Collaborating Authors


Comparison of Read Mapping and Variant Calling Tools for the Analysis of Plant NGS Data


High-throughput sequencing technologies have rapidly developed during the past years and have become an essential tool in plant sciences. However, the analysis of genomic data remains challenging and relies mostly on the performance of automatic pipelines. Frequently applied pipelines involve the alignment of sequence reads against a reference sequence and the identification of sequence variants. Since most benchmarking studies of bioinformatics tools for this purpose have been conducted on human datasets, there is a lack of benchmarking studies in plant sciences. In this study, we evaluated the performance of 50 different variant calling pipelines, including five read mappers and ten variant callers, on six real plant datasets of the model organism Arabidopsis thaliana. Sets of variants were evaluated based on various parameters including sensitivity and specificity. We found that all investigated tools are suitable for analysis of NGS data in plant research. When looking at different performance metrics, BWA-MEM and Novoalign were the best mappers and GATK returned the best results in the variant calling step.

First Ever Artificial Intelligence/Machine Learning Action Plan by FDA


Last week, the U.S. Food and Drug Administration presented the organization's first Artificial Intelligence/Machine Learning (AI/ML)- Based Software as a Medical Device (SaMD) Action Plan. This plan portrays a multi-pronged way to deal with the Agency's oversight of AI/ML-based medical software. The Artificial Intelligence/Machine Learning (AI/ML)- Based Software as a Medical Device (SaMD) Action Plan is a response to stakeholder input on the FDA's 2019 regulatory structure for AI and ML-based medical items. FDA additionally will hold a public workshop on algorithm transparency and draw in its stakeholders and partners on other key activities, for example, assessing predisposition in algorithms. While the Action Plan proposes a guide for propelling a regulatory framework, an operational structure gives off an impression of being further down the road.

2021's crystal ball: 6 AI predictions that will shape a new commercial model - MedCity News


Alan Kalton, Vice President and General Manager of Aktana Europe, is a leader in data analytics and manages all new Contextual Intelligence implementations and developments across Europe. He comes to Aktana from Cape Town, South Africa where he led a data analytics venture called BroadReach and prior was the Analytics Leader of EY in South Africa. He also held prominent executive leadership positions in data analytics at IBM, Elsevier, Cognizant, Steris, Novartis, GSK, and ZS Associates. He graduated with a BS and MSc of industrial and operations engineering from the University of Michigan. Kalton can be reached at

Artificial Intelligence Discovers Potent Antibiotic


Anewly designed artificial intelligence tool based on the structure of the brain has identified a molecule capable of wiping out a number of antibiotic-resistant strains of bacteria, according to a study published on February 20 in Cell. The molecule, halicin, which had previously been investigated as a potential treatment for diabetes, demonstrated activity against Mycobacterium tuberculosis, the causative agent of tuberculosis, and several other hard-to-treat microbes. The discovery comes at a time when novel antibiotics are becoming increasingly difficult to find, reports STAT, and when drug-resistant bacteria are a growing global threat. The Interagency Coordination Group (IACG) on Antimicrobial Resistance convened by United Nations a few years ago released a report in 2019 estimating that drug-resistant diseases could result in 10 million deaths per year by 2050. Despite the urgency in the search for new antibiotics, a lack of financial incentives has caused pharmaceutical companies to scale back their research, according to STAT. "I do think this platform will very directly reduce the cost involved in the discovery phase of antibiotic development," coauthor James Collins of MIT tells STAT.

Artificial intelligence in longevity medicine


Aging is a universal feature shared by all living beings. While the rate of aging may vary among individuals and species, the time elapsed since birth is a strong predictor of health status and mortality. Targeting aging may extend the average life expectancy more substantially than prevention or treatment of individual diseases1. However, within the established drug discovery and development framework, pharmaceutical companies are still searching for compounds and interventions for the treatment of individual chronic diseases such as cancer and cardiovascular or pulmonary diseases. Current biomedical research aims to identify the underlying mechanisms and molecular targets specific to a disease in order to modify the disease, treat its symptoms or cure it.

AI changing dynamics of healthcare


Globally, healthcare organisations have accelerated adoption of artificial intelligence (AI), with the ones still implementing frameworks planning to go live within 24 months. Hardly surprising given the improved consumer engagement that results from the technology. But more than that, the challenging economic climate is seeing healthcare organisations looking for better ways to make processes more efficient, enhance their existing products and services and lower costs. The key to this is AI that brings with it a more innovative environment to automate manual, error-prone processes and introduce a sophisticated layer of analytics that can deliver new insights to the wealth of data already available. These platforms use algorithms and machine learning to analyse and interpret data, while empowering the healthcare organisation with the means to provide more personalised customer experiences.

Love in the time of algorithms: would you let your artificial intelligence choose your partner?


It could be argued artificial intelligence (AI) is already the indispensable tool of the 21st century. From helping doctors diagnose and treat patients to rapidly advancing new drug discoveries, it's our trusted partner in so many ways. Now it has found its way into the once exclusively-human domain of love and relationships. With AI-systems as matchmakers, in the coming decades it may become common to date a personalised avatar. This was explored in the 2014 movie "Her", in which a writer living in near-future Los Angeles develops affection for an AI system. The sci-fi film won an Academy Award for depicting what seemed like a highly unconventional love story.

Oxford-BMS Fellowship

Oxford Comp Sci

Bristol Myers Squibb (BMS) is a global biopharmaceutical company making advancements in oncology, haematology, immunology and cardiovascular disease. BMS are dedicated to helping patients prevail over serious diseases through a diverse and promising pipeline and new scientific platforms. Prior to 2019, in partnership with the Oxford Medical Sciences Division, Celgene co-developed and provided support for fellowships which will continue as the Oxford-BMS Translational Research Fellowship Programme. The goals of this scheme are to stimulate new scientific discovery and translation and to facilitate skills and people transfer between researchers in academia and industry. This programme offers fellows an opportunity to gain exposure to the field of commercial drug discovery and development.

Recent and forthcoming machine learning and AI seminars: January 2021 edition


This post contains a list of the AI-related seminars that are scheduled to take place between now and the end of February 2021. We've also listed recent past seminars that are available for you to watch. All events detailed here are free and open for anyone to attend virtually. This list includes forthcoming seminars scheduled to take place between 15 January and 28 February. Zero-shot (human-AI) coordination (in Hanabi) and ridge rider Speaker: Jakob Foerster (Facebook, University of Toronto & Vector Institute) Organised by: University College London Zoom link is here.

Phylodynamics for cell biologists


Advances in experimental approaches for single-cell analysis allow in situ sequencing, genomic barcoding, and mapping of cell lineages within tissues and organisms. Large amounts of data have thus accumulated and present an analytical challenge. Stadler et al. recognized the need for conceptual and computational approaches to fully exploit these technological advances for the understanding of normal and disease states. The authors review ideas taken from phylodynamics of infectious disease and show how similar tree-building techniques can be applied to monitoring changes in somatic cell lineages for applications ranging from development and differentiation to cancer biology. Science , this issue p. [eaah6266][1] ### BACKGROUND The birth, death, and diversification of individuals are events that drive biological processes across all scales. This is true whether the individuals in question represent nucleic acids, cells, whole organisms, populations, or species. The ancestral relationships of individuals can be visualized as branching trees or phylogenies, which are long-established representations in the fields of evolution, ecology, and epidemiology. Molecular phylogenetics is the discipline concerned with the reconstruction of such trees from gene or genome sequence data. The shape and size of such phylogenies depend on the past birth and death processes that generated them, and in phylodynamics, mathematical models are used to infer and quantify the dynamical behavior of biological populations from ancestral relationships. New technological advances in genetics and cell biology have led to a growing body of data about the molecular state and ancestry of individual cells in multicellular organisms. Ideas from phylogenetics and phylodynamics are being applied to these data to investigate many questions in tissue formation and tumorigenesis. ### ADVANCES Trees offer a valuable framework for tracing cell division and change through time, beginning with individual ancestral stem cells or fertilized eggs and resulting in complex tissues, tumors, or whole organisms (see the figure). They also provide the basis for computational and statistical methods with which to analyze data from cell biology. Our Review explains how “tree-thinking” and phylodynamics can be beneficial to the interpretation of empirical data pertaining to the individual cells of multicellular organisms. We summarize some recent research questions in developmental and cancer biology and briefly introduce the new technologies that allow us to observe the spatiotemporal histories of cell division and change. We provide an overview of the various and sometimes confusing ways in which graphical models, based on or represented by trees, have been applied in cell biology. To provide conceptual clarity, we outline four distinct graphical representations of the history of cell division and differentiation in multicellular organisms. We highlight that cells from an organism cannot be always treated as statistically independent observations but instead are often correlated because of phylogenetic history, and we explain how this can cause difficulties when attempting to infer dynamical behavior from experimental single-cell data. We introduce simple ecological null models for cell populations and illustrate some potential pitfalls in hypothesis testing and the need for quantitative phylodynamic models that explicitly incorporate the dependencies caused by shared ancestry. ### OUTLOOK We expect the rapid growth in the number of cell-level phylogenies to continue, a trend enhanced by ongoing technological advances in cell lineage tracing, genomic barcoding, and in situ sequencing. In particular, we anticipate the generation of exciting datasets that combine phenotypic measurements for individual cells (such as through transcriptome sequencing) with high-resolution reconstructions of the ancestry of the sampled cells. These developments will offer new ways to study developmental, oncogenic, and immunological processes but will require new and appropriate conceptual and computational tools. We discuss how models from phylogenetics and phylodynamics will benefit the interpretation of the data sets generated in the foreseeable future and will aid the development of statistical tests that exploit, and are robust to, cell shared ancestry. We hope that our discussion will initiate the integration of cell-level phylodynamic approaches into experimental and theoretical studies of development, cancer, and immunology. We sketch out some of the theoretical advances that will be required to analyze complex spatiotemporal cell dynamics and encourage explorations of these new directions. Powerful new statistical and computational tools are essential if we are to exploit fully the wealth of new experimental data being generated in cell biology. ![Figure][2] Multicellular organisms develop from a single fertilized egg. The division, apoptosis, and differentiation of cells can be displayed in a development tree, with the fertilized egg being the root of the tree. The development of any particular tissue within an organism can be traced as a subtree of the full developmental tree. Subtrees that represent cancer tumors or B cell clones may exhibit rapid growth and genetic change. Here, we illustrate the developmental tree of a human and expand the subtree representing haematopoiesis (blood formation) in the bone marrow. Stem cells in the bone marrow differentiate, giving rise to the numerous blood cell types in humans. The structure of the tree that underlies haematopoiesis and the formation of all tissues is unclear. Phylogenetic and phylodynamic tools can help to describe and statistically explore questions about this cell differentiation process. Multicellular organisms are composed of cells connected by ancestry and descent from progenitor cells. The dynamics of cell birth, death, and inheritance within an organism give rise to the fundamental processes of development, differentiation, and cancer. Technical advances in molecular biology now allow us to study cellular composition, ancestry, and evolution at the resolution of individual cells within an organism or tissue. Here, we take a phylogenetic and phylodynamic approach to single-cell biology. We explain how “tree thinking” is important to the interpretation of the growing body of cell-level data and how ecological null models can benefit statistical hypothesis testing. Experimental progress in cell biology should be accompanied by theoretical developments if we are to exploit fully the dynamical information in single-cell data. [1]: /lookup/doi/10.1126/science.aah6266 [2]: pending:yes