Low-cost, high throughput DNA and RNA sequencing (HTS) data is now the main workforce for various biological applications. HTS technologies have already started to impact a broad range of research and clinical use for the life sciences. These include but are not limited to, large-scale sequencing studies for population genomics and disease-causing mutation discovery, including cancer, metagenomics, comparative genomics, transcriptome profiling, and outbreak detection and tracking, including COVID-19, Ebola, and Zika. HTS also impacts the whole healthcare system in several directions. Although there is still much room for improvement, sequencing of personal genomes is now becoming a part of preventive and personalized medicine as HTS technologies make it possible to identify genetic mutations that enable rare disease diagnosis, determine cancer subtypes therefore guiding treatment options, and characterize infections and antibiotic resistance.
Currently, all biological data are analyzed using computation platforms that are general-purpose, i.e. they aim to solve a wide range of problems. This means that all current compute grids, servers, and cloud computing platforms are designed to be able to provide solutions for all “computable” problems with amortized efficiency. Analyzing massive amounts of biological data in large clusters and cloud platforms poses two problems. First, transferring the data from where it is generated (hospitals, clinics, or even small villages in the case of virus tracking) to these computer centers is both time and energy-consuming and requires a stable and fast internet connection. Second, these computer platforms themselves are energy-hungry, as the data moves between the processing unit and the memory on the same computer system, a considerable amount of energy is spent.
The BioPIM project aims to develop algorithms and specialized hardware together to improve the speed and cost of various bioinformatics analyses. The project focuses on two algorithm design techniques: combinatorial algorithms such as alignments, pattern matching, genome assembly, and other uses of graphs, as well as methods based on deep learning, machine learning, and AI such as genomic variation discovery. To achieve energy-efficient, cost-efficient, and ultra-fast bioinformatics analysis, the BioPIM project leverages the emerging processing-in-memory (PIM) architectures that couple processing capability with memory and storage devices, therefore minimizing time and energy spent in data transfer. We will also design our hardware to perform some of these analyses on mobile devices therefore enabling edge computing. BioPIM addresses the inability to perform genome analysis on the go to help in the timely investigation of clinical and research data, including viral and bacterial typing in remote locations with little or no access to conventional large-scale computing platforms.
BioPIM’s proposed research is flexible as it aims to develop PIM acceleration for various algorithms. Although the methods the project focuses on will be within the bioinformatics domain, most of these algorithms originated decades ago, and they are also being used for non-bioinformatics applications such as:
● String search and pattern matching (e.g. in natural language processing, data mining)
● Graph theory (e.g. data analytics, web indexing)
● General machine learning
● Specifically, neuromorphic computing (e.g. many applications of deep learning and artificial intelligence)
Additionally, most of our PIM developments will benefit data centers in terms of performance gain and energy efficiency; therefore, the project’s impact is expected to be far beyond our significant aims.
