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18 materials found

Keywords: computer-science  or RNA-Seq 


ADER19F - Analysis of Differential Expression with RNAseq (First course in 2019)

This introductory course covers practical aspects of the analysis of differential gene expression by RNAseq, from planning the gathering of sequence data to the generation of tables of differentially expressed gene lists and visualization of results. For this edition of the course, we will also...

Keywords: RNA-Seq, Genomics, RNA

Resource type: Documentation, Exercise, Handout, Scripts

ADER18S - Analysis of Differential Expression with RNAseq (Second course in 2018)

This introductory course covers practical aspects of the analysis of differential gene expression by RNAseq, from planning the gathering of sequence data to the generation of tables of differentially expressed gene lists and visualization of results. For this edition of the course, we will also...

Keywords: RNA-Seq, Genomics, RNA

Resource type: Documentation, Exercise, Handout, Scripts

ADER18F - Analysis of Differential Expression with RNAseq (First course in 2018)

This introductory course covers practical aspects of the analysis of differential gene expression by RNAseq, from planning the gathering of sequence data to the generation of tables of differentially expressed gene lists and visualization of results. We we will also cover some of the initial...

Keywords: RNA-Seq, Genomics, RNA

Resource type: Documentation, Exercise, Handout, Scripts

Single cell RNA-seq data analysis with R

Programme

Monday 27.5.2019

Introduction to single cell RNA-seq (Jules Gilet)
Quality control and data preprocessing (Åsa Björklund)
Normalisation (Heli Pessa)
Removal of confounding factors (Bishwa Ghimire)
Data integration (CCA, MNN, dataset alignment) (Ahmed Mahfouz)

Tuesday...

Scientific topics: RNA-Seq

Keywords: RNA-Seq, Single Cell technologies, scRNA-seq

Resource type: course materials

RNA-seq data analysis using Chipster

Materials from the ELIXIR tutorial “RNA-seq data analysis using Chipster”, Jan 31, 2017

Scientific topics: Transcriptomics, Genomics

Keywords: transcriptomics, RNA-Seq, eLearning, EeLP

Resource type: course materials, Training materials, Slides, Video

Single cell RNA-seq data analysis with R

This hands-on course introduces the participants to single cell RNA-seq data analysis concepts and popular R packages. It covers the preprocessing steps from raw sequence reads to expression matrix as well as clustering, cell type identification, differential expression analysis and pseudotime...

Scientific topics: RNA-Seq

Keywords: RNA-Seq, Single Cell technologies, scRNA-seq

Resource type: course materials

Single cell RNA-seq data analysis with Chipster

This course introduces single cell RNA-seq data analysis methods, tools and file formats. It covers the preprocessing steps of DropSeq data from raw reads to a digital gene expression matrix (DGE), and how to find sub-populations of cells using clustering with the...

Scientific topics: RNA-Seq

Keywords: RNA-Seq, Single Cell technologies, scRNA-seq

Resource type: course materials, Video

LXDE RStudio with support for bioconductor tools

An LXDE Rstudio with pre-loaded Bioconductor tools for rna-seq DE analysis & visualisation

Keywords: RNA-Seq, IFR, Institute of Food Research, Quadram Institute

IFR Dockerised version of the EBI's Introduction to RNA-seq course

This is basically the EBI RNA-seq course bundled up in a Docker container using LXDE via TightVNC to provide a graphical environment. The material has been repackaged to use an Ipython Notebook as the learning environment which can be annotated by the learner.

Keywords: RNA-Seq, IFR, Institute of Food Research, Quadram Institute

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

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

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 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

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

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

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

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

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