Biology subjects

Rishishwar, L.

Publications and source records attributed to Rishishwar, L..

8 recordsLinked to original sources

bioRxiv2026

Epidemiology of Legionella: Genome-bAsed Typing (el_gato) - a new bioinformatic tool for identifying sequence-based types of Legionella pneumophila from whole genome sequencing data

Sequence-based typing (SBT) via Sanger sequencing has been the standard for describing Legionella pneumophila relationships for two decades. SBT involves sequencing seven loci, identifying alleles using the United Kingdom Health Security Agency (UKHSA) database, and inferring the corresponding sequence type (ST). While similar SBT approaches for other organisms can be easily adapted to whole genome sequencing (WGS), L. pneumophila presents two known challenges for this adaptation: multiple copies of one locus (mompS) and extensive heterogeneity in a second locus (neuA/neuAh). Although several computational methods have been proposed to address these issues, a WGS-based replacement with equal resolution to traditional SBT has been elusive. To address this gap, we developed el_gato (Epidemiology of Legionella: Genome-bAsed Typing; https://github.com/CDCgov/el_gato), which offers several advantages over existing methods: (1) a novel approach for resolving multiple mompS alleles identified in the same isolate, (2) the ability to capture diverse neuA/neuAh alleles, (3) fast runtime with an average of 27.7 seconds per sample, (4) easy installation via Bioconda or Docker and (5) an updated database as of March 2025. el_gato works with either paired-end short reads or genome assemblies, performing more accurately with paired-end short reads at least 250 base pairs (bp) in length. We compared el_gato against two other in silico SBT tools ("mompS", hereafter referred to as mompS tool and "legsta") using a dataset of 441 isolates with sequence types (STs) previously determined by Sanger-based sequencing. el_gato correctly identified the ST for 98.9% of the test isolates, compared to 95.2% for the mompS tool and 42.2% for legsta, demonstrating a significant improvement compared to the mompS tool (adjusted p = 1.06e-3) and legsta (adjusted p = 4.24e-55) in ST identification. Furthermore, el_gatos determination of ST was not significantly different from Sanger sequencing (adjusted p = 0.442). In summary, el_gato significantly improves in silico SBT and given its growing adoption, is poised to support the public health community.

bioinformatics↗

bioRxiv2022

Rye: genetic ancestry inference at biobank scale

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 algorithms 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.

bioinformatics↗

bioRxiv2019

STing: accurate and ultrafast genomic profiling with exact sequence matches

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. We hope that the adoption of STing will help to democratize microbial genomics and thereby maximize its benefit for public health.

bioinformatics↗

bioRxiv2019

Admixture-enabled selection for rapid adaptive evolution in the Americas

BackgroundAdmixture occurs when previously isolated populations come together and exchange genetic material. We hypothesized that admixture can enable rapid adaptive evolution in human populations by introducing novel genetic variants (haplotypes) at intermediate frequencies, and we tested this hypothesis via the analysis of whole genome sequences sampled from admixed Latin American populations in Colombia, Mexico, Peru, and Puerto Rico.\n\nResultsOur screen for admixture-enabled selection relies on the identification of loci that contain more or less ancestry from a given source population than would be expected given the genome-wide ancestry frequencies. We employed a combined evidence approach to evaluate levels of ancestry enrichment at (1) single loci across multiple populations and (2) multiple loci that function together to encode polygenic traits. We found cross-population signals of African ancestry enrichment at the major histocompatibility locus on chromosome 6, consistent with admixture-enabled selection for enhanced adaptive immune response. Several of the human leukocyte antigen genes at this locus (HLA-A, HLA-DRB51 and HLA-DRB5) showed independent evidence of positive selection prior to admixture, based on extended haplotype homozygosity in African populations. A number of traits related to inflammation, blood metabolites, and both the innate and adaptive immune system showed evidence of admixture-enabled polygenic selection in Latin American populations.\n\nConclusionsThe results reported here, considered together with the ubiquity of admixture in human evolution, suggest that admixture serves as a fundamental mechanism that drives rapid adaptive evolution in human populations.

genomics↗

bioRxiv2019

Genomic characterization and computational phenotyping of nitrogen-fixing bacteria isolated from Colombian sugarcane fields

Previous studies have shown that the sugarcane microbiome harbors diverse plant growth promoting (PGP) microorganisms, including nitrogen-fixing bacteria, and the objective of this study was to design a genome-enabled approach to prioritize sugarcane associated nitrogen-fixing bacteria according to their potential as biofertilizers. Using a systematic high throughput approach, 22 pure cultures of nitrogen-fixing bacteria were isolated and tested for diazotrophic potential by PCR amplification of nitrogenase (nifH) genes, common molecular markers for nitrogen fixation capacity. Genome sequencing confirmed the presence of intact nitrogenase nifH genes and operons in the genomes of 18 of the isolates. Isolate genomes also encoded operons for phosphate solubilization, siderophore production operons, and other PGP phenotypes. Klebsiella pneumoniae strains comprised 14 of the 22 nitrogen-fixing isolates, and four others were members of closely related genera to Klebsiella. A computational phenotyping approach was developed to rapidly screen for strains that have high potential for nitrogen fixation and other PGP phenotypes while showing low risk for virulence and antibiotic resistance. The majority of sugarcane isolates were below a genotypic and phenotypic threshold, showing uniformly low predicted virulence and antibiotic resistance compared to clinical isolates. Six prioritized strains were experimentally evaluated for PGP phenotypes: nitrogen fixation, phosphate solubilization, and the production of siderophores, gibberellic acid and indole acetic acid. Results from the biochemical assays were consistent with the computational phenotype predictions for these isolates. Our results indicate that computational phenotyping is a promising tool for the assessment of benefits and risks associated with bacteria commonly detected in agricultural ecosystems.\n\nIMPORTANCEA genome-enabled approach was developed for the prioritization of native bacterial isolates with the potential to serve as biofertilizers for sugarcane fields in Colombias Cauca Valley. The approach is based on computational phenotyping, which entails predictions related to traits of interest based on bioinformatic analysis of whole genome sequences. Bioinformatic predictions of the presence of plant growth promoting traits were validated with experimental assays and more extensive genome comparisons, thereby demonstrating the utility of computational phenotyping for assessing the benefits and risks posed by bacterial isolates that can be used as biofertilizers. The quantitative approach to computational phenotyping developed here for the discovery of biofertilizers has the potential for use with a broad range of applications in environmental and industrial microbiology, food safety, water quality, and antibiotic resistance studies.

microbiology↗

bioRxiv2019

Analysis of Vibrio cholerae genomes using a novel bioinformatic tool identifies new, active Type VI Secretion System gene clusters

BackgroundLike many bacteria, Vibrio cholerae, which causes fatal cholera, deploys a harpoon-like Type VI Secretion System (T6SS) to compete against other microbes in environmental and host settings. The T6SS punctures adjacent cells and delivers toxic effector proteins that are harmless to bacteria carrying cognate immunity factors. Only four effector/immunity pairs encoded on one large and three auxiliary gene clusters have been characterized from largely clonal, patient-derived strains of V. cholerae. ResultsWe sequenced two dozen V. cholerae strain genomes from diverse sources and developed a novel and adaptable bioinformatic tool based on Hidden Markov Models. We identified two new T6SS auxiliary gene clusters; one, Aux 5, is described here. Four Aux 5 loci are present in the host strain, each with an atypical effector/immunity gene organization. Structural prediction of the putative effector indicated it is a lipase, which we name TleV1 (Type VI lipase effector Vibrio, TleV1). Ectopic TleV1 expression induced toxicity in E. coli, which was rescued by co-expression of the TleV1 immunity factor. A clinical V. cholerae reference strain expressing the Aux 5 cluster used TleV1 to lyse its parental strain upon contact via its T6SS but was unable to kill parental cells expressing TleV1s immunity factor. ConclusionWe developed a novel bioinformatic method and identified new T6SS gene clusters in V. cholerae. We also showed the TleV1 toxin is delivered in a T6SS-manner by V. cholerae and can lyse other bacterial cells. Our web-based tool may be modified to identify additional novel T6SS genomic loci in diverse bacterial species.

microbiology↗

bioRxiv2018

Cryptic Native American ancestry recapitulates population-specific migration and settlement of the continental United States

European and African descendants settled the continental US during the 17th-19th centuries, coming into contact with established Native American populations. The resulting admixture among these groups yielded a significant reservoir of cryptic Native American ancestry in the modern US population. We analyzed the patterns of Native American admixture seen for the three largest genetic ancestry groups in the US population: African American, European American, and Hispanic/Latino. The three groups show distinct Native American ancestry profiles, which are indicative of their historical patterns of migration and settlement across the country. Native American ancestry in the modern African American population does not coincide with local geography, instead forming a monophyletic group with origins in the southeastern US, consistent with the Great Migration of the early 20th century. European Americans show Native American ancestry that tracks their geographic origins across the US, indicative of ongoing contact during westward expansion, and Native American ancestry can resolve Hispanic/Latino individuals into distinct local groups formed by more recent migration from Mexico and Puerto Rico. We found an anomalous pattern of Native American ancestry from the US southwest, which most likely corresponds to the Nuevomexicano descendants of early Spanish settlers to the region. We addressed a number of controversies surrounding this population, including the extent of Sephardic Jewish ancestry. Nuevomexicanos are less admixed than nearby Mexican-American individuals, with more European and less Native American and African ancestry, and while they do show demonstrable Sephardic Jewish ancestry, the fraction is no greater than seen for other Hispanic/Latino populations.

genetics↗

bioRxiv2017

Assortative mating on ancestry-variant traits in admixed Latin American populations

BackgroundAssortative mating is a universal feature of human societies, and individuals from ethnically diverse populations are known to mate assortatively based on similarities in genetic ancestry. However, little is currently known regarding the exact phenotypic cues, or their underlying genetic architecture, which inform ancestry-based assortative mating.\n\nResultsWe developed a novel approach, using genome-wide analysis of ancestry-specific haplotypes, to evaluate ancestry-based assortative mating on traits whose expression varies among the three continental population groups - African, European, and Native American - that admixed to form modern Latin American populations. Application of this method to genome sequences sampled from Colombia, Mexico, Peru, and Puerto Rico revealed widespread ancestry-based assortative mating. We discovered a number of anthropometric traits (body mass, height, facial development and waist-hip ratio) and neurological attributes (educational attainment and schizophrenia) that serve as phenotypic cues for ancestry-based assortative mating. Major histocompatibility complex (MHC) loci show population-specific patterns of both assortative and disassortative mating in Latin America. Ancestry-based assortative mating in the populations analyzed here appears to be driven primarily by African ancestry.\n\nConclusionsThis study serves as an example of how population genomic analyses can yield novel insights into human behavior.

genomics↗