This special section includes a selection of articles presented at the 12th International Conference on Computational Advances in Bio and Medical Sciences (ICCABS 2023). ICCABS has the goal of bringing together researchers, scientists, and students from academia, laboratories, and industry to discuss recent advances in computational techniques and applications in the areas of biology, medicine, and drug discovery. ICCABS 2023 was held on December 11–13, 2023 in Norman, Oklahoma, USA. The conference was hosted by the School of Computer Science at the University of Oklahoma, with Dr. Marmar R. Moussa as General Chair and Drs. Pavel Skums and Sharma V. Thankachan as Program Committee Co-Chairs.
The technical program of ICCABS 2023 was comprised of 3 keynote talks, 23 extended abstract presentations, 15 poster presentations, and 34 invited talks presented at four satellite workshops hosted by the conference: 10 invited talks presented at the 12th Workshop on Computational Advances for Next Generation Sequencing (CANGS 2023), 10 invited talks presented at the 11th Workshop on Computational Advances in Molecular Epidemiology (CAME 2023), 7 invited talks presented at the 5th Workshop on Computational Advances for Single-Cell Omics Data Analysis (CASCODA 2023), and 7 invited talks presented at the 2nd Workshop on Advances in Systems Immunology (ASI 2023).
This special section includes a selection of three articles presented at ICCABS 2023. The first article, by Sing-Hoi Sze, explores the use of longest exact matches for taxonomic classification of 16S rRNA amplicon sequence variants. The author presents an efficient method for computing longest exact matches based on heuristically speeding up a divide-and-conquer application of the classical Knuth-Morris-Pratt pattern-matching algorithm. Experimental results on publicly available 16S sequencing data from mock communities of varying complexity show that using longest exact matches yields species assignment accuracy comparable to or better than the use of Basic Local Alignment Search Tool (BLAST) best hits and a naive Bayes classifier.
The remaining two articles in the special section explore computational approaches for inferring tumor phylogenies from sequencing data. The article by Gavryushkina et al. evaluates several data-processing and modeling techniques for inferring tumor phylogenies from spatially resolved, near single-cell RNA-seq data generated by commercial platforms such as 10X Genomics Visium. Based on experimental results on several real tumor and matched tumor-normal datasets, the authors conclude that the highest phylogenetic reconstruction accuracy is achieved by restricting the analysis to the high-quality Visium spots and using only the highly variable genes in the phylogenetic inference process.
Finally, yet importantly, the article by Bristy, Fu, and Schwartz introduces a single cell extension called Sc-TUSV-ext of the Tree Unmixing with Structural Variants (TUSV) integer linear programming-based method for reconstructing tumor phylogenies from bulk DNA sequencing data. Sc-TUSV-ext is the first single cell clonal lineage inference method to simultaneously consider in the reconstruction process single nucleotide variants, copy number alterations, and structural variations. The authors present results of experiments on both synthetic and real tumor data showing that Sc-TUSV-ext yields improved accuracy in clonal lineage reconstruction compared to prior methods that consider only one or two of these variant types.
We would like to thank the Editor-in-Chief Mona Singh for providing us with the opportunity to highlight some of the exciting research presented at ICCABS 2023 in this special section of the Journal of Computational Biology. Finally, we would like to thank all ICCABS authors—the conference could not continue to thrive without their high-quality contributions.
Guest Editors