The high-throughput needs in electron tomography and in single particle analysis have driven the parallel implementation of several reconstruction algorithms and software packages on computing clusters.
Moreover, key features of any validation method should be scalability (that is, the capacity to be adapted for low-, morerate-, or high-throughput needs), reproducibility, and the requirement for minimal amounts of precious clinical sample as input material.
Key features of any validation method should be adaptability for low- or high-throughput needs and high reproducibility, and should only require minimal amounts of precious clinical sample as input material.
Key features of the assay are its low cost, low sample input (critical for limited and precious clinical samples), high reproducibility, and scalability for both low- and high-throughput needs.
Analysis of this data demonstrated that this multiplex assay had high reproducibility (mean standard deviation of 1.4% for methylation values), was low cost, required low sample input (50 ng of DNA or less), and could be scaled for both low- and high-throughput needs.
For example, most high throughput experiments need quality control samples made from a pool of all of the samples being analyzed.
This consideration extends to PPI on both organisms: the protein-protein bindings detected by high throughput essays need not correspond to stable bindings and hence to highly correlated patterns of expression.
In this case, the demand of flexibility is satisfied by run-time reconfigurability, while the array structure provides the high data throughput needed by SDR applications.
High throughput biological data need to be processed, analyzed, and interpreted to address problems in life sciences.
High throughput methods are needed to address, model and understand the constitutive and stochastic differences between individual cells.
