The CAFA challenge reports improved protein function prediction and new functional annotations for hundreds of genes through experimental screens

BackgroundThe Critical Assessment of Functional Annotation (CAFA) is an ongoing, global, community-driven effort to evaluate and improve the computational annotation of protein function.ResultsHere, we report on the results of the third CAFA challenge, CAFA3, that featured an expanded analysis over the previous CAFA rounds, both in terms of volume of data analyzed and the types of analysis performed. In a novel and major new development, computational predictions and assessment goals drove some of the experimental assays, resulting in new functional annotations for more than 1000 genes. Specifically, we performed experimental whole-genome mutation screening in Candida albicans and Pseudomonas aureginosa genomes, which provided us with genome-wide experimental data for genes associated with biofilm formation and motility. We further performed targeted assays on selected genes in Drosophila melanogaster, which we suspected of being involved in long-term memory.ConclusionWe conclude that while predictions of the molecular function and biological process annotations have slightly improved over time, those of the cellular component have not. Term-centric prediction of experimental annotations remains equally challenging; although the performance of the top methods is significantly better than the expectations set by baseline methods in C. albicans and D. melanogaster, it leaves considerable room and need for improvement. Finally, we report that the CAFA community now involves a broad range of participants with expertise in bioinformatics, biological experimentation, biocuration, and bio-ontologies, working together to improve functional annotation, computational function prediction, and our ability to manage big data in the era of large experimental screens.</p

Protein sekanslarının enzimatik özelliklerinin enzim komisyonu terminolojisine dayalı tahmini.

The volume of expert manual annotation of biomolecules is steady due to high costs associated with it, although the number of sequenced genomes continues to grow exponentially. Computational methods have been proposed in order to predict the attributes of gene products. The prediction of Enzyme Commission (EC) numbers is a challenging issue in this area. Enzymes have crucial roles in metabolic pathways, therefore they are widely employed in biotechnological and biomedical pplications. EC numbers are numerical representations of enzymatic functions based on chemical reactions that they catalyze. Due to the cost and labor extensiveness of in vitro experiments EC classification annotation of catalytically active proteins are limited. Therefore, computational tools have been proposed to classify these proteins to annotate them with EC nomenclature. However, the performance of existing tools indicates that EC number prediction still requires improvement. Here, we present an EC number prediction tool, ECPred, to obtain predictions for large-scale protein sets. In ECPred, we employed hierarchical data preparation and evaluation steps by utilizing the functional relations among the four levels of EC annotation system. The main features that distinguish our approach from existing studies are the use of a combination of independent classifiers, and novel data preparation and evaluation methods. Totally, 858 EC classifiers are trained which consists of 6 main, 55 subfamily, 163 sub-subfamily and 634 substrate EC class classifiers. The average F-score value of 0.99 is obtained for all EC classes using the validation datasets. Enzyme or non-enzyme classification is incorporated into ECPred along with a hierarchical prediction approach. To the best of our knowledge, this is the first study that predicts the enzymatic function of proteins starting from Level 0 (enzyme/non-enzyme) going up to Level 4 (substrate class). Finally, ECPred is compared with other similar tools on independent test sets and ECPred obtained better results than existing tools, however, the results show that there is still room for improvement. M.S. - Master of Scienc

Aviation Carbon Accounting for Climate Change Mitigation: The Case of Turkey

Abstract: Global Warming has become one of the biggest challenges of our World. Aviation Industry as a major contributor, is expected to play its own part in fighting the Global Warming by accounting for its carbon emissions. This study examines the approaches and strategies of airline companies for carbon accounting and reducing their contribution to Global Warming. Turkish Airlines, Turkey's largest airline, is studied to reflect how Turkey's air transport industry addresses the challenges of global warming and accounts for its carbon emissions. The study aims to set an example for other countries and businesses by showcasing the approaches and strategies of Turkish airline companies on carbon accounting

Protein İşlevlerinin Altdizi Analizi ile Büyük Ölçekte Öngörme Yöntemleri

Proteinlerin işlevlerinin otomatik olarak etiketlenmesi (automatic protein function annotation), işlemsel biyolojinin (computational biology) önemli ve zor problemlerinden birisidir. Araştırma grubumuzun daha önceki çalışmalarında, yapay öğrenme (machine learning) yöntemleri yardımı ile altdizi benzerliğine dayalı öznitelik uzayı eşlemesi (subsequence similarity based feature mapping) kullanılarak protein dizilerinin işlevsel sınıflandırması için Subsequence Profile Map (SPMap) sistemini geliştirmiştik. Bu projenin amaçları geliştirmiş olduğumuz SPMap sisteminin iyileştirilmesi için bazı adımların yeniden tasarlanıp gerçekleştirilmesi, daha önce üstünde durulmamış olan noktaların açığa çıkartılması ve bu iki maddenin büyük ölçekli veri kümesine uygulanmasının ardından tüm sonuçların genel erişime açık olarak sunulmasıdır

The CAFA challenge reports improved protein function prediction and new functional annotations for hundreds of genes through experimental screens.

BackgroundThe Critical Assessment of Functional Annotation (CAFA) is an ongoing, global, community-driven effort to evaluate and improve the computational annotation of protein function.ResultsHere, we report on the results of the third CAFA challenge, CAFA3, that featured an expanded analysis over the previous CAFA rounds, both in terms of volume of data analyzed and the types of analysis performed. In a novel and major new development, computational predictions and assessment goals drove some of the experimental assays, resulting in new functional annotations for more than 1000 genes. Specifically, we performed experimental whole-genome mutation screening in Candida albicans and Pseudomonas aureginosa genomes, which provided us with genome-wide experimental data for genes associated with biofilm formation and motility. We further performed targeted assays on selected genes in Drosophila melanogaster, which we suspected of being involved in long-term memory.ConclusionWe conclude that while predictions of the molecular function and biological process annotations have slightly improved over time, those of the cellular component have not. Term-centric prediction of experimental annotations remains equally challenging; although the performance of the top methods is significantly better than the expectations set by baseline methods in C. albicans and D. melanogaster, it leaves considerable room and need for improvement. Finally, we report that the CAFA community now involves a broad range of participants with expertise in bioinformatics, biological experimentation, biocuration, and bio-ontologies, working together to improve functional annotation, computational function prediction, and our ability to manage big data in the era of large experimental screens