Trends in IT Innovation to Build a Next Generation Bioinformatics Solution to Manage and Analyse Biological Big Data Produced by NGS Technologies

JSONWP: a static website generator for protein bioinformatics research

Motivation

Presenting the integrated results of bioinformatics research can be challenging and requires sophisticated visualization components, which can be time-consuming to develop. This article presents a new way to effectively communicate research findings.

Results

We have developed a static web page generator, JSONWP, which is specifically designed for protein bioinformatics research. Utilizing React (a JavaScript library used to build interactive and dynamic user interfaces for web applications), we have integrated publicly available bioinformatics visualization components to provide standardized access to these components. JSON (or JavaScript Object Notation, is a lightweight textual data format often used to structure and exchange information between different software tools.) is used as the input source due to its ability to represent nearly all types of data using key and value pairs. This allows researchers to use their preferred programming language to create a JSON representation, which can then be converted into a website by JSONWP. No server or domain is required to host the website, as only the publicly accessible JSON file is required.

Conclusions

Overall, JSONWP provides a useful new tool for bioinformatics researchers to effectively communicate their findings. The open-source implementation is located at https://github.com/MesihK/react-json-wpbuilder, and the tool can be used at jsonwp.onrender.com.

Linear epitope mapping of the humoral response against SARS-CoV-2 in two independent African cohorts

Profiling of the antibody responses to severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) proteins in African populations is scarce. Here, we performed a detailed IgM and IgG epitope mapping study against 487 peptides covering SARS-CoV-2 wild-type structural proteins. A panel of 41 pre-pandemic and 82 COVID-19 RT-PCR confirmed sera from Madagascar and Senegal were used. We found that the main 36 immunodominant linear epitopes identified were (i) similar in both countries, (ii) distributed mainly in the Spike and the Nucleocapsid proteins, (iii) located outside the RBD and NTD regions where most of the reported SARS-CoV-2 variant mutations occur, and (iv) identical to those reported in European, North American, and Asian studies. Within the severe group, antibody levels were inversely correlated with the viral load. This first antibody epitope mapping study performed in patients from two African countries may be helpful to guide rational peptide-based diagnostic assays or vaccine development.

Next-Generation Sequencing Markup Language (NGSML): A Medium for the Representation and Exchange of NGS Data

With the increasing demand for low-cost high-throughput sequencing of large genomes, next-generation sequencing (NGS) technology has developed rapidly. NGS can not only be used in basic scientific research but also in clinical diagnostics and healthcare. Numerous software systems and tools have been developed to analyze NGS data, and various data formats have been produced to accommodate different sequencing equipment providers or analytical software. However, the data interoperability between these tools brings great challenges to researchers. A generic format that could be shared by most of the software and tools in the NGS field would make data interoperability and sharing easier. In this paper, we defined a general XML-based NGS markup language (NGSML) format for the representation and exchange of NGS data. We also developed a user-friendly GUI tool, NGSMLEditor, for presenting, creating, editing, and converting NGSML files. By using NGSML, various types of NGS data can be saved in one unified format. Compared with the unstructured plain text file, a structured data format based on XML technology solves the incompatibility of various NGS data formats. The NGSML specifications are freely available from http://www.sysbio.org.cn/NGSML. NGSMLEditor is open source under GNU GPL and can be downloaded from the website.

Machine Learning Assisted Cervical Cancer Detection

Cervical malignant growth is the fourth most typical reason for disease demise in women around the globe. Cervical cancer growth is related to human papillomavirus (HPV) contamination. Early screening made cervical cancer a preventable disease that results in minimizing the global burden of cervical cancer. In developing countries, women do not approach sufficient screening programs because of the costly procedures to undergo examination regularly, scarce awareness, and lack of access to the medical center. In this manner, the expectation of the individual patient's risk becomes very high. There are many risk factors relevant to malignant cervical formation. This paper proposes an approach named CervDetect that uses machine learning algorithms to evaluate the risk elements of malignant cervical formation. CervDetect uses Pearson correlation between input variables as well as with the output variable to pre-process the data. CervDetect uses the random forest (RF) feature selection technique to select significant features. Finally, CervDetect uses a hybrid approach by combining RF and shallow neural networks to detect Cervical Cancer. Results show that CervDetect accurately predicts cervical cancer, outperforms the state-of-the-art studies, and achieved an accuracy of 93.6%, mean squared error (MSE) error of 0.07111, false-positive rate (FPR) of 6.4%, and false-negative rate (FNR) of 100%.

Social innovation for life expectancy extension utilizing a platform-centered system used in the Iwaki health promotion project: A protocol paper

Introduction:

We are trying to create a platform for social innovation to extend life span.

Methods:

Since 2005, health data (approximately 3000 items per person as of 2020) of approximately 1000 adults have been collected each year during the Iwaki Health Promotion Project. The industry, government, academia, and citizens have involvements in data collection, aiming to build a platform that encourages societal innovation and subsequently extends life expectancy in Aomori. The Iwaki Health Promotion Project has been supported financially by the Japanese government since it was selected as the Center of Innovation program in 2013.

Results:

Since the numbers of academia, industries, governments, and citizens involved in the Iwaki Health Promotion Project increased over the years, the big data produced during the project has become increasingly pluripotent and adaptable. It has been used to promote public health, which has also created a stronger partnership among companies and research organizations. Consequently, the amount of data collected from the project has gained attention and became more open to companies and researchers participating in the Iwaki Health Promotion Project, resulted in establishing a larger platform. It also led to the acquisition of external funding, publications of numerous research papers, creation of new health examinations, and the establishment of the Health Promotion Center (an institution for cultivating health volunteers).

Conclusion:

The Iwaki Health Promotion Project aims not only to produce a pluripotent big data but also to improve the average life expectancy of Aomori by creating a large platform in the society. Its positive impact in the future is infinite and will keep growing as long as it is maintained by the society.

Bioinformatics Workflows With NoSQL Database in Cloud Computing

Scientific workflows can be understood as arrangements of managed activities executed by different processing entities. It is a regular Bioinformatics approach applying workflows to solve problems in Molecular Biology, notably those related to sequence analyses. Due to the nature of the raw data and the in silico environment of Molecular Biology experiments, apart from the research subject, 2 practical and closely related problems have been studied: reproducibility and computational environment. When aiming to enhance the reproducibility of Bioinformatics experiments, various aspects should be considered. The reproducibility requirements comprise the data provenance, which enables the acquisition of knowledge about the trajectory of data over a defined workflow, the settings of the programs, and the entire computational environment. Cloud computing is a booming alternative that can provide this computational environment, hiding technical details, and delivering a more affordable, accessible, and configurable on-demand environment for researchers. Considering this specific scenario, we proposed a solution to improve the reproducibility of Bioinformatics workflows in a cloud computing environment using both Infrastructure as a Service (IaaS) and Not only SQL (NoSQL) database systems. To meet the goal, we have built 3 typical Bioinformatics workflows and ran them on 1 private and 2 public clouds, using different types of NoSQL database systems to persist the provenance data according to the Provenance Data Model (PROV-DM). We present here the results and a guide for the deployment of a cloud environment for Bioinformatics exploring the characteristics of various NoSQL database systems to persist provenance data.

Bioinformatics in Germany: toward a national-level infrastructure

The German Network for Bioinformatics Infrastructure (de.NBI) is a national initiative funded by the German Federal Ministry of Education and Research (BMBF). The mission of de.NBI is (i) to provide high-quality bioinformatics services to users in basic and applied life sciences research from academia, industry and biomedicine; (ii) to offer bioinformatics training to users in Germany and Europe through a wide range of workshops and courses; and (iii) to foster the cooperation of the German bioinformatics community with international network structures such as the European life-sciences Infrastructure for biological Information (ELIXIR). The network was launched by the BMBF in March 2015 and now includes 40 service projects operated by 30 project partners that are organized in eight service centers. The de.NBI staff develops further and maintains almost 100 bioinformatics services for the human, plant and microbial research fields and provides comprehensive training courses to support users with different expertise levels in bioinformatics. In the future, de.NBI will expand its activities to the European level, as the de.NBI consortium was assigned by the BMBF to establish and run the German node of ELIXIR.

AFLP-AFLP in silico-NGS approach reveals polymorphisms in repetitive elements in the malignant genome

The increasing interest in exploring the human genome and identifying genetic risk factors contributing to the susceptibility to and outcome of diseases has supported the rapid development of genome-wide techniques. However, the large amount of obtained data requires extensive bioinformatics analysis. In this work, we established an approach combining amplified fragment length polymorphism (AFLP), AFLP in silico and next generation sequencing (NGS) methods to map the malignant genome of patients with chronic myeloid leukemia. We compared the unique DNA fingerprints of patients generated by the AFLP technique approach with those of healthy donors to identify AFLP markers associated with the disease and/or the response to treatment with imatinib, a tyrosine kinase inhibitor. Among the statistically significant AFLP markers selected for NGS analysis and virtual fingerprinting, we identified the sequences of three fragments in the region of DNA repeat element OldhAT1, LINE L1M7, LTR MER90, and satellite ALR/Alpha among repetitive elements, which may indicate a role of these non-coding repetitive sequences in hematological malignancy. SNPs leading to the presence/absence of these fragments were confirmed by Sanger sequencing. When evaluating the results of AFLP analysis for some fragments, we faced the frequently discussed size homoplasy, resulting in co-migration of non-identical AFLP fragments that may originate from an insertion/deletion, SNP, somatic mutation anywhere in the genome, or combination thereof. The AFLP–AFLP in silico–NGS procedure represents a smart alternative to microarrays and relatively expensive and bioinformatically challenging whole-genome sequencing to detect the association of variable regions of the human genome with diseases.

NGS-FC: A Next-Generation Sequencing Data Format Converter

With the widespread implementation of next-generation sequencing (NGS) technologies, millions of sequences have been produced. A lot of databases were created to store and organize the high-throughput sequencing data. Numerous analysis software programs and tools have been developed over the past years. Most of them use specific formats for data representation and storage. Data interoperability becomes a crucial challenge and many tools have been developed to convert NGS data from one format to another. However, most of them were developed for specific and limited formats. Here, we present NGS-FC (Next-Generation Sequencing Format Converter), which provides a framework to support the conversion between several formats. It supports 14 formats now and provides interfaces to enable users to improve the existing converters and add new ones. Moreover, NGS-FC achieved the overall competitive performance in comparison with some existing converters in terms of RAM usage and running time. The software is written in Java and can be executed standalone. The source code and documentation are freely available at http://sysbio.suda.edu.cn/NGS-FC.

Spfy: an integrated graph database for real-time prediction of bacterial phenotypes and downstream comparative analyses

Public health laboratories are currently moving to whole-genome sequence (WGS)-based analyses, and require the rapid prediction of standard reference laboratory methods based solely on genomic data. Currently, these predictive genomics tasks rely on workflows that chain together multiple programs for the requisite analyses. While useful, these systems do not store the analyses in a genome-centric way, meaning the same analyses are often re-computed for the same genomes. To solve this problem, we created Spfy, a platform that rapidly performs the common reference laboratory tests, uses a graph database to store and retrieve the results from the computational workflows and links data to individual genomes using standardized ontologies. The Spfy platform facilitates rapid phenotype identification, as well as the efficient storage and downstream comparative analysis of tens of thousands of genome sequences. Though generally applicable to bacterial genome sequences, Spfy currently contains 10 243 Escherichia coli genomes, for which in-silico serotype and Shiga-toxin subtype, as well as the presence of known virulence factors and antimicrobial resistance determinants have been computed. Additionally, the presence/absence of the entire E. coli pan-genome was computed and linked to each genome. Owing to its database of diverse pre-computed results, and the ability to easily incorporate user data, Spfy facilitates hypothesis testing in fields ranging from population genomics to epidemiology, while mitigating the re-computation of analyses. The graph approach of Spfy is flexible, and can accommodate new analysis software modules as they are developed, easily linking new results to those already stored. Spfy provides a database and analyses approach for E. coli that is able to match the rapid accumulation of WGS data in public databases.

How viral genetic variants and genotypes influence disease and treatment outcome of chronic hepatitis B. Time for an individualised approach?

Chronic hepatitis B virus (HBV) infection remains a global problem. Several HBV genotypes exist with different biology and geographical prevalence. Whilst the future aim of HBV treatment remains viral eradication, current treatment strategies aim to suppress the virus and prevent the progression of liver disease. Current strategies also involve identification of patients for treatment, namely those at risk of progressive liver disease. Identification of HBV genotype, HBV mutants and other predictive factors allow for tailoured treatments, and risk-surveillance pathways, such as hepatocellular cancer screening. In the future, these factors may enable stratification not only of treatment decisions, but also of patients at risk of higher relapse rates when current therapies are discontinued. Newer technologies, such as next-generation sequencing, to assess drug-resistant or immune escape variants and quasi-species heterogeneity in patients, may allow for more information-based treatment decisions between the clinician and the patient. This article serves to discuss how HBV genotypes and genetic variants impact not only upon the disease course and outcomes, but also current treatment strategies. Adopting a personalised genotypic approach may play a role in future strategies to combat the disease. Herein, we discuss new technologies that may allow more informed decision-making for response guided therapy in the battle against HBV.

Relational Databases and Biomedical Big Data

In various biomedical applications that collect, handle, and manipulate data, the amounts of data tend to build up and venture into the range identified as bigdata. In such occurrences, a design decision has to be taken as to what type of database would be used to handle this data. More often than not, the default and classical solution to this in the biomedical domain according to past research is relational databases. While this used to be the norm for a long while, it is evident that there is a trend to move away from relational databases in favor of other types and paradigms of databases. However, it still has paramount importance to understand the interrelation that exists between biomedical big data and relational databases. This chapter will review the pros and cons of using relational databases to store biomedical big data that previous researches have discussed and used.

Draft Genome Sequence of Yarrowia lipolytica Strain A-101 Isolated from Polluted Soil in Poland

Yarrowia lipolytica is an early diverging species of the Saccharomycotina subphylum, which is recognized as a valuable host for many biotechnological applications exploiting its oleaginous capacities. The 20.5-Mb genome of the Polish Y. lipolytica strain A-101 will greatly help decipher the genetic basis of the regulation of its lipid metabolism.

Vaccinology in the third millennium: scientific and social challenges

The epidemiology of deaths due to vaccine-preventable diseases has been significantly and positively altered through the use of vaccines. Despite this, significant challenges remain in vaccine development and use in the third millennium. Both new (Ebola, Chikungunya, Zika, and West Nile) and re-emerging diseases (measles, mumps, and influenza) require the development of new or next-generation vaccines. The global aging of the population, and accumulating numbers of immunocompromised persons, will require new vaccine and adjuvant development to protect large segments of the population. After vaccine development, significant challenges remain globally in the cost and efficient use and acceptance of vaccines by the public. This article raises issues in these two areas and suggests a way forward that will benefit current and future generations.

miTALOS v2: Analyzing Tissue Specific microRNA Function

MicroRNAs are involved in almost all biological processes and have emerged as regulators of signaling pathways. We show that miRNA target genes and pathway genes are not uniformly expressed across human tissues. To capture tissue specific effects, we developed a novel methodology for tissue specific pathway analysis of miRNAs. We incorporated the most recent and highest quality miRNA targeting data (TargetScan and StarBase), RNA-seq based gene expression data (EBI Expression Atlas) and multiple new pathway data sources to increase the biological relevance of the predicted miRNA-pathway associations. We identified new potential roles of miR-199a-3p, miR-199b-3p and the miR-200 family in hepatocellular carcinoma, involving the regulation of metastasis through MAPK and Wnt signaling. Also, an association of miR-571 and Notch signaling in liver fibrosis was proposed. To facilitate data update and future extensions of our tool, we developed a flexible database backend using the graph database neo4j. The new backend as well as the novel methodology were included in the updated miTALOS v2, a tool that provides insights into tissue specific miRNA regulation of biological pathways. miTALOS v2 is available at http://mips.helmholtz-muenchen.de/mitalos.

Molecular Pathology and Personalized Medicine: The Dawn of a New Era in Companion Diagnostics-Practical Considerations about Companion Diagnostics for Non-Small-Cell-Lung-Cancer

Companion diagnostics (CDx) have become a major tool in molecular pathology and assist in therapy decisions in an increasing number of various cancers. Particularly, the developments in lung cancer have been most impressing in the last decade and consequently lung cancer mutation testing and molecular profiling has become a major business of diagnostic laboratories. However, it has become difficult to decide which biomarkers are currently relevant for therapy decisions, as many of the new biomarkers are not yet approved as therapy targets, remain in the status of clinical studies, or still have not left the experimental phase. The current review is focussed on those markers that do have current therapy implications, practical implications arising from the respective companion diagnostics, and thus is focused on daily practice.