Vocabulary and jargon in microbial ecology and some background information

In response to Hal Levin’s comment on my previous post: Thank you ISIAQ for the “Vocabulary of the Indoor Air Sciences” I am starting to put together some definitions and background information on the terminology of microbial ecology studies.  Please suggest any / all terms that you think would be useful to define and I will do my best to put together text on them.

Work in Progress as of June 9, 2014 (Note – I did not mean to make this live yet, but once I did I decided to just leave it out there – incomplete though it may be.)

Some microbial ecology terminology (with a focus on sequence based studies at least for now).  Links to Wikipedia provided when available. Note – I will make a sortable / alphabetical version of this once it is more complete.

Ecology and Communtiies

• Ecology. The study of the interactions among organisms and between organisms and their environment.
• Microbial ecology. A subset of ecology focusing on microbes.
• Biogeography. Patterns in the distribution of organisms over space
• Microbial biogeography. A subset of biogeography focusing on microbes.
• Community. A group of different kinds of organisms (at least two) that occupy the same space at the same time (alternative definition –  a group of different kinds of organisms (at least two) that interact and share the same environment – see comment).
• Microbiome. A community of microbes that occupy a specific space or collection of spaces.  For example, “the human microbiome” refers generally to the communities of microbes found in and on humans.  The “built environment microbiome” refers to the communities of microbes found in and on entities that have been built by humans.
• Ecosystem. A community of organisms and their environment that operate as a system.

Diversity Terminology

• Within versus between community measures
  • Alpha diversity. Diversity within a community (though note there is a complicated history of this term and its usage is not completely uniform).
  • Beta diversity. Variation in diversity between communities (though note there is a complicated history of this term and its usage is not completely uniform).
• Kinds of diversity
  • Taxonomic diversity. Many metrics focus on measures of the number of species (or sometimes other taxaonomic groups) in a sample.
    • Richness. The total number of a particular taxonomic level (e.g., species, genus, family, etc) in a particular area or set of areas.
    • Species richness. The total number of species in a particular area or set of areas
    • OTU (operational taxonomic unit). Initially defined as, in essence, the group being examined as part of a taxonomic study.  Most commonly used in DNA sequence based studied of organisms where some measure of sequence similarity is used to cluster individual sequences into groups.
  • Phylogenetic diversity.  A measure of diversity that accounts for the evolutionary (aka phylogenetic) distances between organisms.  It is calculated by summing up the total length of branches in an evolutionary (aka phylogenetic) tree.  Some people (myself included) prefer phylogenetic measures of diversity because they do a better job of taking into account the relatedness of organisms.
  • Functional diversity. A measure of the diversity of functions seen in a community.  This can be very hard to measure and currently it is mainly estimated from analyzing metagenomic data.
• These different measures of diversity can be used to calculate both alpha and beta diversity for communities.  For example, alpha diversity can be calculated via taxonomic diversity (i.e., counting the species in sample) or by phylogenetic diversity (the sum total length of branches in a phylogenetic trees). Similarly, beta diversity can be calculated by comparing taxonomic lists between communities or by comparing phylogenetic trees of organisms in different communities.  Thus we can have taxonomic alpha diversity, taxonomic beta diversity, phylogenetic alpha diversity and phylogenetic beta diversity.  The same can be done in theory for “functional diversity” although this is not done extensively at this time.

Kinds of microbes

• Bacteria
• Archaea
• Eukaryotes
• Viruses

Methods for studying microbial diversity (see Table 1 from Eisen 2007).

There are a diversity (pun intended) of approaches for assessing the diversity of microbes.  There are at least three main categories of such methods:

• microscopic (i.e., using a microscope)
• culturing (i.e., growing microbes in defined conditions in the lab)
• molecular (i.e., assaying microbes in samples by directly characterizing their molecular components). Molecular approaches are generally known as “culture-independent”.

Molecular methods for characterizing microbial diveristy and for generally studying microbes directly from environmental samples.

In general, molecular methods for assessing microbial diversity involve collecting samples from environments and then characterizing major molecular components of the organisms in those samples.  Although there are many kinds of molecular components that one could look at, most of the work focuses on one of four kinds of molecules: DNA, RNA, protein, and “metabolites” (which can include a diversity of other compounds).  More on each of these is below:

• DNA. Deoxyribonucleic acid.  Used as the heritable genetic material for all cellular organisms as well as some viruses.
• RNA. Ribonucleic acid.  Used as the heritable genetic material for some viruses.  Used to convert instructions found in the genetic material (either DNA or RNA) into cellular functions.  RNAs in cellular organisms are made by transcription from DNA.  There are multiple kinds of RNAs used by organisms including:
  • mRNA (messenger RNA). These RNAs are translated into proteins using the genetic code.
  • tRNA (transfer RNA). These RNAs are involved in the process of translation – they serve to read the genetic code and transfer that information to the machinery that makes proteins.
  • rRNA (ribosomal RNA) These RNAs are components of the ribosome, which is the machine that carries out translation of mRNA into proteins.
• Proteins.
• Metabolites.

• Sequencing and molecular biology
  • PCR – polymerase chain reaction
  • NGS – next generation sequencing
  • Sanger – Sanger sequencing
  • Marker genes
  • ribosomal RNA. See our fact sheets here and here.
  • Metagenomics
  • Sequence quality
  • Paired end sequencing
• Sequence Analysis and Bioinformatics
  • Alignment
  • Clustering
  • Chimera detection
  • Phylogenetics
  • Assembly
  • Annotation