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Student Council Symposium 2020 Virtual Poster Hall

SCS-01: Discovery and Investigation of Fibrillar Adhesins in Bacterial Proteomes

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  • Vivian Monzon, European Bioinformatics Institute - EMBL-EBI, UK
  • Alex Bateman, European Bioinformatics Institute - EMBL-EBI, UK

Short Abstract: Understanding the interactions between bacteria and humans is essential for preventing diseases by pathogens, but also to explain functions of commensal bacteria. Adhesive proteins bind to host cells directly or via components of the extracellular matrix. One type of adhesive proteins, called fibrillar adhesins, is characterised by repeating domains, which fold into a stalk that projects the adhesive domain away from the cell surface. We could detect fibrillar adhesins in bacteria across various phyla. In gram positive bacteria their domains are arranged in a stable architecture, with the adhesive domain at the N-terminus and the stalk at the C-terminus. In gram negative bacteria less fibrillar adhesins could be found and the domain architecture often differs from the described one. I am characterising fibrillar adhesins to be able to identify them computationally. I developed a prototype pipeline to find binding proteins by applying their characteristics and I tested in on the Staphylococcus aureus NCTC8325 proteome. The pipeline detected 13 out of 14 known adhesive proteins and two potential new ones. Currently, I am working on a machine learning algorithm to find further unknown fibrillar adhesins.

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SCS-02: Characterisation of CYP2D6 Pharmacogenomic Variation in African Populations: An Integrative Bioinformatics Approach

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Topic(s): CYP2D6, Pharmacogenomics, Bioinformatics
Poster: Click here
  • David Twesigomwe, University of the Witwatersrand, South Africa
  • Scott Hazelhurst, University of the Witwatersrand, South Africa
  • Zané Lombard, University of the Witwatersrand, South Africa

Short Abstract: Background: Genetic variation in major drug metabolising genes such as CYP2D6 contributes markedly to inter-individual differences in response to medications. CYP2D6 genotypes are reported as star alleles to represent gene copy number and the exact sequence variations within the gene copies. Functionally annotated star alleles can be used to infer CYP2D6 enzyme metaboliser activity, which is important for adjusting therapy. The advent of next generation sequencing has led to the increase of whole genome sequence datasets from African populations thus providing an excellent resource for calling CYP2D6 star alleles in African datasets. However, CYP2D6 genotyping with short-read next generation sequencing data is challenging due to complex structural variations in the CYP2D gene locus and read misalignment to the pseudogene, CYP2D7. Description: Given these challenges, we benchmarked the performance of three CYP2D6 star allele calling algorithms, that is Aldy, Stargazer, and Astrolabe, using whole genome sequence data from the Centres for Disease Control-based Genetic Testing Reference Materials program. We found that using a consensus genotyping approach with all three algorithms significantly resolves single-tool star allele calling ambiguities and enhances structural variant detection. We then used the consensus genotyping approach to call CYP2D6 star alleles from 458 high coverage African whole genome sequence datasets generated by the Human Heredity and Health in Africa consortium, and the Simon’s Genome Diversity Project. Predefined activity scores were used for phenotype prediction. We were able to confidently call African-specific CYP2D6 star alleles (such as *17, *29, *73 and *74) as well as structural variants (such as *1xN, *2xN, *29xN and *68+*4), and estimate their frequency in various African population groups. Our phenotype prediction from the called diplotypes also highlights the CYP2D6 phenotypic landscape among the various groups represented in our African study population. Conclusion: This research highlights the distribution of CYP2D6 star alleles and predicted phenotypes in African populations, which could inform future precision medicine strategies. Additionally, results and recommendations from comparing the genotyping tools could be useful to other researchers intending to genotype CYP2D6 using whole genome sequence data.

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SCS-03: Modelling Structural Rearrangements in Proteins using Euclidean Distance Matrices

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Topic(s): Protein modelling
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  • Aleix Lafita-Masip, European Bioinformatics Institute, UK
  • Alex Bateman, European Bioinformatics Institute, UK

Short Abstract: Proteins undergo large structural rearrangements, such as circular permutations, dimerisation via domain swapping, and loss of core secondary structure elements in domain atrophy, among others. These structural changes can be naturally represented as distance matrix transformations, exploiting conserved contacts at the protein core. Here we present an approach to formulate structural rearrangements as a Euclidean Distance Matrix (EDM) problem and use it to build their 3D models. This modelling approach aims to be intuitive, flexible and fast. Models are coarse-grained and solely based on protein geometry. We demonstrate how EDM models can be useful to answer fundamental questions in protein structure analysis.

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SCS-04: Boosting the accuracy of protein secondary structure prediction through nearest neighbor search and method hybridization

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Topic(s): Protein secondary structure prediction
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  • Spencer Krieger, University of Arizona, USA
  • John Kececioglu, University of Arizona, USA

Short Abstract: Motivation: Protein secondary structure prediction is a fundamental precursor to many bioinformatics tasks. Nearly all state-of-the-art tools when computing their secondary structure prediction do not explicitly leverage the vast number of proteins whose structure is known. Leveraging this additional information in a so-called template-based method has the potential to significantly boost prediction accuracy. Method: We present a new hybrid approach to secondary structure prediction that gains the advantages of both template- and non-template-based methods. Our core template-based method is an algorithmic approach that uses metric-space nearest neighbor search over a template database of fixed-length amino- acid words to determine estimated class-membership probabilities for each residue in the protein. These probabilities are then input to a dynamic programming algorithm that finds a physically-valid maximum- likelihood prediction for the entire protein. Our hybrid approach exploits a novel accuracy estimator for our core method, that estimates the unknown true accuracy of its prediction, to discern when to switch between template- and non-template-based methods. Results: On challenging CASP benchmarks, the resulting hybrid approach boosts the state-of-the-art Q8 accuracy by more than 2-10%, and Q3 accuracy by more than 1-3%, yielding the most accurate method currently available for both 3- and 8-state secondary structure prediction. Availability: A preliminary implementation in a new tool we call Nnessy is available free for non- commercial use at http://nnessy.cs.arizona.edu.

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SCS-05: Treating T-cell receptor-epitope recognition prediction as an image classification problem

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Topic(s): deep learning for TCR-epitope recognition prediction, immunoinformatics
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  • Pieter Moris, University of Antwerp, Belgium
  • Joey De Pauw, University of Antwerp, Belgium
  • Anna Postovskaya, University of Antwerp, Belgium
  • Benson Ogunjimi, University of Antwerp, Antwerp University Hospital, Belgium
  • Kris Laukens, University of Antwerp, Belgium
  • Pieter Meysman, University of Antwerp, Belgium

Short Abstract: The prediction of epitope recognition by T-cell receptors (TCR) has seen much progress in recent years, with several methods now available that can predict TCR-epitope recognition for a given set of epitopes. However, the generic case of evaluating all possible TCR-epitope pairs remains challenging, mainly due to the high diversity of sequences and the limited amount of currently available training data. In this work, we present a novel feature engineering approach for sequence-based predictive molecular interaction models, and demonstrate its potential in generic TCR-epitope recognition. The approach is based on the pairwise combination of the physicochemical properties of the individual amino acids in both sequences, which can provide a convolutional neural network with a combined representation of both sequences. We found indications that this simplifies the prediction task and that it can improve the generalization capabilities of the model to a certain degree. We postulate that similar feature engineering methods could pave the way towards general epitope-agnostic models, although further improvements and additional data are still necessary. In addition, we highlight that appropriate validation strategies are required to accurately assess the generalization performance of TCR-epitope recognition models when applied to both known and novel epitopes.

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SCS-07: Dynamic Microbial Association Networks in the Ocean

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Topic(s): Dynamic Network
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  • Ina M. Deutschmann, ICM-CSIC, Spain
  • Ramiro Logares, ICM-CSIC, Spain

Short Abstract: Background Ecological interactions among microbes are fundamental for ecosystem function, still they are barely known. High-throughput-omics can help predicting microbial interactions through association networks, which are often dense and static. Yet, microbial interactomes are highly dynamic. Here, we investigate microbial association networks through time aiming to improve our understanding of network dynamics, which can lead to a better comprehension of marine microbial ecosystems. Method and Results We developed a novel method to obtain dynamic networks and applied it on ten years of marine microbial community composition data (16S and 18S rRNA genes), which allowed us to reconstruct one network per month. The median Jaccard similarity used for pairwise network comparison based on their edge sets, suggest low overall similarity (Jall=0.15, range=[0.01, 0.94]). Yet, similarity increases if networks are compared intraseasonally ([Jwinter, Jspring, Jsummer, Jfall]=[0.59, 0.20, 0.46, 0.27]), or monthly (JApr=0.21 to JJan=0.76), pointing to a seasonally-dependent recurrence in network architecture ranging from modest to moderate. Conclusions We quantified network recurrence in a model marine microbial ecosystem and found a low amount of interseasonal recurrent edges, but a modest or moderate amount of intraseasonal ones. This suggest that ocean ecosystems require a moderate amount of reoccurring microbial interactions to function.

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SCS-08: A smoothed bootstrap method for reliable estimation of type II errors of RNA-seq data

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Topic(s): Statistical analysis of RNA-seq data
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  • Karsten Rinas, University of Waterloo, Canada
  • Brendan McConkey, University of Waterloo, Canada

Short Abstract: As a result of the high number of measured gene expressions, low number of replicates, interconnected gene expression networks and many biases introduced by the multistep processing, analysis of RNA-seq data can be challenging. Therefore, inferential statistical analysis must be adjusted for these factors. Type I errors are well studied for RNA-seq data, whereas type II errors are far less frequently analyzed although similarly important. Here, we propose the use of a univariate smoothed bootstrap with a Gaussian kernel density method to accurately estimate the type II errors in fold change within an RNA-seq experiment. A key advantage of this approach is that experiment-level power can be assessed and can verify that the sample replication is sufficient for a given effect size. The two RNA-seq data sets used for testing are a study comparing cell line responses to different drug treatments and data from the human breast cancer experiment TCGA-BRCA project. The results show a high overall power, even for relatively low replicate numbers. Furthermore, the type II errors for genes are highly dependent on the expression level. The proposed method will help provide additional confidence for genes of interest and complete RNA-seq experiments.

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SCS-09: Designing Epitope-based Multivalent and Multipathogenic Vaccines against Dengue and Zika Viruses Utilizing the Immunoinformatics Approach

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  • Bishajit Sarkar, Jahangirnagar University, Bangladesh
  • Md. Asad Ullah, Jahangirnagar University, Bangladesh
  • Yusha Araf, Shahjalal University of Science and Technology, Bangladesh
  • Mohammad Jakir Hosen, Shahjalal University of Science and Technology, Bangladesh

Short Abstract: Background: Filoviruses belong to a group of highly infectious viruses that have caused several outbreaks and epidemics in many countries. Dengue virus (DENV) and zika virus (ZIKV) are two members of this group and both of them are responsible for two most wide spread mosquito borne viral diseases in the world i.e., dengue fever (DENF) and zika fever (ZIKF). Both of these diseases can be lethal and life-threatening in many cases. Although much efforts have been put forward to develop vaccines against these two viruses, however, till now no satisfactory vaccine is available yet in the market. Therefore, in this study, potential multivalent and multipathogenic vaccines have been designed which can combat both DENV and ZIKV, simultaneously. Description: The designed vaccines contain highly antigenic, non-allergenic, non-toxic T-cell epitopes (100% conserved epitopes) as well as B-cell epitopes from all the four DENV serotypes- 1, 2, 3, and 4. Therefore, they might be effective against all these DENV strains as multivalent vaccine constructs. Again, since the vaccines also contain epitopes from ZIKV, for this reason, these vaccines might also be effective against the ZIKV (along with DENV) as multipathogenic vaccines. Several studies such as molecular docking, immune simulation and molecular dynamics simulation also indicated their sound and satisfactory performances as possible vaccines against these viruses. Conclusion: The predicted vaccine might be a potential preventative measure as a multivalent and multipathogenic vaccine against the selected viral species and strains. However, further in vivo and in vitro experiments might be required to finally confirm the safety and efficacy of our suggested vaccine constructs. Keywords: Dengue virus; Zika virus; Vaccine; Immunoinformatics; Multivalent vaccines

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SCS-11: Phylogenetic comparison of the first complete EBV genomes sequenced and analyzed in Argentina.

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Topic(s): Viral evolution
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  • Ana Catalina Blazquez, Instituto Multidisciplinario de Investigación en Patologías Pediátricas (IMIPP) CONICET-GCBA, División Patología, Hospital de Niños “Dr.Ricardo Gutiérrez”, Argentina
  • Berenstein Ariel José, Instituto Multidisciplinario de Investigación en Patologías Pediátricas (IMIPP) CONICET-GCBA, División Patología, Hospital de Niños “Dr.Ricardo Gutiérrez”, Argentina
  • Izqueierdo Agustin, Centro de Investigaciones Endocrinológicas 'Dr. César Bergadá' (CEDIE), CONICET-GCBA-FEI, División de Endocrinología, Hospital de Niños “Dr. Ricardo Gutiérrez”, Argentina
  • Lorenzetti Mario Alejandro, Instituto Multidisciplinario de Investigación en Patologías Pediátricas (IMIPP) CONICET-GCBA, División Patología, Hospital de Niños “Dr.Ricardo Gutiérrez”, Argentina
  • Preciado María Victoria, Instituto Multidisciplinario de Investigación en Patologías Pediátricas (IMIPP) CONICET-GCBA, División Patología, Hospital de Niños “Dr.Ricardo Gutiérrez”, Argentina

Short Abstract: Background: Epstein Barr Virus (EBV) infects more than 90% of the population and can be related to benign and malignant conditions. EBV-related malignancies prevalence, proportion of EBV associated cases and viral types (EBV1 and EBV2) circulation vary among geographical regions. Given NGS methods, more than 800 worldwide complete genomes were sequenced; however, South American EBV isolates are underrepresented. Our aim was to characterize polymorphisms in the entire genome of EBV isolates from our region and compare them with sequences from different geographies. Description: Fifteen isolates from Argentinian pediatric patients were sequenced by NGS methods using EBV custom target enrichment probes. To compare the variability in the context of EBV sequences from other geographies, 203 publicly available raw-NGS data were downloaded from SRA-NCBI database. All the fastq files were analysed with our custom-built mapping-based bioinformatic pipeline. After the preprocessing steps, reads were mapped to both EBV-type reference genomes (EBV1 and EBV2) using BWA and SAMtools. We constructed VCF files with VCFtools and used the information contained in these files to predict EBV type. Type classification was based on coverage, mapped reads and number of variants considering EBNA2 and the entire EBNA3 gene family. All together 194 samples were classificated as EBV1, 20 samples were EBV2, 4 were recombinant. Regarding Argentinean isolates, 9 were EBV1 and 6 EBV2. EBV1 sequences were further studied given their greater worldwide prevalence. In the phylogenetic tree no geographical structure was detected, except for the Asian sequences. Argentinian isolates segregated together with African and European isolates, whereas only three genomes clustered with Asian clades. Asian isolates presented more variants when compared to the reference and, lesser genetic diversity amongst isolates from other geographical regions. Geographical separation of Asiatic sequences was confirmed by PCA. Conclusions: EBV2 resulted more prevalent than previously described. We expanded data on EBV complete genomes from Argentina and inferred a closer phylogenetic relation with Africa and Europe; a fact that may be influenced by the introduction of African slaves during European colonization. Possible virus introduction routes based on phylogeographic analysis of dated sequences and considering the migration history of our country will be performed.

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SCS-13: A Machine Learning Paradigm for Classifying Lipids and Other Metabolites into Major Structural Categories

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Topic(s): Metabolite identification
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  • Elizabeth Mahood, Cornell University, USA
  • Gaurav Moghe, Cornell University, USA

Short Abstract: BACKGROUND/MOTIVATION: The majority of metabolites detected in untargeted LC-MS metabolomic studies of complex biological samples currently receive no or unreliable structural annotation. Here, we have developed machine learning models capable of predicting the structural classes of these metabolites with high accuracy. METHODS/RESULTS: Through a grid-search of feature-based models, we identified random forest models that classified lipids into LipidMAPS and LipidBlast ontologies, with 95-100% accuracy for most classes using just the chemical formula. Addition of in silico MS/MS fragmentation data further improved the multi-class model accuracy for 28/30 LipidBlast classes. To test algorithmic performance in classifying compounds beyond lipids, formula and MS/MS based models were successfully developed for classifying diverse metabolites from PlantCyc and flavonoids from the ReSpect for Phytochemicals databases, respectively. In absence of a well-defined, metabolome-spanning single-label ontology, we trained models for multi-label classification into the ChemOnt ontology, and achieved an overall accuracy of 85% using just the chemical formula, which may be further improved using MS/MS. Ongoing work seeks to expand the utility of this approach to predicting plant-derived compound classes using formula and MS/MS features. SIGNIFICANCE: Accurate structural classification of LC-MS features will reveal the chemistries of thousands of metabolites currently left unannotated and provide deeper insights into many biological processes.

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SCS-15: FrustratometeR: An R package to calculate energetic local frustration in proteins

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Topic(s): Protein frustration,Protein structure
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  • Atilio Osvaldo Rausch, Facultad de Ingeniería de la Universidad Nacional de Entre Ríos, Argentina
  • María Inés Freiberger, Laboratorio de Fisiología de Proteínas, IQUIBICEN N-CONICET, FCEyN, Universidad de Buenos Aires, Argentina
  • Leandro R. Radusky, Centre for Genomic Regulation (CRG), The Barcelona Institute for Science and Technology, Spain
  • Diego U. Ferreiro, Laboratorio de Fisiología de Proteínas, IQUIBICEN N-CONICET, FCEyN, Universidad de Buenos Aires, Argentina
  • R. Gonzalo Parra, Genome Biology Unit, European Molecular Biology Laboratory, Germany

Short Abstract: Background: Energetic local frustration has been extensively linked to multiple functional aspects of proteins. The protein frustratometer has been present as a web server service since 2012, receiving more than 170 citations so far. Here we present, frustratometeR, a standalone R package that extends the set of analysis present at the web server together with brand new functionalities to help elucidate the role of local frustration in proteins function and dynamics. Description: Given a PDB file and a frustration index type, several visualizations and pymol scripts are produced. Additionally the frustratometeR package can compute the frustration index distribution for all alternative amino acids for a given residue to evaluate the impact of point mutations in the structure. A module to analyze frustration change in Molecular Dynamics simulations is implemented. Conclusions: A standalone version of the frustratometer has long been expected by many users. The frustratometeR not only allows to perform frustration calculations locally but also extends its functionalities to study the impact of residue mutations and the mechanistic role of frustration during protein dynamics.

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SCS-16: Pathway Score Benchmarking Study - Generation, Validation and Application

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Topic(s): Pathway scoring
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  • Michelle Meier, ETH Zurich, Switzerland
  • Natalie Davidson, ETH Zurich, Switzerland

Short Abstract: Background: Transcriptomic data has grown rapidly, gene expression omnibus alone has >120,000 distinct experiments, however, it is an open question on how to optimally integrate across all data. One promising approach for heterogeneous data is pathway scoring methods, especially for the prediction of disease phenotypes. While these methods are gaining traction, no comprehensive benchmarking data set nor study that combines across tissue types, perturbation agents, and targeted pathways currently exists. Results: I created a benchmark dataset for six key cancer pathways: Notch, cell cycle, phosphoinositide 3-kinase and mammalian target of rapamycin, tumor protein 53, hypoxia and epithelial-mesenchymal transition. Using the ARCHS4 database, I identified relevant samples for each pathway and divided them into healthy and diseased. The expression data was then normalized and lowly expressed genes removed. I performed a principal component, geneset, and biplot analysis to confirm differential pathway regulation. Through the use of our benchmark dataset, I found the singscore method (out of 5 total) performed the best. Comparisons were done by using a rank statistic metric: the rank of the score difference between case and control samples was compared between the true pathway and randomized pathways. Using this knowledge, I was then able to quantify pathway dysregulation in most pathways of interest. Interestingly, the reliability of the pathway scoring results was not only dependent on the method chosen but also affected by the pathway’s properties. For example, epithelial-mesenchymal transition and phosphoinositide 3-kinase were only identified by one method, whereas hypoxia was detected by all methods. Conclusion: We believe that this benchmarking data set and study is an essential first step to appropriately utilize the growing abundance of heterogeneous expression data. It is a vital step to quantitatively assess different computational omics methods, such as pathway scoring. Building on my benchmark study, I have begun a study to investigate cell-cycle inhibitor resistant cells to understand how different pathways contribute to the resistance phenotype. This shows that our work enables a plethora of relevant studies, especially in the field of precision oncology. Such studies include the identification of phenocopying events and detection of off-target pathway dysregulation.

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SCS-17: AttentionDDI: A Deep Learning method for drug-drug interaction predictions

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Topic(s): deep learning, drugs, side effects
Poster: Poster not uploaded
  • Kyriakos Schwarz, University of Zurich, Switzerland
  • Ahmed Allam, University of Zurich, University Hospital Zurich, Switzerland
  • Michael Krauthammer, University of Zurich, University Hospital Zurich, Switzerland

Short Abstract: Background: Drug-drug interactions (DDI) refer to two or more drugs' effect on each other upon their concurrent administration. Particularly, additional side effects may occur which would not manifest in case of individual drug administration. Due to the combinatorial explosion of possible drug pairs, it is impossible to experimentally test all combinations and discover previously unobserved side effects. Therefore, computational methods and more recently Deep Learning based models are being employed for this task. Description: We propose a self-attention multi-modal neural network that integrates multiple drug similarity measures derived from drugs' gene expression and adverse effect profiles, as well as from the drugs' targets, pathways and drug chemical structures. Initially, a set of drug similarity feature vectors are used as inputs to self-attentive layers. Then, a feature-interaction attention layer learns a set of weights associated with each vector to compute a weighted sum feature vector representation. Finally, this feature vector is passed to a classifier layer to generate a probability distribution over the two possible outcomes (i.e. interacting vs. non-interacting drug pair). Conclusion: We provide a self-attention based multi-modal neural network model for DDI prediction, which (1) is trained end-to-end, 2) offers model interpretability and 3) achieves better prediction performance compared to state-of-the-art DDI models when tested on various benchmark datasets.

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SCS-18: The maternal and infant gut microbiome in Foetal Alcohol Spectrum Disorder

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Topic(s): Gut microbiome, 16S rRNA, Foetal Alcohol Spectrum Disorder
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  • Natasha Kitchin, Department of Psychiatry, Faculty of Medicine and Health Sciences, Stellenbosch University, South Africa
  • Jacqueline S. Womersley, Department of Psychiatry, Faculty of Medicine and Health Sciences, Stellenbosch University, South Africa
  • Andrea Engelbrecht, Department of Psychiatry, Faculty of Medicine and Health Sciences, Stellenbosch University, South Africa
  • Anna-Susan Marais, Department of Psychiatry, Faculty of Medicine and Health Sciences, Stellenbosch University, South Africa
  • Marlene M. de Vries, Department of Psychiatry, Faculty of Medicine and Health Sciences, Stellenbosch University, South Africa
  • Philip A. May, Department of Nutrition, Gillings School of Global Public Health, Nutrition Research Institute, University of North Carolina, United States of America
  • Soraya Seedat, Department of Psychiatry, Faculty of Medicine and Health Sciences, Stellenbosch University, South Africa
  • Sian M. J. Hemmings, Department of Psychiatry, Faculty of Medicine and Health Sciences, Stellenbosch University, South Africa

Short Abstract: Prenatal alcohol exposure is the most preventable cause of birth defects, developmental disorders and mental retardation, yet the prevalence of Foetal Alcohol Spectrum Disorder (FASD) in South Africa is 20%-28%, significantly higher than the global prevalence of 0.77%. Excessive alcohol intake can result in alterations in gut microbial composition. An altered maternal gut microbiome may translate into abnormal infant gut colonisation, thereby resulting in altered gut microbiota functioning, which may result in increased susceptibility to disease. This study compared the gut microbial composition of women who consumed alcohol during pregnancy (cases) and those who abstained from drinking alcohol throughout pregnancy (controls). This study also compared the gut microbial composition of infants who were exposed to alcohol prenatally and those who were not exposed to alcohol prenatally. Pregnant women were recruited from antenatal clinics in Robertson and Wellington. A total of 207 pregnant women (149 drinkers and 58 controls) provided stool samples. Additionally, stool was collected from 211 infants (152 exposed to alcohol prenatally and 59 not exposed to alcohol prenatally) born to these women. 16S rRNA paired-end sequencing of the V1-V2 region was performed on microbial DNA extracted from stool samples. The dada2 pipeline was used to pre-process the fastq sequencing files, create an amplicon sequence variant table, and assign taxonomy using the Ribosomal Database Project reference database. Differential compositional analyses were performed using PhyloSeq, while R was used to compute the statistical analyses of microbial composition and calculate alpha- and beta-diversity. The maternal gut microbiome was dominated by Prevotella, Bacteroides and Succinivibrio. Prevotella and Succinivibrio are associated with plant-rich high fibre diets, while Bacteroides has been linked to a high intake of fat and protein. There were no significant differences in alpha- or beta-diversity measures or relative abundance of genera between cases and controls. Of the variables investigated, trimester may have an effect on Bray-Curtis distance. Major alterations in gut microbial composition occur during the third trimester of pregnancy. Estrogen and progesterone impact the composition of the gut microbiome through their effect on bacterial metabolism and result in an increased abundance of Proteobacteria and Actinobacteria and a decrease in Faecalibacterium. Although no significant differences were identified, differences may become evident with the larger sample size. An altered maternal gut microbiome may exert its influence on the foetal brain via immune-mediated pathways or it may result in abnormal neonate gut colonisation. Abnormal neonate gut colonisation may alter the functioning of the infant’s gut microbiota, which may have significant long-term health consequences.

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SCS-19: Can full-length transcript characterization reveal molecular mechanisms of selection in germinal centre B cells?

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Topic(s): Long-reads, alternative splicing, RNA biology
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  • Ozge Gizlenci, The Babraham Institute, UK
  • Simon Andrews, The Babraham Institute, UK
  • Rebecca Berrens, University of Oxford, UK
  • Louise Matheson, The Babraham Institute, UK
  • Elisa Monzon-Casanova, The Babraham Institute, UK
  • Martin Turner, University of Cambridge, UK

Short Abstract: Background: Germinal centre (GC) reactions are of utmost importance for adaptive immunity, and their abnormal functioning might result in autoimmune disorders and chromosomal translocations leading to lymphomas. The division between the compartments of GC (light and dark zones) help to accommodate distinct cell phenotypes, mediating the activities of the GC. c-Myc is a critical transcription factor for the positive selection of GC B cells. It indirectly regulates the alternative splicing (AS) of transcripts (e.g. Pkm) via induction of the RNA-binding protein PTBP1. There are still unrevealed alternative isoforms of most transcripts which might mediate functional roles in GC B cells. The changes in the AS in the positively selected GC B cells have been previously addressed using the short-read Illumina sequencing. However, the majority of the transcript variants generated by AS and alternative polyadenylation events were not detected. The long-read sequencing platforms emerged out of the need for full-length sequencing to resolve the complexity of isoforms. In our study, we adapted the long-read sequencing, Oxford Nanopore Technology (ONT), using a Smart-seq2 approach to understand the post-transcriptional regulation in positively selected GC B cells. Description: Our Smart-seq2 adapted cDNA sequencing method is highly efficient to obtain higher output in a multiplexed library prepared with low input RNA. Using 1ng total RNA, we reached a depth of approximately 2-million reads per sample and detected transcripts from over 9500 genes with at least 5 supporting reads. This was comparable with the numbers of genes (10925) detected using Illumina on the same samples. One of the challenges of ONT is the bioinformatics analysis of the data which is not yet mature and readily deployed. Additionally, we will provide methodological insight into the detection of the splicing variants of complex isoforms accurately from long-reads using a novel method we developed in our group. Conclusion: Altogether, our findings support that Smart-seq2 adapted ONT RNA sequencing is a suitable method for the identification and quantification of complex isoforms, which will be a powerful workflow for the characterization of positively selected GC B cells and for the development of a single-cell long-read sequencing platform for rare cell populations.

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SCS-20: Computer-aided synthesis of dapsone-phytochemical conjugates against dapsone-resistant Mycobacterium leprae

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Topic(s): Drug Chemistry & Computer-Aided Drug Discovery
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  • Shasank Sekhar Swain, ICMR-Regional Medical Research Centre, INDIA

Short Abstract: Background: Leprosy, a causative disease by Mycobacterium leprae, continues to be the belligerent public health hazard for the causation of high disability and eventual morbidity cases with stable prevalence rates, even with treatment by the ongoing multidrug therapy. Multidrug therapy with the sulphonamide drug, dapsone (4, 4’-diamino diphenyl sulfone) along with rifampicin, clofazimine, and ofloxacin is the ongoing treatment option for leprosy, recommended by World Health Organization today. However, the front-line drugs, dapsone and rifampicin resistance has led to fear of disease in more unfortunate people of certain developing countries. Description: As a possible solution, dapsone was chemically conjugated with five phytochemicals such as coumarin, eugenol, salicylic acid, thymol and vanillin, independently as dapsone-phytochemical conjugates based on azo-coupling reaction protocol. Possible biological activities were verified with computational chemistry and quantum mechanics by molecular dynamics simulation program before chemical synthesis and spectral characterizations viz., proton-HNMR, FTIR, UV, and LC-MS. The in vivo antileprosy activity was monitored with the gold-standard ‘mouse-foot-pad propagation method’, with the WHO recommended concentration 0.01% mg/kg each conjugate for twelve weeks. The host-toxicity testing of the active most active conjugates was carried out in cultured-human-lymphocytes in vitro. Conclusions: Advantageously, one-log bacilli cells in dapsone-resistant infected mice footpads decreased and no bacilli were found in the dapsone-sensitive mice hind pads after twelve weeks treatment with conjugate-4 or ‘dapsone-thymol conjugate’. Additionally, the in vitro host toxicity study confirmed that the ‘dapsone-thymol conjugate’ up to 5,000 mg/L level was safe for oral administration since a minor number of dead cells were found in red colour under a fluorescent microscope. Several advanced bioinformatics tools could help locate the potential chemical entity, reducing the time and resources required for in vitro and in vitro tests. Thus, evidenced from in vivo antileprosy and in vitro host toxicity studies, the potent and less-toxic ‘dapsone-thymol conjugate’ could be used in place of dapsone in multidrug therapy or MDT towards control of gruesome drug-resistance strains of the slow-growing acid-fast bacilli M. leprae.

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SCS-21: Towards CNN Representations for Small Mass Spectrometry Data Classification : From Transfer Learning to Cumulative Learning

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Topic(s): Machine Learning
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  • Khawla Seddiki, Laval university, Canada
  • P. Saudemont, Lille University, Laboratoire Protéomique, Réponse Inflammatoire et Spectrométrie de Masse Villeneuve d’Ascq, France
  • F. Precioso, Cote d’Azur University, CNRS, I3S, France
  • N. Ogrinc, Lille University, Laboratoire Protéomique, Réponse Inflammatoire et Spectrométrie de Masse Villeneuve d’Ascq, France
  • M. Wisztorski, Lille University, Laboratoire Protéomique, Réponse Inflammatoire et Spectrométrie de Masse Villeneuve d’Ascq, France
  • M. Salzet, Lille University, Laboratoire Protéomique, Réponse Inflammatoire et Spectrométrie de Masse Villeneuve d’Ascq, France
  • I. Fournier, Lille University, Laboratoire Protéomique, Réponse Inflammatoire et Spectrométrie de Masse Villeneuve d’Ascq, France
  • A. Droit, CHU de Québec research center – Laval University, Canada

Short Abstract: Rapid and accurate clinical diagnosis from Mass Spectrometry (MS) remains highly challenging. Some Machine Learning (ML) approaches, including Support Vector Machine or Random Forest for instance, have been investigated for this purpose. An important component of this development is the building of effective classification models with MS data. However, these ML algorithms require time-consuming preprocessing steps such as baseline correction, denoising, normalization, and spectra alignment to remove non-sample-related data artifacts. They also depend on the laborious extraction of features, making them unsuitable for real-time analysis. Convolutional Neural Networks (CNNs) have been found to perform well under such circumstances since they can learn efficient representations from data without the need for preprocessing. However, their effectiveness drastically decreases when the number of MS spectra available is small, which is a common situation in medical applications, especially for rarer diseases and pathologies. Transfer learning strategies extend an accurate representation model learnt from a large dataset to a smaller one. In our study, we first investigated transfer learning by a 1D-CNN model designed to classify MS data. Then we developed a new cumulative learning method when transfer learning was not powerful enough as in cases of low-resolution or data heterogeneity. What we propose is to train the same model through several classification tasks over various small datasets in order to accumulate MS knowledge in the resulting representation. Using a cumulative learning approach resulted in a classification accuracy exceeding 98% for 1D clinical canine sarcoma cancer cells, human ovarian cancer serums, and pathogenic microorganisms. We showed for the first time the use of cumulative representation learning using datasets generated in different biological contexts, on different organisms, and acquired by different MS instruments. Our approach thus illustrates a promising strategy for improving classification accuracy when only small numbers of samples are available. The principles demonstrated in this work could even be beneficial to other domains where training samples are scarce.

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SCS-22: GENOMIC INSIGHTS AND BIOTECHNOLOGICAL POTENTIAL OF A MULTI-RESISTANT ACTINOBACTERIUM ISOLATED FROM ANDEAN PUNA SOILS

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Topic(s): Genomics, Comparative Genomics, Extremophiles, High-Altitud Lakes
Poster: Click here
  • Daniel Gonzalo Alonso Reyes, Laboratorio de Microbiología Ultraestructural y Molecular, Centro Integral de Microscopía Electrónica (CIME, CONICET, UNT) CCT, CONICET, Argentina
  • Luciano Raúl Portero, Laboratorio de Microbiología Ultraestructural y Molecular, Centro Integral de Microscopía Electrónica (CIME, CONICET, UNT) CCT, CONICET, Argentina
  • Wolgang Gärtner, Institute for Analytical Chemistry, University of Leipzig,, Germany
  • María Eugenia Farías, Laboratorio de Investigaciones Microbiológicas de Lagunas Andinas (LIMLA), Planta Piloto de Procesos Industriales y Microbiológicos (PROIMI), CCT, CONICET, Argentina
  • Martín P. Vasquez, Instituto de Agrobiotecnologıa de Rosario (INDEAR), Predio CCT Rosario, Argentina.
  • Thierry Douki, Laboratoire “Lésions des Acides Nucléiques” INaC/SCIB UMR-E3 CEA-UJF/ CEA-Grenoble, France

Short Abstract: BACKGROUND: To enable the advance of the industrial bioprocess technology and to identify suitable new enzymes for such technology, the function, enzymatic catalog, and resistance potential of extremophiles inhabiting High-Altitude Andean Lakes (HAAL) merits investigation. Thorough genome mining, this study analyses the potential microbial functions of the polyextremophilic strain Nesterenkonia sp. Act20, a new actinobacterium isolated from the soil surrounding Lake Socompa. DESCRIPTION: Annotations unveiled the polyextremophilic nature of the Act20 genome, having the potential to resist antibiotics, arsenic, nutrient limiting conditions, osmotic stress, UV radiation, low temperatures, low atmospheric O2 pressure, heavy metal stress, fluoride, and chlorite. Also have the promise to produce or metabolize mercaptopurine, fluorouracil, a pesticide, cellulose, ectoine, colicin V and aurachin C. We also found numerous enzymes for degradation and production of compounds of interest and application in industrial processes. UV-B assays and pyrimidine photoproduct measurements showed the strain’s notable resistance to UV-B. CONCLUSIONS: This work indicates that among the extremophiles, Act20 has good potential to be a source of enzymes and compounds of biotechnological interest as well as to be a model to study the early evolution of life and a possible future terraformation of Mars.

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SCS-23: Occurrence of Xeroderma Pigmentosum D helicase in Galliform birds

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Topic(s): Comparative genomics of birds, DNA repair
Poster: Click here
  • Rayana Feltrin, Federal University of Santa Maria, BR
  • Ana Lúcia Segatto, Federal Institute of Rio Grande do Sul, Brazil
  • Tiago de Souza, TauGC Bioinformatics, Brazil
  • André Schuch, Federal University of Santa Maria, Brazil

Short Abstract: Nucleotide excision repair pathway is the most versatile DNA repair mechanism as it removes a wide variety of structurally unrelated DNA lesions. Among some of the main components of this DNA repair pathway, the Xeroderma Pigmentosum D helicase, which integrates transcription fator IIH, is one of the most evolutionarily conserved proteins, being present even in Archaea. However, according to our previous work, a canonical Xeroderma Pigmentosum D ortholog is missing in Gallus gallus. To better investigate this, we performed a refined search of this protein in G. gallus and also searched for its orthologs in genomes of Galliformes, Tinamiformes and Struthioniformes by using similarity and structural criteria. Therefore, we found that the protein DEAD/H-Box Helicase 11 may be replacing Xeroderma Pigmentosum D function in chicken. We also identified likely occurrences of Xeroderma Pigmentosum D in only three genomes out of 19, belonging to species of Tinamiformes and Struthioniformes, that is, the base of the bird phylogeny. In addition, we obtained search results with high sequence identities, but very low coverages. Thus, we suppose that there might be occurred a progressive loss of the Xeroderma Pigmentosum D sequence from the base of the bird phylogeny throughout the evolution of Galliformes until chicken, which reinforces the importance of in silico studies to open perspectives for functional investigations.

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SCS-24: Epitope mimicry between neural proteins and SARS-COV-2 surface proteins explains the occurrence of Guillain Barre syndrome

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Topic(s): Structural biology
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  • Sara Morsy, Medical Biochemistry and Molecular Biology department, Faculty of Medicine, Tanta University, Egypt

Short Abstract: Abstract Purpose In this study, we aim to identify neural proteins that can be attacked by cross-reacting SARS-COV-2 antibodies causing Guillain Barre syndrome. These markers can be used for diagnosis of Guillain Barre syndrome. Methods In this study, proteins implicated in the development of GBS were retrieved from literature. These proteins were compared to SARS-COV-2 surface proteins to identify proteins with homologous sequences using Blastp. MHC-I and MHC-II epitopes were determined and compared to homologous sequences. The similar epitopes were docked to the corresponding MHC molecule to compare the binding pattern of the human and SARS-COV-2 proteins and to confirm the epitope finding. Results Neural cell adhesion molecule is the only neural protein that showed homologous sequence to SARS-COV-2 envelope proteins. The homologous sequence was part of HLA-A68 and HLA-DQA/HLA-DQB. Conclusion The current study suggests that NCAM may play a significant role in the immunopathogenesis of GBS. NCAM antibodies can be used as a marker for Guillain Barre syndrome. Keywords

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SCS-25: An In silico Approach To Identifying and Evaluating Antibodies to Target SARS-CoV 2 Spike Glycoprotein

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Topic(s): monoclonal antibodies, computational immunology
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  • Cameron DeChristopher, Bristol Community College, United States of America

Short Abstract: In December of 2019, a novel coronavirus was discovered in Wuhan, China, a populous city in the country’s central region. This virus, named Severe Acute Respiratory Syndrome Coronavirus 2 or SARS CoV 2, for its genetic similarity to the SARS virus that plagued parts of Asia in the early 2000s, has so far reached pandemic proportions, infecting over ten million people worldwide and killing over 500,000. Here, we investigated monoclonal antibody therapies that could target the virus’s spike glycoprotein, the molecular mechanism believed to be the means by which the virus gains entry into a host’s cells. We identified two potential mAb candidates based on genomic alignments between the DNA sequence of the virus’s spike glycoprotein and the immunoglobulin (IgG) coding regions of the human and mouse genomes. We then sought to computationally estimate the binding affinity of these two mAb candidates to ultimately assess their individual likelihood for therapeutic utility.

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SCS-26: in-silico Study of Human Arachidonate 5-Lipoxygenase Inhibition Potential of Heritiera fomes Extracted Compounds

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Topic(s): Cheminformatics, Ethno-pharmaceutics, Anti-inflammation
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  • Rahagir Salekeen, Biotechnology and Genetic Engineering Discipline, Life Science School, Khulna University, Bangladesh
  • Md Emdadul Islam, Biotechnology and Genetic Engineering Discipline, Life Science School, Khulna University, Bangladesh
  • Kazi Mohammed Didarul Islam, Biotechnology and Genetic Engineering Discipline, Life Science School, Khulna University, Bangladesh

Short Abstract: Driven by our parallel interests in chronic inflammation and the bioactive potentials of Heritiera fomes, we adopt cheminformatic approaches to elucidate a molecular linkage between the two phenomena. Utilizing in-silico pharmacokinetics, molecular docking, molecular dynamics simulation and molecular mechanics calculation, the study progresses our understanding of the molecular events taking place when a pro-inflammtory enzyme, 5-lipoxygenase is subjected to phytochemical interventions. Analysis of the simulations reflect Heritiera fomes as a functional source of multiple safe and potent anti-inflammatory compounds. Our investigation suggests two best candidate phytocompounds: tetraneurin A and apigenin glucoside identified from ethanolic extracts as potent inhibitors compared to natural and non steroidal anti inflammatory drug controls. The findings grant us the foundation of understanding the molecular roles of Heritiera fomes extracts and provides a guideline for future drug discovery against atherosclerosis, coagulopathy, ageing and cancer.

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SCS-27: Mining, analysis, development, and validation of SSR in the cloroplast genomes of genus Capsicum and their use for depicture the genetic structure in peruvian ecotypes of Capsicum Anuum, Capsicum Baccatum and Capsicum Chinense

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Topic(s): Markers moleculars-diversity genetic.
Poster: Click here
  • Richard Estrada Cañari, Universidad Nacional Agraria La Molina, Perù
  • Maziel Cusi Casanova, Universidad Nacional Agraria La Molina, Perù

Short Abstract: The Capsicum genus is distributed throughout the world, and have several important species involved in food crops and spice processing. Peru is one of the countries with the greatest diversity of chili peppers cultivated worldwide, representing an important sector of Peruvian agriculture, both industrially and as small producers of family farming. However, this high diversity has not yet been fully identified and this has been resulting in a taxonomic confusion of the Capsicum genus, due to the evolution of shape, size and colour of the fruit over many centuries, mainly among the ecotypes of Peru. The genetic structure of the ecotype collection is significantly influenced by its echogeographic distribution, and the SSR markers; which due to their broader distribution throughout the genome, are found to be of greater utility in the evaluation of variation intraspecific and interspecific. Chloroplast DNA-based microsatellites( cpSSRs) are extensively exploited to reveal the genetic diversity, also because their cost is relatively low. The availability of the organelle genome sequence helps to understand the organization of the SSRs in them.  In this study, chloroplast genome sequences of the genus Capsicum were screened for the identification of chloroplast simple sequence repeats.The conventional methods of generating SSRs from genomic libraries was replaced by in silico mining of SSRs from DNA sequences available in biological databases. Total number of identified SSRs were from 30 to 38 for each genome sequence. .The density of ∼1SSR/4.0–5.1 kb was observed. Repetitions of more 3 units were very few.Hexanucleotide repeats were completely absent in the chloroplast genomes of genus Capsicum. The single nucleotide repeat was the highest proportion 98% for all chloroplastic genomes.For type A repeats, the highest ratio for number of repeats was 10 to 12, and for type T repeats, it was 18 to 25, with an offside of 14. There are also TTA-type SSRs found for all genomes. , assuming this type as an evolutionary character among chloroplastic genomes.For repetitions of another type, very little proportion was found.. Based on gene ontology analysis, the molecular functions of cpSSRs identified were classified as transporter activity, catalytic activity and binding. The majority involved in biological processes was related to cellular component organization or biogenesis, localization, metabolic process, single-organism process and cellular process. Moreover, the cellular component contained number of groups having macromolecular complex, membrane part, membrane, organelle part, cell part, organelle and cell.A total of 35 cpSSRs in peruvian ecotypes Capsicum , were mined computationally.Nine were selected on the basis of repeats for validation and evaluate a level of genetic intra and interpopulation diversity among 50 peruvian ecotypes six highly polymorphic microsatellite loci were used to depicture the genetic structure of targeted trees.Important allele frequency parameters were estimated. The low genetic differentiation detected among different ecotype was further discussed and clarified.

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SCS-28: Analysis of SNP rs10911021

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Topic(s): Single Nucleotide Polymorphisms, Bioinformatics, Type 2 Diabetes, Variation
Poster: Click here
  • Nadja DiMartino, Johns Hopkins University, USA
  • Amaal Kalds, Johns Hopkins University, USA
  • Myriam Warren, Johns Hopkins University, USA

Short Abstract: Background: In previous studies, the single nucleotide polymorphism rs10911021 was found to be linked with coronary heart diseases only in diabetic patients through downregulation of the GLUL gene. We investigated whether rs10911021 could be a major key factor that increases the risk of coronary heart diseases in these patients. Description: Further investigation of the GLUL gene confirmed that rs10911021 was upstream of the GLUL gene and associated with a long-intergenic noncoding RNA known as linc01344. However, the association of rs10911021 with the lincRNA was found to be associated with a specific variant with little information present. Transcription factors involved with the GLUL gene also revealed an association between rs10911021 and the ratio of pyroglutamic acid to glutamic acid. NGS analysis of a type 2 diabetic sequence did not contain the single nucleotide polymorphism rs10911021. Conclusion: The single nucleotide polymorphism rs10911021 may not be a direct link to the increased risk of coronary heart diseases in diabetic patients. However, the ratio of pyroglutamic acid to glutamic acid found in individuals who have the rs10911021 variant may contribute to already lower ratios in these patients.

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SCS-29: Dominating Balanced Protein Interaction Networks in Cancer

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Topic(s): protein network analysis of cancer networks using signed graphs and dominating sets
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  • Rianna Patricia Cruz, University of the Philippines Diliman, Philippines
  • Henry Adorna, Department of Computer Science - University of the Philippines Diliman, Philippines
  • Joshua Gregor A. Dizon, Philippine Genome Center - University of the Philippines, Philippines

Short Abstract: As available proteomic data grows, so does our need for computational methods to process such data for practical applications such as drug and therapeutic development. This need is critical in development of cancer treatments, where multiple mutations may obscure oncogenes and pathways to target for potential treatments. To identify these driver proteins and significant pathways, we explore these networks’ minimum connected dominating sets (MCDS), a set of topologically significant nodes of a network. We build on existing heuristic algorithms to find driver proteins of selected cancer networks via their MCDS. From sets of known driver proteins (n= [8,10]) and essential proteins(n= [991,1415]) of breast, ovarian, and pancreatic cancer, we generated protein interaction networks for each selected cancer using balanced signed directed graphs to model regulatory function. We identified each regulatory networks’ driver proteins (n= [40,100]) from their MCDS and validated each against sets of known driver proteins for the selected cancers as positive controls. On these driver protein sets, we performed pathway analysis. We then validated pathways found with statistical significance by surveying published cancer research, to verify whether these proteins had a documented association with cancer. Our driver proteins had measures of centrality (betweenness, degree centrality) higher than those of known essential proteins of our selected cancer networks. This implies their topological significance in their respective networks. Pathway analysis based on these driver proteins identified over 300 path-ways with statistical significance. A survey on these pathways found that79−80% of these pathways are linked to cancer. This study not only identifies positive control driver proteins in cancer networks but also validates the potential of minimum connected dominating set-finding algorithms to identify driver proteins in protein regulatory networks. We validate the potential of balanced signed directed graphs in modeling regulatory functions of protein interaction networks.

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SCS-30: Frequency and spectrum of mutations in the BRCA1, BRCA2, PALB2, P53, PTEN, CHEK2, CDH1 genes in women from 3 cities of Colombia

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Topic(s): Genetics of breast cancer
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  • Alejandro Mejía, Universidad de antioquia, Colombia
  • William Hernán Arias Pérez, Universidad de antioquia, Colombia
  • Shalom Gomez Pulgarín, Universidad de antioquia, Colombia
  • Yina Tatiana Zambrano, Universidad de antioquia, Colombia
  • Gabriel Bedoya, Universidad de antioquia, Colombia
  • Roberto Jaramillo, Hemato oncólogos S.A, Colombia
  • Yorlany Rodas, Hemato oncólogos S.A, Colombia
  • Edgar Navarro, Universidad del norte, Colombia
  • Andres Ossa, hospital general de Medellin, Colombia
  • Mauricio Borrero, instituto de Cancerología Las Americas, Colombia
  • Alicia M. Cock-Rada, instituto de Cancerología Las Americas, Colombia
  • Gonzalo Alberto Angel, consultorio privado, Colombia
  • Michael Dean, national institute of health, United states of america
  • Gloria Inés Sánchez, Universidad de antioquia, Colombia

Short Abstract: Background: germline mutations in the BRCA1 and BRCA2 genes confer a life time risk of 40-80% of developing breast cancer, while mutations in TP53, PTEN CDH1, PALB2, CHEK2 confer moderate to high life time risk for this disease; it is important to detect these mutations in order to give genetic counseling and specific treatment. There are a few high frequency mutations in Colombia: A1708E (BRCA1), 3450delCAAG y 3034delACAA (BRCA2). The aim of this study is to determine the frequency and spectrum of mutations in 7 genes in women with breast cancer unselected living in three cities of Colombia. Main findings: 135 patients with breast cancer unselected between the ages of 25-77 were recruited in 6 health centers from Medellin, Cali and Barranquilla. DNA was extracted from blood samples by salting out; then exons and 20 nucleotides in the intron-exon boundaries of the BRCA1, BRCA2, PALB2, P53, PTEN, CHEK2 and CDH1 genes were sequenced by next generation sequencing on the ion torrent platform. Raw signal data were analyzed using Torrent SuiteTM. The pipeline included Quality control, read alignment to human genome 19 reference (with TMAP), quality control of mapping quality, coverage analysis, and variant calling using the torrent variant caller 5.0-7 (SNVs and INDELs) and GATK (for SNVs). The variants were annotated with the Ion reporter software and classified according to the following databases: Clinvar, Leiden Open Variation Database and Wintervar. The new variants were classified using Intervar. Pathogenic mutations were confirmed by sanger sequencing. The carriers received genetic counseling by an oncogenetist. 6 pathogenic mutations (frequency of 4.4%) were found in these patients: BRCA1: C.5186C>A, C.178C>T and C.213-12A>G, BRCA2: C.7007+1G>A y C.631+3A>G and TP53: C.586C>T. one variant of uncertain significance showed pathogenic evidence in silico (CHEK2: C.497A>G). Conclusions: this is the first study in Colombia that evaluates genes different from BRCA1 and BRCA2 in unselected cases in Colombia, and the frequency of pathogenic mutations was 4.4%. three mutations were found in splicing sites, so it is important to include these sites in the sequencing. Here, we report 4 new pathogenic mutations for the Colombian population.

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SCS-32: Analysis of bacterial DNA sequences submitted by Peruvian institutions to public databases

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Topic(s): Public Databases, Bioinformatics Research, Data Mining
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  • Camila Castillo-Vilcahuaman, Universidad Peruana Cayetano Heredia, Peru
  • Pedro E. Romero, Universidad Peruana Cayetano Heredia, Peru

Short Abstract: Background: Peru is a megadiverse country; however, its biodiversity is still not well researched. This problem is recurrent for many of the taxa in the country, including bacteria. Bacteria are a numerous domain of organisms with great importance. Their study using traditional methods can be limited. Therefore, there is a great interest in bacterial sequencing data. Although Peru has a great biodiversity, bacterial data is still scarce and does not reflect the real bacterial diversity that may inhabit the country. Description: We analyzed two public databases to assess the quantity of peruvian sequencing data related to bacteria: NCBI’s Nucleotide database and the PATRIC (Pathosystems Resource Integration Center) database. In our analysis, we found that 14 488 sequences were submitted by 36 Peruvian institutions in the Nucleotide database. 70.60% of the records belonged to the Bacteria domain. Most of the sequences belonged to the following species: Pasteurella multocida, Neisseria meningitidis, Vibrio parahaemolyticus, Bacillus thuringiensis and Escherichia coli. Two national institutions had most of their records in this database, one of them exclusively focused on medical and health research (Instituto Nacional de Salud) and the other one with a broader research interest (Universidad Nacional Mayor de San Marcos). In the PATRIC database, an exclusive database for pathogens, most of the records belonged to bacteria such as Mycobacterium tuberculosis, Yersinia pestis, Shigella sonnei, and Staphylococcus aureus. A private institution (Universidad Peruana Cayetano Heredia) had most of the records in this database. Conclusion: Data analysis such as this one allows researchers to have an overview of the research interests of Peruvian institutions related to sequencing data. Our survey confirmed that the primary interest in Peruvian institutions involving bacteria resides in pathogens. In a country affected by numerous infectious diseases, it should not be surprising to see the majority of data being related to pathogens. However, this should not be the only focus on peruvian institutions. Peru has a vast biological potential and the expansion of sequencing interests should be encouraged by local research funding institutions.

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SCS-34: In Silico Approach for predicting antimicrobial activity and toxicity of analogues of cruzioseptin-3

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Topic(s):
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  • Fernando Valdivieso-Rivera, Laboratorio de Biología Molecular y Bioquímica, Universidad Regional Amazónica Ikiam, Ecuador
  • Jean Ramos-Galarza, Laboratorio de Biología Molecular y Bioquímica, Universidad Regional Amazónica Ikiam, Ecuador
  • Sebastián Bermúdez-Puga, Laboratorio de Biología Molecular y Bioquímica, Universidad Regional Amazónica Ikiam, Ecuador
  • Carolina Proaño-Bolaños, Laboratorio de Biología Molecular y Bioquímica, Universidad Regional Amazónica Ikiam, Ecuador

Short Abstract: Background: The rapid development and spread of bacterial resistance to conventional antibiotics have become a serious global concern and find a new antibiotic is a great challenge. Antimicrobial peptides have attracted attention as an alternative for the treatment of infections with drug-resistant pathogens. Antimicrobial peptides show a potent antimicrobial activity, a broad spectrum of activity, and low potential for resistance development. For example, cruzioseptin-3 is an antimicrobial peptide of 23 amino acids isolated from the skin secretion of Cruziohyla calcarifer that show a wide spectrum of activity against bacteria and fungi. However, the clinical use of this class of antimicrobial therapeutics has limitations such as host toxicity, intolerance to physiological conditions, degradation by proteases, or high manufacturing costs by the length of the peptide sequence. Thus, the translation of antimicrobial peptides into clinical use requires overcome all these limitations. In the present study, we evaluated in silico the toxicity and antimicrobial activity of five analogs (15 amino acid residues of length) of the antimicrobial peptide cruzioseptin-3 using molecular docking. Description: The binding energy value of the analog cruzioseptin3-15C when interacted with the mammalian membrane was less favorable, which would reduce hemolysis. This could be related to the increase in hydrophobicity with concerning Cruzioseptin-3. The interaction of bacterial membrane with cruzioseptin3-15C and cruzioseptin-3 do not have a significant difference therefore this analog could have the same activity but less cost in the synthesis. On the other hand, when Cruzioseptin3-15E interacted with two key enzymes for the growth of Staphylococcus aureus and Escherichia coli present more inhibitory action than its known inhibitor and cruzioseptin-3. The data suggest that the combination of in vitro and in silico assays can give direction to avoid the cost in the synthesis of many analogs that could be inactive or hemolytic. Conclusion: This data suggest that the two analogs of cruzioseptin-3 can disrupt the bacterial membrane due to the increase of net charge, less hemolysis, and inhibitor activity for key enzymes. Also, molecular docking in combination with tools to predict analogs of antimicrobial peptides could be saved time and money for evaluating peptide therapeutics.

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SCS-35: Diversity of soil microorganisms in maize push-pull farming

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Topic(s): Soil microbiome, push-pull farming, metabarcording, 16S
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  • Aneth Mwakilili, Swedish University of Agricultural Sciences (SLU), Sweden
  • Kilaza Mwaikono, Dar es Salaam Institute of Technology (DIT), Tanzania
  • Charles Midega, International Centre of Insect Physiology and Ecology (ICIPE), Kenya
  • Francis Magingo, University of Dar es Salaam (UDSM), Tanzania
  • Beatrix Alsanius, Swedish University of Agricultural Sciences (SLU), Sweden
  • Teun Dekker, Swedish University of Agricultural Sciences (SLU), Sweden
  • Sylvester Lyantagaye, University of Dar es Salaam (UDSM), Tanzania

Short Abstract: Background This study aimed at describing and comparing soil microbial profiles between maize push-pull farming and maize monoculture. Push-pull is an effective intercropping-based pest management technology used smallholder farmers of maize and sorghum in Sub-Saharan Africa. The technology exploits stimulo-deterrent approach to control lepidopteran stem-borers while also suppressing the parasitic weed striga. The roles of each component of the technology and associated mechanisms have been uncovered. However, the impact of the technology on soil microorganisms and potential benefits on plant health and protection remains under investigated despite documented evidence. Main findings Faith PD alpha diversity index showed a slightly higher diversity of soil bacterial species within monoculture plots compared to push-pull farming plots (p = 0.563703). Conversely, diversity of soil fungal populations within push-pull plots was significantly higher than that in monoculture plots (p = 0.006923). PCO of beta diversity index unweighted unifrac emperor showed a marginal difference in diversity of soil bacteria i.e. number of unique taxa between push-pull and monoculture plots. The diversity of soil fungal species was however observed to be significantly higher in push-pull compared to maize monoculture plots. Push-pull plots demonstrated a less diverse profile of microbial populations than maize monoculture samples. This may be due to presence of Desmodium exerting additional selection pressure on soil microorganisms leading to fewer unique taxa count. Major constitutive phyla observed were Chloroflexi, Actinobacteria, Proteobacteria, Acidobacteria and Planctomycetes. Conclusions Uncovering the diversity of soil microbial populations in push-pull systems may open doors into understanding their contribution in management of the insect pests and the parasitic weed of cereals. Soil microorganisms may even have indirect benefits such as the lower mycotoxins incidents observed in cereals harvested from push-pull farms. Eventually, it may be possible to modulate soil microorganisms for improved yield and sustainable farming.

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SCS-36: Intra-bacterial variability of short oligonucleotide frequencies and their applicability to metagenome analysis: Tools and analysis

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Topic(s): Metagenomics, Oligonucleotide frequencies, Reference-free, Binning
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  • Gleb Goussarov, Belgian Nuclear Research centre, Ghent University, Belgium
  • Ilse Cleenweck, Ghent University, Belgium
  • Mohamed Mysara, Belgian Nuclear Research centre, Belgium
  • Natalie Leys, Belgian Nuclear Research centre, Belgium
  • Peter Vandamme, Ghent University, Belgium
  • Rob van Houdt, Belgian Nuclear Research centre, Belgium

Short Abstract: In metagenomics, short oligonucleotide frequencies are used by most de-novo binning tools in order to group unlabeled contigs into genomic bins, intended to correspond to bacterial species. This approach is justified by the presumed stability of these frequencies within genomes, provided that a sufficiently large window is used. We have recently demonstrated that using a transformation of these frequencies, called Karlin signatures, and based on complete genomes, we could distinguish bacterial species from each other. Here we expand upon this previous analysis and show how our method and other oligonucleotide frequency-baCsed methods perform when considering different parts of one genome rather than different genomes. We also show that it is possible to estimate whether or not a bin is likely to correspond to a single complete genome, or whether it cannot be considered as such, based on different oligonucleotide lengths and calculation methods, without using a reference. This last point is of particular interest to environmental studies, where the bacterial species tend to be poorly studied.

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SCS-37: The Black Women in Computational Biology Network

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Topic(s):
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  • Jenea Adams, Unviersity of Pennsylvania, USA

Short Abstract: "Creating a seat at the table" is a term often used throughout corporate culture to indicate a leadership position, or a position with opportunity to shape, influence and impact key decisions being made. This is a phrase that also carries a connotation of ownership of that ""table"" and represents a long exclusionary history of intracommunity politics and inaccessibility. This talk will explore the philosophy of the Black Women in Computational Biology Network as an unapologetic symbol of not fighting for a seat at a table that wasn't created for the success of underrepresented minorities; instead we challenge this belief and demonstrate what it means to create an entirely new table of inclusion and equity. This community is comprised of womxn across all degree and professional levels from an array of backgrounds in biology, computer science, mathematics, bioengineering, chemistry, physics, medicine, public health data science, and much more refining their unique skillset to answer biological questions through a computational and quantitative lens. Beyond building a global community, concepts, practices, and challenges for creating a more equitable landscape in the computational biology through The Network will be shared.

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SCS-38: AntiFam 6.0 : Are We Close to Getting Rid of Spurious Proteins in Sequence Databases?

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Topic(s): Spurious protein, Sequence analysis, Prokaryotic sequence, Database
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  • Syed Muktadir Al Sium, Bangladesh Agricultural University, Bangladesh
  • Alex Bateman, European Bioinformatics Institute (EMBL-EBI), UK

Short Abstract: Most protein sequences come from gene prediction tools and have no experimental supporting evidence for their translation. Many spurious protein predictions exist in the sequence databases. The AntiFam database and Spurio software help to identify spurious proteins. The machine learning based tool Spurio’s performance has a dependency on AntiFam for the training dataset. AntiFam, a collection of profile-HMMs used to identify spurious protein families, only contained 72 spurious families and each family required manual curation to be built and verified as spurious. We aimed to increase the coverage of spurious protein identification by increasing the number of entries in AntiFam. We struggled to decide a protein’s spurious status and position in Shadow ORFs using Spurio version 1.1. So, we developed a new approach using BLASTX or USEARCH for finding spurious proteins from shadow (-1, -2, -3 frames) and alternate (+2, +3 frames) ORFs. 178 new AntiFam families have been added to the new release (AntiFam 6.0) providing 28,704 new spurious prokaryotic sequences for Spurio. Additionally, protein disorder analysis with IUPred2A showed significant differences between SwissProt and AntiFam sequences. Jointly, these findings will help us to get rid of spurious proteins in sequence databases.

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