Bench Scientists Research Articles

Impact of the COVID-19 Pandemic Onset on the Early Careers of Pediatric Oncology Health Professionals and Researchers: A Report From the Children's Oncology Group Young Investigators Committee, Young SIOP Network, and Young SIOPE.

The COVID-19 pandemic onset had a global debilitating impact on individuals and on burgeoning careers. In 2021, the Children's Oncology Group Young Investigators Committee, Young SIOP (International Society of Paediatric Oncology) Network, and Young SIOPE (European Society for Paediatric Oncology) co-sponsored a survey to explore the impacts of the first year of the pandemic on early-career pediatric oncology professionals with respect to working practices, productivity, professional and career development, personal wellbeing, and changing childcare needs. The survey comprised demographic, multiple-choice, and free-text questions, and was distributed via email and social media with English, French, and Spanish versions available. Descriptive statistics and chi-square tests were used to compare quantitative data by self-designated gender and country of origin. Qualitative data were described using content analysis. Professionals (N = 499, 26.3% male, 77.2% MDs) in 48 countries (77.6% high income) responded in English (79.4%), Spanish (12.4%), and French (8.2%). Respondents had difficulty obtaining and keeping jobs (26.9%), worsened overall academic productivity (50.7%, with higher rates among bench scientists, p<0.01), and decreased career opportunities (40.9%). Childcare challenges impacted 56.7% of respondents and was felt more negatively among women (p = 0.008) and in high-income settings (p<0.0001). Qualitative data (n = 300) highlighted these differences were often attributable to diminished professional/personal boundaries and impacted their personal wellbeing. The COVID-19 pandemic significantly impacted early-career academic and clinical professionals working in pediatric oncology, with unique challenges noted among those with childcare responsibilities. Career disruptions that resulted from the pandemic should be considered and mitigated by governing bodies and hiring institutions.

The 59th Annual Meeting of the Society for Low Temperature Biology, 20-22 September 2023, Vigo, Spain

Involving patients and their tissues in the research pathway is crucial for future heath innovations. There are many challenges including sustainability, logistical organisation whilst maintaining robust ethical oversight. For over a decade, TAPb has developed to become a research infrastructure to facilitate patient-centric studies across the research pipeline (from bench science, translation to human tissue and the sample component of interventional clinical trials). The TAPb platform groups researchers across academic, commercial and clinical trial sectors, and clinical services. This spreads the costs of the core team, and introduces capacity to provide specialised research services in an incremental way. TAPb is a not-for-profit, full cost recovery model that has been implemented across 7 patient clinical services within the Royal Free London NHS Foundation Trust. It supports an office of 6 fulltime staff, complemented by a number (currently about 7) part-time staff, to reflect the fluctuations in research demand. The researcher journey starts with TAPb linking NHS standard of care and research requirements to aid study design and feasibility. TAPb then provides suitable ethics and project management to develop timescales and delivery methodologies according to evolving research requirements, throughout the delivery phase. Samples and link-anonymised clinical data, and disease interpretation is provided to place samples in the correct research context. This presentation will provide an overview of the important steps which TAPb office have learnt which can make the infrastructure sustainable. Some examples of the significant research impact will be given as an illustration.

Cloud-based introduction to BASH programming for biologists.

This manuscript describes the development of a resource module that is part of a learning platform named 'NIGMS Sandbox for Cloud-based Learning', https://github.com/NIGMS/NIGMS-Sandbox. The overall genesis of the Sandbox is described in the editorial authored by National Institute of General Medical Sciences: NIGMS Sandbox: A Learning Platform toward Democratizing Cloud Computing for Biomedical Research at the beginning of this supplement. This module delivers learning materials introducing the utility of the BASH (Bourne Again Shell) programming language for genomic data analysis in an interactive format that uses appropriate cloud resources for data access and analyses. The next-generation sequencing revolution has generated massive amounts of novel biological data from a multitude of platforms that survey an ever-growing list of genomic modalities. These data require significant downstream computational and statistical analyses to glean meaningful biological insights. However, the skill sets required to generate these data are vastly different from the skills required to analyze these data. Bench scientists that generate next-generation data often lack the training required to perform analysis of these datasets and require support from bioinformatics specialists. Dedicated computational training is required to empower biologists in the area of genomic data analysis, however, learning to efficiently leverage a command line interface is a significant barrier in learning how to leverage common analytical tools. Cloud platforms have the potential to democratize access to the technical tools and computational resources necessary to work with modern sequencing data, providing an effective framework for bioinformatics education. This module aims to provide an interactive platform that slowly builds technical skills and knowledge needed to interact with genomics data on the command line in the Cloud. The sandbox format of this module enables users to move through the material at their own pace and test their grasp of the material with knowledge self-checks before building on that material in the next sub-module. This manuscript describes the development of a resource module that is part of a learning platform named ``NIGMS Sandbox for Cloud-based Learning'' https://github.com/NIGMS/NIGMS-Sandbox. The overall genesis of the Sandbox is described in the editorial NIGMS Sandbox [1] at the beginning of this Supplement. This module delivers learning materials on the analysis of bulk and single-cell ATAC-seq data in an interactive format that uses appropriate cloud resources for data access and analyses.

Real-Time Self-Assembly of Stereomicroscopically Visible Artificial Constructions in Incubated Specimens of mRNA Products Mainly from Pfizer and Moderna: A Comprehensive Longitudinal Study

Observable real-time injuries at the cellular level in recipients of the “safe and effective” COVID-19 injectables are documented here for the first time with the presentation of a comprehensive description and analysis of observed phenomena. The global administration of these often-mandated products from late 2020 triggered a plethora of independent research studies of the modified RNA injectable gene therapies, most notably those manufactured by Pfizer and Moderna. Analyses reported here consist of precise laboratory “bench science” aiming to understand why serious debilitating, prolonged injuries (and many deaths) occurred increasingly without any measurable protective effect from the aggressively, marketed products. The contents of COVID-19 injectables were examined under a stereomicroscope at up to 400X magnification. Carefully preserved specimens were cultured in a range of distinct media to observe immediate and long-term cause-and-effect relationships between the injectables and living cells under carefully controlled conditions. From such research, reasonable inferences can be drawn about observed injuries worldwide that have occurred since the injectables were pressed upon billions of individuals. In addition to cellular toxicity, our findings reveal numerous — on the order of 3~4 x 106 per milliliter of the injectable — visible artificial self-assembling entities ranging from about 1 to 100 µm, or greater, of many different shapes. There were animated worm-like entities, discs, chains, spirals, tubes, right-angle structures containing other artificial entities within them, and so forth. All these are exceedingly beyond any expected and acceptable levels of contamination of the COVID-19 injectables, and incubation studies revealed the progressive self-assembly of many artifactual structures. As time progressed during incubation, simple one- and two-dimensional structures over two or three weeks became more complex in shape and size developing into stereoscopically visible entities in three-dimensions. They resembled carbon nanotube filaments, ribbons, and tapes, some appearing as transparent, thin, flat membranes, and others as three-dimensional spirals, and beaded chains. Some of these seemed to appear and then disappear over time. Our observations suggest the presence of some kind of nanotechnology in the COVID-19 injectables.

EzSingleCell: an integrated one-stop single-cell and spatial omics analysis platform for bench scientists

ezSingleCell is an interactive and easy-to-use application for analysing various single-cell and spatial omics data types without requiring prior programing knowledge. It combines the best-performing publicly available methods for in-depth data analysis, integration, and interactive data visualization. ezSingleCell consists of five modules, each designed to be a comprehensive workflow for one data type or task. In addition, ezSingleCell allows crosstalk between different modules within a unified interface. Acceptable input data can be in a variety of formats while the output consists of publication ready figures and tables. In-depth manuals and video tutorials are available to guide users on the analysis workflows and parameter adjustments to suit their study aims. ezSingleCell’s streamlined interface can analyse a standard scRNA-seq dataset of 3000 cells in less than five minutes. ezSingleCell is available in two forms: an installation-free web application (https://immunesinglecell.org/ezsc/) or a software package with a shinyApp interface (https://github.com/JinmiaoChenLab/ezSingleCell2) for offline analysis.

Thinking and doing beyond science and risk communication as mere transmission

I look back on my professional experience as both bench and social scientist, and examine my misunderstandings of science communication as a field, which were fueled by my ideologically-driven stance on the primacy of the bench sciences over all other fields of study. I begin with my work as a molecular biologist, continue with my socialization into the social sciences at the PhD level, and narrate my experiences as a researcher attempting to break down the notion of science and risk communication as mere vessels for sharing scientific information. I do this by discussing my research findings, and then ruminating on their meaning for the fields of science and risk communication. These fields must grow beyond their preoccupation with the centrality of scientific data in communication by 1) recognizing the ideological force exerted by such a stance, and 2) acknowledging the many voices of society that allow communication to be a complicated, and yet fruitful enterprise.

Abstract 3493: Read-level methylation pattern extraction for high-multiplex large-scale targeted NGS assay

Abstract The abnormal changes in DNA methylation are linked to the early stages of carcinogenesis. Identifying these epigenetic changes in circulating tumor DNA (ctDNA) can reveal potential biomarkers for the early diagnosis of various cancers. However, analyzing such data poses bioinformatics challenges due to the lack of sensitivity in detecting the low abundance ctDNA signals in biopsy samples, which are often overwhelmed by the complexity of libraries containing hundreds of targeted regions. Read-level methylation analysis holds the promise of more in-depth DNA methylation detection due to the wide coverage and high sensitivity of rare signals. However, this approach is hindered by the absence of a standardized workflow capable of generating interpretable reports suitable for both bench scientists and professional bioinformaticians. Here, we present a bioinformatics workflow that examines next-generation sequencing (NGS) data and characterizes the read-level methylation patterns of amplicons. Compared to other currently available tools, our method is designed to work with high-multiplex, large-scale targeted assays. It effectively eliminates the undesired noise derived from sequencing byproducts such as false CpG calls, dimers, and off-target alignments. Additionally, to accommodate the substantial volume of data generated by state-of-the-art NGS platforms, the workflow enables parallel processing of samples compatible with both cloud-based and on-premises computing resources. This workflow provides a comprehensive per-sample visualization of DNA methylation patterns and reports read-level methylation results in a “pattern-as-a-feature” table. In this table, the occurrence of an amplicon epiallelic haplotype (pattern) for every sample is represented as a “feature column” and is aggregated with all patterns discovered in the experiment. These read-level patterns, along with other information, can be used to develop machine learning algorithms to reiteratively harvest true predictive features and penalize confounding signals in predicting cancer diagnosis. Citation Format: Mingda Jin, Masatomo Kaneko, Steven Cen, Hongtao Li, Wei Guo, Xinyi Zhou, Atsuko Fujihara, Tsuyoshi Iwata, Lorenzo Storino Ramacciotti, Divyangi Paralkar, Giovanni E Cacciamani, Manju Aron, Osamu Ukimura, Inderbir S. Gill, Gangning Liang, Andre L. Abreu, Jeffrey Bhasin, Xiaojing Yang, Xi-Yu Jia. Read-level methylation pattern extraction for high-multiplex large-scale targeted NGS assay [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 3493.

Abstract 6202: Integrating public single-cell transcriptomics and patient profiles to guide clinical development

Abstract Background: Single-cell technologies provide invaluable insights into disease biology and drug development by revealing complex interactions among different cell types within patients. However, harnessing the potential of publicly available single-cell data remains challenging due to the lack of integrated data across diverse single cell platforms. Methods: To maximize the potential of single cell insights, we have created an AI/ML powered curation and data integration process within our Drug Intelligence Science (DIS®) platform. This automated process integrates single cell transcriptomic data from publicly available sources with our in-house generated datasets from ex vivo translational efforts and clinical programs. Relevant public datasets are identified and retrieved using an automated Python-based GEO dataset crawler tool, and standardized metadata are curated. A reference atlas of 31 immune and 4 non-immune cell types is generated from the database and is used to consistently label cells across datasets. Additionally, cell-level gene expression integration and normalization across datasets is achieved using generative modeling techniques. To facilitate multiuser visualization and interrogation of the curated and integrated datasets, we have developed Cellect, a customized version of CellxGene Gateway. Results: The current single-cell database comprises 15 million cells analyzed in &amp;gt;5,000 samples collected from &amp;gt;2,500 patients across 160 curated unique human studies, spanning oncology (56%), autoimmune disease (AID, 21%), and viral infection (15%), with the remaining being from reference healthy tissues (8%). We focused dataset curation on indications of interest for our internal clinical development efforts. Uniquely, data from approximately 300 patients who received standard of care (SOC) treatment including immune checkpoint inhibitor (IO) therapy were integrated to delineate mechanisms of drug response and resistance. The post-SOC resource was used to identify (1) treatment settings where these mechanisms are enriched and (2) markers co-expressed with targets of interest (e.g., PD1, CTLA4, OX40, TNFR2, etc.) in specific cell populations to inform combination strategies. Conclusions: We have presented an AI/ML guided approach to address the key challenge of integrating single-cell data across platforms and demonstrated that relevant disease biology is retained upon integration. We outline a path for deploying this solution at scale for bench and computational scientists to guide target as well as indication selection, as was done for our ongoing clinical programs, including our first-in-class TNFR2 agonist (HFB200301, NCT05238883) and second-generation OX40 agonist (HFB301001, NCT05229601). Citation Format: Jack Russella-Pollard, Joshua Whitener, Jordan Byck, Monika Manne, Hani Alostaz, Marianna Elia, Kamil Krukowski, Gabrielle Wong, Francisco Adrian, Liang Schweizer, Robert H. Andtbacka, Christos Hatzis. Integrating public single-cell transcriptomics and patient profiles to guide clinical development [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 6202.

Neurolaw: Revisiting Huberty v. McDonald’s through the Lens of Nutritional Criminology and Food Crime

Recent studies have illuminated the potential harms associated with ultra-processed foods, including poor mental health, aggression, and antisocial behavior. At the same time, the human gut microbiome has emerged as an important contributor to cognition and behavior, disrupting concepts of the biopsychosocial ‘self’ and raising questions related to free will. Since the microbiome is undeniably connected to dietary patterns and components, the topics of nutrition and microbes are of heightened interest to neuroscience and psychiatry. Research spanning epidemiology, mechanistic bench science, and human intervention trials has brought legitimacy to nutritional criminology and the idea that nutrition is of relevance to the criminal justice system. The individual and community-level relationships between nutrition and behavior are also salient to torts and the relatively new field of food crime—that which examines the vast harms, including grand-scale non-communicable diseases and behavioral outcomes, caused by the manufacturers, distributors, and marketers of ultra-processed food products. Here in this essay, we will synthesize various strands of research, reflecting this emergent science, using a notable case that straddled both neurolaw and food crime, Huberty v. McDonald’s (1987). It is our contention that the legalome—microbiome and omics science applied in neurolaw and forensics—will play an increasing role in 21st-century courtroom discourse, policy, and decision-making.

Recommendations for Bioinformatic Tools in lncRNA Research

Abstract: Long non-coding RNAs (lncRNAs) typically refer to non-protein coding RNAs that are longer than 200 nucleotides. Historically dismissed as junk DNA, over two decades of research have revealed that lncRNAs bind to other macromolecules (e.g., DNA, RNA, and/or proteins) to modulate signaling pathways and maintain organism viability. Their discovery has been significantly aided by the development of bioinformatics tools in recent years. However, the diversity of tools for lncRNA discovery and functional prediction can present a challenge for researchers, especially bench scientists and clinicians. This Perspective article aims to navigate the current landscape of bioinformatic tools suitable for both protein-coding and lncRNA genes. It aims to provide a guide for bench scientists and clinicians to select the appropriate tools for their research questions and experimental designs.

Neuroscience and Society: Supporting and Unsettling Public Engagement.

Advancing neuroscience is one of many topics that pose a challenge often called "the alignment problem"-the challenge, that is, of assuring that science policy is responsive to and in some sense squares with the public's values. This issue of the Hastings Center Report launches a series of scholarly essays and articles on the ethical and social issues raised by this vast body of medical research and bench science. The series, which will run under the banner "Neuroscience and Society," is supported by the Dana Foundation and seeks to promote deliberative public engagement, broadly understood, about neuroscience. As a social goal, deliberative public engagement is both ubiquitous and elusive-called for everywhere yet difficult to undertake at a national level on a complex scientific topic. To be meaningful, deliberative public engagement must occur in many locations in a society and be carried forward by many actors. Scholarly writing might contribute in several ways.

The mixing method used to formulate lipid nanoparticles affects mRNA delivery efficacy and organ tropism

mRNA is a versatile drug molecule with therapeutic applications ranging from protein replacement therapies to in vivo gene engineering. mRNA delivery is often accomplished using lipid nanoparticles, which are formulated via mixing of aqueous and organic solutions. Although this has historically been accomplished by manual mixing for bench scale science, microfluidic mixing is required for scalable continuous manufacturing and batch to batch control. Currently, there is limited understanding on how the mixing process affects mRNA delivery efficacy, particularly in regard to tropism. To address this knowledge gap, we examined the influence of the type of mixing and microfluidic mixing parameters on the performance of lipid nanoparticles in mice. This was accomplished with a Design of Experiment approach using four nanoparticle formulations with varied ionizable lipid chemistry. We found that each formulation required unique optimization of mixing parameters, with the total delivery efficacy of each lipid nanoparticle generated with microfluidics ranging from 100-fold less to 4-fold more than manually mixed LNPs. Further, mixing parameters influenced organ tropism, with the most efficacious formulations disproportionately increasing liver delivery compared to other organs. These data suggest that mixing parameters for lipid nanoparticle production may require optimization for each unique chemical formulation, complicating translational efforts. Further, microfluidic parameters must be chosen carefully to balance overall mRNA delivery efficacy with application-specific tropism requirements.

Deriving protein binding-corrected chemical concentrations for in vitro testing.

Extracellular chemical concentrations are considered physiologically relevant for in vitro testing and are evaluated in traditional in vitro systems using cell culture media containing 5%-10% fetal bovine serum (FBS). However, depending on the physicochemical properties, and in vitro testing conditions, cells could be exposed to variable unbound extracellular concentrations. If in vitro unbound concentrations are not calculated, it is challenging to distinguish the chemical potency and concentration-driven responses. In this study, one- and two-protein binding models were used to estimate protein binding corrected chemical concentrations of various chemicals for in vitro testing. As ceftizoxime, moxifloxacin, and nicotine have low protein binding affinity, the in vitro protein binding in 5%-10% FBS is less than 5% and can be considered negligible. However, protein binding of moderate and highly protein-bound chemicals must be corrected for as the in vitro unbound concentrations in 5%-10% FBS containing cell culture media will vary over a range of chemical concentrations. In vitro pharmacological and toxicological assessments must incorporate protein binding-adjusted in vitro concentrations to ensure physiologically relevant exposures. A user-friendly Excel spreadsheet is provided to help bench scientists calculate protein binding-corrected chemical concentrations for in vitro testing.

Benchmarking RNA Editing Detection Tools.

RNA, like DNA and proteins, can undergo modifications. To date, over 170 RNA modifications have been identified, leading to the emergence of a new research area known as epitranscriptomics. RNA editing is the most frequent RNA modification in mammalian transcriptomes, and two types have been identified: (1) the most frequent, adenosine to inosine (A-to-I); and (2) the less frequent, cysteine to uracil (C-to-U) RNA editing. Unlike other epitranscriptomic marks, RNA editing can be readily detected from RNA sequencing (RNA-seq) data without any chemical conversions of RNA before sequencing library preparation. Furthermore, analyzing RNA editing patterns from transcriptomic data provides an additional layer of information about the epitranscriptome. As the significance of epitranscriptomics, particularly RNA editing, gains recognition in various fields of biology and medicine, there is a growing interest in detecting RNA editing sites (RES) by analyzing RNA-seq data. To cope with this increased interest, several bioinformatic tools are available. However, each tool has its advantages and disadvantages, which makes the choice of the most appropriate tool for bench scientists and clinicians difficult. Here, we have benchmarked bioinformatic tools to detect RES from RNA-seq data. We provide a comprehensive view of each tool and its performance using previously published RNA-seq data to suggest recommendations on the most appropriate for utilization in future studies.

Flexor Tendon Repair: Avoidance and Management of Complications.

The proper technique for flexor tendon repair has been well established through numerous bench science and clinical studies. However, less is known about strategies to avoid and manage postoperative complications. This article discusses the common complications after flexor tendon repair, such as repair site rupture and adhesion formation. This article also addresses strategies to prevent and manage these complications. The foundation for preventing many of these complications is ensuring a strong repair without gapping at time zero, which will enable the accrual of tensile strength through early initiation of motion.

Building a collaborative cloud platform to accelerate heart, lung, blood, and sleep research.

Research increasingly relies on interrogating large-scale data resources. The NIH National Heart, Lung, and Blood Institute developed the NHLBI BioData CatalystⓇ (BDC), a community-driven ecosystem where researchers, including bench and clinical scientists, statisticians, and algorithm developers, find, access, share, store, and compute on large-scale datasets. This ecosystem provides secure, cloud-based workspaces, user authentication and authorization, search, tools and workflows, applications, and new innovative features to address community needs, including exploratory data analysis, genomic and imaging tools, tools for reproducibility, and improved interoperability with other NIH data science platforms. BDC offers straightforward access to large-scale datasets and computational resources that support precision medicine for heart, lung, blood, and sleep conditions, leveraging separately developed and managed platforms to maximize flexibility based on researcher needs, expertise, and backgrounds. Through the NHLBI BioData Catalyst Fellows Program, BDC facilitates scientific discoveries and technological advances. BDC also facilitated accelerated research on the coronavirus disease-2019 (COVID-19) pandemic.

Neurologic Complications With Vaccines: What We Know, What We Don't, and What We Should Do.

Over the previous half century, vaccines have shaped human life by eradicating or nearly eradicating infections that were once a major cause of morbidity and mortality. The number of infections for which vaccines are now available has steadily increased. The types of vaccines have evolved over the years from crude extracts to more refined messenger RNA or protein-based vaccines. With these well-defined manufacturing processes, the safety profile has also improved. Despite such measures, vaccines are not without side effects, including those that affect the nervous system. Numerous case reports and case series point to these possibilities. These issues have gathered much attention during the current mass vaccination against severe acute respiratory syndrome coronavirus 2 and have resulted in some members of the public raising concerns about vaccine safety. The vaccine manufacturers have legal protection against vaccine side effects; however, there are active and passive surveillance programs put in place by the Center for Disease Control and Prevention, the US Food and Drug Administration, the World Health Organization, and the European Union. Action is needed that brings together manufactures, healthcare agencies, clinical and bench scientists, and legislatures on a global platform to investigate vaccine-related neurologic adverse events and develop ways to prevent and treat them.

Synthetic biology open language (SBOL) version 3.1.0

Abstract 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 bench scientists and scientific modelers and software developers, across academia, industry, and other institutions. This document describes SBOL 3.1.0, which improves on version 3.0.0 by including a number of corrections and clarifications as well as several other updates and enhancements. First, this version includes a complete set of validation rules for checking whether documents are valid SBOL 3. Second, the best practices section has been moved to an online repository that allows for more rapid and interactive of sharing these conventions. Third, it includes updates based upon six community approved enhancement proposals. Two enhancement proposals are related to the representation of an object’s namespace. In particular, the Namespace class has been removed and replaced with a namespace property on each class. Another enhancement is the generalization of the CombinatorialDeriviation class to allow direct use of Features and Measures. Next, the Participation class now allow Interactions to be participants to describe higher-order interactions. Another change is the use of Sequence Ontology terms for Feature orientation. Finally, this version of SBOL has generalized from using Unique Reference Identifiers (URIs) to Internationalized Resource Identifiers (IRIs) to support international character sets.

History and Future Directions of rTMS for Treatment of Depressive Disorders

History and Future Directions of rTMS for Treatment of Depressive Disorders

M-CAMPTM: A Cloud-based Web Platform with a Novel Approach for Species-level Classification of 16S rRNA Microbiome Sequences

Background: The M-CAMPTM (Microbiome Computational Analysis for Multi-omic Profiling) Cloud Platform was designed to provide users with an easy-to-use web interface to access best in class microbiome analysis tools. This interface allows bench scientists to conduct bioinformatic analysis on their samples and then download publication-ready graphics and reports. Objective: In this study we aim to describe the M-CAMPTM platform and demonstrate that the taxonomic classification is more accurate than previously described methods on a wide range of microbiome samples. Methods: The core pipeline of the platform is the 16S-seq taxonomic classification algorithm which provides species-level classification of Illumina 16s sequencing. This algorithm uses a novel approach combining alignment and kmer based taxonomic classification methodologies to produce a highly accurate and comprehensive profile. Additionally, a comprehensive proprietary database combining reference sequences from multiple sources was curated and contains 18056 unique V3-V4 sequences covering 11527 species. Results and Discussion: The M-CAMPTM 16S taxonomic classification algorithm was evaluated on 52 sequencing samples from both public and in-house standard sample mixtures with known fractions. The same evaluation process was also performed on 5 well-known 16S taxonomic classification algorithms including Qiime2, Kraken2, Mapseq, Idtaxa and Spingo using the same dataset. Results have been discussed in term of evaluation metrics and classified taxonomic levels. Conclusion: Compared to current popular public classification algorithms, M-CAMPTM 16S taxonomic classification algorithm provides the most accurate species-level classification of 16S rRNA sequencing data.