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The scale of genetic, epigenomic, transcriptomic, cheminformatic and proteomic data available today, coupled with easy-to-use machine learning (ML) toolkits, has propelled the application of supervised learning in genomics research. However, the assumptions behind the statistical models and performance evaluations in ML software frequently are not met in biological systems. In this Review, we illustrate the impact of several common pitfalls encountered when applying supervised ML in genomics. We explore how the structure of genomics data can bias performance evaluations and predictions. To address the challenges associated with applying cutting-edge ML methods to genomics, we describe solutions and appropriate use cases where ML modelling shows great potential., (© 2021. Springer Nature Limited.)

What made us human? Gene expression changes clearly played a significant part in human evolution, but pinpointing the causal regulatory mutations is hard. Comparative genomics enabled the identification of human accelerated regions (HARs) and other human-specific genome sequences. The major challenge in the past decade has been to link diverged sequences to uniquely human biology. This review discusses approaches to this problem, progress made at the molecular level, and prospects for moving towards genetic causes for uniquely human biology.

Gene-regulatory enhancers have been identified using various approaches, including evolutionary conservation, regulatory protein binding, chromatin modifications, and DNA sequence motifs. To integrate these different approaches, we developed EnhancerFinder, a two-step method for distinguishing developmental enhancers from the genomic background and then predicting their tissue specificity. EnhancerFinder uses a multiple kernel learning approach to integrate DNA sequence motifs, evolutionary patterns, and diverse functional genomics datasets from a variety of cell types. In contrast with prediction approaches that define enhancers based on histone marks or p300 sites from a single cell line, we trained EnhancerFinder on hundreds of experimentally verified human developmental enhancers from the VISTA Enhancer Browser. We comprehensively evaluated EnhancerFinder using cross validation and found that our integrative method improves the identification of enhancers over approaches that consider a single type of data, such as sequence motifs, evolutionary conservation, or the binding of enhancer-associated proteins. We find that VISTA enhancers active in embryonic heart are easier to identify than enhancers active in several other embryonic tissues, likely due to their uniquely high GC content. We applied EnhancerFinder to the entire human genome and predicted 84,301 developmental enhancers and their tissue specificity. These predictions provide specific functional annotations for large amounts of human non-coding DNA, and are significantly enriched near genes with annotated roles in their predicted tissues and lead SNPs from genome-wide association studies. We demonstrate the utility of EnhancerFinder predictions through in vivo validation of novel embryonic gene regulatory enhancers from three developmental transcription factor loci. Our genome-wide developmental enhancer predictions are freely available as a UCSC Genome Browser track, which we hope will enable researchers to further investigate questions in developmental biology.

Background: Targeted capture of genomic regions reduces sequencing cost while generating higher coverage by allowing biomedical researchers to focus on specific loci of interest, such as exons. Targeted capture also has the potential to facilitate the generation of genomic data from DNA collected via saliva or buccal cells. DNA samples derived from these cell types tend to have a lower human DNA yield, may be degraded from age and/or have contamination from bacteria or other ambient oral microbiota. However, thousands of samples have been previously collected from these cell types, and saliva collection has the advantage that it is a non-invasive and appropriate for a wide variety of research., Results: We demonstrate successful enrichment and sequencing of 15 South African KhoeSan exomes and 2 full genomes with samples initially derived from saliva. The expanded exome dataset enables us to characterize genetic diversity free from ascertainment bias for multiple KhoeSan populations, including new exome data from six HGDP Namibian San, revealing substantial population structure across the Kalahari Desert region. Additionally, we discover and independently verify thirty-one previously unknown KIR alleles using methods we developed to accurately map and call the highly polymorphic HLA and KIR loci from exome capture data. Finally, we show that exome capture of saliva-derived DNA yields sufficient non-human sequences to characterize oral microbial communities, including detection of bacteria linked to oral disease (e.g. Prevotella melaninogenica). For comparison, two samples were sequenced using standard full genome library preparation without exome capture and we found no systematic bias of metagenomic information between exome-captured and non-captured data., Conclusions: DNA from human saliva samples, collected and extracted using standard procedures, can be used to successfully sequence high quality human exomes, and metagenomic data can be derived from non-human reads. We find that individuals from the Kalahari carry a higher oral pathogenic microbial load than samples surveyed in the Human Microbiome Project. Additionally, rare variants present in the exomes suggest strong population structure across different KhoeSan populations.

The genetic changes underlying the dramatic differences in form and function between humans and other primates are largely unknown, although it is clear that gene regulatory changes play an important role. To identify regulatory sequences with potentially human-specific functions, we and others used comparative genomics to find non-coding regions conserved across mammals that have acquired many sequence changes in humans since divergence from chimpanzees. These regions are good candidates for performing human-specific regulatory functions. Here, we analysed the DNA sequence, evolutionary history, histone modifications, chromatin state and transcription factor (TF) binding sites of a combined set of 2649 non-coding human accelerated regions (ncHARs) and predicted that at least 30% of them function as developmental enhancers. We prioritized the predicted ncHAR enhancers using analysis of TF binding site gain and loss, along with the functional annotations and expression patterns of nearby genes. We then tested both the human and chimpanzee sequence for 29 ncHARs in transgenic mice, and found 24 novel developmental enhancers active in both species, 17 of which had very consistent patterns of activity in specific embryonic tissues. Of these ncHAR enhancers, five drove expression patterns suggestive of different activity for the human and chimpanzee sequence at embryonic day 11.5. The changes to human non-coding DNA in these ncHAR enhancers may modify the complex patterns of gene expression necessary for proper development in a human-specific manner and are thus promising candidates for understanding the genetic basis of human-specific biology.

Even in genomes lacking operons, a gene's position in the genome influences its potential for expression. The mechanisms by which adjacent genes are co-expressed are still not completely understood. Using lactation and the mammary gland as a model system, we explore the hypothesis that chromatin state contributes to the co-regulation of gene neighborhoods. The mammary gland represents a unique evolutionary model, due to its recent appearance, in the context of vertebrate genomes. An understanding of how the mammary gland is regulated to produce milk is also of biomedical and agricultural importance for human lactation and dairying. Here, we integrate epigenomic and transcriptomic data to develop a comprehensive regulatory model. Neighborhoods of mammary-expressed genes were determined using expression data derived from pregnant and lactating mice and a neighborhood scoring tool, G-NEST. Regions of open and closed chromatin were identified by ChIP-Seq of histone modifications H3K36me3, H3K4me2, and H3K27me3 in the mouse mammary gland and liver tissue during lactation. We found that neighborhoods of genes in regions of uniquely active chromatin in the lactating mammary gland, compared with liver tissue, were extremely rare. Rather, genes in most neighborhoods were suppressed during lactation as reflected in their expression levels and their location in regions of silenced chromatin. Chromatin silencing was largely shared between the liver and mammary gland during lactation, and what distinguished the mammary gland was mainly a small tissue-specific repertoire of isolated, expressed genes. These findings suggest that an advantage of the neighborhood organization is in the collective repression of groups of genes via a shared mechanism of chromatin repression. Genes essential to the mammary gland's uniqueness are isolated from neighbors, and likely have less tolerance for variation in expression, properties they share with genes responsible for an organism's survival.

Microbial diversity is typically characterized by clustering ribosomal RNA (SSU-rRNA) sequences into operational taxonomic units (OTUs). Targeted sequencing of environmental SSU-rRNA markers via PCR may fail to detect OTUs due to biases in priming and amplification. Analysis of shotgun sequenced environmental DNA, known as metagenomics, avoids amplification bias but generates fragmentary, non-overlapping sequence reads that cannot be clustered by existing OTU-finding methods. To circumvent these limitations, we developed PhylOTU, a computational workflow that identifies OTUs from metagenomic SSU-rRNA sequence data through the use of phylogenetic principles and probabilistic sequence profiles. Using simulated metagenomic data, we quantified the accuracy with which PhylOTU clusters reads into OTUs. Comparisons of PCR and shotgun sequenced SSU-rRNA markers derived from the global open ocean revealed that while PCR libraries identify more OTUs per sequenced residue, metagenomic libraries recover a greater taxonomic diversity of OTUs. In addition, we discover novel species, genera and families in the metagenomic libraries, including OTUs from phyla missed by analysis of PCR sequences. Taken together, these results suggest that PhylOTU enables characterization of part of the biosphere currently hidden from PCR-based surveys of diversity?

The PHylogenetic Analysis with Space/Time models (PHAST) software package consists of a collection of command-line programs and supporting libraries for comparative genomics. PHAST is best known as the engine behind the Conservation tracks in the University of California, Santa Cruz (UCSC) Genome Browser. However, it also includes several other tools for phylogenetic modeling and functional element identification, as well as utilities for manipulating alignments, trees and genomic annotations. PHAST has been in development since 2002 and has now been downloaded more than 1000 times, but so far it has been released only as provisional ('beta') software. Here, we describe the first official release (v1.0) of PHAST, with improved stability, portability and documentation and several new features. We outline the components of the package and detail recent improvements. In addition, we introduce a new interface to the PHAST libraries from the R statistical computing environment, called RPHAST, and illustrate its use in a series of vignettes. We demonstrate that RPHAST can be particularly useful in applications involving both large-scale phylogenomics and complex statistical analyses. The R interface also makes the PHAST libraries acccessible to non-C programmers, and is useful for rapid prototyping. PHAST v1.0 and RPHAST v1.0 are available for download at http://compgen.bscb.cornell.edu/phast, under the terms of an unrestrictive BSD-style license. RPHAST can also be obtained from the Comprehensive R Archive Network (CRAN; http://cran.r-project.org).

Background: Genes are created by a variety of evolutionary processes, some of which generate duplicate copies of an entire gene, while others rearrange pre-existing genetic elements or co-opt previously non-coding sequence to create genes with 'novel' sequences. These novel genes are thought to contribute to distinct phenotypes that distinguish organisms. The creation, evolution, and function of duplicated genes are well-studied; however, the genesis and early evolution of novel genes are not well-characterized. We developed a computational approach to investigate these issues by integrating genome-wide comparative phylogenetic analysis with functional and interaction data derived from small-scale and high-throughput experiments., Results: We examine the function and evolution of new genes in the yeast Saccharomyces cerevisiae. We observed significant differences in the functional attributes and interactions of genes created at different times and by different mechanisms. Novel genes are initially less integrated into cellular networks than duplicate genes, but they appear to gain functions and interactions more quickly than duplicates. Recently created duplicated genes show evidence of adapting existing functions to environmental changes, while young novel genes do not exhibit enrichment for any particular functions. Finally, we found a significant preference for genes to interact with other genes of similar age and origin., Conclusions: Our results suggest a strong relationship between how and when genes are created and the roles they play in the cell. Overall, genes tend to become more integrated into the functional networks of the cell with time, but the dynamics of this process differ significantly between duplicate and novel genes.

BackgroundA large proportion of genetic risk remains unexplained for structural heart disease involving the interventricular septum (IVS) including hypertrophic cardiomyopathy and ventricular septal defects. This study sought to develop a reproducible proxy of IVS structure from standard medical imaging, discover novel genetic determinants of IVS structure, and relate these loci to diseases of the IVS, hypertrophic cardiomyopathy, and ventricular septal defect.MethodsWe estimated the cross-sectional area of the IVS from the 4-chamber view of cardiac magnetic resonance imaging in 32 219 individuals from the UK Biobank which was used as the basis of genome wide association studies and Mendelian randomization.ResultsMeasures of IVS cross-sectional area at diastole were a strong proxy for the 3-dimensional volume of the IVS (Pearson r=0.814, P=0.004), and correlated with anthropometric measures, blood pressure, and diagnostic codes related to cardiovascular physiology. Seven loci with clear genomic consequence and relevance to cardiovascular biology were uncovered by genome wide association studies, most notably a single nucleotide polymorphism in an intron of CDKN1A (rs2376620; β, 7.7 mm2 [95% CI, 5.8-11.0]; P=6.0×10-10), and a common inversion incorporating KANSL1 predicted to disrupt local chromatin structure (β, 8.4 mm2 [95% CI, 6.3-10.9]; P=4.2×10-14). Mendelian randomization suggested that inheritance of larger IVS cross-sectional area at diastole was strongly associated with hypertrophic cardiomyopathy risk (pIVW=4.6×10-10) while inheritance of smaller IVS cross-sectional area at diastole was associated with risk for ventricular septal defect (pIVW=0.007).ConclusionsAutomated estimates of cross-sectional area of the IVS supports discovery of novel loci related to cardiac development and Mendelian disease. Inheritance of genetic liability for either small or large IVS, appears to confer risk for ventricular septal defect or hypertrophic cardiomyopathy, respectively. These data suggest that a proportion of risk for structural and congenital heart disease can be localized to the common genetic determinants of size and shape of cardiovascular anatomy.

The precise pattern and timing of speciation events that gave rise to all living placental mammals remain controversial. We provide a comprehensive phylogenetic analysis of genetic variation across an alignment of 241 placental mammal genome assemblies, addressing prior concerns regarding limited genomic sampling across species. We compared neutral genome-wide phylogenomic signals using concatenation and coalescent-based approaches, interrogated phylogenetic variation across chromosomes, and analyzed extensive catalogs of structural variants. Interordinal relationships exhibit relatively low rates of phylogenomic conflict across diverse datasets and analytical methods. Conversely, X-chromosome versus autosome conflicts characterize multiple independent clades that radiated during the Cenozoic. Genomic time trees reveal an accumulation of cladogenic events before and immediately after the Cretaceous-Paleogene (K-Pg) boundary, implying important roles for Cretaceous continental vicariance and the K-Pg extinction in the placental radiation.

We reconstruct the phenotype of Balto, the heroic sled dog renowned for transporting diphtheria antitoxin to Nome, Alaska, in 1925, using evolutionary constraint estimates from the Zoonomia alignment of 240 mammals and 682 genomes from dogs and wolves of the 21st century. Balto shares just part of his diverse ancestry with the eponymous Siberian husky breed. Balto's genotype predicts a combination of coat features atypical for modern sled dog breeds, and a slightly smaller stature. He had enhanced starch digestion compared with Greenland sled dogs and a compendium of derived homozygous coding variants at constrained positions in genes connected to bone and skin development. We propose that Balto's population of origin, which was less inbred and genetically healthier than that of modern breeds, was adapted to the extreme environment of 1920s Alaska.

Conserved genomic sequences disrupted in humans may underlie uniquely human phenotypic traits. We identified and characterized 10,032 human-specific conserved deletions (hCONDELs). These short (average 2.56 base pairs) deletions are enriched for human brain functions across genetic, epigenomic, and transcriptomic datasets. Using massively parallel reporter assays in six cell types, we discovered 800 hCONDELs conferring significant differences in regulatory activity, half of which enhance rather than disrupt regulatory function. We highlight several hCONDELs with putative human-specific effects on brain development, including HDAC5, CPEB4, and PPP2CA. Reverting an hCONDEL to the ancestral sequence alters the expression of LOXL2 and developmental genes involved in myelination and synaptic function. Our data provide a rich resource to investigate the evolutionary mechanisms driving new traits in humans and other species.

Annotating coding genes and inferring orthologs are two classical challenges in genomics and evolutionary biology that have traditionally been approached separately, limiting scalability. We present TOGA (Tool to infer Orthologs from Genome Alignments), a method that integrates structural gene annotation and orthology inference. TOGA implements a different paradigm to infer orthologous loci, improves ortholog detection and annotation of conserved genes compared with state-of-the-art methods, and handles even highly fragmented assemblies. TOGA scales to hundreds of genomes, which we demonstrate by applying it to 488 placental mammal and 501 bird assemblies, creating the largest comparative gene resources so far. Additionally, TOGA detects gene losses, enables selection screens, and automatically provides a superior measure of mammalian genome quality. TOGA is a powerful and scalable method to annotate and compare genes in the genomic era.

Human accelerated regions (HARs) are conserved genomic loci that evolved at an accelerated rate in the human lineage and may underlie human-specific traits. We generated HARs and chimpanzee accelerated regions with an automated pipeline and an alignment of 241 mammalian genomes. Combining deep learning with chromatin capture experiments in human and chimpanzee neural progenitor cells, we discovered a significant enrichment of HARs in topologically associating domains containing human-specific genomic variants that change three-dimensional (3D) genome organization. Differential gene expression between humans and chimpanzees at these loci suggests rewiring of regulatory interactions between HARs and neurodevelopmental genes. Thus, comparative genomics together with models of 3D genome folding revealed enhancer hijacking as an explanation for the rapid evolution of HARs.

Thousands of genomic regions have been associated with heritable human diseases, but attempts to elucidate biological mechanisms are impeded by an inability to discern which genomic positions are functionally important. Evolutionary constraint is a powerful predictor of function, agnostic to cell type or disease mechanism. Single-base phyloP scores from 240 mammals identified 3.3% of the human genome as significantly constrained and likely functional. We compared phyloP scores to genome annotation, association studies, copy-number variation, clinical genetics findings, and cancer data. Constrained positions are enriched for variants that explain common disease heritability more than other functional annotations. Our results improve variant annotation but also highlight that the regulatory landscape of the human genome still needs to be further explored and linked to disease.

Genotyping single-nucleotide polymorphisms (SNPs) in microbiomes enables strain-level quantification. In this protocol, we describe a computational pipeline that performs fast and accurate SNP genotyping using metagenomic data. We first demonstrate how to use Maast to catalog SNPs from microbial genomes. Then we use GT-Pro to extract unique SNP-covering k-mers, optimize a data structure for storing these k-mers, and finally perform metagenotyping. For proof of concept, the protocol leverages public whole-genome sequences to metagenotype a synthetic community. For complete details on the use and execution of this protocol, please refer to Shi et al. (2022a)1 and Shi et al. (2022b).2.

The Zoonomia Project is investigating the genomics of shared and specialized traits in eutherian mammals. Here we provide genome assemblies for 131 species, of which all but 9 are previously uncharacterized, and describe a whole-genome alignment of 240 species of considerable phylogenetic diversity, comprising representatives from more than 80% of mammalian families. We find that regions of reduced genetic diversity are more abundant in species at a high risk of extinction, discern signals of evolutionary selection at high resolution and provide insights from individual reference genomes. By prioritizing phylogenetic diversity and making data available quickly and without restriction, the Zoonomia Project aims to support biological discovery, medical research and the conservation of biodiversity.

The CRISPR/Cas system is a highly specific genome editing tool capable of distinguishing alleles differing by even a single base pair. Target sites might carry genetic variations that are not distinguishable by sgRNA designing tools based on one reference genome. AlleleAnalyzer is an open-source software that incorporates single-nucleotide variants and short insertions and deletions to design sgRNAs for precisely editing 1 or multiple haplotypes of a sequenced genome, currently supporting 11 Cas proteins. It also leverages patterns of shared genetic variation to optimize sgRNA design for different human populations. AlleleAnalyzer is available at https://github.com/keoughkath/AlleleAnalyzer .

The potential impact of structural variants includes not only the duplication or deletion of coding sequences, but also the perturbation of noncoding DNA regulatory elements and structural chromatin features, including topological domains (TADs). Structural variants disrupting TAD boundaries have been implicated both in cancer and developmental disease; this likely occurs via "enhancer hijacking," whereby removal of the TAD boundary exposes enhancers to new target transcription start sites (TSSs). With this functional role, we hypothesized that boundaries would display evidence for negative selection. Here we demonstrate that the chromatin landscape constrains structural variation both within healthy humans and across primate evolution. In contrast, in patients with developmental delay, variants occur remarkably uniformly across genomic features, suggesting a potentially broad role for enhancer hijacking in human disease.

The genetic diversity of the gut microbiota has a central role in host health. Here, we created pangenomes for 728 human gut prokaryotic species, quadrupling the genes of strain-specific genomes. Each of these species has a core set of a thousand genes, differing even between closely related species, and an accessory set of genes unique to the different strains. Functional analysis shows high strain variability associates with sporulation, whereas low variability is linked with antibiotic resistance. We further map the antibiotic resistome across the human gut population and find 237 cases of extreme resistance even to last-resort antibiotics, with a predominance among Enterobacteriaceae. Lastly, the presence of specific genes in the microbiota relates to host age and sex. Our study underscores the genetic complexity of the human gut microbiota, emphasizing its significant implications for host health. The pangenomes and antibiotic resistance map constitute a valuable resource for further research. [Display omitted] • Pangenomes showcase the extensive genetic diversity of the human gut microbiota • High strain-level variability associates with species' capacity to sporulate • Extreme antibiotic resistance is widespread in the healthy, non-hospitalized population • The presence of specific microbial genes relates to host age and sex Shoer et al. showcase the extensive genetic diversity of the human gut microbiota and its implications for the host. They find high strain-level variability associates with species' capacity to sporulate, while low variability associates with carrying antibiotic resistance genes. Their insights into microbial evolution open avenues for targeted microbiota interventions. [ABSTRACT FROM AUTHOR]

Mammals have evolved remarkably different sensory, reproductive, metabolic, and skeletal systems. To explore the genetic basis for these differences, we developed a comparative genomics approach to scan whole-genome multiple sequence alignments to identify regions that evolved rapidly in an ancestral lineage but are conserved within extant species. This pattern suggests that ancestral changes in function were maintained in descendants. After applying this test to therian mammals, we identified 4,797 accelerated regions, many of which are noncoding and located near developmental transcription factors. We then used mouse transgenic reporter assays to test if noncoding accelerated regions are enhancers and to determine how therian-specific substitutions affect their activity in vivo. We discovered enhancers with expression specific to the therian version in brain regions involved in the hormonal control of milk ejection, uterine contractions, blood pressure, temperature, and visual processing. This work underscores the idea that changes in developmental gene expression are important for mammalian evolution, and it pinpoints candidate genes for unique aspects of mammalian biology.

The population persistence of schizophrenia despite associated reductions in fitness and fecundity suggests that the genetic basis of schizophrenia has a complex evolutionary history. A recent meta-analysis of schizophrenia genome-wide association studies offers novel opportunities for assessment of the evolutionary trajectories of schizophrenia-associated loci. In this study, we hypothesize that components of the genetic architecture of schizophrenia are attributable to human lineage-specific evolution. Our results suggest that schizophrenia-associated loci enrich in genes near previously identified human accelerated regions (HARs). Specifically, we find that genes near HARs conserved in nonhuman primates (pHARs) are enriched for schizophrenia-associated loci, and that pHAR-associated schizophrenia genes are under stronger selective pressure than other schizophrenia genes and other pHAR-associated genes. We further evaluate pHAR-associated schizophrenia genes in regulatory network contexts to investigate associated molecular functions and mechanisms. We find that pHAR-associated schizophrenia genes significantly enrich in a GABA-related coexpression module that was previously found to be differentially regulated in schizophrenia affected individuals versus healthy controls. In another two independent networks constructed from gene expression profiles from prefrontal cortex samples, we find that pHAR-associated schizophrenia genes are located in more central positions and their average path lengths to the other nodes are significantly shorter than those of other schizophrenia genes. Together, our results suggest that HARs are associated with potentially important functional roles in the genetic architecture of schizophrenia.

The comparison of related genomes has emerged as a powerful lens for genome interpretation. Here we report the sequencing and comparative analysis of 29 eutherian genomes. We confirm that at least 5.5% of the human genome has undergone purifying selection, and locate constrained elements covering ∼4.2% of the genome. We use evolutionary signatures and comparisons with experimental data sets to suggest candidate functions for ∼60% of constrained bases. These elements reveal a small number of new coding exons, candidate stop codon readthrough events and over 10,000 regions of overlapping synonymous constraint within protein-coding exons. We find 220 candidate RNA structural families, and nearly a million elements overlapping potential promoter, enhancer and insulator regions. We report specific amino acid residues that have undergone positive selection, 280,000 non-coding elements exapted from mobile elements and more than 1,000 primate- and human-accelerated elements. Overlap with disease-associated variants indicates that our findings will be relevant for studies of human biology, health and disease.

Abstract Background In previous studies, gene neighborhoods—spatial clusters of co-expressed genes in the genome—have been defined using arbitrary rules such as requiring adjacency, a minimum number of genes, a fixed window size, or a minimum expression level. In the current study, we developed a Gene Neighborhood Scoring Tool (G-NEST) which combines genomic location, gene expression, and evolutionary sequence conservation data to score putative gene neighborhoods across all possible window sizes simultaneously. Results Using G-NEST on atlases of mouse and human tissue expression data, we found that large neighborhoods of ten or more genes are extremely rare in mammalian genomes. When they do occur, neighborhoods are typically composed of families of related genes. Both the highest scoring and the largest neighborhoods in mammalian genomes are formed by tandem gene duplication. Mammalian gene neighborhoods contain highly and variably expressed genes. Co-localized noisy gene pairs exhibit lower evolutionary conservation of their adjacent genome locations, suggesting that their shared transcriptional background may be disadvantageous. Genes that are essential to mammalian survival and reproduction are less likely to occur in neighborhoods, although neighborhoods are enriched with genes that function in mitosis. We also found that gene orientation and protein-protein interactions are partially responsible for maintenance of gene neighborhoods. Conclusions Our experiments using G-NEST confirm that tandem gene duplication is the primary driver of non-random gene order in mammalian genomes. Non-essentiality, co-functionality, gene orientation, and protein-protein interactions are additional forces that maintain gene neighborhoods, especially those formed by tandem duplicates. We expect G-NEST to be useful for other applications such as the identification of core regulatory modules, common transcriptional backgrounds, and chromatin domains. The software is available at http://docpollard.org/software.html

Gut microbiota are shaped by a combination of ecological and evolutionary forces. While the ecological dynamics have been extensively studied, much less is known about how species of gut bacteria evolve over time. Here, we introduce a model-based framework for quantifying evolutionary dynamics within and across hosts using a panel of metagenomic samples. We use this approach to study evolution in approximately 40 prevalent species in the human gut. Although the patterns of between-host diversity are consistent with quasi-sexual evolution and purifying selection on long timescales, we identify new genealogical signatures that challenge standard population genetic models of these processes. Within hosts, we find that genetic differences that accumulate over 6-month timescales are only rarely attributable to replacement by distantly related strains. Instead, the resident strains more commonly acquire a smaller number of putative evolutionary changes, in which nucleotide variants or gene gains or losses rapidly sweep to high frequency. By comparing these mutations with the typical between-host differences, we find evidence that some sweeps may be seeded by recombination, in addition to new mutations. However, comparisons of adult twins suggest that replacement eventually overwhelms evolution over multi-decade timescales, hinting at fundamental limits to the extent of local adaptation. Together, our results suggest that gut bacteria can evolve on human-relevant timescales, and they highlight the connections between these short-term evolutionary dynamics and longer-term evolution across hosts. [ABSTRACT FROM AUTHOR]

The number and proportion of genes with no known function are growing rapidly. To quantify this phenomenon and provide criteria for prioritizing genes for functional characterization, we developed a bioinformatics pipeline that identifies robustly defined protein families with no annotated domains, ranks these with respect to phylogenetic breadth, and identifies them in metagenomics data. We applied this approach to 271 965 protein families from the SFams database and discovered many with no functional annotation, including >118 000 families lacking any known protein domain. From these, we prioritized 6 668 conserved protein families with at least three sequences from organisms in at least two distinct classes. These Function Unknown Families (FUnkFams) are present in Tara Oceans Expedition and Human Microbiome Project metagenomes, with distributions associated with sampling environment. Our findings highlight the extent of functional novelty in sequence databases and establish an approach for creating a “most wanted” list of genes to prioritize for further characterization. [ABSTRACT FROM AUTHOR]

The mechanisms by which different microbes colonize the healthy human gut versus other body sites, the gut in disease states, or other environments remain largely unknown. Identifying microbial genes influencing fitness in the gut could lead to new ways to engineer probiotics or disrupt pathogenesis. We approach this problem by measuring the statistical association between a species having a gene and the probability that the species is present in the gut microbiome. The challenge is that closely related species tend to be jointly present or absent in the microbiome and also share many genes, only a subset of which are involved in gut adaptation. We show that this phylogenetic correlation indeed leads to many false discoveries and propose phylogenetic linear regression as a powerful solution. To apply this method across the bacterial tree of life, where most species have not been experimentally phenotyped, we use metagenomes from hundreds of people to quantify each species’ prevalence in and specificity for the gut microbiome. This analysis reveals thousands of genes potentially involved in adaptation to the gut across species, including many novel candidates as well as processes known to contribute to fitness of gut bacteria, such as acid tolerance in Bacteroidetes and sporulation in Firmicutes. We also find microbial genes associated with a preference for the gut over other body sites, which are significantly enriched for genes linked to fitness in an in vivo competition experiment. Finally, we identify gene families associated with higher prevalence in patients with Crohn’s disease, including Proteobacterial genes involved in conjugation and fimbria regulation, processes previously linked to inflammation. These gene targets may represent new avenues for modulating host colonization and disease. Our strategy of combining metagenomics with phylogenetic modeling is general and can be used to identify genes associated with adaptation to any environment. [ABSTRACT FROM AUTHOR]

The molecular events leading to the development of the bat wing remain largely unknown, and are thought to be caused, in part, by changes in gene expression during limb development. These expression changes could be instigated by variations in gene regulatory enhancers. Here, we used a comparative genomics approach to identify regions that evolved rapidly in the bat ancestor, but are highly conserved in other vertebrates. We discovered 166 bat accelerated regions (BARs) that overlap H3K27ac and p300 ChIP-seq peaks in developing mouse limbs. Using a mouse enhancer assay, we show that five Myotis lucifugus BARs drive gene expression in the developing mouse limb, with the majority showing differential enhancer activity compared to the mouse orthologous BAR sequences. These include BAR116, which is located telomeric to the HoxD cluster and had robust forelimb expression for the M. lucifugus sequence and no activity for the mouse sequence at embryonic day 12.5. Developing limb expression analysis of Hoxd10-Hoxd13 in Miniopterus natalensis bats showed a high-forelimb weak-hindlimb expression for Hoxd10-Hoxd11, similar to the expression trend observed for M. lucifugus BAR116 in mice, suggesting that it could be involved in the regulation of the bat HoxD complex. Combined, our results highlight novel regulatory regions that could be instrumental for the morphological differences leading to the development of the bat wing. [ABSTRACT FROM AUTHOR]

Rapid, sensitive, and specific virus detection is an important component of clinical diagnostics. Massively parallel sequencing enables new diagnostic opportunities that complement traditional serological and PCR based techniques. While massively parallel sequencing promises the benefits of being more comprehensive and less biased than traditional approaches, it presents new analytical challenges, especially with respect to detection of pathogen sequences in metagenomic contexts. To a first approximation, the initial detection of viruses can be achieved simply through alignment of sequence reads or assembled contigs to a reference database of pathogen genomes with tools such as BLAST. However, recognition of highly divergent viral sequences is problematic, and may be further complicated by the inherently high mutation rates of some viral types, especially RNA viruses. In these cases, increased sensitivity may be achieved by leveraging position-specific information during the alignment process. Here, we constructed HMMER3-compatible profile hidden Markov models (profile HMMs) from all the virally annotated proteins in RefSeq in an automated fashion using a custom-built bioinformatic pipeline. We then tested the ability of these viral profile HMMs (“vFams”) to accurately classify sequences as viral or non-viral. Cross-validation experiments with full-length gene sequences showed that the vFams were able to recall 91% of left-out viral test sequences without erroneously classifying any non-viral sequences into viral protein clusters. Thorough reanalysis of previously published metagenomic datasets with a set of the best-performing vFams showed that they were more sensitive than BLAST for detecting sequences originating from more distant relatives of known viruses. To facilitate the use of the vFams for rapid detection of remote viral homologs in metagenomic data, we provide two sets of vFams, comprising more than 4,000 vFams each, in the HMMER3 format. We also provide the software necessary to build custom profile HMMs or update the vFams as more viruses are discovered (http://derisilab.ucsf.edu/software/vFam). [ABSTRACT FROM AUTHOR]

Obesity is an important and intractable public health problem. In addition to the well-known risk factors of behavior, diet, and genetics, gut microbial communities were recently identified as another possible source of risk and a potential therapeutic target. However, human and animal-model studies have yielded conflicting results about the precise nature of associations between microbiome composition and obesity. In this paper, we use publicly available data from the Human Microbiome Project (HMP) and MetaHIT, both surveys of healthy adults that include obese individuals, plus two smaller studies that specifically examined lean versus obese adults. We find that inter-study variability in the taxonomic composition of stool microbiomes far exceeds differences between lean and obese individuals within studies. Our analyses further reveal a high degree of variability in stool microbiome composition and diversity across individuals. While we confirm the previously published small, but statistically significant, differences in phylum-level taxonomic composition between lean and obese individuals in several cohorts, we find no association between BMI and taxonomic composition of stool microbiomes in the larger HMP and MetaHIT datasets. We explore a range of different statistical techniques and show that this result is robust to the choice of methodology. Differences between studies are likely due to a combination of technical and clinical factors. We conclude that there is no simple taxonomic signature of obesity in the microbiota of the human gut. [ABSTRACT FROM AUTHOR]

GC-biased gene conversion (gBGC) is a recombination-associated process that favors the fixation of G/C alleles over A/T alleles. In mammals, gBGC is hypothesized to contribute to variation in GC content, rapidly evolving sequences, and the fixation of deleterious mutations, but its prevalence and general functional consequences remain poorly understood. gBGC is difficult to incorporate into models of molecular evolution and so far has primarily been studied using summary statistics from genomic comparisons. Here, we introduce a new probabilistic model that captures the joint effects of natural selection and gBGC on nucleotide substitution patterns, while allowing for correlations along the genome in these effects. We implemented our model in a computer program, called phastBias, that can accurately detect gBGC tracts about 1 kilobase or longer in simulated sequence alignments. When applied to real primate genome sequences, phastBias predicts gBGC tracts that cover roughly 0.3% of the human and chimpanzee genomes and account for 1.2% of human-chimpanzee nucleotide differences. These tracts fall in clusters, particularly in subtelomeric regions; they are enriched for recombination hotspots and fast-evolving sequences; and they display an ongoing fixation preference for G and C alleles. They are also significantly enriched for disease-associated polymorphisms, suggesting that they contribute to the fixation of deleterious alleles. The gBGC tracts provide a unique window into historical recombination processes along the human and chimpanzee lineages. They supply additional evidence of long-term conservation of megabase-scale recombination rates accompanied by rapid turnover of hotspots. Together, these findings shed new light on the evolutionary, functional, and disease implications of gBGC. The phastBias program and our predicted tracts are freely available. [ABSTRACT FROM AUTHOR]

Background: During the development of the Drosophila central nervous system the process of midline crossing is orchestrated by a number of guidance receptors and ligands. Many key axon guidance molecules have been identified in both invertebrates and vertebrates, but the transcriptional regulation of growth cone guidance remains largely unknown. It is established that translational regulation plays a role in midline crossing, and there are indications that transcriptional regulation is also involved. To investigate this issue, we conducted a genome-wide study of transcription in Drosophila embryos using wild type and a number of well-characterized Drosophila guidance mutants and transgenics. We also analyzed a previously published microarray time course of Drosophila embryonic development with an axon guidance focus. Results: Using hopach, a novel clustering method which is well suited to microarray data analysis, we identified groups of genes with similar expression patterns across guidance mutants and transgenics. We then systematically characterized the resulting clusters with respect to their relevance to axon guidance using two complementary controlled vocabularies: the Gene Ontology (GO) and anatomical annotations of the Atlas of Pattern of Gene Expression (APoGE) in situ hybridization database. The analysis indicates that regulation of gene expression does play a role in the process of axon guidance in Drosophila. We also find a strong link between axon guidance and hemocyte migration, a result that agrees with mounting evidence that axon guidance molecules are co-opted in vertebrate vascularization. Cell cyclin activity in the context of axon guidance is also suggested from our array data. RNA and protein expression patterns of cell cyclins in axon guidance mutants and transgenics support this possible link. Conclusion: This study provides important insights into the regulation of axon guidance in vivo. [ABSTRACT FROM AUTHOR]

Comparative genomics allow us to search the human genome for segments that were extensively changed in the last ∼5 million years since divergence from our common ancestor with chimpanzee, but are highly conserved in other species and thus are likely to be functional. We found 202 genomic elements that are highly conserved in vertebrates but show evidence of significantly accelerated substitution rates in human. These are mostly in non-coding DNA, often near genes associated with transcription and DNA binding. Resequencing confirmed that the five most accelerated elements are dramatically changed in human but not in other primates, with seven times more substitutions in human than in chimp. The accelerated elements, and in particular the top five, show a strong bias for adenine and thymine to guanine and cytosine nucleotide changes and are disproportionately located in high recombination and high guanine and cytosine content environments near telomeres, suggesting either biased gene conversion or isochore selection. In addition, there is some evidence of directional selection in the regions containing the two most accelerated regions. A combination of evolutionary forces has contributed to accelerated evolution of the fastest evolving elements in the human genome. [ABSTRACT FROM AUTHOR]

Hyperthermophilic crenarchaea in the genus Pyrobaculum are notable for respiratory versatility, but relatively little is known about the genetics or regulation of crenarchaeal respiratory pathways. We measured global gene expression in Pyrobaculum aerophilum cultured with oxygen, nitrate, arsenate and ferric iron as terminal electron acceptors to identify transcriptional patterns that differentiate these pathways. We also compared genome sequences for four closely related species with diverse respiratory characteristics (Pyrobaculum arsenaticum, Pyrobaculum calidifontis, Pyrobaculum islandicum, and Thermoproteus neutrophilus) to identify genes associated with different respiratory capabilities. Specific patterns of gene expression in P. aerophilum were associated with aerobic respiration, nitrate respiration, arsenate respiration, and anoxia. Functional predictions based on these patterns include separate cytochrome oxidases for aerobic growth and oxygen scavenging, a nitric oxide-responsive transcriptional regulator, a multicopper oxidase involved in denitrification, and an archaeal arsenate respiratory reductase. We were unable to identify specific genes for iron respiration, but P. aerophilum exhibited repressive transcriptional responses to iron remarkably similar to those controlled by the ferric uptake regulator in bacteria. Together, these analyses present a genome-scale view of crenarchaeal respiratory flexibility and support a large number of functional and regulatory predictions for further investigation. The complete gene expression data set can be viewed in genomic context with the Archaeal Genome Browser at archaea.ucsc.edu. [ABSTRACT FROM AUTHOR]