Todd J. Treangen

Todd J. Treangen

Assistant Professor of Computer Science

Rice University

About

Given the computational challenges presented by the metagenomic data deluge, coupled with the time-sensitive nature of problems specific to tracking pandemics and synthetic DNA screening, the Treangen lab strives to develop efficient and accurate computational solutions to emerging problems in these fields. Specifically, his research group focuses on the design, development, and implementation of novel algorithms, heuristics, and data structures to solve emerging computational research questions specific to biosecurity, infectious disease monitoring, and host-associated microbiome characterization. The Treangen lab is also dedicated to the dissemination and development of novel open-source bioinformatics methods, software, and pipelines, and to providing “hands on” research opportunities to Rice undergraduates.

Interests
  • Computational Biology
  • Bioinformatics
  • Microbial genomics and Metagenomics
Education
  • PhD in Computer Science, 2008

    Polytechnic University of Catalonia

  • MSc in Computer Science, 2006

    Polytechnic University of Catalonia

  • BSc in Computer Science, 2002

    University of Nebraska

Treangen lab members

Principal Investigators

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Todd J. Treangen

Rice University

Assistant Professor of Computer Science

Computational Biology, Bioinformatics, Microbial genomics and Metagenomics

Grad Students

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Advait Balaji

Rice University

PhD student

Bioinformatics, Graph Theory, Pathogen Detection and Analysis, Machine Learning, Microbial genomics and Metagenomics

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Bryce Kille

Rice University

PhD student

String algorithms, Pangenomics, High performance computing, Cheminformatics

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Kristen Curry

Rice University

PhD student

Computational Biology, Bioinformatics, Microbial genomics and Metagenomics

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Michael Wang

Rice University

PhD student

Computational Biology, Bioinformatics, Microbial genomics and Metagenomics

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Nicolae Sapoval

Rice University

PhD student

Computational Biology, Bioinformatics, Microbial genomics and Metagenomics, Graph algorithms and data structures, Deep learning

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Yilei Fu

Rice University

PhD student

Computational Biology, Bioinformatics, Structural Variation, Microbial genomics and Metagenomics

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Yunxi Liu

Rice University

PhD student

Computational Biology, Bioinformatics, Microbial genomics and Metagenomics

Alumni

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Dr. Michael Nute

Anvil Diagnostics

Postdoctoral Scientist from August 2020 through September 2022

Computational Biology, Bioinformatics, Microbial genomics and Metagenomics

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Qi Wang

Illumina

PhD student from September 2018 through January 2022

Computational Biology, Bioinformatics, Microbial genomics and Metagenomics

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R. Matt Barnett

Center for Research Computing, Rice University

Senior Research Programmer (2021-2022)

Computational Biology, Bioinformatics, Microbial genomics and Metagenomics, Programming Languages, Domain Specific Languages

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R.A. Leo Elworth

Rice University

Postdoctoral Scientist from August 2019 through April 2022

Computational Biology, Bioinformatics, Microbial genomics and Metagenomics

Teaching (Since 2021)

 
 
 
 
 
Fall 2022 semester
COMP 347/547 (Computational Microbial Forensics)
Fall 2022 semester
Aug 2022 – Present Rice University
We will review, critique, and discuss computational methods and approaches for microbial forensics and infectious disease monitoring in the genomics era. The seminar will be divided into topic-specific sessions, focusing on emerging research trends and open challenges in the field. Cross-list: COMP 347.
 
 
 
 
 
Spring 2022 semester
COMP 182 (Algorithmic Thinking)
Spring 2022 semester
Jan 2022 – May 2022 Rice University
The objective of the course is to introduce Algorithmic Thinking as a way of problem solving. To achieve this, the course teaches discrete mathematics and algorithms in the context of real-world applications. The goal is for students to learn the foundations on which most of computer science is built and to understand the interplay between mathematics, algorithms, and programming.
 
 
 
 
 
Fall 2021 semester
COMP 416/519 (Genome Scale Algorithms)
Fall 2021 semester
Aug 2021 – Dec 2021 Rice University
Since the advent of Sanger Sequencing in 1977, computer scientists have been devising algorithms and software tools to interpret and analyze DNA sequences. The field of bioinformatics focuses on computational approaches to solving biological questions. This course will serve as an introduction to widely used algorithms in bioinformatics used for pattern searching, genome assembly, sequence alignment, and clustering of biological data. No prior knowledge of biology is assumed. The class involves several programming assignments. Cross-list: COMP 416.
 
 
 
 
 
Spring 2021 semester
COMP 347/547 (Computational Microbial Forensics)
Spring 2021 semester
Jan 2021 – Apr 2021 Rice University
We will review, critique, and discuss computational methods and approaches for microbial forensics and infectious disease monitoring in the genomics era. The seminar will be divided into topic-specific sessions, focusing on emerging research trends and open challenges in the field. Cross-list: COMP 347.

Blog

Notable news and newsworthy notes

Active Projects

2018-2022

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Squeegee

Squeegee

Squeegee is the first computational method for identifying potential microbial contaminants in the absence of environmental negative control samples.

Variabel

Variabel

Variabel is the first method designed for rescuing low frequency intra-host variants from ONT data alone.

Vulcan

Vulcan

Vulcan is the first long-read mapping framework that combines two distinct gap penalty modes for improved structural variant recall and precision.

Harvest Variants

Harvest Variants

Tracking SARS-CoV-2 variants within the Harvest Software Suite

Bakdrive

Bakdrive

A novel approach to inferring microbial interactions across multiple microbiome samples

DYNAMITE

DYNAMITE

Dynamics of Colonization and Infection by Multidrug-resistant Pathogens in Immunocompromised and Critically Ill Patients

Emu, Species-Level Microbial Community Profiling for Full-Length Nanopore 16S Reads

Emu, Species-Level Microbial Community Profiling for Full-Length Nanopore 16S Reads

Background 16S rRNA based analysis is the established standard for elucidating microbial community composition. While short read 16S analyses are largely confined to genus-level resolution at best since only a portion of the gene is sequenced, full-length 16S sequences have the potential to provide species-level accuracy.

Komb

Komb

Taxonomy-Oblivious Graph-Based Characterization of Genome Dynamics in Microbial Communities.

Parsnp

Parsnp

Large-scale microbial core genome alignment

PlasmidHawk

PlasmidHawk

a lab-of-origin detection software

SeqScreen

SeqScreen

Accurate and Sensitive Functional Screening of Pathogenic Sequences via Ensemble Learning.

Wastewater monitoring for SARS-CoV-2 variants of concern in Houston

Wastewater monitoring for SARS-CoV-2 variants of concern in Houston

Collaboration with Stadler lab at Rice University

Great Horned Owl genome

Great Horned Owl genome

PacBio HiFi genome assembly project

Treangen lab Publications

Most recent 10

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Contact

Lab building blocks

Open-access papers

100%

Open science

100%

Biorxiv

100%

Overleaf

50%

Google docs

50%

Gitlab

100%

Python

50%

cpp
C++

49%

Rust

1%

Funding

PI, Co-PI, or Collaborator

nsf
NSF
iarpa
IARPA
cdc
CDC
nih
NIH
c3
C3.ai DTI
houston
Houston Health Department