Chandrakanth Emani’s Post

If you haven't been paying attention to the all of the new RNA discoveries recently (there are a lot!), then this is a great, not-too-technical article to get you up to speed. We all learned about the central dogma that DNA is transcribed to mRNA that is translated into protein; that DNA encoded genes contain the information for proteins that run the molecular machinery of the cell. However, when the human genome was sequenced much of the DNA was found not to code for proteins and many assumed that the extra DNA was non-functional, accumulated through hundreds of millions of years of evolution. It turns out that researchers are finding more and more functions for these so-called non-coding regions. This article walks us through many of the new discoveries including that at least 75% of the genome gets transcribed into RNAs! What are all these extra RNAs doing? Surely they are not all "junk". The functions for these RNAs are a hot topic and many discoveries have shed light on their role in cell biology. These include long noncoding (lnc) RNA molecules that play a role in X-linked inactivation and microRNAs (miRNA) that interfere with the translation of a gene’s mRNA transcript with small interfering RNAs (siRNAs) a subgroup that have therapeutic applications. The article delves into many others. Given that the ENCODE project identified 37,600 non-coding genes (~2X the number of coding genes), there is still a huge amount of work to be done to assign function and deconvolve the role of these RNAs in cell biology, let alone their possible roles in human disease. https://lnkd.in/gGMDvjNP

An informative read!! A great way to start the year. 😊 Wishing my linkedIn family good health, great exploits and a happy 2024. 🙏🙏 #genomics #genomicmedicine #genomicresearch #transcriptomics #bioinformatics #rnaseq #dataanalysis #computationalbiology #learning #learninganddevelopment

Revolutionary Genetics Research Shows RNA May Rule Our Genome https://lnkd.in/eVXD7ZPt “What makes these discoveries revolutionary is what all this noncoding RNA -- abbreviated as ncRNA -- does. Much of it indeed seems involved in gene regulation: not simply turning them off or on but also fine-tuning their activity. So although some genes hold the blueprint for proteins, ncRNA can control the activity of those genes and thus ultimately determine whether their proteins are made. This is a far cry from the basic narrative of biology that has held sway since the discovery of the DNA double helix some 70 years ago, which was all about DNA leading to proteins. "It appears that we may have fundamentally misunderstood the nature of genetic programming," wrote molecular biologists Kevin Morris of Queensland University of Technology and John Mattick of the University of New South Wales in Australia in a 2014 article. Another important discovery is that some ncRNAs appear to play a role in disease, for example, by regulating the cell processes involved in some forms of cancer. So researchers are investigating whether it is possible to develop drugs that target such ncRNAs or, conversely, to use ncRNAs themselves as drugs. If a gene codes for a protein that helps a cancer cell grow, for example, an ncRNA that shuts down the gene might help treat the cancer.”

Struggling to make sense of vast genomic data? Bioinformatics and computational biology are revolutionizing genome analysis, offering powerful tools to decode DNA sequences, gene expression, protein structures, and more. This dynamic duo is: ➡𝗘𝗺𝗽𝗼𝘄𝗲𝗿𝗶𝗻𝗴 𝗽𝗲𝗿𝘀𝗼𝗻𝗮𝗹𝗶𝘇𝗲𝗱 𝗺𝗲𝗱𝗶𝗰𝗶𝗻𝗲 ➡𝗘𝗻𝗵𝗮𝗻𝗰𝗶𝗻𝗴 𝗱𝗿𝘂𝗴 𝗱𝗶𝘀𝗰𝗼𝘃𝗲𝗿𝘆 ➡𝗦𝗵𝗮𝗽𝗶𝗻𝗴 𝘁𝗵𝗲 𝗳𝘂𝘁𝘂𝗿𝗲 𝗼𝗳 𝗵𝗲𝗮𝗹𝘁𝗵𝗰𝗮𝗿𝗲 𝗕𝘂𝘁 𝗻𝗮𝘃𝗶𝗴𝗮𝘁𝗶𝗻𝗴 𝘁𝗵𝗲 𝗲𝘁𝗵𝗶𝗰𝗮𝗹 𝗹𝗮𝗻𝗱𝘀𝗰𝗮𝗽𝗲 𝗼𝗳 𝗴𝗲𝗻𝗼𝗺𝗶𝗰 𝗱𝗮𝘁𝗮 𝗽𝗿𝗶𝘃𝗮𝗰𝘆 𝗶𝘀 𝗰𝗿𝘂𝗰𝗶𝗮𝗹. Learn more about how this synergy is transforming the pharmaceutical industry: https://lnkd.in/gmJHhyp9 #bioinformatics #computationalbiology #genomics #personalizedmedicine #drugdiscovery #futureofhealthcare #pharmafocuseurope #PFE #Pharmaindustry

This protocol details the use of kallisto, bustools, and kb-python for quantifying bulk, single-cell, and single-nucleus RNA sequencing (RNA-seq). It describes how these free, open-source tools can efficiently produce gene expression data from raw sequencing reads, while also distinguishing between nascent and mature RNA species. The kb-python tool simplifies the running of kallisto and bustools into just two steps, making the workflow highly efficient, modular, and accessible for researchers. Read more about the protocol here. https://lnkd.in/gmAebdJ2

Fast and Free RNA-Seq Analysis with kallisto, bustools, and kb-python Need to perform RNA-seq analysis? Check out this open-source and efficient solution! This protocol leverages kallisto, bustools, and kb-python to quantify gene expression from raw sequencing reads. It's compatible with both bulk and single-cell/nucleus data, and can even differentiate between nascent and mature RNA species. Plus, it's incredibly fast, processing moderate datasets in minutes! Learn more: https://lnkd.in/dfQE9yB7 #RNAseq #bioinformatics #opensource #genomics #transcriptomics

🚀 Enhancing Single-Cell RNA Sequencing Accuracy! Single-cell RNA sequencing (scRNA-seq) datasets often contain true single cells (singlets) and cells that stick together during the protocol (doublets). Identifying singlets with high fidelity is crucial to avoid false discoveries. Traditional methods, tested on highly heterogeneous datasets, often lack a priori knowledge of true singlets. 🔍 I “singletCode”: A new study leveraged datasets with synthetically introduced DNA barcodes to extract ground-truth singlets. This framework evaluates existing doublet detection methods and trains a machine learning classifier that outperforms other algorithms. 💡 Key Highlights: Ground-truth singlets for accurate evaluation. Superior doublet detection in non-barcoded datasets. This integrative framework promises robust and reliable scRNA-seq analyses. https://lnkd.in/gAZyTqe6 #Bioinformatics #scRNAseq #Genomics #MachineLearning #Research

Here is a basic mathematical way to understand why we all look, act and perform differently at the biological level. Genes interact within complex networks, akin to a mathematical model using graph theory with nodes representing genes and edges representing interactions. The dynamics of gene regulatory networks can be thought of as something that are governed by nonlinear differential equations. (promise that the math is very high level and simple). https://lnkd.in/geXxtnXp #math #genomics #biology #lifesciences