Biology subjects

Dorman, K.

Publications and source records attributed to Dorman, K..

8 recordsLinked to original sources

bioRxiv2026

CLEAR: Concise List Enrichment Analysis Reducing Redundancy

MotivationHigh-throughput experiments generate genome-wide measurements for thousands of genes, which are often tested marginally. Biological processes are driven by coordinated groups of genes rather than individual genes, making gene set enrichment analysis an essential post hoc interpretation tool. Traditional approaches such as Over-Representation Analysis and Gene Set Enrichment Analysis test gene sets independently, which ignores the hierarchical and overlapping structure of gene set collections such as the Gene Ontology, and often leads to redundant enrichment results. Set-based approaches such as MGSA address this issue by modeling multiple gene sets simultaneously, but they rely on binary gene activation states derived from arbitrary thresholds on gene-level statistics. ResultsWe introduce Concise List Enrichment Analysis Reducing Redundancy (CLEAR), a Bayesian gene set enrichment framework that jointly models gene sets while incorporating continuous gene-level statistics such as test statistics or p-values. CLEAR extends model-based gene set analysis by replacing threshold-based gene activation with a probabilistic model for continuous gene-level statistics. This approach preserves the redundancy-reduction advantages of set-based enrichment methods while avoiding the information loss introduced by binarization. Using both simulated datasets and human gene expression data, we show that CLEAR improves sensitivity compared with existing enrichment approaches while producing a more concise and interpretable set of enriched gene sets. Availability and implementationThe source code, data, and a brief tutorial are freely available at https://github.com/jiatuya/CLEAR

bioinformatics↗

bioRxiv2026

PREMISE: A Quality-Aware Probabilistic Framework for Pathogen Resolution and Source Assignment in Viral mNGS

The circulation of Influenza A viruses (IAVs) in wildlife and livestock presents a significant public health threat due to their zoonotic potential and rapid genomic diversification. Accurate classification of viral subtypes and characterization of within-host diversity are crucial for risk assessment and vaccine development. Although metagenomic sequencing facilitates early detection, prevalent memory-efficient k-mer-based pipelines often discard critical linkage information. This loss of information can result in missed or imprecise pathogen identification, potentially delaying clinical and public health responses. We introduce PREMISE (Pathogen Resolution via Expectation Maximization In Sequencing Experiments), a probabilistic, alignment-based framework implemented in RUST for high-resolution viral genome identification. By integrating advanced string data structures for efficient alignment with a quality-score-aware Expectation-Maximization algorithm, PREMISE accurately identifies source strains, estimates relative abundances, and performs precise read assignments. This framework provides superior source estimation with statistical confidence, enabling the identification of mixed infections, recombination, and IAV-reassortment directly from raw data. Validated against simulated and empirical datasets, PREMISE outperforms state-of-the-art k-mer methods. Ultimately, this framework represents a significant advancement in viral identification, providing a foundation for novel approaches that can automatically flag reassorted viruses or recombination events in the future, thereby improving the detection of emerging pathogens with zoonotic potential. Availabilityhttps://github.com/sriram98v/premise under a MIT license. Contactsriramv@iastate.edu

bioinformatics↗

bioRxiv2025

MULTICLUST - Fast multinomial clustering of multiallelic genotypes to infer genetic population structure

Identifying population structure from multilocus genotype data is key to down-stream genetic analyses, including analysis of genome-wide association (GWAS), genetic genealogy and phylogenetics, in the fields of conservation, forensics, evolution and more. While inference of population structure has been dominated by Bayesian methods, maximum likelihood methods have some benefits in terms of reliability, consistency and efficiency. Here we extend the methods of Tang et al. [53] and Alexander et al. [2] to handle multi-allelic (e.g., SNP, STR, allozyme) and polyploid loci with missing data to infer genetic admixture proportions and subpopulation allele frequencies. Comparative analyses of our method, MULTICLUST, and STRUCTURE [42] on both simulated and empirical data indicate comparable, fast, reproducible, and accurate estimates of population admixture proportions and allele frequencies using MULTICLUST.MULTICLUST is implemented in the C programming language and is publicly accessible via www.github.com/arunsethuraman/multiclust.

evolutionary biology↗

bioRxiv2023

Unsupervised Contrastive Peak Caller for ATAC-seq

The assay for transposase-accessible chromatin with sequencing (ATAC-seq) is a common assay to identify chromatin accessible regions by using a Tn5 transposase that can access, cut, and ligate adapters to DNA fragments for subsequent amplification and sequencing. These sequenced regions are quantified and tested for enrichment in a process referred to as "peak calling". Most unsupervised peak calling methods are based on simple statistical models and suffer from elevated false positive rates. Newly developed supervised deep learning methods can be successful, but they rely on high quality labeled data for training, which can be difficult to obtain. Moreover, though biological replicates are recognized to be important, there are no established approaches for using replicates in the deep learning tools, and the approaches available for traditional methods either cannot be applied to ATAC-seq, where control samples may be unavailable, or are post-hoc and do not capitalize on potentially complex, but reproducible signal in the read enrichment data. Here, we propose a novel peak caller that uses unsupervised contrastive learning to extract shared signals from multiple replicates. Raw coverage data are encoded to obtain low-dimensional embeddings and optimized to minimize a contrastive loss over biological replicates. These embeddings are passed to another contrastive loss for learning and predicting peaks and decoded to denoised data under an autoencoder loss. We compared our Replicative Contrastive Learner (RCL) method with other existing methods on ATAC-seq data, using annotations from ChromHMM genome and transcription factor ChIP-seq as noisy truth. RCL consistently achieved the best performance.

bioinformatics↗

bioRxiv2022

Nod1-dependent NF-kB activation initiates hematopoietic stem cell specification in response to small Rho GTPases

The possibility of specifying functional hematopoietic stem and progenitor cells (HSPCs) from human pluripotent stem cells (hPSCs) would overcome current limitations related to HSPC transplantation. However, generating hPSC-derived HSPCs has been elusive, necessitating a better understanding of the native developmental mechanisms that trigger HSPC specification. Here, we revealed in vivo an intrinsic inflammatory mechanism triggered by Nod1 that drives early hemogenic endothelium (HE) patterning to specify HSPCs. Our genetic and chemical experiments showed that HSPCs failed to specify in the absence of Nod1 and its downstream kinase Ripk2. Rescue experiments demonstrated that Nod1 and Ripk2 acted through NF-kB, and that small Rho GTPases are at the apex of this mechanism. Manipulation of NOD1 in a human system of hPSCs differentiation towards the definitive hematopoietic lineage indicated functional conservation. This work establishes the RAC1-NOD1-RIPK2-NFkB axis as the earliest inflammatory inductor that intrinsically primes the HE for proper HSPC specification. Manipulation of this pathway could help derive a competent HE amenable to specify functional patient specific HSPCs for the treatment of blood disorders. HighlightsO_LINod1 specifies HSPCs in vivo through the early induction of hemogenic endothelium. C_LIO_LINod1-Ripk2 controls HSPC specification by activating the inflammatory master TF NF-kB. C_LIO_LINod1 links small Rho GTPases with pro-inflammatory signaling during the genesis of HSPCs. C_LIO_LIThe function of NOD1 is conserved in the development of definitive human HSPCs. C_LI

developmental biology↗

bioRxiv2022

Accurate Estimation of Molecular Counts from Amplicon Sequence Data with Unique Molecular Identifiers

MotivationAmplicon sequencing is widely applied to explore heterogeneity and rare variants in genetic populations. Resolving true biological variants and quantifying their abundance is crucial for downstream analyses, but measured abundances are distorted by stochasticity and bias in amplification, plus errors during Polymerase Chain Reaction (PCR) and sequencing. One solution attaches Unique Molecular Identifiers (UMIs) to sample sequences before amplification eliminating amplification bias by clustering reads on UMI and counting clusters to quantify abundance. While modern methods improve over naive clustering by UMI identity, most do not account for UMI reuse, or collision, and they do not adequately model PCR and sequencing errors in the UMIs and sample sequences. ResultsWe introduce Deduplication and accurate Abundance estimation with UMIs (DAUMI), a probabilistic framework to detect true biological sequences and accurately estimate their deduplicated abundance from amplicon sequence data. DAUMI recognizes UMI collision, even on highly similar sequences, and detects and corrects most PCR and sequencing errors in the UMI and sampled sequences. DAUMI performs better on simulated and real data compared to other UMI-aware clustering methods. AvailabilitySource code is available at https://github.com/xiyupeng/AmpliCI-UMI.

bioinformatics↗

bioRxiv2019

MetaOmGraph: a workbench for interactive exploratory data analysis of large expression datasets

The diverse and growing omics data in public domains provide researchers with a tremendous opportunity to extract hidden knowledge. However, the challenge of providing domain experts with easy access to these big data has resulted in the vast majority of archived data remaining unused. Here, we present MetaOmGraph (MOG), a free, open-source, standalone software for exploratory data analysis of massive datasets by scientific researchers. Using MOG, a researcher can interactively visualize and statistically analyze the data, in the context of its metadata. Researchers can interactively hone-in on groups of experiments or genes based on attributes such as expression values, statistical results, metadata terms, and ontology annotations. MOGs statistical tools include coexpression, differential expression, and differential correlation analysis, with permutation test-based options for significance assessments. Multithreading and indexing enable efficient data analysis on a personal computer, with no need for writing code. Data can be visualized as line charts, box plots, scatter plots, and volcano plots. A researcher can create new MOG projects from any data or analyze an existing one. An R-wrapper lets a researcher select and send smaller data subsets to R for additional analyses. A researcher can save MOG projects with a history of the exploratory progress and later reopen or share them. We illustrate MOG by case studies of large curated datasets from human cancer RNA-Seq, in which we assembled a list of novel putative biomarker genes in different tumors, and microarray and metabolomics from A. thaliana.

bioinformatics↗

bioRxiv2018

phylostratr: A framework for phylostratigraphy

MotivationThe goal of phylostratigraphy is to infer the evolutionary origin of each gene in an organism. Currently, there are no general pipelines for this task. We present an R package, phylostratr, to fill this gap, making high-quality phylostratigraphic analysis accessible to non-specialists.\n\nResultsPhylostratigraphic analysis entails searching for homologs within increasingly broad clades. The highest clade that contains all homologs of a gene is that genes phylostratum. We have created a general R-based framework, phylostratr, for estimating the phylostratum of every gene in a species. The program can fully automate an analysis: select species for a balanced representation of each strata, retrieve the sequences from UniProt, build BLAST databases, run BLAST, infer homologs for each gene against each subject species, determine phylostrata, and return summaries and diagnostics. phylostratr allows extensive customization. A user may: modify the automatically-generated clade tree or use their own tree; provide custom sequences in place of those automatically retrieved from UniProt; replace BLAST with an alternative algorithm; or tailor the method and sensitivity of the homology inference classifier. phylostratr also offers proteome quality assessments, false-positive diagnostics, and checks for missing organelle genomes. We show the utility of phylostratr through case studies in Arabidopsis thaliana and Saccharomyces cerevisiae.\n\nAvailabilityphylostratr source code and vignettes are available on GitHub at https://github.com/arendsee/phylostratr\n\nContactevewurtele@gmail.com

bioinformatics↗