poolHiTS: A Shifted Transversal Design based pooling strategy for high-throughput drug screening

Abstract

Background: A key goal of drug discovery is to increase the throughput of small molecule screens without sacrificing screening accuracy. High-throughput screening (HTS) in drug discovery involves testing a large number of compounds in a biological assay to identify active compounds. Normally, molecules from a large compound library are tested individually to identify the activity of each molecule. Usually a small number of compounds are found to be active, however the presence of false positive and negative testing errors suggests that this one-drug one-assay screening strategy can be significantly improved. Pooling designs are testing schemes that test mixtures of compounds in each assay, thereby generating a screen of the whole compound library in fewer tests. By repeatedly testing compounds in different combinations, pooling designs also allow for errorcorrection. These pooled designs, for specific experiment parameters, can be simply and efficiently created using the Shifted Transversal Design (STD) pooling algorithm. However, drug screening contains a number of key constraints that require specific modifications if this pooling approach is to be useful for practical screen designs.

Shifted Transversal Design smart-pooling for high coverage interactome mapping

“…STD pooling is also more powerful and flexible than other recently employed pooling designs (Jin et al 2006(Jin et al , 2007Vermeirssen et al 2007). We expect that STD-based smart-pooling can be applied in other large-scale functional genomics experiments that rely on a basic yes-or-no test to identify rare positive events, provided that pools can be tested and yield a positive signal if they contain at least one positive, such as yeast one-hybrid, drug screening (e.g., Kainkaryam and Woolf 2008), or PCR-or hybridization-based analyses (e.g., Wu et al 2008). …”

An accurate clone-based haplotyping method by overlapping pool sequencing

“…Considering the large clone number, a repeated blocks design was employed to split these clones into smaller blocks, where an optimized overlapping pool strategy was conducted repeatedly for each block, which reduced the decoding complexity ( 30 ). According to the above analysis, we could calculate the maximum number of clones that span a variant in different block designs, i.e., the maximum number of positive samples in group testing (Supplementary Table S2, Supplementary Figure S2).…”

“…Normally the clone number ( n ) is >100 000. Studies in the field of overlapping pool sequencing ( 30 ) have proved that it is more efficient to split a large library into smaller blocks of samples, which is called repeated blocks design. Hence, the clones are first randomly divided into several blocks and then pooled “intra-block”.…”

Compressed Genotyping

“…The number of queries in CRS for a given d is: $$t~O(d^2{\text{ log}}^{2}n/(\text{log }d+\text{log log }n))$$ and the weight is: $$w~O(d\text{ log }n/\text{log }d+\text{ log log }n))$$ Notice that the weight scales with the number of specimens, implying that more sequencing lanes and robotic logistic are required with the growth of n even if d is constant. STD is also a number theoretic design, which has been used for several biological applications [18], [19]. …”