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Metagenomics data analysis
Published on Jan 31, 20171002 Views
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Chapter list
Metagenomics data analytics00:00
Map01:16
Main concepts01:58
Microbiome impacts on human health04:48
Diet as modulator of gut microbiota08:06
ML and diet - based therapeutics09:24
Maternal - fetal microbial landscape10:37
Discovery pipeline for developing microbiome characterization12:06
Gut microbiota and inflammation12:54
Microbiome in malnourished children - 114:19
Microbiome in malnourished children - 214:54
Applications of the “metagenomic clock” in preclinical studies17:10
Map/219:41
Next Generation sequencing20:11
Which platforms for metagenomics markers?20:55
Studying Metagenomics with NGS22:26
Bioinformatics and the microbiome23:55
Major reference databases25:10
Bioinformatics + ML Framework26:04
The FBK Kore HPC cluster 27:26
Roche 454 junior sequencer - 129:03
A warning about compositional data31:32
Roche 454 junior sequencer - 232:15
Trimming primers and adaptors32:48
Assigning Taxa33:34
Map/334:38
Conceptual pipelines: meta - blocks34:46
The MAQC/SEQC initiatives34:59
Need for Data Analysis Protocols37:27
Repeat 10 times38:11
A MAQC - II/SEQC Data Analysis Plan39:02
Summary of decisions/Challenges40:16
Minepy40:45
Microbiome: network differences41:57
Map/442:35
Example 1: Diet Induced Diversity42:42
Difference induced by diet: Networks44:11
Example 245:29
Results (Kang 2013)47:31
Results (FBK 2014)48:18
Network dysbiosis49:13
Microbiota & Behaviour49:39
B. fragilis51:02
Results (Hsiao et al 2013)52:24
Results (FBK)52:57
IBD OPBG clinical dataset - 154:05
IBD OPBG clinical dataset - 256:48
IBD Classification models58:08
Top discriminant features59:40
Microbiome characterization01:00:20
Networks: IBD vs. healthy01:00:35
Calprotectin level is associated to increasing dysbiosis in Biopsy Networks - 101:01:01
Calprotectin level is associated to increasing dysbiosis in Biopsy Networks - 201:01:52
Markers patterns vs Calprotectin01:03:08
Biopsy IBD Networks01:03:30
Biopsy networks trajectories01:04:13
Networks: healthy vs IBD01:05:25
Summary 101:05:39
Map/501:07:51
MetAML Results01:10:00
ML Framework for Metagenomics01:13:20
For reproducibility and upscaling01:13:35
Summary 201:14:52
Acknowledgments01:15:51