Treangen lab at Rice University

Treangen Lab, September 2024

Rice University, Houston, TX

Welcome to the Treangen lab website, a bioinformatics and computational biology research group at Rice University, led by Dr. Todd J. Treangen (Associate Professor in the Department of Computer Science and affiliated Associate Professor in the Department of Bioengineering).

Since launching in 2018, the Treangen lab has focused on developing scalable and accurate computational algorithms, methods, and open-source software tools to answer research questions specific to: (i) identifying and characterizing known and novel microbial pathogens, (ii) population scale microbial genome variation and evolution, and (iii) studying microbial communities across time and space in the environment and human-host-associated microbiomes.

The Treangen lab is actively funded by CDC, NIH (NIA, NIAID), and NSF (EF, IIS, SHF). Prior support from: c3.ai DTI, DHS, IARPA, and NIH.

news

Aug 30, 2024	Bryce Kille successfully passed his Thesis Proposal Defense, now a Ph.D. candidate!
Aug 29, 2024	Fall’24 semester COMP 416/519 kicks off
Aug 01, 2024	Genome Sleuths’24 has concluded, huge thanks to all of the undergrad participants and grad student mentors

selected publications

SeqScreen: accurate and sensitive functional screening of pathogenic sequences via ensemble learning

Advait Balaji, Bryce Kille, Anthony D Kappell, and 8 more authors

Genome biology, 2022

Bib

@article{balaji2022seqscreen,
  title = {SeqScreen: accurate and sensitive functional screening of pathogenic sequences via ensemble learning},
  author = {Balaji, Advait and Kille, Bryce and Kappell, Anthony D and Godbold, Gene D and Diep, Madeline and Elworth, RA Leo and Qian, Zhiqin and Albin, Dreycey and Nasko, Daniel J and Shah, Nidhi and others},
  year = {2022},
  journal = {Genome biology},
  publisher = {BioMed Central London},
  volume = {23},
  number = {1},
  pages = {133}
}

Emu: species-level microbial community profiling of full-length 16S rRNA Oxford Nanopore sequencing data

Kristen D Curry, Qi Wang, Michael G Nute, and 8 more authors

Nature methods, 2022

Bib

@article{curry2022emu,
  title = {Emu: species-level microbial community profiling of full-length 16S rRNA Oxford Nanopore sequencing data},
  author = {Curry, Kristen D and Wang, Qi and Nute, Michael G and Tyshaieva, Alona and Reeves, Elizabeth and Soriano, Sirena and Wu, Qinglong and Graeber, Enid and Finzer, Patrick and Mendling, Werner and others},
  year = {2022},
  journal = {Nature methods},
  publisher = {Nature Publishing Group US New York},
  volume = {19},
  number = {7},
  pages = {845--853}
}

Current progress and open challenges for applying deep learning across the biosciences

Nicolae Sapoval, Amirali Aghazadeh, Michael G Nute, and 8 more authors

Nature Communications, 2022

Bib

@article{sapoval2022current,
  title = {Current progress and open challenges for applying deep learning across the biosciences},
  author = {Sapoval, Nicolae and Aghazadeh, Amirali and Nute, Michael G and Antunes, Dinler A and Balaji, Advait and Baraniuk, Richard and Barberan, CJ and Dannenfelser, Ruth and Dun, Chen and Edrisi, Mohammadamin and others},
  year = {2022},
  journal = {Nature Communications},
  publisher = {Nature Publishing Group UK London},
  volume = {13},
  number = {1},
  pages = {1728}
}