Finding Open-Source Tools for Scientific Research

Bioinformatics Reality Check: Save Time by Looking First (And Using AI Wisely) We've all been there: spending days crafting what feels like the "perfect" solution, only to discover someone already built it better. The smarter approach? >> Search GitHub first >> Check Biostars and Stack Overflow >> Browse Bioconductor and PyPI >> Ask LLMs for guidance on existing tools and best practices >> Then consider writing from scratch The AI angle: LLMs can be powerful research assistants for bioinformatics....they excel at suggesting relevant packages, explaining complex algorithms, and helping debug code. But they're not perfect: always validate suggested packages exist, check for deprecated functions, and test thoroughly with your data. -> In bioinformatics, existing tools are often more robust, better tested, and actively maintained than anything we might build in isolation. -> The most efficient code is often the code you don't have to write. Leveraging existing solutions, whether found through traditional search or AI assistance, lets us focus on the unique aspects of our research rather than rebuilding common functionality. Pro tip: When using LLMs for bioinformatics code, always cross-reference suggestions with official documentation and recent publications. The field moves fast, and AI training data might not reflect the latest best practices. What's your experience with this? Have you discovered game-changing tools (or AI prompting strategies) that saved you significant development time? HERE IS THE GITHUB WITH THE COMPREHENSIVE LIST OF BIOINFORMATICS TOOLS AND LIBRARIES https://lnkd.in/gJi-gwyM #Bioinformatics #ComputationalBiology #Research #ScientificComputing #DataScience #Efficiency #OpenSource #AI #LLMs

Why are there so many scientific imaging tools for biology? Each has a distinct function and reveals interesting research questions and methods. Unlike molecular assays, imaging datasets provide clear visual structure. From developing zebrafish embryos to neural activity in two-photon microscopy, the raw outputs are growing in complexity and size. We’ll dive into some open source imaging tools and what they actually do: Napari, ImageJ, Cellpose, CellProfiler, Suite2p, highlighting the original motivation for each tool and how they fit into common lab workflows. We’ll then construct some real-world scientific use cases, from segmenting microglia in brain slices to high-throughput compound screening in cancer models. Finally, we’ll explore some data infrastructure and software principles that lead to better analysis for scientists.

📜 Published today by Springer Nature Group in Nature Methods. Genomics 2 Proteins portal: a resource and discovery tool for linking genetic screening outputs to protein sequences and structures. ➡ https://lnkd.in/ebbvAXaY ➡ The G2P portal (https://lnkd.in/gAFErcdt) is an open-source tool for proteome-wide linking of human genetic variants to protein sequences and structures and hypothesizing the structure-function relationship between natural/synthetic variations and their molecular phenotypes. ➡ An elaborated thread on the amount of integrated #variant, #structural and #functional data, and case studies on analyzing #geneticvariants in #raredisease genes and #syntheticvariants from #baseediting screens are available here: https://lnkd.in/eb6-SZDD (and of course, in the paper ➡ link above!) ➡ The #resource and interactive #method on the G2P portal are developed for a broad community of researchers: #MolecularBiologist, #Bioinformatician, #VariantAnalysts, #TherapeuticScientists, and #MachineLearners, to name a few! Check out the tool if you find it useful, and let us know if you need training and collaborations! 🎯 Finally, I couldn't be more delighted and grateful for the terrific effort by the G2P portal team: Jordan Safer, Seulki Kwon, Duyen Nguyen, colleagues and collaborators: Arthur J. Campbell, David Hoksza, Alan Rubin (and others from Atlas of Variant Effects Alliance), Alex Burgin, thousands of users of the portal, and our funding supports, the Broad Institute of MIT and Harvard SPARC award and David R. Liu, Merkin Institute of Transformative Technologies in Healthcare at the Broad Institute of MIT and Harvard.

CRISPR/Cas9 - The "Molecular Swiss Army Knife" 🧬Yeast (Saccharomyces cerevisiae) continues to be an ideal eukaryotic model system for studying gene editing. 🧬A roadblock for molecular biologists has always been specific strain development when multiple genetic manipulations are required. 🧬Enter "CRISPR/Cas9" - the "molecular swiss army knife" that enables a simple and rapid strain construction in yeast and explores their potential for simultaneous introduction of multiple genetic modifications. 🧬An open-source tool "Yeastriction" is now available for identifying suitable and multiple Cas9 target sites in desired yeast strains. 🧬A transformation strategy, using in vivo assembly of a guideRNA plasmid and subsequent genetic modification, was shown in terms of high accuracies along with another alternative strategy, using in vitro assembled plasmids containing two gRNAs, to simultaneously introduce up to six genetic modifications in a single transformation step with high efficiencies. 🧬While previous researchers used CRISPR/Cas9 for gene inactivation, this study demonstrates the versatility of CRISPR/Cas9-based engineering of yeast by achieving simultaneous integration of a multigene construct combined with gene deletion and the simultaneous introduction of two single-nucleotide mutations at different loci. The article: https://lnkd.in/gvNeuMfK The open-source tool: https://lnkd.in/gaDJSw9f