Unbiased and error-detecting combinatorial pooling experiments with balanced constant-weight Gray codes for...

Area of Science:

• Bioinformatics
• Computational Biology
• Genomics

Background:

• Combinatorial pooling schemes increase experimental throughput by distributing items across multiple reaction pools.
• Existing pooling methods lack balanced item distribution, crucial for biological applications.
• Uniform distribution is essential for accurate analysis and error detection in high-throughput biological experiments.

Purpose of the Study:

• To introduce balanced constant-weight Gray codes (DCP-CWGCs) for constructing efficient combinatorial pooling schemes.
• To address the need for uniform item distribution in biological pooling applications.
• To enable identification of consecutive positives and facilitate error detection.

Main Methods:

• Development of balanced constant-weight Gray codes (DCP-CWGCs).
• Implementation of two core algorithms: a branch-and-bound algorithm (BBA) and a recursive combination with BBA (rcBBA).
• Release of an open-source Python package, codePUB, for constructing DCP-CWGCs.

Main Results:

• Balanced DCP-CWGCs ensure uniform item distribution across pools.
• The method allows identification of consecutive positive items, such as overlapping biological sequences.
• Error detection is enabled by ensuring a constant number of tests per item and consecutive pairs.
• Simulations demonstrate the construction of long, balanced DCP-CWGCs with error detection capabilities in tractable runtimes.

Conclusions:

• Balanced DCP-CWGCs provide an efficient and robust method for combinatorial pooling in biological applications.
• The codePUB package facilitates the construction of these advanced pooling schemes.
• This approach enhances accuracy and error detection in large-scale biological experiments.