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Genome-enabled approaches to molecular epidemiology have become essential to public health agencies and the microbial research community. We developed the algorithm STing to provide turn-key solutions for molecular typing and gene detection directly from next generation sequence data of microbial pathogens. Our implementation of STing uses an innovative k-mer search strategy that eliminates the computational overhead associated with the time-consuming steps of quality control, assembly, and alignment, required by more traditional methods. We compared STing to six of the most widely used programs for genome-based molecular typing and demonstrate its ease of use, accuracy, speed and efficiency. STing shows superior accuracy and performance for standard multilocus sequence typing schemes, along with larger genome-scale typing schemes, and it enables rapid automated detection of antimicrobial resistance and virulence factor genes. STing determines the sequence type of traditional 7-gene MLST with 100% accuracy in less than 10 seconds per isolate. We hope that the adoption of STing will help to democratize microbial genomics and thereby maximize its benefit for public health.

Biobank projects around the world are generating genomic data for many thousands and even millions of individuals. Computational methods are needed to handle these massive data sets, including tools for genetic ancestry (GA) inference. Current methods for GA inference are generally accurate, but they are slow and do not scale to biobank-size genomic datasets. Here we present Rye – a new algorithm for GA inference at biobank scale. We compare the accuracy and runtime performance of Rye to the widely used RFMix and ADMIXTURE programs, and we apply it to a dataset of 488,221 genome-wide variant samples from the UK Biobank. Rye infers GA based on principal component analysis (PCA) of genomic variant samples from ancestral reference populations and query individuals. The algorithm’s accuracy is powered by Metropolis-Hastings optimization and its speed is provided by non-negative least squares (NNLS) regression. Rye produces highly accurate GA estimates for three-way admixed populations – African, European, and Native American – compared to RFMix and ADMIXTURE (R2 = 0.998 – 1.00), and shows 50x runtime improvement compared to ADMIXTURE on the UK Biobank dataset. Rye analysis of UK Biobank samples demonstrates how it can be used to infer GA at different levels of relatedness. We discuss user consideration and options for the use of Rye; the program and its documentation are distributed on the GitHub repository: https://github.com/healthdisparities/rye.

Human populations from around the world show striking phenotypic variation across a wide variety of traits. Genome-wide association studies (GWAS) are used to uncover genetic variants that influence the expression of heritable human traits; accordingly, population-specific distributions of GWAS-implicated variants may shed light on the genetic basis of human phenotypic diversity. With this in mind, we developed the GlobAl Distribution of GEnetic Traits web server (GADGET http://gadget.biosci.gatech.edu). The GADGET web server provides users with a dynamic visual platform for exploring the relationship between worldwide genetic diversity and the genetic architecture underlying numerous human phenotypes. GADGET integrates trait-implicated single nucleotide polymorphisms (SNPs) from GWAS, with population genetic data from the 1000 Genomes Project, to calculate genome-wide polygenic trait scores (PTS) for 818 phenotypes in 2504 individual genomes. Population-specific distributions of PTS are shown for 26 human populations across 5 continental population groups, with traits ordered based on the extent of variation observed among populations. Users of GADGET can also upload custom trait SNP sets to visualize global PTS distributions for their own traits of interest.

Transposable element (TE) derived sequences are known to contribute to the regulation of the human genome. The majority of known TE-derived regulatory sequences correspond to relatively ancient insertions, which are fixed across human populations. The extent to which human genetic variation caused by recent TE activity leads to regulatory polymorphisms among populations has yet to be thoroughly explored. In this study, we searched for associations between polymorphic TE (polyTE) loci and human gene expression levels using an expression quantitative trait loci (eQTL) approach. We compared locus-specific polyTE insertion genotypes to B cell gene expression levels among 445 individuals from 5 human populations. Numerous human polyTE loci correspond to both cis and trans eQTL, and their regulatory effects are directly related to cell type-specific function in the immune system. PolyTE loci are associated with differences in expression between European and African population groups, and a single polyTE loci is indirectly associated with the expression of numerous genes via the regulation of the B cell-specific transcription factor PAX5. The polyTE-gene expression associations we found indicate that human TE genetic variation can have important phenotypic consequences. Our results reveal that TE-eQTL are involved in population-specific gene regulation as well as transcriptional network modification.

We report on the development of an unsupervised algorithm for the genome-wide discovery and analysis of chromatin signatures. Our Chromatin-profile Alignment followed by Tree-clustering algorithm (ChAT) employs dynamic programming of combinatorial histone modification profiles to identify locally similar chromatin sub-regions and provides complementary utility with respect to existing methods. We applied ChAT to genomic maps of 39 histone modifications in human CD4(+) T cells to identify both known and novel chromatin signatures. ChAT was able to detect chromatin signatures previously associated with transcription start sites and enhancers as well as novel signatures associated with a variety of regulatory elements. Promoter-associated signatures discovered with ChAT indicate that complex chromatin signatures, made up of numerous co-located histone modifications, facilitate cell-type specific gene expression. The discovery of novel L1 retrotransposon-associated bivalent chromatin signatures suggests that these elements influence the mono-allelic expression of human genes by shaping the chromatin environment of imprinted genomic regions. Analysis of long gene-associated chromatin signatures point to a role for the H4K20me1 and H3K79me3 histone modifications in transcriptional pause release. The novel chromatin signatures and functional associations uncovered by ChAT underscore the ability of the algorithm to yield novel insight on chromatin-based regulatory mechanisms.

Mammalian genomes encode numerous cis-natural antisense transcripts (cis-NATs). The extent to which these cis-NATs are actively regulated and ultimately functionally relevant, as opposed to transcriptional noise, remains a matter of debate. To address this issue, we analyzed the chromatin environment and RNA Pol II binding properties of human cis-NAT promoters genome-wide. Cap analysis of gene expression data were used to identify thousands of cis-NAT promoters, and profiles of nine histone modifications and RNA Pol II binding for these promoters in ENCODE cell types were analyzed using chromatin immunoprecipitation followed by sequencing (ChIP-seq) data. Active cis-NAT promoters are enriched with activating histone modifications and occupied by RNA Pol II, whereas weak cis-NAT promoters are depleted for both activating modifications and RNA Pol II. The enrichment levels of activating histone modifications and RNA Pol II binding show peaks centered around cis-NAT transcriptional start sites, and the levels of activating histone modifications at cis-NAT promoters are positively correlated with cis-NAT expression levels. Cis-NAT promoters also show highly tissue-specific patterns of expression. These results suggest that human cis-NATs are actively transcribed by the RNA Pol II and that their expression is epigenetically regulated, prerequisites for a functional potential for many of these non-coding RNAs.

Boundary elements partition eukaryotic chromatin into active and repressive domains, and can also block regulatory interactions between domains. Boundary elements act via diverse mechanisms making accurate feature-based computational predictions difficult. Therefore, we developed an unbiased algorithm that predicts the locations of human boundary elements based on the genomic distributions of chromatin and transcriptional states, as opposed to any intrinsic characteristics that they may possess. Application of our algorithm to ChIP-seq data for histone modifications and RNA Pol II-binding data in human CD4(+) T cells resulted in the prediction of 2542 putative chromatin boundary elements genome wide. Predicted boundary elements display two distinct features: first, position-specific open chromatin and histone acetylation that is coincident with the recruitment of sequence-specific DNA-binding factors such as CTCF, EVI1 and YYI, and second, a directional and gradual increase in histone lysine methylation across predicted boundaries coincident with a gain of expression of non-coding RNAs, including examples of boundaries encoded by tRNA and other non-coding RNA genes. Accordingly, a number of the predicted human boundaries may function via the synergistic action of sequence-specific recruitment of transcription factors leading to non-coding RNA transcriptional interference and the blocking of facultative heterochromatin propagation by transcription-associated chromatin remodeling complexes.

The Meningococcus Genome Informatics Platform (MGIP) is a suite of computational tools for the analysis of multilocus sequence typing (MLST) data, at http://mgip.biology.gatech.edu. MLST is used to generate allelic profiles to characterize strains of Neisseria meningitidis, a major cause of bacterial meningitis worldwide. Neisseria meningitidis strains are characterized with MLST as specific sequence types (ST) and clonal complexes (CC) based on the DNA sequences at defined loci. These data are vital to molecular epidemiology studies of N. meningitidis, including outbreak investigations and population biology. MGIP analyzes DNA sequence trace files, returns individual allele calls and characterizes the STs and CCs. MGIP represents a substantial advance over existing software in several respects: (i) ease of use--MGIP is user friendly, intuitive and thoroughly documented; (ii) flexibility--because MGIP is a website, it is compatible with any computer with an internet connection, can be used from any geographic location, and there is no installation; (iii) speed--MGIP takes just over one minute to process a set of 96 trace files; and (iv) expandability--MGIP has the potential to expand to more loci than those used in MLST and even to other bacterial species.