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Welcome to the Community of Special Interest wiki!
Introduction
ISCB Communities of Special Interest (COSIs) are member communities of shared interest that have self-organized and have multiple activities or interactions throughout the year, rather than solely meeting during the ISMB conference in the COSI track. An important goal of any COSI is to foster a topically-focused collaborative community wherein scientists communicate with one another on research problems and/or opportunities in specific areas of computational biology. Such communication is often in the form of meetings, but can also be through other social media tools that allow for vibrant participation in a virtual environment. If you are interested in starting an ISCB COSI please send your exploratory questions to Diane E. Kovats, ISCB Executive Director (dkovats@iscb.org).
COSI groups
- 3D-SIG: Structural Bioinformatics and Computational Biophysics
3D-SIG focuses on structural bioinformatics and computational biophysics and has become the largest meeting in this growing field.
Bioinfo-core is a worldwide body of people that manage or staff bioinformatics cores within organizations of all types including academia, academic medical centers, medical schools, biotechs and pharmas.
Bio-Ontologies Special Interest Group (SIG) covers the latest and most innovative research in the application of ontologies and more generally the organisation, presentation and dissemination of knowledge in biomedicine and the life sciences.
Data visualization cuts across all areas of computational biology. On the one hand, sophisticated data visualization techniques are required to allow the biologist to explore their large/complex datasets and gain insight from them. On the other hand, this approach can lower the black-box nature of complex (bioinformatics) algorithms. The goal of BioVis is to bring together researchers from the visualization, bioinformatics, and biology communities with the purpose of educating, inspiring, and engaging bioinformatics and biology researchers in state-of-the-art visualization research, as well as visualization researchers in problems in biological data visualization.
CAMDA presents a crowd sourcing and open-ended data analysis challenge format which focuses on big heterogeneous data sets that are increasingly produced in several fields of the life sciences.
COSI CompMS promotes the efficient, high quality analysis of mass spectrometry data through dissemination and training in existing approaches and coordination of new, innovative approaches. The CompMS initiative aims to exploit synergies between different application domains, in particular proteomics and metabolomics.
The ISCB EDUCATION COSI focuses on bioinformatics and computational biology education and training across the life sciences. A major goal of this COSI is to foster a mutually supportive, collaborative community in which bioscientists can share bioinformatics education and training resources and experiences, and facilitate the development of education programs, courses, curricula, etc., and teaching tools and methods.
Evolution and comparative genomics are deeply intertwined with computational biology. Computational evolutionary methods, such as phylogenetic inference methods or multiple sequence alignment are widely used, yet remain far from “solved” and are indeed intense areas of research. At the same time, evolutionary and comparative genomics are inherently “transversal” disciplines in that work in many other biological areas of research have some evolutionary component (e.g. cancer genomics, epidemiology, toxicology, population genetics, functional genomics, structural biology just to name a few). The scope of this COSI is intentionally kept broad. The track will feature a mix of proceedings, highlight, and invited talks. Priority will be given to contributions which are relevant to more than a single area of application, or to contributions which are not covered by more specialised COSIs.
The accurate annotation of protein function is key to understanding life at the molecular level. The Function SIG COSI brings together computational biologists, experimental biologists and biocurators who are dealing with gene and gene product function prediction, to share ideas and create collaborations. The Function-SIG holds annual meetings and conducts the multi-year Critical Assessment of protein Function Annotation, or CAFA, experiment.
HiTSeq is devoted to the latest advances in computational techniques for the analysis of high-throughput sequencing data including novel algorithms, analysis methods and applications in biology where high-throughput sequencing data has been transformative. It provides a forum for in depth presentations of novel algorithms, analysis methods, and applications in multiple areas of biology that HTS is transforming.
The Integrative RNA Biology (IRB) group organizes the RNA SIG session at ISMB. The group aims to bring together experts in computational and experimental aspects of research in RNA Biology to cover new developments across this broad field of research. The RNA SIG session at ISMB focuses on two major areas: (1) the development of computational and high-throughput experimental methods, and (2) the application of such methods to break new grounds in the study of RNA biology and disease. We aim to educate and inspire researchers in the field, novice and seasoned alike, by meshing together different aspects of Computational RNA Biology, and promoting cross-disciplinary collaborative research.
Transitioning from a post-doc to a junior PI can be a challenging process requiring careful planning. Once running a group, junior PIs are faced with many new tasks, some of which are learnt on the job. The Junior Principal Investigators group (JPI) aims to provide support during this process via a community of peers.
Machine Learning in Computational and Systems Biology (MLCSB) is a community for researchers interested in the interface of data sciences and life sciences, in particular the method development and application challenges that arise for Machine Learning in Computational and Systems Biology.
The MICROBIOME Community of Special Interest aims at the advancement and evaluation of computational methods in microbiome research, especially metaomic approaches. Based on the Critical Assessment of Metagenome Interpretation (CAMI), the COSI supplies users and developers with exhaustive quantitative data about the performance of methods in relevant scenarios. It therefore guides users in the selection and application of methods and in their proper interpretation. Furthermore, the COSI provides a platform for exchange and networking between method developers, and provides valuable information allowing them to identify promising directions for their future work.
As more research fields turn to network visualization and analysis for perspective, our Network Biology Community serves to introduce novel methods and tools, identify best practices and highlight the latest research in the growing and interconnected field of network biology.
The Open Bioinformatics Foundation (OBF) is a non-profit, volunteer-run group dedicated to promoting the practice and philosophy of Open Source software development and Open Science within the biological research community. Our primary activities are running the annual Bioinformatics Open Source Conference (BOSC, held as an ISMB SIG meeting), and managing the servers, domain names and other assets of our member projects which include BioJava, BioPerl, Biopython, and BioRuby.
The Regulatory and Systems Genomics Community of Special Interest focuses on computational methods that are important in the study of regulation of genes and systems. The RegSys COSI organizes the following activities: (1) ISMB Regulatory Genomics SIG Meeting, (2) RECOMB/ISCB Conference on Regulatory and Systems Genomics and DREAM Challenges, and (3) Top Ten Papers in Regulatory and Systems Genomics.
The SysMod Community of Special Interests aims at bridging the gap between bioinformatics and systems biology modeling. Recently, aspects of the two fields have converged. Systems modeling has become more reliant on bioinformatic network inference to build models and has begun to use transcriptomics and proteomics data to train models. In addition, more communication between systems modellers and bioinformaticians is needed to build models of whole cells, organs and organisms. Furthermore, the promises of precision medicine lie, in part, on the interplay between bioinformatics-based analysis of patient data and model-based predictions of treatments.
Knowledge-based translational medicine is a rapidly growing discipline in biomedical research and aims to expedite the discovery of new diagnostic tools and treatments by using a multi-disciplinary, highly collaborative, "bench-to-bedside" approach. It involves the integration of multiple high dimensional datasets that capture the molecular profiles of patients, as well as detailed clinical information. Within public health, translational medicine is focused on ensuring that proven strategies for disease treatment and prevention are actually implemented within the community, and on progressing towards data-educated personalised therapy. To genuinely realise the promise of Big Data in healthcare, we must consistently collate the data, annotate it with consistent and useful ontologies, apply sophisticated statistical analysis and translate these findings to the clinic. As a community, we will explore the current status of computational biology approaches within the field of clinical and translational medicine. In this COSI we will bring scientists from both academia and industry to exchange knowledge and foster networking, to help in building up of the translational medicine community.
The Variant Interpretation Community of Special Interest (VarI-COSI) is a community of scientists interested in “breaking” the genomic code. The main goal of our COSI is to promote the formation of a collaborative network of scientists interested in the understanding of the meaning of genomic variation as applied to a range of questions, including population studies, functional and evolutionary impacts, and disease.
