Enhancing Estimates of Breakpoints in Genome Copy Number Alteration using Confidence Masks

Chromosomal structural changes in human body known as copy number alteration (CNA) are often associated with diseases, such as various forms of cancer. Therefore, accurate estimation of breakpoints of the CNAs is important to understand the genetic basis of many diseases. The high‐resolution comparative genomic hybridization (HR‐CGH) and single‐nucleotide polymorphism (SNP) technologies enable cost‐efficient and high‐throughput CNA detection. However, probing provided using these profiles gives data highly contaminated by intensive Gaussian noise having white properties. We observe the probabilistic properties of CNA in HR‐CGH and SNP measurements and show that jitter in the breakpoints can statistically be described with either the discrete skew Laplace distribution when the segmental signal‐to‐noise ratio (SNR) exceeds unity or modified Bessel function‐based approximation when SNR is <1. Based upon these approaches, the confidence masks can be developed and used to enhance the estimates of the CNAs for the given confidence probability by removing some unlikely existing breakpoints

DeCiFering the elusive cancer cell fraction in tumor heterogeneity and evolution

The cancer cell fraction (CCF), or proportion of cancerous cells in a tumor containing a single-nucleotide variant (SNV), is a fundamental statistic used to quantify tumor heterogeneity and evolution. Existing CCF estimation methods from bulk DNA sequencing data assume that every cell with an SNV contains the same number of copies of the SNV. This assumption is unrealistic in tumors with copy-number aberrations that alter SNV multiplicities. Furthermore, the CCF does not account for SNV losses due to copy-number aberrations, confounding downstream phylogenetic analyses. We introduce DeCiFer, an algorithm that overcomes these limitations by clustering SNVs using a novel statistic, the descendant cell fraction (DCF). The DCF quantifies both the prevalence of an SNV at the present time and its past evolutionary history using an evolutionary model that allows mutation losses. We show that DeCiFer yields more parsimonious reconstructions of tumor evolution than previously reported for 49 prostate cancer samples

Insights into the genomic histories of diverse human populations using whole-genome sequencing analysis.

Despite the progress in sampling many populations, human genomics research is still not fully reflective of the diversity found globally. Understudied populations limit our knowledge of genetic variation and population history, and their inclusion is needed to ensure they benefit from future developments in genomic medicine. In this thesis, I describe extending our understanding of global genetic diversity and population history by two main projects. The first is focused on structural variation in a diverse set of 54 human populations which are part of the Human Genome Diversity Project (HGDP-CEPH) panel. Using whole-genome sequences previously produced at the Wellcome Sanger Institute, I generated a comprehensive catalogue of structural variation identifying a total of 126,018 variants, of which 78% are novel. Some reach high frequency and are private to continental groups or even individual populations, including regionally-restricted runaway duplications and putatively introgressed variants from archaic hominins. By de novo assembly of 25 genomes using linked-read sequencing, I discovered 1643 breakpoint-resolved unique insertions, in aggregate accounting for 1.9 Mb of sequence absent from the GRCh38 reference genome, highlighting the limitation of a single human reference genome. In the second project I collected and analysed a dataset of 137 high-coverage physically-phased genome sequences from eight Middle Eastern populations using linked-read sequencing. Focusing on the population history using single nucleotide variants, I found no genetic traces of archeologically documented early expansions out-of-Africa in present-day populations in the region. I show that Arabian populations have the lowest Neanderthal ancestry of all non-African populations tested, which is explained by them having elevated Basal Eurasian ancestry. By comparing Levantines and Arabian historical population sizes, I find a divergence that starts before the Neolithic era, when Levantines expanded while Arabians maintained small populations that could have derived ancestry from local epipaleolithic hunter-gatherers. All populations suffered a bottleneck overlapping the archaeologically-documented aridification events, with Arabians decreasing in size with the onset of the desert climate in Arabia ~6 kya while the Levantine bottleneck overlaps the 4.2 kiloyear aridification event. I also identify an ancestry that is associated with the spread of Semitic languages across the region during the Bronze Age. Finally, I identify novel variants that show evidence of selection, including signals of polygenic selection. This thesis fills an important gap in the study of diverse human populations, although further work is needed to sequence and characterize additional genetically underrepresented groups.Government of Dubai - Dubai Police GH

Integrative approaches to high-throughput data in lymphoid leukemias (on transcriptomes, the whole-genome mutational landscape, flow cytometry and gene copy-number alterations)

Within this thesis I developed a new approach for the analysis and integration of heterogeneous leukemic data sets applicable to any high-throughput analysis including basic research. All layers are stored in a semantic graph which facilitates modifications by just adding edges (relationships/attributes) and nodes (values/results) as well as calculating biological consensus and clinical correlation. The front-end is accessible through a GUI (graphical user interface) on a Java-based Semantic Web server. I used this framework to describe the genomic landscape of T-PLL (T-cell prolymphocytic leukemia), which is a rare (~0.6/million) mature T-cell malignancy with aggressive clinical course, notorious treatment resistance, and generally low overall survival. We have conducted gene expression and copy-number profiling as well as NGS (next-generation sequencing) analyses on a cohort comprising 94 T-PLL cases. TCL1A (T-cell leukemia/lymphoma 1A) overexpression and ATM (Ataxia Telangiectasia Mutated) impairment represent central hallmarks of T-PLL, predictive for patient survival, T-cell function and proper DNA damage responses. We identified new chromosomal lesions, including a gain of AGO2 (Argonaute 2, RISC Catalytic Component; 57.14% of cases), which is decisive for the chromosome 8q lesion. While we found significant enrichments of truncating mutations in ATM mut/no del (p=0.01365), as well as FAT (FAT Atypical Cadherin) domain mutations in ATM mut/del (p=0.01156), JAK3 (Janus Kinase 3) mut/ATM del cases may represent another tumor lineage. Using whole-transcriptome sequencing, we identified novel structural variants affecting chromosome 14 that lead to the expression of a TCL1A-TCR (T-cell receptor) fusion transcript and a likely degradated TCL1A protein. Two clustering approaches of normal T-cell subsets vs. leukemia gene expression profiles, as well as immunophenotyping-based agglomerative clustering and TCR repertoire reconstruction further revealed a restricted, memory-like T-cell phenotype. This is to date the most comprehensive, multi-level, integrative study on T-PLL and it led to an evolutionary disease model and a histone deacetylase-inhibiting / double strand break-inducing treatment that performs better than the current standard of chemoimmunotherapy in preclinical testing

Hidden Markov Models

Hidden Markov Models (HMMs), although known for decades, have made a big career nowadays and are still in state of development. This book presents theoretical issues and a variety of HMMs applications in speech recognition and synthesis, medicine, neurosciences, computational biology, bioinformatics, seismology, environment protection and engineering. I hope that the reader will find this book useful and helpful for their own research

Biometrics

Biometrics uses methods for unique recognition of humans based upon one or more intrinsic physical or behavioral traits. In computer science, particularly, biometrics is used as a form of identity access management and access control. It is also used to identify individuals in groups that are under surveillance. The book consists of 13 chapters, each focusing on a certain aspect of the problem. The book chapters are divided into three sections: physical biometrics, behavioral biometrics and medical biometrics. The key objective of the book is to provide comprehensive reference and text on human authentication and people identity verification from both physiological, behavioural and other points of view. It aims to publish new insights into current innovations in computer systems and technology for biometrics development and its applications. The book was reviewed by the editor Dr. Jucheng Yang, and many of the guest editors, such as Dr. Girija Chetty, Dr. Norman Poh, Dr. Loris Nanni, Dr. Jianjiang Feng, Dr. Dongsun Park, Dr. Sook Yoon and so on, who also made a significant contribution to the book

A Practical Hardware Implementation of Systemic Computation

It is widely accepted that natural computation, such as brain computation, is far superior to typical computational approaches addressing tasks such as learning and parallel processing. As conventional silicon-based technologies are about to reach their physical limits, researchers have drawn inspiration from nature to found new computational paradigms. Such a newly-conceived paradigm is Systemic Computation (SC). SC is a bio-inspired model of computation. It incorporates natural characteristics and defines a massively parallel non-von Neumann computer architecture that can model natural systems efficiently. This thesis investigates the viability and utility of a Systemic Computation hardware implementation, since prior software-based approaches have proved inadequate in terms of performance and flexibility. This is achieved by addressing three main research challenges regarding the level of support for the natural properties of SC, the design of its implied architecture and methods to make the implementation practical and efficient. Various hardware-based approaches to Natural Computation are reviewed and their compatibility and suitability, with respect to the SC paradigm, is investigated. FPGAs are identified as the most appropriate implementation platform through critical evaluation and the first prototype Hardware Architecture of Systemic computation (HAoS) is presented. HAoS is a novel custom digital design, which takes advantage of the inbuilt parallelism of an FPGA and the highly efficient matching capability of a Ternary Content Addressable Memory. It provides basic processing capabilities in order to minimize time-demanding data transfers, while the optional use of a CPU provides high-level processing support. It is optimized and extended to a practical hardware platform accompanied by a software framework to provide an efficient SC programming solution. The suggested platform is evaluated using three bio-inspired models and analysis shows that it satisfies the research challenges and provides an effective solution in terms of efficiency versus flexibility trade-off

Unravelling the mystery of migratory behaviour in the Bogong moth Agrotis infusa using genomics and novel automated monitoring techniques

An exceptionally impressive example of animal navigation is presented by the Bogong moth Agrotis infusa, that migrates over 1000 km from widely distributed winter breeding grounds to a relatively confined summer range in the Australian Alps, consistently arriving to the same sites as its predecessors, despite never having an opportunity to learn the migratory route, or indeed, the location of its destination. The Bogong moth then waits out the summer in a dormant state known as aestivation, lining the walls of cool cracks and crevices in high altitude granite outcrops, where it forms massive assemblages with an estimated 17000 moths per square metre. Recent and ongoing investigations into the sensory and neurological capabilities of the Bogong moth have revealed that it possesses a "compass sense" that relies on geomagnetic and stellar information. However, since the migratory direction of the Bogong moth varies across its breeding range, a compass is not sufficient on its own for the moth's navigation. How, for instance, does a Bogong moth know - given its starting location - in which direction to migrate? The objective of this thesis is to understand the basis of the Bogong moth migratory direction. Even though this thesis opens as many questions as it answers, significant progress towards achieving this objective is presented (in two parts) herein, primarily through development of the scientific infrastructure for studying Bogong moth biology more generally. Part I introduces a new method for quantitatively measuring Bogong moth activity and abundance using automated camera-based detection, which is then used to model the influence of abiotic factors on Bogong moth behaviour, and to measure the arrival, departure, and population dynamics of the moths in their summer range. In addition to its utility in addressing ethological questions, this new method enables quantitative long-term monitoring of the Bogong moth population, which may prove invaluable for conservation efforts (the Bogong moth has recently been assessed as endangered for the IUCN Red List). In part II, the annotated sequence of the Bogong moth genome is presented, opening the door to high-throughput molecular research on the moth. Extensive differential gene expression in the sensory and brain tissue of migrating and aestivating moths is observed, along with evidence of epigenomic modification. Finally, the results of re-sequencing the genomes of 77 Bogong moths collected from across their breeding and summer ranges are presented, which show that the Bogong moth population is panmictic, and harbours a vast quantity of rare genetic variants. Interestingly, a small number of variants are highly correlated with migratory direction, indicating promising avenues for further research into the genetic basis of migratory direction

Characterisation and computational modelling of retinal stem cells in medaka (Oryzias latipes)

The central functional unit of the vertebrate eye is the retina, composed of neural retina (NR), retinal pigmented epithelium (RPE), and non-visual retina (NVR). In amphibians and fish, the retina grows throughout life via different pools of stem cells (SCs). In this work, I combined experimental and computational approaches to elucidate SC dynamics in the three retinal tissues of the teleost fish medaka (Oryzias latipes). I developed a cell centred agent based model to recapitulate post-embryonic growth of the NR and RPE. By accounting for 3D tissue geometry and continuous growth, the model reconciled conflicting hypotheses, demonstrating that competition between SCs is not mutually exclusive with lifelong coexistence of multiple SC lineages. To understand how NR and RPE regulate their proliferative output to coordinate growth rates, I developed quantitative methods to compare experiment and simulation. I tested the experimental data against simulations implementing two modes of feedback between cell proliferation and organ growth. Thus, I identified that the NR acts upstream to set the growth pace by sending an inductive growth signal, while the RPE responds downstream to this signal. Leveraging the model, I showed that NR SCs compete for niche space, but tissue geometry biases cells at certain positions to win this competition. Further, NR SCs modulate division axes and proliferation rate to change organ shape and retinal topology. Motivated by model predictions, I experimentally characterised the large SC population of the RPE, which consisted of both cycling and non-cycling quiescent cells. Putative sister cells exhibited similar temporal dynamics in local clusters, indicating that quiescence was the major mechanism for regulating proliferative output in the RPE. Finally, I experimentally showed that the NVR grows post-embryonically from a primordium, and shared all known markers for NR SCs in the same spatial distribution. Unlike NR and RPE, the NVR lacked a dedicated niche, instead proliferative cells were distributed throughout the tissue. Lineage tracing revealed a continuous relationship between RPE, NVR, and NR. Thus, the SCs of NR and RPE, and all cells of the NVR displayed plastic multipotency capable of generating all retinal tissues. By taking advantage of the positive feedback loop between experiment and simulation, this work shines a new light into a fundamental problem – growth coordination of different SC populations in a complex vertebrate organ