BC-store: a program for mgiseq barcode sets analysis

Here we present the devised BC-store - a program for analyzing and selecting sets of barcodes for sequencing on platforms manufactured by MGI Tech (China). The app is available as an open source in Python3 and as a desktop version. The application allows analyzing the compatibility of barcodes on a single lane of a flow cell in a set in the case of equal and arbitrary fractions. In addition, with the help of this tool barcodes can be added to an existing set with custom share options. In this paper we describe how BC-store works for different tasks and consider the effectiveness of using BC-store in sequence lab routine tasks. Author summaryThe performance of modern NGS machines allows considerable amount of data to be obtained which exceed the data required for one specific sample. To pool multiple samples on a single lane of the flow cell, barcoding is used - adapters carrying a unique nucleotide sequence are introduced by ligation [1]. Adapters are sequenced from their specific primers. Their sequences are used for demultiplexing the sequencing results for individual fastq files by programs like zebracall [2] or bcl2fastq [3]. The task of selecting the adapters for a set is similar to sequencing low diversity libraries [4]: if all adapters on the lane have the same nucleotide during this sequencing cycle, the quality of its reading drops dramatically. Therefore, manufacturers recommend grouping the adapters by sets. However, the sets offered by MGI Tech [5] are far from routine practice, as they do not allow for non-equimolar sample pooling by default, and they have other disadvantages (more on them later in the text). To overcome these problems, we created the BC-store program, which allows analyzing the sets of MGI Tech barcodes entered by the user to be further used in sequencing. This tool also provides the opportunity to vary the number of simultaneously sequenced samples, to use reagents more efficiently, and, as a result, to correctly distribute reads across samples, which helps to increase both the quality of sequencing and the level of data interpretation.