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.
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.
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.
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.
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.
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.
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.
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.
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has emerged in Wuhan, and spread across the continents, and caused the COVID-19 pandemic. In this talk, I will talk about our recent study which focuses on comprehensive genomic analysis of the virus isolates in Turkey. We built a phylogenetic tree with globally obtained 15,277 severe SARS-CoV-2 genomes, and clustered the virus isolates based on the phylogenetic tree and previously annotated classification methods. We performed a phylogenetic analysis of the first thirty SARS-CoV-2 genomes isolated and sequenced in Turkey to identify specific groups circulating in the country. Our results suggest that the first introduction of the virus to the country is earlier than the first reported case of infection. Virus genomes isolated from Turkey are scattered among most types in the phylogenetic tree. Two of the seventeen sub-clusters were found enriched with the isolates of Turkey, which possibly have spread expansively in the country. Finally, we traced virus genomes based on heir phylogenetic placements. This analysis suggested multiple independent international introductions of the virus and revealed a hub for the inland transmission.
Cancers are a very complex and heterogeneous set of diseases and therefore, cancer research is by no means trivial. The greater our understanding of the molecular landscape of a particular tumour type the better equipped we will become to combat its growth and spread. Publicly available multi-omic datasets provide a valuable resource to further this understanding. These data sets are commonly used for the identification of novel areas of study, the validation of results and the benchmarking/assessment of novel statistical methods. The Cancer Genome Atlas (TCGA) provides one such dataset with its repository consisting of 11,000 patients across 33 cancer types. This rich resource assists research on both a tumour specific and pan-cancer setting. In this webinar, I will introduce the various ways of accessing The Cancer Genome Atlas repository, navigating the multiple data types available and the tools I use for the multi-omics assessment (single and integrated) of my tumours of interest; renal cell carcinomas.
Date: May 5th, 2020 – 3:00 pm (GMT+3)
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.