Early Detection of Various Types of Skin Cancer Using Deep Learning CNN – Vidit Goyal


Vidit Goyal

Vidit Goyal earned his Bachelors degree in Computer Science Engineering from Dr. A.P.J Abdul Kalam Technical University, India. He is also studying Bioinformatics and Artificial Intelligence from various MOOCs. His thesis focused on Early Detection of Skin Cancers Using Deep Learning CNN. He is currently working on developing vaccine using openAI under the AI firm Montreal.AI.


Over the last few years, there has been a rise in the reports of skin cancer in Asian continents. Regular skin checkups are recommended by dermatologist to identify the skin cancer in their initial stages. Hence, to assist this process, we proposed a mobile application that can detect the position of cancer and also classify into three categories such as Melanoma, Dermatofibroma, and Benign Keratosis lesions. We proposed a convolutional neural network and implemented two models – Modified Inception model and Modified Google’s MobileNet with transfer learning. The evaluation of the proposed method is done using HAM10000 dataset which is a collection of multi-source dermatoscopic images of common pigmented skin lesions. The experimental results shows that modified inception model performs better than Google’s MobileNet. The objective is to develop a commercial mobile application to detect the chances of early cancer so that a proper treatment can be suggested to the patient.

Date: December 5th, 2020 – 6:00 pm (GMT+3)

Language: English

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A short review: Integrative Modeling of Biomolecular Complexes

Structures of biological macromolecules cannot be easily determined, as they are flexible, i.e. their conformations change while they function [1].  Therefore, these molecules should be characterized. This characterization step might be very challenging [2]. Structures of these macromolecules can be specified by using some well-known techniques such as X-ray crystallography, nuclear magnetic resonance (NMR) spectroscopy and Small-angle X-ray scattering (SAXS) [3]. These techniques give different information about structure of a molecule and these alone are not enough to determine whole structure properties [4]. Also, results of these techniques need to be interpreted by using computational analysis in order to specify more precise structures of macromolecules [2]. Integrative modeling is a common technique used in recent years to more accurately determine the molecular structure.

What is integrative modeling?

Figure 1: Integrative structure examples of some systems [5].

As understood from a word meaning, integrative modeling uses more than one information source to model a structure and mechanism of biological molecules in systems [5]. As in all modeling methods, integrative modeling combines all available experimental data as well as with computational techniques to obtain more accurate, precise, complete and efficient model (Figure 1).

Integrative modeling has iterative four stages: (1) gathering information, (2) representing the system by translating information, (3) creating sample of structural models and (4) scoring the model (Figure 2).  

A screenshot of a cell phone

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  Figure 2: Iterative integrative modeling process [5].


  1. Gathering information: Collecting all available data about structure of the system is the first stage in Figure 2 [6]. Structural information coming from any method/technique can be based in the theory.
  2. Representing the system and translating information into spatial restraints: Gained information data from first step can be used to describe a model [5]. Based on the input information, variables are used to define features of the model. These variables can represent atoms, coarse-grained particles and subunit in complex in structural biology.
  3. Structural sampling:  Created models have random configuration as a first. Then, different configurations are sampled based on the scoring functions [6]. Results are fitted to input information for filtering. 
  4. Validating the model: Models which have good-scoring results are chosen for validation (these chosen models creates ensemble) [5]. After some estimation, one or more than one model can be chosen as a result (or not to be chosen). This depends on accuracy calculation based on the input data. 

As a following, you can find another representation of these iterative four stages for integrative modeling which shows different usage areas [7].

A picture containing timeline

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Figure 3: Structure determination of protein complexes and genome assemblies with integrative modeling [7].

Types of Structural Information & Software Resources for Integrative Modeling 

X-ray crystallography and nuclear magnetic resonance (NMR) spectroscopy are common techniques to specify the structure of biological molecules[4]. They give atomic information of the structure. Acquiring crystallographic structures is very challenging for large biological complexes. Therefore, some recent techniques to determine structure of a molecule are used as shown in Table 1.


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Table 1: Types of structural information [4].

Each technique gives a different aspect about structure of a molecule, so they can be used to determine different properties of the structure[5]. Brief explanation of some techniques in Table1:

  • Cryo-EM: Cryo-electron microscopy is commonly used to specify global structural information and structural characterization of large complexes [4]. The three-dimensional (3D) electron density map of a macromolecule complex is obtained with cryo-EM single particle analysis. 
  • XL-MS: Distance restraints between residue pairs in biological molecule can be determined by using cross-linking coupled to mass spectrometry.
  • SAXS: Small-angle X-ray scattering is commonly used method to get information about shapes of macromolecules. 
  • Sequence information is important to get evolutionary conserved positions which can be related with folding, function, interactions and dynamics of the molecule.
  • FRET: Förster resonance energy transfer is used to get information about structures, dynamics and interactions of protein.

In Table 2, some of commonly used suite of tools with their methods are listed which were also used to create structures in Figure 1. 


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   Table 2: Example of existing software resources for integrative modeling [5].

What is the accuracy of integrative modeling?


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Figure 4: Comparing the integrative structures of the Yeast NPC [5].

The modeled structures of the Yeast NPC from two different years are compared in Figure 4 [5]. As it can be seen in Figure 4, 2018 structure of Yeast NPC was modeled more detailed than 2007 structure. Among the years, precision is getting smaller and that leads to more detailed, more accurate model. Accordingly, decrease on the value of precision means increasing the resolution. High resolution allows us to determine structure more clearly. Therefore, 2018 structure has more details. These two structures of the Yeast NPC were not modeled by using only one information resource. They were modeled with multiple information from different resources. The resources used for 2018 model give more detailed information. Purpose of integrative modeling is to reach as true as possible model by using all available data and the quality of data increases the accuracy of integrative modeling. Figure 4 is a good example of how well integrative modeling works and how used resources affect the result.


I suggest you take a look at the articles I used for this short review. “Principles for Integrative Structural Biology Studies” article is very informative review for integrative modeling. If you will choose only one, I highly recommend that article. I hope this short review is helpful for you. Thank you for reading.



[1]      A. Panjkovich and D. I. Svergun, “Deciphering conformational transitions of proteins by small angle X-ray scattering and normal mode analysis,” Phys. Chem. Chem. Phys., vol. 18, no. 8, pp. 5707–5719, 2016.

[2]      E. Karaca and A. M. J. J. Bonvin, “Advances in integrative modeling of biomolecular complexes,” Methods, vol. 59, no. 3, pp. 372–381, 2013.

[3]      C. E. M. Schindler, S. J. de Vries, A. Sasse, and M. Zacharias, “SAXS Data Alone can Generate High-Quality Models of Protein-Protein Complexes,” Structure, vol. 24, no. 8, pp. 1387–1397, 2016.

[4]      M. Braitbard, D. Schneidman-Duhovny, and N. Kalisman, “Integrative Structure Modeling: Overview and Assessment,” Annu. Rev. Biochem., vol. 88, no. 1, pp. 113–135, Jun. 2019.

[5]      M. P. Rout and A. Sali, “Principles for Integrative Structural Biology Studies,” Cell, vol. 177, no. 6, pp. 1384–1403, 2019.

[6]      B. Webb et al., “Integrative structure modeling with the Integrative Modeling Platform,” Protein Sci., vol. 27, no. 1, pp. 245–258, Jan. 2018.

[7]      A. P. Joseph, G. Polles, F. Alber, and M. Topf, “Integrative modelling of cellular assemblies,” Curr. Opin. Struct. Biol., vol. 46, pp. 102–109, 2017.

Sex, Genes and Diplomonads: The Evolution of Sex-related Genes in Hexamita inflata – Begüm Serra Büyüktarakçı


Begüm Serra Büyütarakçı

After I completed BSc at Boğaziçi University, Molecular Biology and Genetics department, I moved to Sweden for MSc and studied Evolutionary Biology at Uppsala University. Meanwhile, I got interested in bioinformatics and focused on phylogenetic analysis in the thesis of my master’s degree. I am currently working as a research assistant in Molecular Evolution group of Jan Andersson at Biomedical Centre (BMC), Uppsala University.


Sexual reproduction is widespread among eukaryotes however it is not very wellknown outside of the animals, land plants and fungi kingdoms. Metamonada, a phylum of single-celled eukaryotes, comprises diverse lineages including diplomonads. Some members of diplomonads have been assumed to be asexual, though the presence of putative meiotic genes were reported in recent studies. I applied a comparative phylogenomic approach to clarify the occurrence of sexual life cycle in diplomonads. Here, I surveyed the sets of sex-related genes in the ongoing Hexamita inflata genome project. The inventory of sex-related genes was compiled based on the major sexual processes: cell fusion (plasmogamy), nuclear fusion (karyogamy) and meiosis. My analysis showed that H. inflata encodes karyogamy protein, Gex1 but not the plasmogamy protein, Hap2. Putatively meiosis specific genes: Spo11, Dmc1, Hop2 and Mnd1 were identified in H. inflata genome. Based on my findings, H. inflata possesses Mer3/Hfm1 gene which is required during meiotic crossover formation and postmeiotic genes (Mlh2/Pms1 and Mad2). I hypothesize that H. inflata is capable of some sex-related processes such as nuclear fusion and meiotic inter-homolog recombination. My results indicate that the sex machinery varies among diplomonads and other Metamonada based on the wide distribution of sex-related genes.

Date: September 30th, 2020 – 4:00 pm (GMT+3)

Language: English

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Density based clustering and error correction of metabarcodes in Nanopore sequencing using the novel bioinformatics algorithm ASHURE – Bilgenur Baloğlu


Bilgenur Baloğlu

Bilgenur Baloglu earned her B.S. in molecular biology and genetics at Istanbul Technical University. She then earned her Ph.D. in Biological Sciences from National University of Singapore in 2018. Her thesis focused on the biological assessment of aquatic habitats using DNA sequencing technologies, which contributed to solving an ecological outbreak caused by aquatic insects as well as led to the discovery of nearly 350 insect species in a tropical swamp forest. Throughout her Ph.D., she provided consulting services to the National Water Agency of Singapore government. Dr. Baloglu worked as/is as postdoctoral researcher at the Centre for Biodiversity Genomics, University of Guelph in Canada, where she focused on developing new methods for DNA sequencing, Nanopore sequencing, and phylogenetics of sub-arctic insects. She is also coordinating a US EPA funded project on the Great Lakes DNA barcoding along with four collaborating universities in the USA.


Metabarcoding (identification of the plant, animal, and fungal taxa present in an environmental sample) rapidly gains importance in ecology, food safety, pest identification, and disease surveillance. It has a compelling advantage over traditional approaches for obtaining data on species distributions, however, it is often difficult to detect all the species present in a bulk sample using High-throughput Sequencing (HTS). This can – in parts – be attributed to the shorter read lengths most HTS instruments generate. Moreover, most HTS platforms are not portable, making in situ field-based sequencing not feasible. Oxford Nanopore sequencing platforms such as the MinION represent an exception to that and they are also known to provide longer reads albeit limited by rather high error rates (~12-15%). We used a freshwater mock community of 50 Operational Taxonomic Units (OTU) to test the capacity of the Oxford Nanopore MinION coupled with a rolling circle amplification protocol to provide long read metabarcoding results. We also propose a new Python pipeline that explores error profiles of nanopore consensus sequences, mapping accuracy, and overall community representation within a complex bulk sample. Using our molecular and bioinformatics workflow, we were able to estimate the diversity of the tested freshwater mock community with an average sequence accuracy of >99% for 1D2 sequencing on the nanopore platform. We also showed that the high error rates associated with long-read single-molecule sequencing can be mitigated by using a rolling circle amplification protocol. Future bioassessment programs will tremendously benefit from such portable, highly accurate, species-level metabarcoding and it appears that we reached a point were cost-effective field-based DNA metabarcoding is possible.

Date: August 28th, 2020 – 3:00 pm (GMT+3)

Language: English

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The Impact of Protein Structure on Sequence Evolution – Julien Y. Dutheil


Julien Y. Dutheil

My research aims at understanding the mechanisms of biological evolution at the molecular level. I am in particular interested in the study of stochastic processes and the role of organisational levels (a.k.a. “systems”). Research in my group combines computational with experimental approaches, applied to population genomics, structural bioinformatics and statistical analysis of “omics” data.


The fate of mutations in populations depends on their impact on the fitness of the individual that carries them. This fitness effect depends, in turn, on the location of the mutation in the genome: a mutation occurring in a non-coding region generates a new allele that will evolve neutrally, while a mutation located within a functional region can have deleterious or advantageous effects, effects that will furthermore depend on the function of the underlying gene. Yet within a given gene, mutations can have very distinct effects. For genes encoding a macromolecule, RNA or protein, an important determinant of these effects is the structure of the encoded molecule. I will here present some insights that we gained regarding the impact of protein structure on the evolution of sequences, with a focus on protein-encoding sequences. In particular, we ask the following questions: (1) what is the distribution of adaptive mutations along 3D protein structures and (2) to which extent does protein structure generate coevolution between positions? To leverage information about the distribution of fitness effects, we relied on comparative genome analyses. I will present two statistical approaches: an extension of the McDonald-Kreitman approach that allows inferring the rate of adaptive non-synonymous substitutions by modeling the distribution of fitness effects of mutations, and a substitution mapping procedure used for inferring coevolving positions.

Date: September 4th, 2020 – 6:00 pm (GMT+3)

Language: English

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Projecting the Course of COVID-19 in Turkey: A Probabilistic Modeling Approach – Hüseyin Cahit Burduroğlu


Hüseyin Cahit Burduroğlu

He graduated from the Molecular Biology and Genetics department of Yildiz Technical University. After working in the area of structural bioinformatics for 3 years in two different projects that are focused on the stability of metalloproteins and peptides to be used on drug-delivery, he joined the Bioinformatics Master Program in METU Informatics Institute in 2019 where he currently works as a research assistant.


The COVID-19 Pandemic originated in Wuhan, China, in December 2019 and became one of the worst global health crises ever. The first confirmed cases were announced early in March and since then, serious containment measures have taken place in Turkey. Here, we present a different approach, a Bayesian negative binomial multilevel model with mixed effects, for the projection of the COVID-19 pandemic and apply this model to the Turkish case. We predicted confirmed daily cases and cumulative numbers for June 6th to June 26th with 80%, 95%, and 99% prediction intervals (PI). Our projections showed that if we continued to comply with measures and no drastic changes are seen in diagnosis or management protocols, the epidemic curve would tend to decrease in this time interval. Also, the predictive validity analysis suggests that proposed model projections should be in the 95% PI band for the first 12 days of the projections.

Date: August 21th, 2020 – 2:00 pm (GMT+3)

Language: English

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Lewontin Paradoksu ve Düşündürdükleri – Ergi Deniz Özsoy


Ergi Deniz Özsoy

Ergi Deniz Özsoy, 1967 yılında Hannover’da doğdu. 1993 yılında Hacettepe Üniversitesi Fen Fakültesi Biyoloji Bölümü’nü bitiren Özsoy, 1996 yılında yine aynı bölümde yüksek lisans tezini vererek bilim uzmanı oldu. 2002 yılında Hacettepe Üniversitesi Biyoloji Bölümü ‘nde doktorasını tamamladı. Doktora deneylerini Groningen Üniversitesi Genetik Bölümü Popülasyon Genetiği biriminde aldığı TÜBİTAK bursuyla tamamladı. 2000 ve 2002 yıllarında Kuzey Karolina Üniversitesi’nde istatistiksel genetik üzerine eğitim aldı. 2004 yılından itibaren çeşitli sürelerle aynı üniversitede Trudy Mackay’ın laboratuvarında kantitatif genetik ve genomik çalıştı. 2010 yılında Fullbright bursiyeri olarak Kaliforniya Üniversitesi San Diego’da Ekoloji ve Evrimsel Biyoloji Bölümü’nde araştırmalarda bulundu. Şu an Hacettepe Üniversitesi Biyoloji Bölümü’nde genotip-fenotip ilişkisinin karmaşık genetiği ve genomiği üzerine evrimsel genetik perspektif kullanarak Drosophila modelleri çerçevesinde çalışmaktadır. Ek olarak egzersiz genetiği ve genomiği, gelişim genetiği ve çeşitli genetik temelli hastalıkları genomdaki genetik varyasyonla ilişkisinin araştırılması gibi çalışmalarda yürütmektedir. Evrimsel biyoloji, genetik, genomik ve kantitatif genetik Özsoy’un çalışma alanlarıdır. Özsoy, evrimsel biyolojinin tarihi ve evrim felsefesi ve biyoloji felsefesi konularıyla da ilgilenmektedir. Bu konularda yurt içinde ve yurt dışında yayınlanmış makaleleri bulunmaktadır.


Bir türün sahip olduğu genetik çeşitlilik miktarının genellikle, nötral (seçilimsel olarak birbirine eş) mutasyonların birikmesiyle oluştuğu düşünülür. Nötral evrim kuramına göre, nötral mutasyonların genetik sürüklenme ile birikmesi sonucunda oluşan heterozigotluk (genetik çeşitlilik) ile populasyonların etkin (efektif) büyüklüğü arasında doğrusal bir ilişki olmalıdır: popülasyon büyüklüğü arttıkça nötral mutasyonların birikme ihtimali de artar ve genomik heterozigotluk düzeyiyle, dolayısıyla, popülasyon büyüklüğü doğru orantılıdır. Bununla birlikte, ilk defa tüm açıklığıyla çağımızın büyük evrimsel genetikçisi Richard Lewontin’in analizinin işaret ettiği gibi, bu ilişki bir yanılsamaya dayalı olabilir ve yapılan pek çok çalışma büyük popülasyon-düşük genetik varyasyon ya da düşük genetik varyasyon büyük popülasyon büyüklüğüne sahip pek çok türe ve tür-içi (popülasyonlar arası) farka işaret etmektedir. Popülasyon büyüklüğü ile nötral genetik çeşitlilik arasındaki bu çelişki- evrimsel biyoloji literatüründe Lewontin Paradoksu olarak anılmaktadır ve evrimsel biyolojinin zorlu problemlerinden biri olarak aktif araştırma konusudur. Bu konuşmada, Lewontin paradoksunun çözümüne işaret eden modern çalışmalar ve yaklaşımlar, klasik Hill-Robertson etkisinin genişletilmiş bağlamında, “bağlantılı seçilim (linked selection)” sürecine vurgu yapılarak özetlenecektir.

Tarih: 8 Ağustos 2020 – 18:00 (GMT+3)

Dil: Türkçe

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Drivers of Genetic Diversity in Regions of Low Recombination – Kimberly Gilbert


Kimberly Gilbert

Dr. Gilbert obtained her PhD from the University of British Columbia in 2016, studying theoretical population genetics and the impact of demography on evolutionary processes and inferences. Her research broadly includes both theoretical and empirical data analysis in topics of evolutionary biology, including population structure, effective population size, local adaptation, and mutation load. She is currently a postdoctoral fellow at the University of Lausanne, Switzerland. More information is available on her website:


Linked selection is a major driver of genetic diversity. Selection against deleterious mutations removes linked neutral diversity (background selection [BGS]), creating a positive correlation between recombination rates and genetic diversity. Purifying selection against recessive variants, however, can also lead to associative overdominance (AOD), due to an apparent heterozygote advantage at linked neutral loci that opposes the loss of neutral diversity by BGS. Zhao and Charlesworth (2016) identified the conditions under which AOD should dominate over BGS in a single-locus model and suggested that the effect of AOD could become stronger if multiple linked deleterious variants co-segregate. We present a model describing how and under which conditions multi-locus dynamics can amplify the effects of AOD. We derive the conditions for a transition from BGS to AOD due to pseudo-overdominance, i.e., a form of balancing selection that maintains complementary deleterious haplotypes that mask the effect of recessive deleterious mutations. Simulations confirm these findings and show that multi-locus AOD can increase diversity in low-recombination regions much more strongly than previously appreciated. While BGS is known to drive genome-wide diversity in humans, the observation of a resurgence of genetic diversity in regions of very low recombination is indicative of AOD. We identify 22 such regions in the human genome consistent with multi-locus AOD. Our results demonstrate that AOD may play an important role in the evolution of low-recombination regions of many species.

Date: July 16th, 2020 – 6:00 pm (GMT+3)

Language: English

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REVIEW: A Brief Introduction to Microencapsulation


The containment of a  core material inside of a  small capsule is called  microencapsulation. A polymeric material coates liquid or solid substances to protect polymeric material from circumambient area1. Microcapsules size vary between 50 nm to 2 mm2. Microcapsule’s size and structure differs according to core material being solid, liquid or gas as in figure 12

Figure 1: (a) Mononuclear microcapsules carrying solid material, (b) Aggregated microcapsules carrying liquid material2.
Figure 2: Schematic presentation of a microcapsule2.

Coating material must be adhesive to the core material  in order to cover core material properly. Coating materials must work as an harmonious aid to core material in required strength, flexibility, impermeability, optical properties, and stability. Its release must be  controllable under required conditions1.

Figure 3 : Coating material examples1

Water Soluble MaterialsWater Insoluble Materials Waxes and Lipid Materials
GelatinCalcium alginateParaffin
Gum ArabicPolyethyleneCarnauba
StarchPolyamide (Nylon)Spermaceti
Polyacrylic acidPolymethacrylateStearic acid
Carboxymethyl-celluloseCellulose nitrateGlyceryl stearates
Figure 4 : Alginate coated adipose stem cells extracted from (A) rat and (B) human3
Figure 5 : Confocal laser scanning microscope image of rhodamine-labeled hydrogel microcapsules4.


  The microencapsulation of adipose stem cells  coating with alginate is shown in figure 6. The cross- linking solution contains calcium chloride and glucose and is buffered with HEPES. Calcium chloride provides divalent cations to alginate during cross-linking. Glucose is useful for maintaining physiological osmolality of the cross-linking solution for the  adipose stem cells. HEPES is used tomaintain pH at or below pH 7.33.

Figure 6: Schematic presentation of method used for  microencapsulation of adipose stem cells3.

The generation of hydrogel microcapsules with a microfluidic system is shown in figure 7. Oligosaccharides and  peptide–starPEG were inserted through two distinct channels. The flow rates of the oil phase and Oligosaccharides and  peptide–starPEG have been set  to get required droplet formation4.

Figure 7 : Scheme of the microfluidic system used for hydrogel microcapsule generation4.


Microencapsulation can be used to encapsulate different materials therefore it is useful for treatment of different diseases that occurs in various tissues. There are various methods to make microcapsules. Microcapsule generation method must be chosen carefully according to the materials that microcapsule made out of. Microcapsules can be used to deliver drug molucules, various cell types into the targeted tissue. As technology improves, microencapsulation mehods will also improve and become more effective. 


1. MICROENCAPSULATION. Int J Pharm Sci Rev Res. 2010;5(2):58-62.

2.  M.N. Singh, K.S.Y. Hemant, M. Ram  and HGS. Microencapsulation: A promising technique for controlled drug delivery. Res Pharm Sci. 2010;5(2):65-77.

3.  Shirae K. Leslie , Ramsey C. Kinney , Zvi Schwartz  and BDB, Abstract. Microencapsulation of Stem Cells for Therapy. In: Vol 1479. ; 2017:225-235. doi:10.1007/978-1-4939-6364-5

4.  Wieduwild R, Krishnan S, Chwalek K, et al. Noncovalent Hydrogel Beads as Microcarriers for Cell Culture. Angew Chemie. 2015;127(13):4034-4038. doi:10.1002/ange.201411400

INSaFLU ve galaxyproject ile SARS-CoV-2 varyantlarının karşılaştırılması – RSG-Türkiye Aktif Üyeleri

Çalışmayı Yapanlar

  • Nazlı S. Kara, İstinye Üniversitesi
  • Meltem Kutnu, ODTÜ
  • Yasemin Utkueri, Sabancı Üniversitesi
  • Funda Yılmaz, Radbound University
  • Elif Bozlak, University of Veterinary Medicine Vienna; Vienna Graduate School of Population Genetics
  • Evrim Fer, University of Arizona


2020 BioHackathon’u, var olan varyant tespit etme iş akışlarının COVID-19 için geliştirilmesi veya üretilen büyük miktardaki verinin analiz edilebilmesi için yeni iş akışları oluşturulmasına ev sahipliği yapmıştır. Bunlardan bazıları Galaxy Project, INSaFLU ve nf-core’dur. Bu iş akışları yeni nesil dizileme teknolojisi ile dizilenen genom verisini analiz eder ve anotasyonu yapılmış tek nükleotid polimorfizm (SNP) ve kısa ekle-sil (indel) varyantlarını çıktı olarak verir. Kullandıkları algoritmalara göre farklı avantaj ve dezavantajları vardır. Bu çalışmada Galaxy Project tarafından yayımlanmış SARS-CoV-2 genom varyantlarını INSaFLU iş akışıyla belirlenen varyantlarla karşılaştırmayı, böylece bu iki iş akışının performanslarını değerlendirebilmeyi amaçladık. Sonuç olarak iki iş akışı tarafından ortak olarak bulunan 600’e yakın varyant bulduk. Bu varyantların neredeyse yarısının replikaz poliprotein 1ab’de olduğunu tespit ettik. Ortak olarak bulunan varyantlarda non-synonymous varyantların synonymous varyantlardan fazla olduğu gördük. Çalışmada tespit edilen ortak ve özgün varyantlar ileriki araştırmalarda daha detaylı incelenebilir.

Tarih: 21 Haziran 2020 – 20:00 (GMT+3)

Dil: Türkçe

Aşağıdaki linkten webinara kayıt olabilirsiniz:

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