The genomic variability of sixty-nine populations of the potato cyst nematodes (PCN) Globodera pallida and G. rostochiensis from Europe and South America were analyzed using the RAPD-PCR technique with sixty-six 10-mer primers. Large genomic differences were found between the two PCN species (i.e. 33%). The genomic pool of British G. pallida populations showed considerably less variation than the Peruvian populations, with 73% and 41% similarity between populations respectively. The genomic similarity among populations of G. rostochiensis was 89% for UK populations and 82% when the two continental European populations were included. Nevertheless, between populations within each species and from the same locality, genomic differences were still found. The RAPD-PCR technique proved to be useful for revealing the genomic variability between and within species using DNA extracted from 50 cysts, but it gave variable results when DNA extracted from individual females or cysts was used, suggesting that for evaluating the genomic variability of individuals it is better to use specific primers. RAPD-PCR was also used successfully to distinguish the two PCN species, individuals selected and selected for virulence and even biotypes using individual cysts. Based on the results found when comparing biotypes of Globodera pallida, it is suggested that all the biotypes considered in the International Pathotype Scheme could be grouped into Pa1 and Pa2/3 when classifying European populations, and Pa1A or Pa1B, P4A, P5A and P6A when analyzing South American populations. However, these groupings should be regarded just as a reference, because virulence bioassay results plus the data found using the RAPD-PCR technique suggested that, at least in G. pallida, virulence seems to be a polygenic trait ruled by several genes with additive effects. On the other hand, based on the same sort of data, virulence in G. rostochiensis seems to be ruled only by major genes. Selected and unselected populations of G. pallida, reared on either potato clone Solanum vernei (VTn)2 62.33.3 or a susceptible control, were distinguished using the RAPD-PCR technique and primers Operon A-07, E-06, G-16 and I-05. Three of the fragments that appeared to distinguish the unselected from the selected populations were cloned into an isolate of E. coli and their sequences obtained. Gpalpha, seems to be part of a promoter region of a gene probably related or linked to virulence. The use of differential clones to characterize PCN populations with different proportions of each virulence gene is a valuable tool. Whilst diagnostic probes for routine identification of virulent populations are being developed, the use of the “gene pool similarities” concept involving the DNA patterns of standard populations as genetic virulence types (i.e. virulence biotypes), integrated with information on their response to differential clones bearing genes for resistance, would represent the best approach towards devising a sustainable control strategy to optimize the usefulness of whatever resistance is available.

Malaria is a deadly disease that causes nearly one million deaths each year. Understanding the demographic history of the malaria parasite Plasmodium falciparum and the genetic basis of its adaptations to antimalarial treatments and the human immune system is important for developing methods to control and eradicate malaria. To study the long-term demographic history and recent effective size of the population in order to identify genes under selection more efficiently and predict the effectiveness of selection, in Chapter 2 we sequenced the complete genomes of 25 cultured P. falciparum isolates from Senegal. In addition, in Chapter 3 we estimated temporal allele frequencies in 24 loci among 528 strains from the same population across six years. Based on genetic diversity of the genome sequences, we estimate the long-term effective population size to be approximately 100,000, and a major population expansion of the parasite population approximately 20,000-40,000 years ago. Based on temporal changes in allele frequencies, however, the recent effective size is estimated to be less than 100 from 2007-2011. The discrepancy may reflect recent aggressive efforts to control malaria in Senegal or migration between populations.

Over half the human genome consists of repetitive sequences. One major class is the tandem repeats (TRs), which are defined by their location in the genome, repeat unit, and copy number. TRs loci that exhibit variant copy numbers are called Variable Number Tandem Repeats (VNTRs). High VNTR mutation rates of approximately 0.0001 per generation make them suitable for forensic studies, and of interest for potential roles in gene regulation and disease. TRs are generally divided into three classes: 1) microsatellites or short tandem repeats (STRs) with patterns <7 bp; 2) minisatellites with patterns of seven to hundreds of base pairs; and 3) macrosatellites with patterns of >100 bp. To date, mini- and macrosatellites have been poorly characterized, mainly due to a lack of computational tools. In this thesis, I utilize a tool, VNTRseek, to identify human minisatellite VNTRs using short-read sequencing data from nearly 2,800 individuals and developed a new computational tool, MaSUD, to identify human macrosatellite VNTRs using data from 2,504 individuals. MaSUD is the first high-throughput tool to genotype macrosatellites using short reads. I identified over 35,000 minisatellite VNTRs and over 4,000 macrosatellite VNTRs, most previously unknown. A small subset in each VNTR class was validated experimentally and in silico. The detected VNTRs were further studied for their effects on gene expression, ability to distinguish human populations, and functional enrichment. Unlike STRs, mini- and macrosatellite VNTRs are enriched in regions with functional importance, e.g., introns, promoters, and transcription factor binding sites. A study of VNTRs across 26 populations shows that minisatellite VNTR genotypes can be used to predict super-populations with >90% accuracy. In addition, genotypes for 195 minisatellite VNTRs and 22 macrosatellite VNTRs were shown to be associated with differential expression in nearby genes (eQTLs). Finally, I developed a computational tool, mlZ, to infer undetected VNTR alleles and to detect false positive predictions. mlZ is applicable to other tools that use read support for predicting short variants. Overall, these studies provide the most comprehensive analysis of mini- and macrosatellites in human populations and will facilitate the application of VNTRs for clinical purposes.

Thesis advisor: Gabor T. Marth / Background: Due to the rapid development and application of next generation sequencing (NGS) techniques, large amounts of NGS data have become available for genome-related biological research, such as population genetics, evolutionary research, and genome wide association studies. A crucial step of these genome-related studies is the detection of genomic variation between different species and individuals. Current approaches for the detection of genomic variation can be classified into alignment-based variation detection and assembly-based variation detection. Due to the limitation of current NGS read length, alignment-based variation detection remains the mainstream approach. The Smith-Waterman algorithm, which produces the optimal pairwise alignment between two sequences, is frequently used as a key component of fast heuristic read mapping and variation detection tools for next-generation sequencing data. Though various fast Smith-Waterman implementations are developed, they are either designed as monolithic protein database searching tools, which do not return detailed alignment, or they are embedded into other tools. These issues make reusing these efficient Smith-Waterman implementations impractical. After the alignment step in the traditional variation detection pipeline, the afterward variation detection using pileup data and the Bayesian model is also facing great challenges especially from low-complexity genomic regions. Sequencing errors and misalignment problems still influence variation detection (especially INDEL detection) a lot. The accuracy of genomic variation detection still needs to be improved, especially when we work on low- complexity genomic regions and low-quality sequencing data. Results: To facilitate easy integration of the fast Single-Instruction-Multiple-Data Smith-Waterman algorithm into third-party software, we wrote a C/C++ library, which extends Farrar's Striped Smith-Waterman (SSW) to return alignment information in addition to the optimal Smith-Waterman score. In this library we developed a new method to generate the full optimal alignment results and a suboptimal score in linear space at little cost of efficiency. This improvement makes the fast Single-Instruction-Multiple-Data Smith-Waterman become really useful in genomic applications. SSW is available both as a C/C++ software library, as well as a stand-alone alignment tool at: https://github.com/mengyao/Complete- Striped-Smith-Waterman-Library. The SSW library has been used in the primary read mapping tool MOSAIK, the split-read mapping program SCISSORS, the MEI detector TAN- GRAM, and the read-overlap graph generation program RZMBLR. The speeds of the mentioned software are improved significantly by replacing their ordinary Smith-Waterman or banded Smith-Waterman module with the SSW Library. To improve the accuracy of genomic variation detection, especially in low-complexity genomic regions and on low-quality sequencing data, we developed PHV, a genomic variation detection tool based on the profile hidden Markov model. PHV also demonstrates a novel PHMM application in the genomic research field. The banded PHMM algorithms used in PHV make it a very fast whole-genome variation detection tool based on the HMM method. The comparison of PHV to GATK, Samtools and Freebayes for detecting variation from both simulated data and real data shows PHV has good potential for dealing with sequencing errors and misalignments. PHV also successfully detects a 49 bp long deletion that is totally misaligned by the mapping tool, and neglected by GATK and Samtools. Conclusion: The efforts made in this thesis are very meaningful for methodology development in studies of genomic variation detection. The two novel algorithms stated here will also inspire future work in NGS data analysis. / Thesis (PhD) — Boston College, 2014. / Submitted to: Boston College. Graduate School of Arts and Sciences. / Discipline: Biology.

Megasatellites are polymorphic tandem repetitive sequences with repeat-units longer than or equal to 1000 base pairs. The novel algorithm Megasatfinder predicts megasatellites in the human genome. A structured method of analysing the algorithm is developed and conducted. The analysis method consists of six test scenarios. Scripts are created, which execute the algorithm using various parameter settings. Three nucleotide sequences are applied; a real sequence extracted from the human genome and two random sequences, generated using different base probabilities. Usability and accuracy are investigated, providing the user with confidence in the algorithm and its output. The results indicate that Megasatfinder is an excellent tool for the detection of megasatellites and that the generated results are highly reliable. The results of the complete analysis suggest alterations in the default parameter settings, presented as user guidelines, and state that artificially generated sequences are not applicable as models for real DNA in computational simulations.

<p>Megasatellites are polymorphic tandem repetitive sequences with repeat-units longer than or equal to 1000 base pairs. The novel algorithm Megasatfinder predicts megasatellites in the human genome. A structured method of analysing the algorithm is developed and conducted. The analysis method consists of six test scenarios. Scripts are created, which execute the algorithm using various parameter settings. Three nucleotide sequences are applied; a real sequence extracted from the human genome and two random sequences, generated using different base probabilities. Usability and accuracy are investigated, providing the user with confidence in the algorithm and its output. The results indicate that Megasatfinder is an excellent tool for the detection of megasatellites and that the generated results are highly reliable. The results of the complete analysis suggest alterations in the default parameter settings, presented as user guidelines, and state that artificially generated sequences are not applicable as models for real DNA in computational simulations.</p>