DNA barcoding, environmental DNA, and long-read genomics from a 5,000+ sq ft Berkeley wet-lab — sample to verifiable, FAIR-compliant published claim, built for the commons.
Agentis — coming soon — BioKEA
An AI-first scientific journal built on the AT Protocol. Fast, verifiable peer review and publications as interactive StoryMaps.
How to SNIFF out Good Scientific Software
New PLOS Computational Biology paper: "Ten Quick Tips to SNIFF Out Sustainable and Secure Scientific Software"
Reinventing the wheel: milk, microbes, and the fight for real cheese
"Reinventing the Wheel is equal parts popular science, history, and muckraking. Over the past hundred and fifty years, dairy farming and cheesemaking have been transformed, and this book explores what has been lost along the way. Today, using cutting-edge technologies like high-throughput DNA sequencing, scientists are beginning to understand the techniques of our great-grandparents. The authors describe how geneticists are helping conservationists rescue rare dairy cow breeds on the brink of extinction, microbiologists are teaching cheesemakers to nurture the naturally occurring microbes in their raw milk rather than destroying them, and communities of cheesemakers are producing "real" cheeses that reunite farming and flavor, rewarding diversity and sustainability at every level."--Provided by publisher.
Holobiont
A holobiont is an assemblage of a host and the many other species living in or around it, which together form a discrete ecological unit through symbiosis, though there is controversy over this discreteness. The components of a holobiont are individual species or bionts, while the combined genome of all bionts is the hologenome. The holobiont concept was initially introduced by the German theoretical biologist Adolf Meyer-Abich in 1943, and then apparently independently by Dr. Lynn Margulis in her 1991 book Symbiosis as a Source of Evolutionary Innovation. The concept has evolved since the original formulations. Holobionts include the host, virome, microbiome, and any other organisms which contribute in some way to the functioning of the whole. Well-studied holobionts include reef-building corals and humans.
How DNA Editing Could Change Life on Earth
Researchers hope to use the powerful biological tool to alter the genetic destiny of species and eliminate diseases.
A Cell So Minimal That It Challenges Definitions of Life | Quanta Magazine
The newly described microbe represents a world of parasitic, intercellular biodiversity only beginning to be revealed by genome sequencing.
#bioinformatics #computationalbiology #aiforscience #singlecell #spatialtranscriptomics #opensource #codex #claudecode #mcp #agenticai | Herry Z.
BioNexus: A Scientific Reliability Layer for Agentic Biology As coding agents such as OpenAI Codex, Claude Code, and Cursor become increasingly capable of running bioinformatics workflows, a new problem emerges: Knowing how to run an analysis does not mean knowing when that analysis is scientifically valid. BioNexus is an open-source project exploring a reliability layer for biological coding agents rather than another standalone AI scientist. Its current architecture focuses on: Machine-readable Capability Contracts — define input semantics, scientific preconditions, canonical backends, refusal conditions, and evidence requirements. EvidenceCard 2.1 — separates execution state from scientific evidence and grades conclusions as PRELIMINARY, SUPPORTED, ROBUST, REPLICATED, FRAGILE, or ABSTAIN. Scientific Intent & Invariant Routing — detects invalid analyses such as pseudoreplication, incompatible data representations, or missing backends. BioNexus Eval — evaluates routing, unsafe claims, refusal behavior, and scientific outcomes. RunBundle Artifact Contract — packages inputs, parameters, results, provenance, environment, and evidence into reproducible machine-readable runs. The long-term goal is simple: Make scientific reliability a first-class infrastructure layer for agentic biology. BioNexus is still evolving, and I am actively looking for contributors in bioinformatics, single-cell/spatial omics, scientific benchmarking, MCP, reproducibility, and AI coding agents. PRs, issues, benchmark datasets, scientific critiques, and failure cases are especially welcome. GitHub: HERRY423/BioNexus #Bioinformatics #ComputationalBiology #AIForScience #SingleCell #SpatialTranscriptomics #OpenSource #Codex #ClaudeCode #MCP #AgenticAI
SOLU / Bioart Society
SOLU / Bioart Society Panimokatu 1, 3rd floor 00580 Helsinki Finland info@bioartsociety.fi
How Life Works: A User’s Guide to the New Biology
“Bold and intriguing.”—Wall Street Journal • “Penetrating. . . . Provocative and profound.”—Publishers Weekly (starred review) • “Offers plenty of food for thought.”—Kirkus Reviews (starred review) “Ball’s marvelous book is both wide-ranging and deep. . . . I could not put it down.”—Siddhartha Mukherjee, author of The Song of the Cell and the Pulitzer Prize–winning The Emperor of All Maladies A new, cutting-edge vision of biology that revises our understanding of what life itself is, how to enhance it, and what possibilities it offers. Biology is undergoing a quiet but profound transformation. Several aspects of the standard picture of how life works—the idea of the genome as a blueprint, of genes as instructions for building an organism, of proteins as precisely tailored molecular machines, of cells as entities with fixed identities, and more—have been exposed as incomplete, misleading, or wrong. In How Life Works, Philip Ball explores the new biology, revealing life to be a far richer, more ingenious affair than we had guessed. Ball explains that there is no unique place to look for an answer to this question: life is a system of many levels—genes, proteins, cells, tissues, and body modules such as the immune system and the nervous system—each with its own rules and principles. How Life Works explains how these levels operate, interface, and work together (most of the time). With this knowledge come new possibilities. Today we can redesign and reconfigure living systems, tissues, and organisms. We can reprogram cells, for instance, to carry out new tasks and grow into structures not seen in the natural world. As we discover the conditions that dictate the forms into which cells organize themselves, our ability to guide and select the outcomes becomes ever more extraordinary. Some researchers believe that ultimately we will be able to regenerate limbs and organs, and perhaps even create new life forms that evolution has never imagined. Incorporating the latest research and insights, How Life Works is a sweeping journey into this new frontier of the life sciences, a realm that will reshape our understanding of life as we know it.
b.next: rebuilding biology for engineering
Meet Biomni – an AI-powered biomedical co-scientist
In creating a comprehensive, AI-enabled research agent for the biomedical sciences, Stanford researchers hope to speed innovation by eliminating the tedium of scientific legwork.
PRIDE - PRoteomics IDEntifications Database
PRIDE - PRoteomics IDEntifications Database
Genspace
Genspace is the world’s first community biology lab—a place where anyone can learn and work on biotechnology.
Biohub releases a world model of protein biology
Open AI models used for a wide range of scientific applications, including accelerating the design of therapeutic molecules.
Citizen science in environmental and ecological sciences
Citizen science is an increasingly acknowledged approach applied in many scientific domains, and particularly within the environmental and ecological sciences, in which non-professional participants contribute to data collection to advance scientific research. We present contributory citizen science as a valuable method to scientists and practitioners within the environmental and ecological sciences, focusing on the full life cycle of citizen science practice, from design to implementation, evaluation and data management. We highlight key issues in citizen science and how to address them, such as participant engagement and retention, data quality assurance and bias correction, as well as ethical considerations regarding data sharing. We also provide a range of examples to illustrate the diversity of applications, from biodiversity research and land cover assessment to forest health monitoring and marine pollution. The aspects of reproducibility and data sharing are considered, placing citizen science within an encompassing open science perspective. Finally, we discuss its limitations and challenges and present an outlook for the application of citizen science in multiple science domains.
