13th Workshop on Computational Advances for Next Generation Sequencing
Workshop Chairs:
Ion Mandoiu (University of Connecticut, ion@engr.uconn.edu)
Pavel Skums (University of Connecticut, pavel.skums@uconn.edu)
Alex Zelikovsky (Georgia State University, alexz@cs.gsu.edu)
Massively parallel DNA and RNA sequencing have become widely available, reducing the cost by several orders of magnitude and placing the capacity to generate gigabases to terabases of sequence data into the hands of individual investigators. These next-generation technologies have the potential to dramatically accelerate biological and biomedical research by enabling the comprehensive analysis of genomes and transcriptomes to become inexpensive, routine and widespread. The exploding volume of data has spurred the development of novel algorithmic approaches for primary analyses of sequence data in such areas as error correction, de novo genome assembly, novel transcript discovery, virus quasispecies assembly, etc. The CANGS workshop will bring together specialists to discuss the various mathematical and computational challenges presented by next-generation sequencing technologies.
Workshop topics of interest include but are not limited to:
3D genome architecture
Cancer sequencing
Characterization of structural variants
Cloud-based NGS analysis
De novo genome and transcriptome assembly
Epigenomics
Haplotype reconstruction
Long single molecule read analysis
Metagenomics and metatranscriptomics
NGS error correction
Population genomics
Read mapping
Small RNA analysis
Scaffolding and genome finishing
Transcriptome quantification
Variant detection and genotyping
The meeting is by invitation only. If you would like to inquire about the possibility of being invited, please contact the workshop chairs as soon as possible, but no later than November 30, 2024. Following the workshop, speakers will be invited to submit extended abstracts for publication in the Springer LNBI (pending approval) post-proceedings volume devoted to ICCABS 2025 and/or full length articles to a special issue of the Journal of Computational Biology.