Dr. Travis J Wheeler

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Academic Career Length

12Years (2015-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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26 November 2015 in Nucleic Acids Research

Journal Article

The Dfam database of repetitive DNA families

Thomas A. Jones

Repetitive DNA, especially that due to transposable elements (TEs), makes up a large fraction of many genomes. Dfam is an open access database of families of repetitive DNA elements, in which each family is represented by a multiple sequence alignment and a profile hidden Markov model (HMM). The initial release of Dfam, featured in the 2013 NAR Database Issue, contained 1143 families of repetitive elements found in humans, and was used to produce more than 100 Mb of additional annotation of TE-derived regions in the human genome, with improved speed. Here, we describe recent advances, most notably expansion to 4150 total families including a comprehensive set of known repeat families from four new organisms (mouse, zebrafish, fly and nematode). We describe improvements to coverage, and to our methods for identifying and reducing false annotation. We also describe updates to the website interface. The Dfam website has moved to http://dfam.org. Seed alignments, profile HMMs, hit lists and other underlying data are available for download.

1 April 2022 in Science

Journal Article

Complete genomic and epigenetic maps of human centromeres

Nicolas Altemose

Glennis Amelia Logsdon

Andrey V. Bzikadze

Pragya Sidhwani

Sasha A. Langley

Karen Miga

+53

Existing human genome assemblies have almost entirely excluded repetitive sequences within and near centromeres, limiting our understanding of their organization, evolution, and functions, which include facilitating proper chromosome segregation. Now, a complete, telomere-to-telomere human genome assembly (T2T-CHM13) has enabled us to comprehensively characterize pericentromeric and centromeric repeats, which constitute 6.2% of the genome (189.9 megabases). Detailed maps of these regions revealed multimegabase structural rearrangements, including in active centromeric repeat arrays. Analysis of centromere-associated sequences uncovered a strong relationship between the position of the centromere and the evolution of the surrounding DNA through layered repeat expansions. Furthermore, comparisons of chromosome X centromeres across a diverse panel of individuals illuminated high degrees of structural, epigenetic, and sequence variation in these complex and rapidly evolving regions.

1 April 2022 in Science

Journal Article

From telomere to telomere: The transcriptional and epigenetic state of human repeat elements

Gabrielle A. Hartley

+21

Mobile elements and repetitive genomic regions are sources of lineage-specific genomic innovation and uniquely fingerprint individual genomes. Comprehensive analyses of such repeat elements, including those found in more complex regions of the genome, require a complete, linear genome assembly. We present a de novo repeat discovery and annotation of the T2T-CHM13 human reference genome. We identified previously unknown satellite arrays, expanded the catalog of variants and families for repeats and mobile elements, characterized classes of complex composite repeats, and located retroelement transduction events. We detected nascent transcription and delineated CpG methylation profiles to define the structure of transcriptionally active retroelements in humans, including those in centromeres. These data expand our insight into the diversity, distribution, and evolution of repetitive regions that have shaped the human genome.

4 August 2015 in Mobile DNA

Journal Article

A call for benchmarking transposable element annotation methods

+12

DNA derived from transposable elements (TEs) constitutes large parts of the genomes of complex eukaryotes, with major impacts not only on genomic research but also on how organisms evolve and function. Although a variety of methods and tools have been developed to detect and annotate TEs, there are as yet no standard benchmarks—that is, no standard way to measure or compare their accuracy. This lack of accuracy assessment calls into question conclusions from a wide range of research that depends explicitly or implicitly on TE annotation. In the absence of standard benchmarks, toolmakers are impeded in improving their tools, annotators cannot properly assess which tools might best suit their needs, and downstream researchers cannot judge how accuracy limitations might impact their studies. We therefore propose that the TE research community create and adopt standard TE annotation benchmarks, and we call for other researchers to join the authors in making this long-overdue effort a success.

22 May 2018 in Science Signaling

Journal Article

Integration of protein phosphorylation, acetylation, and methylation data sets to outline lung cancer signaling networks

Mark Grimes

Benjamin Hall

Lauren Foltz

Tyler Levy

Klarisa Rikova

Michael Comb

Integrated multiomics network analysis reveals signaling profiles in lung cancer.

16 October 2024 in Nature Communications

Journal Article

A unified framework to analyze transposable element insertion polymorphisms using graph genomes

Cristian Groza

Transposable elements are ubiquitous mobile DNA sequences generating insertion polymorphisms, contributing to genomic diversity. We present GraffiTE, a flexible pipeline to analyze polymorphic mobile elements insertions. By integrating state-of-the-art structural variant detection algorithms and graph genomes, GraffiTE identifies polymorphic mobile elements from genomic assemblies 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. 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.

About Me

Travis J. Wheeler is an Associate Professor in the Department of Pharmacy Practice and Science at the University of Arizona. He earned his Bachelor's (Ecology and Evolutionary Biology, 1995) and PhD (Computer Science, 2009, advised by John Kececioglu and Mike Sanderson) at the University of Arizona, with a several-year intermission in industry. He then joined Sean Eddy's group as a Postdoctoral Associate then Research Scientist at HHMI Janelia Research Campus (through 2014). He then served as an Assistant then Associate Professor of Computer Science at University of Montana, before moving to Arizona. His research group designs algorithms, statistical methods, and software solutions for problems motivated by biological data. His group is particularly focused on the annotation of biological sequences and the accompanying problem of searching for similar sequences within large-scale biological sequence databases, but their work also addresses transposable elements, soil microbiomes, drug discovery, natural language processing, and animal tracking and behavior classification. Projects in his group range from statistical modeling, to indexing and search algorithms, to low-level software optimization, to FPGAs, to deep neural networks, to web services.

Dr. Travis J Wheeler

Dr. Travis J Wheeler

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