ...evolution-based measures of potential genomic function. Our approach is to cluster genomic positions into groups exhibiting distinct 'fingerprints' on the basis of high-throughput functional genomic data, then to estimate a probability of fitness consequences for each group from associated patterns of genetic polymorphism and divergence. We have generated fitCons scores for three human cell types on the basis of public data from ENCODE. In comparison with conventional conservation scores, fitCons scores show considerably improved prediction power for cis regulatory elements. In addition, fitCons scores indicate that 4.2-7.5% of nucleotides in the human genome have influenced fitness since the human-chimpanzee divergence, and they suggest that recent evolutionary turnover has had limited impact on the functional content of the genome.
The complex correlation structure of a collection of orthologous DNA sequences is uniquely captured by the "ancestral recombination graph" (ARG), a complete record of coalescence and recombination ...events in the history of the sample. However, existing methods for ARG inference are computationally intensive, highly approximate, or limited to small numbers of sequences, and, as a consequence, explicit ARG inference is rarely used in applied population genomics. Here, we introduce a new algorithm for ARG inference that is efficient enough to apply to dozens of complete mammalian genomes. The key idea of our approach is to sample an ARG of Formula: see text chromosomes conditional on an ARG of Formula: see text chromosomes, an operation we call "threading." Using techniques based on hidden Markov models, we can perform this threading operation exactly, up to the assumptions of the sequentially Markov coalescent and a discretization of time. An extension allows for threading of subtrees instead of individual sequences. Repeated application of these threading operations results in highly efficient Markov chain Monte Carlo samplers for ARGs. We have implemented these methods in a computer program called ARGweaver. Experiments with simulated data indicate that ARGweaver converges rapidly to the posterior distribution over ARGs and is effective in recovering various features of the ARG for dozens of sequences generated under realistic parameters for human populations. In applications of ARGweaver to 54 human genome sequences from Complete Genomics, we find clear signatures of natural selection, including regions of unusually ancient ancestry associated with balancing selection and reductions in allele age in sites under directional selection. The patterns we observe near protein-coding genes are consistent with a primary influence from background selection rather than hitchhiking, although we cannot rule out a contribution from recurrent selective sweeps.
Methods for detecting nucleotide substitution rates that are faster or slower than expected under neutral drift are widely used to identify candidate functional elements in genomic sequences. ...However, most existing methods consider either reductions (conservation) or increases (acceleration) in rate but not both, or assume that selection acts uniformly across the branches of a phylogeny. Here we examine the more general problem of detecting departures from the neutral rate of substitution in either direction, possibly in a clade-specific manner. We consider four statistical, phylogenetic tests for addressing this problem: a likelihood ratio test, a score test, a test based on exact distributions of numbers of substitutions, and the genomic evolutionary rate profiling (GERP) test. All four tests have been implemented in a freely available program called phyloP. Based on extensive simulation experiments, these tests are remarkably similar in statistical power. With 36 mammalian species, they all appear to be capable of fairly good sensitivity with low false-positive rates in detecting strong selection at individual nucleotides, moderate selection in 3-bp elements, and weaker or clade-specific selection in longer elements. By applying phyloP to mammalian multiple alignments from the ENCODE project, we shed light on patterns of conservation/acceleration in known and predicted functional elements, approximate fractions of sites subject to constraint, and differences in clade-specific selection in the primate and glires clades. We also describe new "Conservation" tracks in the UCSC Genome Browser that display both phyloP and phastCons scores for genome-wide alignments of 44 vertebrate species.
Despite the conventional distinction between them, promoters and enhancers share many features in mammals, including divergent transcription and similar modes of transcription factor binding. Here we ...examine the architecture of transcription initiation through comprehensive mapping of transcription start sites (TSSs) in human lymphoblastoid B cell (GM12878) and chronic myelogenous leukemic (K562) ENCODE Tier 1 cell lines. Using a nuclear run-on protocol called GRO-cap, which captures TSSs for both stable and unstable transcripts, we conduct detailed comparisons of thousands of promoters and enhancers in human cells. These analyses identify a common architecture of initiation, including tightly spaced (110 bp apart) divergent initiation, similar frequencies of core promoter sequence elements, highly positioned flanking nucleosomes and two modes of transcription factor binding. Post-initiation transcript stability provides a more fundamental distinction between promoters and enhancers than patterns of histone modification and association of transcription factors or co-activators. These results support a unified model of transcription initiation at promoters and enhancers.
An obligate intermediate during microRNA (miRNA) biogenesis is an ~22-nucleotide RNA duplex, from which the mature miRNA is preferentially incorporated into a silencing complex. Its partner miRNA* ...species is generally regarded as a passenger RNA, whose regulatory capacity has not been systematically examined in vertebrates. Our bioinformatic analyses demonstrate that a substantial fraction of miRNA* species are stringently conserved over vertebrate evolution, collectively exhibit greatest conservation in their seed regions, and define complementary motifs whose conservation across vertebrate 3'-UTR evolution is statistically significant. Functional tests of 22 miRNA expression constructs revealed that a majority could repress both miRNA and miRNA* perfect match reporters, and the ratio of miRNA:miRNA* sensor repression was correlated with the endogenous ratio of miRNA:miRNA* reads. Analysis of microarray data provided transcriptome-wide evidence for the regulation of seed-matched targets for both mature and star strand species of several miRNAs relevant to oncogenesis, including mir-17, mir-34a, and mir-19. Finally, 3'-UTR sensor assays and mutagenesis tests confirmed direct repression of five miR-19* targets via star seed sites. Overall, our data demonstrate that miRNA* species have demonstrable impact on vertebrate regulatory networks and should be taken into account in studies of miRNA functions and their contribution to disease states.
Complete genome sequences contain valuable information about natural selection, but this information is difficult to access for short, widely scattered noncoding elements such as transcription factor ...binding sites or small noncoding RNAs. Here, we introduce a new computational method, called Inference of Natural Selection from Interspersed Genomically coHerent elemenTs (INSIGHT), for measuring the influence of natural selection on such elements. INSIGHT uses a generative probabilistic model to contrast patterns of polymorphism and divergence in the elements of interest with those in flanking neutral sites, pooling weak information from many short elements in a manner that accounts for variation among loci in mutation rates and coalescent times. The method is able to disentangle the contributions of weak negative, strong negative, and positive selection based on their distinct effects on patterns of polymorphism and divergence. It obtains information about divergence from multiple outgroup genomes using a general statistical phylogenetic approach. The INSIGHT model is efficiently fitted to genome-wide data using an approximate expectation maximization algorithm. Using simulations, we show that the method can accurately estimate the parameters of interest even in complex demographic scenarios, and that it significantly improves on methods based on summary statistics describing polymorphism and divergence. To demonstrate the usefulness of INSIGHT, we apply it to several classes of human noncoding RNAs and to GATA2-binding sites in the human genome.
Contrasting the genetic diversity of the human X chromosome (X) and autosomes has facilitated understanding historical differences between males and females and the influence of natural selection. ...Previous studies based on smaller data sets have left questions regarding how empirical patterns extend to additional populations and which forces can explain them. Here, we address these questions by analyzing the ratio of X-to-autosomal (X/A) nucleotide diversity with the complete genomes of 569 females from 14 populations. Results show that X/A diversity is similar within each continental group but notably lower in European (EUR) and East Asian (ASN) populations than in African (AFR) populations. X/A diversity increases in all populations with increasing distance from genes, highlighting the stronger impact of diversity-reducing selection on X than on the autosomes. However, relative X/A diversity (between two populations) is invariant with distance from genes, suggesting that selection does not drive the relative reduction in X/A diversity in non-Africans (0.842 ± 0.012 for EUR-to-AFR and 0.820 ± 0.032 for ASN-to-AFR comparisons). Finally, an array of models with varying population bottlenecks, expansions, and migration from the latest studies of human demographic history account for about half of the observed reduction in relative X/A diversity from the expected value of 1. They predict values between 0.91 and 0.94 for EUR-to-AFR comparisons and between 0.91 and 0.92 for ASN-to-AFR comparisons. Further reductions can be predicted by more extreme demographic events in excess of those captured by the latest studies but, in the absence of these, also by historical sex-biased demographic events or other processes.
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