Fall 2019: Investigating the Microbial Communities in Mortality Composts (2/8) -- BIT 477/577 Metagenomics

Fall 2019: Investigating the Microbial Communities in Mortality Composts 2 Different metrics. BIT 477/577 Fall 2019 Students and Carlos Goller Learning Objectives - Define and explain the concepts of metadata, OTU, rarefaction curve. - Explain three different diversity metrics. - Identify and describe the limitations and assumptions of certain diversity metrics. What is diversity? Definitions - Metadata - Data about the data. For example, date, location of sample collection, the concentration of DNA samples, etc. - Standards for metadata can be found on the Genomics Standards Consortium (gensc.org) → creates standard descriptors for metadata and sequencing approaches. - - - Genomics Standards Consortium used to help classify the data - Metadata can be distinct to specific fields (clinical microbiology has different metadata than environmental microbiology) - Indicates the “where, when and what” conditions of samples. - - OTU (Operational Taxonomic Units): defines a species (classify sequences together)- used to classify closely related groups based on sequence similarity - - Not reproducible - - Traditionally used as a means of species identification or classifying sequence clusters - - Generally 16S or 18S (ribotyping) - - Reference- Compares against known reference standards - - - It can miss species or misidentify. Only as good as the reference data set. - - - de novo- Compares against data in the set - - Captures information based on what is in the sample. - Based on rarefaction curve - Allows researchers to assess species richness from sampling results - Added parameter: Read number (x) and sequence variability (specification)(y) - Not to be confused with “rarifying” - Normalizing based on the number of sequences present in various samples so that all samples have the same number of sequences - Go back to sample to sub-sample take into account subsample for each sample - Controversy due to exclusion of some collected data. - High abundance organisms affect the likelihood of finding low abundance organisms - Our experiment: Superimposing our data - Comparing species present - Used to determine whether or not we need additional sequencing - Assumptions - Assumes differences are genuine and not errors - Shotgun will be very difficult to capture the rare members - How likely is it that more sequencing will help identify low abundance organisms (i.e. not factored into the graph) - Equal probability of identifying species in samples - Rare organisms may have minimal effect - A higher plateau has more variability. A plateau usually suggests that you sequenced “enough” to identify the majority of the organisms. - Bioinformatics pipeline has minimized sequencing errors - Every “new” read is a new organism - Alpha diversity (focuses on one sample) - General: Within a sample - - Species richness: How many different species are present? - - Species diversity: How different is the distribution? - Beta diversity (across many distinct samples) - - General