Open Student Webinars Acıbadem University


Alara Erenel

The Co-Occurrence of X, Y, and Z SNPs in BRCA1 Gene: an in silico Investigation


Breast Cancer (BC) is the most common cancer type seen in women and the third most common one worldwide with an increasing rate of cases. Genomic studies revealed X, Y, and Z SNPs on Breast Cancer Gene 1 (BRCA1) may be co-occurring and affecting the BC formation. If they co-occur, investigating them individually would be misdirecting. In this research, the aim was to investigate whether 1) X, Y, and Z are on the same haplotype and co-occur and 2) co-occurrence of X, Y, and Z is pathogenic. During this study, frequencies and conservation scores of SNPs’, haplotype status, linkage disequilibrium (LD), dual and triple co-occurrence statuses, BRCA1 transcripts, and possible protein changes are investigated through data portals and R. By the comparison of the healthy dataset (2,504), general cancer dataset (296), and BC dataset (98), association between co-occurrence of X, Y, and Z with cancer/BC formation is done. Associations are tested with logistic regression, odds ratio, Fisher’s Exact Test, and Chi-Square test. All results are cross-checked with the variant classification guidelines for pathogenicity. As a result, these SNPs coherent with the same haplotype pattern, co-occurrence experiments supported the co-occurrence of these three SNPs and also strengthen the pathogenicity hypothesis. It was shown that odds to have cancer (Odds Ratio (OR): 34.28, probability value (p-value): 0.0006) and BC (OR: 52.15, p-value: 0.0041) are significantly higher for the individuals with triple co-occurrence. More in vitro research needs to be done to strengthen the pieces of evidence obtained in silico.

Alper Bülbül

Variant pathgenicity prediction tool with 3-D and sequence analyzes of protein-protein interactions


Motivation: A large number of patient samples can be analyzed with the developing next-generation sequence and protein interaction technologies. In this way, we see that many genes are involved, especially in autoinflammatory monogenic diseases. At the same time, the number of variants associated with diseases is increasing. We used protein-protein interactions and 3D structure analysis for the classification of large number of variants. Results: 3D docking analysis of proteins, sequence-based interaction scores and delta delta Gibbs free energy (ddG) were created using stability analysis based on protein binary interactions from STRING and Intact databases. ZDOCK and SPRINT values were weighted according to the HGPEC gene rank scores with a variant in 36 monogenic autoinflammatory diseases. When the relationships between ZDOCK, SPRINT, and ddG values were examined in the benign and pathogenic variant groups, we find that the ZDOCK and SPRINT values were positively correlated with each other. In addition, ddG values are negatively correlated with ZDOCK and SPRINT values. 702 missense disease associated variants are retrieved from infevers database. Since there was an imbalance between the sample number of 130 Bening and 572 pathogenic mutations, we created synthetic data with the SMOTE algorithm. The ROC AUC values of the model, created with the Random Forest algorithm, are 97%.

Ekin Köni

Integrated Analysis of Mutated Genes in Leptomeningeal Metastasis Caused by Breast Cancer


Background/aim: Leptomeningeal carcinomatosis (LMC) is a rare type of cancer that settles through metastasis from a tumor in the body to the meninges and affects the brain, spinal cord, and nerves, causing sudden neurological disorders and death. Most common solid tumors causing LMC include breast, lung, and melanoma. The average life expectancy of LMC patients with the prescribed treatments is an average of 6 months. Due to the unknown molecular mechanism and genetic state of the disease, next-generation sequencing (NGS), Whole-exome sequencing (WES) and RNA sequencing (RNA-seq) are being performed to investigate the transcriptome properties of circulating tumor cells (CTCs) found in cerebrospinal fluid (CSF). Currently, the diversification of cancer treatment and the prolonged patient survival have also led to increased LMC incidence. Therefore, molecular studies investigating the development of LMC are required. The aim of this study is to gather information about the genes that are mutated in Breast-LMC studies to analyze possible molecular interactions. Results: According to our results in Breast cancer-LMC there were in total 24 mutated genes. 7 of these were only seen in Breast cancer-LMC, only one mutual gene with melanoma-LMC and 11 common genes with NSCLC-LMC. The PPI network constructed with STRING showed interactions among these 24 genes. In addition, pathway enrichment analysis which was observed with g:profiler and Cytoscape revealed the enriched pathways. The networks contained 24 nodes and 87 edges. Chromatin organization, modification of cellular content were some of the enriched pathways. Moreover, transcription regulation, immune system development, activation and regulation pathways were some of the most important pathways in which the mutated genes were involved. Finally, drugs that interact with breast cancer genes, have been approved or are under clinical trials, were identified with DrugBank and online tools.

İrem Çongur

Bioinformatic Analysis of Mutated Genes in Leptomeningeal Carcinoma Caused by Non-Small Cell Lung Cancer


Background/aim: Leptomeningeal carcinoma (LM) is mostly seen as a result of metastasis caused by melanoma, breast, and non-small cell lung cancer (NSCLC) and is formed by the placement of tumor cells in the meninges of the brain. As a result of this metastasis tumor cells also leak into the cerebrospinal fluid (CSF). The average survival time for LM patients is less than one year. Mutations and gene expression changes in patients are being studied with next-generation sequencing (NGS), whole exome sequencing (WES) and single-cell RNA sequencing (scRNA-seq), but no study has yet been conducted to elucidate the molecular mechanism of this disease. Since LM disease has a narrow patient population, studies on candidate marker genes are limited. Therefore, there is a great lack of information in the literature about its mechanism. The aim of this study is to analyze genes mutated in NSCLC-LM in order to determine which pathways may be involved in the development of LM. Results: 87 genes were found to be mutated in NSCLC-LM patients after classifying the mutated genes from 11 articles. Among 87 genes, 65 were mutated only in NSCLC-LM patients. There were common mutations: 5 with both breast and melanoma LM, 6 with melanoma-LM, and 11 with breast-LM patients. PPI network of mutated genes in NSCLC-LM was composed of 87 nodes and 1181 edges which was constructed using the String database. EnrichmentMap plug-in of Cytoscape was used to construct a network of enriched pathways to visualize the output of g:Profiler. The network contained 856 nodes and 31411 edges. Using the MCODE plug-in 25 clusters were created. Some of the clusters included the following pathways: regulation of cell cycle, DNA damage and repair, cell adhesion, regulation of cytoskeleton and cellular response to environmental stimulus. Finally, drugs that interact with 8 NSCLC biomarkers were identified with DrugBank and publicly available articles.

Date: May 21st, 2022 – 11:00 AM (GMT+3)

Language: English

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Open Student Webinars – Gebze Technical University


Dilara Uzuner

Transcriptional landscape of cellular networks reveal interactions driving the dormancy mechanisms in cancer


Primary cancer cells exert unique capacity to disseminate and nestle in distant organs. Once seeded in secondary sites, cancer cells may enter a dormant state, becoming resistant to current treatment approaches, and they remain silent until they reactivate and cause overt metastases. To illuminate the complex mechanisms of cancer dormancy, 10 transcriptomic datasets from the literature enabling 21 dormancy–cancer comparisons were mapped on protein–protein interaction networks and gene-regulatory networks to extract subnetworks that are enriched in significantly deregulated genes. The genes appearing in the subnetworks and significantly upregulated in dormancy with respect to proliferative state were scored and filtered across all comparisons, leading to a dormancy–interaction network for the first time in the literature, which includes 139 genes and 1974 interactions. The dormancy interaction network will contribute to the elucidation of cellular mechanisms orchestrating cancer dormancy, paving the way for improvements in the diagnosis and treatment of metastatic cancer.

Ecehan Abdik

Systematic investigation of mouse models of Parkinson’s disease by transcriptome mapping on a brain-specific genome-scale metabolic network


Genome-scale metabolic networks enable systemic investigation of metabolic alterations caused by diseases by providing interpretation of omics data. Although Mus musculus (mouse) is one of the most commonly used model organisms for neurodegenerative diseases, a brain-specific metabolic network model of mouse has not yet been reconstructed. Here we reconstructed the first brain-specific metabolic network model of mouse, iBrain674-Mm, by a homology-based approach, which consisted of 992 reactions controlled by 674 genes and distributed over 48 pathways. We validated the newly reconstructed network model by showing that it predicts healthy resting-state metabolic phenotypes of mouse brain compatible with literature. We later used iBrain674-Mm to interpret various experimental mouse models of Parkinson’s Disease (PD) at the transcriptome level. To this aim, we applied a constraint-based modelling based biomarker prediction method called TIMBR (Transcriptionally Inferred Metabolic Biomarker Response) to predict altered metabolite productions from transcriptomic data. Systemic analysis of seven different PD mouse models by TIMBR showed that neuronal levels of glutamate, lactate, creatine phosphate, neuronal acetylcholine, bilirubin and formate increased in most of PD mouse models whereas levels of melatonin, epinephrine, astrocytic formate and astrocytic bilirubin decreased. Although most of the predictions were consistent with the literature, there were some inconsistencies among different PD mouse models, signifying that there is no perfect experimental model to reflect PD metabolism. The newly reconstructed brain-specific genome-scale metabolic network model of mouse can make important contributions to the interpretation and development of experimental mouse models of PD and other neurodegenerative diseases.

Hatice Büşra Lüleci

iMAT application as an integration method in Alzheimer’s disease in order to predict reaction activity


Alzheimer’s disease (AD) is the most common cause of dementia. There is increasing evidence of a possible link between the incidence and progression of AD and metabolic dysfunction. Determining the changes in the activity of metabolic pathways should be a major interest in the treatment of AD. Mapping sample-based gene expression levels by using Integrative Metabolic Analysis Tool (iMAT) optimization algorithm on Human-GEM led to personalized metabolic networks. Each personalized metabolic network for healthy and disease cases has a different number of reactions and genes. This variation across personalized models reveals the inherent heterogeneity of control and AD samples and justifies our personalized approach. Reactions in each model were converted to binary vectors. This categorized data was analyzed by performing Fisher-Exact test. Based on these calculations, significantly changed reactions and pathways were detected. Mapping biochemical alterations associated with AD is crucial to fill knowledge gaps on the disease mechanisms.

Müberra Fatma Cesur

Network-based metabolism-centered screening of potential drug targets in Klebsiella pneumoniae at genome scale


Klebsiella pneumoniae is an opportunistic bacterial pathogen leading to life-threatening nosocomial infections. Emergence of highly resistant strains poses a major challenge in the management of the infections by healthcare-associated K. pneumoniae isolates. Thus, despite intensive efforts, the current treatment strategies remain insufficient to eradicate such infections. Failure of the conventional infection-prevention and treatment efforts explicitly indicates the requirement of new therapeutic approaches. This prompted us to systematically analyze the K. pneumoniae metabolism to investigate drug targets. Genome-scale metabolic networks (GMNs) facilitating the systematic analysis of the metabolism are promising platforms. Thus, we used a GMN of K. pneumoniae MGH 78578 to determine putative targets through gene- and metabolite-centric approaches. To develop more realistic infection models, we performed the bacterial growth simulations within different host-mimicking media, using an improved biomass formation reaction. We selected more suitable targets based on several property-based prioritization procedures. KdsA was identified as the high-ranked putative target satisfying most of the target prioritization criteria specified under the gene-centric approach. Through a structure-based virtual screening protocol, we identified potential KdsA inhibitors. In addition, the metabolite-centric approach extended the drug target list based on synthetic lethality. This revealed the importance of combined metabolic analyses for a better understanding of the metabolism. To our knowledge, this is the first comprehensive effort on the investigation of the K. pneumoniae metabolism for drug target prediction through the constraint-based analysis of its GMN in conjunction with several bioinformatic approaches. This study can guide the researchers for the future drug designs by providing initial findings regarding crucial components of the Klebsiella metabolism.

Date: April 8th, 2022 – 2:00 PM (GMT+3)

Language: English

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As one of the biggest studen-driven organizations in Turkey, we are familiar with the challenges the students, especially the post-graduate students face. One of these problems the students face is not having enough time or financial resources to present their data to fellow students and researchers in a scientific meeting which hampers their visibility within the scientific community. As RSG Turkey, we have been conducting a webinar series called BioInfoNet for a long time. Throughout the years it has come to our attention that there was a rapid ramp up in the number of students who want to present their work but we also noticed that there have been many more who have been refraining themselves from presenting their data simply because BioInfoNet was seen as a project in which only Postdocs and PIs could present their work. To overcome this misreading and to create a safe zone to all students we have decided to start a new student webinar series: OPEN STUDENT WEBINARS.

In this new project, we aim for an open conference concept but with an online webinar approach meaning all talks will be online and open to the public. However, instead of letting students give presentations in a random order, the students from same university will be given a single day to present their work one after another as 30-40 minutes presentations. Depending on the participation requests from the university, this can be rearranged as a two day event. At the end of each talk there will be a discussion session in which all attendees can ask their questions. We initially aim to create a platform where students can present their work and improve their presentation skills. Secondly we aim to encourage students from the same university to get to know each other’s work better and help each other out. Our third aim with this compact presentation concept is letting students and researchers from other universities learn about the bioinformatics-related studies of the presenting university as well as their approaches to the studies. And finally, our main and most important goal is to increase intra&inter university collaborations.

We will collect demands until the end of April and will arrange the presentations’ dates of a particular university by creating a consensus of the availability of students from that particular university.

RSG-Turkey is a member of The International Society for Computational Biology (ISCB) Student Council (SC) Regional Student Groups (RSG). We are a non-profit community composed of early career researchers interested in computational biology and bioinformatics.


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