This course will provide an introduction to cloud computing and some practical experience in building, deploying and running applications in cloud platforms - OpenStack, Google, Amazon and Azure.

By the end of the course you will be able to:
Discuss user-centric hybrid cloud strategy
Describe architectural considerations around porting applications to a cloud
Deploy across different clouds

This quick tour provides a brief introduction to Rfam, the EBI's database of RNA families.

By the end of the course you will be able to:
Outline what Rfam provides
Search RNA-related information with Rfam
Describe where to find out more about Rfam

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:

"Disruption of the gut microbiome composition is associated with many metabolic, inflammatory, and infectious diseases in humans. Accurately profiling microbiomes at the strain level is important for gaining a clear understanding of the microbiome’s role in disease. However, the existing tools can’t reliably classify microbes at strain resolution, making it difficult to detect composition changes. A new bioinformatic tool, SameStr, addresses this difficulty by identifying strains that are shared between metagenomes according to the similarity of single-nucleotide variants (SNVs) in certain species-specific marker genes. In validation studies, SameStr was more sensitive than other tools in identifying shared strains among mock populations, while remaining robust against false-positives..."

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

Literature search is an essential and routine task in scientific research. Following the release of the new Europe PMC website, this webinar will show you the new full-text searching and browsing features built to optimise the literature research. Learn how to find the most recent, most relevant and most cited articles in a topic of interest. Also, learn how to find and explore the enhanced content available in Europe PMC.

Who is this course for?
This webinar is aimed at life science scientists who are interested in optimising their literature search skills. No prior knowledge of bioinformatics is required.

This webinar will introduce the PDBe search API, a powerful Solr-based query system that underlies the PDBe search pages. The breadth of search options will be highlighted, along with specific case studies that demonstrate what is possible through the search API.

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:

Describe the basic functionality of PDBe search API
Identify the applications of using the search API via specific case studies

BLAST and multiple sequence alignments are essential methods commonly used to find and compare protein or nucleotide sequences. In this webinar, we will demonstrate how you can access and run these sequence analysis tools in UniProt, a comprehensive resource of protein sequence and function. We will show you how to navigate the results and how you can exploit UniProt protein annotation to expand further the analysis of your results.

Who is this course for?
This webinar is aimed at students or early stage researchers beginning to use bioinformatics resources in their studies/research who wish to learn how to run a BLAST or a sequence alignment in UniProt. 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:
• Use Align and BLAST sequence analysis tools
• Retrieve and analyse the results

In this webinar, we will look at a Galaxy interface for single cell analysis. Specifically, we will run Scanpy (which would otherwise require Python programming skills) to analyse a Drop-seq dataset located in EMBL-EBI's Single Cell Expression Atlas.

Who is this course for?
This webinar is aimed at individuals interested in single cell RNA-seq analysis. No prior knowledge of bioinformatics is required, but an undergraduate level understanding of biology would be useful.

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:

"Freshwater salinization, which can be caused by saltwater intrusion, urbanization, and climate change, is becoming an extensive global environmental problem. Microeukaryotic plankton are key components of aquatic ecosystems and play significant ecological roles. However, few studies have investigated the influences of small salinity shifts on microeukaryotic plankton community assembly and co-occurrence networks in inland freshwaters. In a recent study, researchers used high-throughput sequencing to analyze microeukaryotic plankton communities in a subtropical urban reservoir. They found that increasing salinity altered the community composition and led to a significant decrease in plankton diversity. The salinity changes influenced the microeukaryotic plankton community assembly primarily by regulating the deterministic-stochastic balance. The core plankton sub-network had greater robustness at low salinity levels, while the satellite sub-networks had greater robustness at medium/high salinity levels..."

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

Focuses on the problem of supervised learning from the perspective of modern statistical learning theory starting with the theory of multivariate function approximation from sparse data. Develops basic tools such as Regularization including Support Vector Machines for regression and classification. Derives generalization bounds using both stability and VC theory. Discusses topics such as boosting and feature selection. Examines applications in several areas: computer vision, computer graphics, text classification and bioinformatics. Final projects and hands-on applications and exercises are planned, paralleling the rapidly increasing practical uses of the techniques described in the subject.

This course is for upper-level graduate students who are planning careers in computational neuroscience. This course focuses on the problem of supervised learning from the perspective of modern statistical learning theory starting with the theory of multivariate function approximation from sparse data. It develops basic tools such as Regularization including Support Vector Machines for regression and classification. It derives generalization bounds using both stability and VC theory. It also discusses topics such as boosting and feature selection and examines applications in several areas: Computer Vision, Computer Graphics, Text Classification, and Bioinformatics. The final projects, hands-on applications, and exercises are designed to illustrate the rapidly increasing practical uses of the techniques described throughout the course.

Statistical Physics in Biology is a survey of problems at the interface of statistical physics and modern biology. Topics include: bioinformatic methods for extracting information content of DNA; gene finding, sequence comparison, and phylogenetic trees; physical interactions responsible for structure of biopolymers; DNA double helix, secondary structure of RNA, and elements of protein folding; considerations of force, motion, and packaging; protein motors, membranes. We also look at collective behavior of biological elements, cellular networks, neural networks, and evolution.

This course provides a basic introduction to volume matching techniques in the field of structural bioinformatics. It explains the use of electron microscopy (EM) and electron tomography (ET) data in creating 3D volume maps of proteins, and the approaches that can be used to compare volumes. It describes how volume matching can be used to find similar structures, or to highlight specific differences between related structures.

By the end of the course you will be able to:
Describe what structural volume data is
Appreciate the value of volume data even in the absence of atomic coordinates
List the variety of methods available to compare volumes

In order to have your metagenomic data analysed by MGnify your sequence data has to be captured by the European Nucleotide Archive (ENA). ENA is a comprehensive database of nucleotide sequences, providing information on raw sequencing data, sequence assemblies and functional annotation.

This webinar will discuss how you can submit metagenomic studies to ENA, make sure they are well annotated, and help people understand your work. It will also cover Webin-CLI, the newly developed Webin Command Line Submission tool. The webinar is presented by Sam Holt who works for ENA.

Who is this course for?
This webinar is aimed at life scientists who are working in the field of metagenomics and who want to learn more about how submitting their data to ENA.

The GWAS Catalog is a comprehensive and highly accessed resource for GWAS data, including full summary statistics. We encourage authors to submit their data to us, to make them accessible and reusable by the wider GWAS community. This webinar provides a step-by-step guide to the submission process, including setting up your account, converting your summary statistics to our standard format, providing metadata and receiving accession numbers to cite in publications.

Who is this course for?
This webinar is for any academic, clinical or industrial researcher who is generating genome wide association data.

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

Access the GWAS Catalog submission system
Format, validate and submit your own summary statistics

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:

"Microbial communities are an important part of our everyday lives. They cycle nutrients across the planet, keep our metabolism balanced, impact our infrastructure, and lend their services for the production of clean water and fuel. One important property researchers use to understand microbes is the dissimilarity between microbial communities. The problem is that there are many dissimilarity indices to choose from and choosing one over another could influence the conclusions drawn from an experiment. To provide a measure of standardization, a new study proposes a framework for capturing dissimilarity based on Hill numbers. Hill numbers measure diversity within a macroscopic community or dissimilarity between communities and now, it appears, Hill numbers can do the same for microbial communities. When coupled with a null model, Hill numbers allow for the analysis of the mechanisms driving microbial community assembly..."

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

This activity uses DNA sequences, protein sequence, and chromosome-density maps to re-trace the ancestry of humans and some of their closest relatives.

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:

"Bacteriophages are everywhere, influencing everything from microbial evolution to biogeochemical cycling. Phages, however, remain among the least understood members of complex microbiomes. Do the tools used to identify phages introduce biases? A recent study compared ten of the most widely used bioinformatics tools designed to detect phages from metagenomics data. Overall, tool performance varied substantially in the analysis of different benchmarking datasets. For a set of artificial RefSeq contigs, PPR Meta and VirSorter2 showed the highest performance. Kraken2 showed the highest accuracy for a mock community benchmark. And generally, k-mer tools performed better than similarity- or gene-based tools. The study offers insight into the biases introduced by different tools, offers guidance into which one is best suited for different use cases, and suggests that rather than relying on any one tool, researchers may do well to combine different ones to suit their research needs..."

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

This webinar will discuss what functional information is available in UniProt and where that information comes from. It will also describe how it the information is added to the resource and how to find it on the UniProt website.

Who is this course for?
This webinar is aimed at students or early stage researchers beginning to use bioinformatics resources in their studies/research who wish to learn more about using UniProt to learn more about function of proteins. 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 what functional information is available in UniProt
Find functional information on the UniProt website

This is the first part of a webinar series on InterPro which is held between 6 May - 20 May 2020.

InterPro is a database that helps users to understand the possible functions of proteins sequence by identifying what family it belongs to or what domains and motifs it contains. InterPro provides the world’s most comprehensive predictions by combining predictions from 13 member databases. This webinar defines the basic InterPro notions, concepts of protein classification and describes how InterPro ensures the accuracy of its data. This webinar also explains the different methodologies used by the InterPro member databases in their predictions and how we integrate them into the 5 different InterPro entry types: Homologous superfamily, family, domain, repeat and site. Finally, it presents some examples of different external resources using the InterPro database as an underlying resource.

Who is this course for?
This webinar is aimed at scientists with an interest in learning more about the InterPro database.

No prior knowledge of bioinformatics is required, but an undergraduate level knowledge of biology would be useful.

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:

"Targeted therapy with BRAF and MEK inhibitors has significantly improved the outcomes of patients with BRAF-mutated metastatic melanoma. Unfortunately, a combination of genetic and epigenetic events can create resistance to such treatment, causing most patients to relapse. A new study suggests that this resistance could be due to dysregulation of the miR-146a-5p-regulated NFκB signaling network. Initial experiments showed the gene COX2 to be largely regulated by miR-146a-5p and NFκB. Further experiments involved manipulating miR-146a-5p/COX2 in vitro and in 3D cultures of treatment-resistant tumors explanted from patients undergoing therapy, which demonstrated that miR-146a-5p expression increased drug sensitivity and reduced COX2 expression. Tumor cells’ susceptibility to therapy could be significantly enhanced by decreasing COX2 expression through forced miR-146a-5p expression or by inhibiting COX2 expression with the drug celecoxib..."

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