Systolic building block for high performance recursive filtering

A novel bit level systolic array is presented that can be used as a building block in the construction of recursive digital filters. The circuit accepts bit-parallel input data, is pipelined at the bit level, and exhibits a very high throughput rate. The most important feature of the circuit is that it allows recursive operations to be implemented directly without incurring the large m cycle latency (where m is approximately the word length) normally associated with such systems. The use of this circuit in the construction of both first- and second-order IIR (infinite-impulse-response) filters is described.

Extremely Complex Populations of Small RNAs in the Mouse Retina and RPE/Choroid

Purpose: MicroRNAs (miRNAs) are small non-coding RNAs of ~18-22 nucleotides in length that regulate gene expression. They are widely expressed in the retina, being both required for its normal development and perturbed in disease. The aim of this study was to apply new high-throughput sequencing techniques to more fully characterise the microRNAs and other small RNAs expressed in the retina and retinal pigment epithelium (RPE)/choroid of the mouse.

Methods: Retina and RPE/choroid were dissected from eyes of 3 month-old C57BL/6J mice. Small RNA libraries were prepared and deep sequencing performed on a Genome Analyzer (Illumina). Reads were annotated by alignment to miRBase, other non-coding RNA databases and the mouse genome.

Results: Annotation of 9 million reads to 320 microRNAs in retina and 340 in RPE/choroid provides the most comprehensive profiling of microRNAs to date. Two novel microRNAs were identified in retina. Members of the sensory organ specific miR-183,-182,-96 cluster were amongst the most highly expressed, retina-enriched microRNAs. Remarkably, microRNA 'isomiRs', which vary slightly in length and are differentially detected by Taqman RT-PCR assays, existed for all the microRNAs identified in both tissues. More variation occurred at the 3' ends, including non-templated additions of T and A. Drosha-independent mirtron microRNAs and other small RNAs derived from snoRNAs were also detected.

Conclusions: Deep sequencing has revealed the complexity of small RNA expression in the mouse retina and RPE/choroid. This knowledge will improve the design and interpretation of future functional studies of the role of microRNAs and other small RNAs in retinal disease.

Improved Detection of Mycobacterium bovis in Bovine Tissues Using Immunomagnetic Separation Approaches

Immunomagnetic separation (IMS) represents a simple but effective method of selectively capturing and concentrating Mycobacterium bovis, the causative agent of bovine tuberculosis (bTB), from tissue samples. It is a physical cell separation technique that does not impact cell viability, unlike traditional chemical decontamination prior to culture. IMS is performed with paramagnetic beads coated with M. bovis-specific antibody and peptide binders. Once captured by IMS, M. bovis cells can be detected by either PCR or cultural detection methods. Increased detection rates of M. bovis, particularly from non-visibly lesioned lymph node tissues from bTB reactor animals, have recently been reported when IMS-based methods were employed.

MICROBIAL COMMUNITY COMPOSITION IN THE LUNGS OF PATIENTS WITH CYSTIC FIBROSIS AND NON-CF BRONCHIECTASIS

Introduction and Aims: Persistent bacterial infection is a major cause of morbidity and mortality in patients with both Cystic Fibrosis (CF) and non-CF Bronchiectasis (non-CFBX). Numerous studies have shown that CF and non-CFBX airways are colonised by a complex microbiota. However, many bacteria are difficult, if not impossible, to culture by conventional laboratory techniques. Therefore, molecular detection techniques offer a more comprehensive view of bacterial diversity within clinical specimens. The objective of this study was to characterise and compare bacterial diversity and relative abundance in patients with CF and non-CFBX during exacerbation and when clinically stable.

Methods: Sputum samples were collected from CF (n=50 samples) and non-CFBX (n=52 samples) patients at the start and end of treatment for an infective exacerbation and when clinically stable. Pyrosequencing was used to assess the microbial diversity and relative genera (or the closest possibly taxonomic order) abundance within the samples. Each sequence read was defined based on 3% difference.

Results: High-throughput pyrosequencing allowed a sensitive and detailed examination of microbial community composition. Rich microbial communities were apparent within both CF (171 species-level phylotypes per genus) and non-CFBX airways (144 species-level phylotypes per genus). Relative species distribution within those two environments was considerably different; however, relatively few genera formed a core of microorganisms, representing approximately 90% of all sequences, which dominated both environments. Relative abundance based on observed operational taxonomic units demonstrated that the most abundant bacteria in CF were Pseudomonas (28%), Burkholderia (22%), Streptococcus (13%), family Pseudomonadaceae (8%) and Prevotella (6%). In contrast, the most commonly detected operational taxonomic units in non-CFBX were Haemophilus (22%), Streptococcus (14%), other (unassigned taxa) (11%), Pseudomonas (10%), Veillonella (7%) and Prevotella (6%).

Conclusions: These results suggest that distinctive microbial communities are associated with infection and/or colonisation in patients with both CF and non-CFBX. Although relatively high species richness was observed within the two environments, each was dominated by different core taxa. This suggests that differences in the lung environment of these two diseases may affect adaptability of the relevant bacterial taxa.

DNA methylation subgroups and the CpG island methylator phenotype in gastric cancer: a comprehensive profiling approach

BACKGROUND: Methylation-induced silencing of promoter CpG islands in tumor suppressor genes plays an important role in human carcinogenesis. In colorectal cancer, the CpG island methylator phenotype (CIMP) is defined as widespread and elevated levels of DNA methylation and CIMP+ tumors have distinctive clinicopathological and molecular features. In contrast, the existence of a comparable CIMP subtype in gastric cancer (GC) has not been clearly established. To further investigate this issue, in the present study we performed comprehensive DNA methylation profiling of a well-characterised series of primary GC.

METHODS: The methylation status of 1,421 autosomal CpG sites located within 768 cancer-related genes was investigated using the Illumina GoldenGate Methylation Panel I assay on DNA extracted from 60 gastric tumors and matched tumor-adjacent gastric tissue pairs. Methylation data was analysed using a recursively partitioned mixture model and investigated for associations with clinicopathological and molecular features including age, Helicobacter pylori status, tumor site, patient survival, microsatellite instability and BRAF and KRAS mutations.

RESULTS: A total of 147 genes were differentially methylated between tumor and matched tumor-adjacent gastric tissue, with HOXA5 and hedgehog signalling being the top-ranked gene and signalling pathway, respectively. Unsupervised clustering of methylation data revealed the existence of 6 subgroups under two main clusters, referred to as L (low methylation; 28% of cases) and H (high methylation; 72%). Female patients were over-represented in the H tumor group compared to L group (36% vs 6%; P = 0.024), however no other significant differences in clinicopathological or molecular features were apparent. CpG sites that were hypermethylated in group H were more frequently located in CpG islands and marked for polycomb occupancy.

CONCLUSIONS: High-throughput methylation analysis implicates genes involved in embryonic development and hedgehog signaling in gastric tumorigenesis. GC is comprised of two major methylation subtypes, with the highly methylated group showing some features consistent with a CpG island methylator phenotype.

Computational cancer biology: education is a natural key to many locks

Background: Oncology is a field that profits tremendously from the genomic data generated by high-throughput technologies, including next-generation sequencing. However, in order to exploit, integrate, visualize and interpret such high-dimensional data efficiently, non-trivial computational and statistical analysis methods are required that need to be developed in a problem-directed manner.

Discussion: For this reason, computational cancer biology aims to fill this gap. Unfortunately, computational cancer biology is not yet fully recognized as a coequal field in oncology, leading to a delay in its maturation and, as an immediate consequence, an under-exploration of high-throughput data for translational research.

Summary: Here we argue that this imbalance, favoring 'wet lab-based activities', will be naturally rectified over time, if the next generation of scientists receives an academic education that provides a fair and competent introduction to computational biology and its manifold capabilities. Furthermore, we discuss a number of local educational provisions that can be implemented on university level to help in facilitating the process of harmonization.

LC- HRMS based metabolomics in food safety and doping control – DeTECH21

Issues surrounding the misuse of prohibited and licensed substances in animals destined for food production and performance sport competition continue to be an enormous challenge to regulatory authorities charged with enforcing their control. Efficient analytical strategies are implemented to screen and confirm the presence of a wide range of exogenous substances in various biological matrices. However, such methods rely on the direct measurement of drugs and/or their metabolites in a targeted mode, allowing the detection of restricted number of compounds. As a consequence, emerging practices, in particular the use of natural hormones, designer drugs and low-dose cocktails, remain difficult to handle from a control point of view. A new SME-led FP7 funded project, DeTECH21, aims to overcome current limitations by applying an untargeted metabolomics approach based on liquid chromatography coupled to high resolution mass spectrometry and bioinformatic data analysis to identify bovine and equine animals which have been exposed to exogenous substances and assist in the identification of administered compounds. Markerbased strategies, dealing with the comprehensive analysis of metabolites present in a biological sample (urine/plasma/tissue), offer a reliable solution in the areas of food safety and animal sport doping control by effective, high-throughput and sensitive detection of exogenously administered agents. Therefore, the development of the first commercially available forensic test service based on metabolomics profiling will meet 21st century demands in animal forensics.

PICan: An integromics framework for dynamic cancer biomarker discovery

Modern cancer research on prognostic and predictive biomarkers demands the integration of established and emerging high-throughput technologies. However, these data are meaningless unless carefully integrated with patient clinical outcome and epidemiological information. Integrated datasets hold the key to discovering new biomarkers and therapeutic targets in cancer. We have developed a novel approach and set of methods for integrating and interrogating phenomic, genomic and clinical data sets to facilitate cancer biomarker discovery and patient stratification. Applied to a known paradigm, the biological and clinical relevance of TP53, PICan was able to recapitulate the known biomarker status and prognostic significance at a DNA, RNA and protein levels.

Mapping protein-DNA interactions using ChIP-sequencing

Chromatin immunoprecipitation (ChIP) allows enrichment of genomic regions which are associated with specific transcription factors, histone modifications, and indeed any other epitopes which are present on chromatin. The original ChIP methods used site-specific PCR and Southern blotting to confirm which regions of the genome were enriched, on a candidate basis. The combination of ChIP with genomic tiling arrays (ChIP-chip) allowed a more unbiased approach to map ChIP-enriched sites. However, limitations of microarray probe design and probe number have a detrimental impact on the coverage, resolution, sensitivity, and cost of whole-genome tiling microarray sets for higher eukaryotes with large genomes. The combination of ChIP with high-throughput sequencing technology has allowed more comprehensive surveys of genome occupancy, greater resolution, and lower cost for whole genome coverage. Herein, we provide a comparison of high-throughput sequencing platforms and a survey of ChIP-seq analysis tools, discuss experimental design, and describe a detailed ChIP-seq method.Chromatin immunoprecipitation (ChIP) allows enrichment of genomic regions which are associated with specific transcription factors, histone modifications, and indeed any other epitopes which are present on chromatin. The original ChIP methods used site-specific PCR and Southern blotting to confirm which regions of the genome were enriched, on a candidate basis. The combination of ChIP with genomic tiling arrays (ChIP-chip) allowed a more unbiased approach to map ChIP-enriched sites. However, limitations of microarray probe design and probe number have a detrimental impact on the coverage, resolution, sensitivity, and cost of whole-genome tiling microarray sets for higher eukaryotes with large genomes. The combination of ChIP with high-throughput sequencing technology has allowed more comprehensive surveys of genome occupancy, greater resolution, and lower cost for whole genome coverage. Herein, we provide a comparison of high-throughput sequencing platforms and a survey of ChIP-seq analysis tools, discuss experimental design, and describe a detailed ChIP-seq method.

Chromatin immunoprecipitation (ChIP) methodology and readouts

The identification of direct nuclear hormone receptor gene targets provides clues to their contribution to both development and cancer progression. Until recently, the identification of such direct target genes has relied on a combination of expression analysis and in silico searches for consensus binding motifs in gene promoters. Consensus binding motifs for transcription factors are often defined using in vitro DNA binding strategies. Such in vitro strategies fail to account for the many factors that contribute significantly to target selection by transcription factors in cells beyond the recognition of these short consensus DNA sequences. These factors include DNA methylation, chromatin structure, posttranslational modifications of transcription factors, and the cooperative recruitment of transcription factor complexes. Chromatin immunoprecipitation (ChIP) provides a means of isolating transcription factor complexes in the context of endogenous chromatin, allowing the identification of direct transcription factor targets. ChIP can be combined with site-specific PCR for candidate binding sites or alternatively with cloning, genomic microarrays or more recently direct high throughput sequencing to identify novel genomic targets. The application of ChIP-based approaches has redefined consensus binding motifs for transcription factors and provided important insights into transcription factor biology.

High field chemistry with scanning probes

Fabricating Ge and Si integrated structures with nanoscale accuracy is a challenging pursuit essential for novel advances in electronics and photonics. While several scanning probe-based techniques have been proposed, no current technique offers control of nanostructure size, shape, placement, and chemical composition. To this end, atomic force microscope direct write uses a high electric field (> 109 V m-1) to create nanoscale features as fast as 1 cm s-1 by reacting a liquid precursor with a biased AFM tip. In this work, I present the first results on fabricating inorganic nanostructures via AFM direct write. Using diphenylgermane (DPG) and diphenylsilane (DPS), carbon-free germanium and silicon nanostructures (SIMS, x-ray PEEM) are fabricated. For this chemistry, I propose a model that involves electron capture and precursor fragmentation under the high electric field. To verify this model, experimental data and simulations are presented. High field chemistry for DPG and DPS has also been demonstrated for both sequential deposition and the creation of nanoscale heterostuctures, in addition to microscale deposition using a flexible stamp approach. This high field chemistry approach to the deposition of organometallic precursors could offer a low-cost, high throughput alternative for future optical, electronic, and photovoltaic applications.

Application of Machine Learning Techniques to Acute Myeloid Leukemia

Thesis (Master's)--University of Washington, 2016-03

Disease-Gene Association Using a Genetic Algorithm

Understanding the relationship between genetic diseases and the genes associated with them is an important problem regarding human health. The vast amount of data created from a large number of high-throughput experiments performed in the last few years has resulted in an unprecedented growth in computational methods to tackle the disease gene association problem. Nowadays, it is clear that a genetic disease is not a consequence of a defect in a single gene. Instead, the disease phenotype is a reflection of various genetic components interacting in a complex network. In fact, genetic diseases, like any other phenotype, occur as a result of various genes working in sync with each other in a single or several biological module(s). Using a genetic algorithm, our method tries to evolve communities containing the set of potential disease genes likely to be involved in a given genetic disease. Having a set of known disease genes, we first obtain a protein-protein interaction (PPI) network containing all the known disease genes. All the other genes inside the procured PPI network are then considered as candidate disease genes as they lie in the vicinity of the known disease genes in the network. Our method attempts to find communities of potential disease genes strongly working with one another and with the set of known disease genes. As a proof of concept, we tested our approach on 16 breast cancer genes and 15 Parkinson's Disease genes. We obtained comparable or better results than CIPHER, ENDEAVOUR and GPEC, three of the most reliable and frequently used disease-gene ranking frameworks.

Développement d’outils pour l’analyse de données de ChIP-seq et l’identification des facteurs de transcription

La méthode ChIP-seq est une technologie combinant la technique de chromatine immunoprecipitation avec le séquençage haut-débit et permettant l’analyse in vivo des facteurs de transcription à grande échelle. Le traitement des grandes quantités de données ainsi générées nécessite des moyens informatiques performants et de nombreux outils ont vu le jour récemment. Reste cependant que cette multiplication des logiciels réalisant chacun une étape de l’analyse engendre des problèmes de compatibilité et complique les analyses. Il existe ainsi un besoin important pour une suite de logiciels performante et flexible permettant l’identification des motifs. Nous proposons ici un ensemble complet d’analyse de données ChIP-seq disponible librement dans R et composé de trois modules PICS, rGADEM et MotIV. A travers l’analyse de quatre jeux de données des facteurs de transcription CTCF, STAT1, FOXA1 et ER nous avons démontré l’efficacité de notre ensemble d’analyse et mis en avant les fonctionnalités novatrices de celui-ci, notamment concernant le traitement des résultats par MotIV conduisant à la découverte de motifs non détectés par les autres algorithmes.

Genèse de l’immunopeptidome du CMH de classe I

La différentiation entre le « soi » et le « non-soi » est un processus biologique essentiel à la vie. Les peptides endogènes présentés par les complexes majeurs d’histocompatibilité de classe I (CMH I) représentent le fondement du « soi » pour les lymphocytes T CD8+. On donne le nom d’immunopeptidome à l’ensemble des peptides présentés à la surface cellulaire par les molécules du CMH I. Nos connaissances concernant l’origine, la composition et la plasticité de l’immunopeptidome restent très limitées. Dans le cadre de cette thèse, nous avons développé une nouvelle approche par spectrométrie de masse permettant de définir avec précision : la nature et l’abondance relative de l’ensemble des peptides composant l’immunopeptidome. Nous avons trouvé que l’immunopeptidome, et par conséquent la nature du « soi » immun, est surreprésenté en peptides provenant de transcrits fortement abondants en plus de dissimuler une signature tissu-spécifique. Nous avons par la suite démontré que l’immunopeptidome est plastique et modulé par l’activité métabolique de la cellule. Nous avons en effet constaté que les modifications du métabolisme cellulaire par l’inhibition de mTOR (de l’anglais mammalian Target Of Rapamycin) provoquent des changements dynamiques dans la composition de l’immunopeptidome. Nous fournissons également la première preuve dans l’étude des systèmes que l’immunopeptidome communique à la surface cellulaire l’activité de certains réseaux biochimiques ainsi que de multiples événements métaboliques régulés à plusieurs niveaux à l’intérieur de la cellule. Nos découvertes ouvrent de nouveaux horizons dans les domaines de la biologie des systèmes et de l’immunologie. En effet, notre travail de recherche suggère que la composition de l’immunopeptidome est modulée dans l’espace et le temps. Il est par conséquent très important de poursuivre le développement de méthodes quantitatives au niveau des systèmes qui nous permettront de modéliser la plasticité de l’immunopeptidome. La simulation et la prédiction des variations dans l’immunopeptidome en réponse à différents facteurs cellulaires intrinsèques et extrinsèques seraient hautement pertinentes pour la conception de traitements immunothérapeutiques.