Leading Professional Society for Computational Biology and Bioinformatics
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Upcoming ISCBacademy Webinars



To view previous webinars use the links below

2020 Webinars | 2021 Webinars | 2022 Webinars


ISCB in collaboration with our Communities of Special Interest is pleased to announce the ISCBacademy COSI Webinar Series.  Mark your calendars for Tuesdays at 11 AM Eastern Time Zone to participate in a COSI themed webinar.

Upcoming Webinars (check back regularly for speaker and registration details):
January 18, 2022 - SysMod
January 25, 2022 - Text Mining
February 1, 2022 - TransMed
February 8, 2022 - VarI
February 15, 2022 - 3DSIG
February 22, 2022 - BOSC/OBF
March 1, 2022 - Bio-Ontologies
March 8, 2022 - BIOINFO-CORE
March 15, 2022 - BioVis
March 22, 2022 - CAMDA
March 29, 2022 - CompMS
April 5, 2022 - Education
April 12, 2022 - EvolCompGen
April 19, 2022 - Function
April 26, 2022 - HiTSeq
May 3, 2022 - iRNA
May 10, 2022 - MICROBIOME
May 17, 2022 - MLCSB
May 24, 2022 - NetBio


Join us for our upcoming ISCBacademy Webinars.  Check back regularly for updates.

To propose a talk for an ISCBacademy Webinar click here.


Accelerating biomedical discovery with large-scale knowledge assembly and human-machine collaboration
by Benjamin Gyori

January 18, 2022 at 11:00 AM EST

The rate at which biomedical knowledge is produced (both at the level of new publications and data sets) is accelerating, and there is an increasing need to monitor, extract and assemble this knowledge in an actionable form. Classic mechanistic models take substantial human effort to construct and rarely scale to the level of omics datasets, while statistical approaches often do not make use of prior knowledge about mechanisms. To address these challenges, we present INDRA, an automated knowledge assembly system which integrates multiple text mining tools that process the scientific literature, and structured sources (pathway databases, drug-target databases, etc.). INDRA standardizes knowledge extracted from these sources and corrects errors, resolves redundancies, fills in missing information, and calculates confidence to create a coherent knowledge base. From this knowledge, various executable model types (ODEs, Boolean networks, etc.) and causal networks can be generated automatically for further analysis. We discuss technology built on top of INDRA, including human-machine dialogue systems, and EMMAA, a framework which makes available a set of self-updating and self-analyzing models of specific diseases and pathways. We present applications of these tools to automatically construct explanations for experimental observations in multiple disease areas.

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Tidy Transcriptomics for Single-cell RNA Sequencing Analyses
by Stefano Mangiola and Maria Doyle

February 18, 2022 at 4:00 PM CET

Description:

This tutorial will present how to perform analysis of single-cell RNA sequencing data following the tidy data paradigm. The tidy data paradigm provides a standard way to organise data values within a dataset, where each variable is a column, each observation is a row, and data is manipulated using an easy-to-understand vocabulary. Most importantly, the data structure remains consistent across manipulation and analysis functions. This can be achieved with the integration of packages present in the R CRAN and Bioconductor ecosystem, including tidyseurat, tidySingleCellExperiment, and tidyverse. These packages are part of the tidytranscriptomics suite that introduces a tidy approach to RNA sequencing data representation and analysis.

Instructors:

Dr. Stefano Mangiola is a Postdoctoral researcher in the laboratory of Prof. Tony Papenfuss. His background spans from biotechnology to bioinformatics and biostatistics. His research focuses on prostate and breast tumour microenvironment, the development of statistical model for the analysis of RNA sequencing data, and data analysis and visualisation interfaces.

Dr. Maria Doyle is the Application and Training Specialist for Research Computing at the Peter MacCallum Cancer Centre in Melbourne, Australia. She has a PhD in Molecular Biology and currently works in bioinformatics and data science education and training. She is passionate about supporting researchers, reproducible research, open source and tidy data.

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Growing open source communities with internships
by Yo Yehudi

February 22, 2022 at 11:00 AM EST

Building communities for your open source computational tooling requires more than just technical expertise, and often isn't as straightforward as building the tool itself. Having a community of contributors and users can make a big difference in many ways - additional community members will spot opportunities and bugs in your code that previously you didn't notice, and may be able to offer unique skillsets to your team.

One effective way to grow your community can be via internships. Programs such as Google Summer of Code and Outreachy offer the chance to work with interns for 6-12 weeks, working on individual supervised projects whilst getting paid for their work.

This webinar will cover the ins-and-outs of participating in internship programs like this, from the perspective of a mentoring organisation. Topics will include:

  1. Getting started with internship programs - finding mentors and defining a set of projects
  2. Time commitments for mentors, before the application period and after interns are selected.
  3. Funding for internship programs! (it's not as tricky as you may fear - others handle this bit!)
  4. Keeping interns engaged during the program and bringing them in as long-term contributors afterwards.

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Spinning a semantic web of protein information
by Monique Zahn

March 18, 2022 at 11:00 AM UTC

Description:

Life science is the most demanding research field in terms of data quantity and complexity, with many relevant reference databases. To generate knowledge, heterogeneous data from various sources must often be combined. Semantic Web technologies, and in particular RDF and its companion query language SPARQL, provide a common framework allowing data to be shared and reused between resources. Many life science databases have recently turned to RDF to model their data, developed SPARQL endpoints and joined the Linked (Open) Data cloud. This tutorial will introduce neXtProt (www.nextprot.org/), one of the major public knowledge bases on human proteins, its comprehensive RDF data model, and its large collection of reusable example queries, including federated queries to other resources.

At the end of the course, the participants are expected to:
•    Describe the neXtProt data model
•    Run example queries that answer biological questions
•    Search for data by modifying existing SPARQL queries
•    Understand how federated queries are constructed

Instructor:

Monique Zahn is the Quality Manager of the CALIPHO group which develops neXtProt. She is responsible for testing user interfaces and the contents of each release. She has established quality control procedures involving SPARQL queries carried out at each data release. She has taught biology in undergraduate degree programs in Switzerland and is also Training Manager at the SIB.

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