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    Bayesian network prior: network analysis of biological data using external knowledge
    (Oxford Univ Press, 2014-03-15) Otu, Hasan H.; Doğan, Haluk; İşçi, Şenol; Öztürk, Cengizhan
    Motivation: Reverse engineering GI networks from experimental data is a challenging task due to the complex nature of the networks and the noise inherent in the data. One way to overcome these hurdles would be incorporating the vast amounts of external biological knowledge when building interaction networks. We propose a framework where GI networks are learned from experimental data using Bayesian networks (BNs) and the incorporation of external knowledge is also done via a BN that we call Bayesian Network Prior (BNP). BNP depicts the relation between various evidence types that contribute to the event 'gene interaction' and is used to calculate the probability of a candidate graph (G) in the structure learning process. Results: Our simulation results on synthetic, simulated and real biological data show that the proposed approach can identify the underlying interaction network with high accuracy even when the prior information is distorted and outperforms existing methods.
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    Bayesian Pathway Analysis of Cancer Microarray Data
    (Public Library Science, 2014-07-18) Otu, Hasan H.; Özgur, Arzucan; İşçi, Şenol; Korucuoğlu, Melike
    High Throughput Biological Data (HTBD) requires detailed analysis methods and from a life science perspective, these analysis results make most sense when interpreted within the context of biological pathways. Bayesian Networks (BNs) capture both linear and nonlinear interactions and handle stochastic events in a probabilistic framework accounting for noise making them viable candidates for HTBD analysis. We have recently proposed an approach, called Bayesian Pathway Analysis (BPA), for analyzing HTBD using BNs in which known biological pathways are modeled as BNs and pathways that best explain the given HTBD are found. BPA uses the fold change information to obtain an input matrix to score each pathway modeled as a BN. Scoring is achieved using the Bayesian-Dirichlet Equivalent method and significance is assessed by randomization via bootstrapping of the columns of the input matrix. In this study, we improve on the BPA system by optimizing the steps involved in "Data Preprocessing and Discretization'', "Scoring'', "Significance Assessment'', and "Software and Web Application''. We tested the improved system on synthetic data sets and achieved over 98% accuracy in identifying the active pathways. The overall approach was applied on real cancer microarray data sets in order to investigate the pathways that are commonly active in different cancer types. We compared our findings on the real data sets with a relevant approach called the Signaling Pathway Impact Analysis (SPIA).
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    Gene expression profiling of granulosa cells from PCOS patients following varying doses of human chorionic gonadotropin
    (Springer/Plenum Publishers, 2013-03) Otu, Hasan H.
    Human chorionic gonadotrophin (hCG) has been used to induce ovulation and oocyte maturation. Although the most common dose of hCG used in IVF is 10,000 IU, there are reports that suggest 5,000 IU is sufficient to yield similar results. The objective of this study is to evaluate the dose dependent differences in gene expression of granulosa cells following various doses of hCG treatment. Patients with polycystic ovarian syndrome (PCOS) were stimulated for IVF treatment. The hCG injection was either withheld or given at 5,000 or 10,000 IU. Granulosa cells from the follicular fluids have been collected for RNA isolation and analyzed using Affymetrix genechip arrays. Unsupervised hierarchical clustering based on whole gene expression revealed two distinct groups of patients in this experiment. All untreated patients were clustered together whereas hCG-treated patients separated to a different group regardless of the dose. A large number of the transcripts were similarly up- or down-regulated across both hCG doses (2229 and 1945 transcripts, respectively). However, we observed dose-dependent statistically significant differences in gene expression in only 15 transcripts. Although hCG injection caused a major change in the gene expression profile of granulosa cells, 10,000 IU hCG resulted in minimal changes in the gene expression profiles of granulosa cells as compared with 5,000 IU. Thus, based on our results, we suggest the use of 10,000 IU hCG should be reconsidered in PCOS patients.
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    Identification of a novel gene set in human cumulus cells predictive of an oocyte's pregnancy potential
    (Elsevier Science Inc, 2013) Iager, Amy E.; Kocabas, Arif M.; Otu, Hasan H.; Ruppel, Patricia; Langerveld, Anna; Schnarr, Patricia; Suarez, Mariluz
    Objective: To identify a gene expression signature in human cumulus cells (CCs) predictive of pregnancy outcome across multiple clinics, taking into account the clinic and patient variations inherent in IVF practice. Design: Retrospective analysis of single human cumulus-oocyte complexes with the use of a combined microarray and quantitative reverse-transcription polymerase chain reaction (qRT-PCR) approach. Setting: Multiple private IVF clinics. Patient(s): Fifty-eight patients. Samples from 55 patients underwent qRT-PCR analysis, and samples from 27 patients resulted in live birth. Intervention(s): Gene expression analysis for correlation with pregnancy outcome on individual human CCs collected immediately after oocyte retrieval. Pregnancy prediction analysis used leave-one-out cross-validation with weighted voting. Main Outcome Measure(s): Combinatorial expression of 12 genes in 101 samples from 58 patients. Result(s): We found a set of 12 genes predictive of pregnancy outcome based on their expression levels in CCs. This pregnancy prediction model had an accuracy of 78%, a sensitivity of 72%, a specificity of 84%, a positive predictive value of 81%, and a negative predictive value of 76%. Receiver operating characteristic analysis found an area under the curve of 0.763 +/- 0.079, significantly greater than 0.5 (random chance prediction). Conclusion(s): Gene expression analysis in human CCs should be considered in identifying oocytes with a high potential to lead to pregnancy in IVF-ET. (Fertil Steril (R) 2013;99:745-52. (C) 2013 by American Society for Reproductive Medicine.)
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    Objective Functions
    (Humana Press Inc, 2014) Dogan, Haluk; Otu, Hasan H.
    Multiple sequence alignment involves alignment of more than two sequences and is an NP-complete problem. Therefore, heuristic algorithms that use different criteria to find an approximation to the optimal solution are employed. At the heart of these approaches lie the scoring and objective functions that a given algorithm uses to compare competing solutions in constructing a multiple sequence alignment. These objective functions are often motivated by the biological paradigms that govern functional similarities and evolutionary relations. Most existing approaches utilize a progressive process where the final alignment is constructed sequentially by adding new sequences into an existing multiple sequence alignment matrix, which is dynamically updated. In doing this, the core scoring function to assess accuracies of pairwise alignments generally remains the same, while the objective functions used in intermediary steps differ. Nevertheless, the overall assessment of the final multiple sequence alignment is generally calculated by an extension of pairwise scorings. In this chapter, we explore different scoring and objective functions used in calculating the accuracy and optimization of a multiple sequence alignment and provide utilization of these criteria in popularly used multiple sequence alignment algorithms.
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    Prediction of peptides binding to MHC class I and II alleles by temporal motif mining
    (Bmc, 2013-01-21) Otu, Hasan H.; Meydan, Cem; Sezerman, Osman Uğur
    Background: MHC (Major Histocompatibility Complex) is a key player in the immune response of most vertebrates. The computational prediction of whether a given antigenic peptide will bind to a specific MHC allele is important in the development of vaccines for emerging pathogens, the creation of possibilities for controlling immune response, and for the applications of immunotherapy. One of the problems that make this computational prediction difficult is the detection of the binding core region in peptides, coupled with the presence of bulges and loops causing variations in the total sequence length. Most machine learning methods require the sequences to be of the same length to successfully discover the binding motifs, ignoring the length variance in both motif mining and prediction steps. In order to overcome this limitation, we propose the use of time-based motif mining methods that work position-independently. Results: The prediction method was tested on a benchmark set of 28 different alleles for MHC class I and 27 different alleles for MHC class II. The obtained results are comparable to the state of the art methods for both MHC classes, surpassing the published results for some alleles. The average prediction AUC values are 0.897 for class I, and 0.858 for class II. Conclusions: Temporal motif mining using partial periodic patterns can capture information about the sequences well enough to predict the binding of the peptides and is comparable to state of the art methods in the literature. Unlike neural networks or matrix based predictors, our proposed method does not depend on peptide length and can work with both short and long fragments. This advantage allows better use of the available training data and the prediction of peptides of uncommon lengths.
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    Testing robustness of relative complexity measure method constructing robust phylogenetic trees for Galanthus L. Using the relative complexity measure
    (Bmc, 2013-01-17) Otu, Hasan H.
    Background: Most phylogeny analysis methods based on molecular sequences use multiple alignment where the quality of the alignment, which is dependent on the alignment parameters, determines the accuracy of the resulting trees. Different parameter combinations chosen for the multiple alignment may result in different phylogenies. A new non-alignment based approach, Relative Complexity Measure (RCM), has been introduced to tackle this problem and proven to work in fungi and mitochondrial DNA. Result: In this work, we present an application of the RCM method to reconstruct robust phylogenetic trees using sequence data for genus Galanthus obtained from different regions in Turkey. Phylogenies have been analyzed using nuclear and chloroplast DNA sequences. Results showed that, the tree obtained from nuclear ribosomal RNA gene sequences was more robust, while the tree obtained from the chloroplast DNA showed a higher degree of variation. Conclusions: Phylogenies generated by Relative Complexity Measure were found to be robust and results of RCM were more reliable than the compared techniques. Particularly, to overcome MSA-based problems, RCM seems to be a reasonable way and a good alternative to MSA-based phylogenetic analysis. We believe our method will become a mainstream phylogeny construction method especially for the highly variable sequence families where the accuracy of the MSA heavily depends on the alignment parameters.
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    Whole Genome Sequence of a Turkish Individual
    (Public Library Science, 2014-01-09) Doğan, Haluk; Can, Handan; Otu, Hasan H.
    Although whole human genome sequencing can be done with readily available technical and financial resources, the need for detailed analyses of genomes of certain populations still exists. Here we present, for the first time, sequencing and analysis of a Turkish human genome. We have performed 35x coverage using paired-end sequencing, where over 95% of sequencing reads are mapped to the reference genome covering more than 99% of the bases. The assembly of unmapped reads rendered 11,654 contigs, 2,168 of which did not reveal any homology to known sequences, resulting in similar to 1 Mbp of unmapped sequence. Single nucleotide polymorphism (SNP) discovery resulted in 3,537,794 SNP calls with 29,184 SNPs identified in coding regions, where 106 were nonsense and 259 were categorized as having a high-impact effect. The homo/hetero zygosity (1,415,123:2,122,671 or 1:1.5) and transition/transversion ratios (2,383,204:1,154,590 or 2.06:1) were within expected limits. Of the identified SNPs, 480,396 were potentially novel with 2,925 in coding regions, including 48 nonsense and 95 high-impact SNPs. Functional analysis of novel high-impact SNPs revealed various interaction networks, notably involving hereditary and neurological disorders or diseases. Assembly results indicated 713,640 indels (1:1.09 insertion/deletion ratio), ranging from -52 bp to 34 bp in length and causing about 180 codon insertion/deletions and 246 frame shifts. Using paired-end-and read-depth-based methods, we discovered 9,109 structural variants and compared our variant findings with other populations. Our results suggest that whole genome sequencing is a valuable tool for understanding variations in the human genome across different populations. Detailed analyses of genomes of diverse origins greatly benefits research in genetics and medicine and should be conducted on a larger scale.