🧾Midterm and Final Exam Questions of a Bioinformatics Course
Dr. Brian Fristensky's 10 years archive
🌐 Page link
📲Channel: @Bioinformatics
Dr. Brian Fristensky's 10 years archive
🌐 Page link
📲Channel: @Bioinformatics
📑Network approaches to systems biology analysis of complex disease
Our goal is to review extant and emerging network approaches that can be applied across multiple biological layers to facilitate a more comprehensive and integrative multilayered omics analysis of complex diseases.
🌐 Read the paper
📲Channel: @Bioinformatics
Our goal is to review extant and emerging network approaches that can be applied across multiple biological layers to facilitate a more comprehensive and integrative multilayered omics analysis of complex diseases.
🌐 Read the paper
📲Channel: @Bioinformatics
👨🏻💻Deep learning on computational biology and bioinformatics tutorial: from DNA to protein folding and alphafold2
💥With biology basics for computer science readers
📑 Study the article
📲Channel: @Bioinformatics
💥With biology basics for computer science readers
📑 Study the article
📲Channel: @Bioinformatics
👷♀️Career Guide To Jobs in Bioinformatics
Bioinformatics is a relatively new field that is providing individuals with innovative roles. To get a job in bioinformatics, you'll likely need a specific skill set and additional qualifications that can help you succeed in the role. Learning how to obtain these qualifications and what types of jobs are available can help you determine if this field is right for you. In this article, we offer a complete guide on careers in the bioinformatics field.
🌐Study the article
📲Channel: @Bioinformatics
Bioinformatics is a relatively new field that is providing individuals with innovative roles. To get a job in bioinformatics, you'll likely need a specific skill set and additional qualifications that can help you succeed in the role. Learning how to obtain these qualifications and what types of jobs are available can help you determine if this field is right for you. In this article, we offer a complete guide on careers in the bioinformatics field.
🌐Study the article
📲Channel: @Bioinformatics
🧠AI Transforms Genomic Data into Actionable Insights
A creative company has developed an AI-powered “all in human” platform that can shift through information from genetically segmented patient populations and identify drug targets that function as “master switches” in genetic diseases. To validate the targets, it applies its human-based discovery capabilities, which include drug screening in cell cultures derived from human stem cells.
🌐 Study full story
📲Channel: @Bioinformatics
A creative company has developed an AI-powered “all in human” platform that can shift through information from genetically segmented patient populations and identify drug targets that function as “master switches” in genetic diseases. To validate the targets, it applies its human-based discovery capabilities, which include drug screening in cell cultures derived from human stem cells.
🌐 Study full story
📲Channel: @Bioinformatics
🎬 Recorded Workshop
🎦Unravelling the mysteries of the human proteome: From tools to workflows and analyses
🥽Watch
📲Channel: @Bioinformatics
🎦Unravelling the mysteries of the human proteome: From tools to workflows and analyses
🥽Watch
📲Channel: @Bioinformatics
YouTube
CGC Proteomics Virtual Workshop Recording
This workshop (originally on April 21st, 2021) will feature a hands-on demonstration of navigating the PDC website, linking publicly-available PDC data to personal projects on the CGC, without the need to download or upload, finding files in the CGC Public…
🔧 Amazon Genomics CLI
A new Amazon tool for genomics customers to process genomics data at petabyte scale
🔗 Full story
📲Channel: @Bioinformatics
A new Amazon tool for genomics customers to process genomics data at petabyte scale
🔗 Full story
📲Channel: @Bioinformatics
📹Deciphering Cancer Genomes and Networks
NIH Wednesday Afternoon Lectures
🔗 Watch the movie
📲Channel: @Bioinformatics
NIH Wednesday Afternoon Lectures
🔗 Watch the movie
📲Channel: @Bioinformatics
YouTube
Deciphering Cancer Genomes and Networks
Deciphering Cancer Genomes and Networks
Air date: Wednesday, February 5, 2020, 3:00:00 PM
Category: WALS - Wednesday Afternoon Lectures
Runtime: 01:00:59
Denoscription: NIH Director's Wednesday Afternoon Lecture Series
Large-scale cancer genome sequencing…
Air date: Wednesday, February 5, 2020, 3:00:00 PM
Category: WALS - Wednesday Afternoon Lectures
Runtime: 01:00:59
Denoscription: NIH Director's Wednesday Afternoon Lecture Series
Large-scale cancer genome sequencing…
🎬 Free Webinar
🧬Cancer Genomics Cloud Summer Symposium 2021
In this symposium, the speakers will focus on three areas of cancer research: Epigenomics, Image Processing using Machine Learning, and Single-Cell Analysis.
🗓 Time:
Wednesday, August 18, 2021
12:00 PM 2:30 PM
✍️Registration Link
ℹ️ Symposium details
📲Channel: @Bioinformatics
🧬Cancer Genomics Cloud Summer Symposium 2021
In this symposium, the speakers will focus on three areas of cancer research: Epigenomics, Image Processing using Machine Learning, and Single-Cell Analysis.
🗓 Time:
Wednesday, August 18, 2021
12:00 PM 2:30 PM
✍️Registration Link
ℹ️ Symposium details
📲Channel: @Bioinformatics
🗄full archive of
- Belgrade BioInformatics Conference 2021 -
🎞 Presentation videos
📑 Posters
📘 Book of Abstracts
📲Channel: @Bioinformatics
- Belgrade BioInformatics Conference 2021 -
🎞 Presentation videos
📑 Posters
📘 Book of Abstracts
📲Channel: @Bioinformatics
🧑💻Machine Learning in Genomics:
Tools, Resources, Clinical Applications and Ethics
🎞 Watch
📲Channel: @Bioinformatics
Tools, Resources, Clinical Applications and Ethics
🎞 Watch
📲Channel: @Bioinformatics
YouTube
Welcome and Keynote Session: What are the opportunities and challenges for ML in genomics research?
April 13-14, 2021 - The NHGRI Genomic Data Science Working Group hosted Machine Learning in Genomics: Tools, Resources, Clinical Applications and Ethics. This virtual workshop highlights the opportunities and obstacles that occur when applying machine learning…
📖Online practical book
An Introduction to Applied Bioinformatics
An Introduction to Applied Bioinformatics, or IAB, is a bioinformatics text available at http://readIAB.org. It introduces readers to core concepts in bioinformatics in the context of their implementation and application to real-world problems and data. IAB makes extensive use of common Python libraries, such as scikit-learn and scikit-bio, which provide production-ready implementations of algorithms and data structures taught in the text. Readers therefore learn the concepts in the context of tools they can use to develop their own bioinformatics software and pipelines, enabling them to rapidly get started on their own projects. While some theory is discussed, the focus of IAB is on what readers need to know to be effective, practicing bioinformaticians.
🌐Study
📲Channel: @Bioinformatics
An Introduction to Applied Bioinformatics
An Introduction to Applied Bioinformatics, or IAB, is a bioinformatics text available at http://readIAB.org. It introduces readers to core concepts in bioinformatics in the context of their implementation and application to real-world problems and data. IAB makes extensive use of common Python libraries, such as scikit-learn and scikit-bio, which provide production-ready implementations of algorithms and data structures taught in the text. Readers therefore learn the concepts in the context of tools they can use to develop their own bioinformatics software and pipelines, enabling them to rapidly get started on their own projects. While some theory is discussed, the focus of IAB is on what readers need to know to be effective, practicing bioinformaticians.
🌐Study
📲Channel: @Bioinformatics
👍1
Bioinformatics
🎬 Free Webinar 🧬Cancer Genomics Cloud Summer Symposium 2021 In this symposium, the speakers will focus on three areas of cancer research: Epigenomics, Image Processing using Machine Learning, and Single-Cell Analysis. 🗓 Time: Wednesday, August 18, 2021…
🎬Free webinar
💻Where to go when your bioinformatics outgrows your compute
Bioinformatics analyses are often complex, requiring multiple software tools and specialised compute resources. “I don’t know what compute resources I will need”, “My analysis won’t run and I don’t know why” and "Just getting it to work" are common pain points for researchers. In this webinar, you will learn how to understand the compute requirements for your bioinformatics workflows.
🗣 Who the webinar is for
This webinar is intended for biological researchers who wish to understand what computational infrastructure is required and available for their need.
🗓 Time:
Thursday, 19 August 2021
12:00 pm 1:00 pm AEST
✍️Registration Link
ℹ️ Webinar details
📲Channel: @Bioinformatics
💻Where to go when your bioinformatics outgrows your compute
Bioinformatics analyses are often complex, requiring multiple software tools and specialised compute resources. “I don’t know what compute resources I will need”, “My analysis won’t run and I don’t know why” and "Just getting it to work" are common pain points for researchers. In this webinar, you will learn how to understand the compute requirements for your bioinformatics workflows.
🗣 Who the webinar is for
This webinar is intended for biological researchers who wish to understand what computational infrastructure is required and available for their need.
🗓 Time:
Thursday, 19 August 2021
12:00 pm 1:00 pm AEST
✍️Registration Link
ℹ️ Webinar details
📲Channel: @Bioinformatics
🧬 Analyzing the SARS-CoV-2 trannoscriptome
In this post, I will take a deep dive into the trannoscriptome of COVID-19 infected patients. I will use the DESeq2 package in Bioconductor and perform QC, normalization, and differential expression analysis of expression data obtained through high-throughput sequencing. I will further try to validate the list of gene biomarkers (defined in the text) obtained from our analysis.
The entire article is divided into two parts:
📑 Read Part 1:
▫️A short primer on RNA
▫️RNA therapies in general (with a focus on mRNA vaccines)
▫️The trannoscriptome
📑 Read Part 2:
▫️Expression data download and preprocessing
▫️QC and normalization
▫️Differential expression analysis using the DESeq2 package in Bioconductor
▫️Generating heatmaps and volcano plots for the list of DEGs
▫️Gene Ontology (GO) analysis of the gene markers
▫️Literature evidence of the generated markers
📲Channel: @Bioinformatics
In this post, I will take a deep dive into the trannoscriptome of COVID-19 infected patients. I will use the DESeq2 package in Bioconductor and perform QC, normalization, and differential expression analysis of expression data obtained through high-throughput sequencing. I will further try to validate the list of gene biomarkers (defined in the text) obtained from our analysis.
The entire article is divided into two parts:
📑 Read Part 1:
▫️A short primer on RNA
▫️RNA therapies in general (with a focus on mRNA vaccines)
▫️The trannoscriptome
📑 Read Part 2:
▫️Expression data download and preprocessing
▫️QC and normalization
▫️Differential expression analysis using the DESeq2 package in Bioconductor
▫️Generating heatmaps and volcano plots for the list of DEGs
▫️Gene Ontology (GO) analysis of the gene markers
▫️Literature evidence of the generated markers
📲Channel: @Bioinformatics
👨🏫 Differential Expression Analysis with limma package in R
💥Free online course
📑Course content:
You'll review the goals of differential expression analysis, manage gene expression data using R and Bioconductor, and run your first differential expression analysis with limma package.
✍️Requires free registration
🌐 Start Course
📲Channel: @Bioinformatics
💥Free online course
📑Course content:
You'll review the goals of differential expression analysis, manage gene expression data using R and Bioconductor, and run your first differential expression analysis with limma package.
✍️Requires free registration
🌐 Start Course
📲Channel: @Bioinformatics
👨🏻💻Webtools for DNA to protein translation + three Expasy exercises
💥With biology basics for computer science readers
📑 Study the article
📲Channel: @Bioinformatics
💥With biology basics for computer science readers
📑 Study the article
📲Channel: @Bioinformatics
👨🏫 RNA-seq Analysis
💥Free online course
📑Course content:
This is an introductory course about RNA-seq data analysis. During this course you will learn the basics of RNa-seq data analysis in a Linux environment, current used software and best practices will be explained. We evaluate also some ways to do RNA-seq analisys using galaxy. This course is focused on trannoscriptomics (RNA-seq) and his shades (Mirna-seq,Fusion-seq,RIP-seq,Ribo-seq). Particular attention was used for demonstrate differents approach. This is course is scheduled for a 2 days and assumes a very basic knowledge of NGS data analysis and Linux. All materials in this is course are free and open and derived from some pulic avaible online course.
🌐 Start reading
📲Channel: @Bioinformatics
💥Free online course
📑Course content:
This is an introductory course about RNA-seq data analysis. During this course you will learn the basics of RNa-seq data analysis in a Linux environment, current used software and best practices will be explained. We evaluate also some ways to do RNA-seq analisys using galaxy. This course is focused on trannoscriptomics (RNA-seq) and his shades (Mirna-seq,Fusion-seq,RIP-seq,Ribo-seq). Particular attention was used for demonstrate differents approach. This is course is scheduled for a 2 days and assumes a very basic knowledge of NGS data analysis and Linux. All materials in this is course are free and open and derived from some pulic avaible online course.
🌐 Start reading
📲Channel: @Bioinformatics
👍1
📊 Grand Challenges in Bioinformatics Data Visualization
Increasingly, the life sciences rely on data science, an emerging discipline in which visualization plays a critical role. Visualization is particularly important with challenging data from cutting-edge experimental techniques, such as 3D genomics, spatial trannoscriptomics, 3D proteomics, epiproteomics, high-throughput imaging, and metagenomics. Data visualization also plays an increasing role in how research is communicated. Some scientists still think of data visualization as optional; however, as more realize it is an essential tool for revealing insights buried in complex data, bioinformatics visualization is emerging as a subdiscipline. This article outlines current and future grand challenges in bioinformatics data visualization, and announces the first publication venue dedicated to this subdiscipline.
📑 Study the paper
📲Channel: @Bioinformatics
Increasingly, the life sciences rely on data science, an emerging discipline in which visualization plays a critical role. Visualization is particularly important with challenging data from cutting-edge experimental techniques, such as 3D genomics, spatial trannoscriptomics, 3D proteomics, epiproteomics, high-throughput imaging, and metagenomics. Data visualization also plays an increasing role in how research is communicated. Some scientists still think of data visualization as optional; however, as more realize it is an essential tool for revealing insights buried in complex data, bioinformatics visualization is emerging as a subdiscipline. This article outlines current and future grand challenges in bioinformatics data visualization, and announces the first publication venue dedicated to this subdiscipline.
📑 Study the paper
📲Channel: @Bioinformatics
❤1