SEVA 4.0: an update of the Standard European Vector Architecture database for advanced analysis and programming of bacterial phenotypes.

The SEVA platform (https://seva-plasmids.com) was launched one decade ago, both as a database (DB) and as a physical repository of plasmid vectors for genetic analysis and engineering of Gram-negative bacteria with a structure and nomenclature that follows a strict, fixed architecture of functional DNA segments. While the current update keeps the basic features of earlier versions, the platform has been upgraded not only with many more ready-to-use plasmids but also with features that expand the range of target species, harmonize DNA assembly methods and enable new applications. In particular, SEVA 4.0 includes (i) a sub-collection of plasmids for easing the composition of multiple DNA segments with MoClo/Golden Gate technology, (ii) vectors for Gram-positive bacteria and yeast and [iii] off-the-shelf constructs with built-in functionalities. A growing collection of plasmids that capture part of the standard-but not its entirety-has been compiled also into the DB and repository as a separate corpus (SEVAsib) because of its value as a resource for constructing and deploying phenotypes of interest. Maintenance and curation of the DB were accompanied by dedicated diffusion and communication channels that make the SEVA platform a popular resource for genetic analyses, genome editing and bioengineering of a large number of microorganisms.

GENETTA: a Network-Based Tool for the Analysis of Complex Genetic Designs.

GENETTA is a software tool that transforms synthetic biology designs into networks using graph theory for analysis and manipulation. By representing complex data as interconnected points, GENETTA allows dynamic customization of visualizations, including interaction networks and parts hierarchies. It can also merge design data from multiple databases, providing a unified perspective. The generated interactive network can be edited by adding nodes and edges, simplifying changes to existing design files. This article presents GENETTA and its features through specific use cases, showcasing its practical applications.

A Network Approach to Genetic Circuit Designs.

As genetic circuits become more sophisticated, the size and complexity of data about their designs increase. The data captured goes beyond genetic sequences alone; information about circuit modularity and functional details improves comprehension, performance analysis, and design automation techniques. However, new data types expose new challenges around the accessibility, visualization, and usability of design data (and metadata). Here, we present a method to transform circuit designs into networks and showcase its potential to enhance the utility of design data. Since networks are dynamic structures, initial graphs can be interactively shaped into subnetworks of relevant information based on requirements such as the hierarchy of biological parts or interactions between entities. A significant advantage of a network approach is the ability to scale abstraction, providing an automatic sliding level of detail that further tailors the visualization to a given situation. Additionally, several visual changes can be applied, such as coloring or clustering nodes based on types (e.g., genes or promoters), resulting in easier comprehension from a user perspective. This approach allows circuit designs to be coupled to other networks, such as metabolic pathways or implementation protocols captured in graph-like formats. We advocate using networks to structure, access, and improve synthetic biology information.

A comparison between visible wavelength hyperspectral imaging and digital photography for the detection and identification of bloodstained footwear marks.

One of the first challenges that crime scene examiners have is determining if a substance is blood before performing analysis. Conventional methods of detecting blood involve the use of chemicals and different wavelengths of light in tandem with digital photography. However, these methods are destructive or provide false positives. Visible wavelength hyperspectral imaging (HSI) is a noncontact blood detection method that has been proven to provide accurate and reliable results. A novel application of this technique has been used for the detection and positive identification of bloodstained footwear marks, of different dilutions ranging from undiluted to 1:50 with distilled water, and on a range of substrates, and colors. Comparisons between HSI and conventional digital photography were made using a grading scale and analyzed using Mann-Whitney U-tests. The HSI technique was able to detect a statistically significant greater amount of tread detail on white tiles, laminate, carpet, and blue tiles compared with the digital photography technique, which was only superior on black tiles. Critically, the HSI technique was also able to determine that the footwear marks were made in blood. These results show that HSI will be useful in forensic investigations, where it is known that the perpetrator has walked through the victim's blood and left a trail of footwear marks at the crime scene. Even if the perpetrator had time to clean up afterward resulting in diluted stains, HSI would still be able to detect bloodstained footwear marks with a greater amount of detail compared with digital photography.

Application of non-contact scanning to forensic podiatry: A feasibility study.

Foot impression evidence recovered from crime scenes can be available in the form of barefoot prints, sock-clad footprints, or as impressions within footwear. In some cases, suspects leave their footwear at the scene of the crime, and the insoles from the footwear can be important in linking a person to the footwear. The application of 3D data-collecting technology is becoming more and more popular within forensic science and has been used to recover footwear impression evidence. The present study is a feasibility study to discover if 3D data capturing devices can be applied to insoles; to capture the footprint impression for measurement using the Gunn method (a method used in forensic podiatry casework). Three different methods of data capture were conducted; Adobe Photoshop, MeshLab, and calipers used directly on the insole. Paired t-tests and Intraclass Correlation Coefficient (ICC) were conducted for all three data capture methods. Seven measurements used in this study were significantly different across all three methods. ICC scores were moderate to excellent for the Photoshop method, poor to good for the 3D method, and moderate to excellent for the Direct method.

ShortBOL: A Language for Scripting Designs for Engineered Biological Systems Using Synthetic Biology Open Language (SBOL).

The Synthetic Biology Open Language (SBOL) is an emerging synthetic biology data exchange standard, designed primarily for unambiguous and efficient machine-to-machine communication. However, manual editing of SBOL is generally difficult for nontrivial designs. Here, we describe ShortBOL, a lightweight SBOL scripting language that bridges the gap between manual editing, visual design tools, and direct programming. ShortBOL is a shorthand textual language developed to enable users to create SBOL designs quickly and easily, without requiring strong programming skills or visual design tools.

Synthetic biology open language (SBOL) version 3.0.0.

Synthetic biology builds upon genetics, molecular biology, and metabolic engineering by applying engineering principles to the design of biological systems. When designing a synthetic system, synthetic biologists need to exchange information about multiple types of molecules, the intended behavior of the system, and actual experimental measurements. The Synthetic Biology Open Language (SBOL) has been developed as a standard to support the specification and exchange of biological design information in synthetic biology, following an open community process involving both wet bench scientists and dry scientific modelers and software developers, across academia, industry, and other institutions. This document describes SBOL 3.0.0, which condenses and simplifies previous versions of SBOL based on experiences in deployment across a variety of scientific and industrial settings. In particular, SBOL 3.0.0, (1) separates sequence features from part/sub-part relationships, (2) renames Component Definition/Component to Component/Sub-Component, (3) merges Component and Module classes, (4) ensures consistency between data model and ontology terms, (5) extends the means to define and reference Sub-Components, (6) refines requirements on object URIs, (7) enables graph-based serialization, (8) moves Systems Biology Ontology (SBO) for Component types, (9) makes all sequence associations explicit, (10) makes interfaces explicit, (11) generalizes Sequence Constraints into a general structural Constraint class, and (12) expands the set of allowed constraints.