📽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
🎬 Free Virtual Event
🧬 Bioinformatics and Computational Biology Symposium
presented by the NIH Library Bioinformatics Support Program
🟫 Some Keynote Speaks:
▫️Big Data for Health and Disease
▫️Novel Topological Approach to Secondary Structure Assignment and Identification of Distorted Helices and Strands
▫️Multi-omics Data Integration to Study COVID-19 Severity
▫️Employing Machine Learning Approaches in the Identification of Genes Containing Spatial Information from Single Cell Trannoscriptomics Data
▫️Identification of Effective Targets for Cancer Immunotherapy
🗓 Time:
September 9, 9:30 A.M.–3:00 P.M
✍️Registration Link
ℹ️ Symposium details
📲Channel: @Bioinformatics
🧬 Bioinformatics and Computational Biology Symposium
presented by the NIH Library Bioinformatics Support Program
🟫 Some Keynote Speaks:
▫️Big Data for Health and Disease
▫️Novel Topological Approach to Secondary Structure Assignment and Identification of Distorted Helices and Strands
▫️Multi-omics Data Integration to Study COVID-19 Severity
▫️Employing Machine Learning Approaches in the Identification of Genes Containing Spatial Information from Single Cell Trannoscriptomics Data
▫️Identification of Effective Targets for Cancer Immunotherapy
🗓 Time:
September 9, 9:30 A.M.–3:00 P.M
✍️Registration Link
ℹ️ Symposium details
📲Channel: @Bioinformatics
🧑💻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.
🌐 Study the paper
📲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
🌐 Watch
📲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
🌐 Watch
📲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
🌐 Study the article
📲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
🌐 Study the article
📲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
🌐 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, 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
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.
🌐 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
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