SHAHID Manzoor’s Post

- HAPPY FRIDAY ! 🌾🧬🧩👨🎓💻💥📈📲🧑🌾🌾👍💚🍞 RT: Bioinformatics & Computational Biology (B&CB) - Manuel García-Ulloa #bioinformatics #computationalbiology #AI #machinelearning #modeling **** #CRISPR, #genomeediting, #nanotechnology, #nanoparticles, speedy crop improvement, crop enhancement **** 🌾🧬🧩👨🎓💥📈📲🧑🌾🌾👍💚🍞 - Plant Breeding and Time-Saving Strategies for Crop Improvement **** ➡️Modern #agriculture faces enormous challenges over the coming decades. ➡️ #plantbreeding #FoodSecurity #biotechnology Modern #agriculture faces enormous challenges over the coming decades. #plantbreeding #FoodSecurity #biotechnology #genebank #germplasm #seedbank #Genomics #EC #EU #StrongerTogether #resistance #agroecosystem #agrobiodiversity #farmers #agriculture #CAP #greendeal #ecology #OneHealth #AI #IoT #DSS 🌾🧬🧩👨🎓💥📈📲🧑🌾🌾👍💚🍞 “You can’t build a peaceful world on empty stomachs and human misery”. #NormanBorlaug Norman Borlaug #NobelPrize Plantbreeding is increasingly being recognised as a key factor in addressing foodsecurity. ---- 820+ million people suffer from hunger ---- - #wisdom #strength #beauty - #onehealth - #ZeroHunger - #science #knowledge #nature #society #prosperity

This week’s recap highlights a new method for gene-level alignment of single-cell trajectories, an R package for integrating gene and protein identifiers across biological sequence databases, characterization of SVs across humans and apes, universal prediction of cellular phenotypes, a method to quantify cell state heritability versus plasticity and infer cell state transition with single cell data, and a new AI-driven, natural language-oriented bioinformatics pipeline assists with automatic and codeless execution of biological analyses. Others that caught my attention include pangenome-informed privacy preserving synthetic sequence generation, a paper showing generative haplotype prediction outperforms statistical methods for small variant detection, a metadata standardizer for genomic region attributes, a web-based platform for reference-based analysis of single-cell datasets, a new taxa-specific normalization approach for microbiome data, a deep-learning-based splice site predictor, models for human metabologenomics, a Snakemake pipeline to automatically generate pangenomes from metagenome assembled genomes and a new paper on COVID-19 origins.

🧬 Demystifying the Complexity of Next-Generation Sequencing Analysis   In the world of genomics and molecular biology, Next-Generation Sequencing (NGS) stands as a revolutionary technology, offering unprecedented insights into the secrets of our genes. However, the analysis from raw NGS data to meaningful biological conclusions is intricate and multifaceted. Here, we briefly unravel this complexity, highlighting the depth of NGS analysis.   🧬 Sequencing and Data Generation: The first challenge lies in generating high-quality raw sequence data. This involves obtaining millions of short DNA reads, a process replete with nuances and technical sophistication.   🔍 Quality Control: Ensuring data integrity is next. This critical phase ensures the reliability of our data, setting the stage for accurate interpretation.   🌐 Read Mapping/Alignment: Perhaps one of the most computationally intensive stages, aligning these short reads to a reference genome or assembling them de novo is a task of enormous complexity.   🧬 Variant Calling: Identifying genetic variations meaning finding these variations and understanding their significance in the vast genomic context.   📖 Functional Annotation: The identified variants are annotated to understand their potential functional implications. This includes known variants, or novel variants which we predict their effects on gene function and protein structure.   🔬 Data Analysis and Interpretation: Integrating sequencing results with biological knowledge requires analytical skills and a profound understanding of biological systems.   🌟 Secondary and Tertiary Analysis: Further analyses that extend beyond the initial sequencing data interpretation. This may involve integrative genomics studies, longitudinal studies, multi-omics analysis, and clinical applications like identifying biomarkers or understanding the genetic basis of diseases.   Each of these steps requires specialized expertise and a multidisciplinary approach. The complexity of this process emphasis the need for automated and AI powered solutions that can streamline these tasks, transforming raw data into valuable biological insights. 👩💻   As we continue to push the boundaries of what's possible in genomic research, the role of automation and Artificial Intelligence in simplifying and refining this process becomes ever more pivotal. The future of genomics is not just in sequencing DNA but in unlocking its vast, untapped potential through intelligent analysis.   #Genomics #NGS #Bioinformatics #DataAnalysis #InnovationInScience

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

🧬 Navigating the Genomic Landscape: A Comparison of Top Alignment Algorithms! 🚀 In the vast realm of Genomics, choosing the right alignment algorithm is crucial for accurate and efficient analyses. Let's explore some of the most widely used alignment tools shaping the future of genomic research. 🔍 1. BWA (Burrows-Wheeler Aligner): 🔹Strengths: Efficient alignment of short DNA sequences, memory efficiency, versatile applications in variant calling and structural variations. 🔹Applications: Ideal for resequencing, DNA mapping, and population genomics. 🧩 2. Bowtie2: 🔹Strengths: Ultra-fast alignment, excellent for high-throughput sequencing data, supports gapped alignments. 🔹Applications: Widely used for RNA-Seq, ChIP-Seq, and whole-genome sequencing. 🌟 3. STAR (Spliced Transcripts Alignment to a Reference): 🔹 Strengths: Specialized for RNA-Seq, accurate mapping of spliced sequences, handles large introns efficiently. 🔹Applications: Essential for studying gene expression, alternative splicing, and transcriptome analysis. 💻 4. HISAT2 (Hierarchical Indexing for Spliced Alignment of Transcripts 2): 🔹Strengths: Integrates genomic and splice junction information, suitable for large eukaryotic genomes. 🔹Applications: Particularly effective for RNA-Seq analysis in eukaryotic organisms. 🔄 5. SAMtools (Sequence Alignment/Map tools): 🔹Strengths: Versatile toolkit for manipulating sequence data, includes tools for format conversion, sorting, and indexing. 🔹Applications: Widely used for post-alignment processing and variant calling. 📚 Choosing the Right Path: Selecting the best alignment algorithm depends on the specific goals of your genomic study. Consider factors such as read length, data type, and computational resources to optimize results. 💬 Join the Discussion: Share your experiences with alignment algorithms! What tools have you found most effective in your genomic analyses? 🔗 #Genomics #Bioinformatics #AlignmentAlgorithms #ResearchTools #GenomicAnalysis

Vector Databases in Genomics In the rapidly evolving field of genomics, the ability to efficiently store, manage, and analyze vast amounts of genomic data is crucial. Enter vector database powerful tools that are transforming how researchers approach genomic data management. By leveraging these databases, we can significantly accelerate biomedical research. The Role of Vector Databases in Genomics Vector databases are designed to handle high-dimensional data, making them particularly well-suited for genomic information, which often involves complex datasets with numerous variables. These databases allow researchers to store genomic sequences, gene expression profiles, and other biological data in a way that facilitates rapid querying and analysis. This capability is essential for drawing meaningful insights from the data, ultimately leading to advancements in personalized medicine. Technical Challenges and Solutions Despite their advantages, the integration of vector databases into genomic research is not without challenges. Some of the primary obstacles include: 1. Data Scalability As genomic research generates increasingly large datasets, traditional databases often struggle to keep up. Vector databases, however, are designed to scale efficiently, allowing researchers to manage and analyze vast amounts of data without compromising performance. 2. Integration with Existing Systems Many research institutions rely on legacy systems that may not easily interface with modern vector databases. To address this, researchers can implement middleware solutions that facilitate seamless data transfer and integration, ensuring that valuable historical data can be utilized alongside new genomic insights. Real-World Applications One notable example of vector databases in action is the All of Us Research Program, an initiative aimed at gathering diverse genomic data to improve health outcomes. By utilizing vector databases, the program can efficiently manage and analyze the extensive data collected from participants, leading to valuable insights into the genetic basis of diseases and the effectiveness of various treatments. Impact on Trading Strategies and Market Analysis The implications of vector databases extend beyond research: they also have significant potential for informing trading strategies and market analysis in the biotech sector. Investors can leverage insights derived from genomic research to identify emerging trends, assess the potential of new therapies, and make informed decisions about biotech investments. Conclusion Vector databases are at the forefront of transforming genomic research enabling scientists to unlock the potential of large-scale genomic data. By addressing technical challenges and providing innovative solutions, these databases not only accelerate biomedical research but also pave the way for advancements in personalized medicine. #VectorDatabases #Genomics #Research #Bioinformatics #AI #Healthcare #DataScience #BigData

New Generation Sequencing (NGS) is a cutting-edge technology that revolutionizes the way scientists decode genetic information. Unlike traditional sequencing methods, NGS allows researchers to analyze millions of DNA fragments simultaneously, enabling a rapid and cost-effective approach to deciphering entire genomes. This advancement has significantly accelerated genetic research and opened doors to various applications in fields like medicine, agriculture, and environmental science. By providing comprehensive insights into an organism's DNA, NGS facilitates the study of genetic variations, diseases, and evolutionary relationships with unprecedented detail and efficiency. NGS works by breaking down DNA samples into smaller fragments and then sequencing these fragments in parallel using high-throughput platforms. The resulting sequence data is then analyzed using sophisticated bioinformatics tools to reconstruct the original genome sequence. This approach not only speeds up the sequencing process but also allows for the detection of rare genetic variants and the exploration of complex genomic landscapes. With its remarkable speed, accuracy, and scalability, NGS has become an indispensable tool for advancing our understanding of genetics and unlocking the mysteries of life at the molecular level. _______ Website: pawitrabiotech.com Instagram: @pawitrabiotech Email: info@pawitrabiotech.com  Linkedin: PT Pawitra Jaya Sakti Biotek Phone: +62 85 607 124 424

🔬 Excited to Share My Latest Bioinformatics Project with BioinformHER !🧬 I recently completed a comprehensive bioinformatics project focusing on the human TNF gene, which has the potential to contribute significantly to future biological research. Project Overview: 🔹 Retrieved the human TNF gene sequence from NCBI in FASTA format. 🔷 Visualized the nucleotide sequence and translated it into amino acids using the BioEdit tool. 🔹 Identified Open Reading Frames (ORFs) and analyzed sequence composition with BioEdit. 🔹 Utilized the PROMO tool to detect potential Transcription Factor Binding Sites. 🔹 Discovered functional motifs within the TNF gene with MEME Suite. 🔹 Predicted coding and non-coding regions using GENSCAN. 🔹 Converted the FASTA sequence of the TNF gene into PHYLIP format using BioEdit. This project has enhanced my understanding of various bioinformatics tools and their usage in genomics and molecular biology research. 🔗 GitHub project link : https://lnkd.in/dkG_8nZc #Bioinformatics #Genomics #TNFGene #DataScience #NCBI #BioEdit #PROMO #MEMESuite #GENSCAN #MolecularBiology #Research #ContinuousLearning #BioinformHER #opentowork

Accelerating Genomics with Vector Databases Vector databases are revolutionizing genomic research by enabling efficient storage, analysis, and management of vast datasets. These tools are transforming how we approach personalized medicine. Key Benefits: - High-dimensional data management - Rapid insights from large datasets - Scalability to handle growing data volumes - Integration with existing systems Real-World Impact: - The All of Us Research Program uses vector databases to enhance understanding of disease genetics and treatment strategies - Informs trading strategies in biotech by identifying trends and informing investment decisions As genomics evolves, vector databases will be pivotal in unlocking the potential of genomic data. Embracing this technology is key to improving human health outcomes. Read the full post to learn more about how vector databases are accelerating biomedical research, and follow Design Datascience for the latest updates on genomics and data science innovations! #VectorDatabases #Genomics #GenomicResearch #PrecisionMedicine #PersonalizedMedicine #Bioinformatics #Healthcare #DataScience #BigData #AIinHealthcare #GeneticTesting #DNASequencing #MachineLearning

Weekly genomics+bioinformatics recap is out: https://lnkd.in/eBJGVY4c. This week’s recap highlights a new multispecies codon optimization method, personalized pangenome references with vg, a commentary on the wild west of spike-in normalization, a new pipeline for comprehensive and scalable polygenic scoring across ancestrally diverse populations, a paper showing deep learning / transformer-based methods don’t outperform simple linear models for predicting gene expression after genetic perturbations, and finally, a fascinating demonstration of engineered E. coli that form artificial neurosynapses called “bactoneurons” that can perform simple calculations like determining if a number is prime or if a letter is a vowel. Others that caught my attention include a paper on the genetic history of Portugal over the past 5,000 years, a new visualization tool for single cell RNA-seq data, a Snakemake workflow for complete chromosome-scale de novo genome assembly, a phylogeny-based method for accurate community profiling of large-scale metabarcoding datasets, an R package for genome size prediction, the polygenic basis of seedlessness in grapevine, a review on DNA methylation in mammalian development and disease, and an R package for analysis of multi-ethnic GWAS summary stats.