Bioinformatics Workflows for Genomic Variant Discovery, Interpretation and Prioritization

Next-generation sequencing (NGS) techniques allow high-throughput detection of a vast amount of variations in a cost-efficient manner. However, there still are inconsistencies and debates about how to process and analyse this ‘big data’. To accurately extract clinically relevant information from genomics data, choosing appropriate tools, knowing how to best utilize them and interpreting the results correctly is crucial. This chapter reviews state-of-the-art bioinformatics approaches in clinically relevant genomic variant detection. Best practices of reads-to-variant discovery workflows for germline and somatic short genomic variants are presented along with the most commonly utilized tools for each step. Additionally, methods for detecting structural variations are overviewed. Finally, approaches and current guidelines for clinical interpretation of genomic variants are discussed. As emphasized in this chapter, data processing and variant discovery steps are relatively well-understood. The differences in prioritization algorithms on the other hand can be perplexing, thus creating a bottleneck during interpretation. This review aims to shed light on the pros and cons of these differences to help experts give more informed decisions

iNucs:Inter-nucleosome interactions

[Motivation] Deciphering nucleosome–nucleosome interactions is an important step toward mesoscale description of chromatin organization but computational tools to perform such analyses are not publicly available. [Results] We developed iNucs, a user-friendly and efficient Python-based bioinformatics tool to compute and visualize nucleosome-resolved interactions using standard pairs format input generated from pairtools

Prediction of Phosphorylation Sites from Protein Sequences

Phosphorylation is amongst the most crucial and well-studied post-translational modifications. It is involved in multiple cellular processes which makes phosphorylation prediction vital for understanding protein functions. However, wet-lab techniques are labour and time intensive. Thus, computational tools are required for efficiency. This project aims to provide a novel way to predict phosphorylation sites from protein sequences by adding flexibility and Sezerman Grouping amino acid similarity measure to previous methods, as discovering new protein sequences happens at a greater rate than determining protein structures. The predictor – NOPAY - relies on Support Vector Machines (SVMs) for classification. The features include amino acid encoding, amino acid grouping, predicted secondary structure, predicted protein disorder, predicted protein flexibility, solvent accessibility, hydrophobicity and volume. As a result, we have managed to improve phosphorylation prediction accuracy for Homo sapiens by 3% and 6.1% for Mus musculus. Sensitivity at 99% specificity was also increased by 6% for Homo sapiens and for Mus musculus by 5% on independent test sets. In this study, we have managed to increase phosphorylation prediction accuracy for Homo sapiens and Mus musculus. When there is enough data, future versions of the software may also be able to predict other organisms.Siirretty Doriast

Analysis of Correlation between the Seven Important Helicobacter pylori (H. pylori) Virulence Factors and Drug Resistance in Patients with Gastritis

The aim of this study is to evaluate the association between seven important H. pylori virulence factors and antibiotic resistance in patients with gastritis. H. pylori strains isolated from 33 patients with gastritis were examined. Antimicrobial susceptibilities were tested by GenoType® HelicoDR (Hain Life Science, Germany) test kit and RT-PCR. The virulence-factors were determined using conventional PCR. 39% of patients were resistant for clarithromycin and 27% of patients were resistant for fluoroquinolone. 15% of patients were resistant to both clarithromycin and fluoroquinolone. The H. pylori vacA m1/s2 genotype was the most frequent allelic combination. Patients were possessed the vacA s1, m1 (6.1%); s1, m2 (6.1%); s2, m1 (15.1%); and s2, m2 (3.0%) genotypes. 94% of patients with gastritis were positive for H. pylori napA gene. Also, there were no dupA gene-positive gastritis patients. There was no significant correlation between the vacA, cagA, oipA, hpaA, babA, napA, dupA, ureA, ureB virulence genes, clarithromycin, and fluoroquinolone resistance. Herein, we report that the relationship between the H. pylori napA gene and gastritis. Although we found a correlation between H. pylori virulence factor and clinical outcome, there is a need for further studies to enlighten the relation between H. pylori virulence genes and antibiotic resistance

The importance of dysregulated miRNAs on ovarian cysts and tumors

Ovarian cancer is the fifth most important cause of cancer-related deaths among women and the most lethal gynecologic malignancy. Epithelial ovarian cancer (EOC) is asymptomatic and few screening tests are available. We aimed to identify novel circulating miRNAs to be used as therapeutic prediction of EOC. Moreover, another goal of our study is the importance of dysregulated miRNAs in ovarian cyst and their expression profile difference between ovarian cancer cases. In this study, we studied three different samples: serums of EOC patients, healthy individuals (HI) and benign ovarian cysts (BOC). Their miRNA expressions have been compared by microarray. Microarray data were analyzed according to miRNA expressions the relation between miRNAs target genes and EOC were examined by bioinformatic tools. 75 and 66 significantly dysregulated miRNAs were identified by microarray in BOC vs. EOC and BOC vs. HI comparison, respectively. Then, we focused on common miRNAs that found in both comparison and finally 46 important miRNAs were detected which can represent the only common sample group, BOC. After these findings, we also considered miRNA profiling in EOC and HI, and surprisingly any common miRNAs were found with these 46 miRNAs. Thus, we analyzed them depending on their potential importance on BOC pathogenesis. After bioinformatic analysis, our findings indicated that there are several biological processes and pathways which can be considered to be related BOC development