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Despite the wide use of plasmids in research and clinical production, the need to verify plasmid sequences is a bottleneck that is too often underestimated in the manufacturing process. Although sequencing platforms continue to improve, the method and assembly pipeline chosen still influence the final plasmid assembly sequence. Furthermore, few dedicated tools exist for plasmid assembly, especially for de novo assembly. Here, we evaluated short-read, long-read, and hybrid (both short and long reads) de novo assembly pipelines across three replicates of a 24-plasmid library. Consistent with previous characterizations of each sequencing technology, short-read assemblies had issues resolving GC-rich regions, and long-read assemblies commonly had small insertions and deletions, especially in repetitive regions. The hybrid approach facilitated the most accurate, consistent assembly generation and identified mutations relative to the reference sequence. Although Sanger sequencing can be used to verify specific regions, some GC-rich and repetitive regions were difficult to resolve using any method, suggesting that easily sequenced genetic parts should be prioritized in the design of new genetic constructs. 

Abstract SummaryPlasCAT (Plasmid Cloud Assembly Tool) is an easy-to-use cloud-based bioinformatics tool that enables de novo plasmid sequence assembly from raw sequencing data. Nontechnical users can now assemble sequences from long reads and short reads without ever touching a line of code. PlasCAT uses high-performance computing servers to reduce run times on assemblies and deliver results faster. Availability and implementationPlasCAT is freely available on the web at https://sequencing.genofab.com. The assembly pipeline source code and server code are available for download at https://bitbucket.org/genofabinc/workspace/projects/PLASCAT. Click the Cancel button to access the source code without authenticating. Web servers implemented in React.js and Python, with all major browsers supported. 

The raw data and generated assemblies from Illumina MiSeq, Oxford Nanopore MinION, and Sanger Sequencing. 

Information is the cornerstone of research, from experimental (meta)data and computational processes to complex inventories of reagents and equipment. These 10 simple rules discuss best practices for leveraging laboratory information management systems to transform this large information load into useful scientific findings. 

Abstract Plasmids are a foundational tool for basic and applied research across all subfields of biology. Increasingly, researchers in synthetic biology are relying on and developing massive libraries of plasmids as vectors for directed evolution, combinatorial gene circuit tests, and for CRISPR multiplexing. Verification of plasmid sequences following synthesis is a crucial quality control step that creates a bottleneck in plasmid fabrication workflows. Crucially, researchers often elect to forego the cumbersome verification step, potentially leading to reproducibility and—depending on the application—security issues. In order to facilitate plasmid verification to improve the quality and reproducibility of life science research, we developed a fast, simple, and open source pipeline for assembly and verification of plasmid sequences from Illumina reads. We demonstrate that our pipeline, which relies on de novo assembly, can also be used to detect contaminating sequences in plasmid samples. In addition to presenting our pipeline, we discuss the role for verification and quality control in the increasingly complex life science workflows ushered in by synthetic biology. 

Abstract Laboratory strains, cell lines, and other genetic materials change hands frequently in the life sciences. Despite evidence that such materials are subject to mix-ups, contamination, and accumulation of secondary mutations, verification of strains and samples is not an established part of many experimental workflows. With the plummeting cost of next generation technologies, it is conceivable that whole genome sequencing (WGS) could be applied to routine strain and sample verification in the future. To demonstrate the need for strain validation by WGS, we sequenced haploid yeast segregants derived from a popular commercial mutant collection and identified several unexpected mutations. We determined that available bioinformatics tools may be ill-suited for verification and highlight the importance of finishing reference genomes for commonly used laboratory strains.