scholarly journals HaVoC, a bioinformatic pipeline for reference-based consensus assembly and lineage assignment for SARS-CoV-2 sequences

2021 ◽  
Author(s):  
Phuoc Truong Nguyen ◽  
Ilya Plyusnin ◽  
Tarja Sironen ◽  
Olli Vapalahti ◽  
Ravi Kant ◽  
...  
Keyword(s):  
South African ◽  
Genetic Variance ◽  
Source Code ◽  
Bioinformatic Pipeline ◽  
Related Research ◽  
Bioinformatic Tools ◽  
Link Type ◽  
Novel Variants ◽  
The Uk ◽  
Virus Genomes

AbstractBackgroundSARS-CoV-2 related research has increased in importance worldwide since December 2019. Several new variants of SARS-CoV-2 have emerged globally, of which the most notable and concerning currently are the UK variant B.1.1.7, the South African variant B1.351 and the Brazilian variant P.1. Detecting and monitoring novel variants is essential in SARS-CoV-2 surveillance. While there are several tools for assembling virus genomes and performing lineage analyses to investigate SARS-CoV-2, each is limited to performing singular or a few functions separately.ResultsDue to the lack of publicly available pipelines, which could perform fast reference-based assemblies on raw SARS-CoV-2 sequences in addition to identifying lineages to detect variants of concern, we have developed an open source bioinformatic pipeline called HaVoC (Helsinki university Analyzer for Variants Of Concern). HaVoC can reference assemble raw sequence reads and assign the corresponding lineages to SARS-CoV-2 sequences.ConclusionsHaVoC is a pipeline utilizing several bioinformatic tools to perform multiple necessary analyses for investigating genetic variance among SARS-CoV-2 samples. The pipeline is particularly useful for those who need a more accessible and fast tool to detect and monitor the spread of SARS-CoV-2 variants of concern during local outbreaks. HaVoC is currently being used in Finland for monitoring the spread of SARS-CoV-2 variants. HaVoC user manual and source code are available at https://www.helsinki.fi/en/projects/havoc and https://bitbucket.org/auto_cov_pipeline/havoc, respectively.

scholarly journals HAVoC, a bioinformatic pipeline for reference-based consensus assembly and lineage assignment for SARS-CoV-2 sequences

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Phuoc Thien Truong Nguyen ◽  
Ilya Plyusnin ◽  
Tarja Sironen ◽  
Olli Vapalahti ◽  
Ravi Kant ◽  
...  
Keyword(s):  
South African ◽  
Genetic Variance ◽  
Source Code ◽  
Bioinformatic Pipeline ◽  
Related Research ◽  
Bioinformatic Tools ◽  
Link Type ◽  
Novel Variants ◽  
The Uk ◽  
Virus Genomes

Abstract Background SARS-CoV-2 related research has increased in importance worldwide since December 2019. Several new variants of SARS-CoV-2 have emerged globally, of which the most notable and concerning currently are the UK variant B.1.1.7, the South African variant B1.351 and the Brazilian variant P.1. Detecting and monitoring novel variants is essential in SARS-CoV-2 surveillance. While there are several tools for assembling virus genomes and performing lineage analyses to investigate SARS-CoV-2, each is limited to performing singular or a few functions separately. Results Due to the lack of publicly available pipelines, which could perform fast reference-based assemblies on raw SARS-CoV-2 sequences in addition to identifying lineages to detect variants of concern, we have developed an open source bioinformatic pipeline called HAVoC (Helsinki university Analyzer for Variants of Concern). HAVoC can reference assemble raw sequence reads and assign the corresponding lineages to SARS-CoV-2 sequences. Conclusions HAVoC is a pipeline utilizing several bioinformatic tools to perform multiple necessary analyses for investigating genetic variance among SARS-CoV-2 samples. The pipeline is particularly useful for those who need a more accessible and fast tool to detect and monitor the spread of SARS-CoV-2 variants of concern during local outbreaks. HAVoC is currently being used in Finland for monitoring the spread of SARS-CoV-2 variants. HAVoC user manual and source code are available at https://www.helsinki.fi/en/projects/havoc and https://bitbucket.org/auto_cov_pipeline/havoc, respectively.

scholarly journals ASaiM: a Galaxy-based framework to analyze raw shotgun data from microbiota

2017 ◽  
Author(s):  
Bérénice Batut ◽  
Kévin Gravouil ◽  
Clémence Defois ◽  
Saskia Hiltemann ◽  
Jean-François Brugère ◽  
...  
Keyword(s):  
Technological Progress ◽  
Source Code ◽  
Command Line ◽  
Bioinformatic Tools ◽  
Link Type ◽  
Data Analyses ◽  
The Galaxy ◽  
Sequencing Platforms ◽  
User Friendly ◽  
New Generation

AbstractBackgroundNew generation of sequencing platforms coupled to numerous bioinformatics tools has led to rapid technological progress in metagenomics and metatranscriptomics to investigate complex microorganism communities. Nevertheless, a combination of different bioinformatic tools remains necessary to draw conclusions out of microbiota studies. Modular and user-friendly tools would greatly improve such studies.FindingsWe therefore developed ASaiM, an Open-Source Galaxy-based framework dedicated to microbiota data analyses. ASaiM provides a curated collection of tools to explore and visualize taxonomic and functional information from raw amplicon, metagenomic or metatranscriptomic sequences. To guide different analyses, several customizable workflows are included. All workflows are supported by tutorials and Galaxy interactive tours to guide the users through the analyses step by step. ASaiM is implemented as Galaxy Docker flavour. It is scalable to many thousand datasets, but also can be used a normal PC. The associated source code is available under Apache 2 license at https://github.com/ASaiM/framework and documentation can be found online (http://asaim.readthedocs.io/)ConclusionsBased on the Galaxy framework, ASaiM offers sophisticated analyses to scientists without command-line knowledge. ASaiM provides a powerful framework to easily and quickly explore microbiota data in a reproducible and transparent environment.

scholarly journals UKBCC: a cohort curation package for UK Biobank

2020 ◽  
Author(s):  
Isabell Kiral ◽  
Nathalie Willems ◽  
Benjamin Goudey
Keyword(s):  
Source Code ◽  
Heterogeneous Data ◽  
Use Case ◽  
Uk Biobank ◽  
Link Type ◽  
Search Terms ◽  
Heterogeneous Data Sources ◽  
Wide Range ◽  
Critical Resource ◽  
The Uk

AbstractSummaryThe UK Biobank (UKB) has quickly become a critical resource for researchers conducting a wide-range of biomedical studies (Bycroft et al., 2018). The database is constructed from heterogeneous data sources, employs several different encoding schemes, and is disparately distributed throughout UKB servers. Consequently, querying these data remains complicated, making it difficult to quickly identify participants who meet a given set of criteria. We have developed UK Biobank Cohort Curator (UKBCC), a Python tool that allows researchers to rapidly construct cohorts based on a set of search terms. Here, we describe the UKBCC implementation, critical sub-modules and functions, and outline its usage through an example use case for replicable cohort creation.AvailabilityUKBCC is available through PyPi (https://pypi.org/project/ukbcc) and as open source code on GitHub (https://github.com/tool-bin/ukbcc)[email protected]

scholarly journals ZGA: a flexible pipeline for read processing, de novo assembly and annotation of prokaryotic genomes

2021 ◽  
Author(s):  
A.A. Korzhenkov
Keyword(s):  
Genome Sequencing ◽  
De Novo ◽  
Wide Spectrum ◽  
Source Code ◽  
Routine Method ◽  
Genome Sequences ◽  
Bioinformatic Pipeline ◽  
Internet Connection ◽  
Link Type ◽  
Prokaryotic Genomes

AbstractWhole genome sequencing (WGS) became a routine method in modern days and may be applied to study a wide spectrum of scientific problems. Despite increasing availability of genome sequencing by itself, genome assembly and annotation could be a challenge for an inexperienced researcher. To solve this problem, a bioinformatic pipeline was developed to conduct a user from raw sequencing reads to annotated bacterial or archaeal genome ready for deposition to any INSDC database as NCBI, ENA or DDBJ. The pipeline is fully automated and doesn’t require internet connection after installation which prevents data leakage and premature publication of genome sequences. The source code of the pipeline is freely available at https://github.com/laxeye/zga/. The software may be installed from popular repositories: Anaconda Cloud (https://anaconda.org/bioconda/zga/) and PyPI (https://pypi.org/project/zga/).

scholarly journals Introduction of the South African SARS-CoV-2 variant 501Y.V2 into the UK

2021 ◽  
Author(s):  
Julian W. Tang ◽  
Oliver T.R. Toovey ◽  
Kirsty N. Harvey ◽  
David D.S. Hui
Keyword(s):  
South African ◽  
The South ◽  
The Uk

scholarly journals AGILE: a seamless phase I/IIa platform for the rapid evaluation of candidates for COVID-19 treatment: an update to the structured summary of a study protocol for a randomised platform trial letter

Trials ◽  
2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Gareth O. Griffiths ◽  
Richard FitzGerald ◽  
Thomas Jaki ◽  
Andrea Corkhill ◽  
Helen Reynolds ◽  
...  
Keyword(s):  
Phase I ◽  
Early Stage ◽  
Late Phase ◽  
Optimum Dose ◽  
Open Label ◽  
Link Type ◽  
Hospitalised Patients ◽  
Volunteer Study ◽  
The Uk ◽  
Phase Trial

Abstract Background There is an urgent unmet clinical need for the identification of novel therapeutics for the treatment of COVID-19. A number of COVID-19 late phase trial platforms have been developed to investigate (often repurposed) drugs both in the UK and globally (e.g. RECOVERY led by the University of Oxford and SOLIDARITY led by WHO). There is a pressing need to investigate novel candidates within early phase trial platforms, from which promising candidates can feed into established later phase platforms. AGILE grew from a UK-wide collaboration to undertake early stage clinical evaluation of candidates for SARS-CoV-2 infection to accelerate national and global healthcare interventions. Methods/design AGILE is a seamless phase I/IIa platform study to establish the optimum dose, determine the activity and safety of each candidate and recommend whether it should be evaluated further. Each candidate is evaluated in its own trial, either as an open label single arm healthy volunteer study or in patients, randomising between candidate and control usually in a 2:1 allocation in favour of the candidate. Each dose is assessed sequentially for safety usually in cohorts of 6 patients. Once a phase II dose has been identified, efficacy is assessed by seamlessly expanding into a larger cohort. AGILE is completely flexible in that the core design in the master protocol can be adapted for each candidate based on prior knowledge of the candidate (i.e. population, primary endpoint and sample size can be amended). This information is detailed in each candidate specific trial protocol of the master protocol. Discussion Few approved treatments for COVID-19 are available such as dexamethasone, remdesivir and tocilizumab in hospitalised patients. The AGILE platform aims to rapidly identify new efficacious and safe treatments to help end the current global COVID-19 pandemic. We currently have three candidate specific trials within this platform study that are open to recruitment. Trial registration EudraCT Number: 2020-001860-27 14 March 2020 ClinicalTrials.gov Identifier: NCT04746183 19 February 2021 ISRCTN reference: 27106947

scholarly journals BioCommons: a robust java library for RNA structural bioinformatics

2021 ◽  
Author(s):  
Tomasz Zok
Keyword(s):  
Source Code ◽  
Structural Bioinformatics ◽  
Supplementary Information ◽  
Supplementary Data ◽  
Bioinformatic Tools ◽  
Data Formats ◽  
Central Repository ◽  
Diverse Data ◽  
2D And 3D ◽  
Java Library

Abstract Motivation Biomolecular structures come in multiple representations and diverse data formats. Their incompatibility with the requirements of data analysis programs significantly hinders the analytics and the creation of new structure-oriented bioinformatic tools. Therefore, the need for robust libraries of data processing functions is still growing. Results BioCommons is an open-source, Java library for structural bioinformatics. It contains many functions working with the 2D and 3D structures of biomolecules, with a particular emphasis on RNA. Availability and implementation The library is available in Maven Central Repository and its source code is hosted on GitHub: https://github.com/tzok/BioCommons Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals In Silico Investigation of the New UK (B.1.1.7) and South African (501Y.V2) SARS-CoV-2 Variants with a Focus at the ACE2–Spike RBD Interface

2021 ◽  
Vol 22 (4) ◽  
pp. 1695
Author(s):  
Bruno O. Villoutreix ◽  
Vincent Calvez ◽  
Anne-Geneviève Marcelin ◽  
Abdel-Majid Khatib
Keyword(s):  
Amino Acid ◽  
South African ◽  
In Silico ◽  
Neutralizing Antibodies ◽  
Viral Escape ◽  
Spike Protein ◽  
Amino Acid Substitutions ◽  
The South ◽  
African Strain ◽  
The Uk

SARS-CoV-2 exploits angiotensin-converting enzyme 2 (ACE2) as a receptor to invade cells. It has been reported that the UK and South African strains may have higher transmission capabilities, eventually in part due to amino acid substitutions on the SARS-CoV-2 Spike protein. The pathogenicity seems modified but is still under investigation. Here we used the experimental structure of the Spike RBD domain co-crystallized with part of the ACE2 receptor, several in silico methods and numerous experimental data reported recently to analyze the possible impacts of three amino acid replacements (Spike K417N, E484K, N501Y) with regard to ACE2 binding. We found that the N501Y replacement in this region of the interface (present in both the UK and South African strains) should be favorable for the interaction with ACE2, while the K417N and E484K substitutions (South African strain) would seem neutral or even unfavorable. It is unclear if the N501Y substitution in the South African strain could counterbalance the K417N and E484K Spike replacements with regard to ACE2 binding. Our finding suggests that the UK strain should have higher affinity toward ACE2 and therefore likely increased transmissibility and possibly pathogenicity. If indeed the South African strain has a high transmission level, this could be due to the N501Y replacement and/or to substitutions in regions located outside the direct Spike–ACE2 interface but not so much to the K417N and E484K replacements. Yet, it should be noted that amino acid changes at Spike position 484 can lead to viral escape from neutralizing antibodies. Further, these amino acid substitutions do not seem to induce major structural changes in this region of the Spike protein. This structure–function study allows us to rationalize some observations made for the UK strain but raises questions for the South African strain.

Knowledge as a Revenue Driver, Not a Cost Centre: the LexisNexis GLP and How to Use Market Intelligence to Make Knowledge a Vital Growth Engine for Law Firms

2020 ◽  
Vol 20 (4) ◽  
pp. 213-218
Author(s):  
Christopher O'Connor
Keyword(s):  
Quantitative Measure ◽  
Law Firms ◽  
Legal Services ◽  
Market Intelligence ◽  
Link Type ◽  
Marketing Team ◽  
Virtual Conference ◽  
The Uk ◽  
Individual Metrics ◽  
Legal Product

AbstractThis article by the LexisNexis Segment Marketing team explains the approach, methodology and findings of the LexisNexis Gross Legal Product (GLP) report, first presented at the BIALL's Virtual Conference in June 2020. The GLP is a quantitative measure of underlying demand for legal services in the UK, comprised of 250 individual metrics which serve as proxies for legal activity. The article outlines the methodology and sources used to build the GLP; headline findings for Q2 2020 YTD; and provides suggestions for how firm leaders and knowledge professionals could use the information in their work. The GLP Q2 model found that demand for legal activity has declined by 7% since the start of 2020.

Sign in / Sign up

Export Citation Format

Share Document