Biology subjects

Huff, C.

Publications and source records attributed to Huff, C..

4 recordsLinked to original sources

bioRxiv2026

SPrOUT: A computational and targeted sequencing approach for mixed plant DNA identification with Angiosperms353

PremiseThe identification of plant species from mixed samples is crucial in various fields, including ecological surveys, conservation efforts, and food and dietary supplement safety. Traditional methods face potential challenges due to the high costs of DNA sequencing, inefficiencies in computational workflows, and incomplete sequence databases. Methods and ResultsThis study introduces a novel approach using the Angiosperms353 target sequencing kit for efficient taxonomic identification of angiosperm DNA in mixed samples. Our method assembles short pair-end reads for each mixed sample. Using gene sets of Angiosperms353 from 871 species, we apply phylogenetic inference to categorize the variance in phylogenetic distance across genes to identify the presence of taxa in mixed plant samples. The pipeline reaches 98.1 to 99.6% accuracy, 92.9 to 100% precision for identifying unknown taxa in in-silico mixes, and 90.7% accuracy and 98.0% precision for mock supplement mixtures. We explored the parameter cutoffs of the pipeline to offer an empirical range for different applications. ConclusionsThe Angiosperms353 and HybPiper assembly proved effective in sorting mixed plant DNA samples. Our method offers a framework for scientific and practical applications in plant species identification in both single and mixed samples.

bioinformatics↗

bioRxiv2025

Large Impact of Genetic Data Processing Steps on Stability and Reproducibility of Set-Based Analyses in Genome-Wide Association Studies

Genome-wide association studies (GWAS) are crucial to human genetics research, yet their stability and reproducibility are often questioned. This work describes, analyzes, and provides tools for overcoming reproducibility challenges in two highly popular components of GWAS: set-based (a) hypothesis testing and (b) effect size estimation. Specifically, we focus on how the set-based natures of (a) and (b) often fuel non-reproducible results due to differences in data processing pipelines that are rarely discussed. First, we describe the processing challenges in a statistical model misspecification framework. Second, we analytically calculate the differences in power and amounts of bias that can arise in (a) and (b), respectively, due to small data processing choices. Third, we provide tools for quantifying and avoiding the data processing obstacles in GWAS. We validate our analytical calculations through a simulation study, and we demonstrate the aforementioned challenges empirically through analysis of a whole-exome sequencing study of pancreatic cancer. Author SummaryThe lack of reproducibility and stability in genome-wide association studies (GWAS) have been widely reported. Here, we demonstrate how such reproducibility challenges arise in a common component of GWAS, set-based hypothesis testing and estimation studies. Specifically, we show how minor, seemingly harmless decisions in how scientists prepare their data can lead to major differences in the final conclusions. These data processing steps are rarely reported in detail, further obscuring their importance. Our work precisely measures the impact of data processing steps on power and bias of common modeling approaches. As a partial solution for future GWAS, we also provide an R software package to interact with our results, which can be used to assess the impact of choices at the design stage of GWAS studies. We further analyze a pancreatic cancer dataset using two modern pipelines and show how the pipelines produce very different results for ATM, a gene that has been previously linked with pancreatic cancer. Using the tools provided by this work can help significantly improve the reproducibility and stability of set-based results, enhancing the translational potential of GWAS investigations.

genetics↗

bioRxiv2024

Benchmarking DNA Foundation Models for Genomic Sequence Classification

The rapid evolution of DNA foundation models promises to revolutionize genomics, yet comprehensive evaluations are lacking. Here, we present a comprehensive, unbiased benchmark of five models (DNABERT-2, Nucleotide Transformer V2, HyenaDNA, Caduceus-Ph, and GROVER) across diverse genomic and genetic tasks including sequence classification, gene expression prediction, variant effect quantification, and topologically associating domain (TAD) region recognition, using zero-shot embeddings. Our analysis reveals that mean token embedding consistently and significantly improves sequence classification performance, outperforming other pooling strategies. Model performance varies among tasks and datasets; while general purpose DNA foundation models showed competitive performance in pathogenic variant identification, they were less effective in predicting gene expression and identifying putative causal QTLs compared to specialized models. Our findings offer a framework for model selection, highlighting the impact of architecture, pre-training data, and embedding strategies on performance in genomic and genetic tasks.

genomics↗

bioRxiv2019

A novel ribosomal protein 20 variant in a family with unexplained colorectal cancer and polyposis

Colorectal cancer (CRC) has a large hereditary component, which is only partially explained by known genetic causes. Recently, variants in ribosomal protein S20 (RPS20, [OMIM: 603682]) were identified in a family with familial CRC type X and in a CRC cancer case-control screen. This study describes a novel splice donor variant in RPS20, NM_001023.3:c.177+1G>A. It segregates with CRC [OMIM: 114500] and polyposis [HP: 0200063] within the probands family. Reverse transcription-polymerase chain reaction (RT-PCR) confirms the variant results in two aberrantly-spliced transcripts that are absent in controls. The location of the novel RPS20 variant is near two previously-reported truncating RPS20 variants associated with CRC. DNA from colon adenocarcinoma showed no evidence of loss-of-heterozygosity, supporting a haploinsufficiency or dominant negative disease mechanism. These findings support designation of RPS20 as a CRC predisposition gene, and expand the phenotypic spectrum of RPS20 truncating variants to include polyposis.

genetics↗