Anil Makhija’s Post

Understanding biology at both the macro and micro levels has been exhaustively explored over the years. Nowadays, there is a focus on fine-tuning medical and surgical interventions to optimize treatment modalities, concentrating on immuno, regenerative, and gene therapies that have the potential to bring relief to millions of people living with diseases. The optimization largely depends on relevant and pertinent data available at the most critical times. This is where the field of precision medicine fills the information divide that currently exists in "modern" medicine. Data-driven decision-making, adopting preventives, diagnostics, and treatment therapeutics guided by data aim to optimize more effective outcomes. The days of a "one size fits all" approach to medicine are numbered; precision medicine is personalized and customized to the individual's gene profile. This is where gene sequencing, splicing, and editing come into play. Additionally, practicing evidenced-based medicine, where the data guides us, is crucial. Precision medicine introduces a new dynamic in the field of healthcare. It extends beyond the exclusive preserve of Medical and Surgical Practitioners to include Data Scientists, Data Engineers, and Artificial Cognition Engineers playing key roles in optimizing Preventives, Diagnostics, and Treatment Therapeutics. The subject is vast, and there could be continuous breakthroughs, leaving the Information exploration of Quantum BioSciences for another day. The challenge lies in the fact that the data loads from DNA profiling are humongous, and the processing resources required for just one individual's genome are relatively hyperscale, when multiplied by population scale, we need data system resources to "crunch" big data for the population loads. Systems require electrical power to operate, and electrical power is a controlled resource these days due to global warming and greenhouse gas emissions (GHGs). There is pushback by environmental activists, who label Data Centers as guzzlers of electrical power, and the story just goes on. However, I believe we will end up with an amicable solution in the future.

BioDiscoveryAgent: Revolutionizing Genetic Experiment Design with AI-Powered Insights https://lnkd.in/dAyyfEeW Practical AI Solutions for Accelerating Scientific Discovery Enhancing Efficiency in Gene Perturbation Screens AI solutions based on LLMs show promise in accelerating scientific discovery, especially in biomedical research. They use extensive background knowledge to design and interpret experiments, aiding in identifying drug targets through CRISPR-based genetic perturbation. Challenges include balancing exploration freedom with biological validity and maintaining decision-making transparency with literature citations and human feedback. BioDiscoveryAgent: AI Tool for Genetic Perturbation Experiments Researchers at Stanford University and UCSF have developed BioDiscoveryAgent, an AI tool that designs genetic perturbation experiments without requiring a pre-trained machine learning model. It suggests genes to perturb based on prior knowledge and experimental results, improving the detection of desired phenotypes and accurately predicting gene combinations. Its transparent decision-making process enhances the design of genetic experiments, providing a valuable resource for biomedical research. Advances in AI-Driven Lab Experiments Artificial intelligence has shown promise in various scientific fields, including simulating human behavior and exploring mathematical functions. AI models are effective in mining scientific literature, data analysis, and report generation. In biology, LLMs capture detailed information about biological pathways and processes and can simulate these processes. AI for generating hypotheses in functional genomics is well-established, addressing the vast experimental space and combinatorial challenges. BioDiscoveryAgent: Automating Scientific Discovery in Biology BioDiscoveryAgent uses the Claude v1 Anthropic LLM to automate scientific discovery in biology. It accesses scientific knowledge, generates hypotheses, plans experiments, and interprets results. The agent selects batches of genes for testing, incorporating previous results into its prompts, and surpasses machine learning baselines in gene perturbation experiments. Its comprehensive approach enhances the design of genetic perturbation experiments by utilizing extensive biological knowledge. Revolutionizing Genetic Experiment Design with AI-Powered Insights BioDiscoveryAgent introduces a new approach to biological experiment design, efficiently integrating prior biological knowledge and observational data. It solves the cold start problem and leverages various tools for information from literature and datasets, accelerating research. While effective, it performs variably across cell types and excels mainly in early experimentation stages, complementing existing methods and offering improved reasoning and interpretability. Evolve Your Company with AI To evolve yo...

Exploring Cellular Diversity: Unveiling Insights with CellRanger 🔬 In today's installment of the bioinformatics tools series, let's shining a spotlight on CellRanger, an essential tool in the field of single-cell genomics. Developed by 10x Genomics, CellRanger empowers researchers to dissect cellular heterogeneity and uncover novel insights from single-cell RNA sequencing (scRNA-seq) data. What is CellRanger? CellRanger is a comprehensive analysis software suite tailored for processing, analyzing, and visualizing scRNA-seq data generated using 10x Genomics' Chromium platform. It provides a seamless workflow from raw data to biological insights, facilitating the exploration of gene expression patterns across thousands of individual cells. Key Features: 🧬 Data Processing Pipeline: Offers a streamlined pipeline for processing raw sequencing data, including read alignment, barcode processing, and unique molecular identifier (UMI) counting. 📊 Quality Control and Visualization: Includes tools for quality control assessment, data normalization, dimensionality reduction, clustering, and visualization, enabling comprehensive exploratory data analysis. 🌐 Integration with Existing Tools: Seamlessly integrates with other bioinformatics tools and workflows, facilitating downstream analysis and interpretation. Applications of CellRanger: 🧪 Biological Discovery: Enables the identification of novel cell types, states, and regulatory networks in complex tissues and biological systems. 🏥 Disease Research: Facilitates the investigation of cellular dynamics and molecular mechanisms underlying diseases such as cancer, neurodegenerative disorders, and immune-related conditions. 🌱 Developmental Biology: Supports studies of cellular differentiation, lineage tracing, and developmental trajectories during embryogenesis and tissue regeneration. Getting Started with CellRanger: Data Acquisition: Generate scRNA-seq data using the 10x Genomics Chromium platform or obtain publicly available datasets. Installation and Setup: Download and install the CellRanger software package, following the instructions provided in the documentation. Exploratory Analysis: Explore the functionalities of CellRanger through tutorials, user guides, and example datasets, gaining proficiency in data analysis and interpretation. CellRanger empowers researchers to unlock the full potential of single-cell RNA sequencing data, providing a comprehensive toolkit for studying cellular heterogeneity and dynamics in health and disease. Whether you're unraveling the complexities of the immune system, dissecting neural circuits, or exploring the intricacies of tissue development, CellRanger offers the analytical power to transform scRNA-seq data into biological insights. #Bioinformatics #CellRanger #SingleCellRNASeq #GenomicAnalysis #10xGenomics #BioinformaticsTools

Here's my first one to start: I think Bioinformatics is revolutionizing the scientific landscape, driving innovations that are reshaping our approach to disease understanding and treatment. 🌟 The Intersection of Genomics and Data Science: Bioinformatics lies at the crossroads of biology and data science, leveraging computational power to decode complex biological data. Here’s how it’s making a significant impact: 1. Uncovering Hidden Patterns: The integration of vast genomic datasets allows us to identify patterns and correlations that were previously undetectable. This deep dive into genetic information reveals the intricate mechanisms behind diseases, paving the way for novel therapeutic targets. For example, by analyzing large-scale RNA-Seq data, we can pinpoint gene expression changes that contribute to disease progression. 🧬 2. Advancing Precision Medicine: Personalized treatment plans based on an individual’s genetic makeup are becoming a reality. Bioinformatics enables us to tailor therapies to each patient’s unique genetic profile, enhancing treatment efficacy and minimizing adverse effects. This approach is transforming oncology, where targeted therapies are designed to attack specific genetic mutations in tumors, improving patient outcomes and quality of life. 🧠 3. Machine Learning in Genomics: The application of machine learning algorithms in genomics is a game-changer. These advanced techniques help predict disease outcomes, identify potential biomarkers, and accelerate drug discovery. For instance, machine learning models can analyze genetic variations to predict an individual’s risk of developing certain diseases, enabling proactive and preventative healthcare measures. 🤖 4. Integrative Data Analysis: Bioinformatics excels in integrating diverse datasets, from genomic sequences to clinical records. This holistic analysis provides a comprehensive view of the biological landscape, enhancing the accuracy and reliability of research findings. By combining genetic, proteomic, and clinical data, researchers can gain deeper insights into disease etiology and progression. 🔬 Why This Matters: - Accelerated Research: Bioinformatics reduces the time from discovery to application, speeding up the development of new treatments. By automating data analysis and leveraging computational power, researchers can quickly identify potential drug candidates and therapeutic targets. - Improved Patient Care: Precision medicine ensures that patients receive the most effective treatments based on their genetic profiles. This approach not only improves clinical outcomes but also enhances patient satisfaction and adherence to treatment plans. Join the Conversation and let me know what you think ! #Bioinformatics#Genomics#MachineLearning#PrecisionMedicine#HealthcareInnovation#DataScience#BigData#ArtificialIntelligence#PersonalizedMedicine#NextGenSequencing#ComputationalBiology#BioTech#Pharma#MedicalResearch#GeneticResearch#LifeSciences#HealthTech

Day 9: Transcriptomics Tools - Transcript Quantification with Kallisto Hello LinkedIn community! Welcome to Day 9 of my bioinformatics series! Today, I'll focus on Kallisto, a tool for fast and accurate transcript quantification, which is essential for understanding gene expression levels. 🌟 Importance of Transcript Quantification Transcript quantification allows us to measure the abundance of transcripts in a sample. This is crucial for identifying differentially expressed genes, understanding gene regulation, and discovering biomarkers. 🔧 Key Tools for Transcript Quantification 1. Kallisto Kallisto is a lightweight, highly efficient tool for quantifying transcript abundances from RNA-seq data. It uses pseudoalignment to quickly determine the compatibility of reads with transcripts, significantly speeding up the quantification process. How to Use Kallisto: 1. Installation: - Download Kallisto from (https://lnkd.in/eiKDJKz5). - Follow the installation instructions for your operating system. 2. Building the Transcriptome Index: - Before quantifying reads, you need to build a transcriptome index: ```bash kallisto index -i transcriptome.idx transcripts.fasta - This command creates an index from the transcriptome FASTA file. 3. Quantifying RNA-Seq Reads: - Once the index is built, you can quantify your RNA-seq reads: ```bash kallisto quant -i transcriptome.idx -o output_directory --single -l 200 -s 20 input_reads.fastq ``` - For paired-end reads, use the `--paired` option and specify the two input files. 4. Output Files: - abundance.tsv: A tab-delimited file with estimated transcript abundances. - abundance.h5: A binary file for downstream analysis with Sleuth or other tools. 🛠️ Interpreting Kallisto Results 1. Transcript Abundances: - The `abundance.tsv` file contains important metrics such as estimated counts (est_counts) and transcripts per million (TPM), which indicate the relative abundance of each transcript. 2. Quality Metrics: - Review the log files generated by Kallisto for summary statistics, including the number of processed reads and mapping rates. 3. Visualization: - Use tools like R and Python to visualize the expression levels and perform differential expression analysis. 📅 Stay Tuned! Tomorrow,I'll explore differential expression analysis tools like DESeq2 and EdgeR. Follow my page to stay updated and join me on this journey to explore the exciting world of bioinformatics. Feel free to share your thoughts, ask questions, and engage with the posts. Your feedback and interactions will make this series even more enriching! #Bioinformatics #ComputationalBiology #Transcriptomics #RNASeq #Kallisto #Genomics #BioinformaticsTools #DataScience #LifeSciences #Research #Biotech #BioinformaticsJobs #HealthcareInnovation #PrecisionMedicine #BiomedicalResearch

#NGS Next-Generation Sequencing (NGS) is a high-throughput technique used to determine the sequence of nucleotides in DNA or RNA. Unlike traditional sequencing methods like Sanger sequencing, which are slower and more limited in scale, NGS allows for the rapid sequencing of large volumes of genetic material. Here’s a closer look at NGS: Key Concepts of NGS High Throughput: NGS can sequence millions of DNA fragments simultaneously, allowing for comprehensive genomic analysis in a fraction of the time required by older methods. Library Preparation: DNA or RNA samples are first fragmented into smaller pieces. These fragments are then prepared into a library with added adapters that allow them to be sequenced. The preparation steps often include: Fragmentation of the nucleic acids. Addition of sequencing adapters. Amplification of the fragments to ensure sufficient quantity. Sequencing Platforms: Several technologies are available for NGS, including: Illumina Sequencing: Uses reversible dye terminators to determine the sequence. It is widely used due to its accuracy and throughput. Ion Torrent Sequencing: Detects changes in pH caused by nucleotide incorporation. PacBio Sequencing (SMRT): Uses single-molecule real-time sequencing to read long DNA sequences. Nanopore Sequencing: Measures changes in ionic current as DNA passes through a nanopore, allowing for ultra-long reads. Data Analysis: The raw data generated by NGS is processed and analyzed using bioinformatics tools. This involves: Quality Control: Assessing the accuracy and reliability of the sequence data. Alignment: Mapping the sequenced fragments to a reference genome or assembling them de novo. Variant Calling: Identifying genetic variations such as single nucleotide polymorphisms (SNPs), insertions, deletions, and structural variants. Interpretation: Correlating genetic findings with biological and clinical informat

𝐂𝐎𝐌𝐏𝐔𝐓𝐀𝐓𝐈𝐎𝐍𝐀𝐋 𝐓𝐎𝐎𝐋𝐒 & 𝐀𝐏𝐏𝐑𝐎𝐀𝐂𝐇𝐄𝐒 𝐅𝐎𝐑 𝐂𝐑𝐈𝐒𝐏𝐑/𝐂𝐀𝐒 𝐓𝐄𝐂𝐇𝐍𝐎𝐋𝐎𝐆𝐘 In the realm of genetic engineering, CRISPR/Cas systems have emerged as revolutionary tools, fundamentally altering our approach to genome editing. Originally identified as an adaptive immune mechanism in bacteria and archaea, these systems have been repurposed to enable precise genetic modifications across diverse organisms, including plants. This commentary delves into the computational innovations propelling CRISPR/Cas technologies forward, highlighting the crucial role of bioinformatics in optimizing these systems for enhanced precision and efficiency. 𝑻𝒉𝒆 𝑪𝑹𝑰𝑺𝑷𝑹 𝑻𝒐𝒐𝒍𝒃𝒐𝒙: 𝑭𝒓𝒐𝒎 𝑰𝒅𝒆𝒏𝒕𝒊𝒇𝒊𝒄𝒂𝒕𝒊𝒐𝒏 𝒕𝒐 𝑫𝒆𝒔𝒊𝒈𝒏 The first step in leveraging CRISPR/Cas for genome editing involves identifying the CRISPR arrays and Cas proteins, necessitating robust computational tools for accurate system characterization. Subsequent stages involve the meticulous design of guide RNAs (gRNAs), with algorithms playing a pivotal role in predicting gRNA efficacy and minimizing off-target effects. This process is critical, as the precision of gRNA targeting dictates the success of the editing outcome. 𝑵𝒂𝒗𝒊𝒈𝒂𝒕𝒊𝒏𝒈 𝒕𝒉𝒆 𝑪𝒐𝒎𝒑𝒖𝒕𝒂𝒕𝒊𝒐𝒏𝒂𝒍 𝑳𝒂𝒏𝒅𝒔𝒄𝒂𝒑𝒆: 𝑻𝒐𝒐𝒍𝒔 𝒂𝒏𝒅 𝑻𝒆𝒄𝒉𝒏𝒐𝒍𝒐𝒈𝒊𝒆𝒔 A plethora of computational tools have been developed to tackle various challenges associated with CRISPR/Cas applications. From CRISPR array identification tools like CRISPRFinder and PILER-CR to gRNA design algorithms such as CHOPCHOP and CRISPOR, each tool offers unique capabilities to enhance the precision and efficiency of CRISPR/Cas-mediated editing. Additionally, novel classes of tools utilize differences in DNA repair outcomes to predict editing results, further refining the editing process. 𝑬𝒏𝒉𝒂𝒏𝒄𝒊𝒏𝒈 𝑺𝒑𝒆𝒄𝒊𝒇𝒊𝒄𝒊𝒕𝒚: 𝑻𝒉𝒆 𝑸𝒖𝒆𝒔𝒕 𝒇𝒐𝒓 𝒁𝒆𝒓𝒐 𝑶𝒇𝒇-𝑻𝒂𝒓𝒈𝒆𝒕 𝑬𝒇𝒇𝒆𝒄𝒕𝒔 Despite its transformative potential, the CRISPR/Cas system is not without its challenges. Off-target effects remain a significant concern, prompting the development of computational strategies to predict and mitigate unintended edits. Tools such as Cas-OFFinder and CRISPRera provide sophisticated analyses of potential off-target sites, enabling researchers to design gRNAs with maximum specificity. 𝑷𝒐𝒔𝒕-𝑬𝒅𝒊𝒕𝒊𝒏𝒈 𝑨𝒏𝒂𝒍𝒚𝒔𝒊𝒔: 𝑫𝒆𝒄𝒊𝒑𝒉𝒆𝒓𝒊𝒏𝒈 𝑶𝒖𝒕𝒄𝒐𝒎𝒆𝒔 Computational tools like TIDE and CRISPResso2 facilitate the interpretation of editing results, providing insights into indel patterns, off-target effects, and the efficiency of homology-directed repair. 𝑭𝒐𝒐𝒅 𝒇𝒐𝒓 𝑻𝒉𝒐𝒖𝒈𝒉𝒕: In light of the computational tools for CRISPR/Cas technology, how do emerging AI and ML algorithms specifically improve the precision of gRNA design and mitigate off-target effects, given the complexity of genomic and epigenetic variations?

85 million cells at your fingertips: Chan Zuckerberg CELL by GENE Discover - A free and open-source platform for single-cell research In an era where data is as valuable as gold, the Chan Zuckerberg CELL by GENE Discover (CZ CELLxGENE) is leading a transformative change in biomedicine. This platform, featured in the latest issue of Nature, is a treasure trove of over 85 million single-cell RNA sequencing data collected and curated to provide a seamless experience for researchers around the world. What's the big deal? CZ CELLxGENE offers comprehensive tools that drastically reduce the time and effort required to access and analyze high-quality single-cell data. This means that what used to take months now takes minutes! Imagine the possibilities for advances in understanding diseases and developing new treatments. The platform is not just a database, but an innovation hub where users can employ R or Python to delve into the data via a robust API, enhancing reproducibility and accessibility. It is particularly suited to projects that aim to map complicated cellular environments and predict the effects of genetic modifications. From discovering rare cell types in different tissues to helping researchers like Meera Prasad at Caltech with their cancer studies, CZ CELLxGENE is proving to be an indispensable part of the modern scientific infrastructure. Read more about how CZ CELLxGENE is changing the landscape of biomedicine in the Nature article by Jeffrey M. Perkel, published on 29 April, here: https://lnkd.in/dMuNuAFj Or check out the CZ CELLxGENE website: https://lnkd.in/e25PbWv9 What impact do you think such tools will have on future research? Let us know in the comments below and follow us on LinkedIn to get more exciting research updates every week! #Biomedicine #Biotechnology #Cells #ChanZuckerberg #CELLxGENE #Data #Database #DataScience #GeneExpression #Innovation #Nature #OpenSource #Platform #Research #RNAseq #SingleCell #Tool #WeeklyPublication

Technological advances in gene sequencing and computing have led to an explosion in the availability of bioinformatic data and processing power, respectively, creating a ripe nexus for artificial intelligence (AI) to design strategies for controlling cell behavior.

🔬🚀 Let’s talk about the scRNA-seq saga, where 10x Genomics not only shows off its library efficiency muscles but also boasts about its stellar fraction of reads in this bioRxiv pre-print! 🌌 With an out-of-this-world 98% fraction of valid reads, it's like 10x is saying, 'In the realm of scRNA-seq, I'm the head of the castle 🏰✨ But don't take our word for it; this study and numbers speak for themselves. Meanwhile, our competitor tries to keep up by bringing a higher proportion of intronic reads to the table. 📚 While introns might be the dark matter of the genome, 10x prefers to shine bright with a higher proportion of exonic reads, illuminating the path to precise gene expression insights like a beacon in the night. 🌟🔦 But here's where the plot thickens: despite valiant efforts in gene detection by our rival, it seems to struggle with a lower cell recovery rate and a slightly muddled library, like a hero battling against the odds. Loosing 70% of your samples in the process, oh! those precious cells, just LOST without any biological signal 🦸♂️🆚🦹♂️ So, as we cheer on these titans of transcriptomics, let's not forget that every good story needs a bit of drama. And in this genomic tale, 10x Genomics wears the crown with grace, offering a performance that's hard to match. 🎭👑 🌈 But wait, there's a twist in our genomic saga! Just when you thought the story was over, 10x Genomics rolls out the red carpet for our newest marvel, the GEM-X assays! 🚀🎉 Say goodbye to 'old news' because with GEM-X, we're talking a jaw-dropping 2X more genes, a staggering 80% cell recovery, and a 2-fold higher throughput. So buckle up, single cell enthusiasts, as we zoom into the future of scRNA-seq with GEM-X, where 10x Genomics is not just leading the race; it's rewriting the rules! 🏁📚🌍🔬 Pre-print: Comparative analysis of single-cell RNA sequencing methods with and without sample multiplexing https://lnkd.in/g52XCP26 GEM-X: https://lnkd.in/gmcd5-u6 #GenomicsEpic #ReadsRoyalty #IntronVsExon #ScienceSaga #ScienceUnleashed