Gyaanalya’s Post

Bioinformatics Tool Insights: NGS Toolkit - Simplifying Next-Generation Sequencing Analysis 🔬 As we continue our journey through the exciting world of bioinformatics tools, today's focus is on the NGS Toolkit (NGSTK). This newer addition to the bioinformatics toolkit is streamlining the way scientists handle and analyze next-generation sequencing data. What is NGSTK? NGSTK stands for Next-Generation Sequencing Toolkit, a comprehensive suite of tools designed to simplify the processing, analysis, and visualization of NGS data. It's tailored to assist both novices and experienced researchers in handling the complex data generated by modern sequencing technologies. Key Features: 🧬 Versatile Data Handling: Capable of processing various types of NGS data, including RNA-seq, ChIP-seq, and whole-genome sequencing. 🖥️ User-Friendly Interface: NGSTK is developed with a focus on usability, making complex analyses more accessible. 📊 Integrated Analysis Tools: Comes packed with tools for alignment, variant calling, data visualization, and more. Applications of NGSTK: 🌐 Genomic Research: Facilitates genomic sequencing projects, allowing for efficient data processing and analysis. 🎯 Targeted Research: Supports targeted sequencing and analysis, crucial for understanding specific genetic regions or mutations. 🏥 Clinical Applications: Can be applied in clinical settings for patient genomic data analysis, aiding in personalized medicine approaches. Getting Started with NGSTK: Download and Setup: Access NGSTK from its official repository. Follow the detailed installation guide to set it up on your system. Learning Resources: Utilize the tutorials and documentation provided to familiarize yourself with the toolkit's functionalities. Community Engagement: Join forums or user groups dedicated to NGSTK for support, tips, and sharing best practices. NGSTK is a testament to the continuous evolution of bioinformatics tools, catering to the growing and diverse needs of genomic research. Whether you're dealing with large-scale genomic data or focused sequencing projects, NGSTK offers a robust and user-friendly solution. Join me next time as we uncover more tools reshaping the bioinformatics landscape. Share your experiences or queries about NGSTK, and let's delve into the transformative impact of these technologies on research! #Bioinformatics #NGSToolkit #NextGenerationSequencing #NGS #GenomicAnalysis #BioinformaticsTools

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

Great insights

🔬🌐 Charting New Horizons in Science with Comprehensive Bioinformatics Services 🧬📊 In the realm where science and data converge, our Bioinformatics Services are your compass 🧭. From unraveling the mysteries of DNA and genes to pioneering the future of drug discovery 💊🧪, we offer a vast spectrum of specialized tools to decode the secrets of life. 🧬 **For Computational Drug Discovery:** 1. Identify drug targets 🎯. 2. Discover hits and optimize leads 💡. 3. Model protein structures 🧬. 4. Analyze molecular docking and binding modes 🧲. 5. Screen compounds virtually 👓. 6. Create pharmacophore models 🧩. 7. Perform 2D/3D QSAR analysis 📊. 8. Analyze structure-activity relationships (SAR) 🧪. 9. Evaluate ADMET properties 💊. 10. Investigate drug repositioning and design new drugs 💊💡. 🧪 **For Bioinformatics for Proteins:** 11. Analyze protein sequences 🧬. 12. Investigate protein structures 🔍. 13. Explore evolutionary data 🌿. 🧬 **For Bioinformatics for Genes:** 14. Analyze primary gene sequences 🧬. 15. Decode coding regions and annotate functions 📘. 16. Examine evolutionary data 🌍. 17. Compare genomes across species 🧬🌳. 18. Construct phylogenetic trees 🌲🌿. 19. Analyze RNA sequencing data 📊. 20. Perform exploratory gene expression analysis 📈. 21. Study differential gene expression 🧬📊. 22. Explore gene ontology and pathway analysis 🧩📚. 23. Assemble transcriptomes from scratch 🧬🧩. 24. Investigate single-cell gene expression 🕵️♂️🔬. 25. Detect fusion genes 🔍🧬. 26. Analyze microarray data 📉🧬. 27. Profile gene expression 📊🧬. 28. Conduct meta-analyses 📊🔬. 29. Implement clustering analysis 🧬🧩. 30. Use weighted gene co-expression network analysis (WGCNA) 🧬🔗. 31. Analyze DNA sequencing data, including whole-genome, whole-exome, targeted, and clinical sequencing 🧬🔬. 32. Investigate genetic variants 🧬🔍. 33. Examine the evolution of tumors 📊🦠. 34. Assemble genomes from scratch 🧬🔍. 35. Explore metagenomic data 🌐🧬. 36. Analyze population genetics 🌍🧬. 37. Conduct genome-wide association studies (GWAS) 🧬🌐. Your scientific journey deserves the best tools, and that's precisely what we offer 🛠🔍. Explore, discover, and innovate with confidence as you uncover the boundless potential of biological data 🚀🌟. At DrOmics Labs🏢, we're here to elevate your scientific endeavors 📈🧬. Visit us at: www.dromicslabs.com Follow Us On: Instagram: https://lnkd.in/dTsv2FjG Facebook: https://lnkd.in/gjFh8uKr LinkedIn: https://lnkd.in/gNNCat63 Youtube: https://lnkd.in/gdnZdTHk #bioinformatics #services #publication #datadriven #research #data #onlinework #career #knowledge #facts #blogs #students #visualization #programming #bioinformatician #biologists #biology #biotechnology #lifescience #microbiology #follow #posts

-Top R Packages for Bioinformaticians ... 📊 Bioconductor: - A vast collection of R packages designed for the analysis and comprehension of high-throughput genomic data. 🔬 DESeq2: - Facilitates differential gene expression analysis based on count data, commonly used for RNA-Seq analysis. 📈 EdgeR: - Another popular package for differential expression analysis of RNA-Seq and other count data. 🌐 GenomicRanges: - Provides efficient handling and manipulation of genomic intervals and variables defined along a genome. 🧬 Biostrings: - Provides efficient manipulation of biological strings, particularly DNA, RNA, and protein sequences. 🔍 VariantAnnotation: - Enables annotation of genetic variants, focusing on single nucleotide polymorphisms (SNPs) and insertion/deletion polymorphisms (indels). 📊 limma: - Linear models for microarray and RNA-Seq data analysis, widely used for analyzing gene expression data. 🌱 phyloseq: - An essential package for microbiome analysis, integrating phylogenetic trees, OTU tables, and sample metadata. 🧬 GenomicFeatures: - Facilitates the representation and manipulation of transcript-centric annotations in R. 🔬 clusterProfiler: - Facilitates statistical analysis and visualization of functional profiles for genes and gene clusters. 🧩 ComplexHeatmap: - A package for creating richly annotated heatmaps for complex datasets. 📈 Gviz: - Provides tools to visualize genomic data and annotations along the genome. 🚀 SummarizedExperiment: - Provides a container for storing experiment data, including row and column annotations, commonly used in genomics data analysis. What are your favorite R packages for bioinformatics? Share in the comments! 💡 Follow for more! #Bioinformatics #RStats #DataScience #Genomics #BioinformaticsTools #Bioconductor

Excited to dive into the realm of Next-Generation Sequencing (NGS) and explore its transformative potential in unraveling the mysteries of our genetic code. What is NGS? NGS is a state-of-the-art genomic technology that allows us to unveil the intricacies of our DNA, enabling a deeper understanding of genetic variations, gene expression patterns, biological processes, and more. It's like deciphering the hidden language of life itself! Let's delve into the key aspects of Next-Gen Sequencing: 1️⃣ Genetic Variations: NGS empowers us to explore the small, yet significant, genetic variations that make each of us unique. From pinpointing single nucleotide changes to identifying structural rearrangements, we can uncover the genetic tapestry that influences our health, traits, and susceptibility to diseases. 2️⃣ DNA Fragmentation: Imagine breaking down the DNA puzzle into tiny fragments, like unlocking hidden secrets piece by piece. DNA fragmentation is a pivotal step in NGS, allowing us to analyze multiple fragments concurrently, expediting the sequencing process and expanding our understanding of the genome. 3️⃣ Gene Expression: NGS paints a vivid picture of gene expression, revealing a symphony of activity within cells and tissues. By examining messenger RNA levels, we can unravel the complex interplay of genes, identify key players in developmental pathways, and gain insights into the mechanisms driving diseases and biological processes. 4️⃣ Biological Process: NGS unlocks the door to a world of biological wonders. It empowers us to explore intricate signaling pathways, understand gene regulation nuances, and trace the footprints of evolution. With NGS, we can navigate the inner workings of life and uncover the mechanisms that shape our existence. 5️⃣ Bioinformatics & Analysis: Turning raw sequencing data into meaningful insights requires sophisticated bioinformatics tools and a touch of computational wizardry. By analyzing and annotating genomic features, bioinformatics helps us make sense of the data deluge, unleashing the power of NGS to its fullest potential. 6️⃣ Data Interpretation: Making sense of NGS data is an art form. Skilled scientists and researchers blend vast datasets, computational prowess, and domain expertise to uncover the hidden stories within the genome. It's through data interpretation that we unlock discoveries, predict clinical outcomes, and pave the way for precision medicine. How do you envision Next-Generation Sequencing transforming healthcare, genomics research, and personalized medicine? #NextGenSequencing #GenomicsRevolution #Bioinformatics #PrecisionMedicine #LifeSciences #NGSInsights

- 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

📃Scientific paper: High-throughput sequencing analysis reveals genomic similarity in phenotypic heterogeneous Photorhabdus luminescens cell populations Abstract: Purpose Phenotypic heterogeneity occurs in many bacterial populations: single cells of the same species display different phenotypes, despite being genetically identical. The Gram-negative entomopathogenic bacterium Photorhabdus luminescens is an excellent example to investigate bacterial phenotypic heterogeneity. Its dualistic life cycle includes a symbiotic stage interacting with entomopathogenic nematodes (EPNs) and a pathogenic stage killing insect larvae. P. luminescens appears in two phenotypically different cell forms: the primary (1°) and the secondary (2°) cell variants. While 1° cells are bioluminescent, pigmented, and produce a huge set of secondary metabolites, 2° cells lack all these phenotypes. The main difference between both phenotypic variants is that only 1° cells can undergo symbiosis with EPNs, a phenotype that is absent from 2° cells. Recent comparative transcriptome analysis revealed that genes mediating 1° cell-specific traits are modulated differently in 2° cells. Although it was previously suggested that heterogeneity in P. luminescens cells cultures is not genetically mediated by, e.g., larger rearrangements in the genome, the genetic similarity of both cell variants has not clearly been demonstrated yet. Methods Here, we analyzed the genomes of both 1° and 2° cells by genome sequencing of each six single 1° and 2° clones that emerged from a single 1° clone after prolonged growth. Using different bioinformatics tools, the sequence data wer... Discover the rest of the scientific article on es/iode ➡️https://etcse.fr/UoK

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