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: 20
    Citation - Scopus: 25
    Microrna Categorization Using Sequence Motifs and K-Mers
    (BioMed Central Ltd., 2017) Yousef, Malik; Khalifa, Waleed; Acar, İlhan Erkin; Allmer, Jens
    Background: Post-transcriptional gene dysregulation can be a hallmark of diseases like cancer and microRNAs (miRNAs) play a key role in the modulation of translation efficiency. Known pre-miRNAs are listed in miRBase, and they have been discovered in a variety of organisms ranging from viruses and microbes to eukaryotic organisms. The computational detection of pre-miRNAs is of great interest, and such approaches usually employ machine learning to discriminate between miRNAs and other sequences. Many features have been proposed describing pre-miRNAs, and we have previously introduced the use of sequence motifs and k-mers as useful ones. There have been reports of xeno-miRNAs detected via next generation sequencing. However, they may be contaminations and to aid that important decision-making process, we aimed to establish a means to differentiate pre-miRNAs from different species. Results: To achieve distinction into species, we used one species' pre-miRNAs as the positive and another species' pre-miRNAs as the negative training and test data for the establishment of machine learned models based on sequence motifs and k-mers as features. This approach resulted in higher accuracy values between distantly related species while species with closer relation produced lower accuracy values. Conclusions: We were able to differentiate among species with increasing success when the evolutionary distance increases. This conclusion is supported by previous reports of fast evolutionary changes in miRNAs since even in relatively closely related species a fairly good discrimination was possible.
  • Article
    Citation - WoS: 40
    Citation - Scopus: 46
    Genome-Wide Snp Discovery and Qtl Mapping for Fruit Quality Traits in Inbred Backcross Lines (ibls) of Solanum Pimpinellifolium Using Genotyping by Sequencing
    (BioMed Central Ltd., 2017) Çelik, İbrahim; Gürbüz, Nergiz; Uncu, Ali Tevfik; Frary, Anne; Doğanlar, Sami
    Background: Solanum pimpinellifolium has high breeding potential for fruit quality traits and has been used as a donor in tomato breeding programs. Unlocking the genetic potential of S. pimpinellifolium requires high-throughput polymorphism identification protocols for QTL mapping and introgression of favourable alleles into cultivated tomato by both positive and background selection. Results: In this study we identified SNP loci using a genotyping by sequencing (GBS) approach in an IBL mapping population derived from the cross between a high yielding fresh market tomato and S. pimpinellifolium (LA1589) as the recurrent and donor parents, respectively. A total of 120,983,088 reads were generated by the Illumina HiSeq next-generation sequencing platform. From these reads 448,539 sequence tags were generated. A majority of the sequence tags (84.4%) were uniquely aligned to the tomato genome. A total of 3.125 unique SNP loci were identified as a result of tag alignment to the genome assembly and were used in QTL analysis of 11 fruit quality traits. As a result, 37 QTLs were identified. S. pimpinellifolium contributed favourable alleles for 16 QTLs (43.2%), thus confirming the high breeding potential of this wild species. Conclusions: The present work introduced a set of SNPs at sufficiently high density for QTL mapping in populations derived from S. pimpinellifolium (LA1589). Moreover, this study demonstrated the high efficiency of the GBS approach for SNP identification, genotyping and QTL mapping in an interspecific tomato population.