🧑💻Ten simple rules for biologists learning to program
✍🏻Introduction:
As big data and multi-omics analyses are becoming mainstream, computational proficiency and literacy are essential skills in a biologist’s tool kit. All “omics” studies require computational biology: the implementation of analyses requires programming skills, while experimental design and interpretation require a solid understanding of the analytical approach. While academic cores, commercial services, and collaborations can aid in the implementation of analyses, the computational literacy required to design and interpret omics studies cannot be replaced or supplemented. However, many biologists are only trained in experimental techniques. We write these 10 simple rules for traditionally trained biologists, particularly graduate students interested in acquiring a computational skill set.
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📲Channel: @Bioinformatics
✍🏻Introduction:
As big data and multi-omics analyses are becoming mainstream, computational proficiency and literacy are essential skills in a biologist’s tool kit. All “omics” studies require computational biology: the implementation of analyses requires programming skills, while experimental design and interpretation require a solid understanding of the analytical approach. While academic cores, commercial services, and collaborations can aid in the implementation of analyses, the computational literacy required to design and interpret omics studies cannot be replaced or supplemented. However, many biologists are only trained in experimental techniques. We write these 10 simple rules for traditionally trained biologists, particularly graduate students interested in acquiring a computational skill set.
🌐 Study the paper
📲Channel: @Bioinformatics
🎞Good and fast introduction to Gene set enrichment analysis
💥The presentation provides a minimal introduction to the basic idea of enrichment analysis, correction for multiple testing, importance of custom backgrounds, analysis of ranked gene lists, and applications beyond gene function.
✅Video details
▫️0:00 Introduction: characterization of a gene list by finding overrepresented classes of genes
▫️0:20 Basic idea: testing for enrichment of a single term, Gene Ontology, and systematic enrichment analysis
▫️1:32 Multiple testing: 20 colors of jelly beans, Bonferroni correction, and false discovery rate
▫️3:00 Custom background: the problem of using genome-wide background,
▫️4:42 Ranked lists: mapping GO terms on a ranked gene list and significance testing
▫️6:15 Beyond gene functions: gene set enrichment for diseases/tissues/trannoscription factor, kinase enrichment analysis, and organism set enrichment in microbiomes
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📲Channel: @Bioinformatics
💥The presentation provides a minimal introduction to the basic idea of enrichment analysis, correction for multiple testing, importance of custom backgrounds, analysis of ranked gene lists, and applications beyond gene function.
✅Video details
▫️0:00 Introduction: characterization of a gene list by finding overrepresented classes of genes
▫️0:20 Basic idea: testing for enrichment of a single term, Gene Ontology, and systematic enrichment analysis
▫️1:32 Multiple testing: 20 colors of jelly beans, Bonferroni correction, and false discovery rate
▫️3:00 Custom background: the problem of using genome-wide background,
▫️4:42 Ranked lists: mapping GO terms on a ranked gene list and significance testing
▫️6:15 Beyond gene functions: gene set enrichment for diseases/tissues/trannoscription factor, kinase enrichment analysis, and organism set enrichment in microbiomes
🌐 Watch
📲Channel: @Bioinformatics
YouTube
Enrichment analysis: A short introduction to the core concepts of gene set enrichment analysis
A short introduction to the core concepts of enrichment analysis and its applications to bioinformatics analysis of gene lists. The presentation provides a minimal introduction to the basic idea of enrichment analysis, correction for multiple testing, importance…
💻 Analyzing Gene Sequence Results with BLAST
✍️ Level: Elementary
💥In this video, you learn how to...
1) Obtain gene sequences from a sequencing company (e.g., GeneWiz)
2) Select which part of the sequence is best for analysis
3) Analyze results with BLAST
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📲Channel: @Bioinformatics
✍️ Level: Elementary
💥In this video, you learn how to...
1) Obtain gene sequences from a sequencing company (e.g., GeneWiz)
2) Select which part of the sequence is best for analysis
3) Analyze results with BLAST
🌐 Watch
📲Channel: @Bioinformatics
YouTube
Analyzing Gene Sequence Results with BLAST
⚡ Welcome to Catalyst University! I am Kevin Tokoph, PT, DPT.
I hope you enjoy the video! Please leave a like and subscribe! 🙏
INSTAGRAM | @thecatalystuniversity
Follow me on Instagram @thecatalystuniversity for additional helpful content and for my more…
I hope you enjoy the video! Please leave a like and subscribe! 🙏
INSTAGRAM | @thecatalystuniversity
Follow me on Instagram @thecatalystuniversity for additional helpful content and for my more…
👨🏻💻Deep Learning for Network Biology
💥Free online tutorial from Stanford University
Networks are ubiquitous in biology where they encode connectivity patterns at all scales of organization, from molecular to the biome. This tutorial investigates key advancements in representation learning for networks over the last few years, with an emphasis on fundamentally new opportunities in network biology enabled by these advancements.
Titles:
▫️Introduction: Introduction to networks and overview of network biology
▫️Part 1: Network propagation and node embeddings
▫️Part 2: Graph autoencoders and deep representation learning
▫️Part 3: Heterogeneous networks
▫️Conclusion: End-to-end Tensorflow examples and new directions
🌐 Study online
📲Channel: @Bioinformatics
💥Free online tutorial from Stanford University
Networks are ubiquitous in biology where they encode connectivity patterns at all scales of organization, from molecular to the biome. This tutorial investigates key advancements in representation learning for networks over the last few years, with an emphasis on fundamentally new opportunities in network biology enabled by these advancements.
Titles:
▫️Introduction: Introduction to networks and overview of network biology
▫️Part 1: Network propagation and node embeddings
▫️Part 2: Graph autoencoders and deep representation learning
▫️Part 3: Heterogeneous networks
▫️Conclusion: End-to-end Tensorflow examples and new directions
🌐 Study online
📲Channel: @Bioinformatics
🖥 Machine learning and complex biological data
💥 Sections:
▫️The revolution of biological techniques and demands for new data mining methods
▫️Machine learning versus statistics
▫️The applications of machine learning in biology
▫️Challenges and future outlooks
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📲Channel: @Bioinformatics
💥 Sections:
▫️The revolution of biological techniques and demands for new data mining methods
▫️Machine learning versus statistics
▫️The applications of machine learning in biology
▫️Challenges and future outlooks
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📲Channel: @Bioinformatics
📕 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
▫️Denoscription 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
▫️Denoscription 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
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📲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, trannoscriptomic, 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.
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📲Channel: @Bioinformatics
From Abstract: High-throughput data such as genomic, epigenetic, genome architecture, cistromic, trannoscriptomic, 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.
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📲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
👍1
📑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