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In this paper we present the work executed on BioHackrXiv during the international ELIXIR BioHackathon in Barcelona, Spain, 2021.

BackgroundMany open-source workflow systems have made bioinformatics data analysis procedures portable. Sharing these workflows provides researchers easy access to high-quality analysis methods without the requirement of computational expertise. However, published workflows are not always guaranteed to be reliably reusable. Therefore, a system is needed to lower the cost of sharing workflows in a reusable form.ResultsWe introduce Yevis, a system to build a workflow registry that automatically validates and tests workflows to be published. The validation and test are based on the requirements we defined for a workflow being reusable with confidence. Yevis runs on GitHub and Zenodo and allows workflow hosting without the need of dedicated computing resources. A Yevis registry accepts workflow registration via a GitHub pull request, followed by an automatic validation and test process for the submitted workflow. As a proof of concept, we built a registry using Yevis to host workflows from a community to demonstrate how a workflow can be shared while fulfilling the defined requirements.ConclusionsYevis helps in the building of a workflow registry to share reusable workflows without requiring extensive human resources. By following Yevis’s workflow-sharing procedure, one can operate a registry while satisfying the reusable workflow criteria. This system is particularly useful to individuals or communities that want to share workflows but lacks the specific technical expertise to build and maintain a workflow registry from scratch.

biohackrxiv.org is a scholarly publication service forBioHackathons and Codefests where papers are generated from Markdowntemplates where the header is a YAML/JSON record that includes thetitle, authors, affiliations and tags. Many projects in BioHackathons are about using FAIR data. Because the current setup is lacking in the findable (F) andaccessible (A) of FAIR, for the ELIXIR BioHackathon 2020, we decidedto add an additional service that provides a SPARQL endpoint forqueries and some simple HTML output that can be embedded in aBioHackathon website.

Background Reproducibility of data analysis workflow is a key issue in the field of bioinformatics. Recent computing technologies, such as virtualization, have made it possible to reproduce workflow execution with ease. However, the reproducibility of results is not well discussed; that is, there is no standard way to verify whether the biological interpretation of reproduced results is the same. Therefore, it still remains a challenge to automatically evaluate the reproducibility of results. Results We propose a new metric, a reproducibility scale of workflow execution results, to evaluate the reproducibility of results. This metric is based on the idea of evaluating the reproducibility of results using biological feature values (e.g., number of reads, mapping rate, and variant frequency) representing their biological interpretation. We also implemented a prototype system that automatically evaluates the reproducibility of results using the proposed metric. To demonstrate our approach, we conducted an experiment using workflows used by researchers in real research projects and the use cases that are frequently encountered in the field of bioinformatics. Conclusions Our approach enables automatic evaluation of the reproducibility of results using a fine-grained scale. By introducing our approach, it is possible to evolve from a binary view of whether the results are superficially identical or not to a more graduated view. We believe that our approach will contribute to more informed discussion on reproducibility in bioinformatics.

Validating RDF data becomes necessary in order to ensure data compliance against the conceptualization model it follows, e.g., schema or ontology behind the data, and improve data consistency and completeness. There are different approaches to validate RDF data, for instance, JSON schema, particularly for data in JSONLD format, as well as Shape Expression and Shapes Constraint Language, which can be used with other serialization as well, e.g., RDF/XML or Turtle. Currently, no validation approach is prevalent regarding others, selection commonly depends on data characteristics, background knowledge and personal preferences . In some cases, the approaches are interchangeable; however, that is not always the case, making it necessary to identify a subset among them that can be seamlessly translated from one to another. During the NBDC/DBCLS 2019 BioHackathon, we worked on a variety of topics related to RDF data validation, including (i) development of ShEx shapes for a number of datasets, (ii) development of a tool to semi-automatically create ShEx shapes, (iii) improvements to the RDFShape tool, and (iv) enabling validation schema conversion from one format to the other. Here we report on our BioHackathon achievements.

Poor trans-ethnic portability of polygenic risk score (PRS) models is a critical issue that may be partially due to limited knowledge of causal variants shared among populations. Hence, leveraging noncoding regulatory annotations that capture genetic variation across populations has the potential to enhance the trans-ethnic portability of PRS. To this end, we constructed a unique resource of 707 cell-type-specific IMPACT regulatory annotations by aggregating 5,345 public epigenetic datasets to predict binding patterns of 142 cell-type-regulating transcription factors across 245 cell types. With this resource, we partitioned the common SNP heritability of diverse polygenic traits and diseases from 111 GWAS summary statistics of European (EUR, average N=180K) and East Asian (EAS, average N=157K) origin. For 95 traits, we were able to identify a single IMPACT annotation most strongly enriched for trait heritability. Across traits, these annotations captured an average of 43.3% of heritability (se = 13.8%) with the top 5% of SNPs. Strikingly, we observed highly concordant polygenic trait regulation between populations: the same regulatory annotations captured statistically indistinguishable SNP heritability (fitted slope = 0.98, se = 0.04). Since IMPACT annotations capture both large and consistent proportions of heritability across populations, prioritizing variants in IMPACT regulatory elements may improve the trans-ethnic portability of PRS. Indeed, we observed that EUR PRS models more accurately predicted 21 tested phenotypes of EAS individuals when variants were prioritized by key IMPACT tracks (49.9% mean relative increase in R2). Notably, the improvement afforded by IMPACT was greater in the trans-ethnic EUR-to-EAS PRS application than in the EAS-to-EAS application (47.3% vs 20.9%, P < 1.7e-4). Overall, our study identifies a crucial role for functional annotations such as IMPACT to improve the trans-ethnic portability of genetic data, and this has important implications for future risk prediction models that work across populations.

Poor trans-ancestry portability of polygenic risk scores is a consequence of Eurocentric genetic studies and limited knowledge of shared causal variants. Leveraging regulatory annotations may improve portability by prioritizing functional over tagging variants. We constructed a resource of 707 cell-type-specific IMPACT regulatory annotations by aggregating 5,345 epigenetic datasets to predict binding patterns of 142 transcription factors across 245 cell types. We then partitioned the common SNP heritability of 111 genome-wide association study summary statistics of European (average n ≈ 189,000) and East Asian (average n ≈ 157,000) origin. IMPACT annotations captured consistent SNP heritability between populations, suggesting prioritization of shared functional variants. Variant prioritization using IMPACT resulted in increased trans-ancestry portability of polygenic risk scores from Europeans to East Asians across all 21 phenotypes analyzed (49.9% mean relative increase in R(2)). Our study identifies a crucial role for functional annotations such as IMPACT to improve the trans-ancestry portability of genetic data.

We report on the activities of the 2015 edition of the BioHackathon, an annual event that brings together researchers and developers from around the world to develop tools and technologies that promote the reusability of biological data. We discuss issues surrounding the representation, publication, integration, mining and reuse of biological data and metadata across a wide range of biomedical data types of relevance for the life sciences, including chemistry, genotypes and phenotypes, orthology and phylogeny, proteomics, genomics, glycomics, and metabolomics. We describe our progress to address ongoing challenges to the reusability and reproducibility of research results, and identify outstanding issues that continue to impede the progress of bioinformatics research. We share our perspective on the state of the art, continued challenges, and goals for future research and development for the life sciences Semantic Web.

In the integrated database project in Japan, Database Center for Life Science (DBCLS) has developed various computational tools for reuse of huge amount of data archived in the public repository (https://dbcls.rois.ac.jp/services-en.html). Meanwhile, DNA Data Bank of Japan (DDBJ) has archived and maintained data from the high-throughput sequencing platforms in the International Nucleotide Sequence Database Collaboration (INSDC) with NCBI Genbank and EBI ENA (http://www.insdc.org/). In collaboration with DDBJ, we made a search engine for metadata of these INSDC databases which consist of Bioproject, Biosample and Sequence Read Archive (SRA), which is called DBCLS SRA (http://sra.dbcls.jp/). DBCLS SRA is now linked from DDBJ website, and it is planned to be used in DDBJ officially. Because of the high-throughput sequencing platform, tens of thousands of RNA-seq data have been archived as transcriptome data in SRA described above. On the other hand, transcriptomic data from microarray is still the majority of data in the public gene expression databases known as NCBI Gene Expression Omnibus (GEO) and EBI ArrayExpress (AE). Furthermore, DDBJ started yet another gene expression data repository called Genomic Expression Archive (GEA) in 2018. Thus, it is not easy to draw new discoveries by comparing datasets from those transcriptomic data because of the complexity of relationships among those databases. We therefore constructed an index for those gene expression data repositories, called all of gene expression (AOE) to integrate publicly available transcriptomic data (GEO, AE and GEA). The web interface of AOE (https://aoe.dbcls.jp/) can graphically query data in addition to the application programming interface. By collecting gene expression data by RNA-seq from SRA, AOE also includes data not included in GEO, AE and GEA. Both DBCLS SRA and AOE are freely available without any registration.

Mosaic loss of chromosome Y (mLOY) is frequently observed in the leukocytes of ageing men. However, the genetic architecture and biological mechanisms underlying mLOY are not fully understood. In a cohort of 95,380 Japanese men, we identify 50 independent genetic markers in 46 loci associated with mLOY at a genome-wide significant level, 35 of which are unreported. Lead markers overlap enhancer marks in hematopoietic stem cells (HSCs, P ≤ 1.0 × 10−6). mLOY genome-wide association study signals exhibit polygenic architecture and demonstrate strong heritability enrichment in regions surrounding genes specifically expressed in multipotent progenitor (MPP) cells and HSCs (P ≤ 3.5 × 10−6). ChIP-seq data demonstrate that binding sites of FLI1, a fate-determining factor promoting HSC differentiation into platelets rather than red blood cells (RBCs), show a strong heritability enrichment (P = 1.5 × 10−6). Consistent with these findings, platelet and RBC counts are positively and negatively associated with mLOY, respectively. Collectively, our observations improve our understanding of the mechanisms underlying mLOY., Mosaic loss of chromosome Y (mLOY) is associated with age and smoking but also genetic factors play a role. Here, Terao et al. perform GWAS for mLOY in 95,380 Japanese men and identify 46 loci that overlap with hematopoietic stem cell enhancers and transcription factor binding sites critical for hematopoiesis.

This is the open peer reviewers comments and recommendations regarding the submitted GigaScience article and/or dataset.

We have fully integrated public chromatin chromatin immunoprecipitation sequencing (ChIP‐seq) and DNase‐seq data (n > 70,000) derived from six representative model organisms (human, mouse, rat, fruit fly, nematode, and budding yeast), and have devised a data‐mining platform—designated ChIP‐Atlas (http://chip-atlas.org). ChIP‐Atlas is able to show alignment and peak‐call results for all public ChIP‐seq and DNase‐seq data archived in the NCBI Sequence Read Archive (SRA), which encompasses data derived from GEO, ArrayExpress, DDBJ, ENCODE, Roadmap Epigenomics, and the scientific literature. All peak‐call data are integrated to visualize multiple histone modifications and binding sites of transcriptional regulators (TRs) at given genomic loci. The integrated data can be further analyzed to show TR–gene and TR–TR interactions, as well as to examine enrichment of protein binding for given multiple genomic coordinates or gene names. ChIP‐Atlas is superior to other platforms in terms of data number and functionality for data mining across thousands of ChIP‐seq experiments, and it provides insight into gene regulatory networks and epigenetic mechanisms.

Noncoding regions of the human genome possess enhancer activity and harbor risk loci for heritable diseases. Whereas the binding profiles of multiple transcription factors (TFs) have been investigated, integrative analysis with the large body of public data available so as to provide an overview of the function of such noncoding regions has remained a challenge. Here we have fully integrated public ChIP-seq and DNase-seq data (n ~ 70,000), including those for 743 human transcription factors (TFs) with 97 million binding sites, and have devised a data-mining platform —designated ChIP-Atlas—to identify significant TF-genome, TF-gene, and TF-TF interactions. Using this platform, we found that TFs enriched at macrophage or T-cell enhancers also accumulated around risk loci for autoimmune diseases, whereas those enriched at hepatocyte or macrophage enhancers were preferentially detected at loci associated with HDL-cholesterol levels. Of note, we identified “hotspots” around such risk loci that accumulated multiple TFs and are therefore candidates for causal variants. Integrative analysis of public chromatin-profiling data is thus able to identify TFs and tissues associated with heritable disorders.

Recent studies have shown that environmental DNA is found almost everywhere. Flower petal surfaces are an attractive tissue to use for investigation of the dispersal of environmental DNA in nature as they are isolated from the external environment until the bud opens and only then can the petal surface accumulate environmental DNA. Here, we performed a crowdsourced experiment, the “Ohanami Project”, to obtain environmental DNA samples from petal surfaces of Cerasus × yedoensis ‘Somei-yoshino’ across the Japanese archipelago during spring 2015. C. × yedoensis is the most popular garden cherry species in Japan and clones of this cultivar bloom simultaneously every spring. Data collection spanned almost every prefecture and totaled 577 DNA samples from 149 collaborators. Preliminary amplicon-sequencing analysis showed the rapid attachment of environmental DNA onto the petal surfaces. Notably, we found DNA of other common plant species in samples obtained across a wide distribution; this DNA likely originated from pollen of the Japanese cedar. Our analysis supports our belief that petal surfaces after blossoming are a promising target to reveal the dynamics of environmental DNA in nature. The success of our experiment also shows that crowdsourced environmental DNA analyses have considerable value in ecological studies.

Recent studies have shown that environmental DNA is found almost everywhere. Flower petal surfaces are an attractive tissue to use for investigation of the dispersal of environmental DNA in nature as they are isolated from the external environment until the bud opens and only then can the petal surface accumulate environmental DNA. Here, we performed a crowdsourced experiment, the “Ohanami Project”, to obtain environmental DNA samples from petal surfaces of Cerasus × yedoensis ‘Somei-yoshino’ across the Japanese archipelago during spring 2015. C. × yedoensis is the most popular garden cherry species in Japan and clones of this cultivar bloom simultaneously every spring. Data collection spanned almost every prefecture and totaled 577 DNA samples from 149 collaborators. Preliminary amplicon-sequencing analysis showed the rapid attachment of environmental DNA onto the petal surfaces. Notably, we found DNA of other common plant species in samples obtained from a wide distribution; this DNA likely originated from the pollen of the Japanese cedar. Our analysis supports our belief that petal surfaces after blossoming are a promising target to reveal the dynamics of environmental DNA in nature. The success of our experiment also shows that crowdsourced environmental DNA analyses have considerable value in ecological studies.

Publishing databases in the Resource Description Framework (RDF) model is becoming widely accepted to maximize the syntactic and semantic interoperability of open data in life sciences. Here we report advancements made in the 6th and 7th annual BioHackathons which were held in Tokyo and Miyagi respectively. This review consists of two major sections covering: 1) improvement and utilization of RDF data in various domains of the life sciences and 2) meta-data about these RDF data, the resources that store them, and the service quality of SPARQL Protocol and RDF Query Language (SPARQL) endpoints. The first section describes how we developed RDF data, ontologies and tools in genomics, proteomics, metabolomics, glycomics and by literature text mining. The second section describes how we defined descriptions of datasets, the provenance of data, and quality assessment of services and service discovery. By enhancing the harmonization of these two layers of machine-readable data and knowledge, we improve the way community wide resources are developed and published. Moreover, we outline best practices for the future, and prepare ourselves for an exciting and unanticipatable variety of real world applications in coming years.

It is important for public data repositories to promote the reuse of archived data. In the growing field of omics science, however, the increasing number of submissions of high-throughput sequencing (HTSeq) data to public repositories prevents users from choosing a suitable data set from among the large number of search results. Repository users need to be able to set a threshold to reduce the number of results to obtain a suitable subset of high-quality data for reanalysis. We calculated the quality of sequencing data archived in a public data repository, the Sequence Read Archive (SRA), by using the quality control software FastQC. We obtained quality values for 1 171 313 experiments, which can be used to evaluate the suitability of data for reuse. We also visualized the data distribution in SRA by integrating the quality information and metadata of experiments and samples. We provide quality information of all of the archived sequencing data, which enable users to obtain sufficient quality sequencing data for reanalyses. The calculated quality data are available to the public in various formats. Our data also provide an example of enhancing the reuse of public data by adding metadata to published research data by a third party.

This paper proposes an intercloud brokerage method for system infrastructure deployments of genomic big data analytics workflows. The proposed method utilizes a conjunction of universally quantified atomic formula to describe requirements given by users, and selects combinations of cloud services based on logical reasoning by the replacement of definite clause sets created from conjunction of the atomic formulas, while preserving the declarative meaning of the system infrastructures' constraint conditions. We also define algorithms for the replacement of definite clause sets, and present an example of the use of the proposed intercloud brokerage method.

With the rapid advances in next-generation sequencing (NGS), datasets for DNA polymorphisms among various species and strains have been produced, stored, and distributed. However, reliability varies among these datasets because the experimental and analytical conditions used differ among assays. Furthermore, such datasets have been frequently distributed from the websites of individual sequencing projects. It is desirable to integrate DNA polymorphism data into one database featuring uniform quality control that is distributed from a single platform at a single place. DNA polymorphism annotation database (DNApod; http://tga.nig.ac.jp/dnapod/) is an integrated database that stores genome-wide DNA polymorphism datasets acquired under uniform analytical conditions, and this includes uniformity in the quality of the raw data, the reference genome version, and evaluation algorithms. DNApod genotypic data are re-analyzed whole-genome shotgun datasets extracted from sequence read archives, and DNApod distributes genome-wide DNA polymorphism datasets and known-gene annotations for each DNA polymorphism. This new database was developed for storing genome-wide DNA polymorphism datasets of plants, with crops being the first priority. Here, we describe our analyzed data for 679, 404, and 66 strains of rice, maize, and sorghum, respectively. The analytical methods are available as a DNApod workflow in an NGS annotation system of the DNA Data Bank of Japan and a virtual machine image. Furthermore, DNApod provides tables of links of identifiers between DNApod genotypic data and public phenotypic data. To advance the sharing of organism knowledge, DNApod offers basic and ubiquitous functions for multiple alignment and phylogenetic tree construction by using orthologous gene information.

Predicting responsible transcription regulators on the basis of transcriptome data is one of the most promising computational approaches to understanding cellular processes and characteristics. Here, we present a novel method employing vast amounts of chromatin immunoprecipitation (ChIP) experimental data to address this issue. Global high-throughput ChIP data was collected to construct a comprehensive database, containing 8 578 738 binding interactions of 454 transcription regulators. To incorporate information about heterogeneous frequencies of transcription factor (TF)-binding events, we developed a flexible framework for gene set analysis employing the weighted t-test procedure, namely weighted parametric gene set analysis (wPGSA). Using transcriptome data as an input, wPGSA predicts the activities of transcription regulators responsible for observed gene expression. Validation of wPGSA with published transcriptome data, including that from over-expressed TFs, showed that the method can predict activities of various TFs, regardless of cell type and conditions, with results totally consistent with biological observations. We also applied wPGSA to other published transcriptome data and identified potential key regulators of cell reprogramming and influenza virus pathogenesis, generating compelling hypotheses regarding underlying regulatory mechanisms. This flexible framework will contribute to uncovering the dynamic and robust architectures of biological regulation, by incorporating high-throughput experimental data in the form of weights.

A Lotus japonicus mutant, Ljsym75, which forms ineffective symbiotic nodules and defines a new locus involved in the process of nitrogen fixation, was characterized in detail in order to identify the stage of developmental arrest of the nodules. No nitrogen-fixing activity was detectable in Ljsym75 nodules at any stage during plant development, and plant growth was markedly retarded. Ljsym75 plants formed twice as many nodules as the wild-type Gifu, and this phenotype was not influenced by the application of low concentrations of nitrate. Although the ineffective nodules formed on Ljsym75 were anatomically similar to effective Gifu nodules, Ljsym75 nodules senesced prematurely. Microscopic examination revealed that bacteria endocytosed into Ljsym75 nodules failed to differentiate into bacteroids. Moreover, the bacteria contained no nitrogenase proteins, whereas leghemoglobin was detected in the cytosol of the nodules. These results indicate that Ljsym75 is required for bacterial differentiation into nitrogen-fixing bacteroids in nodules, and thus the Ljsym75 gene was renamed sen1 (for stationary endosymbiont nodule). Linkage analysis using DNA markers showed that Sen1 is located on chromosome 4.

Presentation slide for PAG(Plant & Animal Genome Meeting) XXIII. Abstract: In order to promote life science researches in Japan, National Bioscience Database Center (NBDC: http://biosciencedbc.jp/en/) makes biological databases easier to use. In collaboration with NBDC on technology development, Database Center for Life Science (DBCLS: http://dbcls.rois.ac.jp/en/) has been tackling the problem how to organize big data in lifescience including huge amount of nucleotide sequence data from next generation sequencers(next-gen sequence data) and various types of gene expression data.In order to promote the use of next-gen sequence data, our team has just moved to National Institute of Genetics, where DNA DataBank of Japan (DDBJ) is located, and we have sorted out data deposited in Sequence Read Archive (SRA) with DDBJ, which collaboratively holds SRA. We will present current status of the project and utility of the developed systems.

Poster presentation at Cold Spring Harbor meeting on biological data science. -- In order to promote life science researches in Japan, National Bioscience Database Center (NBDC: http://biosciencedbc.jp/en/) makes biological databases easier to use. In collaboration with NBDC on technology development, Database Center for Life Science (DBCLS: http://dbcls.rois.ac.jp/en/) has been tackling the problem how to organize big data in lifescience including huge amount of nucleotide sequence data from next generation sequencers(next-gen sequence data) and various types of gene expression data. In order to promote the use of next-gen sequence data, our team has just moved to National Institute of Genetics, where DNA DataBank of Japan (DDBJ) is located, and we have sorted out data deposited in Sequence Read Archive (SRA) with DDBJ, which collaboratively holds SRA. The statistics of SRA has been maintained based on study types, sequencer types (platform) and species of samples by analyzing metadata of SRA, and these information is available from our DBCLS SRA website (http://sra.dbcls.jp/). Notably, we are collecting SRA entries associated with publications and diseases, and these search forms are also accessible for use from DBCLS SRA website. Recently, we started collecting gene expression datasets from SRA for re-use of precious data in collaboration with DDBJ. We will present current status of the project and utility of the system developed.

Computational Workflows are widely used in data analysis, enabling innovation and decision-making. In many domains (bioinformatics, image analysis, & radio astronomy) the analysis components are numerous and written in multiple different computer languages by third parties. However, many competing workflow systems exist, severely limiting portability of such workflows, thereby hindering the transfer of workflows between different systems, between different projects and different settings, leading to vendor lock-ins and limiting their generic re-usability. Here we present the Common Workflow Language (CWL) project which produces free and open standards for describing command-line tool based workflows. The CWL standards provide a common but reduced set of abstractions that are both used in practice and implemented in many popular workflow systems. The CWL language is declarative, which allows expressing computational workflows constructed from diverse software tools, executed each through their command-line interface. Being explicit about the runtime environment and any use of software containers enables portability and reuse. Workflows written according to the CWL standards are a reusable description of that analysis that are runnable on a diverse set of computing environments. These descriptions contain enough information for advanced optimization without additional input from workflow authors. The CWL standards support polylingual workflows, enabling portability and reuse of such workflows, easing for example scholarly publication, fulfilling regulatory requirements, collaboration in/between academic research and industry, while reducing implementation costs. CWL has been taken up by a wide variety of domains, and industries and support has been implemented in many major workflow systems., 8 pages, 3 figures. For the LaTex source code of this paper, see https://github.com/mr-c/cwl_methods_included