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Project Organization and Management for Metagenomics: Glossary

Key Points

Data Tidiness
  • Assigning and keeping track of appropriate unique ID identifiers must be a well-thought process.

  • Metadata is key for you and others to work with your data.

  • Tabular data needs to be structured to work with it effectively.

  • Human microbiome data requires informed consent and confidentiality.

Planning for NGS Projects
  • Data being sent to a sequencing center also needs to be structured so you can use it.

  • Raw sequencing data should be kept raw somewhere, so you can always go back to the original files.

Examining Data on the NCBI SRA Database
  • Public data repositories are a great source of genomic data.

  • You are likely to put your data on a public repository.


a unique identifier assigned to each sequence or set of sequences
The Basic Local Alignment Search Tool at NCBI that searches for similarities between known and unknown biomolecules like DNA
categorical variable
Variables can be classified as categorical (aka, qualitative) or quantitative (aka, numerical). Categorical variables take on a fixed number of values that are names or labels.
cleaned data
data that has been manipulated post-collection to remove errors or inaccuracies, introduce desired formatting changes, or otherwise prepare the data for analysis
conditional formatting
formatting that is applied to a specific cell or range of cells depending on a set of criteria
CSV (comma separated values) format
a plain text file format in which values are separated by commas
a variable that takes on a limited number of possible values (i.e. categorical data)
gigabyte of file storage or file size
a gigabase represents one billion nucleic acid bases (Gbp may indicate one billion base pairs of nucleic acid)
names at tops of columns that are descriptive about the column contents (sometimes optional)
data which describes other data
common acronym for “Next Generation Sequencing” currently being replaced by “High Throughput Sequencing”
null value
a value used to record observations missing from a dataset
a single measurement or record of the object being recorded (e.g. the weight of a particular mouse)
plain text
unformatted text
quality assurance
any process which checks data for validity during entry
quality control
any process which removes problematic data from a dataset
raw data
data that has not been manipulated and represents actual recorded values
rich text
formatted text (e.g. text that appears bolded, colored or italicized)
a collection of characters (e.g. “thisisastring”)
TSV (tab separated values) format
a plain text file format in which values are separated by tabs
a category of data being collected on the object being recorded (e.g. a mouse’s weight)


This page is adapted from the Project Organization and Management for Genomics corresponding page.