Integrative analysis if environmental sequences

1 hour 47 secs, 225.39 MB, iPod Video 480x270, 29.97 fps, 44100 Hz, 506.26 kbits/sec

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Description:	Huson, D (University of Tubingen) Thursday 23 June 2011, 14:00-15:00

Created:

2011-06-27 18:09

Collection:

Phylogenetics

Publisher:

Isaac Newton Institute

Huson, D

Language:

eng (English)

Credits:

Author:	Huson, D
Director:	Steve Greenham


Abstract:	In metagenomics, the aim is to understand the composition and operation of complex microbial assemblages in environmental samples through sequencing and analysis of their DNA. Similarly, metatranscriptomics and metaproteomics target the RNA and proteins obtained from such samples. A major challenge in the analysis of environmental sequences is data integration. The question is how to anaylze different types of data in a unified approach, addressing both taxonomic and functional aspects. We will discuss how to perform taxonomic analysis based on the NCBI taxonomy. Functional analysis can be based on classifications such as the SEED classification of functional roles and subsystems or the KEGG classification of pathways and enzymes. We will also discuss how to compare different datasets using statistical approaches, ecological indices and methods such as Neighbor-net and multi-dimensional scaling.

Abstract:

In metagenomics, the aim is to understand the composition and operation of complex microbial assemblages in environmental samples through sequencing and analysis of their DNA. Similarly, metatranscriptomics and metaproteomics target the RNA and proteins obtained from such samples. A major challenge in the analysis of environmental sequences is data integration. The question is how to anaylze different types of data in a unified approach, addressing both taxonomic and functional aspects. We will discuss how to perform taxonomic analysis based on the NCBI taxonomy. Functional analysis can be based on classifications such as the SEED classification of functional roles and subsystems or the KEGG classification of pathways and enzymes. We will also discuss how to compare different datasets using statistical approaches, ecological indices and methods such as Neighbor-net and multi-dimensional scaling.

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