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  • Küçük Resim
    Öğe
    Identification of protein-protein interaction bridges for multiple sclerosis
    (Oxford University Press, 2023) Yazıcı, Gözde; Kurt Vatandaşlar, Burcu; Aydın Cantürk, İlknur; Aydınlı, Fatmagül İlayda; Arıcı Düz, Özge; Karakoç, Emre; Kerman, Bilal Ersen; Alkan, Can
    Motivation: Identifying and prioritizing disease-related proteins is an important scientific problem to develop proper treatments. Network science has become an important discipline to prioritize such proteins. Multiple sclerosis, an autoimmune disease for which there is still no cure, is characterized by a damaging process called demyelination. Demyelination is the destruction of myelin, a structure facilitating fast transmission of neuron impulses, and oligodendrocytes, the cells producing myelin, by immune cells. Identifying the proteins that have special features on the network formed by the proteins of oligodendrocyte and immune cells can reveal useful information about the disease.Results: We investigated the most significant protein pairs that we define as bridges among the proteins providing the interaction between the two cells in demyelination, in the networks formed by the oligodendrocyte and each type of two immune cells (i.e. macrophage and T-cell) using network analysis techniques and integer programming. The reason, we investigated these specialized hubs was that a problem related to these proteins might impose a bigger damage in the system. We showed that 61%-100% of the proteins our model detected, depending on parameterization, have already been associated with multiple sclerosis. We further observed the mRNA expression levels of several proteins we prioritized significantly decreased in human peripheral blood mononuclear cells of multiple sclerosis patients. We therefore present a model, BriFin, which can be used for analyzing processes where interactions of two cell types play an important role.
  • Küçük Resim
    Öğe
    Improving genome assemblies using multi-platform sequence data
    (Springer Verlag, 2016) Kavak, Pınar; Ergüner, Bekir; Üstek, Duran; Yüksel, Bayram; Sağıroglu, Mahmut Şamil; Güngör, Tunga; Alkan, Can
    Accurate de novo assembly using short reads generated by next generation sequencing technologies is still an open problem. Although there are several assembly algorithms developed for data generated with different sequencing technologies, and some that can make use of hybrid data, the assemblies are still far from being perfect. There is still a need for computational approaches to improve draft assemblies. Here we propose a new method to correct assembly mistakes when there are multiple types of data generated using different sequencing technologies that have different strengths and biases. We exploit the assembly of highly accurate short reads to correct the contigs obtained from less accurate long reads. We apply our method to Illumina, 454, and Ion Torrent data, and also compare our results with existing hybrid assemblers, Celera and Masurca.