Microbiome association studies typically link host disease or other traits to summary statistics measured in metagenomics data, such as diversity or taxonomic composition. But identifying disease-associated species based on their relative abundance does not provide insight into why these microbes act as disease markers, and it overlooks cases where disease risk is related to specific strains with unique biological functions. To bridge this knowledge gap, we developed microSLAM, a mixed-effects model and an R package that performs association tests that connect host traits to the presence/absence of genes within each microbiome species, while accounting for strain genetic relatedness across hosts. Traits can be quantitative or binary (such as case/control). MicroSLAM is fit in three steps for each species. The first step estimates population structure across hosts. Step two calculates the association between population structure and the trait, enabling detection of species for which a subset of related strains confer risk. To identify specific genes whose presence/absence across diverse strains is associated with the trait, step three models the trait as a function of gene occurrence plus random effects estimated from step two. Applying microSLAM to 710 gut metagenomes from inflammatory bowel disease (IBD) samples, we discovered 56 species whose population structure correlates with IBD, meaning that different lineages are found in cases versus controls. After controlling for population structure, 20 species had genes significantly associated with IBD. Twenty-one of these genes were more common in IBD patients, while 32 genes were enriched in healthy controls, including a seven-gene operon in Faecalibacterium prausnitzii that is involved in utilization of fructoselysine from the gut environment. The vast majority of species detected by microSLAM were not significantly associated with IBD using standard relative abundance tests. These findings highlight the importance of accounting for within-species genetic variation in microbiome studies.

The investigation of chromatin organization in single cells holds great promise for identifying causal relationships between genome structure and function. However, analysis of single-molecule data is hampered by extreme yet inherent heterogeneity, making it challenging to determine the contributions of individual chromatin fibers to bulk trends. To address this challenge, we propose ChromaFactor, a novel computational approach based on non-negative matrix factorization that deconvolves single-molecule chromatin organization datasets into their most salient primary components. ChromaFactor provides the ability to identify trends accounting for the maximum variance in the dataset while simultaneously describing the contribution of individual molecules to each component. Applying our approach to two single-molecule imaging datasets across different genomic scales, we find that these primary components demonstrate significant correlation with key functional phenotypes, including active transcription, enhancer-promoter distance, and genomic compartment. Also, we find that some bulk trends exist at the single-cell level, but only in a small fraction of cells, suggesting that critical changes in genome organization may be driven by specific rare subpopulations rather than occurring uniformly across all cells. ChromaFactor offers a robust tool for understanding the complex interplay between chromatin structure and function on individual DNA molecules, pinpointing which subpopulations drive functional changes and fostering new insights into cellular heterogeneity and its implications for bulk genomic phenomena.

SUMMARY: The increasing development of sequence-based machine learning models has raised the demand for manipulating sequences for this application. However, existing approaches to edit and evaluate genome sequences using models have limitations, such as incompatibility with structural variants, challenges in identifying responsible sequence perturbations, and the need for vcf file inputs and phased data. To address these bottlenecks, we present Sequence Mutator for Predictive Models (SuPreMo), a scalable and comprehensive tool for performing and supporting in silico mutagenesis experiments. We then demonstrate how pairs of reference and perturbed sequences can be used with machine learning models to prioritize pathogenic variants or discover new functional sequences. AVAILABILITY AND IMPLEMENTATION: SuPreMo was written in Python, and can be run using only one line of code to generate both sequences and 3D genome disruption scores. The codebase, instructions for installation and use, and tutorials are on the GitHub page: https://github.com/ketringjoni/SuPreMo.

SUMMARY: The Metagenomic Intra-Species Diversity Analysis System (MIDAS) is a scalable metagenomic pipeline that identifies single nucleotide variants (SNVs) and gene copy number variants in microbial populations. Here, we present MIDAS2, which addresses the computational challenges presented by increasingly large reference genome databases, while adding functionality for building custom databases and leveraging paired-end reads to improve SNV accuracy. This fast and scalable reengineering of the MIDAS pipeline enables thousands of metagenomic samples to be efficiently genotyped. AVAILABILITY AND IMPLEMENTATION: The source code is available at https://github.com/czbiohub/MIDAS2. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

SUMMARY: CellWalkR is an R package that integrates single-cell open chromatin data with cell type labels and bulk epigenetic data to identify cell type-specific regulatory regions. A Graphics Processing Unit (GPU) implementation and downsampling strategies enable thousands of cells to be processed in seconds. CellWalkR's user-friendly interface provides interactive analysis and visualization of cell labels and regulatory region mappings. AVAILABILITY AND IMPLEMENTATION: CellWalkR is freely available as an R package under a GNU GPL-2.0 License and can be accessed from https://github.com/PFPrzytycki/CellWalkR with an accompanying vignette. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

SUMMARY: Phylogenetic comparative methods are powerful but presently under-utilized ways to identify microbial genes underlying differences in community composition. These methods help to identify functionally important genes because they test for associations beyond those expected when related microbes occupy similar environments. We present phylogenize, a pipeline with web, QIIME 2 and R interfaces that allows researchers to perform phylogenetic regression on 16S amplicon and shotgun sequencing data and to visualize results. phylogenize applies broadly to both host-associated and environmental microbiomes. Using Human Microbiome Project and Earth Microbiome Project data, we show that phylogenize draws similar conclusions from 16S versus shotgun sequencing and reveals both known and candidate pathways associated with host colonization. AVAILABILITY AND IMPLEMENTATION: phylogenize is available at https://phylogenize.org and https://bitbucket.org/pbradz/phylogenize. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

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.

Chromatin immunoprecipitation followed by sequencing (ChIP-seq) is an important tool for studying gene regulatory proteins, such as transcription factors and histones. Peak calling is one of the first steps in the analysis of these data. Peak calling consists of two sub-problems: identifying candidate peaks and testing candidate peaks for statistical significance. We surveyed 30 methods and identified 12 features of the two sub-problems that distinguish methods from each other. We picked six methods GEM, MACS2, MUSIC, BCP, Threshold-based method (TM) and ZINBA] that span this feature space and used a combination of 300 simulated ChIP-seq data sets, 3 real data sets and mathematical analyses to identify features of methods that allow some to perform better than the others. We prove that methods that explicitly combine the signals from ChIP and input samples are less powerful than methods that do not. Methods that use windows of different sizes are more powerful than the ones that do not. For statistical testing of candidate peaks, methods that use a Poisson test to rank their candidate peaks are more powerful than those that use a Binomial test. BCP and MACS2 have the best operating characteristics on simulated transcription factor binding data. GEM has the highest fraction of the top 500 peaks containing the binding motif of the immunoprecipitated factor, with 50% of its peaks within 10 base pairs of a motif. BCP and MUSIC perform best on histone data. These findings provide guidance and rationale for selecting the best peak caller for a given application.

BACKGROUND: Cluster heatmaps are commonly used in biology and related fields to reveal hierarchical clusters in data matrices. This visualization technique has high data density and reveal clusters better than unordered heatmaps alone. However, cluster heatmaps have known issues making them both time consuming to use and prone to error. We hypothesize that visualization techniques without the rigid grid constraint of cluster heatmaps will perform better at clustering-related tasks. RESULTS: We developed an approach to "unbox" the heatmap values and embed them directly in the hierarchical clustering results, allowing us to use standard hierarchical visualization techniques as alternatives to cluster heatmaps. We then tested our hypothesis by conducting a survey of 45 practitioners to determine how cluster heatmaps are used, prototyping alternatives to cluster heatmaps using pair analytics with a computational biologist, and evaluating those alternatives with hour-long interviews of 5 practitioners and an Amazon Mechanical Turk user study with approximately 200 participants. We found statistically significant performance differences for most clustering-related tasks, and in the number of perceived visual clusters. Visit git.io/vw0t3 for our results. CONCLUSIONS: The optimal technique varied by task. However, gapmaps were preferred by the interviewed practitioners and outperformed or performed as well as cluster heatmaps for clustering-related tasks. Gapmaps are similar to cluster heatmaps, but relax the heatmap grid constraints by introducing gaps between rows and/or columns that are not closely clustered. Based on these results, we recommend users adopt gapmaps as an alternative to cluster heatmaps.

UNLABELLED: Microbiome researchers frequently want to know how abundant a particular microbial gene or pathway is across different human hosts, including its association with disease and its co-occurrence with other genes or microbial taxa. With thousands of publicly available metagenomes, these questions should be easy to answer. However, computational barriers prevent most researchers from conducting such analyses. We address this problem with MetaQuery, a web application for rapid and quantitative analysis of specific genes in the human gut microbiome. The user inputs one or more query genes, and our software returns the estimated abundance of these genes across 1267 publicly available fecal metagenomes from American, European and Chinese individuals. In addition, our application performs downstream statistical analyses to identify features that are associated with gene variation, including other query genes (i.e. gene co-variation), taxa, clinical variables (e.g. inflammatory bowel disease and diabetes) and average genome size. The speed and accessibility of MetaQuery are a step toward democratizing metagenomics research, which should allow many researchers to query the abundance and variation of specific genes in the human gut microbiome. AVAILABILITY AND IMPLEMENTATION: http://metaquery.docpollard.org. CONTACT: [email protected] UPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

BACKGROUND: When biomolecules physically interact, natural selection operates on them jointly. Contacting positions in protein and RNA structures exhibit correlated patterns of sequence evolution due to constraints imposed by the interaction, and molecular arms races can develop between interacting proteins in pathogens and their hosts. To evaluate how well methods developed to detect coevolving residues within proteins can be adapted for cross-species, inter-protein analysis, we used statistical criteria to quantify the performance of these methods in detecting inter-protein residues within 8 angstroms of each other in the co-crystal structures of 33 bacterial protein interactions. We also evaluated their performance for detecting known residues at the interface of a host-virus protein complex with a partially solved structure. RESULTS: Our quantitative benchmarking showed that all coevolutionary methods clearly benefit from alignments with many sequences. Methods that aim to detect direct correlations generally outperform other approaches. However, faster mutual information based methods are occasionally competitive in small alignments and with relaxed false positive rates. Two commonly used null distributions are anti-conservative and have high false positive rates in some scenarios, although the empirical distribution of scores performs reasonably well with deep alignments. CONCLUSIONS: We conclude that coevolutionary analysis of cross-species protein interactions holds great promise but requires sequencing many more species pairs.

Shotgun metagenomic DNA sequencing is a widely applicable tool for characterizing the functions that are encoded by microbial communities. Several bioinformatic tools can be used to functionally annotate metagenomes, allowing researchers to draw inferences about the functional potential of the community and to identify putative functional biomarkers. However, little is known about how decisions made during annotation affect the reliability of the results. Here, we use statistical simulations to rigorously assess how to optimize annotation accuracy and speed, given parameters of the input data like read length and library size. We identify best practices in metagenome annotation and use them to guide the development of the Shotgun Metagenome Annotation Pipeline (ShotMAP). ShotMAP is an analytically flexible, end-to-end annotation pipeline that can be implemented either on a local computer or a cloud compute cluster. We use ShotMAP to assess how different annotation databases impact the interpretation of how marine metagenome and metatranscriptome functional capacity changes across seasons. We also apply ShotMAP to data obtained from a clinical microbiome investigation of inflammatory bowel disease. This analysis finds that gut microbiota collected from Crohn's disease patients are functionally distinct from gut microbiota collected from either ulcerative colitis patients or healthy controls, with differential abundance of metabolic pathways related to host-microbiome interactions that may serve as putative biomarkers of disease.

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: 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.

BACKGROUND: New computational resources are needed to manage the increasing volume of biological data from genome sequencing projects. One fundamental challenge is the ability to maintain a complete and current catalog of protein diversity. We developed a new approach for the identification of protein families that focuses on the rapid discovery of homologous protein sequences. RESULTS: We implemented fully automated and high-throughput procedures to de novo cluster proteins into families based upon global alignment similarity. Our approach employs an iterative clustering strategy in which homologs of known families are sifted out of the search for new families. The resulting reduction in computational complexity enables us to rapidly identify novel protein families found in new genomes and to perform efficient, automated updates that keep pace with genome sequencing. We refer to protein families identified through this approach as "Sifting Families," or SFams. Our analysis of ~10.5 million protein sequences from 2,928 genomes identified 436,360 SFams, many of which are not represented in other protein family databases. We validated the quality of SFam clustering through statistical as well as network topology-based analyses. CONCLUSIONS: We describe the rapid identification of SFams and demonstrate how they can be used to annotate genomes and metagenomes. The SFam database catalogs protein-family quality metrics, multiple sequence alignments, hidden Markov models, and phylogenetic trees. Our source code and database are publicly available and will be subject to frequent updates (http://edhar.genomecenter.ucdavis.edu/sifting_families/).

The evolutionary history of a protein reflects the functional history of its ancestors. Recent phylogenetic studies identified distinct evolutionary signatures that characterize proteins involved in cancer, Mendelian disease, and different ontogenic stages. Despite the potential to yield insight into the cellular functions and interactions of proteins, such comparative phylogenetic analyses are rarely performed, because they require custom algorithms. We developed ProteinHistorian to make tools for performing analyses of protein origins widely available. Given a list of proteins of interest, ProteinHistorian estimates the phylogenetic age of each protein, quantifies enrichment for proteins of specific ages, and compares variation in protein age with other protein attributes. ProteinHistorian allows flexibility in the definition of protein age by including several algorithms for estimating ages from different databases of evolutionary relationships. We illustrate the use of ProteinHistorian with three example analyses. First, we demonstrate that proteins with high expression in human, compared to chimpanzee and rhesus macaque, are significantly younger than those with human-specific low expression. Next, we show that human proteins with annotated regulatory functions are significantly younger than proteins with catalytic functions. Finally, we compare protein length and age in many eukaryotic species and, as expected from previous studies, find a positive, though often weak, correlation between protein age and length. ProteinHistorian is available through a web server with an intuitive interface and as a set of command line tools; this allows biologists and bioinformaticians alike to integrate these approaches into their analysis pipelines. ProteinHistorian's modular, extensible design facilitates the integration of new datasets and algorithms. The ProteinHistorian web server, source code, and pre-computed ages for 32 eukaryotic genomes are freely available under the GNU public license at http://lighthouse.ucsf.edu/ProteinHistorian/.

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).

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 DBA/2J mouse is not only the oldest inbred strain, but also one of the most widely used strains. DBA/2J exhibits many unique anatomical, physiological, and behavior traits. In addition, DBA/2J is one parent of the large BXD family of recombinant inbred strains [ 1 ]. The genome of the other parent of this BXD family—C57BL/6J—has been sequenced and serves as the mouse reference genome [ 2 ]. We sequenced the genome of DBA/2J using SOLiD and Illumina high throughput short read protocols to generate a comprehensive set of ~5 million sequence variants segregating in the BXD family that ultimately cause developmental, anatomical, functional and behavioral differences among these 80+ strains. We generated approximately 13.2 and 38.9× whole-genome short reads of DBA/2J females using Illumina GA2 and ABI SOLiD massively parallel DNA sequencing platforms. Comparing to the C57BL/6J reference genome sequence, we identified over 4.5 million single nucleotide polymorphisms (SNPs), including 84 nonsense and ~11,000 missense mutations, 78% of which are novel. We also detected ~568,000 insertions and deletions (indels) within single short reads and ~9,400 between mate-paired reads. Approximately 300 inversions were detected by SOLiD mate-pair reads, 46 of which span at least one exon. In addition, we identified ~22,000 copy number variants (CNVs) in the range of 1 Kb to 100 Kb (Figure 1 ). Concentric circles represent the sequence and structural variation across mouse chromosomes. Moving inward from the outer circle, circle 1 denotes each chromosome. Circle 2, read depth with 100kb window. Circle 3, SNP density with 100kb windows (black is lowest density and orange is highest density). Circle 4, Indels density with 100kb window. Circle 4, Inversion. Circle 5, CNVs, blue (outward) denotes loss of CNVs and green (inward) denotes gains of CNVs. Our study generates the first consensus sequence for the DBA/2J and creates a compendium of sequence and structural variations that will be used by the community of researchers who study complex traits in mouse models. The sequence data provide a novel resource with which to initiate reverse genetic analysis of complex traits, particularly by exploiting strong alleles (premature stop codons, frame-shift mutations, and deletion) that differentially affect members of the BXD strain family. The DBA/2J genome is also an essential prerequisite to unbiased alignment of RNA-seq and ChIP-seq data generated using BXD strains and any other cross involving these two common parental strains.