3rd Workshop on Statistical and Algorithmic Challenges in Microbiome Data Analysis

Apr 1, 2019, 8:00 AM → Apr 2, 2019, 9:35 PM America/New_York

Gerald D. Fischbach Auditorium (Simons Foundation )

Eric Alm (Massachusetts Institute of Technology Biological Engineering), Michelle Badri (New York University), Richard Bonneau (Flatiron Institute / New York University), Claire Duvallet (Center for Microbiome Informatics and Therapeutics), Rob Knight, Jamie Morton (Flatiron Institute / Knights Lab), Vicki Mountain (Center for Microbiome Informatics and Therapeutics)

Center for Computational Biology (Simons Foundation, NYU), and the Center for Microbiome Informatics and Therapeutics (at MIT) will hold their Third Workshop on Statistical and Algorithmic Challenges in Microbiome Data Analysis at The Simons Foundation, 160 Fifth Avenue, 2nd Floor, G.D.F. Auditorium, New York, New York 10010 on April 1 – 2, 2019.

As with previous years, the SACMDA workshop is a forum to advance research within the growing community of developers of computational and statistical methods from the microbiome field and related disciplines. Dissemination, exchange, and synthesis of ideas is encouraged by inviting by junior faculty members or postdoctoral associates to present the tools and algorithms that they conceived and developed. Plenty of time will be allocated for discussion so that peers and senior faculty can provide constructive feedback and initiate collaboration.

Susan Holmes has been selected as our Keynote to inspire the group to rise to new challenges.

Monday, April 1

8:00 AM Breakfast

1 Welcome Remarks by Eric Alm and Rich Bonneau

2 Embracing Ambiguity In The Taxonomic Classification Of Microbiome Sequencing Data

Shah_v1.pptx

3 Metagenomics Strain Resolution On Assembly Graphs

4 Pangenome Graph Structures Reveal More Genetic Variation Between Divergent Genomes

Colquhoun_v1.pdf

5 Multi-Resolution Phylogenetic Analysis Of Known And Unexplored Members Of The Human Microbiome With PhyloPhlAn2

Asnicar_v1.pdf

10:40 AM Break

6 Environmental Selection Of Butyrate Producers Drives Genetic But Not Species Convergence

7 Spatial Metagenomic Characterization Of Microbial Biogeography In The Gut

8 Challenges And Approaches To System-Level Investigation In The HIV-Associate Microbiome And Metabolic Health

9 Meta-Analysis Of Colorectal Cancer Metagenomics Studies Reveals Robust Microbial Biomarkers

12:35 PM Lunch

10 FEAST: Fast Expectation-Maximization For Microbial Source Tracking

11 Controlling For Spurious Associations In Case-Control Microbiome Studies

12 Structural Variation In The Gut Microbiome Associates With Host Health

13 Methods For Statistical Inference With Predicted Outcome Data

3:30 PM Break

14 Computational Non-Ribosomal Peptide Antibiotic Discovery: Metabolomics Meets Microbiome

15 Discovering Novel Small Molecules From Microbial Communities By Integrating Computational Mass Spectrometry And Metagenome Mining

16 Learning Accurate Representations Of Microbe-Metabolite Interactions

5:00 PM Break

17 A Machine Learning Framework Reveals Common Molecular Interactions Between Gut Microbiome And Host Genes Across Human Disease States

18 Geometric Deep Learning Methods For Large-Scale Structure-Based Function Prediction Of Microbial Proteins

19 The Second Round Of CAMI Challenges

6:25 PM Poster Session and Cocktail Reception

Tuesday, April 2

8:00 AM Breakfast

Continental

20 Opening Remarks by Rob Knight

21 A Scale Normalization Strategy For The Correction Of Compositional Bias In Sparse Sequencing Count Data

22 Bayesian Inference For Removing Contamination In Microbiome Studies

23 Consistent And Correctable Bias In Metagenomic Sequencing Measurements

24 A Probabilistic Compositional Effect Size Metric For Use In Microbiome Datasets

10:40 AM Break

25 Detecting and Correcting for Differential Detection Effects in 16S Data

26 Phylofactorization - Identifying Lineages Of Interest In Microbiome Datasets

27 Selbal: An R Package For The Identification Of Microbial Signatures

28 Balance Regression In Microbiome Studies Using Stochastic Search

29 Weighted Kernel Machine Regression For Microbiome Compositional Data Analysis

1:05 PM Lunch

30 Challenges In The Analyses Of Multidomain Longitudinal Data: Layered Solutions.

31 Near-Causal Inference With Time-Ordered Association Testing For Multiomic Time Series

32 A Generic Multivariate Framework For The Integration Of Microbiome Longitudinal Studies With Other Data Types

3:50 PM Break

33 A Bayesian, Probabilistic Method For Analyzing Multinomially Distributed Data.

34 Joint Modeling Of Longitudinal Microbiome And Time-To-Event Data With Application To A Type I Diabetes Study

35 Scalable Bayesian Multinomial Logistic-Normal Models For The Analysis Of Microbiome Data

5:25 PM Break

36 Robust And Scalable Models Of Microbiome Dynamics

37 Elucidating Microbial Spatiotemporal Dynamics With Compositional Tensor Factorization

38 QIIME 2 Enables Robust And Reproducible Bioinformatics

39 Closing Remarks by Eric Alm, Rich Bonneau & Rob Knight

7:00 PM Cocktail Reception