📕 Intro to R and RStudio for Genomics
📃Online tutorial
🌐 Study the online document
📲Channel: @Bioinformatics
📃Online tutorial
🌐 Study the online document
📲Channel: @Bioinformatics
📖 Biopython coronavirus notebook tutorial
How to use Biopython to identity and perform some basic characterization of a coronavirus genome sequence. The objective of this tutorial is to introduce some of the Biopython modules in an applied biological context. Note, the use of a coronavirus genome is merely illustrative, the analyses are generic, and could be applied to any small genome.
🌐 Open the Notebook link here
💻 Running the Notebook online or locally via Google Colab or via Binder
📲Channel: @Bioinformatics
How to use Biopython to identity and perform some basic characterization of a coronavirus genome sequence. The objective of this tutorial is to introduce some of the Biopython modules in an applied biological context. Note, the use of a coronavirus genome is merely illustrative, the analyses are generic, and could be applied to any small genome.
🌐 Open the Notebook link here
💻 Running the Notebook online or locally via Google Colab or via Binder
📲Channel: @Bioinformatics
🧑🏫 Computational Biology Course full details
🇳🇱Utrecht University, Netherlands
Computational Biology uses computer modeling to investigate biological problems. The course teaches a variety of modeling techniques and techniques to analyse the model behaviour. Moreover, biological theory obtained by computational modeling is examined.
💠 Some parts of the course:
▫️Course booklet
▫️Recorded lectures 2020
▫️Software used in the course
▫️Description of miniprojects
🌐 Course link with more details
📲Channel: @Bioinformatics
🇳🇱Utrecht University, Netherlands
Computational Biology uses computer modeling to investigate biological problems. The course teaches a variety of modeling techniques and techniques to analyse the model behaviour. Moreover, biological theory obtained by computational modeling is examined.
💠 Some parts of the course:
▫️Course booklet
▫️Recorded lectures 2020
▫️Software used in the course
▫️Description of miniprojects
🌐 Course link with more details
📲Channel: @Bioinformatics
👨🏫Computational Genomics Class
This class is presented by Dr. Rob Edwards at San Diego State University, and is based on classes he has taught there and workshops taught around the world.
📹 61 Videos of the class
📂 Course material complementing the class
📲Channel: @Bioinformatics
This class is presented by Dr. Rob Edwards at San Diego State University, and is based on classes he has taught there and workshops taught around the world.
📹 61 Videos of the class
📂 Course material complementing the class
📲Channel: @Bioinformatics
📑Disease networks and their contribution to disease understanding
A review of their evolution, techniques and data sources
🌐 Study the paper
📲Channel: @Bioinformatics
A review of their evolution, techniques and data sources
🌐 Study the paper
📲Channel: @Bioinformatics
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🧬 Why rapid genome sequencing is key to finding out how long Delta has been in NZ, and how large this outbreak might be?
🌐 Study the news
📲Channel: @Bioinformatics
🌐 Study the news
📲Channel: @Bioinformatics
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🏢 RECOMB2021 Computational Molecular Biology Conference
▫️One of the most prestigious bioinformatics conferences
▫️Fully virtual
🗓 Date: August 29 - September 1, 2021
🆓 Free of charge participation (Registration is required)
✍🏻 Registration link
💣 Registration deadline:
Sunday August 22nd, 2021
📑 Accepted paper list
🖇Website & more info.:
https://www.recomb2021.org
📲Channel: @Bioinformatics
▫️One of the most prestigious bioinformatics conferences
▫️Fully virtual
🗓 Date: August 29 - September 1, 2021
🆓 Free of charge participation (Registration is required)
✍🏻 Registration link
💣 Registration deadline:
Sunday August 22nd, 2021
📑 Accepted paper list
🖇Website & more info.:
https://www.recomb2021.org
📲Channel: @Bioinformatics
💊Bioinformatics and Drug Discovery
From Abstract: High-throughput data such as genomic, epigenetic, genome architecture, cistromic, transcriptomic, proteomic, and ribosome profiling data have all made significant contribution to mechanism-based drug discovery & drug repurposing. Accumulation of protein and RNA structures, as well as development of homology modeling and protein structure simulation, coupled with large structure databases of small molecules & metabolites, paved the way for more realistic protein-ligand docking experiments and more informative virtual screening. I present the conceptual framework that drives the collection of these high-throughput data, summarize the utility and potential of mining these data in drug discovery, outline a few inherent limitations in data & software mining these data, point out news ways to refine analysis of these diverse types of data, and highlight commonly used software and databases relevant to drug discovery.
🌐Study the paper
📲Channel: @Bioinformatics
From Abstract: High-throughput data such as genomic, epigenetic, genome architecture, cistromic, transcriptomic, proteomic, and ribosome profiling data have all made significant contribution to mechanism-based drug discovery & drug repurposing. Accumulation of protein and RNA structures, as well as development of homology modeling and protein structure simulation, coupled with large structure databases of small molecules & metabolites, paved the way for more realistic protein-ligand docking experiments and more informative virtual screening. I present the conceptual framework that drives the collection of these high-throughput data, summarize the utility and potential of mining these data in drug discovery, outline a few inherent limitations in data & software mining these data, point out news ways to refine analysis of these diverse types of data, and highlight commonly used software and databases relevant to drug discovery.
🌐Study the paper
📲Channel: @Bioinformatics
🧬A Beginner’s Guide to Analysis of RNA Sequencing Data
From Abstract: Since the first publications coining the term RNA-seq (RNA sequencing) appeared in 2008, the number of publications containing RNA-seq data has grown exponentially. With this wealth of RNA-seq data being generated, it is a challenge to extract maximal meaning from these datasets, and without the appropriate skills and background, there is risk of misinterpretation of these data. However, a general understanding of the principles underlying each step of RNA-seq data analysis allows investigators without a background in programming and bioinformatics to critically analyze their own datasets as well as published data. Our goals in the present review are to break down the steps of a typical RNA-seq analysis and to highlight the pitfalls and checkpoints along the way that are vital for bench scientists and biomedical researchers performing experiments that use RNA-seq.
🌐 Study the paper
📲Channel: @Bioinformatics
From Abstract: Since the first publications coining the term RNA-seq (RNA sequencing) appeared in 2008, the number of publications containing RNA-seq data has grown exponentially. With this wealth of RNA-seq data being generated, it is a challenge to extract maximal meaning from these datasets, and without the appropriate skills and background, there is risk of misinterpretation of these data. However, a general understanding of the principles underlying each step of RNA-seq data analysis allows investigators without a background in programming and bioinformatics to critically analyze their own datasets as well as published data. Our goals in the present review are to break down the steps of a typical RNA-seq analysis and to highlight the pitfalls and checkpoints along the way that are vital for bench scientists and biomedical researchers performing experiments that use RNA-seq.
🌐 Study the paper
📲Channel: @Bioinformatics
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📑Network bioinformatics analysis provides insight into drug repurposing for COVID-19
💥New paper with inspiring process method
🌐Study the paper
📲Channel: @Bioinformatics
💥New paper with inspiring process method
🌐Study the paper
📲Channel: @Bioinformatics
👍1
👨🏫 Free Online Course: Statistical Inference and Modeling for High-throughput Experiments
from Harvard University supporting by NIH Grant
🗓 Duration: 4 weeks long
🕦 Time Commitment: 2-4 hours per week
✍️ Level: Intermediate, self paced
💥What you'll learn:
▫️Organizing high throughput data
▫️Multiple comparison problem
▫️Family Wide Error Rates
▫️False Discovery Rate
▫️Error Rate Control procedures
▫️Bonferroni Correction
🔰Open till September 14, 2021
ℹ️ More information and Participation
📲Channel: @Bioinformatics
from Harvard University supporting by NIH Grant
🗓 Duration: 4 weeks long
🕦 Time Commitment: 2-4 hours per week
✍️ Level: Intermediate, self paced
💥What you'll learn:
▫️Organizing high throughput data
▫️Multiple comparison problem
▫️Family Wide Error Rates
▫️False Discovery Rate
▫️Error Rate Control procedures
▫️Bonferroni Correction
🔰Open till September 14, 2021
ℹ️ More information and Participation
📲Channel: @Bioinformatics
Harvard University
Statistical Inference and Modeling for High-throughput Experiments | Harvard University
A focus on the techniques commonly used to perform statistical inference on high throughput data.
🗄Cancer Analysis recorded workshop
🎞 Some sessions:
▫️Data, formats and Databases
▫️Genome Alignment
▫️Somatic Genomic Alteration
▫️Single Cell Genomics - DNA
▫️Gene Expression Profiling
▫️Genes to Pathways
▫️Genes to Networks
▫️Multi-omic data integration
▫️Integration of Clinical Data
📲Channel: @Bioinformatics
🎞 Some sessions:
▫️Data, formats and Databases
▫️Genome Alignment
▫️Somatic Genomic Alteration
▫️Single Cell Genomics - DNA
▫️Gene Expression Profiling
▫️Genes to Pathways
▫️Genes to Networks
▫️Multi-omic data integration
▫️Integration of Clinical Data
📲Channel: @Bioinformatics
📑On the use of Networks in Biomedicine
💥From Abstract: Most biological networks are still far from being complete and they are often difficult to interpret due to the complexity of relationships and the peculiarities of the data. Starting from preliminary notions about neural networks, we focus on biological networks and discuss some well-known applications, like protein-protein interaction networks, gene regulatory networks (DNA-protein interaction networks), metabolic networks, signaling networks, neuronal network, phylogenetic trees and special networks.
🌐 Study the paper
📲Channel: @Bioinformatics
💥From Abstract: Most biological networks are still far from being complete and they are often difficult to interpret due to the complexity of relationships and the peculiarities of the data. Starting from preliminary notions about neural networks, we focus on biological networks and discuss some well-known applications, like protein-protein interaction networks, gene regulatory networks (DNA-protein interaction networks), metabolic networks, signaling networks, neuronal network, phylogenetic trees and special networks.
🌐 Study the paper
📲Channel: @Bioinformatics
📹 Free webcast from nature
Applications of single-cell multi-omics techniques in molecular biology, genetics and cancer research
🗓 Date: September 22, 2021
🕐 Time: 9am PDT / 12pm EDT / 5pm BST / 6pm CEST
✍️ Registration link
🖇 More information
📲Channel: @Bioinformatics
Applications of single-cell multi-omics techniques in molecular biology, genetics and cancer research
🗓 Date: September 22, 2021
🕐 Time: 9am PDT / 12pm EDT / 5pm BST / 6pm CEST
✍️ Registration link
🖇 More information
📲Channel: @Bioinformatics
🥛Probiotics has no bad effect on any diseases
💥Twenty years review of probiotic meta-analyses articles: Effects on disease prevention and treatment
From abstract: Here, we collected close to 300 meta-analysis articles for 20 years, investigating the effect of probiotics in the prevention and treatment of diseases. The goal of this study is to provide an overview of all meta-analysis articles of the effects of probiotics on various human diseases. Papers studied and categorized and investigated in order to present valuable insights for researchers in the field. According to the results, most meta-analyses indicated probiotics were 79% effective in preventing or treating the diseases. Some articles have also reported no positive effects, but there is not any paper in our study confirming the detrimental influence of probiotic effect on human health.
🌐 Read the interesting review
📲Channel: @Bioinformatics
💥Twenty years review of probiotic meta-analyses articles: Effects on disease prevention and treatment
From abstract: Here, we collected close to 300 meta-analysis articles for 20 years, investigating the effect of probiotics in the prevention and treatment of diseases. The goal of this study is to provide an overview of all meta-analysis articles of the effects of probiotics on various human diseases. Papers studied and categorized and investigated in order to present valuable insights for researchers in the field. According to the results, most meta-analyses indicated probiotics were 79% effective in preventing or treating the diseases. Some articles have also reported no positive effects, but there is not any paper in our study confirming the detrimental influence of probiotic effect on human health.
🌐 Read the interesting review
📲Channel: @Bioinformatics
📑Review of machine learning methods for RNA secondary structure prediction
🌐 Study the paper
📲Channel: @Bioinformatics
🌐 Study the paper
📲Channel: @Bioinformatics
📑The Incredible Convergence Of Deep Learning And Genomics
🌐Study the article
📲Channel: @Bioinformatics
🌐Study the article
📲Channel: @Bioinformatics
👨🏻💻Accurate predictions of the structures of almost all human proteins
from Nature News
🌐 Read the article
📲Channel: @Bioinformatics
from Nature News
🌐 Read the article
📲Channel: @Bioinformatics