🎬 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
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👨🏫 Bioinformatics for Biologists: An Introduction to Linux, Bash Scripting, and R
💥Free online course
⏳ Duration:
3 weeks, 5 hours each week
🗓 Start from:
6 Sep 2021
ℹ️ More information
✍🏻 Registration
📲Channel: @Bioinformatics
💥Free online course
⏳ Duration:
3 weeks, 5 hours each week
🗓 Start from:
6 Sep 2021
ℹ️ More information
✍🏻 Registration
📲Channel: @Bioinformatics
📽25 years of human gene finding: are we there yet?
💥Great lecture from Steven Salzberg
🌐Watch
📲Channel: @Bioinformatics
💥Great lecture from Steven Salzberg
🌐Watch
📲Channel: @Bioinformatics
YouTube
ISMB 2018 Distinguished Keynote: 25 years of human gene finding... - Steven Salzberg - ISMB 2018
25 years of human gene finding: are we there yet? - Steven Salzberg - ISMB 2018
📑 Multi-Omics Model Applied to Cancer Genetics
In this review, we focus on bioinformatic oncology as an integrative discipline that incorporates knowledge from the mathematical, physical, and computational fields to further the biomedical understanding of cancer. Before providing a deeper insight into the bioinformatics approach and utilities involved in oncology, we must understand what is a system biology framework and the genetic connection, because of the high heterogenicity of the backgrounds of people approaching precision medicine. In fact, it is essential to providing general theoretical information on genomics, epigenomics, and trannoscriptomics to understand the phases of multi-omics approach. We consider how to create a multi-omics model. In the last section, we describe the new frontiers and future perspectives of this field.
🌐 Study the paper
📲Channel: @Bioinformatics
In this review, we focus on bioinformatic oncology as an integrative discipline that incorporates knowledge from the mathematical, physical, and computational fields to further the biomedical understanding of cancer. Before providing a deeper insight into the bioinformatics approach and utilities involved in oncology, we must understand what is a system biology framework and the genetic connection, because of the high heterogenicity of the backgrounds of people approaching precision medicine. In fact, it is essential to providing general theoretical information on genomics, epigenomics, and trannoscriptomics to understand the phases of multi-omics approach. We consider how to create a multi-omics model. In the last section, we describe the new frontiers and future perspectives of this field.
🌐 Study the paper
📲Channel: @Bioinformatics
👨🏫Next-Gen Sequence Analysis Workshop
🗄Full archive 2019
💥Some headings:
▫️Intro to cloud computing
▫️Command Line BLAST
▫️Accessing The Jetstream Cloud
▫️Environment management with Conda
▫️Short read quality and trimming
▫️RNA-seq read quantification with salmon
▫️R and RStudio introduction
▫️Differential Expression and Visualization in R
▫️Mapping and variant calling on yeast trannoscriptome
▫️Automating workflows using bash
▫️Workflow Management using Snakemake
▫️De novo genome assembly
▫️De novo genome exploration
▫️Some more practical use of Unix
▫️Version Control with Github
▫️Microbial Ecology - a discussion and overview of amplicon sequencing and metagenomics
▫️De novo trannoscriptome assembly
▫️Annotating and evaluating a de novo trannoscriptome assembly
▫️Quick Insights from Sequencing Data with sourmash
▫️RNA-seq Analysis
🧾Table of contents
📲Channel: @Bioinformatics
🗄Full archive 2019
💥Some headings:
▫️Intro to cloud computing
▫️Command Line BLAST
▫️Accessing The Jetstream Cloud
▫️Environment management with Conda
▫️Short read quality and trimming
▫️RNA-seq read quantification with salmon
▫️R and RStudio introduction
▫️Differential Expression and Visualization in R
▫️Mapping and variant calling on yeast trannoscriptome
▫️Automating workflows using bash
▫️Workflow Management using Snakemake
▫️De novo genome assembly
▫️De novo genome exploration
▫️Some more practical use of Unix
▫️Version Control with Github
▫️Microbial Ecology - a discussion and overview of amplicon sequencing and metagenomics
▫️De novo trannoscriptome assembly
▫️Annotating and evaluating a de novo trannoscriptome assembly
▫️Quick Insights from Sequencing Data with sourmash
▫️RNA-seq Analysis
🧾Table of contents
📲Channel: @Bioinformatics
📝A Cloud-Based Tutorial
Combining Bioinformatics Software, Interactive Coding, and Visualization Exercises
for Distance Learning on Structural Bioinformatics
Following a cloud-based approach, we migrated the practical activities of a course on molecular modeling and simulation into the Google Colaboratory environment resulting in 12 tutorials that introduce students to topics such as phylogenetic analysis, molecular modeling, molecular docking, several flavors of molecular dynamics, and coevolutionary analysis. Each of these notebooks includes a brief introduction to the topic, software installation, execution of the required tools, and analysis of results, with each step properly described.
🌐 Read the main article
🧑💻 All tutorials are freely available at:
https://github.com/pb3lab/ibm3202
📲Channel: @Bioinformatics
Combining Bioinformatics Software, Interactive Coding, and Visualization Exercises
for Distance Learning on Structural Bioinformatics
Following a cloud-based approach, we migrated the practical activities of a course on molecular modeling and simulation into the Google Colaboratory environment resulting in 12 tutorials that introduce students to topics such as phylogenetic analysis, molecular modeling, molecular docking, several flavors of molecular dynamics, and coevolutionary analysis. Each of these notebooks includes a brief introduction to the topic, software installation, execution of the required tools, and analysis of results, with each step properly described.
🌐 Read the main article
🧑💻 All tutorials are freely available at:
https://github.com/pb3lab/ibm3202
📲Channel: @Bioinformatics