LibGuides: HSLS MolBio Workshops: Bioinformatics Data Analysis: Pitt Resources

Data Analysis: what's involved?

Data analysis is a process of inspecting, cleaning, transforming, and modeling data with the goal of discovering useful information, suggesting conclusions, and supporting decision making.

This process results in four sequential categories of collected data:

raw data = original data collected from a source, without any manipulation
cleaned data = modified data, after removal of incorrect, incomplete, irrelevant, duplicated, or improperly formatted data
processed data = data that has been validated, sorted, classified, calculated, or otherwise transformed
analyzed data = data that has been interpreted for meaning

Example of bioinformatics data analysis techniques:

high throughput (microarrays, RNA/DNA-Seq)
DNA/protein sequence manipulation
SNP, genetic variation, Genome Wide Association
functional analysis
signaling, network, and pathway analysis
transcription factor and gene regulatory sequence analysis

The Four C's

The "four C's" cover the broad categories of options for analyzing bioinformatics data, including paying someone else to do it (Core labs), working with another researcher (Collaboration), and doing it yourself either by learning to program (Coding) or using out-of-the-box software (Commercially licensed tools).

Core Labs

Core facilities are centralized technology-based laboratories that maintain and support sophisticated equipment, training, and computational services for a fee. These links are to Pitt/UPMC cores of particular relevance for bioinformatics data analysis.