π Free recorded webinar
π₯Mastering Phylogenetic Tree Creation & Optimizationπ₯
https://www.dnastar.com/mastering-phylogenetic-tree-creation-and-optimization-with-megalign-pro/
π²Channel: @Bioinformatics
π₯Mastering Phylogenetic Tree Creation & Optimizationπ₯
https://www.dnastar.com/mastering-phylogenetic-tree-creation-and-optimization-with-megalign-pro/
π²Channel: @Bioinformatics
π Biological data visualization important notes
https://www.youtube.com/watch?v=a2PxHtmfCK0
π²Channel: @Bioinformatics
https://www.youtube.com/watch?v=a2PxHtmfCK0
π²Channel: @Bioinformatics
YouTube
The essence of data visualization in bioinformatics (Webinar)
Presenter: Martin Krzywinski, Canada's Michael Smith Genome Sciences Center.
Progress in science requires effective communicationβdo not leave your audience confused, bored, or uninspired. Using critique by redesign and drawing from published examples inβ¦
Progress in science requires effective communicationβdo not leave your audience confused, bored, or uninspired. Using critique by redesign and drawing from published examples inβ¦
π Network modeling methods for precision medicine
Abstract:We discuss in this survey several network modeling methods and their applicability to precision medicine. We review several network centrality methods and two systems controllability methods . We demonstrate their applicability to precision medicine on three multiple myeloma patient disease networks. Each network consists of protein-protein interactions built around a specific patient's mutated genes, around the targets of the drugs used in the standard of care in multiple myeloma, and around multiple myeloma-specific essential genes. For each network we demonstrate how the network methods we discuss can be used to identify personalized, targeted drug combinations uniquely suited to that patient.
https://arxiv.org/pdf/2104.09206.pdf
π²Channel: @Bioinformatics
Abstract:We discuss in this survey several network modeling methods and their applicability to precision medicine. We review several network centrality methods and two systems controllability methods . We demonstrate their applicability to precision medicine on three multiple myeloma patient disease networks. Each network consists of protein-protein interactions built around a specific patient's mutated genes, around the targets of the drugs used in the standard of care in multiple myeloma, and around multiple myeloma-specific essential genes. For each network we demonstrate how the network methods we discuss can be used to identify personalized, targeted drug combinations uniquely suited to that patient.
https://arxiv.org/pdf/2104.09206.pdf
π²Channel: @Bioinformatics
π1
π½ Bioinformatics Project from Scratch
Data professor learning series
https://www.youtube.com/watch?v=plVLRashaA8&list=RDCMUCV8e2g4IWQqK71bbzGDEI4Q&index=2
π²Channel: @Bioinformatics
Data professor learning series
https://www.youtube.com/watch?v=plVLRashaA8&list=RDCMUCV8e2g4IWQqK71bbzGDEI4Q&index=2
π²Channel: @Bioinformatics
YouTube
Bioinformatics Project from Scratch - Drug Discovery Part 1 (Data Collection and Pre-Processing)
Do you want to collect your very own novel and original dataset in biology that you can use in your Data Science Project? In this video, I will show you how to download and pre-process biological activity data from the ChEMBL database that you can use toβ¦
π€ -π New atlas can help understand the relationship between healthy and breast cancer cells
Australian researchers have documented the diversity of cells in the human breast, explaining the relationship between healthy breast cells and breast cancer cells.
The research, which relied on expertise spanning from breast cancer biology through to bioinformatics, measured gene expression in single cells taken from healthy women and cancerous breast tissue, including tissue carrying a faulty BRCA1 gene. This enabled the researchers to create an 'RNA atlas' that details the different cells found in these tissues.
The atlas, which was described in EMBO Journal, will enable researchers to better understand the different cell types that constitute breast tissue and how these change during the development of cancer.
π Paper link:
https://www.embopress.org/doi/full/10.15252/embj.2020107333
π²Channel: @Bioinformatics
Australian researchers have documented the diversity of cells in the human breast, explaining the relationship between healthy breast cells and breast cancer cells.
The research, which relied on expertise spanning from breast cancer biology through to bioinformatics, measured gene expression in single cells taken from healthy women and cancerous breast tissue, including tissue carrying a faulty BRCA1 gene. This enabled the researchers to create an 'RNA atlas' that details the different cells found in these tissues.
The atlas, which was described in EMBO Journal, will enable researchers to better understand the different cell types that constitute breast tissue and how these change during the development of cancer.
π Paper link:
https://www.embopress.org/doi/full/10.15252/embj.2020107333
π²Channel: @Bioinformatics
π Data Visualization with R - Online Book
R is an amazing platform for data analysis, capable of creating almost any type of graph. This book helps you create the most popular visualizations - from quick and dirty plots to publication-ready graphs. The text relies heavily on the ggplot2 package for graphics, but other approaches are covered as well.
π§βπ« Read the book from here:
https://rkabacoff.github.io/datavis/
π²Channel: @Bioinformatics
R is an amazing platform for data analysis, capable of creating almost any type of graph. This book helps you create the most popular visualizations - from quick and dirty plots to publication-ready graphs. The text relies heavily on the ggplot2 package for graphics, but other approaches are covered as well.
π§βπ« Read the book from here:
https://rkabacoff.github.io/datavis/
π²Channel: @Bioinformatics
𧬠Sample Bioinformatics Analyses for Hub Gene Identification
π https://www.hindawi.com/journals/cmmm/2021/5548918/
π²Channel: @Bioinformatics
π https://www.hindawi.com/journals/cmmm/2021/5548918/
π²Channel: @Bioinformatics
𧫠An insilico method to predict genetics that underpin adverse drug reactions
https://www.sciencedirect.com/science/article/pii/S2215016119303516#fig0015
π²Channel: @Bioinformatics
https://www.sciencedirect.com/science/article/pii/S2215016119303516#fig0015
π²Channel: @Bioinformatics
π Current trend and development in bioinformatics research
https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-020-03874-y
π²Channel: @Bioinformatics
https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-020-03874-y
π²Channel: @Bioinformatics
π¨βπ« Two Days Hands on training on Cancer Genomics
π Duration: 22 May and 23 May 2021
β° 11 a.m. to 12:30 p.m. and 1 p.m. to 2:30 pm. (IST)
πLocation: Virtual via Zoom
π»Software used: R studio
π§Eligibility: Anyone from Life science or related fields.
βοΈ Registration Link
https://forms.gle/r5PADzXaZG5RmqBr5
π² Fees: Rs. 799 | 25 USD
π Website: https://lifegenbio.com/
π²Channel: @Bioinformatics
π Duration: 22 May and 23 May 2021
β° 11 a.m. to 12:30 p.m. and 1 p.m. to 2:30 pm. (IST)
πLocation: Virtual via Zoom
π»Software used: R studio
π§Eligibility: Anyone from Life science or related fields.
βοΈ Registration Link
https://forms.gle/r5PADzXaZG5RmqBr5
π² Fees: Rs. 799 | 25 USD
π Website: https://lifegenbio.com/
π²Channel: @Bioinformatics
π’ ISCB-Africa ASBCB Bioinformatics Conference 2021
Online Event
π£ Deadline for abstract submission: April 19, 2021
π Date: 7-10 June, 2021
πWebsite & more info.: https://www.iscb.org/iscbafrica2021
π²Channel: @Bioinformatics
Online Event
π£ Deadline for abstract submission: April 19, 2021
π Date: 7-10 June, 2021
πWebsite & more info.: https://www.iscb.org/iscbafrica2021
π²Channel: @Bioinformatics
π Network based Approach to Drug Discovery: A Mini Review
π Paper link
π²Channel: @Bioinformatics
π Paper link
π²Channel: @Bioinformatics
βοΈ7 Key points about big data in pandemics
The use of data has played a central role in the COVID-19 pandemic, but it should come as no surprise that researchers are already looking at ways to improve data acquisition, management, and access. EMBLβs most recent Science and Society seminar, βHarnessing Big Data to Monitor and Tackle Pandemicsβ, featured a panel of three speakers who shared their experiences and conclusions about these topics.
π https://www.miragenews.com/just-gist-big-data-and-pandemics-in-seven-key-563697/
π²Channel: @Bioinformatics
The use of data has played a central role in the COVID-19 pandemic, but it should come as no surprise that researchers are already looking at ways to improve data acquisition, management, and access. EMBLβs most recent Science and Society seminar, βHarnessing Big Data to Monitor and Tackle Pandemicsβ, featured a panel of three speakers who shared their experiences and conclusions about these topics.
π https://www.miragenews.com/just-gist-big-data-and-pandemics-in-seven-key-563697/
π²Channel: @Bioinformatics
π The entire genome of a woman from 35,000 years ago sequenced
https://phys.org/news/2021-05-entire-genome-petera-muierii-sequenced.html
π²Channel: @Bioinformatics
https://phys.org/news/2021-05-entire-genome-petera-muierii-sequenced.html
π²Channel: @Bioinformatics
phys.org
The entire genome from PeΕtera Muierii 1 sequenced
For the first time, researchers have successfully sequenced the entire genome from the skull of PeΕtera Muierii 1, a woman who lived in today's Romania 35,000 years ago. Her high genetic diversity shows ...
π₯Network Analysis of Herbs Recommended for the Treatment of COVID-19
In this study, we aimed to identify the pattern and combination of herbs used in the formulae recommended for treating different stages of COVID-19 using a network analysis approach. A total of 142 herbal formulae comprising 416 herbs were analyzed. All possible herbal pairs were examined, and the top frequently used herbal pairs were identified for each disease stage. The herb Glycyrrhizae radix et rhizoma is only identified in one herb pair, even though this herb is identified as one of the herbs with high frequency of use for every disease stage. This study may provide new insights and ideas for clinical research in the future.
πStudy more: https://doi.org/10.2147/IDR.S305176
π²Channel: @Bioinformatics
In this study, we aimed to identify the pattern and combination of herbs used in the formulae recommended for treating different stages of COVID-19 using a network analysis approach. A total of 142 herbal formulae comprising 416 herbs were analyzed. All possible herbal pairs were examined, and the top frequently used herbal pairs were identified for each disease stage. The herb Glycyrrhizae radix et rhizoma is only identified in one herb pair, even though this herb is identified as one of the herbs with high frequency of use for every disease stage. This study may provide new insights and ideas for clinical research in the future.
πStudy more: https://doi.org/10.2147/IDR.S305176
π²Channel: @Bioinformatics
π¬Introduction to Biological Network Analysis
π©βπ«Mini Courses from Donna Slonim at Tufts University
Session 1: Network Basics and Properties
Session 2: From Graphs to Function
Session 3: Identifying Network Modules
Session 4: Network Alignment and Querying
π²Channel: @Bioinformatics
π©βπ«Mini Courses from Donna Slonim at Tufts University
Session 1: Network Basics and Properties
Session 2: From Graphs to Function
Session 3: Identifying Network Modules
Session 4: Network Alignment and Querying
π²Channel: @Bioinformatics
π§βπ¨ Tasks, Techniques, and Tools for Genomic Data Visualization
https://onlinelibrary.wiley.com/doi/full/10.1111/cgf.13727
π²Channel: @Bioinformatics
https://onlinelibrary.wiley.com/doi/full/10.1111/cgf.13727
π²Channel: @Bioinformatics
πDrug Repurposing for the Treatment of COVID-19: A Knowledge Graph Approach
π https://onlinelibrary.wiley.com/doi/full/10.1002/adtp.202100055
A COVID-19 knowledge graph by integrating 14 public bioinformatic databases containing information on drugs, genes, proteins, viruses, diseases, symptoms and their linkages is developed. An algorithm is developed to extract hidden linkages connecting drugs and COVID-19 from the knowledge graph, to generate and rank proposed drug candidates for repurposing as treatments by integrating three scores for each drug: motif scores, knowledge graph PageRank scores, and knowledge graph embedding scores. The knowledge graph contains over 48 000 nodes and 13 37 000 edges, including 13 563 molecules in the DrugBank database. From the 5624 molecules identified by the motif-discovery algorithms, ranking results show that 112 drug molecules had the top 2% scores, of which 50 existing drugs with other indications approved by health administrations reported.
π²Channel: @Bioinformatics
π https://onlinelibrary.wiley.com/doi/full/10.1002/adtp.202100055
A COVID-19 knowledge graph by integrating 14 public bioinformatic databases containing information on drugs, genes, proteins, viruses, diseases, symptoms and their linkages is developed. An algorithm is developed to extract hidden linkages connecting drugs and COVID-19 from the knowledge graph, to generate and rank proposed drug candidates for repurposing as treatments by integrating three scores for each drug: motif scores, knowledge graph PageRank scores, and knowledge graph embedding scores. The knowledge graph contains over 48 000 nodes and 13 37 000 edges, including 13 563 molecules in the DrugBank database. From the 5624 molecules identified by the motif-discovery algorithms, ranking results show that 112 drug molecules had the top 2% scores, of which 50 existing drugs with other indications approved by health administrations reported.
π²Channel: @Bioinformatics
πVery good introduction to RNA-seq
Including: 1)Prepare a sequencing library 2)Sequence 3)Data analysis
βοΈ Level: Elementary
https://www.youtube.com/watch?v=tlf6wYJrwKY
π²Channel: @Bioinformatics
Including: 1)Prepare a sequencing library 2)Sequence 3)Data analysis
βοΈ Level: Elementary
https://www.youtube.com/watch?v=tlf6wYJrwKY
π²Channel: @Bioinformatics
YouTube
StatQuest: A gentle introduction to RNA-seq
RNA-seq may sound mysterious, but it's not. Here's go over the main ideas behind how it's done and how the data is analyzed.
NOTE: If you want to learn about ChIP-seq, check out the StatQuest: https://youtu.be/nkWGmaYRues
For a complete index of all theβ¦
NOTE: If you want to learn about ChIP-seq, check out the StatQuest: https://youtu.be/nkWGmaYRues
For a complete index of all theβ¦
πͺProteomics reveals how exercise increases the efficiency of muscle energy production
πhttps://phys.org/news/2021-05-proteomics-reveals-efficiency-muscle-energy.html
π²Channel: @Bioinformatics
πhttps://phys.org/news/2021-05-proteomics-reveals-efficiency-muscle-energy.html
π²Channel: @Bioinformatics
phys.org
Proteomics reveals how exercise increases the efficiency of muscle energy production
Mitochondria are the cell's power plants and produce the majority of a cell's energy needs through an electrochemical process called electron transport chain coupled to another process known as oxidative ...
π§βπ«Workshop RNA-Seq using high-performance computing
πhttps://hbctraining.github.io/Intro-to-rnaseq-hpc-salmon-flipped/schedule/links-to-lessons.html
π₯Learning Objectives
β«οΈUnderstand the necessity for, and use of, the command line interface (bash) and HPC for analyzing high-throughput sequencing data.
β«οΈUnderstand best practices for designing an RNA-seq experiment and analysis the resulting data.
π²Channel: @Bioinformatics
πhttps://hbctraining.github.io/Intro-to-rnaseq-hpc-salmon-flipped/schedule/links-to-lessons.html
π₯Learning Objectives
β«οΈUnderstand the necessity for, and use of, the command line interface (bash) and HPC for analyzing high-throughput sequencing data.
β«οΈUnderstand best practices for designing an RNA-seq experiment and analysis the resulting data.
π²Channel: @Bioinformatics