Computation-in-Memory for Modern Applications using Emerging Technologies

Modern applications like Genomics and Machine Learning (ML) hold the potential to reshape our understanding of diseases’ genetic origins and guide machines in executing tasks and making predictions without our explicit programming. The successful, widespread integration of these modern applications can usher in advancements in di-agnostics,...

ApHMM: Accelerating Profile Hidden Markov Models for Fast and Energy-efficient Genome Analysis

Profile hidden Markov models (pHMMs) are widely employed in various bioinformatics applications to identify similarities between biological sequences, such as DNA or protein sequences. In pHMMs, sequences are represented as graph structures, where states and edges capture modifications (i.e., insertions, deletions, and substitutions) by...

Single-cell Analysis from the perspective of how to Interact, Identify and Integrate cells

Single-cell technologies have emerged as powerful tools to analyze complex tissues at the single-cell resolution, resolving the cellular heterogeneity within a tissue through the discovery of different cell populations. Over the past decade, single-cell technologies have greatly developed allowing the profiling of various molecular features...

QuantTB- A method to classify mixed Mycobacterium tuberculosis infections within whole genome sequencing data

Background: Mixed infections of Mycobacterium tuberculosis and antibiotic heteroresistance continue to complicate tuberculosis (TB) diagnosis and treatment. Detection of mixed infections has been limited to molecular genotyping techniques, which lack the sensitivity and resolution to accurately estimate the multiplicity of TB infections. In...

Molecular interactomes: Network-guided cancer prognosis prediction & multi-way chromatin interaction analysis

In the last two decades, our understanding of the molecular mechanisms within the cell has witnessed a great leap forward. For the most part this is due to the fast innovation of the genomic measurements technologies and wide spread usage of computational methods which enables knowledge extraction from the massive datasets produced by these...

A Comparison of Seed-and-Extend Techniques in Modern DNA Read Alignment Algorithms

DNA read alignment is a major step in genome analysis. However, as DNA reads continue to become longer, new approaches need to be developed to effectively use these longer reads in the alignment process. Modern aligners commonly use a two-step approach for read alignment: 1. seeding, 2. extension. In this paper, we have investigated various...

RUBIC identifies driver genes by detecting recurrent DNA copy number breaks

The frequent recurrence of copy number aberrations across tumour samples is a reliable hallmark of certain cancer driver genes. However, state-of-the-art algorithms for detecting recurrent aberrations fail to detect several known drivers. In this study, we propose RUBIC, an approach that detects recurrent copy number breaks, rather than...

An FPGA-based systolic array to accelerate the BWA-MEM genomic mapping algorithm

We present the first accelerated implementation of BWA-MEM, a popular genome sequence alignment algorithm widely used in next generation sequencing genomics pipelines. The Smith-Waterman-like sequence alignment kernel requires a significant portion of overall execution time. We propose and evaluate a number of FPGA-based systolic array...

Algorithms for sequence-based reverse metabolic engineering

Microorganisms are employed on a large scale in a variety of industrial production processes, such as production of food and ingredients, beverages, pharmaceutical compo- nents and fine- and bulk chemicals. Improving the behaviour of these microorganisms is of crucial importance in the design of efficient production processes. In this thesis we...

Hardware Acceleration of Bioinformatics Sequence Alignment Applications

Biological sequence alignment is an important and challenging task in bioinformatics. Alignment may be defined as an arrangement of two or more DNA or protein sequences to highlight the regions of their similarity. Sequence alignment is used to infer the evolutionary relationship between a set of protein or DNA sequences. An accurate alignment...