Search bioRxiv⌕ Search

Biology subjects

Mateyko, N.

Publications and source records attributed to Mateyko, N..

2 recordsLinked to original sources

Culture wars: Empirically determining the bestapproach for plasmid library amplification

DNA libraries are critical components of many biological assays. These libraries are often kept in plasmids that are amplified in E. coli to generate sufficient material for an experiment. Library uniformity is critical for ensuring that every element in the library is tested similarly, and is thought to be influenced by the culture approach used during library amplification. We tested five commonly used culturing methods for their ability to uniformly amplify plasmid libraries: liquid, semisolid agar, cell spreader-spread plates with high or low colony density, and bead-spread plates. Each approach was evaluated with two library types: a random 80-mer library, representing high complexity low coverage of similar sequence lengths, and a human TF ORF library, representing low complexity high coverage of diverse sequence lengths. We found that no method was better than liquid culture, which produced relatively uniform libraries regardless of library type. However, when libraries were transformed with high coverage, culturing method had minimal impact on uniformity or amplification bias. Plating libraries was the worst approach by almost every measure for both library types, and, counter-intuitively, produced the strongest biases against long sequence representation. Semisolid agar amplified most elements of the library uniformly but also included outliers with orders of magnitude higher abundance. For amplifying DNA libraries, liquid culture, the simplest method, appears to be best.

genomics↗

GIL: A Python package for designing custom indexing primers

Next-generation sequencing allows samples to be multiplexed by adding a unique DNA index to each sample. Multiplexing greatly reduces the price of sequencing large numbers of samples, yet the minimum cost per sample remains high when using commercially available indexing kits and designing custom indexes is challenging. To address these issues, we created GIL (Generate Indexes for Libraries), a software tool that designs indexing primers for producing multiplexed sequencing libraries. GIL can be customized in numerous ways to meet user specifications, including index length, sequencing modality, color balancing, and compatibility with existing primers, and produces ordering and demultiplexing-ready outputs. GIL is written in Python and is freely available on GitHub at https://github.com/de-Boer-Lab/GIL. It can also be accessed as a web-application implemented in Streamlit at https://dbl-gil.streamlitapp.com.

bioinformatics↗