Implementation of parallel algorithm for k-mer enrichment analysis of genomic sequences

The goal of this thesis was to implement a sequential algorithm that would search for subsequences in a genome. To accelerate the execution time of this algorithm we designed a parallel version and implemented the parallel version on a graphics card. The sequential algorithm had to search for predefined subsequences in a genome that was represented as a sequence of characters. It had to calculate the frequencies of sequence occurrences and the frequencies of interactions on predefined positions and on randomly modified positions in the genome, for each subsequence. Based on these frequencies it had to identify sequences that were more frequent on certain locations in a given genome. Based on data about protein-RNA interactions on certain locations in the genome, and based on the found character sequences, the algorithm had to calculate and statistically evaluate the frequencies of interactions. The sequential algorithm was implemented in the C programming language, while the parallelization was implemented on the OpenCL architecture

Implementation of parallel algorithm for k-mer enrichment analysis of genomic sequences

The goal of this thesis was to implement a sequential algorithm that would search for subsequences in a genome. To accelerate the execution time of this algorithm we designed a parallel version and implemented the parallel version on a graphics card. The sequential algorithm had to search for predefined subsequences in a genome that was represented as a sequence of characters. It had to calculate the frequencies of sequence occurrences and the frequencies of interactions on predefined positions and on randomly modified positions in the genome, for each subsequence. Based on these frequencies it had to identify sequences that were more frequent on certain locations in a given genome. Based on data about protein-RNA interactions on certain locations in the genome, and based on the found character sequences, the algorithm had to calculate and statistically evaluate the frequencies of interactions. The sequential algorithm was implemented in the C programming language, while the parallelization was implemented on the OpenCL architecture

Implementation of parallel algorithm for k-mer enrichment analysis of genomic sequences

Cilj diplomske naloge je bil implementirati sekvenčni algoritem za iskanje krajših zaporedij v genomih, ga pohitriti s paralelizacijo ter implementirati paralelno verzijo na grafični kartici. Sekvenčni algoritem je moral poiskati krajša zaporedja znakov v danem zaporedju, ki predstavlja genom. Izračunati je moral frekvence pojavitev zaporedij in pogostost interakcij na podanih položajih ter na naključno premaknjenih položajih, za vsako krajše zaporedje posebej. Na podlagi pojavitev je nato moral določiti, katera krajša zaporedja so za določene položaje v genomu bolj značilna. Na podlagi podatkov o interakcijah med proteini in genomom na določenih položajih, ter na podlagi najdenih krajših zaporedij znakov, je moral nato izračunati in statistično ovrednotiti pogostost pojavitve interakcij. Sekvenčni algoritem smo implementirali v programskem jeziku C, paralelizacijo sekvenčnega algoritma pa smo izvedli na podlagi arhitekture OpenCL, ki omogoča implementacijo algoritmov na grafičnih karticah.The goal of this thesis was to implement a sequential algorithm that would search for subsequences in a genome. To accelerate the execution time of this algorithm we designed a parallel version and implemented the parallel version on a graphics card. The sequential algorithm had to search for predefined subsequences in a genome that was represented as a sequence of characters. It had to calculate the frequencies of sequence occurrences and the frequencies of interactions on predefined positions and on randomly modified positions in the genome, for each subsequence. Based on these frequencies it had to identify sequences that were more frequent on certain locations in a given genome. Based on data about protein-RNA interactions on certain locations in the genome, and based on the found character sequences, the algorithm had to calculate and statistically evaluate the frequencies of interactions. The sequential algorithm was implemented in the C programming language, while the parallelization was implemented on the OpenCL architecture

Implementation of parallel algorithm for k-mer enrichment analysis of genomic sequences

Cilj diplomske naloge je bil implementirati sekvenčni algoritem za iskanje krajših zaporedij v genomih, ga pohitriti s paralelizacijo ter implementirati paralelno verzijo na grafični kartici. Sekvenčni algoritem je moral poiskati krajša zaporedja znakov v danem zaporedju, ki predstavlja genom. Izračunati je moral frekvence pojavitev zaporedij in pogostost interakcij na podanih položajih ter na naključno premaknjenih položajih, za vsako krajše zaporedje posebej. Na podlagi pojavitev je nato moral določiti, katera krajša zaporedja so za določene položaje v genomu bolj značilna. Na podlagi podatkov o interakcijah med proteini in genomom na določenih položajih, ter na podlagi najdenih krajših zaporedij znakov, je moral nato izračunati in statistično ovrednotiti pogostost pojavitve interakcij. Sekvenčni algoritem smo implementirali v programskem jeziku C, paralelizacijo sekvenčnega algoritma pa smo izvedli na podlagi arhitekture OpenCL, ki omogoča implementacijo algoritmov na grafičnih karticah.The goal of this thesis was to implement a sequential algorithm that would search for subsequences in a genome. To accelerate the execution time of this algorithm we designed a parallel version and implemented the parallel version on a graphics card. The sequential algorithm had to search for predefined subsequences in a genome that was represented as a sequence of characters. It had to calculate the frequencies of sequence occurrences and the frequencies of interactions on predefined positions and on randomly modified positions in the genome, for each subsequence. Based on these frequencies it had to identify sequences that were more frequent on certain locations in a given genome. Based on data about protein-RNA interactions on certain locations in the genome, and based on the found character sequences, the algorithm had to calculate and statistically evaluate the frequencies of interactions. The sequential algorithm was implemented in the C programming language, while the parallelization was implemented on the OpenCL architecture

Stability of erythrocyte-derived nanovesicles assessed by light scattering and electron microscopy

Extracellular vesicles (EVs) are gaining increasing amounts of attention due to their potential use in diagnostics and therapy, but the poor reproducibility of the studies that have been conducted on these structures hinders their breakthrough into routine practice. We believe that a better understanding of EVs stability and methods to control their integrity are the key to resolving this issue. In this work, erythrocyte EVs (hbEVs) were isolated by centrifugation from suspensions of human erythrocytes that had been aged in vitro. The isolate was characterised by scanning (SEM) and cryo-transmission electron microscopy (cryo-TEM), flow cytometry (FCM), dynamic/static light scattering (LS), protein electrophoresis, and UV-V spectrometry. The hbEVs were exposed to various conditions (pH (4–10), osmolarity (50–1000 mOsm/L), temperature (15–60 °C), and surfactant Triton X-100 (10–500 μM)). Their stability was evaluated by LS by considering the hydrodynamic radius (R$_h$), intensity of scattered light (I), and the shape parameter (ρ). The morphology of the hbEVs that had been stored in phosphate-buffered saline with citrate (PBS–citrate) at 4 °C remained consistent for more than 6 months. A change in the media properties (50–1000 mOsm/L, pH 4–10) had no significant effect on the R$_h$ (=100–130 nm). At pH values below 6 and above 8, at temperatures above 45 °C, and in the presence of Triton X-100, hbEVs degradation was indicated by a decrease in I of more than 20%. Due to the simple preparation, homogeneous morphology, and stability of hbEVs under a wide range of conditions, they are considered to be a suitable option for EV reference material

Autologous Platelet and Extracellular Vesicle-Rich Plasma as Therapeutic Fluid: A Review

The preparation of autologous platelet and extracellular vesicle-rich plasma (PVRP) has been explored in many medical fields with the aim to benefit from its healing potential. In parallel, efforts are being invested to understand the function and dynamics of PVRP that is complex in its composition and interactions. Some clinical evidence reveals beneficial effects of PVRP, while some report that there were no effects. To optimize the preparation methods, functions and mechanisms of PVRP, its constituents should be better understood. With the intention to promote further studies of autologous therapeutic PVRP, we performed a review on some topics regarding PVRP composition, harvesting, assessment and preservation, and also on clinical experience following PVRP application in humans and animals. Besides the acknowledged actions of platelets, leukocytes and different molecules, we focus on extracellular vesicles that were found abundant in PVRP

Autologous Platelet and Extracellular Vesicle-Rich Plasma as Therapeutic Fluid:A Review

The preparation of autologous platelet and extracellular vesicle-rich plasma (PVRP) has been explored in many medical fields with the aim to benefit from its healing potential. In parallel, efforts are being invested to understand the function and dynamics of PVRP that is complex in its composition and interactions. Some clinical evidence reveals beneficial effects of PVRP, while some report that there were no effects. To optimize the preparation methods, functions and mechanisms of PVRP, its constituents should be better understood. With the intention to promote further studies of autologous therapeutic PVRP, we performed a review on some topics regarding PVRP composition, harvesting, assessment and preservation, and also on clinical experience following PVRP application in humans and animals. Besides the acknowledged actions of platelets, leukocytes and different molecules, we focus on extracellular vesicles that were found abundant in PVRP.</p