Molecular Biology and Genetics / Moleküler Biyoloji ve Genetik
Permanent URI for this collectionhttps://hdl.handle.net/11147/9
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Article Citation - WoS: 9Citation - Scopus: 11Development of Simple Sequence Repeat Markers in Hazelnut (corylus Avellana L.) by Next-Generation Sequencing and Discrimination of Turkish Hazelnut Cultivars(Springer, 2018) Özturk, Süleyman Can; Göktay, Mehmet; Doğanlar, Sami; Allmer, Jens; Frary, AnneEuropean hazelnut (Corylus avellana) is a diploid tree species and is widely used in confections. Hazelnuts are, to a large part, produced in Turkey with the cultivar "Tombul" widely grown in the Black Sea region. In this work, the "Tombul" genome was partially sequenced by next-generation sequencing technology yielding 29.2% (111.85 Mb) of the similar to 385 Mb (1C). This sequence information was used to develop genetic markers in order to enable differentiation of material before the long maturation process and to facilitate future breeding strategies. A total of 90,142 simple sequence repeats (SSRs) were identified in the contigs giving a frequency of 1 SSR per 1240 nt in the assembly. Mononucleotides were the most abundant SSR marker type (60.9%) followed by di- and trinucleotides. Primer pairs were designed for 75,139 (83.3%) of the SSRs. Fifty SSR primers were applied to 47 hazelnut accessions from nine countries to test their effectiveness and polymorphism. The markers amplified an average of 3.2 fragments. The highest polymorphism information content value was for cavSSR11062 (0.97) and the lowest (0.04) was for cavSSR13386. Two markers were monomorphic: cavSSR12855 and cavSSR13267. Single-copy SSR primers were also assessed for their ability to discriminate 19 Turkish cultivars, and it was found that seven primer pairs (Cav4217, Cav14875, Cav14418, Cav2704, Cav12862, Cav3909, Cav1361) were sufficient for this task. Thus, this study developed new SSR markers for use in hazelnut breeding and genetic studies and also provide a method to distinguish and identify true-type Turkish cultivars.Article Citation - WoS: 20Citation - Scopus: 24Newly Developed Ssr Markers Reveal Genetic Diversity and Geographical Clustering in Spinach (spinacia Oleracea)(Springer Verlag, 2017) Göl, Şurhan; Göktay, Mehmet; Allmer, Jens; Doğanlar, Sami; Frary, AnneSpinach is a popular leafy green vegetable due to its nutritional composition. It contains high concentrations of vitamins A, E, C, and K, and folic acid. Development of genetic markers for spinach is important for diversity and breeding studies. In this work, Next Generation Sequencing (NGS) technology was used to develop genomic simple sequence repeat (SSR) markers. After cleaning and contig assembly, the sequence encompassed 2.5% of the 980 Mb spinach genome. The contigs were mined for SSRs. A total of 3852 SSRs were detected. Of these, 100 primer pairs were tested and 85% were found to yield clear, reproducible amplicons. These 85 markers were then applied to 48 spinach accessions from worldwide origins, resulting in 389 alleles with 89% polymorphism. The average gene diversity (GD) value of the markers (based on a GD calculation that ranges from 0 to 0.5) was 0.25. Our results demonstrated that the newly developed SSR markers are suitable for assessing genetic diversity and population structure of spinach germplasm. The markers also revealed clustering of the accessions based on geographical origin with clear separation of Far Eastern accessions which had the overall highest genetic diversity when compared with accessions from Persia, Turkey, Europe, and the USA. Thus, the SSR markers have good potential to provide valuable information for spinach breeding and germplasm management. Also they will be helpful for genome mapping and core collection establishment.Article Citation - WoS: 6Citation - Scopus: 5Development of Genomic Simple Sequence Repeat Markers in Faba Bean by Next-Generation Sequencing(Springer Verlag, 2017) Abuzayed, Mazen A.; Göktay, Mehmet; Allmer, Jens; Doğanlar, Sami; Frary, AnneFaba bean (Vicia faba L.) is an important food legume crop with a huge genome. Development of genetic markers for faba bean is important to study diversity and for molecular breeding. In this study, we used Next Generation Sequencing (NGS) technology for the development of genomic simple sequence repeat (SSR) markers. A total of 14,027,500 sequence reads were obtained comprising 4,208 Mb. From these reads, 56,063 contigs were assembled (16,367 Mb) and 2138 SSRs were identified. Mono and dinucleotides were the most abundant, accounting for 57.5 % and 20.9 % of all SSR repeats, respectively. A total of 430 primer pairs were designed from contigs larger than 350 nucleotides and 50 primers pairs were tested for validation of SSR locus amplification. Nearly all (96 %) of the markers were found to produce clear amplicons and to be reproducible. Thirty-nine SSR markers were then applied to 46 faba bean accessions from worldwide origins, resulting in 161 alleles with 87.5 % polymorphism, and an average of 4.1 alleles per marker. Gene diversity (GD) of the markers ranged from 0 to 0.48 with an average of 0.27. Testing of the markers showed that they were useful in determining genetic relationships and population structure in faba bean accessions.Article Citation - WoS: 14Citation - Scopus: 13Delineating the Impact of Machine Learning Elements in Pre-Microrna Detection(PeerJ Inc., 2017) Saçar Demirci, Müşerref Duygu; Allmer, JensGene regulation modulates RNA expression via transcription factors. Posttranscriptional gene regulation in turn influences the amount of protein product through, for example, microRNAs (miRNAs). Experimental establishment of miRNAs and their effects is complicated and even futile when aiming to establish the entirety of miRNA target interactions. Therefore, computational approaches have been proposed. Many such tools rely on machine learning (ML) which involves example selection, feature extraction, model training, algorithm selection, and parameter optimization. Different ML algorithms have been used for model training on various example sets, more than 1,000 features describing pre-miRNAs have been proposed and different training and testing schemes have been used for model establishment. For pre-miRNA detection, negative examples cannot easily be established causing a problem for two class classification algorithms. There is also no consensus on what ML approach works best and, therefore, we set forth and established the impact of the different parts involved in ML on model performance. Furthermore, we established two new negative datasets and analyzed the impact of using them for training and testing. It was our aim to attach an order of importance to the parts involved in ML for pre-miRNA detection, but instead we found that all parts are intricately connected and their contributions cannot be easily untangled leading us to suggest that when attempting ML-based pre-miRNA detection many scenarios need to be explored.Article Citation - WoS: 14Citation - Scopus: 12The Impact of Feature Selection on One and Two-Class Classification Performance for Plant Micrornas(PeerJ Inc., 2016) Khalifa, Waleed; Yousef, Malik; Saçar Demirci, Müşerref Duygu; Allmer, JensMicroRNAs (miRNAs) are short nucleotide sequences that form a typical hairpin structure which is recognized by a complex enzyme machinery. It ultimately leads to the incorporation of 18-24 nt long mature miRNAs into RISC where they act as recognition keys to aid in regulation of target mRNAs. It is involved to determine miRNAs experimentally and, therefore, machine learning is used to complement such endeavors. The success of machine learning mostly depends on proper input data and appropriate features for parameterization of the data. Although, in general, two-class classification (TCC) is used in the field; because negative examples are hard to come by, one-class classification (OCC) has been tried for pre-miRNA detection. Since both positive and negative examples are currently somewhat limited, feature selection can prove to be vital for furthering the field of pre-miRNA detection. In this study, we compare the performance of OCC and TCC using eight feature selection methods and seven different plant species providing positive pre-miRNA examples. Feature selection was very successful for OCC where the best feature selection method achieved an average accuracy of 95.6%, thereby being ~29% better than the worst method which achieved 66.9% accuracy. While the performance is comparable to TCC, which performs up to 3% better than OCC, TCC is much less affected by feature selection and its largest performance gap is ~13% which only occurs for two of the feature selection methodologies. We conclude that feature selection is crucially important for OCC and that it can perform on par with TCC given the proper set of features.Article Citation - WoS: 27Citation - Scopus: 31Development of Genomic Simple Sequence Repeat Markers in Opium Poppy by Next-Generation Sequencing(Springer Verlag, 2014) Çelik, İbrahim; Gültekin, Visam; Allmer, Jens; Doğanlar, Sami; Frary, AnneOpium poppy (Papaver somniferum L.) is an important pharmaceutical crop with very few genetic marker resources. To expand these resources, we sequenced genomic DNA using pyrosequencing technology and examined the DNA sequences for simple sequence repeats (SSRs). A total of 1,244,412 sequence reads were obtained covering 474 Mb. Approximately half of the reads (52 %) were assembled into 166,724 contigs representing 105 Mb of the opium poppy genome. A total of 23,283 non-redundant SSRs were identified in 18,944 contigs (11.3 % of total contigs). Trinucleotide and tetranucleotide repeats were the most abundant SSR repeats, accounting for 49.0 and 27.9 % of all SSRs, respectively. The AAG/TTC repeat was the most abundant trinucleotide repeat, representing 19.7 % of trinucleotide repeats. Other SSR repeat types were AT-rich. A total of 23,126 primer pairs (98.7 % of total SSRs) were designed to amplify SSRs. Fifty-three genomic SSR markers were tested in 37 opium poppy accessions and seven Papaver species for determination of polymorphism and transferability. Intraspecific polymorphism information content (PIC) values of the genomic SSR markers were intermediate, with an average 0.17, while the interspecific average PIC value was slightly higher, 0.19. All markers showed at least 88 % transferability among related species. This study increases sequence coverage of the opium poppy genome by sevenfold and the number of opium poppy-specific SSR markers by sixfold. This is the first report of the development of genomic SSR markers in opium poppy, and the genomic SSR markers developed in this study will be useful in diversity, identification, mapping and breeding studies in opium poppy.Article Citation - WoS: 24Citation - Scopus: 26Development of Est-Ssr Markers for Diversity and Breeding Studies in Opium Poppy(John Wiley and Sons Inc., 2013) Şelale, Hatice; Çelik, İbrahim; Gültekin, Visam; Allmer, Jens; Doğanlar, Sami; Frary, AnneAll publicly available opium poppy expressed sequence tag (EST) sequences, totalling 20 885, were assembled into unigenes and examined for simple sequence repeats (SSRs). Nearly 19% of the 14 957 unigenes contained SSRs with 4% harbouring more than one SSR. Average density of the SSRs was 1 SSR per 3.6 kb of non-redundant EST sequence. Trinucleotide SSRs were most frequently identified (39%), and many of the most prevalent motifs were AT-rich. Flanking primers were designed for 86% of the SSRs and 67 primer pairs were tested on 37 opium poppy accessions and seven related species. All markers were transferable to the related species. Polymorphism information content (PIC) values for the markers were intermediate for comparisons within opium poppy (average of 0.27) and slightly higher for comparisons across species (average of 0.29). The markers were found to be useful for diversity analysis as they successfully distinguished among Turkish opium poppy accessions and land races.