Biology subjects

Wheeler, T. J.

Publications and source records attributed to Wheeler, T. J..

17 recordsLinked to original sources

bioRxiv2026

Updated Transposable Element Libraries for Drosophila melanogaster in Dfam 4.0

Drosophila melanogaster repeatome, comprising roughly 20% of the genome, is characterized by a large fraction of active TE families counterbalanced by efficient purifying selection. Consequently, many TE families persist at low copy numbers and are frequently population specific. Hybridization and horizontal transfer provide new families, which can spread through natural populations within decades. These dynamics, together with years of independent curation efforts, left the D. melanogaster mobilome distributed across several, partly redundant, libraries. Prompted by submissions of population-specific data, we undertook a complete overhaul of the D. melanogaster TE libraries, begun in Dfam 3.9 and finalized in Dfam 4.0. We cross-referenced the new submissions against Repbase, FlyBase, the Berkeley Drosophila Genome Project, and our own Dfam 3.8 to resolve redundancy and reconcile names, then rebuilt or newly constructed the seed alignment for most families. Seeds came from four sources: the dm6 reference itself, which supported the majority of models; insertions >100 bp from 13 samples of a recently published D. melanogaster pangenome; the genomes of other members of the D. melanogaster subgroup, which supplied copies for older families too degraded in dm6 alone; and diverged matches recovered during iterative curation, which resolved into subfamilies and previously undescribed relatives. Rebuilding the seeds corrected consensus sequences that were truncated, chimeric, or skewed by co-duplicated fragments, and lowered the mean Kimura divergence of annotated copies from their consensus. The revision also added families with no prior Dfam representation, including the DNA P-element (absent from dm6) and a collection of novel families that have recently invaded natural populations. Following manual curation, the new library contains 398 models, up from 226 in Dfam 3.8, and annotates an additional 1.5% of the dm6 reference.

genomics↗

bioRxiv2026

Alternatively Spliced Dual-Coding Regions Contribute to the Human Gene Regulatory Program

In eukaryotes, alternative splicing allows a single gene to encode multiple protein isoforms, by conditionally using only a subset of the genes exons. In some cases, distinct isoforms utilize the same exon(s) in a different reading frame, thus encoding a distinct sequence of amino acids. Here, we provide a genome-wide view of such dual coding regions (DCRs) in humans. By mapping all reviewed human UniProtKB/Swiss-Prot isoforms to the human genome and tracking reading frames used by each isoform, we identified 1296 DCR-containing genes. Though it is possible for an exon to contain multiple reading frames that lead to DCRs simply due to noisy splicing, (i) mouse orthologs to human DCR genes appear to share a dual-coding nature with much greater frequency than is expected by chance and (ii) many human and mouse DCR isoforms show differential tissue-specific expression levels, suggesting a conserved functional role. DCRs are typically short (average: 95nt), confined to a single exon, and mostly appear to introduce early stop codons that lead to loss of C-terminal coding regions. At least one third of DCRs are likely to cause nonsense-mediated decay. DCR genes are not restricted to any particular functional category, suggesting that dual coding is broadly permissive rather than confined to specialized pathways. Structure prediction indicates that most amino acids produced by canonical-frame regions are involved in some secondary structural element, while non-canonical reading frames generally produce disordered peptides, supporting a model in which dual coding primarily rewires terminal regions and isoform stability rather than creating new folded domains. Our work characterizes DCRs as a fairly common byproduct of alternative splicing, sporadically co-opted and conserved in eukaryotes through evolution, contributing to gene regulation and functional diversity. We also provide web interfaces to enable visual exploration of DCR architecture and usage patterns. Significance statementMore than a thousand human genes are known to harbor coding regions that are conditionally translated in alternative reading frames through alternative splicing or transcription start site selection; however, the functional relevance of these dual-coding regions remains poorly understood. We demonstrate that this dual-coding nature is conserved: most genes containing dual-coding regions in humans also show dual-coding potential in mice. We further show that the relative abundance of proteins encoded by the alternative reading frames can vary across tissues in humans and mice and that in most cases the non-canonical reading frame produces disordered peptide domains and protein truncation. We conclude that dual coding in eukaryotes is an emergent property of noisy transcription that has been repeatedly co-opted to fine-tune gene regulatory programs.

genomics↗

bioRxiv2025

MDCompress: better, faster compression of molecular dynamics simulation trajectories

MotivationMolecular dynamics (MD) simulations model the physical movements of atoms in biomolecular systems over time, providing atomic-resolution insight into conformational changes, binding events, and dynamic behaviors that cannot be captured by static structures alone. As such, MD simulations are playing an increasingly important role in understanding the functional roles and molecular interactions of proteins. However, trajectories from these simulations can be extremely large, often reaching tens of gigabytes for a single simulation of modest duration. This creates substantial challenges for storage and data transfer, motivating efficient compression strategies. Furthermore, many downstream analyses require extraction of only a subset of frames or specific atoms from the full trajectory, so an ideal compression format should support rapid random-access decompression of such samplings without requiring full file decompression. ResultsHere, we introduce MDCompress, a new trajectory compression format and accompanying software implementation that meets these goals. MDCompress produces compressed trajectory files that are 15-37% smaller than those generated by the widely-used XTC format, while achieving faster compression and decompression speeds through efficient multithreading. Availability and ImplementationThe MDCompress software and library are released under an open license (BSD-3) and may be downloaded at https://github.com/refresh-bio/mdcompress Contacttwheeler@arizona.edu, sebastian.deorowicz@polsl.pl Supplementary informationSupplementary data are available online.

biochemistry↗

bioRxiv2025

Simpatico: accurate and ultra-fast virtual drug screening with atomic embeddings

Virtual screening, the in-silico assessment of large libraries of small molecules for binding to a therapeutic protein target, is a critical early step in drug discovery. The dominant approach, molecular docking, requires a separate calculation for each protein-molecule pair, and is too slow to apply alone at the billion-compound scale of modern compound libraries. A recent embedding-retrieval paradigm, exemplified by DrugCLIP, addresses this bottleneck by training deep models to map proteins and small molecules into a shared embedding space, such that proteins are co-located with their likely binding partners; candidate ligands can then be retrieved directly by nearest-neighbors search, with no per-pair calculation. However, current embedding-retrieval methods collapse each protein and each ligand into a single embedding, creating an information bottleneck that limits the representation of partial or alternative binding compatibility. We present simpatico, an embedding-retrieval virtual screening tool that instead produces a unique embedding for each atom in a protein pocket or ligand. A CLIP-style contrastive objective trains these atomic embeddings so that protein-ligand atom pairs known to interact are nearby in embedding space. To screen a protein target, each protein-atom embedding is used as a query against a vector database of precomputed small-molecule atomic embeddings, returning the closest atoms in the library; a simple aggregation step assigns a binding score to each candidate molecule containing retrieved atoms. Where prior retrieval-based methods index one vector per ligand, simpatico indexes one per heavy atom; query time grows sublinearly in library size. On challenging decoy benchmarks, simpatico achieves state-of-the-art predictive accuracy, outperforming recent dense-retrieval methods despite training on only [~]15,000 protein-ligand complexes from PDBBind, with no pretraining and no 3D ligand pose estimation. Simpatico also exceeds the accuracy of physics-based docking and deep-learning-augmented docking methods, is competitive with diffusion-based docking, and runs orders of magnitude faster than all three. Simpatico is open source software; all code, weights, and data may be accessed at https://github.com/TravisWheelerLab/Simpatico.

pharmacology and toxicology↗

bioRxiv2025

Snekmer Learn/Apply: A kmer-based vector similarity approach to proteinclassification suitable for metagenomic datasets

Advances in whole genome sequencing have led to a rapid and ongoing increase in the amount of sequence data available, but 40-50% of known genes have no functional annotation and only 25-30% have specific functional annotations. Current functional annotation approaches typically rely on computationally expensive pairwise or multiple sequence alignments, preventing rapid development of models for novel protein functions and sometimes limiting methods to one ontology. Representation of sequence in short segments (kmers) has been used in many applications for nucleotide sequence, and more recently has been applied to protein sequence as well. We previously developed Snekmer, a tool which uses kmer patterns to develop alignment-free individual protein family models. Other approaches, such as MMSeqs2 and DIAMOND, use protein kmers as a fast filter to reduce search space for subsequent sequence alignment. Here, we describe a novel addition to the Snekmer tool which builds kmer libraries for protein families and uses those libraries to map functional annotations to new sequences. We first demonstrate that our method accurately applies TIGRFAMs annotations to protein fragments and to a low-sequence identity benchmark dataset, and further use it to annotate a set of drought stress associated soil and rhizosphere metagenome sequences with higher sensitivity towards several important protein function classes than that shown by HMMs. We have incorporated this workflow into Snekmer.

bioinformatics↗

bioRxiv2024

Inhibition of PIM kinase in tumor associated macrophages suppresses inflammasome 1 activation and sensitizes prostate cancer to immunotherapy

Immunotherapy has changed the treatment paradigm for many types of cancer, but immune checkpoint inhibitors (ICIs) have not shown benefit in prostate cancer (PCa). Chronic inflammation contributes to the immunosuppressive prostate tumor microenvironment (TME) and is associated with poor response to ICIs. The primary source of inflammatory cytokine production is the inflammasome. Here, we identify PIM kinases as important regulators of inflammasome activation in tumor associated macrophages (TAMs). Analysis of clinical data from a cohort of treatment naive, hormone responsive PCa patients revealed that tumors from patients with high PIM1/2/3 display an immunosuppressive TME characterized by high inflammation (IL-1{beta} and TNF) and a high density of repressive immune cells, most notably TAMs. Strikingly, macrophage-specific knockout of PIM reduced tumor growth in syngeneic models of prostate cancer. Transcriptional analyses indicate that eliminating PIM from macrophages enhanced the adaptive immune response and increased cytotoxic immune cells. Combined treatment with PIM inhibitors and ICIs synergistically reduced tumor growth. Immune profiling revealed that PIM inhibitors sensitized PCa tumors to ICIs by increasing tumor suppressive TAMs and increasing the activation of cytotoxic T cells. Collectively, our data implicate macrophage PIM as a driver of inflammation that limits the potency of ICIs and provides preclinical evidence that PIM inhibitors are an effective strategy to improve the efficacy of immunotherapy in prostate cancer.

cancer biology↗

bioRxiv2024

MDRepo - an open environment for data warehousing and knowledge discovery from molecular dynamics simulations

BackgroundMolecular Dynamics (MD) simulation of biomolecules provides important insights into conformational changes and dynamic behavior, revealing critical information about folding and interactions with other molecules. This enables advances in drug discovery and the design of therapeutic interventions. The collection of simulations stored in computers across the world holds immense potential to serve as training data for future Machine Learning models that will transform the prediction of structure, dynamics, drug interactions, and more. A needIdeally, there should exist an open access repository that enables scientists to submit and store their MD simulations of proteins and protein-drug interactions, and to find, retrieve, analyze, and visualize simulations produced by others. However, despite the ubiquity of MD simulation in structural biology, no such repository exists; as a result, simulations are instead stored in scattered locations without uniform metadata or access protocols. A solutionHere, we introduce MDRepo, a robust infrastructure that supports a relatively simple process for standardized community contribution of simulations, activates common downstream analyses on stored data, and enables search, retrieval, and visualization of contributed data. MDRepo is built on top of the open-source CyVerse research cyberinfrastructure, and is capable of storing petabytes of simulations, while providing high bandwidth upload and download capabilities and laying a foundation for cloud-based access to its stored data.

biochemistry↗

bioRxiv2024

ULTRA-Effective Labeling of Repetitive Genomic Sequence

In the age of long read sequencing, genomics researchers now have access to accurate repetitive DNA sequence (including satellites) that, due to the limitations of short read sequencing, could previously be observed only as unmappable fragments. Tools that annotate repetitive sequence are now more important than ever, so that we can better understand newly uncovered repetitive sequences, and also so that we can mitigate errors in bioinformatic software caused by those repetitive sequences. To that end, we introduce the 1.0 release of our tool for identifying and annotating locally-repetitive sequence, ULTRA (ULTRA Locates Tandemly Repetitive Areas). ULTRA is fast enough to use as part of an efficient annotation pipeline, produces state-of-the-art reliable coverage of repetitive regions containing many mutations, and provides interpretable statistics and labels for repetitive regions. It released under an open license, and available for download at https://github.com/TravisWheelerLab/ULTRA.

bioinformatics↗

bioRxiv2024

nail: software for high-speed, high-sensitivity protein sequence annotation

Fast is fine, but accuracy is final. --Wyatt Earp BackgroundProfile hidden Markov models (pHMMs) deliver state-of-the-art sensitivity for sequence annotation, but the Forward/Backward algorithm fills a dynamic programming matrix sized by the product of model and sequence lengths, making pHMM search slower than fast heuristic alignment tools like MMseqs2 by an order of magnitude or more. ResultsWe introduce nail, which approximates Forward/Backward by computing only a sparse cloud of high-probability matrix cells, recovering accurate pHMM scores, E-values, and alignments at a fraction of the cost. nail annotates the ~2.4 billion protein MGnify metagenomic dataset with all of Pfam in 73.9 hours on a single 48 core machine, recovering nearly all of HMMERs recall advantage over MMseqs2, with run time ~8.7x faster than HMMER3. Detailed analysis of HMMER-only multi-domain hits suggests that many of these matches missed by nail are the result of accumulating score across short, often fragmentary alignments to repetitive regions of the target, consistent with spurious hits rather than genuine homology. We also derive a closed-form approximation for single-sequence E-value calibration, eliminating a per-model simulation step. nail is released under the open BSD-3-clause license at https://github.com/TravisWheelerLab/nail.

bioinformatics↗

bioRxiv2023

VIBES: A Workflow for Annotating and Visualizing Viral Sequences Integrated into Bacterial Genomes

Bacteriophages are viruses that infect bacteria. Many bacteriophages integrate their genomes into the bacterial chromosome and become prophages. Prophages may substantially burden or benefit host bacteria fitness, acting in some cases as parasites and in others as mutualists, and have been demonstrated to increase host virulence. The increasing ease of bacterial genome sequencing provides an opportunity to deeply explore prophage prevalence and insertion sites. Here we present VIBES, a workflow intended to automate prophage annotation in complete bacterial genome sequences. VIBES provides additional context to prophage annotations by annotating bacterial genes and viral proteins in user-provided bacterial and viral genomes. The VIBES pipeline is implemented as a Nextflow-driven workflow, providing a simple, unified interface for execution on local, cluster, and cloud computing environments. For each step of the pipeline, a container including all necessary software dependencies is provided. VIBES produces results in simple tab separated format and generates intuitive and interactive visualizations for data exploration. Despite VIBES primary emphasis on prophage annotation, its generic alignment-based design allows it to be deployed as a general-purpose sequence similarity search manager. We demonstrate the utility of the VIBES prophage annotation workflow by searching for 178 Pf phage genomes across 1,072 Pseudomonas spp. genomes. VIBES software is available at https://github.com/TravisWheelerLab/VIBES.

bioinformatics↗

bioRxiv2023

GraffiTE: a Unified Framework to Analyze Transposable Element Insertion Polymorphisms using Genome-graphs

Transposable Elements are ubiquitous mobile DNA sequences evolving among their hosts genomes, generating insertion polymorphisms that contribute to genomic diversity. We present GraffiTE, a flexible pipeline to analyze polymorphic mobile elements. By integrating state-of-the-art structural variant detection algorithms and graph genomes, GraffiTE identifies polymorphic mobile elements from genomic assemblies and/or long-read sequencing data, and genotypes these variants using short or long read sets. Benchmarking on simulated and real datasets reports high precision and recall rates. GraffiTE is designed to allow non-expert users to perform comprehensive analyses, including in models with limited transposable element knowledge and is compatible with various sequencing technologies. GraffiTE is freely available at https://github.com/cgroza/GraffiTE. Here, we demonstrate the versatility of GraffiTE by analyzing human, Drosophila melanogaster, maize, and Cannabis sativa pangenome data. These analyses reveal the landscapes of polymorphic mobile elements and their frequency variations across individuals, strains, and cultivars.

bioinformatics↗

bioRxiv2023

WAS IT A MATch I SAW? Approximate palindromes lead to overstated false match rates in benchmarks using reversed sequences

BackgroundSoftware for labeling biological sequences typically produces a theory-based statistic for each match (the E-value) that indicates the likelihood of seeing that matchs score by chance. E-values accurately predict false match rate for comparisons of random (shuffled) sequences, and thus provide a reasoned mechanism for setting score thresholds that enable high sensitivity with low expected false match rate. This threshold-setting strategy is challenged by real biological sequences, which contain regions of local repetition and low sequence complexity that cause excess matches between non-homologous sequences. Knowing this, tool developers often develop benchmarks that use realistic-seeming decoy sequences to explore empirical tradeoffs between sensitivity and false match rate. A recent trend has been to employ reversed biological sequences as realistic decoys, because these preserve the distribution of letters and the existence of local repeats, while disrupting the original sequences functional properties. However, we and others have observed that sequences appear to produce high scoring alignments to their reversals with surprising frequency, leading to overstatement of false match risk that may negatively effect downstream analysis. ResultsWe demonstrate that an alignment between a sequence S and its (possibly mutated) reversal tends to produce higher scores than alignment between truly unrelated sequences, even when S is a shuffled string with no notable repetitive or low-complexity regions. This phenomenon is due to the unintuitive fact that (even randomly shuffled) sequences contain palin-dromes that are on average longer than the longest common substrings shared between permuted variants of the same sequence. Though the expected palindrome length is only slightly larger than the expected longest common substring, the distribution of alignment scores involving reversed sequences is strongly right-shifted, leading to greatly increased frequency of high-scoring alignments to reversed sequences. ImpactOverestimates of false match risk can motivate unnecessarily high score thresholds, leading to potentially reduced true match sensitivity. Also, when tool sensitivity is only reported up to the score of the first matched decoy sequence, a large decoy set consisting of reversed sequences can obscure sensitivity differences between tools. As a result of these observations, we advise that reversed biological sequences be used as decoys only when care is taken to remove positive matches in the original (un-reversed) sequences, or when overstatement of false labeling is not a concern. Though the primary focus of the analysis is on sequence annotation, we also demonstrate that the prevalence of internal palindromes may lead to an overstatement of the rate of false labels in protein identification with mass spectrometry.

bioinformatics↗

bioRxiv2023

DISCO: A deep learning ensemble for uncertainty-aware segmentation of acoustic signals

Recordings of animal sounds enable a wide range of observational inquiries into animal communication, behavior, and diversity. Automated labeling of sound events in such recordings can improve both throughput and reproducibility of analysis. Here, we describe our software package for labeling sound elements in recordings of animal sounds and demonstrate its utility on recordings of beetle courtships and whale songs. The software, DISCO, computes sensible confidence estimates and produces labels with high precision and accuracy. In addition to the core labeling software, it provides a simple tool for labeling training data, and a visual system for analysis of resulting labels. DISCO is open-source and easy to install, it works with standard file formats, and it presents a low barrier of entry to use.

bioinformatics↗

bioRxiv2022

Mirage2's high-quality spliced protein-to-genome mappings produce accurate multiple-sequence alignments of isoforms

The organization of homologous protein sequences into multiple sequence alignments (MSAs) is a cornerstone of modern analysis of proteins. Recent focus on the importance of alternatively-spliced isoforms in disease and cell biology has highlighted the need for MSA software that can appropriately account for isoforms and the exon-length insertions or deletions that isoforms may have relative to each other. We previously developed Mirage, a software package for generating MSAs for isoforms spanning multiple species. Here, we present Mirage2, which retains the fundamental algorithms of the original Mirage implementation while providing substantially improved translated mapping and improving several aspects of usability. We demonstrate that Mirage2 is highly effective at mapping proteins to their encoding exons, and that these protein-genome mappings lead to extremely accurate intron-aware alignments. Additionally, Mirage2 implements a number of engineering improvements that simplify installation and use.

bioinformatics↗

bioRxiv2021

Accuracy of multiple sequence alignment methods in the reconstruction of transposable element families

The construction of a high-quality multiple sequence alignment (MSA) from copies of a transposable element (TE) is a critical step in the characterization of a new TE family. Most studies of MSA accuracy have been conducted on protein or RNA sequence families where structural features and strong signals of selection may assist with alignment. Less attention has been given to the quality of sequence alignments involving neutrally evolving DNA sequences such as those resulting from TE replication. Such alignments play an important role in understanding and representing TE family history. Transposable element sequences are challenging to align due to their wide divergence ranges, fragmentation, and predominantly-neutral mutation patterns. To gain insight into the effects of these properties on MSA accuracy, we developed a simulator of TE sequence evolution, and used it to generate a benchmark with which we evaluated the MSA predictions produced by several popular aligners, along with Refiner, a method we developed in the context of our RepeatModeler software. We find that MAFFT and Refiner generally outperform other aligners for low to medium divergence simulated sequences, while Refiner is uniquely effective when tasked with aligning high-divergent and fragmented instances of a family. As a result, consensus sequences derived from Refiner-based MSAs are more similar to the true consensus.

genomics↗

bioRxiv2021

PolyA: a tool for adjudicating competing annotations of biological sequences

Annotation of a biological sequence is usually performed by aligning that sequence to a database of known sequence elements. When that database contains elements that are highly similar to each other, the proper annotation may be ambiguous, because several entries in the database produce high-scoring alignments. Typical annotation methods work by assigning a label based on the candidate annotation with the highest alignment score; this can overstate annotation certainty, mislabel boundaries, and fails to identify large scale rearrangements or insertions within the annotated sequence. Here, we present a new software tool, PolyA, that adjudicates between competing alignment-based annotations by computing estimates of annotation confidence, identifying a trace with maximal confidence, and recursively splicing/stitching inserted elements. PolyA communicates annotation certainty, identifies large scale rearrangements, and detects boundaries between neighboring elements.

bioinformatics↗

bioRxiv2021

An optimized FM-index library for nucleotide and amino acid search

Pattern matching is a key step in a variety of biological sequence analysis pipelines. The FM-index is a compressed data structure for pattern matching, with search run time that is independent of the length of the database text. We present AvxWindowedFMindex (AWFM-index), an open-source, thread-parallel FM-index library written in C that is optimized for indexing nucleotide and amino acid sequences. AWFM-index is easy to incorporate into bioinformatics software and is able to perform exact match count and locate queries ~2-4x faster than SeqAn3s FM-index implementation for nucleotide search, and ~2-6x faster for amino acid search in a single-threaded context. This performance is due to (i) a new approach to storing FM-index data in a strided bit-vector format that enables extremely efficient computation of the FM-index occurrence function via AVX2 bitwise instructions, and (ii) inclusion of a cache-efficient lookup table for partial k-mer searches. AWFM-index also trivially parallelizes to multiple threads with good scaling, and enables efficient on-disk storage of the memory-intensive suffix array. The open-source library is available for download at https://github.com/TravisWheelerLab/AvxWindowFmIndex.

bioinformatics↗