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Special Issue: 11th International Computational Advances in Bio and Medical Sciences (ICCABS 2021)

Year: 2023

Type: article

Abstract: T his special issue of the Journal of Computational Biology includes a selection of articles presented at the 11th International Computational Advances in Bio and Medical Sciences (ICCABS 2021), which was held in a virtual format during December 16-18, 2021.ICCABS has the goal of bringing together researchers, scientists, and students from academia, laboratories, and industry to discuss recent advances on computational techniques and applications in the areas of biology, medicine, and drug discovery.In 2021, 13 extended abstracts were submitted in response to the call for papers, out of which 10 extended abstracts were accepted for oral presentation at the conference and publication in the ICCABS 2021 postproceedings published as volume 13,254 of Springer Verlag's Lecture Notes in Bioinformatics series.The technical program of ICCABS 2021 also included 9 invited talks presented at the 11th Workshop on Computational Advances for Next Generation Sequencing (CANGS 2021), 16 invited talks presented at the 10th Workshop on Computational Advances in Molecular Epidemiology (CAME 2021), 12 invited talks presented at the 4th Workshop on Computational Advances for Single-Cell Omics Data Analysis (CAS-CODA 2021), and 7 invited talks presented at the 1st Workshop on Advances in Systems Immunology (ASI 2021).This special issue includes a selection of 12 articles presented at ICCABS 2021 and the joint CANGS, CAME, CASCODA, and ASI workshops.The first article, by Mitchell et al., was selected from the extended abstracts presented at the main ICCABS 2021 conference.In this article, the authors introduce the excerno R package for filtering false variant calls induced by DNA degradation in formalin-fixed paraffinembedded (FFPE) samples.The package integrates the known signature of FFPE artifacts within a Bayesian framework to annotate mutations called from sequencing data generated from FFPE samples, which are commonly used in cancer genomics studies.The specificity and sensitivity of excerno are assessed using data sets simulating varying degree of FFPE-induced DNA degradation and mutations generated by different mutational processes captured from the Catalogue of Somatic Mutations in Cancer (COSMIC) databases.

Source: Journal of Computational Biology

DOI: 10.1089/cmb.2023.29085.msb