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Keywords: computer-science  or Data analysis 

Helis Academy course FAIR data stewardship 2021, Day 3, Electronic Lab Notebooks (ELN)

This presentation is part of the 3rd edition of the Helis Academy FAIR data stewardship (for life sciences) course Day 3, March 22, 2021

Scientific topics: Data management, FAIR data

Operations: Data handling

Keywords: Electronic Lab Notebooks (ELN), Data processing, Data analysis

Resource type: Slidedeck

Helis Academy course FAIR data stewardship 2021, Day 3, Electronic Lab Notebooks (ELN) http://tess.elixir-uk.org/materials/helis-academy-course-fair-data-stewardship-2021-day-3-electronic-lab-notebooks-eln This presentation is part of the 3rd edition of the Helis Academy FAIR data stewardship (for life sciences) course Day 3, March 22, 2021 Data management FAIR data Electronic Lab Notebooks (ELN), Data processing, Data analysis PhD candidate
Helis Academy course FAIR data stewardship 2021, Day 4, Data carpentry

This presentation is part of the 3rd edition of the Helis Academy FAIR data stewardship (for life sciences) course Day 4, March 24, 2021

Scientific topics: Data management, FAIR data

Operations: Data handling

Keywords: Data carpentry, Data analysis

Resource type: Slidedeck

Helis Academy course FAIR data stewardship 2021, Day 4, Data carpentry http://tess.elixir-uk.org/materials/helis-academy-course-fair-data-stewardship-2021-day-4-data-carpentry This presentation is part of the 3rd edition of the Helis Academy FAIR data stewardship (for life sciences) course Day 4, March 24, 2021 Data management FAIR data Data carpentry, Data analysis PhD candidate
Helis Academy course FAIR data stewardship 2021, Day 4, Software carpentry

This presentation is part of the 3rd edition of the Helis Academy FAIR data stewardship (for life sciences) course Day 4, March 24, 2021

Scientific topics: Data management, FAIR data

Operations: Data handling

Keywords: Data analysis, Software Carpentry

Resource type: Slidedeck

Helis Academy course FAIR data stewardship 2021, Day 4, Software carpentry http://tess.elixir-uk.org/materials/helis-academy-course-fair-data-stewardship-2021-day-4-software-carpentry This presentation is part of the 3rd edition of the Helis Academy FAIR data stewardship (for life sciences) course Day 4, March 24, 2021 Data management FAIR data Data analysis, Software Carpentry PhD candidate
Helis Academy course FAIR data stewardship 2021, Day 2, Infrastructure for storing and sharing data

This presentation is part of the 3rd edition of the Helis Academy FAIR data stewardship (for life sciences) course Day 2, March 18, 2021

Scientific topics: FAIR data, Data management

Operations: Data handling, Analysis

Keywords: Data analysis, Data processing, Data sharing

Resource type: Slidedeck

Helis Academy course FAIR data stewardship 2021, Day 2, Infrastructure for storing and sharing data http://tess.elixir-uk.org/materials/helis-academy-course-fair-data-stewardship-2021-day-2-infrastructure-for-storing-and-sharing-data This presentation is part of the 3rd edition of the Helis Academy FAIR data stewardship (for life sciences) course Day 2, March 18, 2021 FAIR data Data management Data analysis, Data processing, Data sharing PhD candidate
Helis Academy course FAIR data stewardship 2021, Day 2, Tools for processing and analysing data

This presentation is part of the 3rd edition of the Helis Academy FAIR data stewardship (for life sciences) course Day 2, March 18, 2021

Scientific topics: Data management, FAIR data

Operations: Analysis, Data handling

Keywords: Data analysis, Data processing

Resource type: Slidedeck

Helis Academy course FAIR data stewardship 2021, Day 2, Tools for processing and analysing data http://tess.elixir-uk.org/materials/helis-academy-course-fair-data-stewardship-2021-day-2-tools-for-processing-and-analysing-data This presentation is part of the 3rd edition of the Helis Academy FAIR data stewardship (for life sciences) course Day 2, March 18, 2021 Data management FAIR data Data analysis, Data processing PhD candidate
Workshop FAIR & Data stewardship for the 2021 ITN ProEVLifeCycle

This material is part of a FAIR data stewardship follow up workshop for the Marie Curie ITN ProEVLifeCycle (The prostate cancer Extracellular Vesicle LifeCycle)

Scientific topics: FAIR data, Data management

Operations: Analysis, Data handling

Keywords: Data analysis, Data management planning, Data processing, Data sharing, Data collection, Data preserving, Data reuse

Resource type: Slidedeck

Workshop FAIR & Data stewardship for the 2021 ITN ProEVLifeCycle http://tess.elixir-uk.org/materials/workshop-fair-data-stewardship-for-the-2021-itn-proevlifecycle This material is part of a FAIR data stewardship follow up workshop for the Marie Curie ITN ProEVLifeCycle (The prostate cancer Extracellular Vesicle LifeCycle) FAIR data Data management Data analysis, Data management planning, Data processing, Data sharing, Data collection, Data preserving, Data reuse PhD candidate
Workshop FAIR & Data stewardship for the 2020 ITN ProEVLifeCycle

This material is part of an introductory FAIR data stewardship workshop for the Marie Curie ITN ProEVLifeCycle (The prostate cancer Extracellular Vesicle LifeCycle)

Scientific topics: FAIR data, Data management

Operations: Analysis, Data handling

Keywords: Data analysis, Data management planning, Data processing, Data sharing, Data collection, Data preserving, Data reuse

Resource type: Slidedeck

Workshop FAIR & Data stewardship for the 2020 ITN ProEVLifeCycle http://tess.elixir-uk.org/materials/workshop-fair-data-stewardship-for-the-2020-itn-proevlifecycle This material is part of an introductory FAIR data stewardship workshop for the Marie Curie ITN ProEVLifeCycle (The prostate cancer Extracellular Vesicle LifeCycle) FAIR data Data management Data analysis, Data management planning, Data processing, Data sharing, Data collection, Data preserving, Data reuse PhD candidate
Workshop FAIR & Data stewardship for the 2020 ITN COSMIC

This material is part of an introductory data stewardship workshop for the Marie Curie ITN COSMIC project (Combatting Disorders of Adaptive Immunity with Systems MedICine).

Scientific topics: Data management, FAIR data

Operations: Data handling, Analysis

Keywords: Data analysis, Data collection, Data management planning, Data preserving, Data processing, Data reuse, Data sharing

Resource type: Slidedeck

Workshop FAIR & Data stewardship for the 2020 ITN COSMIC http://tess.elixir-uk.org/materials/workshop-fair-data-stewardship-for-the-2020-itn-cosmic This material is part of an introductory data stewardship workshop for the Marie Curie ITN COSMIC project (Combatting Disorders of Adaptive Immunity with Systems MedICine). Data management FAIR data Data analysis, Data collection, Data management planning, Data preserving, Data processing, Data reuse, Data sharing PhD candidate
Using the Norwegian e-infrastructure for Life Science and usegalaxy.no

Online course on using the Norwegian e-infrastructure for Life Science (NeLS) and the national supported Galaxy (usegalaxy.no). The course will consist of lessons covering the necessary topics alternating with hands-on sessions to gain practical experience.

Keywords: NeLS, Data storage, data sharing, Data analysis

Using the Norwegian e-infrastructure for Life Science and usegalaxy.no http://tess.elixir-uk.org/materials/using-the-norwegian-e-infrastructure-for-life-science-and-usegalaxy-no Online course on using the Norwegian e-infrastructure for Life Science (NeLS) and the national supported Galaxy (usegalaxy.no). The course will consist of lessons covering the necessary topics alternating with hands-on sessions to gain practical experience. NeLS, Data storage, data sharing, Data analysis PhD postdocs Researchers
Reproducible analysis

Slides for the "Reproducible analysis" session of the "Best practices in research data management and stewardship" held regularly by ELIXIR-Luxembourg.

Scientific topics: Data architecture, analysis and design

Keywords: Workflows, Literate programming, Reproducible Science, Data analysis

Resource type: Presentation

Reproducible analysis http://tess.elixir-uk.org/materials/reproducible-analysis Slides for the "Reproducible analysis" session of the "Best practices in research data management and stewardship" held regularly by ELIXIR-Luxembourg. Pinar Alper Vilem Ded Data architecture, analysis and design Workflows, Literate programming, Reproducible Science, Data analysis PhD candidates Researchers
Data Gravity in the Life Sciences: Lessons learned from the HCA and other federated data projects

CINECA webinar discussing when to bring compute to the data

Scientific topics: Data architecture, analysis and design

Keywords: Cloud computing, Data analysis, Standards, Translational research

Resource type: Video

Data Gravity in the Life Sciences: Lessons learned from the HCA and other federated data projects http://tess.elixir-uk.org/materials/data-gravity-in-the-life-sciences-lessons-learned-from-the-hca-and-other-federated-data-projects CINECA webinar discussing when to bring compute to the data Data architecture, analysis and design Cloud computing, Data analysis, Standards, Translational research
Reproducible data analysis with RStudio, github and Rmarkdown

Best practices for writing reproducible data-analysis Creating a reproducible and re-usable data-analysis environment with Rstudio Input: https://github.com/vibbits/RDM-LS Output: https://github.com/vibbits/RDM-LS-solution

Scientific topics: Data management, Data architecture, analysis and design

Keywords: Data analysis

Resource type: Presentation

Reproducible data analysis with RStudio, github and Rmarkdown http://tess.elixir-uk.org/materials/reproducible-data-analysis-with-rstudio-github-and-rmarkdown Best practices for writing reproducible data-analysis Creating a reproducible and re-usable data-analysis environment with Rstudio Input: https://github.com/vibbits/RDM-LS Output: https://github.com/vibbits/RDM-LS-solution Data management Data architecture, analysis and design Data analysis life scientists
FAIR principles applied to bioinformatics

Content of the training material: - Introduction to reproducibility - encapsulate a work environment (docker) - design and execute workflows (snakemake) - IFB infrastructure (Slurm cluster) - managing software versions (git) - managing software environments (conda) - ensure...

Keywords: FAIR, Reproducible Science, Open science, Data analysis, Data processing

Resource type: Training materials

FAIR principles applied to bioinformatics http://tess.elixir-uk.org/materials/fair-principles-applied-to-bioinformatics Content of the training material: - Introduction to reproducibility - encapsulate a work environment (docker) - design and execute workflows (snakemake) - IFB infrastructure (Slurm cluster) - managing software versions (git) - managing software environments (conda) - ensure the traceability of analysis using Notebooks. The training material is in french Hélène Chiapello FAIR, Reproducible Science, Open science, Data analysis, Data processing bioinformaticians software developers, bioinformaticians computational scientists Researchers
Data Carpentry in R

Data Carpentry workshops are for any researcher who has data they want to analyze, and no prior computational experience is required. This hands-on workshop teaches basic concepts, skills and tools for working more effectively with data. We will cover Data organization in spreadsheets, data...

Keywords: Data analysis, Data carpentry, SoftwareCarpentry

Resource type: course materials

Data Carpentry in R http://tess.elixir-uk.org/materials/data-carpentry-in-r Data Carpentry workshops are for any researcher who has data they want to analyze, and no prior computational experience is required. This hands-on workshop teaches basic concepts, skills and tools for working more effectively with data. We will cover Data organization in spreadsheets, data cleaning with OpenRefine, and learn how to use the statistical program R. If time allows we will also talk about Interacting with databases from R. Participants will be encouraged to help one another and to apply what they have learned to their own research problems Data analysis, Data carpentry, SoftwareCarpentry Anyone
InterMine user tutorial

A tutorial for end users of InterMine

Keywords: Data querying, Data analysis, Data download, FAIR

Resource type: Tutorial

InterMine user tutorial http://tess.elixir-uk.org/materials/intermine-user-tutorial A tutorial for end users of InterMine Data querying, Data analysis, Data download, FAIR Life Science Researchers Bioinformaticians
InterMine operator manual

Documentation on how to install, configure and operate an InterMine instance.

Keywords: Data integration, Data analysis, Data publishing, FAIR

Resource type: Documentation

InterMine operator manual http://tess.elixir-uk.org/materials/intermine-operator-manual Documentation on how to install, configure and operate an InterMine instance. Data integration, Data analysis, Data publishing, FAIR Bioinformaticians software engineers
Introduction to Analysing Repeated Measures Data

Training session with Dr Helen Brown, Senior Statistician, at The Roslin Institute, March 2016. These training sessions were given to staff and research students at the Roslin Institute. The material is also used for the Animal Biosciences MSc course taught at the Institute.

Keywords: Data analysis, Repeated measures, Roslin Institute

Introduction to Analysing Repeated Measures Data http://tess.elixir-uk.org/materials/introduction-to-analysing-repeated-measures-data Training session with Dr Helen Brown, Senior Statistician, at The Roslin Institute, March 2016. These training sessions were given to staff and research students at the Roslin Institute. The material is also used for the Animal Biosciences MSc course taught at the Institute. Data analysis, Repeated measures, Roslin Institute
Interpretation and automated analysis of proteomic data

Interpretation and automated analysis of proteomic data by Karl R. Clauser

Scientific topics: Proteomics

Keywords: Proteomic data analysis, Data analysis

Interpretation and automated analysis of proteomic data http://tess.elixir-uk.org/materials/interpretation-and-automated-analysis-of-proteomic-data Interpretation and automated analysis of proteomic data by Karl R. Clauser Proteomics Proteomic data analysis, Data analysis
Big Data, Genes, and Medicine

This course distills for you expert knowledge and skills mastered by professionals in Health Big Data Science and Bioinformatics. You will learn exciting facts about the human body biology and chemistry, genetics, and medicine that will be intertwined with the science of Big Data and skills to...

Keywords: life-sciences, computer-science, bioinformatics, algorithms

Big Data, Genes, and Medicine http://tess.elixir-uk.org/materials/big-data-genes-and-medicine This course distills for you expert knowledge and skills mastered by professionals in Health Big Data Science and Bioinformatics. You will learn exciting facts about the human body biology and chemistry, genetics, and medicine that will be intertwined with the science of Big Data and skills to harness the avalanche of data openly available at your fingertips and which we are just starting to make sense of. We’ll investigate the different steps required to master Big Data analytics on real datasets, including Next Generation Sequencing data, in a healthcare and biological context, from preparing data for analysis to completing the analysis, interpreting the results, visualizing them, and sharing the results. Needless to say, when you master these high-demand skills, you will be well positioned to apply for or move to positions in biomedical data analytics and bioinformatics. No matter what your skill levels are in biomedical or technical areas, you will gain highly valuable new or sharpened skills that will make you stand-out as a professional and want to dive even deeper in biomedical Big Data. It is my hope that this course will spark your interest in the vast possibilities offered by publicly available Big Data to better understand, prevent, and treat diseases. life-sciences, computer-science, bioinformatics, algorithms 2017-03-24
Bioinformatics Capstone: Big Data in Biology

In this course, you will learn how to use the BaseSpace cloud platform developed by Illumina (our industry partner) to apply several standard bioinformatics software approaches to real biological data. In particular, in a series of Application Challenges will see how genome assembly can be used...

Keywords: life-sciences, computer-science, health-informatics, algorithms

Bioinformatics Capstone: Big Data in Biology http://tess.elixir-uk.org/materials/bioinformatics-capstone-big-data-in-biology In this course, you will learn how to use the BaseSpace cloud platform developed by Illumina (our industry partner) to apply several standard bioinformatics software approaches to real biological data. In particular, in a series of Application Challenges will see how genome assembly can be used to track the source of a food poisoning outbreak, how RNA-Sequencing can help us analyze gene expression data on the tissue level, and compare the pros and cons of whole genome vs. whole exome sequencing for finding potentially harmful mutations in a human sample. Plus, hacker track students will have the option to build their own genome assembler and apply it to real data! life-sciences, computer-science, health-informatics, algorithms 2017-03-25
Finding Hidden Messages in DNA (Bioinformatics I)

Named a top 50 MOOC of all time by Class Central! This course begins a series of classes illustrating the power of computing in modern biology. Please join us on the frontier of bioinformatics to look for hidden messages in DNA without ever needing to put on a lab coat. In the first half of the...

Keywords: life-sciences, computer-science, health-informatics, algorithms

Finding Hidden Messages in DNA (Bioinformatics I) http://tess.elixir-uk.org/materials/finding-hidden-messages-in-dna-bioinformatics-i Named a top 50 MOOC of all time by Class Central! This course begins a series of classes illustrating the power of computing in modern biology. Please join us on the frontier of bioinformatics to look for hidden messages in DNA without ever needing to put on a lab coat. In the first half of the course, we investigate DNA replication, and ask the question, where in the genome does DNA replication begin? We will see that we can answer this question for many bacteria using only some straightforward algorithms to look for hidden messages in the genome. In the second half of the course, we examine a different biological question, when we ask which DNA patterns play the role of molecular clocks. The cells in your body manage to maintain a circadian rhythm, but how is this achieved on the level of DNA? Once again, we will see that by knowing which hidden messages to look for, we can start to understand the amazingly complex language of DNA. Perhaps surprisingly, we will apply randomized algorithms, which roll dice and flip coins in order to solve problems. Finally, you will get your hands dirty and apply existing software tools to find recurring biological motifs within genes that are responsible for helping Mycobacterium tuberculosis go "dormant" within a host for many years before causing an active infection. life-sciences, computer-science, health-informatics, algorithms 2017-10-09
Finding Mutations in DNA and Proteins (Bioinformatics VI)

In previous courses in the Specialization, we have discussed how to sequence and compare genomes. This course will cover advanced topics in finding mutations lurking within DNA and proteins. In the first half of the course, we would like to ask how an individual's genome differs from the...

Keywords: life-sciences, computer-science, health-informatics, algorithms

Finding Mutations in DNA and Proteins (Bioinformatics VI) http://tess.elixir-uk.org/materials/finding-mutations-in-dna-and-proteins-bioinformatics-vi In previous courses in the Specialization, we have discussed how to sequence and compare genomes. This course will cover advanced topics in finding mutations lurking within DNA and proteins. In the first half of the course, we would like to ask how an individual's genome differs from the "reference genome" of the species. Our goal is to take small fragments of DNA from the individual and "map" them to the reference genome. We will see that the combinatorial pattern matching algorithms solving this problem are elegant and extremely efficient, requiring a surprisingly small amount of runtime and memory. In the second half of the course, we will learn how to identify the function of a protein even if it has been bombarded by so many mutations compared to similar proteins with known functions that it has become barely recognizable. This is the case, for example, in HIV studies, since the virus often mutates so quickly that researchers can struggle to study it. The approach we will use is based on a powerful machine learning tool called a hidden Markov model. Finally, you will learn how to apply popular bioinformatics software tools applying hidden Markov models to compare a protein against a related family of proteins. life-sciences, computer-science, health-informatics, algorithms 2017-10-09
Genome Sequencing (Bioinformatics II)

You may have heard a lot about genome sequencing and its potential to usher in an era of personalized medicine, but what does it mean to sequence a genome? Biologists still cannot read the nucleotides of an entire genome as you would read a book from beginning to end. However, they can read...

Keywords: life-sciences, computer-science, health-informatics, algorithms

Genome Sequencing (Bioinformatics II) http://tess.elixir-uk.org/materials/genome-sequencing-bioinformatics-ii You may have heard a lot about genome sequencing and its potential to usher in an era of personalized medicine, but what does it mean to sequence a genome? Biologists still cannot read the nucleotides of an entire genome as you would read a book from beginning to end. However, they can read short pieces of DNA. In this course, we will see how graph theory can be used to assemble genomes from these short pieces. We will further learn about brute force algorithms and apply them to sequencing mini-proteins called antibiotics. In the first half of the course, we will see that biologists cannot read the 3 billion nucleotides of a human genome as you would read a book from beginning to end. However, they can read shorter fragments of DNA. In this course, we will see how graph theory can be used to assemble genomes from these short pieces in what amounts to the largest jigsaw puzzle ever put together. In the second half of the course, we will discuss antibiotics, a topic of great relevance as antimicrobial-resistant bacteria like MRSA are on the rise. You know antibiotics as drugs, but on the molecular level they are short mini-proteins that have been engineered by bacteria to kill their enemies. Determining the sequence of amino acids making up one of these antibiotics is an important research problem, and one that is similar to that of sequencing a genome by assembling tiny fragments of DNA. We will see how brute force algorithms that try every possible solution are able to identify naturally occurring antibiotics so that they can be synthesized in a lab. Finally, you will learn how to apply popular bioinformatics software tools to sequence the genome of a deadly Staphylococcus bacterium that has acquired antibiotics resistance. life-sciences, computer-science, health-informatics, algorithms 2017-10-09
Biology Meets Programming: Bioinformatics for Beginners

Are you interested in learning how to program (in Python) within a scientific setting? This course will cover algorithms for solving various biological problems along with a handful of programming challenges helping you implement these algorithms in Python. It offers a gently-paced introduction...

Keywords: life-sciences, computer-science, health-informatics, software-development

Biology Meets Programming: Bioinformatics for Beginners http://tess.elixir-uk.org/materials/biology-meets-programming-bioinformatics-for-beginners Are you interested in learning how to program (in Python) within a scientific setting? This course will cover algorithms for solving various biological problems along with a handful of programming challenges helping you implement these algorithms in Python. It offers a gently-paced introduction to our Bioinformatics Specialization (https://www.coursera.org/specializations/bioinformatics), preparing learners to take the first course in the Specialization, "Finding Hidden Messages in DNA" (https://www.coursera.org/learn/dna-analysis). Each of the four weeks in the course will consist of two required components. First, an interactive textbook provides Python programming challenges that arise from real biological problems. If you haven't programmed in Python before, not to worry! We provide "Just-in-Time" exercises from the Codecademy Python track (https://www.codecademy.com/learn/python). And each page in our interactive textbook has its own discussion forum, where you can interact with other learners. Second, each week will culminate in a summary quiz. Lecture videos are also provided that accompany the material, but these videos are optional. life-sciences, computer-science, health-informatics, software-development 2017-05-04
Genomic Data Science and Clustering (Bioinformatics V)

How do we infer which genes orchestrate various processes in the cell? How did humans migrate out of Africa and spread around the world? In this class, we will see that these two seemingly different questions can be addressed using similar algorithmic and machine learning techniques arising from...

Keywords: life-sciences, computer-science, health-informatics, algorithms

Genomic Data Science and Clustering (Bioinformatics V) http://tess.elixir-uk.org/materials/genomic-data-science-and-clustering-bioinformatics-v How do we infer which genes orchestrate various processes in the cell? How did humans migrate out of Africa and spread around the world? In this class, we will see that these two seemingly different questions can be addressed using similar algorithmic and machine learning techniques arising from the general problem of dividing data points into distinct clusters. In the first half of the course, we will introduce algorithms for clustering a group of objects into a collection of clusters based on their similarity, a classic problem in data science, and see how these algorithms can be applied to gene expression data. In the second half of the course, we will introduce another classic tool in data science called principal components analysis that can be used to preprocess multidimensional data before clustering in an effort to greatly reduce the number dimensions without losing much of the "signal" in the data. Finally, you will learn how to apply popular bioinformatics software tools to solve a real problem in clustering. life-sciences, computer-science, health-informatics, algorithms 2017-10-09
Comparing Genes, Proteins, and Genomes (Bioinformatics III)

Once we have sequenced genomes in the previous course, we would like to compare them to determine how species have evolved and what makes them different. In the first half of the course, we will compare two short biological sequences, such as genes (i.e., short sequences of DNA) or proteins. We...

Keywords: life-sciences, computer-science, health-informatics, algorithms

Comparing Genes, Proteins, and Genomes (Bioinformatics III) http://tess.elixir-uk.org/materials/comparing-genes-proteins-and-genomes-bioinformatics-iii Once we have sequenced genomes in the previous course, we would like to compare them to determine how species have evolved and what makes them different. In the first half of the course, we will compare two short biological sequences, such as genes (i.e., short sequences of DNA) or proteins. We will encounter a powerful algorithmic tool called dynamic programming that will help us determine the number of mutations that have separated the two genes/proteins. In the second half of the course, we will "zoom out" to compare entire genomes, where we see large scale mutations called genome rearrangements, seismic events that have heaved around large blocks of DNA over millions of years of evolution. Looking at the human and mouse genomes, we will ask ourselves: just as earthquakes are much more likely to occur along fault lines, are there locations in our genome that are "fragile" and more susceptible to be broken as part of genome rearrangements? We will see how combinatorial algorithms will help us answer this question. Finally, you will learn how to apply popular bioinformatics software tools to solve problems in sequence alignment, including BLAST. life-sciences, computer-science, health-informatics, algorithms 2017-10-09
Molecular Evolution (Bioinformatics IV)

In the previous course in the Specialization, we learned how to compare genes, proteins, and genomes. One way we can use these methods is in order to construct a "Tree of Life" showing how a large collection of related organisms have evolved over time. In the first half of the course, we will...

Keywords: life-sciences, computer-science, health-informatics, algorithms

Molecular Evolution (Bioinformatics IV) http://tess.elixir-uk.org/materials/molecular-evolution-bioinformatics-iv In the previous course in the Specialization, we learned how to compare genes, proteins, and genomes. One way we can use these methods is in order to construct a "Tree of Life" showing how a large collection of related organisms have evolved over time. In the first half of the course, we will discuss approaches for evolutionary tree construction that have been the subject of some of the most cited scientific papers of all time, and show how they can resolve quandaries from finding the origin of a deadly virus to locating the birthplace of modern humans. In the second half of the course, we will shift gears and examine the old claim that birds evolved from dinosaurs. How can we prove this? In particular, we will examine a result that claimed that peptides harvested from a T. rex fossil closely matched peptides found in chickens. In particular, we will use methods from computational proteomics to ask how we could assess whether this result is valid or due to some form of contamination. Finally, you will learn how to apply popular bioinformatics software tools to reconstruct an evolutionary tree of ebolaviruses and identify the source of the recent Ebola epidemic that caused global headlines. life-sciences, computer-science, health-informatics, algorithms 2017-10-09
Metagenome data analysis Workshop, May 21-23, 2014

SciLifelab and AllBio are arranging a workshop on the analysis and interpretation of metagenomics data. In this workshop we will combine talks by experts in the field with practical tutorials guiding the participants in the analysis of representative datasets. Speakers: Lex Nederbragt, Oslo...

Keywords: Allbio, Assembly, Binning, Bioinformatics, Data analysis, Metagenomics

Metagenome data analysis Workshop, May 21-23, 2014 http://tess.elixir-uk.org/materials/metagenome-data-analysis-workshop-may-21-23-2014 SciLifelab and AllBio are arranging a workshop on the analysis and interpretation of metagenomics data. In this workshop we will combine talks by experts in the field with practical tutorials guiding the participants in the analysis of representative datasets. Speakers: Lex Nederbragt, Oslo University, Norway, Saskia Smits, Erasmus University Rotterdam, Netherlands, Joakim Larsson, Göteborg University, Sweden Paul Wilmes, University of Luxembourg, Luxembourg, Anders Andersson, SciLifeLab, Sweden Noan Le Bescot, UPMC (Tara expedition), France Tutorial supervisors: Ino de Bruijn, SciLifeLab, Sweden Johannes Alneberg, SciLifeLab, Sweden Luisa Hugerth, SciLifeLab, Sweden Johan Bengtsson, Göteborg University, Sweden Mikael Huss, SciLifeLab, Sweden Venue: Science for Life Laboratory, Tomtebodavägen 23A, 17165 Solna, Sweden Conference room Air/Fire, Gamma building Organisers: Mikael Huss (mikael.huss@scilifelab.se) Thomas Svensson (thomas.svensson@scilifelab.se) Allbio, Assembly, Binning, Bioinformatics, Data analysis, Metagenomics 2014-09-29 2017-10-09