Explore gene expression profiles across various species and conditions at single cell resolution.

Single Cell Expression Atlas (SCEA) is an open resource committed to making the information from single cell sequencing datasets readily accessible to the research community and beyond. At SCEA we accumulate, curate and re-analyse available raw single-cell sequencing data from multiple species and across experimental conditions to make them cross-comparable and we present the analysis results in a user-friendly interface for public consumption. This allows researchers to gain a quick insight into the expression pattern of their gene of interest at the level of individual cells across different species from human to Saccharomyces.

At SCEA we aim to keep pace with the rapidly growing single cell transcriptomics research and to make the knowledge thus obtained truly open and widely available so that it can serve as basis for further studies.

The webinar will provide a quick overview of:

Different single cell sequencing methods
Various ways of searching SCEA
How to filter and interpret the analysis results on the experiment page
Additional features and resources available on the experiment page
Who is this course for?
This webinar is for individuals who wish to learn more about Single Cell Expression Atlas. No prior knowledge of bioinformatics is required, but an undergraduate level knowledge of biology would be useful.

This webinar will provide an overview of the MGnify API. MGnify offers an automated pipeline for the analysis and archiving of microbiome data to help determine the taxonomic diversity and functional & metabolic potential of environmental samples. Users can submit their own data for analysis or freely browse all of the analysed public datasets held within the repository.

The webinar will include how to utilise the API using a browser, where to find documentation, how to use filtering and pagination, available output formats, and scripting examples in Python.

Who is this course for?
This webinar is aimed at individuals who would like to learn more about using the MGnify API, however some familiarity with MGnify and programmatic access methods is recommended.

Outcomes
By the end of the webinar you will be able to:

Access the MGnify API
Find documentation for the MGnify API

This quick tour provides a brief introduction to MGnify - a free resource for analysis, visualisation and discovery of microbiome (metagenomic, metatranscriptomic, amplicon and assembly) datasets.

By the end of the course you will be able to:
Identify what data the MGnify resource provides
Employ MGnify to search for and interpret microbiome data analysis
Describe the various data-types and analysis results available within MGnify

This tutorial provides you with a step-by-step guide for submitting metagenomics data to the European Nucleotide Archive (ENA) in order for it to be analysed by the MGnify resource.

By the end of the course you will be able to:
Submit your metagenomics data to the ENA
Describe why MGnify requires you to archive your data in the ENA
Evaluate why it is important to provide accurate contextual metadata with your metagenomic sequence data

This webinar will describe how MGnify can assist you in your microbiome research by providing a broad overview of resources available in MGnify.

MGnify offers an automated pipeline for the analysis and archiving of microbiome data to help determine the taxonomic diversity and functional & metabolic potential of environmental samples. Users can submit their own data for analysis or freely browse all of the analysed public datasets held within the repository. During this webinar, we will cover what types of microbiome data are included, improvements we have made to our analysis pipeline, as well as ways to search for data.

Who is this course for?
This webinar is aimed at individuals who would like to learn more about using MGnify. No prior knowledge of bioinformatics is required, but undergraduate level knowledge of biology would be useful.

Outcomes
By the end of the webinar you will be able to:

Describe the types of data included in MGnify
Explore the MGnify resources
Search MGnify

What is machine learning and how can it be applied in drug discovery to identify or prioritise new drug targets? Find out more with this introduction to machine learning applications in drug discovery using WEKA.

By the end of the course you will be able to:
Identify common types of ML algorithms that can be applied to tackle drug discovery challenges
Illustrate some applications of machine learning and other artificial intelligence frameworks in drug discovery
Get started with WEKA, an easy-to-use open-source machine learning software
Use standalone web resources to explore the WEKA results and see if the identified genes could be potential targets in drug discovery

This resource is a video abstract of a research paper created by Research Square on behalf of its authors. It provides a synopsis that's easy to understand, and can be used to introduce the topics it covers to students, researchers, and the general public. The video's transcript is also provided in full, with a portion provided below for preview:

"The human gut is home to a diverse community of microbes. Variations in the makeup of this community between individuals have been linked to diseases such as inflammatory bowel disease, diabetes, and cancer. Efforts to understand these differences have revealed three community types, or enterotypes, in humans, each representing the dominance of a single microbe. But because microbes co-mingle with many partners, studying the gut microbiome solely in terms of enterotypes misses on the highly nuanced nature of microbial interactions. Researchers recently addressed that shortcoming using a machine learning technique called latent Dirichlet allocation, or LDA. Their goal was to determine whether and how recurring microbial partnerships, or assemblages, are linked to the three enterotypes. Using gut metagenomic data gathered from 861 healthy adults across 12 countries LDA revealed three assemblages corresponding to each enterotype as well as a fourth wild-card assemblage that could be found in any gut..."

The rest of the transcript, along with a link to the research itself, is available on the resource itself.

This course provides an intensive introduction to artificial intelligence and its applications to problems of medical diagnosis, therapy selection, and monitoring and learning from databases. It meets with lectures and recitations of 6.034 Artificial Intelligence, whose material is supplemented by additional medical-specific readings in a weekly discussion session. Students are responsible for completing all homework assignments in 6.034 and for additional problems and/or papers.

This resource is a video abstract of a research paper created by Research Square on behalf of its authors. It provides a synopsis that's easy to understand, and can be used to introduce the topics it covers to students, researchers, and the general public. The video's transcript is also provided in full, with a portion provided below for preview:

"Metagenomics is a powerful technique for studying complex microbial communities. The key computational step in this method is clustering genomic sequences from mixed samples into potential microbial genomes, but accurately classifying sequences from complex metagenomes remains challenging. Some tools depend on k-mer frequency and coverage, but such methods struggle to distinguish between similar genomes. Methods that address the similar genomes problem, like ones that rely on single-copy marker genes, in turn struggle with complex datasets. The newly developed MetaDecoder balances these challenges by using both types of methods broken into two steps. First, MetaDecoder simplifies the dataset by generating preliminary groups of sequences with the Dirichlet Process Gaussian Mixture Model (DPGMM). Then, these preliminary clusters are clustered further with a k-mer frequency probabilistic model and a modified Gaussian Mixture Model of single-copy marker gene coverage..."

The rest of the transcript, along with a link to the research itself, is available on the resource itself.

This quick tour provides a brief introduction to EMBL-EBI's database for Metabolomics experiments and derived information, MetaboLights.

By the end of the course you will be able to:
Describe what MetaboLights is
Navigate the MetaboLights website
Outline what MetaboLights can be used for

This course provides a basic introduction into the rapidly emerging field of metabolomics and its importance and applications.

By the end of the course you will be able to:
Comprehend the purpose and importance of the field of metabolomics
Describe some principles of metabolomic study design
Evaluate advantages and limitations of some analytical techniques used in metabolomics studies
Discuss some of the modern-day applications of metabolomics
Access metabolomics resources at the EMBL-EBI

Metagenomics, the genomic analysis of microbial communities from samples like water and soil, involves high-throughput sequencing of the microbial DNA, collecting, archiving and re-sharing the genomic data for taxonomic and functional analysis.

By the end of the course you will be able to:
Conduct appropriate quality control and decontamination of metagenomic data and run simple assembly pipelines on short-read data
Utilise public datasets and resources to identify relevant data for analysis
Apply appropriate tools in the analysis of metagenomic data
Submit metagenomics data to online repositories for sharing and future analysis
Apply relevant knowledge in strain resolution and comparative metagenomic analysis to their own research

This webinar will focus on the use of the online molecular visualisation tool, Mol* at PDBe. We will show basic usage, observation of electron density maps and EM volumes, interrogation of ligand structures, and the visualisation of sequence annotations. We will also highlight the advantages to using Mol* in comparison to other tools and show the different ways the Mol* viewer is presented and accessed on PDBe's pages.

Who is this course for?
This webinar is for individuals with an interest in visualising molecular structures. No prior knowledge of bioinformatics is required, but undergraduate level knowledge of protein biology would be useful.

Outcomes
By the end of the webinar you will be able to:

Visualise molecular structures using Mol*
Identify advantages of using Mol* over other visualisation tools

Small molecules are chemicals that can interact with proteins to affect their functions. Learn about the structure and biological functions of various small molecules like sugar and caffeine. Also featured on the HHMI DVD, Scanning Life's Matrix: Genes, Proteins, and Small Molecules. Available free from HHMI.

Ensembl not only provides up-to-date annotation of the mouse reference genome assembly, but also allows you to browse the genomes of 16 other mouse strains.

This webinar will consist of a short presentation that will describe the origin of the data, followed by a demonstration on how to use the Ensembl web browser to browse and compare data between the different strains, looking specifically at homology and variation data.

Who is this course for?
This webinar is for scientists with an interest in mouse strains, genomes and genotypes. No prior knowledge of bioinformatics is required, but an undergraduate level knowledge of biology would be useful.

The webinar will demonstrate how to perform multiomics comparative pathway analyses using ReactomeGSA. This will cover the web interface and R package. We will demonstrate how to use ReactomeGSA to quickly derive novel biological knowledge by combining multiple datasets. In addition, we will also cover the function of ReactomeGSA for investigating scRNA-seq cell clusters at the pathway level.

Who is this course for?
Bioinformaticians and wet-lab scientists who are interested in analysing their proteomics/transcriptomics/scRNA-seq data.

Outcomes
By the end of the webinar you will be able to:

Identify the application of ReactomeGSA
Apply ReactomeGSA to perform multiomics comparative pathway analyses

This course provides an introduction to the theory and concepts of network analysis. It explores some of the features of protein-protein interaction networks and their implications for biology. Finally, the course discusses the tools and strategies that can be used to build and analyse biological networks.

By the end of the course you will be able to:
List some types of biological networks
Describe topological features of networks
Compare different sources of protein-protein interaction data
Discuss the features of protein-protein interaction networks and their biological implications
Identify tools used for network analysis and their advantages and disadvantages
Evaluate different network analysis strategies and know when to use them

The course focuses on the problem of supervised learning within the framework of Statistical Learning Theory. It starts with a review of classical statistical techniques, including Regularization Theory in RKHS for multivariate function approximation from sparse data. Next, VC theory is discussed in detail and used to justify classification and regression techniques such as Regularization Networks and Support Vector Machines. Selected topics such as boosting, feature selection and multiclass classification will complete the theory part of the course. During the course we will examine applications of several learning techniques in areas such as computer vision, computer graphics, database search and time-series analysis and prediction. We will briefly discuss implications of learning theories for how the brain may learn from experience, focusing on the neurobiology of object recognition. We plan to emphasize hands-on applications and exercises, paralleling the rapidly increasing practical uses of the techniques described in the subject.

This resource is a video abstract of a research paper created by Research Square on behalf of its authors. It provides a synopsis that's easy to understand, and can be used to introduce the topics it covers to students, researchers, and the general public. The video's transcript is also provided in full, with a portion provided below for preview:

"Lignocellulose is a major component of the woody portion of plants. The polymers it houses, like xylan and cellulose, could be used as biofuels or in other plant-based materials. The breakdown of lignocellulose requires specialized carbohydrate-active enzymes (CAZymes), but targeted discovery of novel CAZymes is difficult due, in part, to their structural diversity. In a recent paper, researchers have proposed a new method to speed up this process. They combined phenotype-based selective pressure with functional profiling to screen unknown enzymes. Feeding cattle a forage-based diet applies selective pressure on their rumen microbiota for microbes with specialized fiber-degrading enzymes. Three glycoside hydrolase families had increased abundance in feed-efficient cattle compared to their inefficient counterparts on this diet. Screening some members of those families against a database of uncharacterized enzymes led to the identification of putative xylanases and endoglucanases..."

The rest of the transcript, along with a link to the research itself, is available on the resource itself.

This resource is a video abstract of a research paper created by Research Square on behalf of its authors. It provides a synopsis that's easy to understand, and can be used to introduce the topics it covers to students, researchers, and the general public. The video's transcript is also provided in full, with a portion provided below for preview:

"Biopesticides are widely available insect control applications derived from plant, animal, or bacterial proteins. They do not leave harmful residues and are more target-specific than chemical pesticides, but long-term use has led to resistance. Insecticidal protein genes (IPGs) are frequently found encoded in the genomes of arthropod pathogens, especially in the large plasmids found in soil bacteria. However, there are often several similar IPGs found on the same plasmid, which fragments their assembly. Further complicating the search, existing prediction tools analyze one contig at a time, and many IPGs are spread across multiple contigs, but the structure of the genome assembly graph can be used to combine multiple contigs. A new tool, ORFograph, uses this ‘graph-aware’ technique to predict IPGs. Benchmarking ORFograph on genomic and metagenomic datasets yielded both known IPGs that were “hidden” in assembly graphs and potential novel IPGs that had evaded existing tools..."

The rest of the transcript, along with a link to the research itself, is available on the resource itself.