15 materials found
Content provider:
Coursera
Research Data Management and Sharing
This course will provide learners with an introduction to research data management and sharing. After completing this course, learners will understand the diversity of data and their management needs across the research data lifecycle, be able to identify the components of good data management...
Scientific topics: Data management
Resource type: e-learning
Research Data Management and Sharing
https://www.coursera.org/learn/data-management
https://tess.elixir-europe.org/materials/research-data-management-and-sharing
This course will provide learners with an introduction to research data management and sharing. After completing this course, learners will understand the diversity of data and their management needs across the research data lifecycle, be able to identify the components of good data management plans, and be familiar with best practices for working with data including the organization, documentation, and storage and security of data. Learners will also understand the impetus and importance of archiving and sharing data as well as how to assess the trustworthiness of repositories.
Today, an increasing number of funding agencies, journals, and other stakeholders are requiring data producers to share, archive, and plan for the management of their data. In order to respond to these requirements, researchers and information professionals will need the data management and curation knowledge and skills that support the long-term preservation, access, and reuse of data. Effectively managing data can also help optimize research outputs, increase the impact of research, and support open scientific inquiry. After completing this course, learners will be better equipped to manage data throughout the entire research data lifecycle from project planning to the end of the project when data ideally are shared and made available within a trustworthy repository.
This course was developed by the Curating Research Assets and Data Using Lifecycle Education (CRADLE) Project in collaboration with EDINA at the University of Edinburgh.
This course was made possible in part by the Institute of Museum and Library Services under award #RE-06-13-0052-13. The views, findings, conclusions or recommendations expressed in this Research Data Management and Sharing MOOC do not necessarily represent those of the Institute of Museum and Library Services.
Data management
Life Science Researchers
Plant Bioinformatics
The past 15 years have been exciting ones in plant biology. Hundreds of plant genomes have been sequenced, RNA-seq has enabled transcriptome-wide expression profiling, and a proliferation of "-seq"-based methods has permitted protein-protein and protein-DNA interactions to be determined cheaply...
Keywords: life-sciences, physical-science-and-engineering, health-informatics, research-methods
Plant Bioinformatics
https://www.coursera.org/learn/plant-bioinformatics
https://tess.elixir-europe.org/materials/plant-bioinformatics
The past 15 years have been exciting ones in plant biology. Hundreds of plant genomes have been sequenced, RNA-seq has enabled transcriptome-wide expression profiling, and a proliferation of "-seq"-based methods has permitted protein-protein and protein-DNA interactions to be determined cheaply and in a high-throughput manner. These data sets in turn allow us to generate hypotheses at the click of a mouse. For instance, knowing where and when a gene is expressed can help us narrow down the phenotypic search space when we don't see a phenotype in a gene mutant under "normal" growth conditions. Coexpression analyses and association networks can provide high-quality candidate genes involved in a biological process of interest. Using Gene Ontology enrichment analysis and pathway visualization tools can help us make sense of our own 'omics experiments and answer the question "what processes/pathways are being perturbed in our mutant of interest?"
Structure: each of the 6 week hands-on modules consists of a ~2 minute intro, a ~20 minute theory mini-lecture, a 1.5 hour hands-on lab, an optional ~20 minute lab discussion if experiencing difficulties with lab, and a ~2 minute summary.
Tools covered:
Module 1: GENOMIC DBs / PRECOMPUTED GENE TREES / PROTEIN TOOLS. Araport, TAIR, Gramene, EnsemblPlants Compara, PLAZA; SUBA4 and Cell eFP Browser, 1001 Genomes Browser
Module 2: EXPRESSION TOOLS. eFP Browser / eFP-Seq Browser, Araport, Genevestigator, TravaDB, NCBI Genome Data Viewer for exploring RNA-seq data for many plant species other than Arabidopsis, MPSS database for small RNAs
Module 3: COEXPRESSION TOOLS. ATTED II, Expression Angler, AraNet, AtCAST2
Module 4: PROMOTER ANALYSIS. Cistome, Athena, ePlant
Module 5: GO ENRICHMENT ANALYSIS AND PATHWAY VIZUALIZATION. AgriGO, AmiGO, Classification SuperViewer, TAIR, g:profiler, AraCyc, MapMan (optional: Plant Reactome)
Module 6: NETWORK EXPLORATION. Arabidopsis Interactions Viewer 2, ePlant, TF2Network, Virtual Plant, GeneMANIA
[Material updated in June 2019]
life-sciences, physical-science-and-engineering, health-informatics, research-methods
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
https://www.coursera.org/learn/data-genes-medicine
https://tess.elixir-europe.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: Life Sciences on Your Computer
What makes bioinformatics education exciting is that people of a variety of education levels can get started quickly, with just a computer and internet access.
Keywords: life-sciences, bioinformatics
Bioinformatics: Life Sciences on Your Computer
https://www.coursera.org/learn/bioinform
https://tess.elixir-europe.org/materials/bioinformatics-life-sciences-on-your-computer
What makes bioinformatics education exciting is that people of a variety of education levels can get started quickly, with just a computer and internet access.
life-sciences, bioinformatics
2016-06-29
Bioinformatic Methods II
Large-scale biology projects such as the sequencing of the human genome and gene expression surveys using RNA-seq, microarrays and other technologies have created a wealth of data for biologists. However, the challenge facing scientists is analyzing and even accessing these data to extract useful...
Keywords: life-sciences, bioinformatics
Bioinformatic Methods II
https://www.coursera.org/learn/bioinformatics-methods-2
https://tess.elixir-europe.org/materials/bioinformatic-methods-ii
Large-scale biology projects such as the sequencing of the human genome and gene expression surveys using RNA-seq, microarrays and other technologies have created a wealth of data for biologists. However, the challenge facing scientists is analyzing and even accessing these data to extract useful information pertaining to the system being studied. This course focuses on employing existing bioinformatic resources – mainly web-based programs and databases – to access the wealth of data to answer questions relevant to the average biologist, and is highly hands-on.
Topics covered include multiple sequence alignments, phylogenetics, gene expression data analysis, and protein interaction networks, in two separate parts.
The first part, Bioinformatic Methods I, dealt with databases, Blast, multiple sequence alignments, phylogenetics, selection analysis and metagenomics.
This, the second part, Bioinformatic Methods II, will cover motif searching, protein-protein interactions, structural bioinformatics, gene expression data analysis, and cis-element predictions.
This pair of courses is useful to any student considering graduate school in the biological sciences, as well as students considering molecular medicine.
These courses are based on one taught at the University of Toronto to upper-level undergraduates who have some understanding of basic molecular biology. If you're not familiar with this, something like https://learn.saylor.org/course/bio101 might be helpful. No programming is required for this course although some command line work (though within a web browser) occurs in the 5th module.
life-sciences, bioinformatics
2017-05-04
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
https://www.coursera.org/learn/bioinformatics-project
https://tess.elixir-europe.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
Bioinformatics: Introduction and Methods 生物信息学: 导论与方法
A big welcome to “Bioinformatics: Introduction and Methods” from Peking University! In this MOOC you will become familiar with the concepts and computational methods in the exciting interdisciplinary field of bioinformatics and their applications in biology, the knowledge and skills in...
Keywords: life-sciences, health-informatics
Bioinformatics: Introduction and Methods 生物信息学: 导论与方法
https://www.coursera.org/learn/bioinformatics-pku
https://tess.elixir-europe.org/materials/bioinformatics-introduction-and-methods
A big welcome to “Bioinformatics: Introduction and Methods” from Peking University! In this MOOC you will become familiar with the concepts and computational methods in the exciting interdisciplinary field of bioinformatics and their applications in biology, the knowledge and skills in bioinformatics you acquired will help you in your future study and research.
Course materials are available under the CC BY-NC-SA License.
life-sciences, health-informatics
2017-10-09
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)
https://www.coursera.org/learn/dna-analysis
https://tess.elixir-europe.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)
https://www.coursera.org/learn/dna-mutations
https://tess.elixir-europe.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)
https://www.coursera.org/learn/genome-sequencing
https://tess.elixir-europe.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
Bioinformatic Methods I
Large-scale biology projects such as the sequencing of the human genome and gene expression surveys using RNA-seq, microarrays and other technologies have created a wealth of data for biologists. However, the challenge facing scientists is analyzing and even accessing these data to extract useful...
Keywords: life-sciences, bioinformatics
Bioinformatic Methods I
https://www.coursera.org/learn/bioinformatics-methods-1
https://tess.elixir-europe.org/materials/bioinformatic-methods-i
Large-scale biology projects such as the sequencing of the human genome and gene expression surveys using RNA-seq, microarrays and other technologies have created a wealth of data for biologists. However, the challenge facing scientists is analyzing and even accessing these data to extract useful information pertaining to the system being studied. This course focuses on employing existing bioinformatic resources – mainly web-based programs and databases – to access the wealth of data to answer questions relevant to the average biologist, and is highly hands-on.
Topics covered include multiple sequence alignments, phylogenetics, gene expression data analysis, and protein interaction networks, in two separate parts.
The first part, Bioinformatic Methods I (this one), deals with databases, Blast, multiple sequence alignments, phylogenetics, selection analysis and metagenomics.
This, the second part, Bioinformatic Methods II, covers motif searching, protein-protein interactions, structural bioinformatics, gene expression data analysis, and cis-element predictions.
This pair of courses is useful to any student considering graduate school in the biological sciences, as well as students considering molecular medicine. Both provide an overview of the many different bioinformatic tools that are out there.
These courses are based on one taught at the University of Toronto to upper-level undergraduates who have some understanding of basic molecular biology. If you're not familiar with this, something like https://learn.saylor.org/course/bio101 might be helpful. No programming is required for this course.
life-sciences, bioinformatics
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
https://www.coursera.org/learn/bioinformatics
https://tess.elixir-europe.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)
https://www.coursera.org/learn/genomic-data
https://tess.elixir-europe.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)
https://www.coursera.org/learn/comparing-genomes
https://tess.elixir-europe.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)
https://www.coursera.org/learn/molecular-evolution
https://tess.elixir-europe.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
