Most RNA structure studies show you a crowd snapshot. sm‑PORE‑cupine lets you watch individuals. #RNAstructure #Nanopore #DirectRNASequencing #SingleMolecule #RNAseq #Bioinformatics #Genomics #Transcriptomics #ComputationalBiology #NatureMethods #Research #Biology #LifeSciences #Trinobia

Working all day with no fun? 😩🥱 Not anymore! 🎲🎯♟️ Take some time off coding and running scripts all day and try out this fun R package called Rcade It also contains my favourite game: Pacman 😄✌️ github.com/RLesur/Rcade #RStats #OpenSource #CodingFun #RProgramming #DataScience #Pacman

🔬 AI tools are changing how researchers communicate science. We’re moving beyond static PDFs. Now, you can turn a research paper into a video.

1/ Why does my code run perfectly, but the results make zero sense? 😅 #Bioinformatics #Research #DataScience #AIinScience #Genomics #RNAseq #ComputationalBiology #MachineLearning #Statistics #Reproducibility #OpenScience #Biology #Biotech #AcademicResearch #PhDLife #Science #AI

Most m6A pipelines skip the very controls that would tell us how many calls are wrong. That gap matters more than basecaller choice. New reviewed eLife preprint puts numbers under pressure. #m6A #Nanopore #Bioinformatics #RNAseq #Epitranscriptomics #Genomics #MethodsMatter

"A to B isn’t the goal. The journey is.” This image finally put into words something I have struggled to explain. 👇 #Learning #AITools #GrowthMindset #Education #CriticalThinking #Bioinformatics #ComputationalBiology #Medical #research #science #biology #medicine #AI

🧬 10 Tips for Wet-Lab Scientists Starting in Computational Biology #Bioinformatics #ComputationalBiology #DataScience #WetLab #Genomics #RNAseq #BiomedicalResearch #LifeSciences #Python #RStats #BioinformaticsTraining #Trinobia

1/ AI plus NGS is starting to change how we monitor relapse after transplant. Interim results from the ACROBAT study evaluated an AI-supported blood-based assay for AML and MDS. Strong performance, earlier detection, and honest discussion of limitations. article here: ascopost.com/issues/may-1...

Looking for some R cheat-sheets? Here are some for the most widely used R packages 📦 opensource.posit.co/resources/ch... #RStats #DataScience #RProgramming #DataAnalysis

When starting in computational biology, you quickly run into many file formats. Most store sequences, their quality, or their genomic coordinates. Here is a concise overview of the formats you will see in RNA‑seq, variant calling, and other NGS workflows. #Trinobia #Bioinformatics

1/ Learning scRNA seq analysis is hard because the field moves fast. This 2026 review in Briefings in Bioinformatics is one of the clearest learning resources I have seen for the full scRNA seq analysis pipeline. #scRNAseq #Bioinformatics #SingleCell

If your bulk RNA-seq results do not reproduce, the problem usually happened before differential expression. Most failures are upstream, silent, and structural. Here is what people miss, and how to design around it + Resources to help you learn. 🧬 #RNAseq #Bioinformatics #Trinobia #Research

MYC drives more cancer deaths than almost any other gene. We still cannot drug it directly. A new paper suggests why MYC has been so resilient, and where a real vulnerability might exist. #MYC #CancerGenomics #Oncology #CancerResearch #Trinobia #Bioinformatics #Genomics

1/ Computational biology is no longer supporting research. It is setting the direction. Expectations for how we work in this field are changing fast. #ComputationalBiology #Bioinformatics #Research #LifeSciences #DataDriven #Trinobia

1/ If you do not factor cell location into single-cell studies, your immune signatures may be telling you a story that never happened. This Nature Communications paper shows exactly why spatial context is not optional. 🔗 www.nature.com/articles/s41... #SpatialTranscriptomics #CancerImmunology

“Is AI in biomedicine truly useful, or just hype with unreliable results?” We hear this question from researchers all the time from our students. The answer depends on context, but one thing is clear. AI is already becoming essential in research and clinics. 🧠📊 #AI #Bioinformatics #Trinobia

Single‑cell survival analysis sounds technical. But to actually understand papers in this space, you need only three concepts. Most people meet them buried in dense methods sections and reverse‑engineer the intuition later. This thread is the shortcut. 🧵+ Resources #Trinobia #bioinformatics

Another Trinobia workshop wrapped up last week 🎉 We delivered an online, hands-on training on Introduction to Python for Data Analysis for researchers and clinicians at the Max Delbrück Center for Molecular Medicine. @mdc-berlin.bsky.social Led by Dr. Monah Abou Alezz. #Trinobia #Python

🔬💻 Why should scientists and clinicians learn computational biology? Because biology is no longer just observed. It is decoded. Here are 10 reasons this skill can change your biomedical career ⬇️ #Bioinformatics #ComputationalBiology #Trinobia

Seeing more biology does not make it easier to explain. ⚖️ Reference‑free methods expand discovery, but they move the hardest work elsewhere. #Genomics #Bioinformatics #ComputationalBiology #Biology #LifeSciences #Omics #Research #DataScience #ScientificRigor #Trinobia

Last week we wrapped up an online, hands-on training on RNA-seq data analysis at the German Cancer Research Center, DKFZ. PhD students, researchers, and clinicians joined to build real, practical skills. 🧬 #RNAseq #Bioinformatics #Trinobia

In single‑cell RNA‑seq, most pipelines start by aligning reads to a reference genome. Anything that doesn’t align is usually discarded. Reference‑free methods ask a different question: what if those reads matter? #scRNAseq #Bioinformatics #Trinobia

What if the most important RNA in your dataset never aligned? 🧬 In single‑cell RNA‑seq, unaligned reads are usually discarded. Noise. But what if they are biology? #SingleCell #RNAseq

❗A guide to artificial intelligence for cancer researchers This Nature Reviews Cancer article is a practical learning resource for labs navigating AI in imaging, pathology, genomics, and language tools. Clear, grounded, and written for researchers, not engineers. Worth bookmarking.

5 ways researchers distinguish upstream drivers from downstream markers 🧵👇+ RESOURCES Finding signals in complex disease is easy. Knowing which ones actually matter is hard. Use the links below to learn. #Bioinformatics #SingleCell #Genomics

Syndromic diseases are messy by definition. Everything breaks at once, but nothing tells you why. Preeclampsia is the classic example. 🧬🤰

🧬 A key discovery in preeclampsia broken down A new Circulation paper moves the field forward by identifying VGLL3 as a regulator linking multiple disease features. Here is why it matters 👇

📣 FDA NEWS The FDA just dropped a draft guidance on genome editing therapies. We read it so you do not have to. Here is the short version 👇

If EGFR amplification carries functional passengers, how many other driver oncogenes do the same? A recent Nature Cell Biology paper on EGFR-amplified GBM opens that question up. 🧬

1/ Why do some findings replicate and others fall apart? Rarely the data. Almost always the methodological choices made before the data existed. 🧵

1/ EGFR is amplified in roughly 40% of glioblastomas, yet 80 to 90% of patients see little or no benefit from EGFR inhibitors. We've studied this gene for decades. What if we've been looking at only half the picture? #CancerResearch #ComputationalBiology

❓ What if understanding autoimmune inflammation depends on learning to read what survives, not just what is intact? A new collaboration suggests fragmented cell free RNA may be an upstream trigger of chronic inflammation. 👇 www.prnewswire.com/news-release...

⚠️ When RNA‑seq results look convincing, how do you decide what you can actually claim? #RNAseq #singlecell

🧠What question is RNA‑seq actually answering in disease research? In our last post, we pointed out how single‑cell RNA‑seq changes the questions you’re allowed to ask. That shift builds on something more fundamental: RNA‑seq itself. #RNAseq #bioinformatics

🔬 Why single‑cell RNA‑seq keeps showing up in disease papers: because it changes what question you’re allowed to ask. #singlecell #RNAseq

💊💻As bioinformatics becomes essential in biomedical research, many labs generate large datasets but lack the resources or training to analyze them effectively. Today in Trinobia (trinobia.com), we help researchers transform biological data into clear insights. 📊