Eleazar Eskin

Research in bioinformatics demands that many workflows and operations be consistently repeated across different tools. These operations include managing a taxdump, dealing with accession mappings, and performing analysis on the taxonomic tree. We introduce taxutils to address and alleviate the strain of these common workflows. To exhibit the extent of the package’s capabilities, we introduce a strategy for assessing quality of an assembly via its potential impact on and perturbation of its mapping to a lowest-common-ancestor index. We illustrate the steps required for such an analysis, and show how the tool makes data management easy and otherwise-complex workflows simple.

Coccidioidomycosis, also known as Valley Fever, is a fungal disease endemic to the Americas that kills hundreds annually, yet the host factors that lead to increased risk of life-threatening dissemination of coccidioidomycosis remain poorly understood. We assembled the largest comprehensively sequenced coccidioidomycosis cohort to date, comprising 795 individuals with laboratory confirmed coccidioidomycosis and clinical disease severity phenotyping, many with paired whole blood genomic and transcriptomic data. Individuals with greater than 50% African genetic ancestry have increased risk for disseminated coccidioidomycosis (DCM) cases (OR=13.37, p=1.08×10⁻¹⁸), reflecting ancestry-associated differences in allele frequencies at immune loci. Transcriptomic profiling (n=267) revealed upregulation of interferon-inducible genes IFI44 and IFI44L , the fungal recognition receptor CLEC4D , and pro-inflammatory protein S100A12 , with sex-specific expression differences in immune cell composition. Gene-burden testing identified NOD-like receptor NLRX1 as the only gene carrying significantly more damaging rare variants than expected by chance (p=5.85×10⁻⁴). We identified a rare missense variant, NLRX1 p.Arg252Trp (rs145644388), in five patients with DCM that represents a founder variant: all carriers share African local genetic ancestry and carry 0.6–1.1 centimorgans of identical-by-descent sequence, indicating origin from a common ancestor. In gnomAD, NLRX1 p.Arg252Trp shows has higher allele frequency in African (AF=0.00615) compared to European (AF= 2.25x10 ^-5 ) populations, directly linking this rare variant to population-level African genetic ancestry enrichment in DCM. NLRX1 disruption impairs LC3-associated phagocytosis, an antifungal mechanism in macrophages. Together, these findings reveal both immune gene expression dysregulation and rare-variant architectures associated with African genetic ancestry underlying severe coccidioidomycosis and identify new targets for risk stratification and treatment. Highlights: Patients with disseminated coccidioidomycosis are significantly more likely to have African genetic ancestry. Whole-blood transcriptomics identifies upregulation of interferon-inducible genes IFI44 , IFI44L , and fungal pattern-recognition receptor CLEC4D in disseminated disease. Rare missense variants in NLRX1 , a mitochondrial NOD-like receptor involved in LC3-associated phagocytosis, are significantly enriched in disseminated cases by gene-burden testing. Context and Significance: Valley fever (coccidioidomycosis) is a fungal infection endemic to the southwestern United States that causes life-threatening disseminated disease in a small fraction of those infected. The biological determinants underlying why some develop severe, disseminated infection remains poorly understood. Epidemiological studies have noted that individuals of African American or Filipino background face disproportionately higher risk for severe disease, but these studies relied on self-reported race, a social construct that is not biologically based. By assembling the largest genomically characterized Valley Fever cohort to date and using genome sequencing to directly quantify genetic ancestry, we show that genetic variation that is more common in populations with African ancestry is associated with risk for dissemination. We further identify disruption of interferon signaling and LC3-associated phagocytosis — a cellular mechanism by which macrophages contain fungal infections — as likely contributors to severe disease. These findings open new avenues for risk stratification and potential therapeutic targeting in this neglected fungal infection.

Metagenomic sequencing can identify pathogens from clinical samples without prior knowledge of the causative agent. Yet, as sequencing workflows scale to process thousands of multiplexed samples simultaneously, classifying these samples against massive reference databases creates a significant computational bottleneck. Furthermore, large-scale applications such as screening public sequence repositories remain computationally challenging. Existing metagenomic classifiers are designed for full-taxon classification, where the goal is to identify all organisms in a sample. However, many diagnostic applications focus on detecting a specific set of clinically relevant pathogens. This constraint can be exploited to significantly lower computational costs. Here we present TDKC (Target Distilled K-mer Classifier), a method for targeted metagenomic classification. TDKC constructs a compact index by distilling target-specific k-mers from a full-taxon reference database. When classifying clinical samples, TDKC uses 16.9-33.6x less memory and is 5.2-34.3x faster than per-read full-taxon and targeted classifiers (Kraken2, Centrifuger, CLARK), while maintaining high sensitivity and low false positive rates. Against the sketch-based profiler Sylph, TDKC remains 4.2x faster and uses 8.5x less memory. TDKC also supports per-k-mer accession tracking across over 3 million source accessions for downstream subtype analysis, and domain-level detection of bacteria, archaea, and viruses. By reducing the index to only the pathogens of interest, TDKC makes targeted pathogen detection feasible at scale.

To identify genes that regulate the response to the potentially addictive drug cocaine, we performed a control experiment using genome-wide association studies (GWASs) and RNA-Seq of a panel of inbred and recombinant inbred mice undergoing intravenous self-administration of saline. A linear mixed model increased statistical power for analysis of the longitudinal behavioral data, which was acquired over 10 days. A total of 145 loci were identified for saline compared to 17 for the corresponding cocaine GWAS. Only one locus overlapped. Transcriptome-wide association studies (TWASs) using RNA-Seq data from the nucleus accumbens and medial frontal cortex identified 5031434O11Rik and Zfp60 as significant for saline self-administration. Two other genes, Myh4 and Npc1 , were nominated based on proximity to loci for multiple endpoints or a cis locus regulating expression. All four genes have previously been implicated in locomotor activity, despite the absence of a strong relationship between saline taking and distance traveled in the open field. Our results indicate a distinct genetic basis for saline and cocaine self-administration, and suggest some common genes for saline self-administration and locomotor activity.

Structural variation (SV) refers to insertions, deletions, inversions, and duplications in human genomes. SVs are present in approximately 1.5% of the human genome. Still, this small subset of genetic variation has been implicated in the pathogenesis of psoriasis, Crohn’s disease and other autoimmune disorders, autism spectrum and other neurodevelopmental disorders, and schizophrenia. Since identifying structural variants is an important problem in genetics, several specialized computational techniques have been developed to detect structural variants directly from sequencing data. With advances in whole-genome sequencing (WGS) technologies, a plethora of SV detection methods have been developed. However, dissecting SVs from WGS data remains a challenge, with the majority of SV detection methods prone to a high false-positive rate, and no existing method able to precisely detect a full range of SVs present in a sample. Previous studies have shown that none of the existing SV callers can maintain high accuracy across various SV lengths and genomic coverages. Here, we report an integrated structural variant calling framework, Variant Identification and Structural Variant Analysis (VISTA), that leverages the results of individual callers using a novel and robust filtering and merging algorithm. In contrast to existing consensus-based tools which ignore the length and coverage, VISTA overcomes this limitation by executing various combinations of top-performing callers based on variant length and genomic coverage to generate SV events with high accuracy. We evaluated the performance of VISTA on comprehensive gold-standard datasets across varying organisms and coverage. We benchmarked VISTA using the Genome-in-a-Bottle gold standard SV set, haplotype-resolved de novo assemblies from the Human Pangenome Reference Consortium, along with an in-house polymerase chain reaction (PCR)-validated mouse gold standard set. VISTA maintained the highest F1 score among top consensus-based tools measured using a comprehensive gold standard across both mouse and human genomes. VISTA also has an optimized mode, where the calls can be optimized for precision or recall. VISTA-optimized can attain 100% precision and the highest sensitivity among other variant callers. In conclusion, VISTA represents a significant advancement in structural variant calling, offering a robust and accurate framework that outperforms existing consensus-based tools and sets a new standard for SV detection in genomic research.

The rapid advancements in artificial intelligence, particularly in Large Language Models (LLMs) such as GPT-4, Gemini, and LLaMA, have opened new avenues for computational biology and bioinformatics. We report the development of BioLLMBench, a novel framework designed to evaluate LLMs in bioinformatics tasks. This study assessed GPT-4, Gemini, and LLaMA through 2,160 experimental runs, focusing on 24 distinct tasks across six key areas: domain expertise, mathematical problem-solving, coding proficiency, data visualization, research paper summarization, and machine learning model development. Tasks ranged from fundamental to expert-level challenges, and each area was evaluated using seven specific metrics. A Contextual Response Variability Analysis was implemented to understand how model responses varied under different conditions. Results showed diverse performance: GPT-4 led in most tasks, achieving a 91.3% proficiency in domain knowledge, while Gemini excelled in mathematical problem-solving with a 97.5% proficiency score. GPT-4 also outperformed in machine learning model development, though Gemini and LLaMA struggled to generate executable code. All models faced challenges in research paper summarization, scoring below 40% using the ROUGE metric. Model performance variance increased when using a new chat window, though average scores remained similar. The study also discusses the limitations and potential misuse risks of these models in bioinformatics.

Mendelian randomization (MR) has emerged as a powerful approach to leverage genetic instruments to infer causality between pairs of traits in observational studies. However, the results of such studies are susceptible to biases owing to weak instruments, as well as the confounding effects of population stratification and horizontal pleiotropy. Here, we show that family data can be leveraged to design MR tests that are provably robust to confounding from population stratification, assortative mating, and dynastic effects. We show in simulations that our approach, MR-Twin, is robust to confounding from population stratification and is not affected by weak instrument bias, whereas standard MR methods yield inflated false positive rates. We then conduct an exploratory analysis of MR-Twin and other MR methods applied to 121 trait pairs in the UK Biobank data set. Our results suggest that confounding from population stratification can lead to false positives for existing MR methods, whereas MR-Twin is immune to this type of confounding, and that MR-Twin can help assess whether traditional approaches may be inflated owing to confounding from population stratification.

Mendelian Randomization (MR) has emerged as a powerful approach to leverage genetic instruments to infer causality between pairs of traits in observational studies. However, the results of such studies are susceptible to biases due to weak instruments as well as the confounding effects of population stratification and horizontal pleiotropy. Here, we show that family data can be leveraged to design MR tests that are provably robust to confounding from population stratification, assortative mating, and dynastic effects. We demonstrate in simulations that our approach, MR-Twin, is robust to confounding from population stratification and is not affected by weak instrument bias, while standard MR methods yield inflated false positive rates. We applied MR-Twin to 121 trait pairs in the UK Biobank dataset and found that MR-Twin identifies likely causal trait pairs and does not identify trait pairs that are unlikely to be causal. Our results suggest that confounding from population stratification can lead to false positives for existing MR methods, while MR-Twin is immune to this type of confounding.

We introduce pleiotropic association test (PAT) for joint analysis of multiple traits using genome-wide association study (GWAS) summary statistics. The method utilizes the decomposition of phenotypic covariation into genetic and environmental components to create a likelihood ratio test statistic for each genetic variant. Though PAT does not directly interpret which trait(s) drive the association, a per trait interpretation of the omnibus p-value is provided through an extension to the meta-analysis framework, m-values. In simulations, we show PAT controls the false positive rate, increases statistical power, and is robust to model misspecifications of genetic effect. Additionally, simulations comparing PAT to three multi-trait methods, HIPO, MTAG, and ASSET, show PAT identified 15.3% more omnibus associations over the next best method. When these associations were interpreted on a per trait level using m-values, PAT had 37.5% more true per trait interpretations with a 0.92% false positive assignment rate. When analyzing four traits from the UK Biobank, PAT discovered 22,095 novel variants. Through the m-values interpretation framework, the number of per trait associations for two traits were almost tripled and were nearly doubled for another trait relative to the original single trait GWAS.