#RNASeq

10/10 love it sm #transcriptomics #rnaseq #reels

💻 R packages for RNA-seq analysis ✨ Many have asked for this, so I am going to explain the purpose of each of these packages. DM me if you have any questions regarding this! - GEOquery: to download RNA-seq files from SRA. This is just one way of downloading files. You may also use download.file() function in R if you have the ftp link to your data - fastqcr: a wrapper to run FastQC in R. Current standard for running quality control - QuasR: Trim and filter RAW sequencing reads - RSubread: Align reads to the genome and quantify gene/transcript read counts - DESeq2: Differential gene expression. Check my IGTV for a tutorial! --------------------------------------------- #bioinformatics #bioinformatician #sequencing #sequence #rnaseq #ngs #genomics #transcriptomics #dataanalysis #dataprocessing #bigdata #data #expression #illumina

Year 1: Full of hair and hope Year 3: Slight thinning… maybe it’s the RNA-Seq Year 7: Bald like the FASTA file—no annotations, no hair. #BioinformaticsLife #BaldByYear7 #FASTAAndFurious #DataLossHairLoss #DryLabDiaries #GenomicStress #bioinformatics #phd #PhDLife #phdstruggles

I spend part of my afternoon putting some data through our RNA SEQ pipeline. I just didn’t some basic QC while referencing the project description form. I used DESEQ2 and r studio to do this:). #womenintech #genomics #womeninstem #codingdiaries #studygram #rstudio #deseq2 #latinaswhocode #codingjourney #dataanalysis #cozystudy #rnaseq #thisiswhatsciencelookslike

New to RNA-seq data? Don’t know how to code? First step should ALWAYS be quality checks on your raw reads. Follow this video on how to use FastQC on an internet browser with no coding experience needed #rnaseq #bioinformatics #phdjourney #gradschool #codingeducation

Let me show you how to do Single-Cell RNA-Seq Analysis for any cancer dataset. Here, I’m executing single-cell RNA-seq pipeline on prostate cancer dataset. #scRNA #singlecell #rnaseq #ngs #python #linux #cancergenomics #genomics #biology #research

𝐄𝐯𝐞𝐫 𝐬𝐞𝐞𝐧 𝐚 𝐩𝐥𝐨𝐭 𝐟𝐮𝐥𝐥 𝐨𝐟 𝐜𝐨𝐥𝐨𝐫𝐟𝐮𝐥 𝐝𝐨𝐭𝐬 𝐚𝐧𝐝 𝐛𝐞𝐞𝐧 𝐭𝐨𝐥𝐝, “𝐞𝐚𝐜𝐡 𝐨𝐧𝐞 𝐢𝐬 𝐚 𝐜𝐞𝐥𝐥”? Yep, that’s probably a t-SNE plot — one of the most popular ways to visualize single-cell RNA-seq data. 🤔 But hold on— 𝘏𝘰𝘸 𝘥𝘰 𝘸𝘦 𝘨𝘰 𝘧𝘳𝘰𝘮 20,000+ 𝘨𝘦𝘯𝘦𝘴 𝘱𝘦𝘳 𝘤𝘦𝘭𝘭...𝘵𝘰 𝘢 𝘯𝘦𝘢𝘵 𝘭𝘪𝘵𝘵𝘭𝘦 𝘱𝘭𝘰𝘵 𝘴𝘩𝘰𝘸𝘪𝘯𝘨 𝘤𝘭𝘶𝘴𝘵𝘦𝘳𝘴, 𝘱𝘢𝘵𝘵𝘦𝘳𝘯𝘴, 𝘢𝘯𝘥 𝘤𝘦𝘭𝘭 𝘵𝘺𝘱𝘦𝘴? 𝐋𝐞𝐭’𝐬 𝐛𝐫𝐞𝐚𝐤 𝐢𝐭 𝐝𝐨𝐰𝐧. 👇 🎯 Single-cell RNA-seq gives us massive data — each cell is basically a mini spreadsheet of gene expression. 🧠 Our brains can’t visualize 20,000 dimensions — so we need something to reduce it to 2D or 3D. That’s where 𝐭-𝐒𝐍𝐄 (𝐭-𝐝𝐢𝐬𝐭𝐫𝐢𝐛𝐮𝐭𝐞𝐝 𝐒𝐭𝐨𝐜𝐡𝐚𝐬𝐭𝐢𝐜 𝐍𝐞𝐢𝐠𝐡𝐛𝐨𝐫 𝐄𝐦𝐛𝐞𝐝𝐝𝐢𝐧𝐠) steps in. Think of t-SNE like this: It takes cells that are similar, based on their gene expression, and puts them close together in the plot. 𝘊𝘦𝘭𝘭𝘴 𝘵𝘩𝘢𝘵 𝘢𝘳𝘦 𝘷𝘦𝘳𝘺 𝘥𝘪𝘧𝘧𝘦𝘳𝘦𝘯𝘵? 𝘛𝘩𝘦𝘺 𝘭𝘢𝘯𝘥 𝘧𝘢𝘳 𝘢𝘱𝘢𝘳𝘵. 💡 The result? You get a plot that looks like art but actually reflects meaningful biological patterns — like cell types, disease states, or treatment response. 🚨 𝘗𝘳𝘰 𝘵𝘪𝘱: 𝘈𝘭𝘸𝘢𝘺𝘴 𝘤𝘰𝘮𝘣𝘪𝘯𝘦 𝘵-𝘚𝘕𝘌 𝘸𝘪𝘵𝘩 𝘣𝘪𝘰𝘭𝘰𝘨𝘪𝘤𝘢𝘭 𝘬𝘯𝘰𝘸𝘭𝘦𝘥𝘨𝘦 𝘢𝘯𝘥 𝘧𝘰𝘭𝘭𝘰𝘸-𝘶𝘱 𝘢𝘯𝘢𝘭𝘺𝘴𝘪𝘴. 📚 If you're figuring this out for the first time—you’re not alone. We're hosting a 𝟒-𝐃𝐚𝐲 𝐇𝐚𝐧𝐝𝐬-𝐎𝐧 𝐎𝐧𝐥𝐢𝐧𝐞 𝐈𝐧𝐭𝐫𝐨𝐝𝐮𝐜𝐭𝐢𝐨𝐧 𝐭𝐨 𝐒𝐜𝐑𝐍𝐀-𝐒𝐞𝐪 𝐃𝐚𝐭𝐚 𝐀𝐧𝐚𝐥𝐲𝐬𝐢𝐬 𝐰𝐨𝐫𝐤𝐬𝐡𝐨𝐩. Master key workflows—from preprocessing and quality control to clustering, cell type annotation, and functional analysis. Learn how to analyze ScRNA-seq data gaining hands-on experience in data cleaning, visualization, and interpretation. 🔗 𝐑𝐞𝐠𝐢𝐬𝐭𝐞𝐫 𝐡𝐞𝐫𝐞: https://forms.gle/eNJp1T32U6Soa4Af9 #scRNASeq #Bioinformatics #SingleCell #Transcriptomics #Seurat #CellRanger #DataScience #Genomics #Workshop #ResearchTraining

A chill scientist’s weekday ft RNA-seq prep One of my goals during my PhD is to create a comprehensive single nuclear RNA-seq dataset for human abdominal aneurysms compared to healthy aortas. Not necessarily part of my thesis, but I thought it’d be great to experience the data prep part as I’ve mostly been doing computational analysis of these types of data. I am extremely lucky to have a lab that can fund this experience and have access to clinical samples from the hospital! We top off the day with a bunch of cell culture. I am culturing smooth muscle, endothelial cells, and THP1 monocytes. These are for larger experiments where I have these cell types try to eat/phagocytose apoptosis versions of each other (efferocytosis assay). Needs lots of cells for these and need to stain to track well for flow. We top it off with a 2.4K swim featuring sets containing longer endurance sets and small 50m repeating interval for speed. It was great :) Time to do it all over again tomorrow. #premed #medicalschool #medstudent #gradschool #phd #research #scientist

🤔#이런피규어어떻게그리지 Bioinformatics 왕초보(나!🤣)들을 위한 RNA-seq 데이터 분석따라하기📊✨ GSEA(Gene Set Enrichment Analysis) 피규어, 논문에서 많이 봤지만 그려본 적은 없다😂 이번에 공부하면서 정말 간절했던 <그대~로 따라하기만 하면 그릴 수 있는 영상>을 내가 찍어봤다😆👍💖 1️⃣ GSEA 소프트웨어 다운로드 (https://www.gsea-msigdb.org/gsea/index.jsp) 2️⃣ RNA-seq 데이터 텍스트로 저장 (탭으로 분리된 txt 확장자) 3️⃣ GSEA 실행 👉 Data import (끌어다 놓기) 4️⃣ Phenotype label 설정: (텍스트파일 그대로 입력 or 메모장으로 작성 후 임포트!) 이 소프트웨어 처음 접하고 가장 헤맸던 부분😂 메모장으로 cls 라는 확장자명으로 파일 생성도 가능하지만, 소프트웨어 내에서 입력하는게 간편한 듯 하다ㅎㅎ(취향껏 선택!) GSEA는 기본적으로 <2개의 그룹>간 비교이기 때문에, 비교할 그룹 🅰️/🅱️ 를 입력해야한다. 각 그룹의 replicates(반복실험에서 나온 데이터)에 지정한 열(row) 이름을 한 줄에 하나씩 적어준다. 5️⃣ 기타 설정 (Geneset DB/Chip 등) ✅ Geneset DB: GSEA의 데이터베이스에 있는 여러 유전자의 기능별(혹은 다른 특징들) 그룹이다. 연구 주제와 방향에 맞는 DB를 골라야하므로, 처음엔 이것 저것 시도해보는 것이 좋다. 너무 많아서 막막하다면.. 내가 영상에서 선택한 <c5. GOBP(Gene Ontology Biological Process)> 추천! 👉 홈페이지에 각 그룹별 설명이 자세히 적혀있다😘 ✅Chip 👉 내가 입력한 유전자 그룹의 <이름 형태>이다. 만약에 내 데이터의 유전자 이름 형태를 잘 모르겠다면.. GPT에게 이름 몇 개 던져주고 형식 알려달라고 하면 잘 알려준다🤣👍✨ 6️⃣ html 포맷으로 결과 확인 👀💖 --- 분석법 공부하면서 진짜 생초보를 위한 눈높이 자료가 없길래 직접 만들었다😂 ㅋㅋㅋㅋㅋ 처음 배우던 나처럼 헤매는 사람에게 도움이 될 수 있기를!😘💖 . . . #GSEA #데이터분석 #연구이야기 #rnaseq #NGS #bioinformatics #생물정보학 #대학원 #대학원생 #대학원생그램 #대학원생공스타그램 #대학원생활 #대학원생멘탈관리 #laboratory #laboratorylife #lab #lablife #laboratorywork #experiment #phd #phdlife #phdstudent #phdjourney #실험실 #실험 #실험스타그램 #실험정보 #연구이야기

Unlock the secrets of gene expression with RNA-Seq Data Analysis. Master the workflows, expression profiling, and interpretation of real datasets — all under expert guidance. Don’t just learn it. Apply it. Join our course running every Sunday and Wednesday. Register now: https://forms.gle/Sr4ecHvR4UMK5NCN8 #ColorsHub #RNASeq #Bioinformatics #DataScience #NGS #MolecularBiology

Your degree gives you knowledge. These skills give you JOBS. 🧪 Top 5 Wet Lab Skills (Hands-on Industry Techniques) 1️⃣ PCR / qPCR – Detects and amplifies DNA. Used in diagnostics, genetic testing, infectious disease detection & IVF labs. 2️⃣ Cell Culture – Growing and maintaining cells in controlled conditions. Essential in pharma, vaccine production, stem cell & reproductive labs. 3️⃣ Western Blotting – Identifies specific proteins in a sample. Widely used in biotech research & drug development. 4️⃣ ELISA – Detects antibodies or antigens in blood samples. Core technique in diagnostic and immunology labs. 5️⃣ CRISPR Gene Editing – Precise DNA modification tool. High-demand skill in gene therapy and biotech startups. 💻 Top 5 Dry Lab Skills (Computational Power Skills) 6️⃣ BLAST & Sequence Analysis – Compares DNA/protein sequences. Basic but essential skill in genomics labs. 7️⃣ NGS Data Analysis – Interpreting next-generation sequencing data. Crucial for precision medicine & genetic research. 8️⃣ Molecular Docking – Predicts drug–protein interactions. Important in pharmaceutical drug discovery. 9️⃣ Python / R for Bioinformatics – Used to analyze biological datasets and automate research workflows. 🔟 RNA-Seq Analysis – Studies gene expression patterns. Widely used in disease research and personalized medicine. If you’re serious about biotech, start building SKILLS alongside your degree. Save this. Share with your lab partner. #BiotechCareers #WetLabSkills #DryLabSkills #LifeScienceStudents #FutureBiotechnologist

🚀 Learn RNAseq Analysis in Bioinformatics Ka Chilla! RNAseq analysis is a crucial skill in bioinformatics, and Bioinformatics Ka Chilla will teach you everything you need to know about analyzing RNA sequencing data from start to finish. 🧬📊 Join Dr. Aammar Tufail in this step-by-step course and gain hands-on experience with real-world RNAseq workflows, including data quality control, alignment, and expression analysis. 📌 Here is the complete detail about Bioinformatics ka Chilla: https://codanics.com/bioinformatics-ka-chilla/ #RNAseq, #Bioinformatics, #DataScience, #BioinformaticsKaChilla, #LearnBioinformatics #biology #codanics #babaaammar