166 research outputs found
A novel method for detecting SNV genotypes from personal genome sequencing data
No full textGenoomi variatsioonide uuringud on olulised mitme erineva valdkonna jaoks nagu näiteks personaalne meditsiin, evolutsiooniline analüüs või bakteritüvede tuvastamine. SNV-d, üksiku nukleotiidi variandid, on kõige põhjalikumalt uuritud variatsioonid genoomis ning seostatud mitmete tunnuste ja haigustega. Genoomiuuringud sõltuvad olulisel määral genoomist antud variatsioonide alleeli variantide määramise võimekusest, olemasolevad SNV genotüüpide määramise meetodid on aga võrdlemisi aeglased ja ebausaldusväärsed. Käesoleva magistritöö eesmärk on arendada välja uudne meetod SNV genotüüpide määramiseks kiiresti ning usaldusväärselt, jättes vahele kõige vigaderohkema etapi tavalisest SNV määramise töövoost.
Selles töös tutvustati uut, k-meeridel põhinevat lähenemist SNV genotüüpide määramiseks. Arendati välja meetod SNV asukohti katvate unikaalsete k-meeride kasutamiseks antud SNV-de alleeli variantide leidmiseks. Töö käigus loodi programmid etteantud SNV-de jaoks unikaalsete k-meeride leidmiseks ning personaalse genoomi sekveneerimisandmetest genotüübi määramise metoodika testimiseks.
Tutvustatud meetodit testiti nii simuleeritud kui reaalsete sekveneerimisandmetega, ühtlasi mõõdeti programmi aja- ja mälukasutust. Tulevaseks tööks toodi välja ka mõned soovitused programmi ajakulu vähendamiseks ning sekveneerimisandmetest määratud genotüüpide arvu suurendamiseks.The genome variation studies are important for many areas like personal medicine, evolutionary analysis or bacterial strain identification. The single nucleotide variants (SNVs) are the most thoroughly studied variations in the genome, associated with different traits and diseases. Genomic studies depend greatly on the ability of detecting the allele variants of these variations present in personal genome. However, the methods used for calling SNV genotypes from personal sequencing data are not very fast nor reliable. The aim of this master's thesis was to develop a novel method for detecting SNV genotypes fast and reliably with a new approach that allows omitting the often error-prone step of read mapping used in the general variant calling pipelines.
A k-mer based approach was introduced in this study for detecting SNV genotypes. A method was developed for using the unique k-mers covering the SNV locations for different allele variants to identify the genotypes of these SNVs. A program was created for compiling a list of unique k-mers for the allele variants of given SNVs and the method was tested using a program for detecting the genotype of these SNVs from the personal genome sequencing data.
The method introduced in this study was tested on both simulated and real sequencing data and the memory and time usage was measured. Some recommendations were made for future work to reduce the time usage of the program as well as improving the detection of SNV genotypes
High-density genotyping and linkage disequilibrium in the human genome using chromosome 22 as a model
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Method for the Identification of Taxon-Specific k-mers from Chloroplast Genome: A Case Study on Tomato Plant (Solanum lycopersicum)
No full textPolymerase chain reaction and different barcoding methods commonly used for plant identification from metagenomics samples are based on the amplification of a limited number of pre-selected barcoding regions. These methods are often inapplicable due to DNA degradation, low amplification success or low species discriminative power of selected genomic regions. Here we introduce a method for the rapid identification of plant taxon-specific k-mers, that is applicable for the fast detection of plant taxa directly from raw sequencing reads without aligning, mapping or assembling the reads. We identified more than 800 Solanum lycopersicum specific k-mers (32 nucleotides in length) from 42 different chloroplast genome regions using the developed method. We demonstrated that identified k-mers are also detectable in whole genome sequencing raw reads from S. lycopersicum. Also, we demonstrated the usability of taxon-specific k-mers in artificial mixtures of sequences from closely related species. Developed method offers a novel strategy for fast identification of taxon-specific genome regions and offers new perspectives for detection of plant taxa directly from sequencing raw reads
GeneToCN: an alignment-free method for gene copy number estimation directly from next-generation sequencing reads
No full textAbstract Genomes exhibit large regions with segmental copy number variation, many of which include entire genes and are multiallelic. We have developed a computational method GeneToCN that counts the frequencies of gene-specific k-mers in FASTQ files and uses this information to infer copy number of the gene. We validated the copy number predictions for amylase genes (AMY1, AMY2A, AMY2B) using experimental data from digital droplet PCR (ddPCR) on 39 individuals and observed a strong correlation (R = 0.99) between GeneToCN predictions and experimentally determined copy numbers. An additional validation on FCGR3 genes showed a higher concordance for FCGR3A compared to two other methods, but reduced accuracy for FCGR3B. We further tested the method on three different genomic regions (SMN, NPY4R, and LPA Kringle IV-2 domain). Predicted copy number distributions of these genes in a set of 500 individuals from the Estonian Biobank were in good agreement with the previously published studies. In addition, we investigated the possibility to use GeneToCN on sequencing data generated by different technologies by comparing copy number predictions from Illumina, PacBio, and Oxford Nanopore data of the same sample. Despite the differences in variability of k-mer frequencies, all three sequencing technologies give similar predictions with GeneToCN
K-meeridel põhinevad meetodid bakterite ja plasmiidide tuvastamiseks
No full textVäitekirja elektrooniline versioon ei sisalda publikatsiooneMikroorganismid on Maad asustanud juba miljardeid aastaid ning neid leidub peaaegu kõikjal. Isegi meie oleme nendega lahutamatult seotud – baktereid elab nii meie nahal kui ka soolestikus. Osad bakteritest võivad aga olla patogeensed ja põhjustada haigusi. Näiteks oli keskajal suure hulga elanikkonnast tapnud Musta Surma põhjustajaks katkubakter Yersinia pestis. Tänapäeval aitavad meid bakterite vastu antibiootikumid, kuid järjest suurem probleem on antibiootikumiresistentsuse laialdane levik. Sellele aitavad kaasa plasmiidid – bakterites olevad DNA järjestused, mis on bakteri enda kromosoomist eraldiseisvad ning mida bakterid võivad kiirelt üksteisele edasi anda. Käesoleva doktoritöö eesmärgiks oli luua bakterite ja plasmiidide tuvastamiseks meetodid, mis võimaldaksid töötada sekveneerimiskeskuste poolt toodetud toorandmetega. Ülesande lahendamiseks otsustasime kasutada k-meeridel põhinevat analüüsi. K-meer tähistab lühikest DNA juppi pikkusega k nukleotiidi. Pikema DNA järjestuse, näiteks bakterigenoomi, saab jagada lühemateks k-meerideks ning vaadelda seda kui k-meeride kogumit. Sellise lähenemise eeliseks on sõltumatus lugemi pikkusest – kõik lugemid sisaldavad k-meere ning analüüsides k-meeride hulki, on võimalik määrata algse proovi koostis. StrainSeeker on meie töögrupis loodud programm bakteritüvede määramiseks. Me arendasime välja uudse algoritmi, mis näitab proovis esineva bakteri eeldatavat asukohta kasutaja poolt ette antaval fülogeneetilisel puul. Lõime ka visuaalse kasutajaliidesega veebiserveri. Plasmiidide tuvastamiseks eeldasime, et plasmiidide arv bakteri rakus on tavaliselt suurem bakteri kromosoomi omast, seega võiks ka plasmiidi k-meeride keskmine esinemissagedus olla suurem kui bakteri kromosoomi k-meeride puhul. Me testisime oma programmi, mis sai nimeks PlasmidSeeker, nii simuleeritud kui ka reaalsete bakteri täisgenoomi sekveneerimisandmestikega, millede puhul oli teada proovide tegelik koostis. PlasmidSeeker leidis üles kõik proovides olnud plasmiidid ning määras täpselt ka nende koopiaarvu. Kokkuvõttes oleme oma tööga andnud panuse arvutuslikku mikrobioloogiasse, luues uued võimalused bakteriaalsete proovide analüüsiks.Microbes have roamed Earth for billions of years and can be found almost anywhere. They are present even on our skin and in our gut. However, some bacteria can be pathogenic and cause diseases. For instance, the Black Death, which killed millions during the Middle Ages, was caused by the bacterium Yersinia pestis. Nowadays, antibiotics protect us against the bacterial threat, but a new problem is looming – widespread antibiotic resistance. This is partly facilitated by plasmids – DNA sequences which are separate from the bacterial chromosome and can be readily passed from one bacterium to the other. The general goal of this work was to develop methods for the identification of bacteria and plasmids from raw data produced by sequencing centers. We decided to use k-mer based analysis for this task. K-mer itself is simply a short stretch of DNA with a length of k nucleotides. A long DNA sequence, such as a bacterial genome, can be divided into shorter k-mers and analyzed as a whole. This has the advantage of not being limited by read length – any read contains k-mers and by analyzing these, we can identify the contents of the sample. StrainSeeker is a bacterial identification program developed by our group. We developed a novel algorithm that predicts the location of an isolated bacterium on the user-provided phylogenetic tree. Also, we created a web server with a visual interface for users with limited bioinformatics experience. For plasmid detection, we assumed that the plasmid copy number is usually higher compared to the bacterial chromosome. This means that the average frequency of plasmid k-mers should also be higher than the frequency of chromosomal k-mers. We named the program PlasmidSeeker and tested it with real and simulated bacterial whole genome sequencing samples, in which the real plasmid content was known. PlasmidSeeker detected all plasmids and accurately estimated their copy numbers. With our work, we have made a contribution to the field of computational microbiology and provided novel means for the analysis of bacterial samples
Taimede DNA tuvastamine metagenoomsetest proovidest
No full textVäitekirja elektrooniline versioon ei sisalda publikatsiooneToit sisaldab DNA jälgi taimedest, loomadest ja mikroorganismidest, mida on selle valmistamisel kasutatud või millega toit on kokku puutunud. DNA analüüs võimaldab tuvastada usaldusväärselt toidus olevaid komponente ja nende bioloogilist päritolu, mis omakorda võimaldab tuvastada inimese tervise seisukohast olulisi toidu koostisosi ja võltsinguid. Doktoritöö keskendub toidus olevate taimsete allergeenide DNA põhise tuvastamise metoodika arendamisele kasutades tänapäevast DNA sekveneerimise tehnoloogiat.
Toidus olevate taimsete komponentide DNA analüüs eeldab taimegenoomide eripärade arvestamist ja unikaalsete genoomipiirkondade tuvastamist laboris. Toidu DNA analüüsiks kasutatavate PCR-põhiste meetodite puhul kasutatakse valitud liigi või organismirühma tuvastamiseks eelnevalt disainitud spetsiifilisi PCR praimereid. Samas võivad taimede genoomid sisaldada hulganisti praimerite disainiks sobimatuid kordusjärjestustustega genoomiregioone. Doktoritöö raames rakendati primer3_masker programmi taimede genoomide kordusjärjestustuste analüüsiks, et tuvastada veelgi täpsemalt taimede eristamiseks sobivaid genoomiregioone.
Paljude erinevate taimede tuvastamine töödeldud toidust on kulutõhusam kasutades teise põlvkonna DNA sekveneerimise tehnoloogiat ja efektiivsemaid andmeanalüüsi meetodeid. Doktoritöö käigus töötati välja unikaalne bioinformaatiline metoodika taimede tuvastamiseks, mille käigus analüüsitakse kogu toidus leiduvat DNA-d. Huvipakkuvate taimede tuvastamiseks kasutatakse sadu lühikesi ja spetsiifilisi plastiidi genoomist pärit DNA järjestusi ehk k-meere. Lühikeste k-meeride rakendamine võimaldab taimede tuvastamist ka töödeldud toidust, milles on DNA lagunenud lühikesteks fragmentideksFood contains traces of DNA from plants, animals, and micro-organisms that have been used in its preparation or with which it has been in contact. DNA analysis makes it possible to reliably identify the components in food and their biological origin, which in turn makes it possible to identify counterfeits and food ingredients that are important for human health. The dissertation focuses on the development of DNA-based detection methods for the identification of plant allergens in food using modern DNA sequencing technology.
DNA analysis of plant components in food requires consideration of the specifics of plant genomes and the identification of unique genomic regions in the laboratory. PCR-based methods for food DNA analysis use pre-designed specific PCR primers to identify the selected species or group of organisms. However, plant genomes may contain a large number of repetitive genomic regions that are not suitable for primer design. In the doctoral thesis, the primer3_masker program was applied to the analysis of the extent of repeated regions in the plant genome to identify more precisely the genome regions suitable for plant differentiation.
Detection of many different plants in processed foods is more cost-effective using second-generation DNA sequencing technology and more efficient data analysis methods. In the doctoral thesis, a unique bioinformatics methodology for plant identification was developed, during which all the DNA in food is analyzed. Hundreds of short and specific DNA sequences, or k-mers, from the plastid genome, are used to identify plants of interest. The use of short k-mers also allows the detection of plants in processed foods in which DNA has been broken down into short fragments.https://www.ester.ee/record=b545210
GenePointer - Bakteritel esinevate antibiootikumi resistentsuse näitajate tuvastamise automatiseeriv tarkvara
No full textAntimicrobial resistance has been a growing threat on the horizon for more than 70 years now, and recent improvements in sequencing technology and bioinformatics tools are
making it easier to analyze bacterial genomes. To add upon the already existing functionality of PhenotypeSeeker, a program for phenotype prediction from genotypes, the author created
the resistance marker identification pipeline, GenePointer. The program aims to identify the elements in bacterial genomes most associated with their resistance phenotypes, using mapping
and alignment approaches and is made to provide insight into novel associations to make the study of resistance mechanisms faster and more efficient. The program managed to identify
some of the resistance associated genes in two of the three species and antibiotic combinations analyzed. However, the current version of GenePointer did not identify any novel associations
and lacks the statistical power to identify all the known resistance genes. The program in its current form is useful for analyzing and summarizing the resistance associated elements in large
numbers of genomes at a time and has potential in future developments to improve on the detection of associations
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