DNA Data Bank of Japan

The central role of transfer RNAs in mistranslation

Transfer RNAs (tRNA) are essential small non-coding RNAs that enable the translation of genomic information into proteins in all life forms. The principal function of tRNAs is to bring amino acid building blocks to the ribosomes for protein synthesis. In the ribosome, tRNAs interact with messenger RNA (mRNA) to mediate the incorporation of amino acids into a growing polypeptide chain following the rules of the genetic code. Accurate interpretation of the genetic code requires tRNAs to carry amino acids matching their anticodon identity and decode the correct codon on mRNAs. Errors in these steps cause the translation of codons with the wrong amino acids (mistranslation), compromising the accurate flow of information from DNA to proteins. Accumulation of mutant proteins due to mistranslation jeopardizes proteostasis and cellular viability. However, the concept of mistranslation is evolving, with increasing evidence indicating that mistranslation can be used as a mechanism for survival and acclimatization to environmental conditions. In this review, we discuss the central role of tRNAs in modulating translational fidelity through their dynamic and complex interplay with translation factors. We summarize recent discoveries of mistranslating tRNAs and describe the underlying molecular mechanisms and the specific conditions and environments that enable and promote mistranslation.

The African Human Microbiome Portal: a public web portal of curated metagenomic metadata

There is growing evidence that comprehensive and harmonized metadata are fundamental for effective public data reusability. However, it is often challenging to extract accurate metadata from public repositories. Of particular concern is the metagenomic data related to African individuals, which often omit important information about the particular features of these populations. As part of a collaborative consortium, H3ABioNet, we created a web portal, namely the African Human Microbiome Portal (AHMP), exclusively dedicated to metadata related to African human microbiome samples. Metadata were collected from various public repositories prior to cleaning, curation and harmonization according to a pre-established guideline and using ontology terms. These metadata sets can be accessed at https://microbiome.h3abionet.org/. This web portal is open access and offers an interactive visualization of 14 889 records from 70 bioprojects associated with 72 peer reviewed research articles. It also offers the ability to download harmonized metadata according to the user's applied filters. The AHMP thereby supports metadata search and retrieve operations, facilitating, thus, access to relevant studies linked to the African Human microbiome. Database URL:  https://microbiome.h3abionet.org/.

Clinical manifestations and susceptibility of Scedosporium/Lomentospora infections at a tertiary medical centre in Taiwan from 2014 to 2021: A retrospective cohort study

BACKGROUND

Scedosporiosis/lomentosporiosis is a globally emerging and crucial fungal infection. However, clinical data on Scedosporium/Lomentospora infections in Taiwan are scarce.

OBJECTIVES

This study aimed to explore the clinical characteristics of Scedosporium/Lomentospora-infected patients and evaluate the susceptibility of these isolates to antifungal agents.

METHODS

The clinical features of Scedosporium/Lomentospora-infected patients at a tertiary teaching hospital in Northern Taiwan between 2014 and 2021 were retrospectively reviewed; isolates from these patients were identified to species level for antifungal susceptibility testing.

RESULTS

Among 44 patients, 27 (61.4%) had scedosporiosis/lomentosporiosis, whereas 17 (38.6%) were colonised with Scedosporium/Lomentospora species. Scedosporium apiospermum was the main coloniser; scedosporiosis was primarily caused by S. boydii. Trauma history, steroid and immunosuppressant use were the most common risk factors for developing these infections. Among 27 patients with scedosporiosis/lomentosporiosis, one was lost to follow-up and seven (7/26, 26.9%) died. Most patients with S. apiospermum infection have a history of trauma, leading to cutaneous, bone and ocular infections. Pulmonary, sinus and disseminated infections and mortality were frequently reported in patients with S. boydii infection. Voriconazole's minimum inhibitory concentration was low for S. boydii, S. apiospermum and S. aurantiacum. Caspofungin, micafungin and anidulafungin were active against S. boydii and S. apiospermum. A potentially novel Scedosporium species was identified in this study, with distinct clinical manifestations and antifungal susceptibility.

CONCLUSIONS

At our centre, S. boydii is the main causative species of scedosporiosis; voriconazole could be the first-line treatment in Taiwan. Our study supports the importance of speciation, rather than only categorising these isolates into S. apiospermum species complex.

Going through phages: a computational approach to revealing the role of prophage in Staphylococcus aureus

Prophages have important roles in virulence, antibiotic resistance, and genome evolution in Staphylococcus aureus . Rapid growth in the number of sequenced S. aureus genomes allows for an investigation of prophage sequences at an unprecedented scale. We developed a novel computational pipeline for phage discovery and annotation. We combined PhiSpy, a phage discovery tool, with VGAS and PROKKA, genome annotation tools to detect and analyse prophage sequences in nearly 10 011  S . aureus genomes, discovering thousands of putative prophage sequences with genes encoding virulence factors and antibiotic resistance. To our knowledge, this is the first large-scale application of PhiSpy on a large-scale set of genomes (10 011  S . aureus ). Determining the presence of virulence and resistance encoding genes in prophage has implications for the potential transfer of these genes/functions to other bacteria via transduction and thus can provide insight into the evolution and spread of these genes/functions between bacterial strains. While the phage we have identified may be known, these phages were not necessarily known or characterized in S. aureus and the clustering and comparison we did for phage based on their gene content is novel. Moreover, the reporting of these genes with the S. aureus genomes is novel.

HERV-K (HML-2) insertion polymorphisms in the 8q24.13 region and their potential etiological associations with acute myeloid leukemia

Human endogenous retroviruses (HERVs) are LTR retrotransposons that are present in the human genome. Among them, members of the HERV-K (HML-2) group are suspected to play a role in the development of different types of cancer, including lung, ovarian, and prostate cancer, as well as leukemia. Acute myeloid leukemia (AML) is an important disease that causes 1% of cancer deaths in the United States and has a survival rate of 28.7%. Here, we describe a method for assessing the statistical association between HERV-K (HML-2) transposable element insertion polymorphisms (or TIPs) and AML, using whole-genome sequencing and read mapping using TIP_finder software. Our results suggest that 101 polymorphisms involving HERV-K (HML-2) elements were correlated with AML, with a percentage between 44.4 to 56.6%, most of which (70) were located in the region from 8q24.13 to 8q24.21. Moreover, it was found that the TRIB1, LRATD2, POU5F1B, MYC, PCAT1, PVT1, and CCDC26 genes could be displaced or fragmented by TIPs. Furthermore, a general method was devised to facilitate analysis of the correlation between transposable element insertions and specific diseases. Finally, although the relationship between HERV-K (HML-2) TIPs and AML remains unclear, the data reported in this study indicate a statistical correlation, as supported by the χ² test with p-values < 0.05.

LDMD: A database of microbes in human lung disease

Background

Lungs were initially thought to be sterile. However, with the development of sequencing technologies, various commensal microorganisms, especially bacteria, have been observed in the lungs of healthy humans. Several studies have also linked lung microbes to infectious lung diseases. However, few databases have focused on the metagenomics of lungs to provide microbial compositions and corresponding metadata information. Such a database would be handy for researching and treating lung diseases.

Methods

To provide researchers with a preliminary understanding of lung microbes and their research methods, the LDMD collated nearly 10,000 studies in the literature covering over 30 diseases, gathered basic information such as the sources of lung microbe samples, sequencing methods, and processing software, as well as analyzed the metagenomic sequencing characteristics of lung microbes. Besides, the LDMD also contained data collected in our laboratory.

Results

In this study, we established the Lung Disease Microorganisms Database (LDMD), a comprehensive database of microbes involved in lung disease. The LDMD offered sequence analysis capabilities, allowing users to upload their sequencing results, align them with the data collated in the database, and visually analyze the results.

Conclusion

In conclusion, the LDMD possesses various functionalities that provide a convenient and comprehensive resource to study the lung metagenome and treat lung diseases.

Reproducible acquisition, management and meta-analysis of nucleotide sequence (meta)data using q2-fondue

MOTIVATION

The volume of public nucleotide sequence data has blossomed over the past two decades and is ripe for re- and meta-analyses to enable novel discoveries. However, reproducible re-use and management of sequence datasets and associated metadata remain critical challenges. We created the open source Python package q2-fondue to enable user-friendly acquisition, re-use and management of public sequence (meta)data while adhering to open data principles.

RESULTS

q2-fondue allows fully provenance-tracked programmatic access to and management of data from the NCBI Sequence Read Archive (SRA). Unlike other packages allowing download of sequence data from the SRA, q2-fondue enables full data provenance tracking from data download to final visualization, integrates with the QIIME 2 ecosystem, prevents data loss upon space exhaustion and allows download of (meta)data given a publication library. To highlight its manifold capabilities, we present executable demonstrations using publicly available amplicon, whole genome and metagenome datasets.

AVAILABILITY AND IMPLEMENTATION

q2-fondue is available as an open-source BSD-3-licensed Python package at https://github.com/bokulich-lab/q2-fondue. Usage tutorials are available in the same repository. All Jupyter notebooks used in this article are available under https://github.com/bokulich-lab/q2-fondue-examples.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Recommendations for the formatting of Variant Call Format (VCF) files to make plant genotyping data FAIR

In this opinion article, we discuss the formatting of files from (plant) genotyping studies, in particular the formatting of metadata in Variant Call Format (VCF) files. The flexibility of the VCF format specification facilitates its use as a generic interchange format across domains but can lead to inconsistency between files in the presentation of metadata. To enable fully autonomous machine actionable data flow, generic elements need to be further specified. We strongly support the merits of the FAIR principles and see the need to facilitate them also through technical implementation specifications. They form a basis for the proposed VCF extensions here. We have learned from the existing application of VCF that the definition of relevant metadata using controlled standards, vocabulary and the consistent use of cross-references via resolvable identifiers (machine-readable) are particularly necessary and propose their encoding. VCF is an established standard for the exchange and publication of genotyping data. Other data formats are also used to capture variant data (for example, the HapMap and the gVCF formats), but none currently have the reach of VCF. For the sake of simplicity, we will only discuss VCF and our recommendations for its use, but these recommendations could also be applied to gVCF. However, the part of the VCF standard relating to metadata (as opposed to the actual variant calls) defines a syntactic format but no vocabulary, unique identifier or recommended content. In practice, often only sparse descriptive metadata is included. When descriptive metadata is provided, proprietary metadata fields are frequently added that have not been agreed upon within the community which may limit long-term and comprehensive interoperability. To address this, we propose recommendations for supplying and encoding metadata, focusing on use cases from plant sciences. We expect there to be overlap, but also divergence, with the needs of other domains.

Fusarium Keratitis in Taiwan: Molecular Identification, Antifungal Susceptibilities, and Clinical Features

We performed molecular identification and antifungal susceptibilities of pathogens and investigated clinical features of 43 culture-proven Fusarium keratitis cases from 2015–2020 in Taiwan. The pathogens were identified by sequencing of their internal transcribed spacer regions of ribosomal DNA and translation elongation factor 1α gene; their antifungal susceptibilities (to seven agents) were determined by broth microdilution method. We also collected clinical data to compare the drug susceptibilities and clinical features of Fusarium solani species complex (FSSC) isolates with those of other Fusarium species complexes (non-FSSC). The FSSC accounted for 76.7% pathogens, among which F. falciforme (32.6%) and F. keratoplasticum (27.9%) were the most common species. Among clinically used antifungal agents, amphotericin B registered the lowest minimal inhibitory concentration (MIC), and the new azoles efinaconazole, lanoconazole and luliconazole, demonstrated even lower MICs against Fusarium species. The MICs of natamycin, voriconazole, chlorhexidine, lanoconazole, and luliconazole were higher for the FSSC than the non-FSSC, but no significant differences were noted in clinical outcomes, including corneal perforation and final visual acuity. In Taiwan, the FSSC was the most common complex in Fusarium keratitis; its MICs for five tested antifungal agents were higher than those of non-FSSC, but the clinical outcomes did not differ significantly.

Molecular Acquisition, Cleaning and Evaluation in R (MACER) - A tool to assemble molecular marker datasets from BOLD and GenBank

Molecular sequence data is an essential component for many biological fields of study. The strength of these data is in their ability to be centralised and compared across research studies. There are many online repositories for molecular sequence data, some of which are very large accumulations of varying data types like NCBI’s GenBank. Due to the size and the complexity of the data in these repositories, challenges arise in searching for data of interest. While data repositories exist for molecular markers, taxa and other specific research interests, repositories may not contain, or be suitable for, more specific applications. Manually accessing, searching, downloading, accumulating, dereplicating and cleaning data to construct project-specific datasets is time-consuming. In addition, the manual assembly of datasets presents challenges with reproducibility. Here, we present the MACER package to assist researchers in assembling molecular datasets and provide reproducibility in the process.

Eudiplozoon nipponicum (Monogenea, Diplozoidae) and its adaptation to haematophagy as revealed by transcriptome and secretome profiling

Background

Ectoparasites from the family Diplozoidae (Platyhelminthes, Monogenea) belong to obligate haematophagous helminths of cyprinid fish. Current knowledge of these worms is for the most part limited to their morphological, phylogenetic, and population features. Information concerning the biochemical and molecular nature of physiological processes involved in host–parasite interaction, such as evasion of the immune system and its regulation, digestion of macromolecules, suppression of blood coagulation and inflammation, and effect on host tissue and physiology, is lacking. In this study, we report for the first time a comprehensive transcriptomic/secretome description of expressed genes and proteins secreted by the adult stage of Eudiplozoon nipponicum (Goto, 1891) Khotenovsky, 1985, an obligate sanguivorous monogenean which parasitises the gills of the common carp (Cyprinus carpio).

Results

RNA-seq raw reads (324,941 Roche 454 and 149,697,864 Illumina) were generated, de novo assembled, and filtered into 37,062 protein-coding transcripts. For 19,644 (53.0%) of them, we determined their sequential homologues. In silico functional analysis of E. nipponicum RNA-seq data revealed numerous transcripts, pathways, and GO terms responsible for immunomodulation (inhibitors of proteolytic enzymes, CD59-like proteins, fatty acid binding proteins), feeding (proteolytic enzymes cathepsins B, D, L1, and L3), and development (fructose 1,6-bisphosphatase, ferritin, and annexin). LC-MS/MS spectrometry analysis identified 721 proteins secreted by E. nipponicum with predominantly immunomodulatory and anti-inflammatory functions (peptidyl-prolyl cis-trans isomerase, homolog to SmKK7, tetraspanin) and ability to digest host macromolecules (cathepsins B, D, L1).

Conclusions

In this study, we integrated two high-throughput sequencing techniques, mass spectrometry analysis, and comprehensive bioinformatics approach in order to arrive at the first comprehensive description of monogenean transcriptome and secretome. Exploration of E. nipponicum transcriptome-related nucleotide sequences and translated and secreted proteins offer a better understanding of molecular biology and biochemistry of these, often neglected, organisms. It enabled us to report the essential physiological pathways and protein molecules involved in their interactions with the fish hosts.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12864-021-07589-z.

Bacterial avidins are a widely distributed protein family in Actinobacteria, Proteobacteria and Bacteroidetes

BACKGROUND

Avidins are biotin-binding proteins commonly found in the vertebrate eggs. In addition to streptavidin from Streptomyces avidinii, a growing number of avidins have been characterized from divergent bacterial species. However, a systematic research concerning their taxonomy and ecological role has never been done. We performed a search for avidin encoding genes among bacteria using available databases and classified potential avidins according to taxonomy and the ecological niches utilized by host bacteria.

RESULTS

Numerous avidin-encoding genes were found in the phyla Actinobacteria and Proteobacteria. The diversity of protein sequences was high and several new variants of genes encoding biotin-binding avidins were found. The living strategies of bacteria hosting avidin encoding genes fall mainly into two categories. Human and animal pathogens were overrepresented among the found bacteria carrying avidin genes. The other widespread category were bacteria that either fix nitrogen or live in root nodules/rhizospheres of plants hosting nitrogen-fixing bacteria.

CONCLUSIONS

Bacterial avidins are a taxonomically and ecologically diverse group mainly found in Actinobacteria, Proteobacteria and Bacteroidetes, associated often with plant invasiveness. Avidin encoding genes in plasmids hint that avidins may be horizontally transferred. The current survey may be used as a basis in attempts to understand the ecological significance of biotin-binding capacity.

Formalization and Semantic Integration of Heterogeneous Omics Annotations for Exploratory Searches

Aim:

To facilitate researchers and practitioners for unveiling the mysterious functional aspects of human cellular system through performing exploratory searching on semantically integrated heterogeneous and geographically dispersed omics annotations.

Background:

Improving health standards of life is one of the motives which continuously instigates researchers and practitioners to strive for uncovering the mysterious aspects of human cellular system. Inferring new knowledge from known facts always requires reasonably large amount of data in well-structured, integrated and unified form. Due to the advent of especially high throughput and sensor technologies, biological data is growing heterogeneously and geographically at astronomical rate. Several data integration systems have been deployed to cope with the issues of data heterogeneity and global dispersion. Systems based on semantic data integration models are more flexible and expandable than syntax-based ones but still lack aspect-based data integration, persistence and querying. Furthermore, these systems do not fully support to warehouse biological entities in the form of semantic associations as naturally possessed by the human cell.

Objective:

To develop aspect-oriented formal data integration model for semantically integrating heterogeneous and geographically dispersed omics annotations for providing exploratory querying on integrated data.

Method:

We propose an aspect-oriented formal data integration model which uses web semantics standards to formally specify its each construct. Proposed model supports aspect-oriented representation of biological entities while addressing the issues of data heterogeneity and global dispersion. It associates and warehouses biological entities in the way they relate with

Result:

To show the significance of proposed model, we developed a data warehouse and information retrieval system based on proposed model compliant multi-layered and multi-modular software architecture. Results show that our model supports well for gathering, associating, integrating, persisting and querying each entity with respect to its all possible aspects within or across the various associated omics layers.

Conclusion:

Formal specifications better facilitate for addressing data integration issues by providing formal means for understanding omics data based on meaning instead of syntax

gutMEGA: a database of the human gut MEtaGenome Atlas

The gut microbiota plays important roles in human health through regulating both physiological homeostasis and disease emergence. The accumulation of metagenomic sequencing studies enables us to better understand the temporal and spatial variations of the gut microbiota under different physiological and pathological conditions. However, it is inconvenient for scientists to query and retrieve published data; thus, a comprehensive resource for the quantitative gut metagenome is urgently needed. In this study, we developed gut MEtaGenome Atlas (gutMEGA), a well-annotated comprehensive database, to curate and host published quantitative gut microbiota datasets from Homo sapiens. By carefully curating the gut microbiota composition, phenotypes and experimental information, gutMEGA finally integrated 59 132 quantification events for 6457 taxa at seven different levels (kingdom, phylum, class, order, family, genus and species) under 776 conditions. Moreover, with various browsing and search functions, gutMEGA provides a fast and simple way for users to obtain the relative abundances of intestinal microbes among phenotypes. Overall, gutMEGA is a convenient and comprehensive resource for gut metagenome research, which can be freely accessed at http://gutmega.omicsbio.info.

Integrative computational epigenomics to build data-driven gene regulation hypotheses

BACKGROUND

Diseases are complex phenotypes often arising as an emergent property of a non-linear network of genetic and epigenetic interactions. To translate this resulting state into a causal relationship with a subset of regulatory features, many experiments deploy an array of laboratory assays from multiple modalities. Often, each of these resulting datasets is large, heterogeneous, and noisy. Thus, it is non-trivial to unify these complex datasets into an interpretable phenotype. Although recent methods address this problem with varying degrees of success, they are constrained by their scopes or limitations. Therefore, an important gap in the field is the lack of a universal data harmonizer with the capability to arbitrarily integrate multi-modal datasets.

RESULTS

In this review, we perform a critical analysis of methods with the explicit aim of harmonizing data, as opposed to case-specific integration. This revealed that matrix factorization, latent variable analysis, and deep learning are potent strategies. Finally, we describe the properties of an ideal universal data harmonization framework.

CONCLUSIONS

A sufficiently advanced universal harmonizer has major medical implications, such as (i) identifying dysregulated biological pathways responsible for a disease is a powerful diagnostic tool; (2) investigating these pathways further allows the biological community to better understand a disease's mechanisms; and (3) precision medicine also benefits from developments in this area, particularly in the context of the growing field of selective epigenome editing, which can suppress or induce a desired phenotype.

TypeLoader2: Automated submission of novel HLA and killer-cell immunoglobulin-like receptor alleles in full length

The Immuno Polymorphism Database (IPD) databases provide global, curated repositories for information regarding polymorphisms of genes of the immune system, thereby generating immense value for the research and clinical communities. The advent of high‐throughput genotyping in immunogenetics has led to dramatically growing numbers of heretofore unknown HLA and lately also killer‐cell immunoglobulin‐like receptor (KIR) alleles, which are to be curated and deposited in the IPD‐IMGT/HLA and IPD‐KIR databases, respectively. It is highly desirable that these novel alleles are characterised and submitted in full length, and that known alleles are extended to cover the complete gene sequence.

However, the manual annotation and submission of sequences to European Molecular Biology Laboratory's European Nucleotide Archive and the IPD‐IMGT/HLA and IPD‐KIR databases is time‐consuming and error‐prone. Here, we report the substantial extension of the HLA allele submission tool TypeLoader, which now also supports the annotation and submission of KIR alleles. To enable a more widespread use of this tool, we have made it available as a stand‐alone application that can easily be installed on standard Windows or Linux computers. Furthermore, an internal SQLite database was added to store a wide range of metadata about each allele. This allows TypeLoader2 to be used as a lab's central information platform for the annotation, curation and submission of full‐length HLA and KIR allele sequences. The software is freely available from GitHub (https://github.com/DKMS-LSL/typeloader).

We hope that the increased convenience and scope of TypeLoader2 will foster the submission of more full‐length sequences to the IPD‐IMGT/HLA and IPD‐KIR databases, ultimately promoting the use of full‐length sequencing for genotyping both HLA and KIR.

Integration and Querying of Heterogeneous Omics Semantic Annotations for Biomedical and Biomolecular Knowledge Discovery

Background:

Exploring various functional aspects of a biological cell system has been a focused research trend for last many decades. Biologists, scientists and researchers are continuously striving for unveiling the mysteries of these functional aspects to improve the health standards of life. For getting such understanding, astronomically growing, heterogeneous and geographically dispersed omics data needs to be critically analyzed. Currently, omics data is available in different types and formats through various data access interfaces. Applications which require offline and integrated data encounter a lot of data heterogeneity and global dispersion issues.

Objective:

For facilitating especially such applications, heterogeneous data must be collected, integrated and warehoused in such a loosely coupled way so that each molecular entity can computationally be understood independently or in association with other entities within or across the various cellular aspects.

Methods:

In this paper, we propose an omics data integration schema and its corresponding data warehouse system for integrating, warehousing and presenting heterogeneous and geographically dispersed omics entities according to the cellular functional aspects.

Results & Conclusion:

Such aspect-oriented data integration, warehousing and data access interfacing through graphical search, web services and application programing interfaces make our proposed integrated data schema and warehouse system better and useful than other contemporary ones.

Principal Component Analysis applied directly to Sequence Matrix

Sequence data is now widely used to observe relationships among organisms. However, understanding structure of the qualitative data is challenging. Conventionally, the relationships are analysed using a dendrogram that estimates a tree shape. This approach has difficulty in verifying the appropriateness of the tree shape; rather, horizontal gene transfers and mating can make the shape of the relationship as networks. As a connection-free approach, principal component analysis (PCA) is used to summarize the distance matrix, which records distances between each combination of samples. However, this approach is limited regarding the treatment of information of sequence motifs; distances caused by different motifs are mixed up. This hides clues to figure out how the samples are different. As any bases may change independently, a sequence is multivariate data essentially. Hence, differences among samples and bases that contribute to the difference should be observed coincidentally. To archive this, the sequence matrix is transferred to boolean vector and directly analysed by using PCA. The effects are confirmed in diversity of Asiatic lion and human as well as environmental DNA. Resolution of samples and robustness of calculation is improved. Relationship of a direction of difference and causative nucleotides has become obvious at a glance.

Bioengineering tools for the production of pharmaceuticals: current perspective and future outlook

The bioengineering tools have significant advantages through less time-consuming and utilized as a promising stage for the production of pharmaceutical bioproducts under the single platform. This review highlighted the advantages and current improvement in the plant, animal and microbial bioengineering tools and outlines feasible approaches by biological and process's bioengineering levels for advancing the economic feasibility of pharmaceutical's production. The critical analysis results revealed that system biology and synthetic biology along with advanced bioengineering tools like transcriptome, proteome, metabolome and nano bioengineering tools have shown a promising impact on the development of pharmaceutical's bioproducts. Tools to overcome and resolve the accompanying encounters of pharmaceutical's production that include nano bioengineering tools are also discussed. As a summary and prospect, it also gives new insight into the challenges and possible breakthrough of the development of pharmaceutical's bioproducts through bioengineering tools.

Gene regulatory network inference resources: A practical overview

Transcriptional regulation is a fundamental molecular mechanism involved in almost every aspect of life, from homeostasis to development, from metabolism to behavior, from reaction to stimuli to disease progression. In recent years, the concept of Gene Regulatory Networks (GRNs) has grown popular as an effective applied biology approach for describing the complex and highly dynamic set of transcriptional interactions, due to its easy-to-interpret features. Since cataloguing, predicting and understanding every GRN connection in all species and cellular contexts remains a great challenge for biology, researchers have developed numerous tools and methods to infer regulatory processes. In this review, we catalogue these methods in six major areas, based on the dominant underlying information leveraged to infer GRNs: Coexpression, Sequence Motifs, Chromatin Immunoprecipitation (ChIP), Orthology, Literature and Protein-Protein Interaction (PPI) specifically focused on transcriptional complexes. The methods described here cover a wide range of user-friendliness: from web tools that require no prior computational expertise to command line programs and algorithms for large scale GRN inferences. Each method for GRN inference described herein effectively illustrates a type of transcriptional relationship, with many methods being complementary to others. While a truly holistic approach for inferring and displaying GRNs remains one of the greatest challenges in the field of systems biology, we believe that the integration of multiple methods described herein provides an effective means with which experimental and computational biologists alike may obtain the most complete pictures of transcriptional relationships. This article is part of a Special Issue entitled: Transcriptional Profiles and Regulatory Gene Networks edited by Dr. Federico Manuel Giorgi and Dr. Shaun Mahony.

Review of applications of high-throughput sequencing in personalized medicine: barriers and facilitators of future progress in research and clinical application

There has been an exponential growth in the performance and output of sequencing technologies (omics data) with full genome sequencing now producing gigabases of reads on a daily basis. These data may hold the promise of personalized medicine, leading to routinely available sequencing tests that can guide patient treatment decisions. In the era of high-throughput sequencing (HTS), computational considerations, data governance and clinical translation are the greatest rate-limiting steps. To ensure that the analysis, management and interpretation of such extensive omics data is exploited to its full potential, key factors, including sample sourcing, technology selection and computational expertise and resources, need to be considered, leading to an integrated set of high-performance tools and systems. This article provides an up-to-date overview of the evolution of HTS and the accompanying tools, infrastructure and data management approaches that are emerging in this space, which, if used within in a multidisciplinary context, may ultimately facilitate the development of personalized medicine.

GenBank

GenBank^® (www.ncbi.nlm.nih.gov/genbank/) is a comprehensive database that contains publicly available nucleotide sequences for 400 000 formally described species. These sequences are obtained primarily through submissions from individual laboratories and batch submissions from large-scale sequencing projects, including whole genome shotgun and environmental sampling projects. Most submissions are made using BankIt, the National Center for Biotechnology Information (NCBI) Submission Portal, or the tool tbl2asn. GenBank staff assign accession numbers upon data receipt. Daily data exchange with the European Nucleotide Archive and the DNA Data Bank of Japan ensures worldwide coverage. GenBank is accessible through the NCBI Nucleotide database, which links to related information such as taxonomy, genomes, protein sequences and structures, and biomedical journal literature in PubMed. BLAST provides sequence similarity searches of GenBank and other sequence databases. Complete bimonthly releases and daily updates of the GenBank database are available by FTP. Recent updates include changes to sequence identifiers, submission wizards for 16S and Influenza sequences, and an Identical Protein Groups resource.

DNA Data Bank of Japan: 30th anniversary

The DNA Data Bank of Japan (DDBJ) Center (http://www.ddbj.nig.ac.jp) has been providing public data services for 30 years since 1987. We are collecting nucleotide sequence data and associated biological information from researchers as a member of the International Nucleotide Sequence Database Collaboration (INSDC), in collaboration with the US National Center for Biotechnology Information and the European Bioinformatics Institute. The DDBJ Center also services the Japanese Genotype-phenotype Archive (JGA) with the National Bioscience Database Center to collect genotype and phenotype data of human individuals. Here, we outline our database activities for INSDC and JGA over the past year, and introduce submission, retrieval and analysis services running on our supercomputer system and their recent developments. Furthermore, we highlight our responses to the amended Japanese rules for the protection of personal information and the launch of the DDBJ Group Cloud service for sharing pre-publication data among research groups.

The international nucleotide sequence database collaboration

For more than 30 years, the International Nucleotide Sequence Database Collaboration (INSDC; http://www.insdc.org/) has been committed to capturing, preserving and providing access to comprehensive public domain nucleotide sequence and associated metadata which enables discovery in biomedicine, biodiversity and biological sciences. Since 1987, the DNA Data Bank of Japan (DDBJ) at the National Institute for Genetics in Mishima, Japan; the European Nucleotide Archive (ENA) at the European Molecular Biology Laboratory's European Bioinformatics Institute (EMBL-EBI) in Hinxton, UK; and GenBank at National Center for Biotechnology Information (NCBI), National Library of Medicine, National Institutes of Health in Bethesda, Maryland, USA have worked collaboratively to enable access to nucleotide sequence data in standardized formats for the worldwide scientific community. In this article, we reiterate the principles of the INSDC collaboration and briefly summarize the trends of the archival content.

The European Nucleotide Archive in 2017

For 35 years the European Nucleotide Archive (ENA; https://www.ebi.ac.uk/ena) has been responsible for making the world’s public sequencing data available to the scientific community. Advances in sequencing technology have driven exponential growth in the volume of data to be processed and stored and a substantial broadening of the user community. Here, we outline ENA services and content in 2017 and provide insight into a selection of current key areas of development in ENA driven by challenges arising from the above growth.

VirtualCytometry: a webserver for evaluating immune cell differentiation using single-cell RNA sequencing data

MOTIVATION

The immune system has diverse types of cells that are differentiated or activated via various signaling pathways and transcriptional regulation upon challenging conditions. Immunophenotyping by flow and mass cytometry are the major approaches for identifying key signaling molecules and transcription factors directing the transition between the functional states of immune cells. However, few proteins can be evaluated by flow cytometry in a single experiment, preventing researchers from obtaining a comprehensive picture of the molecular programs involved in immune cell differentiation. Recent advances in single-cell RNA sequencing (scRNA-seq) have enabled unbiased genome-wide quantification of gene expression in individual cells on a large scale, providing a new and versatile analytical pipeline for studying immune cell differentiation.

RESULTS

We present VirtualCytometry, a web-based computational pipeline for evaluating immune cell differentiation by exploiting cell-to-cell variation in gene expression with scRNA-seq data. Differentiating cells often show a continuous spectrum of cellular states rather than distinct populations. VirtualCytometry enables the identification of cellular subsets for different functional states of differentiation based on the expression of marker genes. Case studies have highlighted the usefulness of this subset analysis strategy for discovering signaling molecules and transcription factors for human T-cell exhaustion, a state of T-cell dysfunction, in tumor and mouse dendritic cells activated by pathogens. With more than 226 scRNA-seq datasets precompiled from public repositories covering diverse mouse and human immune cell types in normal and disease tissues, VirtualCytometry is a useful resource for the molecular dissection of immune cell differentiation.

AVAILABILITY AND IMPLEMENTATION

www.grnpedia.org/cytometry.

In-silico Design of DNA Oligonucleotides: Challenges and Approaches

DNA oligonucleotides are essential components of a high number of technologies in molecular biology. The key event of each oligonucleotide-based assay is the specific binding between oligonucleotides and their target DNA. However, single-stranded DNA molecules also tend to bind to unintended targets or themselves. The probability of such unspecific binding increases with the complexity of an assay. Therefore, accurate data management and design workflows are necessary to optimize the in-silico design of primers and probes. Important considerations concerning computational infrastructure and run time need to be made for both data management and the design process. Data retrieval, data updates, storage, filtering and analysis are the main parts of a sequence data management system. Each part needs to be well-implemented as the resulting sequences form the basis for the oligonucleotide design. Important key features, such as the oligonucleotide length, melting temperature, secondary structures and primer dimer formation, as well as the specificity, should be considered for the in-silico selection of oligonucleotides. The development of an efficient oligonucleotide design workflow demands the right balance between the precision of the applied computer models, the general expenditure of time, and computational workload. This paper gives an overview of important parameters during the design process, starting from the data retrieval, up to the design parameters for optimized oligonucleotide design.

DNA microarray technology and bioinformatic web services

The pan-genomic microarray technique is used for environmental and/or clinical studies. Although microarray is an accurate and sharp diagnostic tool, the expertized bioinformaticians were able to minimize the outcome biases and maximize the flexibility and accuracy of the technique. The knowledge of bioinformatics plays a key role in association with probe designing and the utilization of correct probe sets and platforms. This technique is divided into two parts as dry lab (in silico studies) and wet lab (in vitro studies). Each part covers the other and are known as complementary divisions. In the case of microarray probe designing, a wide range of software, tools, and databases are necessary. Obviously, the application of right databases, software, and tools decreases the probable biases in the outcomes. Due to the importance of suitable probe designing, this article has focused its look onto a variety of online/offline databases, software, and tools.

DFAST: a flexible prokaryotic genome annotation pipeline for faster genome publication

Summary

We developed a prokaryotic genome annotation pipeline, DFAST, that also supports genome submission to public sequence databases. DFAST was originally started as an on-line annotation server, and to date, over 7000 jobs have been processed since its first launch in 2016. Here, we present a newly implemented background annotation engine for DFAST, which is also available as a standalone command-line program. The new engine can annotate a typical-sized bacterial genome within 10 min, with rich information such as pseudogenes, translation exceptions and orthologous gene assignment between given reference genomes. In addition, the modular framework of DFAST allows users to customize the annotation workflow easily and will also facilitate extensions for new functions and incorporation of new tools in the future.

Availability and implementation

The software is implemented in Python 3 and runs in both Python 2.7 and 3.4—on Macintosh and Linux systems. It is freely available at https://github.com/nigyta/dfast_core/under the GPLv3 license with external binaries bundled in the software distribution. An on-line version is also available at https://dfast.nig.ac.jp/.

Supplementary information

Supplementary data are available at Bioinformatics online.

TogoGenome/TogoStanza: modularized Semantic Web genome database

TogoGenome is a genome database that is purely based on the Semantic Web technology, which enables the integration of heterogeneous data and flexible semantic searches. All the information is stored as Resource Description Framework (RDF) data, and the reporting web pages are generated on the fly using SPARQL Protocol and RDF Query Language (SPARQL) queries. TogoGenome provides a semantic-faceted search system by gene functional annotation, taxonomy, phenotypes and environment based on the relevant ontologies. TogoGenome also serves as an interface to conduct semantic comparative genomics by which a user can observe pan-organism or organism-specific genes based on the functional aspect of gene annotations and the combinations of organisms from different taxa. The TogoGenome database exhibits a modularized structure, and each module in the report pages is separately served as TogoStanza, which is a generic framework for rendering an information block as IFRAME/Web Components, which can, unlike several other monolithic databases, also be reused to construct other databases. TogoGenome and TogoStanza have been under development since 2012 and are freely available along with their source codes on the GitHub repositories at https://github.com/togogenome/ and https://github.com/togostanza/, respectively, under the MIT license.

MPD: a pathogen genome and metagenome database

Advances in high-throughput sequencing have led to unprecedented growth in the amount of available genome sequencing data, especially for bacterial genomes, which has been accompanied by a challenge for the storage and management of such huge datasets. To facilitate bacterial research and related studies, we have developed the Mypathogen database (MPD), which provides access to users for searching, downloading, storing and sharing bacterial genomics data. The MPD represents the first pathogenic database for microbial genomes and metagenomes, and currently covers pathogenic microbial genomes (6604 genera, 11 071 species, 41 906 strains) and metagenomic data from host, air, water and other sources (28 816 samples). The MPD also functions as a management system for statistical and storage data that can be used by different organizations, thereby facilitating data sharing among different organizations and research groups. A user-friendly local client tool is provided to maintain the steady transmission of big sequencing data. The MPD is a useful tool for analysis and management in genomic research, especially for clinical Centers for Disease Control and epidemiological studies, and is expected to contribute to advancing knowledge on pathogenic bacteria genomes and metagenomes.

Database URL: http://data.mypathogen.org

Exploring biogeographic patterns of bacterioplankton communities across global estuaries

Estuaries provide an ideal niche to study structure and function of bacterioplankton communities owing to the presence of a multitude of environmental stressors. Bacterioplankton community structures from nine global estuaries were compared to understand their broad‐scale biogeographic patterns. Bacterioplankton community structure from four estuaries of Sundarbans, namely Mooriganga, Thakuran, Matla, and Harinbhanga, was elucidated using Illumina sequencing. Bacterioplankton communities from these estuaries were compared against available bacterioplankton sequence data from Columbia, Delaware, Jiulong, Pearl, and Hangzhou estuaries. All nine estuaries were dominated by Proteobacteria. Other abundant phyla included Bacteroidetes, Firmicutes, Acidobacteria, Actinobacteria, Cyanobacteria, Planctomycetes, and Verrucomicrobia. The abundant bacterial phyla showed a ubiquitous presence across the estuaries. At class level, the overwhelming abundance of Gammaproteobacteria in the estuaries of Sundarbans and Columbia estuary clearly stood out amidst high abundance of Alphaproteobacteria observed in the other estuaries. Abundant bacterial families including Rhodobacteriaceae, Shingomonadaceae, Acidobacteriaceae, Vibrionaceae, and Xanthomondaceae also showed ubiquitous presence in the studied estuaries. However, rare taxa including Chloroflexi, Tenericutes, Nitrospirae, and Deinococcus‐Thermus showed clear site‐specific distribution patterns. Such distribution patterns were also reinstated by nMDS ordination plots. Such clustering patterns could hint toward the potential role of environmental parameters and substrate specificity which could result in distinct bacterioplankton communities at specific sites. The ubiquitous presence of abundant bacterioplankton groups along with their strong correlation with surface water temperature and dissolved nutrient concentrations indicates the role of such environmental parameters in shaping bacterioplankton community structure in estuaries. Overall, studies on biogeographic patters of bacterioplankton communities can provide interesting insights into ecosystem functioning and health of global estuaries.

Best practice data life cycle approaches for the life sciences

Throughout history, the life sciences have been revolutionised by technological advances; in our era this is manifested by advances in instrumentation for data generation, and consequently researchers now routinely handle large amounts of heterogeneous data in digital formats. The simultaneous transitions towards biology as a data science and towards a 'life cycle' view of research data pose new challenges. Researchers face a bewildering landscape of data management requirements, recommendations and regulations, without necessarily being able to access data management training or possessing a clear understanding of practical approaches that can assist in data management in their particular research domain. Here we provide an overview of best practice data life cycle approaches for researchers in the life sciences/bioinformatics space with a particular focus on 'omics' datasets and computer-based data processing and analysis. We discuss the different stages of the data life cycle and provide practical suggestions for useful tools and resources to improve data management practices.

A comparison of the effects of Lawsonia inermis (Iranian henna) and clotrimazole on Candida albicans in rats

BACKGROUND

Candida albicans may cause vaginal infections in women. The aim of this study was to compare the antifungal effect of Lawsonia inermis with that of clotrimazole on rats.

METHODS

A total of 35female Wistar rats were randomly assigned into 5groups. Four groups were infected vaginally with C. albicans and one group was not (negative control). The four infected groups received the following treatments: two groups received vaginal creams of 2% or 4% of L. inermis, one group received 1% clotrimazole and one infected group did not receive any treatment (positive control). The hydro-ethanolic henna extract was prepared from the powder of henna leaves using maceration method. Samples were taken for culture from the vaginae of all rats before the treatment, one and two weeks after treatment. An ANOVA test was used to analyze the data.

RESULTS

Before the treatment, the mean colony forming units (CFU) was 213.6±10.08 and 334.42±20.32 in the 2% and 4% henna groups, respectively, 312.7±28.32 in the clotrimazole group, 233.85±8.15 in the positive control group, and zero in the negative control group. The mean CFUs were zero for all groups except for the 2% henna and positive control groups (P<0.001) one week after the treatment and zero in all groups except for the positive control group two weeks after the treatment (P<0.001).

CONCLUSION

L. inermis (henna) in form of vaginal cream could treat C. albicans infections in female rats; however, 4% henna was more effective and had an effect similar to that of clotrimazole.

Nomenclature for the KIR of non-human species

The increasing number of Killer Immunoglobulin-like Receptor (KIR) sequences available for non-human primate species and cattle has prompted development of a centralized database, guidelines for a standardized nomenclature, and minimum requirements for database submission. The guidelines and nomenclature are based on those used for human KIR and incorporate modifications made for inclusion of non-human species in the companion IPD-NHKIR database. Included in this first release are the rhesus macaque (Macaca mulatta), chimpanzee (Pan troglodytes), orangutan (Pongo abelii and Pongo pygmaeus), and cattle (Bos taurus).

BLAST-based validation of metagenomic sequence assignments

When performing bioforensic casework, it is important to be able to reliably detect the presence of a particular organism in a metagenomic sample, even if the organism is only present in a trace amount. For this task, it is common to use a sequence classification program that determines the taxonomic affiliation of individual sequence reads by comparing them to reference database sequences. As metagenomic data sets often consist of millions or billions of reads that need to be compared to reference databases containing millions of sequences, such sequence classification programs typically use search heuristics and databases with reduced sequence diversity to speed up the analysis, which can lead to incorrect assignments. Thus, in a bioforensic setting where correct assignments are paramount, assignments of interest made by "first-pass" classifiers should be confirmed using the most precise methods and comprehensive databases available. In this study we present a BLAST-based method for validating the assignments made by less precise sequence classification programs, with optimal parameters for filtering of BLAST results determined via simulation of sequence reads from genomes of interest, and we apply the method to the detection of four pathogenic organisms. The software implementing the method is open source and freely available.

Ensembl Genomes 2018: an integrated omics infrastructure for non-vertebrate species

Ensembl Genomes (http://www.ensemblgenomes.org) is an integrating resource for genome-scale data from non-vertebrate species, complementing the resources for vertebrate genomics developed in the Ensembl project (http://www.ensembl.org). Together, the two resources provide a consistent set of programmatic and interactive interfaces to a rich range of data including genome sequence, gene models, transcript sequence, genetic variation, and comparative analysis. This paper provides an update to the previous publications about the resource, with a focus on recent developments and expansions. These include the incorporation of almost 20 000 additional genome sequences and over 35 000 tracks of RNA-Seq data, which have been aligned to genomic sequence and made available for visualization. Other advances since 2015 include the release of the database in Resource Description Framework (RDF) format, a large increase in community-derived curation, a new high-performance protein sequence search, additional cross-references, improved annotation of non-protein-coding genes, and the launch of pre-release and archival sites. Collectively, these changes are part of a continuing response to the increasing quantity of publicly-available genome-scale data, and the consequent need to archive, integrate, annotate and disseminate these using automated, scalable methods.

The NCBI BioCollections Database

The rapidly growing set of GenBank submissions includes sequences that are derived from vouchered specimens. These are associated with culture collections, museums, herbaria and other natural history collections, both living and preserved. Correct identification of the specimens studied, along with a method to associate the sample with its institution, is critical to the outcome of related studies and analyses. The National Center for Biotechnology Information BioCollections Database was established to allow the association of specimen vouchers and related sequence records to their home institutions. This process also allows cross-linking from the home institution for quick identification of all records originating from each collection. Database URL: https://www.ncbi.nlm.nih.gov/biocollections

NBDC RDF portal: a comprehensive repository for semantic data in life sciences

In the life sciences, researchers increasingly want to access multiple databases in an integrated way. However, different databases currently use different formats and vocabularies, hindering the proper integration of heterogeneous life science data. Adopting the Resource Description Framework (RDF) has the potential to address such issues by improving database interoperability, leading to advances in automatic data processing. Based on this idea, we have advised many Japanese database development groups to expose their databases in RDF. To further promote such activities, we have developed an RDF-based life science dataset repository called the National Bioscience Database Center (NBDC) RDF portal. All the datasets in this repository have been reviewed by the NBDC to ensure interoperability and queryability. As of July 2018, the service includes 21 RDF datasets, comprising over 45.5 billion triples. It provides SPARQL endpoints for all datasets, useful metadata and the ability to download RDF files. The NBDC RDF portal can be accessed at https://integbio.jp/rdf/.

Genome-Wide Analysis and Expression Profiles of the MYB Genes in Brachypodium distachyon

MYB transcription factors are widespread in plants and play key roles in plant development. Although MYB transcription factors have been thoroughly characterized in many plants, genome-wide analysis of the MYB gene family has not yet been undertaken in Brachypodium distachyon. In this study, 122 BdMYB transcription factors were identified, comprising 85 MYB-R2R3, 34 MYB-related and three MYB-R1R2R3. Phylogenetic analysis showed that BdMYBs, OsMYBs and AtMYBs with similar functions were clustered in the same subgroup, and the phylogenetic relationships of BdMYB transcription factors were supported by highly conserved motifs and gene structures. Two cis-elements were found in the promoters of BdMYB genes. One is related to plant growth/development, the other is related to stress responses. Gene Ontology (GO) analysis indicated that most of the BdMYB genes are involved in various biological processes. The chromosome distribution pattern strongly indicated that genome-wide tandem and segment duplication mainly contributed to the expansion of the BdMYB gene family. Synteny analysis showed that 56, 58 and 61 BdMYB genes were orthologous to rice, maize and sorghum, respectively. We further demonstrated that BdMYB genes have evolved under strong purifying selection. The expression profiles indicated that most BdMYB genes might participate in floral development and respond to abiotic stresses. Additionally, 338 pairs of proteins were predicted to interact by constructing the interaction network. This work laid the foundation and provided clues for understanding the biological functions of these transcription factors.

Best practice data life cycle approaches for the life sciences

Throughout history, the life sciences have been revolutionised by technological advances; in our era this is manifested by advances in instrumentation for data generation, and consequently researchers now routinely handle large amounts of heterogeneous data in digital formats. The simultaneous transitions towards biology as a data science and towards a 'life cycle' view of research data pose new challenges. Researchers face a bewildering landscape of data management requirements, recommendations and regulations, without necessarily being able to access data management training or possessing a clear understanding of practical approaches that can assist in data management in their particular research domain. Here we provide an overview of best practice data life cycle approaches for researchers in the life sciences/bioinformatics space with a particular focus on 'omics' datasets and computer-based data processing and analysis. We discuss the different stages of the data life cycle and provide practical suggestions for useful tools and resources to improve data management practices.

A modification of the PHYLIP program: A solution for the redundant cluster problem, and an implementation of an automatic bootstrapping on trees inferred from original data

Felsenstein's PHYLIP package of molecular phylogeny tools has been used globally since 1980. The programs are receiving renewed attention because of their character-based user interface, which has the advantage of being scriptable for use with large-scale data studies based on super-computers or massively parallel computing clusters. However, occasionally we found, the PHYLIP Consense program output text file displays two or more divided bootstrap values for the same cluster in its result table, and when this happens the output Newick tree file incorrectly assigns only the last value to that cluster that disturbs correct estimation of a consensus tree. We ascertained the cause of this aberrant behavior in the bootstrapping calculation. Our rewrite of the Consense program source code outputs bootstrap values, without redundancy, in its result table, and a Newick tree file with appropriate, corresponding bootstrap values. Furthermore, we developed an add-on program and shell script, add_bootstrap.pl and fasta2tre_bs.bsh, to generate a Newick tree containing the topology and branch lengths inferred from the original data along with valid bootstrap values, and to actualize the automated inference of a phylogenetic tree containing the originally inferred topology and branch lengths with bootstrap values, from multiple unaligned sequences, respectively. These programs can be downloaded at: https://github.com/ShimadaMK/PHYLIP_enhance/.