GS2: Sustainability, Equity, and Efficiency in Computational Biology

Date: July 12, 2022               

16:00 - 20:00 UTC

Abstract submission deadline:

Geo region/Timezone:

Genome sequencing and powerful computing have revolutionized molecular evolutionary studies. Through comparative sequence analysis, scientists in more than 100 biological disciplines gain key insights into the origins of species and genes, evolutionary relationships, adaptive evolution, and genotype-phenotype connections.  These molecular evolutionary analyses involve many computationally intensive steps, particularly the search for homologous sequences, alignment of sequences, selection of optimal substitution model, and inference of evolutionary trees along with bootstrap tests of their robustness. With the rise of large genomic datasets assembled from burgeoning sequence databases, molecular evolutionary analyses require increasingly larger computing resources that leave increasingly bigger carbon footprints.  This symposium brings together the community to highlight the need and development of efficient methods, algorithms, resources, and software that require fewer compute cycles and less computer memory, shrinking the carbon footprint of molecular evolutionary analyses. Additionally, the development of more efficient computational methods is urgently needed to promote science in low and middle-income countries, where this issue is aggravated by the limited investments in research due to limited computing resources and poor infrastructure. The symposium will highlight how green computing is science- and economics-friendly, in addition to being environmentally friendly. We will promote an open discussion on the current state-of-the-art in efficient and economical computing and future directions for greener computing made possible by developing accurate resource-thrifty methods and algorithms and crafting smarter software and greener analysis practices.

Invited Speakers:
Jason Grealey (Children's Medical Research Institute, Australia), Sudhir Kumar (Temple Univ)  

Beatriz Mello (Universidade Federal do Rio de Janeiro, Brazil), Alessandra Lamarca (Laboratório Nacional de Computação Científica, Brazil), and Sudhir Kumar (Temple Univ, USA)

@OfficialSMBE Feed

MBE | Most Read

Molecular Biology and Evolution

GBE | Most Read

Genome Biology & Evolution