Genomics:GTL

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Table 1. GTL Data: Thousands of Times Greater than Genome Data
Experiment Templates for a Single Microbe (acronyms)

Class of Experiment	Time Points	Treatments	Conditions	Genetic Variants	Biological Replication	Total Biological Samples	Proteomics Data Volume in Terabytes	Metabolite Data in Terabytes	Transcription Data in Terabytes
Simple	10	1	3	1	3	90	18.0	13.5	0.018
Moderate	25	3	5	1	3	1,125	225.0	168.8	0.225
Upper mid	50	3	5	5	3	11,250	2,250.0	1,687.5	2.25
Complex	20	5	5	20	3	30,000	6,000.0	4,500.0	6
Comprehensive	20	5	5	50	3	75,000	15,000.0	11,250.0	15

Profiling Methods

Proteomics: Looking at a possible 6000 proteins per microbe, assuming ~200 gigabytes per sample

Metabolites: Looking a panel of 500 to 1000 different molecules, assuming ~150 gigabytes per sample

Transcription: 6000 genes and 2 arrays per sample ~100 megabytes

Typically, a single significant scientific question takes the multidimensional analysis of at least 1000 biological samples.

This table shows how quickly GTL experiments will generate terabytes (10¹² bytes) of proteomic, metabolomic, and transcriptomic data. Global proteomics currently generates ~1.0 terabytes (TB) a day with expected 5- to 10-fold increases per year. Not only massive in volume but also very complex, these data span many levels of scale and dimensionality. For example, in a simple study of a microbial system under a single treatment (such as pH or toxin exposure), three different growth states may be studied, with ten samples taken over the growth of the culture. Replicates of each of these samples will be run as part of quality-assurance protocols. This will result in a total of 90 (3 × 10 × 3) analyses and the generation of more than 18 TB of proteomics data, 13.5 TB of metabolomics data, and 0.018 TB of transcriptomics data. If, however, a more complete set of data is taken to achieve greater temporal fidelity and better understand mechanistic response, the amount of data can grow rapidly. This example of growth in data output demonstrates one of the major data-management challenges of GTL. Strategies and technologies for data compression must be developed that avoid "data decimation," which means knowing all the information that must be extracted from raw data before any is discarded. Current proteomics efforts are employing preliminary technologies for near real-time data reduction.