TAU CS 0368-3116-01
Seminar in Computational Methods in Metagenomics and Microbiome Research

Instructor: Elhanan Borenstein (elbo [ a t ] tauex.tau.ac.il)
Schedule: Wednesdays, 13:00-15:00, Ornstein 110.

The human microbiome – the diverse ensemble of microorganisms that live in and on the human body – is an incredibly complex ecosystem with a tremendous impact on our health. Recent years have witnessed a revolution in our ability to profile the microbiome across multiple functional levels, driven by exciting advances in high-throughput next generation technologies that can assay the composition of species, genes, transcripts, proteins, and metabolites in the microbiome. Importantly, however, such 'meta-genomic' technologies also pose daunting and unique computational challenges that cannot be addressed by traditional bioinformatic and genomic analysis methods. In this seminar, we will cover some of the key algorithms recently developed for processing and analyzing such metagenomic data and for accurately mapping the composition of the microbiome and its role in human health.

The seminar is open for both BSc and MSc students.

No prior knowledge in biology is required – all necessary background in biology will be given in the first meetings!


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Lectures and Resources:
(Note: Links to resources will become live as the course progresses)

1Feb 27 Introduction; Seminar logistics; Background: Microbes, microbiomes, metagenomics 1,2BorensteinSlides
2Mar 6 Taxonomic and functional profiling of the microbiome 3,4BorensteinSlides
3Mar 13 NO CLASS --
4Mar 20 From 16S sequencing to taxonomic profiles 5Gal CohenSlides1
5Mar 27 From 16S sequencing to taxonomic profiles 7,8Hagai Levi, Michael Khaitov Slides1 Slides2
6Apr 3 From 16S sequencing to taxonomic profiles 9,10Guy Bivas, Judith Brener Slides1 Slides2
7Apr 10 From shotgun metagenomics to species- and strain-level taxonomic profiles 11,12Shahar Azulay, Danielle Miller Slides1 Slides2
8May 1 From shotgun metagenomics to species- and strain-level taxonomic profiles 14Alon Tzur Slides2
9May 15 Comparing Taxonomic Profiles: Beta Diversity and Balance Trees 15,16Yael Ben Ari, Itamar Curiel Slides1 Slides2
10May 22 Comparing Taxonomic Profiles: Beta Diversity and Balance Trees 17,18Yotam Nitzan, Roee Wodislawski Slides1 Slides2 Slides3
11May 29 Inferring Species Interactions 19,20Omer Tirosh Slides1
12Jun 5 Inferring Species Interactions 21,22Ido Rozenberg, Guy Shapira Slides1 Slides2
13Jun 12 Other 13Dan Coster Slides1


  1. Cho, I. & Blaser, M. J. The human microbiome: at the interface of health and disease. Nat. Rev. Genet. 13, 260–270 (2012)
  2. Gilbert, J. A. et al. Current understanding of the human microbiome. Nat. Med. (2018)
  3. Quince, C., Walker, A. W., Simpson, J. T., Loman, N. J. & Segata, N. Shotgun metagenomics, from sampling to analysis. Nat. Biotechnol. (2017)
  4. Noecker, C., McNally, C. P., Eng, A. & Borenstein, E. High-resolution characterization of the human microbiome. Transl. Res. (2017)
  5. Robert C Edgar. UPARSE: highly accurate OTU sequences from microbial amplicon reads. Nature Methods volume 10, 996–998 (2013)
  6. Xiaoyu Wang et al. M-pick, a modularity-based method for OTU picking of 16S rRNA sequences. BMC Bioinformatics, 14:43 (2013)
  7. A Murat Eren et al. Minimum entropy decomposition: Unsupervised oligotyping for sensitive partitioning of high-throughput marker gene sequences. The ISME Journal 9, 968–979 (2015)
  8. Mahé F et al. Swarm: robust and fast clustering method for amplicon-based studies. PeerJ 2:e593 (2014) [Check also Swarm V2]
  9. Callahan BJ et al. DADA2: High-resolution sample inference from Illumina amplicon data. Nature Methods, 13, 581–583 (2016)
  10. Amnon Amir et al. Deblur Rapidly Resolves Single-Nucleotide Community Sequence Patterns. mSystems 2,2,00191-16 (2017)
  11. Nicola Segata et al. Metagenomic microbial community profiling using unique clade-specific marker genes. Nature Methods volume 9, pages 811–814 (2012)
  12. Chengwei Luo et al. ConStrains identifies microbial strains in metagenomic datasets. Nature Biotechnology volume 33, pages 1045–1052 (2015)
  13. Brian Cleary et al. Detection of low-abundance bacterial strains in metagenomic datasets by eigengenome partitioning, Nature Biotechnology volume 33, pages 1053–1060 (2015)
  14. Tae-Hyuk Ahn et al. Sigma: Strain-level inference of genomes from metagenomic analysis for biosurveillance. Bioinformatics, 31, 2, 170–177 (2015)
  15. Lozupone and Knight. UniFrac: a New Phylogenetic Method for Comparing Microbial Communities. Applied and Environmental Microbiology, 71:8228-8235 (2005)
  16. Martino C et al. A Novel Sparse Compositional Technique Reveals Microbial Perturbations. mSystems, 4(1), 813–13 (2019)
  17. James T. Morton et al. Balance Trees Reveal Microbial Niche Differentiation. mSystems. Volume 2 Issue 1 e00162-16 (2017)
  18. Justin D Silverman et al. A phylogenetic transform enhances analysis of compositional microbiota data. eLife;6:e21887 (2017)
  19. Faust K et al. Microbial Co-occurrence Relationships in the Human Microbiome. PLoS Comput Biol 8(7) (2012)
  20. Friedman and Alm. Inferring Correlation Networks from Genomic Survey Data. PLoS Comput Biol 8(9): e1002687(2012)
  21. Levy and Borenstein. Metabolic modeling of species interaction in the human microbiome elucidates community-level assembly rules. PNAS (Proc. Natl. Acad. Sci. USA), 110(31), 12804-12809 (2013)
  22. Stein RR et al. Ecological Modeling from Time-Series Inference: Insight into Dynamics and Stability of Intestinal Microbiota. PLoS Comput Biol 9(12): e1003388 (2013)


Elhanan Borenstein
Blavatnik School of Computer Science
Sackler Faculty of Medicine
Tel Aviv University