Systematic overview of challenges and opportunities of computational data-driven research in biology

During the past decade, the rapid advancement of high-throughput technologies has reshaped modern biomedical research by vastly extending the diversity, richness, and availability of data and methods across various domains. Currently, computational researchers are empowered with data, methods, and tools that allow for the possibility of making important contributions in biomedicine –– through primary analysis of pre-clinical and clinical datasets, the application and development of novel machine learning algorithms towards task automation and diagnostic or treatment predictions, and secondary analysis of existing public omics data. Here we discuss the challenges and pitfalls researchers from dry labs are facing and how they are gaining independence and leading high impact projects

Effective and feasible mechanisms to support Ukrainian researchers and students at risk: opportunities, challenges and pitfalls

Since February 24, 2022, invasion of Ukraine by Russia has affected the entire population of Ukraine, including the scientific community. The attack by Russian forces has resulted in the destruction of infrastructure of Ukrainian cities, including universities, research centers, and educational institutions. Millions of people, including educators and scientists, have been displaced from their homes, their futures uncertain. This conflict is unfolding in a country with a large and diverse scientific community and a rich history of higher education and scientific research. The global research community has provided effective and timely response by offering research opportunities and fellowships for displaced Ukrainian academic faculty and students. However, many of these opportunities were primarily targeted to individuals capable of leaving the country. Many Ukrainian scholars remaining in the country need the help of the international community to help find short- or long-term employment. Moving forward, we hope to increase collaboration, provision of training provided by educational organizations worldwide, and spur rapid advancements in science and technology that will lead and expedite the postwar reconstruction of Ukraine

Remote opportunities for scholars in Ukraine

Russia’s unprovoked attack on Ukraine has destroyed civilian infrastructure, including universities, research centers, and other academic infrastructure (1). Many Ukrainian scholars and researchers remain in Ukraine, and their work has suffered from major setbacks (2–4). We call on international scientists and institutions to support them

A comprehensive benchmarking of WGS-based deletion structural variant callers.

Advances in whole-genome sequencing (WGS) promise to enable the accurate and comprehensive structural variant (SV) discovery. Dissecting SVs from WGS data presents a substantial number of challenges and a plethora of SV detection methods have been developed. Currently, evidence that investigators can use to select appropriate SV detection tools is lacking. In this article, we have evaluated the performance of SV detection tools on mouse and human WGS data using a comprehensive polymerase chain reaction-confirmed gold standard set of SVs and the genome-in-a-bottle variant set, respectively. In contrast to the previous benchmarking studies, our gold standard dataset included a complete set of SVs allowing us to report both precision and sensitivity rates of the SV detection methods. Our study investigates the ability of the methods to detect deletions, thus providing an optimistic estimate of SV detection performance as the SV detection methods that fail to detect deletions are likely to miss more complex SVs. We found that SV detection tools varied widely in their performance, with several methods providing a good balance between sensitivity and precision. Additionally, we have determined the SV callers best suited for low- and ultralow-pass sequencing data as well as for different deletion length categories

Scientists without borders: Lessons from Ukraine

Conflicts and natural disasters affect entire populations of the countries involved and, in addition to the thousands of lives destroyed, have a substantial negative impact on the scientific advances these countries provide. The unprovoked invasion of Ukraine by Russia, the devastating earthquake in Turkey and Syria, and the ongoing conflicts in the Middle East are just a few examples. Millions of people have been killed or displaced, their futures uncertain. These events have resulted in extensive infrastructure collapse, with loss of electricity, transportation, and access to services. Schools, universities, and research centers have been destroyed along with decades’ worth of data, samples, and findings. Scholars in disaster areas face short- and long-term problems in terms of what they can accomplish now for obtaining grants and for employment in the long run. In our interconnected world, conflicts and disasters are no longer a local problem but have wide-ranging impacts on the entire world, both now and in the future. Here, we focus on the current and ongoing impact of war on the scientific community within Ukraine and from this draw lessons that can be applied to all affected countries where scientists at risk are facing hardship. We present and classify examples of effective and feasible mechanisms used to support researchers in countries facing hardship and discuss how these can be implemented with help from the international scientific community and what more is desperately needed. Reaching out, providing accessible training opportunities, and developing collaborations should increase inclusion and connectivity, support scientific advancements within affected communities, and expedite postwar and disaster recovery

Unlocking capacities of genomics for the COVID-19 response and future pandemics

During the COVID-19 pandemic, genomics and bioinformatics have emerged as essential public health tools. The genomic data acquired using these methods have supported the global health response, facilitated the development of testing methods and allowed the timely tracking of novel SARS-CoV-2 variants. Yet the virtually unlimited potential for rapid generation and analysis of genomic data is also coupled with unique technical, scientific and organizational challenges. Here, we discuss the application of genomic and computational methods for efficient data-driven COVID-19 response, the advantages of the democratization of viral sequencing around the world and the challenges associated with viral genome data collection and processing