Hardware / Software System for Portable and Low-Cost Genome Assembly

“The enjoyment of the highest attainable standard of health is one of the fundamental rights of every human being without distinction of race, religion, political belief, economic or social condition” [56]. Genomics (the study of the entire DNA) provides such a standard of health for people with rare diseases and helps control the spread of pandemics. Still, millions of human beings are unable to access genomics due to its cost, and portability. In genomics, DNA sequencers digitise DNA information, and computers analyse the digitised information. We have desktop and thumb-sized DNA sequencers, that digitise the DNA data rapidly. But computations necessary for the analysis of this data are inevitably performed on high-performance computers (HPCs) and cloud computers. These computations not only require powerful computers but also necessitate high-speed networks since the data generated are in the hundreds of gigabytes. Relying on HPCs and high-speed networks, deny the benefits that can be reaped by genomics for the masses who live in remote areas and in poorer nations. Developing a low-cost and portable genomics computation platform would provide personalised treatment based on an individual’s DNA and identify the source of the fast-spreading epidemics in remote areas and areas without HPC or network infrastructure. But developing a low-cost and portable genome analysing computing platform is a challenging task. This thesis develops novel computer architecture solutions to assemble the whole human DNA and COVID-19 virus RNA on a low-cost and portable platform. The first phase of the solution describes a ring-pipelined processor architecture for a key genome assembly algorithm. The human genome is partitioned to fit into the small memory footprint of embedded processors. These techniques allow an entire human genome to be assembled using highly portable and low-cost embedded processor cores. These processor cores can be housed within a single chip. Each processor was only 0.08 mm 2 and consumed just 37.5 mW. It has only 2 GB memory, 32-bit instruction width, and a clock with a 1 GHz frequency. The second phase of the solution describes how application-specific instruction-set processors can be sped up to execute a key genome assembly algorithm. A fully automated design system is presented, which improves the performance of large applications (such as genome assembly algorithm) and generates application-specific instructions for a commercial processor design tool (Xtensa). The tool enhances the base processor, which was used in the ring pipeline processor architecture. Thus, the alignment algorithms execute 2.1 times faster with only 11% additional hardware. The energy-delay product was reduced by 7.3× compared to the base processor. This tool is the only one of its type which can handle applications which are large. The third phase of the solution designs a portable low-cost genome assembly computer (PGA). PGA enhances the ring pipeline architecture with the customised processor found in phase two and with improved inter-processor communication. The results show that the COVID-19 virus RNA can be assembled in under 10 minutes and the whole human genome can be assembled in 11 days on a portable platform (HPC take around two days) for 30× coverage. PGA has an area footprint of just 5.68 mm 2 in a 28 nm technology node and is far smaller than a high-performance computer processor chip. The PGA consumes only 4W of power, which is lower than the power requirement of a high-performance processor chip. The manufacturing cost of the PGA also would be much cheaper than the high-performance system cost, when produced in volume. The developed solution can be powered by a USB port of a laptop. This thesis is the first of its type to show the design of a single-chip solution to be able to process a complex genomic problem. This thesis contributes to attaining one of the fundamental rights of every human being wherever they may live

Single cell RNA-sequencing data generated from human pluripotent stem cell-derived lens epithelial cells

Detailed transcriptomic analyses of differentiated cell populations derived from human pluripotent stem cells is routinely used to assess the identity and utility of the differentiated cells. Here we provide single cell RNA-sequencing data obtained from ROR1-expressing lens epithelial cells (ROR1e LECs), obtained via directed differentiation of CA1 human embryonic stem cells. Analysis of the data using principal component analysis, heat maps and gene ontology assessments revealed phenotypes associated with lens epithelial cells. These data provide a resource for future characterisation of both normal and cataractous human lens biology. Corresponding morphological and functional data obtained from ROR1e LECs are reported in the associated research article “A simplified method for producing human lens epithelial cells and light-focusing micro-lenses from pluripotent stem cells “ (Dewi et al., 2020)

Portfolio optimization using genetic algorithm

65 p.Portfolio optimization is a multi-objective, non-linear optimization problem for maximum return with minimum risk. It normally has a huge number of input variables (assets) and numerous local optima; Therefore mathematical derivatives based optimization is very difficult or impossible to be applied. Genetic algorithms (GA) have been used for this kind of problem, as it is good to deal with optimization involving a large number of inputs and non-linear multi-model objectives. But performance of GA optimization degrades when the number of inputs of the problem increases.Master of Science (Computer Control and Automation

A simplified method for producing human lens epithelial cells and light-focusing micro-lenses from pluripotent stem cells

Here we describe a modified method for harvesting tens-of-millions of human lens epithelial-like cells from differentiated pluripotent stem cell cultures. To assess the utility of this method, we analysed the lens cell population via: light microscopy; single cell RNA-sequencing and gene ontology analyses; formation of light-focusing micro-lenses; mass spectrometry; and electron microscopy. Both individually and collectively, the data indicate this simplified harvesting method provides a large-scale source of stem cell-derived lens cells and micro-lenses for investigating human lens and cataract formation

A single-cell and spatially resolved atlas of human breast cancers

Breast cancers are complex cellular ecosystems where heterotypic interactions play central roles in disease progression and response to therapy. However, our knowledge of their cellular composition and organization is limited. Here we present a single-cell and spatially resolved transcriptomics analysis of human breast cancers. We developed a single-cell method of intrinsic subtype classification (SCSubtype) to reveal recurrent neoplastic cell heterogeneity. Immunophenotyping using cellular indexing of transcriptomes and epitopes by sequencing (CITE-seq) provides high-resolution immune profiles, including new PD-L1/PD-L2+ macrophage populations associated with clinical outcome. Mesenchymal cells displayed diverse functions and cell-surface protein expression through differentiation within three major lineages. Stromal-immune niches were spatially organized in tumors, offering insights into antitumor immune regulation. Using single-cell signatures, we deconvoluted large breast cancer cohorts to stratify them into nine clusters, termed ‘ecotypes’, with unique cellular compositions and clinical outcomes. This study provides a comprehensive transcriptional atlas of the cellular architecture of breast cancer