Komb

KOMB Pipeline

Background

Characterizing metagenomes via kmer-based, database-dependent taxonomic classification has provided insight into underlying microbiome dynamics. However, novel approaches are needed to track community dynamics and genomic flux within metagenomes particularly in response to perturbations. We describe KOMB, a novel method for tracking genome level dynamics within microbiomes. KOMB utilizes K-core decomposition to identify flux in repetitive and homologous regions within microbiomes. K-core decomposition partitions the graph into shells containing nodes of degree at least K, yielding reduced computational complexity compared to prior approaches. Through validation on a synthetic community, we show KOMB recovers and profiles repetitive genomic regions in the sample. KOMB is shown to identify functionally-important regions in Human Microbiome Project datasets, and was used to analyze longitudinal data and identify keystone taxa in Fecal Microbiota Transplantation (FMT) samples. In summary, KOMB represents a novel graph-based reference-free approach to microbiome characterization.

Collaborators

  • Dr. Santiago Segarra (Rice University)
  • Dr. Charlie Seto (NCBI)
  • Dr. Tor Savidge (Texas Children’s Hospital)
Advait Balaji
Advait Balaji
PhD student

Advait (4th year PhD student) obtained a dual degree, B.E Computer Science and MS Biological Sciences from BITS, Pilani in India. During his undergraduate degree, he received the Khorana Scholarship (2016) from the Indo-US Science and Technology Forum and also a thesis fellowship (2017-18) to work at Icahn School of Medicine, Mount Sinai, NY. At Mount Sinai, he worked on creating a Sub-cellular process-based ontology that predicts whole cell function using Natural Language Processing. His research interests are at the intersection of genomic data science and designing efficient algorithms to analyze genomic data.

Nicolae Sapoval
Nicolae Sapoval
PhD student

Nick (3rd year PhD student) obtained a B.S. degree in Computer Science and a B.S. with Honors in Mathematics from the University of Chicago. At the University of Chicago Nick worked in wireless networks research and later in computational biophysics focusing on conformational transition modeling for insulin degrading enzyme. His current interests are in the areas of computational biology with a focus on genomic data.

Dr. Mike Nute
Dr. Mike Nute
Postdoctoral Scientist

Mike (Postdoctoral Scientist) received his Ph.D. in Statistics in 2019 from the University of Illinois at Urbana-Champaign where he was advised by Dr. Tandy Warnow in the Department of Computer Science and worked on algorithms related to multiple sequence alignment and phylogenetic tree estimation, in particular applying these methods to studying microbial communities. He was co-advised by Dr. Rebecca Stumpf in the Department of Anthropology where he and other lab members developed novel methods to compare the microbiomes of human and non-human primates. His research interest is in discovering a new applications for our understanding of microbial communities.

Todd J. Treangen
Todd J. Treangen
Assistant Professor of Computer Science

My research interests include algorithms and data structures for efficient analysis of microbial genomes and metagenomes

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