Fluorescence in situ hybridization of potato somatohaploids and their somatic hybrid donors using two Solanum brevidens specific sequences

Selostus: Perunan somaattisten hybridien ja niiden somatohaploidien fluoresenssi in situ -hybridisaatio Solanum brevidens -lajin spesifisten sekvenssien avulla

Automatic recognition of fish from video sequences

The problem of automatic recognition of the fish from the video sequences is discussed in this Master’s Thesis. This is a very urgent issue for many organizations engaged in fish farming in Finland and Russia because the process of automation control and counting of individual species is turning point in the industry. The difficulties and the specific features of the problem have been identified in order to find a solution and propose some recommendations for the components of the automated fish recognition system. Methods such as background subtraction, Kalman filtering and Viola-Jones method were implemented during this work for detection, tracking and estimation of fish parameters. Both the results of the experiments and the choice of the appropriate methods strongly depend on the quality and the type of a video which is used as an input data. Practical experiments have demonstrated that not all methods can produce good results for real data, whereas on synthetic data they operate satisfactorily.

Metal-Ion-Binding Oligonucleotides: High-Affinity Probes for Nucleic Acid Sequences

Small non-coding RNAs have numerous biological functions in cell and are divided into different classes such as: microRNA, snoRNA, snRNA and siRNA. MicroRNA (miRNA) is the most studied non-coding RNA to date and is found in plants, animals and some viruses. miRNA with short sequences is involved in suppressing translation of target genes by binding to their mRNA post-transcriptionally and silencing it. Their function besides silencing of the viral gene, can be oncogenic and therefore the cause of cancer. Hence, their roles are highlighted in human diseases, which increases the interest in using them as biomarkers and drug targets. One of the major problems to overcome is recognition of miRNA. Owing to a stable hairpin structure, chain invasion by conventional Watson-Crick base-pairing is difficult. One way to enhance the hybridization is exploitation of metal-ion mediated base-pairing, i. e. oligonucleotide probes that tightly bind a metal ions and are able to form a coordinative bonds between modified and natural nucleobases. This kind of metallo basepairs containing short modified oligonucleotides can also be useful for recognition of other RNA sequences containing hairpin-like structural motives, such as the TAR sequence of HIV. In addition, metal-ion-binding oligonucleotides will undoubtedly find applications in DNA-based nanotechnology. In this study, the 3,5-dimethylpyrazol-1-yl substituted purine derivatives were successfully incorporated within oligonucleotides, into either a terminal or non-terminal position. Among all of the modified oligonucleotides studied, a 2-(3,5-dimethylpyrazol-1-yl)-6-oxopurine base containing oligonucleotide was observed to bind most efficiently to their unmodified complementary sequences in the presence of both Cu2+ or Zn2+. The oligonucleotide incorporating 2,6-bis(3,5-dimethylpyrazol-1-yl)purine base also markedly increased the stability of duplexes in the presence of Cu2+ without losing the selectivity.

Prediction of Phosphorylation Sites from Protein Sequences

Phosphorylation is amongst the most crucial and well-studied post-translational modifications. It is involved in multiple cellular processes which makes phosphorylation prediction vital for understanding protein functions. However, wet-lab techniques are labour and time intensive. Thus, computational tools are required for efficiency. This project aims to provide a novel way to predict phosphorylation sites from protein sequences by adding flexibility and Sezerman Grouping amino acid similarity measure to previous methods, as discovering new protein sequences happens at a greater rate than determining protein structures. The predictor – NOPAY - relies on Support Vector Machines (SVMs) for classification. The features include amino acid encoding, amino acid grouping, predicted secondary structure, predicted protein disorder, predicted protein flexibility, solvent accessibility, hydrophobicity and volume. As a result, we have managed to improve phosphorylation prediction accuracy for Homo sapiens by 3% and 6.1% for Mus musculus. Sensitivity at 99% specificity was also increased by 6% for Homo sapiens and for Mus musculus by 5% on independent test sets. In this study, we have managed to increase phosphorylation prediction accuracy for Homo sapiens and Mus musculus. When there is enough data, future versions of the software may also be able to predict other organisms.