A Versatile Computational Pipeline for Bacterial Genome Annotation Improvement and Comparative Analysis, with \u3cem\u3eBrucella\u3c/em\u3e as a Use Case

We present a bacterial genome computational analysis pipeline, called GenVar. The pipeline, based on the program GeneWise, is designed to analyze an annotated genome and automatically identify missed gene calls and sequence variants such as genes with disrupted reading frames (split genes) and those with insertions and deletions (indels). For a given genome to be analyzed, GenVar relies on a database containing closely related genomes (such as other species or strains) as well as a few additional reference genomes. GenVar also helps identify gene disruptions probably caused by sequencing errors. We exemplify GenVar’s capabilities by presenting results from the analysis of four Brucella genomes. Brucella is an important human pathogen and zoonotic agent. The analysis revealed hundreds of missed gene calls, new split genes and indels, several of which are species specific and hence provide valuable clues to the understanding of the genome basis of Brucella pathogenicity and host specificity

Economic Losses From COVID-19 Cases in the Philippines: A Dynamic Model of Health and Economic Policy Trade-Offs

The COVID-19 pandemic forced governments globally to impose lockdown measures and mobility restrictions to curb the transmission of the virus. As economies slowly reopen, governments face a trade-off between implementing economic recovery and health policy measures to control the spread of the virus and to ensure it will not overwhelm the health system. We developed a mathematical model that measures the economic losses due to the spread of the disease and due to different lockdown policies. This is done by extending the subnational SEIR model to include two differential equations that capture economic losses due to COVID-19 infection and due to the lockdown measures imposed by the Philippine government. We then proceed to assess the trade-off policy space between health and economic measures faced by the Philippine government. The study simulates the cumulative economic losses for 3 months in 8 scenarios across 5 regions in the country, including the National Capital Region (NCR), to capture the trade-off mechanism. These scenarios present the various combinations of either retaining or easing lockdown policies in these regions. Per region, the trade-off policy space was assessed through minimising the 3-month cumulative economic losses subject to the constraint that the average health-care utilisation rate (HCUR) consistently falls below 70%, which is the threshold set by the government before declaring that the health system capacity is at high risk. The study finds that in NCR, a policy trade-off exists where the minimum cumulative economic losses comprise 10.66% of its Gross Regional Domestic Product. Meanwhile, for regions that are non-adjacent to NCR, a policy that hinges on trade-off analysis does not apply. Nevertheless, for all simulated regions, it is recommended to improve and expand the capacity of the health system to broaden the policy space for the government in easing lockdown measures

Current status and unanswered questions on the use of Denosumab in giant cell tumor of bone

Denosumab is a monoclonal antibody to RANK ligand approved for use in giant cell tumour (GCT) of bone. Due to its efficacy, Denosumab is recommended as the first option in inoperable or metastatic GCT. Denosumab has also been used pre-operatively to downstage tumours with large soft tissue extension to allow for less morbid surgery. The role of Denosumab for conventional limb GCT of bone is yet to be defined. Further studies are required to determine whether local recurrence rates will be decreased with the adjuvant use of Denosumab along with surgery. The long term use and toxicity of this agent is unknown as is the proportion of patients with primary or secondary resistance. It is advised that complicated cases of GCT requiring Denosumab treatment should be referred and followed up at expert centres. Collaborative studies involving further clinical trials and rigorous data collection are strongly recommended to identify the optimum use of this drug

CloneQC: lightweight sequence verification for synthetic biology

Synthetic biology projects aim to produce physical DNA that matches a designed target sequence. Chemically synthesized oligomers are generally used as the starting point for building larger and larger sequences. Due to the error rate of chemical synthesis, these oligomers can have many differences from the target sequence. As oligomers are joined together to make larger and larger synthetic intermediates, it becomes essential to perform quality control to eliminate intermediates with errors and retain only those DNA molecules that are error free with respect to the target. This step is often performed by transforming bacteria with synthetic DNA and sequencing colonies until a clone with a perfect sequence is identified. Here we present CloneQC, a lightweight software pipeline available as a free web server and as source code that performs quality control on sequenced clones. Input to the server is a list of desired sequences and forward and reverse reads for each clone. The server generates summary statistics (error rates and success rates target-by-target) and a detailed report of perfect clones. This software will be useful to laboratories conducting in-house DNA synthesis and is available at http://cloneqc.thruhere.net/ and as Berkeley Software Distribution (BSD) licensed source

Automatic Compilation from High-Level Biologically-Oriented Programming Language to Genetic Regulatory Networks

Background The field of synthetic biology promises to revolutionize our ability to engineer biological systems, providing important benefits for a variety of applications. Recent advances in DNA synthesis and automated DNA assembly technologies suggest that it is now possible to construct synthetic systems of significant complexity. However, while a variety of novel genetic devices and small engineered gene networks have been successfully demonstrated, the regulatory complexity of synthetic systems that have been reported recently has somewhat plateaued due to a variety of factors, including the complexity of biology itself and the lag in our ability to design and optimize sophisticated biological circuitry. Methodology/Principal Findings To address the gap between DNA synthesis and circuit design capabilities, we present a platform that enables synthetic biologists to express desired behavior using a convenient high-level biologically-oriented programming language, Proto. The high level specification is compiled, using a regulatory motif based mechanism, to a gene network, optimized, and then converted to a computational simulation for numerical verification. Through several example programs we illustrate the automated process of biological system design with our platform, and show that our compiler optimizations can yield significant reductions in the number of genes () and latency of the optimized engineered gene networks. Conclusions/Significance Our platform provides a convenient and accessible tool for the automated design of sophisticated synthetic biological systems, bridging an important gap between DNA synthesis and circuit design capabilities. Our platform is user-friendly and features biologically relevant compiler optimizations, providing an important foundation for the development of sophisticated biological systems.National Institutes of Health (U.S.) (Grant # 7R01GM74712-5)United States. Defense Advanced Research Projects Agency (contract HR0011-10-C-0168)National Science Foundation (U.S.) (NSF CAREER award 0968682)BBN Technologie

Characterization of Sulfolobus islandicus rod-shaped virus 2 gp19, a single-strand specific endonuclease

The hyperthermophilic Sulfolobus islandicus rod-shaped virus 2 (SIRV2) encodes a 25-kDa protein (SIRV2gp19) annotated as a hypothetical protein with sequence homology to the RecB nuclease superfamily. Even though SIRV2gp19 homologs are conserved throughout the rudivirus family and presumably play a role in the viral life cycle, SIRV2gp19 has not been functionally characterized. To define the minimal requirements for activity, SIRV2gp19 was purified and tested under varying conditions. SIRV2gp19 is a single-strand specific endonuclease that requires Mg2+ for activity and is inactive on double-stranded DNA. A conserved aspartic acid in RecB nuclease superfamily Motif II (D89) is also essential for SIRV2gp19 activity and mutation to alanine (D89A) abolishes activity. Therefore, the SIRV2gp19 cleavage mechanism is similar to previously described RecB nucleases. Finally, SIRV2gp19 single-stranded DNA endonuclease activity could play a role in host chromosome degradation during SIRV2 lytic infection

Sequence verification of synthetic DNA by assembly of sequencing reads

This is the publisher’s final pdf. The published article is copyrighted by Oxford University Press and can be found at: http://www.oxfordjournals.org/Gene synthesis attempts to assemble user-defined DNA sequences with base-level precision. Verifying the sequences of construction intermediates and the final product of a gene synthesis project is a critical part of the workflow, yet one that has received the least attention. Sequence validation is equally important for other kinds of curated clone collections. Ensuring that the physical sequence of a clone matches its published sequence is a common quality control step performed at least once over the course of a research project. GenoREAD is a web-based application that breaks the sequence verification process into two steps: the assembly of sequencing reads and the alignment of the resulting contig with a reference sequence. GenoREAD can determine if a clone matches its reference sequence. Its sophisticated reporting features help identify and troubleshoot problems that arise during the sequence verification process. GenoREAD has been experimentally validated on thousands of gene-sized constructs from an ORFeome project, and on longer sequences including whole plasmids and synthetic chromosomes. Comparing GenoREAD results with those from manual analysis of the sequencing data demonstrates that GenoREAD tends to be conservative in its diagnostic. GenoREAD is available at www.genoread.org

A versatile computational pipeline for bacterial genome annotation improvement and comparative analysis, with Brucella as a use case

We present a bacterial genome computational analysis pipeline, called GenVar. The pipeline, based on the program GeneWise, is designed to analyze an annotated genome and automatically identify missed gene calls and sequence variants such as genes with disrupted reading frames (split genes) and those with insertions and deletions (indels). For a given genome to be analyzed, GenVar relies on a database containing closely related genomes (such as other species or strains) as well as a few additional reference genomes. GenVar also helps identify gene disruptions probably caused by sequencing errors. We exemplify GenVar's capabilities by presenting results from the analysis of four Brucella genomes. Brucella is an important human pathogen and zoonotic agent. The analysis revealed hundreds of missed gene calls, new split genes and indels, several of which are species specific and hence provide valuable clues to the understanding of the genome basis of Brucella pathogenicity and host specificity

Modeling Structure-Function Relationships in Synthetic DNA Sequences using Attribute Grammars

Recognizing that certain biological functions can be associated with specific DNA sequences has led various fields of biology to adopt the notion of the genetic part. This concept provides a finer level of granularity than the traditional notion of the gene. However, a method of formally relating how a set of parts relates to a function has not yet emerged. Synthetic biology both demands such a formalism and provides an ideal setting for testing hypotheses about relationships between DNA sequences and phenotypes beyond the gene-centric methods used in genetics. Attribute grammars are used in computer science to translate the text of a program source code into the computational operations it represents. By associating attributes with parts, modifying the value of these attributes using rules that describe the structure of DNA sequences, and using a multi-pass compilation process, it is possible to translate DNA sequences into molecular interaction network models. These capabilities are illustrated by simple example grammars expressing how gene expression rates are dependent upon single or multiple parts. The translation process is validated by systematically generating, translating, and simulating the phenotype of all the sequences in the design space generated by a small library of genetic parts. Attribute grammars represent a flexible framework connecting parts with models of biological function. They will be instrumental for building mathematical models of libraries of genetic constructs synthesized to characterize the function of genetic parts. This formalism is also expected to provide a solid foundation for the development of computer assisted design applications for synthetic biology