Browsing by Subject "bioinformatics"

Acute lymphoblastic leukemia (ALL) is a hematological malignancy that is characterized by uncontrolled proliferation and blocked maturation of lymphoid progenitor cells. It is divided into B- and T-cell types both of which have multiple subtypes defined by different somatic genetic changes. Also, germline predisposition has been found to play an important role in multiple hematological malignancies and several germline variants that contribute to the ALL risk have already been identified in pediatric and familial settings. There are only few studies including adult ALL patients but thanks to the findings in acute myeloid leukemia, where they found the germline predisposition to consider also adult patients, there is now more interest in studying adult patients. The prognosis of adult ALL patients is much worse compared to pediatric patients and many are still lacking clear genetic markers for diagnosis. Thus, identifying genetic lesions affecting ALL development is important in order to improve treatments and prognosis. Germline studies can provide additional insight on the predisposition and development of ALL when there are no clear somatic biomarkers. Single nucleotide variants are usually of interest when identifying biomarkers from the genome, but also structural variants can be studied. Their coverage on the genome is higher than that of single nucleotide variants which makes them suitable candidates to explore association with prognosis. Copy number changes can be detected from next generation sequencing data although the detection specificity and sensitivity vary a lot between different software. Current approach is to identify the most likely regions with copy number change by using multiple tools and to later validate the findings experimentally. In this thesis the copy number changes in germline samples of 41 adult ALL patients were analyzed using ExomeDepth, CODEX2 and CNVkit.

Uterine leiomyomas are benign smooth muscle tumors arising in myometrium. They are very common, and the incidence in women is up to 70% by the age of 50. Usually, leiomyomas are asymptomatic, but some patients suffer from various symptoms, including abnormal uterine bleeding, pelvic pain, urinary frequency, and constipation. Uterine leiomyomas may also cause subfertility. Genetic alterations in the known driver genes MED12, HMGA2, FH, and COL4A5-6 account for about 90 % of all leiomyomas. These initiator mutations result in distinct molecular subtypes of leiomyomas. The majority of whole-genome sequencing (WGS) studies analyzing chromosomal rearrangements have been performed using fresh frozen tissues. One aim of this study was to examine the feasibility of detecting chromosomal rearrangements from WGS data of formalin-fixed paraffin embedded (FFPE) tissue samples. Previous results from 3’RNA-sequencing data revealed a subset of uterine leiomyoma samples that displayed similar gene expression patterns with HMGA2-positive leiomyomas but were previously classified as HMGA2-negative by immunohistochemistry. According to 3’RNA-sequencing, all these tumors overexpressed PLAG1, and some of them overexpressed HMGA2 or HMGA1. Thus, the second aim of this study was to identify driver mutations in these leiomyoma samples using WGS. In this study, WGS was performed for 16 leiomyoma and 4 normal myometrium FFPE samples. The following bioinformatic tools were used to detect somatic alterations at multiple levels: Delly for chromosomal rearrangements, CNVkit for copy-number alterations, and Mutect for point mutations and small insertions and deletions. Sanger sequencing was used to validate findings. The quality of WGS data obtained from FFPE samples was sufficient for detecting chromosomal rearrangements, although the number of calls were quite high. We identified recurrent chromosomal rearrangements affecting HMGA2, HMGA1, and PLAG1, mutually exclusively. One sample did not harbor any of these rearrangements, but a deletion in COL4A5-6 was found. Biallelic loss of DEPDC5 was seen in one sample with an HMGA2 rearrangement and in another sample with an HMGA1 rearrangement. HMGA2 and HMGA1 encode architectural chromatin proteins regulating several transcription factors. It is well-known that HMGA2 upregulates PLAG1 expression. The structure and functionality of HMGA2 and HMGA1 are very similar and conserved, so it might be that HMGA1 may also regulate PLAG1 expression. The results of this study suggest that HMGA2 and HMGA1 drive tumorigenesis by regulating PLAG1, and thus, PLAG1 rearrangements resulting in PLAG1 overexpression can also drive tumorigenesis. A few samples, previously classified as HMGA2-negative by immunohistochemistry, revealed to harbor HMGA2 rearrangements, suggesting that the proportion of HMGA2-positive leiomyomas might be underestimated in previous studies using immunohistochemistry. Only one study has previously reported biallelic inactivation of DEPDC5 in leiomyomas, and the results of this study support the idea that biallelic loss of DEPDC5 is a secondary driver event in uterine leiomyomas.

Mycosporines and mycosporine-like amino acids (MAAs) are small-molecules that provide UV protection in a broad range of organisms. Cyanobacteria produce a diverse set of MAA chemical variants, many of which are glycosylated. Even though the biosynthetic pathway for the production of a common cyanobacterial MAA, shinorine, is known, the biosynthetic origins of the glycosylated variants remains unclear. In this work, bioinformatics analyses were performed to catalogue the genetic diversity encoded in the MAA gene clusters in cyanobacterial genomes and identify a set of enzymes that might be involved in MAA biosynthesis. A total of 211 cyanobacterial genomes were found to contain the MAA gene cluster, with six containing glycosyltransferase genes within the gene cluster. Afterwards, 38 strains from the University of Helsinki Culture Collection were tested for the production of MAAs using QTOF-LC/MS analyses. This resulted in the identification of several novel glycosylated MAA chemical variants from Nostoc sp. UHCC 0302, which contained a 7.4 kb MAA biosynthetic gene cluster consisting of 7 genes, including two for glycosyltransferases and one for dioxygenase. Heterologous expression of this gene cluster in Escherichia coli TOP10 resulted in the production of a glycosylated porphyra-334 variant of 509 m/z by the transformant cells, showing that colanic acid biosynthesis glycosyltransferases can catalyse the addition of hexose to MAAs. These results suggested a biosynthetic route for the production of glycosylated MAAs in cyanobacteria and allowed to propose a putative role for dioxygenases in MAA biosynthesis. Further characterization of additional glycosyltransferases is necessary to improve our understanding of glycosylated MAA biosynthesis and functionality, which could be applied to large scale processes and be used in industrial applications.

The volume of data generated by high-throughput DNA sequencing has grown to a magnitude that leads to substantial computational challenges in storing and searching the data. To tackle this problem, various computational methodologies have been developed in recent years to space-efficiently index collections of data sets and enable efficient searches. One of the most recent indexing methods, Spectral Burrows-Wheeler Transform (SBWT), presents all distinct k-mers of a DNA sequence using only 4 bits and a small additional space for the rank data structures per k-mer. In addition to being space-efficient, it also enables k-mer membership queries in linear time relative to k, and constant time relative to the number of distinct k-mers in the sequence. The queries rely on rank queries over bit vectors. Experiments run on a single CPU thread have shown that in one second, hundreds of thousands of k-mer membership queries can be performed over SBWT. By parallelizing the queries on a CPU, it is possible to execute millions of queries per second. However, Graphic Processing Units (GPUs) have much more parallelization potential. The main contribution of the thesis is an implementation of the k-mer membership queries over SBWT with GPU computing. Optimizing the queries to be performed on a GPU made it possible to perform over a billion queries per second. Furthermore, the thesis presents a new enhancement for the queries over SBWT called presearching, which doubles the speed of the original SBWT search query. The rank query needed for the membership queries is implemented using space-efficient poppy rank data structures, and its derivative cumulative-poppy data structure which is one of the contributions of the thesis.

ScRNA-seq captures a static picture of a cell's transcriptome including abundances of unspliced and spliced RNA. RNA velocity methods offer the opportunity to infer future RNA abundances and thus future states of a cell based on the temporal change of these unspliced and spliced RNA. Early RNA velocity methods have shed light on transcriptional dynamics in many biological processes. However, due to strict assumptions in the underlying model, these models are not reliable when analysing and inferring velocity for genes with complex expression dynamics such as genes with transcriptional boosts. These genes can for example be observed in erythropoietic and hematopoietic data. Several new RNA velocity methods have been proposed recently. Among these, veloVI and Pyro-Velocity both employ Bayesian methods to estimate the reaction rate and latent parameters. Thus the problem of estimating RNA velocity is turned into a posterior probability inference, that allows for more flexible inference of model parameters and the quantification of uncertainty. The objectives of this thesis were to investigate newly published RNA velocity methods, veloVI and Pyro-Velocity, in comparison to the established tool scVelo. To achieve this, we applied the methods to data obtained from scRNA-seq of healthy and ERCC6L2 disease bone marrow cells. ERCC6L2 disease can cause bone marrow failure with a risk of progression to acute myeloid leukemia with erythroid predominance. Specifically, we evaluated whether RNA velocity results reflect hematopoietic differentiation, if genes with transcriptional boosts affect the velocity results, and if RNA velocity analysis can indicate why erythropoiesis in ERCC6L2 disease is affected. We find that new RNA velocity methods can not produce velocity estimations that are fully in line with what is known of hematopoiesis in our data. Further, the results suggest that velocity estimations by veloVI are affected by genes with transcriptional boosts. Moreover, RNA velocity methods examined in this thesis are not robust and cannot reliably predict cell transitions based on the estimated velocity. Subsequently, velocity estimations for disease data such as ERCC6L2 disease must be evaluated carefully before drawing any conclusion about the differentiation process. In conclusion, this thesis highlights the need for models that can model complex transcription kinetics. Still, as this field is rapidly growing and promising new methods are being developed, improvement of RNA velocity analysis, in general, is possible.