The effect of recentness of consumer-grade wearable training data on the ability of a DNN to identify users

Heart rate data and other data collected by consumer-grade wearable devices can give away quite useful information about the user. It can for example be used by machine learning algorithms such as Deep Neural Networks (DNN) to learn patterns about cardiovascular disease and fitness, or be used for identification. Heart rate patterns can also...

Finding Biomarkers for Type 2 Diabetes

Type 2 Diabetes is a very prevalent disease in current times and leads to significant adverse effects. Recently, there has been a growing interest in the association of the human gut microbiome with respect to chronic diseases like Type 2 Diabetes with the aim to identify biomarkers. In this study, we researched the effect of different machine...

Using metagenomic Hi-C data to discover broad host-range plasmids conferring antimicrobial resistance

Horizontal gene transfer (HGT) trough plasmids is one of the main contributors to the rapid increase of antimicrobial resistance (AMR). Studying wastewater from wastewater treatment plants (WWTPs) allows us new insights into HGT as bacteria from different sources come together. Currently the analysis of HGT is limited, as plasmids cannot be...

Reference-free biomarker mining in metagenomic data using language embedding

Metagenomic Next-Generation Sequencing (mNGS) presents a promising avenue to generate massive volume of sequence reads in a short period of time. This has opened opportunities for disease diagnosis based on individual variations and mutations by considering the microbiome profile of each patient. However, the effective use of this data requires...

GPU Acceleration of BWA-MEM DNA Sequence Alignment

Over the past 20 years, the cost of sequencing genomes has reduced drastically. As DNA data grows at an unprecedented rate, the need for fast and affordable software and hardware solutions for DNA analysis is higher than ever. A critical and time-consuming component in any DNA analysis pipeline is sequence alignment, which refers to mapping...

Predicting immune responses on multi-modal single-cell data with variational inference

Single-cell sequencing allows measuring individual cells' molecular features and their responses to perturbations. Understanding which cells respond to a particular perturbation and how these responses vary across populations can be used to, for example, improve vaccine immunogenicity. However, an exhaustive exploration of single-cell...

Cell type deconvolution of methylated cell-free DNA at the resolution of individual reads

Cell-free DNA (cfDNA) are DNA fragments originating from dying cells that enter the plasma. Uncontrolled cell death, for example caused by cancer, induces an elevated concentration of cfDNA. As a result, determining the cell type origins of cfDNA can provide information about an individual's health. This research looks into how to increase the...

Comparing the performance of variant calling algorithms based on HiFi reads

A multi-omic approach to discovering methylation-based markers of colon cancer in cfDNA

In recent years the advent of multi-omic techniques have shown great promise in the field of oncology. In light of these advancements, this thesis focuses on the use of multiple data types to find methylation markers around transcription start site regions for colorectal cancer in the cell-free DNA (cfDNA) domain. It combines several methods of...

Unsupervised Manifold Alignment with TopoGAN: Aligning multi-modal biological data without correspondence information available across modalities

Single-cell multi-modal omics promises to open new doors in bioinformatics by measuring different aspects of cells, thus offering multiple perspectives on the underlying biological phenomenon. Although simultaneous multi-modal measurement protocols do exist, their inherent technical limitations necessitate focus on single modality measurements....

Powerful world of (meta-)genomics held back by lack of Standard Operating Procedures: A computer science-oriented analysis on automated metagenomic approaches and pipelines, their common practices, and technical shortcomings

Context: The study and analysis of (meta-)genomics have been providing scientists with valuable insights into the functioning and composition of microbial communities. Latest advancements in next-gen and high throughput sequencing technologies have resulted in significant growth in the data produced and made available for further research. These...

Classifying Candida species using Mixed Integer Optimization based optimal classification trees

Global medical use of azole antifungals and echinocandins has led to an enormous increase in resistant <i>Candida</i> species, that are most commonly associated with fungal infections. A possible mechanism causing resistance are single or simultaneous point mutations in the genes responsible for encoding antifungal target enzymes. The aim of...

Accelerating Protein Sequence Alignment with Different Parallel Hardware Platforms

Detecting similarities between (RNA, DNA, and protein) sequences is an important part of bioinformatics. Among the algorithms used to accomplish this, the Smith-Waterman algorithm is very popular. A sequential implementation of Smith-Waterman requires quadratic running time with respect to the length of the sequences. As the amount of data in...

Multimodal Interface for DNA Alignment of Sequences

Major research efforts in Bioinformatics include sequence alignment, gene finding, genome assembly, protein structure alignment, protein structure prediction, prediction of gene expression and protein-protein interactions, and the modeling of evolution. To perform these specific tasks different tools are used. Using these tools separately is a...