A network of online databases provides essential information for researchers in molecular biology. These databases give researchers quick access to current information on genes, proteins, model organisms and publications. This module introduces students to the basic structure of databases and the process for submitting information to databases. Students use several of these databases to collect information on a yeast gene of their choice. This module is part on a semester-long introductory lab course, Investigations in Molecular Biology, at Boston College

This quick tour provides a brief introduction to Open Targets, an industry-academia collaboration, and the informatics tools it maintains.

By the end of the course you will be able to:
Explain the vision of the Open Targets consortium
Describe how the Open Targets Platform can help you prioritise targets associated with particular diseases or phenotypes, explore diseases associated with specific targets, and how these are linked to known drugs
Describe how the Open Targets Genetics Portal allows you to investigate evidence on genes, genetic variants, and GWAS studies, and identify prioritised candidate causal variants at trait-associated loci
Determine where to find out more about Open Targets and its informatics ecosystem

As part of EMBL-EBI’s celebration of Open Access week, this special one-off webinar will give you insight into the benefits of open access data, alongside guidance and tips on managing your data.

Featuring four members of EMBL-EBI’s resource teams (from BioSamples, ENA, IntAct/Complex Portal & Europe PMC), you’ll get an overview of Open and FAIR data, insights into submission processes and support, the importance of open literature and some hints, tips and tricks for ensuring your data can easily be made available to others. You’ll also get an opportunity to ask live questions to all four of our speakers, and find out where you can learn more.

Who is this course for?
This webinar is for anyone who is interested in learning more about open access data and how they can make their data available to others.

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:

"Phosphorus is essential for life to function. It is a critical component of the energy metabolism molecules, genetic materials, and cell structures of all life. Phosphorus only enters natural ecosystems through the slow weathering of stone. Then microbes help maintain and regulate phosphorus by cycling it between its organic and inorganic forms. Understanding microbial phosphorus cycling is critical to many fields of study, like ecology and agriculture. However, researchers lack a comprehensive understanding of the phosphorus cycling genes microbes use, but the recently developed curated phosphorus cycling database (PCycDB) could help close that gap. PCycDB covers 10 phosphorus metabolic processes and 139 gene families, including several that have been missed elsewhere. Testing PCycDB with simulated datasets revealed high annotation accuracy, positive predictive value, sensitivity, specificity, and negative predictive value..."

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

PDBeChem allows you to search for chemical components (ligands, small molecules and monomers) that appear in PDB entries, and discover which protein structures bind a particular ligand. This course will show you how to use PDBeChem and what you can do with it.

By the end of the course you will be able to:
Evaluate what PDBeChem is and what is can be used for
Launch PDBeChem and use different ways to search for small molecules
Use the SMILES molecular editor to draw parts of your chemical structure to explore related small molecules on PDB
Evaluate your search results from the chemical component summary page

This course will show you how to explore the structural information in a PDB entry using PDBe - EMBL-EBI's resource for the collection, organisation and dissemination of data on biological macromolecular structures.

By the end of the course you will be able to:
Navigate and extract the information from a particular PDB entry using the PDBe
Download data in an appropriate format

PDBeFold can be used to identify structural homologues in the PDB. PDBe's Secondary Structure Matching service (SSM) allows you to interactively compare, align and superimpose protein structures in 3D. This course will show you how to use PDBeFold and what you can do with it.

By the end of the course you will be able to:
Evaluate what PDBeFold is and what it can do
Launch PDBeFold and search for 3D structures that are similar to your protein of interest
Interpret structural similarity of your results based on Q-scores
View and analyse structures from your returned results

This webinar will introduce the concepts of a graph database and describe how we used a graph-approach for integrating the structural data of the Protein Data Bank.

The presented PDBe knowledge graph combines annotations provided by PDBe-KB partners with the core PDB archive. Each PDB entry can be thought of as a tree, with the root being the PDB entry, connected to chains and entities, which are then connected to residues. Each of the more than 150 million PDB residues, the leaves of the tree representation, are linked to annotations that make it easier to answer scientific questions such as: Can we find small molecules which have the same scaffold, and interact with the same binding sites? Or, can we find macromolecular interaction interfaces which have highly conserved residues, and are annotated to be structurally flexible?

The webinar will help the audience answer these, and other similar scientific questions and will provide a solid foundation for taking advantage of the PDBe knowledge graph and use it as an invaluable research tool.

Who is this course for?
This webinar is best suited for bioinformaticians with some experience in analysing structural data from the PDB using programmatic and/or high-throughput data access.

Our understanding of biological function can be advanced by the knowledge of biomolecular structures. Yet, it requires compiling data from individual entries in the Protein Data Bank (PDB), where each entry may represent only a segment of a complete system and may lack biological context.

The Protein Data Bank in Europe - Knowledge Base (PDBe-KB) is a community-driven, integrated resource whose goal is to place macromolecular structures in their biological context and thus promote fundamental and translational research. PDBe-KB combines residue-level structural data with functional annotations and sequence information in a highly interconnected, complex graph database, on the scale of 1 billion nodes and 4 billion edges. PDBe-KB infrastructure allows compilation of data from multiple PDB entries into a context-specific view of the data that is made available via an extensive set of API endpoints and modular web components.

This webinar presents an updated version of the first, protein-specific aggregated view that summarises all the data from the PDB and associated functional annotations from PDBe-KB partner resources in a user-friendly and accessible web-based view, providing a novel way to navigate structural data.

Who is this course for?
This webinar is aimed at individuals working on protein biology. No prior knowledge of bioinformatics is required, but an undergraduate level understanding of biology would be useful.

PDBePISA allows you to explore macromolecular (protein, DNA/RNA and ligand) interfaces and predict the quaternary structure of your protein. This course will show you how to use PDBePISA and what you can do with it.

By the end of the course you will be able to:
Evaluate what PDBePISA is and what it can do
Launch PDBePISA and search for interfaces related to your structure of interest
Analyse and interpret probable assemblies of your structure of interest

This course will show you how to search the Protein Data Bank in Europe (PDBe) - EMBL-EBI's resource for the collection, organisation and dissemination of data on biological macromolecular structures.

By the end of the course you will be able to:
Search the PDB archive using PDBe’s search and navigation system
Know how to refine and save your search query
Sort and download search results
Evaluate the best representative structure from the search results

This webinar will show you how to access some of the software packages created at PDBe, used to generate the data displayed on our website. This will include a walkthrough of our github repositories, highlighting the types of packages available, how to access them and some examples of how they can be used.

This webinar is part of a 6-part PDBe API webinar series, introducing different levels of programmatic access at PDBe.The series will range from basic data retrieval and search using the PDBe API to more advanced features, including access and reuse of PDBe data visualisation components.

Who is this course for?
This webinar is open to anyone who is interested in learning about the programmatic access of PDBe.

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

Discover some of the software packages created at PDBe
Explore PDBe's GitHub repositories to discover types of packages available and their application

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:

"Uncovering the links between microbiome profiles and host conditions is critical to understanding the roles microbes play in human health. However, analysis of microbiome profiles with traditional testing frameworks is made difficult by the high-dimensionality and sparsity of the data. Often this is addressed by incorporating information from closely related microbes. This phylogenetic information is added under the assumption that operational taxonomic units (OTUs) from related taxa will tend to behave similarly, but the complexity of microbe interactions can make this assumption invalid. The recently developed tool, Phylogeny-guided microbiome OTU-Specific association Test (POST), addresses this issue. POST determines how much, if any, information to borrow from neighboring OTUs based on phylogenetic distance and outcome-OTU association. This tool is a local collapsing test built under the kernel machine framework to accommodate complex OTU effects..."

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

This quick tour provides a short introduction to EMBL-EBI's repository for mass spectrometry-based proteomics data, PRIDE.

By the end of the course you will be able to:
Describe what PRIDE is and how data submission and data visualisation work
Search and download datasets from PRIDE Archive
Know where to find out more about PRIDE

This webinar will provide an introduction to proteomics approaches and the PRIDE database, followed by more detailed examples of PRIDE public data re-use, including “big data” approaches.

Who is this course for?
This webinar is suitable for biologists and bioinformaticians with an interest in proteomics, as well as proteomics bioinformaticians or scientific programmers expert in proteomics.

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

Recall the role of the PRIDE database
Describe examples of PRIDE public data re-use

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:

"Most of the microorganisms in the human microbiome are commensals, contributing essential functions to their hosts, while in contrast, pathogenic microorganisms cause disease by invading, colonizing, and damaging their hosts. Virulence factors, including bacterial toxins, contribute to their pathogenicity, and antimicrobial resistance (AMR) genes allow them to evade otherwise curative treatments. A new tool seeks to improve our understanding of virulence factors and antimicrobial resistance genes. PathoFact is a tool for the contextualized prediction of virulence factors, bacterial toxins, and AMR genes. In an evaluation with simulated metagenomic datasets, PathoFact outperformed two other existing analysis workflows, and in a test with three case-control datasets representing infection and chronic disease, PathoFact identified virulence factors that were either secreted or not secreted..."

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:

"Microbiome sequencing data are very complex. In order to simplify analyses, researchers often perform unsupervised clustering to identify naturally occurring clusters and then investigate the clusters’ associations with various characteristics of interest. However, clustering performance and related conclusions can vary depending on the algorithm or beta diversity metric used. To improve microbiome analysis methods, a new study tested the performance of several metrics on four datasets with well-separated groups and a clinical dataset with less-clear group separation. None of the metrics was universally superior, but certain metrics underperformed under certain conditions. For example, the Bray-Curtis metric performed poorly in a dataset with rare high-abundance OTUs (groups of related bacteria), while the unweighted UniFrac metric performed poorly in a dataset with prevalent low-abundance OTUs..."

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

Detection of conserved evolutionary units by profile hidden Markov Models (HMM): discover how different types of entries are created in the Pfam database.

By the end of the course you will be able to:
List the six Pfam entry categories
Describe the concept of Hidden Markov Models
Outline the process of creating a Pfam model
List the features that define a clan of entries

This quick tour provides a brief introduction to the protein families database Pfam, based at EMBL-EBI. Each protein family is represented by multiple sequence alignments and a profile hidden Markov model, which is used to create a probabilistic model that represents the family.

By the end of the course you will be able to:
Provide a basic description of what Pfam provides
Search and discover protein-related information with Pfam
Describe where to find out more about Pfam

This online course provides an introduction to tandem repeats (TR) in proteins. This course offers a description of TRs and their importance in biology. Furthermore, we describe one method of TR detection which is based on sequence homology, as well as discussing the challenges in characterising them.

By the end of the course you will be able to:
list features of tandem repeats
describe the importance of repeats in biology
list the differences between repeats and domains
describe the process used to create repeat entries in Pfam