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  • [Photo] Linna Green • May 6, 2026
    June 9th, 2026 10:00 AM EDT

    Dr. Kayvon Pedra will talk about a novel, cell-impermeant fluorophore that red-shifts and activates upon binding glycans. This allows for seamless, wash-free imaging of glycosylated ECM architecture – from in vitro models to in vivo mouse tumors – providing a powerful new tool for live fluorescence microscopy.

    Highlights of the presentation:
    • A cell-impermeable small molecule fluorophore turns on and red-shifts upon binding glycosylated biomolecules.
    • Application of the dye to live biological samples enables wash-free visualization of the extracellular matrix structure.
    • Observations demonstrate no toxicity, a broad substrate profile, deep tissue penetration, and negligible photobleaching in a variety of model organisms.
    Webinar information and registration at https://www.bocsci.com/webinar-live-imaging-of-the-extracellular-matrix-with-a-glycan-binding-fluorophore.html.

    About the speaker:

    Kayvon is a Group Leader at HHMI's Janelia Research Campus near Washington, D.C. He received his bachelor's degree in 2015 from MIT working with Prof. Alice Ting and his Ph.D. in 2021 from Stanford working with Prof. Carolyn Bertozzi. The Pedram Lab, established in 2021, aims to discover principles by which extracellular assemblies govern mammalian biology across spatial scales, develop therapies that take advantage of the unique and underexplored properties of extracellular biomolecules, and widely distribute methods that will allow others to join those long-term efforts. For more information, see https://pedramlab.com.
  • [Photo] Caroline Green • May 6, 2026
    As a leading provider of biological products – including recombinant and native proteins and cell/tissue lysates – as well as specialized custom services for academic research, diagnostics, therapeutics, and industrial applications, Creative BioMart is committed to supporting the next generation of scientists.

    To further encourage research and higher education in the biomedical and life sciences, Creative BioMart is proud to announce the 2026 Creative BioMart Scholarship Program, which will award a $1,000 scholarship to an outstanding student.

    https://www.creativebiomart.net/creative-biomart-scholarship-program.htm
  • [Photo] Umberto F • March 20, 2026
    September 2-4, 2026
    The 21st International Conference on Computational Intelligence Methods for Bioinformatics and Biostatistics (CIBB 2026)
    Sapienza University of Rome, Italy
    https://cibb2026.teralab.ai

    CIBB focuses on machine learning and computational intelligence methods applied to bioinformatics, biostatistics, and medical informatics.

    Topics include (but are not limited to):
    • AI and machine learning for omics data
    • Interpretable models in healthcare
    • Systems and synthetic biology
    • Statistical learning for biomedical research
    • Scalable algorithms for large-scale biological data
    Short paper submissions (4-6 pages) are now open.

    Submission deadline: May 3, 2026

    Confirmed keynote speakers include:
    • Per Kragh Andersen (University of Copenhagen)
    • Marianna Rapsomaniki (Lausanne University Hospital, CHUV)
    Accepted papers will be presented at the conference and will be invited to submit extended versions to Springer Lecture Notes in Bioinformatics (LNBI) or international journals.

    Submission platform:
    https://easychair.org/conferences/?conf=cibb2026
  • [Photo] J.W. Bizzaro • March 10, 2026
    Philip E. Bourne, founding dean of the University of Virginia School of Data Science, died on March 8, 2026, at age 72 after a long battle with mesothelioma.

    In 2009, I presented Phil with the Benjamin Franklin Award for Open Access in the Life Sciences. The award recognized his extensive work promoting free and open access to scientific materials, methods, and data in bioinformatics and computational biology.

    Phil served as founding editor-in-chief of PLoS Computational Biology, helping establish it as a leading open-access journal from the Public Library of Science. He co-founded SciVee.tv, a platform that let scientists share videos, presentations, and posters openly. His efforts also supported broad public access to biomolecular structure data through the Protein Data Bank.

    From 2014 to 2017, Phil directed data science at the National Institutes of Health as associate director for data science, leading the Big Data to Knowledge initiative. Throughout his career, he consistently advocated for data sharing, preprints, and open scholarly communication.

    In 2017, Phil came to UVA to launch the School of Data Science, the university's newest school, and guided its emphasis on open, collaborative, data-driven research. His dedication helped create a more transparent and inclusive scientific community. Phil's contributions were generous, persistent, and far-reaching; he will be remembered and missed by many of us who worked alongside him in the open science movement.
  • [Photo] Dr. David Langenberger • February 6, 2026
    June 15-19, 2026
    Berlin, Germany
    http://www.ecseq.com/summer-school

    Hi all,

    We're organizing our annual Berlin Summer School in NGS Data Analysis (), and thought it may be of interest to this community.

    This intensive 1-week, in-person course is designed for researchers and life scientists who want to gain a practical, hands-on understanding of next-generation sequencing data analysis without assuming extensive prior experience in bioinformatics. The course focuses on:
    • essential computing skills for everyday NGS analysis
    • understanding key NGS data formats and common pitfalls
    • running real RNA-seq workflows (QC -> mapping -> visualization -> differential expression)
    • structured introduction to DNA variant calling (VCF)
    • working with a real Illumina RNA-seq dataset
    The school uses proven bioinformatics tools to inspect and analyze real sequencing data with trainer support throughout. Note: single-cell RNA-seq is not covered as part of this Summer School.

    We're also pleased to have invited speakers contributing perspectives from both research and tool development:
    • Prof. Dr. Martin Kircher (Berlin Institute of Health at Charité / University of Lübeck / UKSH) - computational genomics & variant effect interpretation
    • Dr. Vladimir Jovanovic (Freie Universität Berlin) - genomic data analysis & functional interpretation
    • Dr. Jeremy Leipzig (TileDB) - scalable genomic data infrastructure & TileDB-VCF
    This combination of structured hands-on training and insights from active researchers and developers tends to work very well for participants who want to apply NGS workflows directly to real projects.

    Details and registration (first-come, first-served): http://www.ecseq.com/summer-school

    Happy to answer any questions about prerequisites or the program.
  • [Photo] Editor • January 31, 2026
    A new deep learning model called AlphaGenome, developed by researchers at Google DeepMind, analyzes up to one million base pairs of DNA sequence to forecast thousands of functional genomic features at single-base resolution. These predictions span 11 modalities, from gene expression and RNA splicing to chromatin accessibility, histone modifications, transcription factor binding, and spatial chromatin contacts, drawing on extensive human and mouse datasets. In benchmark tests, the model equals or surpasses leading alternatives on 25 out of 26 variant effect prediction tasks, including those for expression quantitative trait loci (eQTLs), splicing changes, and chromatin interactions. AlphaGenome also reconstructs the regulatory mechanisms behind clinically significant variants near the TAL1 oncogene.

    ARTICLE

    Avsec Ž, Latysheva N, Cheng J, Novati G, Taylor KR, Ward T, et al. Advancing regulatory variant effect prediction with AlphaGenome. Nature. 2026;649(8099):1206-1218. https://doi.org/10.1038/s41586-025-10014-0.

    AVAILABILITY

    AlphaGenome's primary website is hosted by Google DeepMind at https://deepmind.google.com/science/alphagenome

    It is also on GitHub. There are two key repositories:

    https://github.com/google-deepmind/alphagenome – This provides the Python SDK and programmatic access to the hosted API.
    https://github.com/google-deepmind/alphagenome_research – This contains the model source code, weights, variant scoring tools, and related research materials, as stated in the Nature paper.

    Additional resources include detailed documentation at https://www.alphagenomedocs.com and a community forum.
  • [Photo] Elsa Dubar • January 28, 2026
    August 31 - September 4, 2026
    Geneva, Switzerland
    https://eccb2026.org

    The city of Geneva hosts the 25th European Conference on Computational Biology (ECCB), from 31 August until 4 September 2026, under the theme "Biodiversity, AI & Health: computational biology to address the challenges of our time".

    The conference will explore how computational biology and bioinformatics contribute to understanding biological diversity, advancing health research, and addressing global societal challenges through data-driven approaches.

    Organized by the SIB Swiss Institute of Bioinformatics, ECCB 2026 will bring together 1,000-1,200 experts from around the world, including bioinformaticians, computational biologists, developers, biocurators, and clinicians from academia, industry, and public healthcare.

    CALL FOR PROCEEDINGS

    The ECCB 2026 Scientific Committee welcomes the submission of full manuscripts describing original and previously unpublished work in computational biology. Accepted papers will be presented as 15-minute talks during the conference and published in a supplementary issue (online-only and open access) of the Bioinformatics journal (Oxford University Press).

    Deadline to submit: 5 March 2026.

    More info : https://eccb2026.org/call-proceedings

    CALL FOR HIGHLIGHT TALKS & POSTERS

    ECCB 2026 also welcomes abstract submissions for highlight talks on significant advances in one of the conference's five scientific areas in computational biology and bioinformatics, published in a scientific journal on or after 1 March 2025 or accepted for publication in a scientific journal and available online as a preprint.

    Posters can also be submitted across all areas of computational biology and bioinformatics.

    Accepted contributions (highlight talks and posters) will be presented in person at the conference. Highlight talks will be delivered as 15-minute presentations followed by 3 minutes of discussion.

    Deadline to submit: 20 April 2026.

    More info: https://eccb2026.org/call-highlight-talk-poster
  • [Photo] Editor • January 22, 2026
    Adults who reach their eighties and nineties while keeping memory sharp enough to match much younger people often have a distinct profile in their APOE gene. SuperAgers, as researchers call them, carry the APOE-ε4 allele less frequently than those diagnosed with Alzheimer's dementia, and the APOE-ε2 allele more often. This pattern emerged clearly from analyses of over 18,000 participants in several major cohorts. Among non-Hispanic White individuals, SuperAgers differed significantly from both dementia patients and age-matched peers with average cognitive aging. In a smaller sample of non-Hispanic Black SuperAgers, the trends pointed in the same direction: lower APOE-ε4 and somewhat elevated APOE-ε2 compared with dementia cases, although some differences versus cognitively normal controls did not meet the threshold for strong statistical significance. The results suggest APOE genotype plays a role in maintaining cognitive function late in life. Larger studies focused on Black participants would help determine whether these associations hold similarly across racial or ethnic groups.

    ARTICLE

    Durant A, Mukherjee S, Lee ML, et al. Evaluating the association of apolipoprotein E genotype and cognitive resilience in SuperAgers. Alzheimers Dement. 2026;22(1):e71024. https://doi.org/10.1002/alz.71024
  • [Photo] Editor • January 8, 2026
    Genomics teams often rely on static cloud tags (e.g., project, pipeline_run) and service-level metrics to monitor compute costs. These signals provide limited visibility into how workloads behave at the execution-layer. There's no default mapping from a pipeline task to an EC2 process, it is difficult to determine whether slowdowns are caused by application logic or infrastructure constraints such as disk or network I/O.

    This guide analyzes a real-world RNA-seq pipeline that was initially assumed to be memory- or compute-bound. By correlating pipeline run identifiers with kernel-level execution data, the team identified a different root cause: sustained disk and network I/O saturation. The pipeline consistently ran for more than three hours per sample. Cloud metrics showed high memory reservations, leading the team to vertically scale the infrastructure:
    • Baseline: r6i.8xlarge (256 GB RAM, EBS-backed)
    • Scaled: r6i.16xlarge (512 GB RAM, EBS-backed)
    Costs doubled, but runtime did not improve. By correlating the specific pipeline_run ID to kernel-level traces, using Tracer (https://www.tracer.cloud), the team isolated the following metrics:
    • CPU utilization remained below ~25%
    • Peak memory usage stayed well below requested limits
    • Disk and network throughput were saturated for large portions of the run
    • STAR frequently stalled while waiting on data rather than compute
    The team evaluated newer, memory-optimized instance families with improved CPU generation, memory bandwidth, and network characteristics:
    • r7a.12xlarge: ~33% faster runtime at ~37% lower cost
    • r8i.8xlarge: near-baseline runtime at ~61% lower cost
    After the change:
    • Runtime decreased from 3+ hours to ~2 hours (~30% faster)
    • Cost per pipeline dropped by 36-60%, depending on configuration
    Effective optimization needs more signals. Tracing execution from pipeline-level identifiers down to kernel behavior, helps teams avoid paying for unused resources and select infrastructure that matches how workloads actually run.
  • [Photo] Prashanth N Suravajhala • January 3, 2026
    A comprehensive European study examining the impacts of open science practices, such as publishing articles in open access formats and sharing data or software freely, confirms certain advantages while revealing limited evidence for broader effects. Open-access papers earn more citations from other research and appear in patent applications more often, citizen scientists gain knowledge through participation, and reused open resources during the COVID-19 pandemic correlated with increased industry collaborations in some cases. Users of shared databases like UniProt save substantial time compared to the effort required to maintain them. Yet the analysis, which combined quantitative data, literature reviews, and case studies, identifies sparse causal proof linking these practices to accelerated scientific discovery, economic growth, or widespread societal benefits, underscoring challenges in measuring long-term outcomes.

    Source: https://www.science.org/content/article/open-science-delivering-benefits-major-study-finds-proof-sparse

    Prash
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