The Human Pangenome Project: a global resource to map genomic diversity.

The human reference genome is the most widely used resource in human genetics and is due for a major update. Its current structure is a linear composite of merged haplotypes from more than 20 people, with a single individual comprising most of the sequence. It contains biases and errors within a framework that does not represent global human genomic variation. A high-quality reference with global representation of common variants, including single-nucleotide variants, structural variants and functional elements, is needed. The Human Pangenome Reference Consortium aims to create a more sophisticated and complete human reference genome with a graph-based, telomere-to-telomere representation of global genomic diversity. Here we leverage innovations in technology, study design and global partnerships with the goal of constructing the highest-possible quality human pangenome reference. Our goal is to improve data representation and streamline analyses to enable routine assembly of complete diploid genomes. With attention to ethical frameworks, the human pangenome reference will contain a more accurate and diverse representation of global genomic variation, improve gene-disease association studies across populations, expand the scope of genomics research to the most repetitive and polymorphic regions of the genome, and serve as the ultimate genetic resource for future biomedical research and precision medicine.

FAVOR: functional annotation of variants online resource and annotator for variation across the human genome.

Large biobank-scale whole genome sequencing (WGS) studies are rapidly identifying a multitude of coding and non-coding variants. They provide an unprecedented resource for illuminating the genetic basis of human diseases. Variant functional annotations play a critical role in WGS analysis, result interpretation, and prioritization of disease- or trait-associated causal variants. Existing functional annotation databases have limited scope to perform online queries and functionally annotate the genotype data of large biobank-scale WGS studies. We develop the Functional Annotation of Variants Online Resources (FAVOR) to meet these pressing needs. FAVOR provides a comprehensive multi-faceted variant functional annotation online portal that summarizes and visualizes findings of all possible nine billion single nucleotide variants (SNVs) across the genome. It allows for rapid variant-, gene- and region-level queries of variant functional annotations. FAVOR integrates variant functional information from multiple sources to describe the functional characteristics of variants and facilitates prioritizing plausible causal variants influencing human phenotypes. Furthermore, we provide a scalable annotation tool, FAVORannotator, to functionally annotate large-scale WGS studies and efficiently store the genotype and their variant functional annotation data in a single file using the annotated Genomic Data Structure (aGDS) format, making downstream analysis more convenient. FAVOR and FAVORannotator are available at https://favor.genohub.org.

The Data Use Ontology to streamline responsible access to human biomedical datasets.

Human biomedical datasets that are critical for research and clinical studies to benefit human health also often contain sensitive or potentially identifying information of individual participants. Thus, care must be taken when they are processed and made available to comply with ethical and regulatory frameworks and informed consent data conditions. To enable and streamline data access for these biomedical datasets, the Global Alliance for Genomics and Health (GA4GH) Data Use and Researcher Identities (DURI) work stream developed and approved the Data Use Ontology (DUO) standard. DUO is a hierarchical vocabulary of human and machine-readable data use terms that consistently and unambiguously represents a dataset's allowable data uses. DUO has been implemented by major international stakeholders such as the Broad and Sanger Institutes and is currently used in annotation of over 200,000 datasets worldwide. Using DUO in data management and access facilitates researchers' discovery and access of relevant datasets. DUO annotations increase the FAIRness of datasets and support data linkages using common data use profiles when integrating the data for secondary analyses. DUO is implemented in the Web Ontology Language (OWL) and, to increase community awareness and engagement, hosted in an open, centralized GitHub repository. DUO, together with the GA4GH Passport standard, offers a new, efficient, and streamlined data authorization and access framework that has enabled increased sharing of biomedical datasets worldwide.

The third pillar of bacterial signal transduction: classification of the extracytoplasmic function (ECF) sigma factor protein family.

The ability of a bacterial cell to monitor and adaptively respond to its environment is crucial for survival. After one- and two-component systems, extracytoplasmic function (ECF) sigma factors - the largest group of alternative sigma factors - represent the third fundamental mechanism of bacterial signal transduction, with about six such regulators on average per bacterial genome. Together with their cognate anti-sigma factors, they represent a highly modular design that primarily facilitates transmembrane signal transduction. A comprehensive analysis of the ECF sigma factor protein family identified more than 40 distinct major groups of ECF sigma factors. The functional relevance of this classification is supported by the sequence similarity and domain architecture of cognate anti-sigma factors, genomic context conservation, and potential target promoter motifs. Moreover, this phylogenetic analysis revealed unique features indicating novel mechanisms of ECF-mediated signal transduction. This classification, together with the web tool ECFfinder and the information stored in the Microbial Signal Transduction (MiST) database, provides a comprehensive resource for the analysis of ECF sigma factor-dependent gene regulation.

BioGraphE: high-performance bionetwork analysis using the Biological Graph Environment.

BACKGROUND: Graphs and networks are common analysis representations for biological systems. Many traditional graph algorithms such as k-clique, k-coloring, and subgraph matching have great potential as analysis techniques for newly available data in biology. Yet, as the amount of genomic and bionetwork information rapidly grows, scientists need advanced new computational strategies and tools for dealing with the complexities of the bionetwork analysis and the volume of the data. RESULTS: We introduce a computational framework for graph analysis called the Biological Graph Environment (BioGraphE), which provides a general, scalable integration platform for connecting graph problems in biology to optimized computational solvers and high-performance systems. This framework enables biology researchers and computational scientists to identify and deploy network analysis applications and to easily connect them to efficient and powerful computational software and hardware that are specifically designed and tuned to solve complex graph problems. In our particular application of BioGraphE to support network analysis in genome biology, we investigate the use of a Boolean satisfiability solver known as Survey Propagation as a core computational solver executing on standard high-performance parallel systems, as well as multi-threaded architectures. CONCLUSION: In our application of BioGraphE to conduct bionetwork analysis of homology networks, we found that BioGraphE and a custom, parallel implementation of the Survey Propagation SAT solver were capable of solving very large bionetwork problems at high rates of execution on different high-performance computing platforms.

Scalable Open Science Approach for Mutation Calling of Tumor Exomes Using Multiple Genomic Pipelines.

The Cancer Genome Atlas (TCGA) cancer genomics dataset includes over 10,000 tumor-normal exome pairs across 33 different cancer types, in total >400 TB of raw data files requiring analysis. Here we describe the Multi-Center Mutation Calling in Multiple Cancers project, our effort to generate a comprehensive encyclopedia of somatic mutation calls for the TCGA data to enable robust cross-tumor-type analyses. Our approach accounts for variance and batch effects introduced by the rapid advancement of DNA extraction, hybridization-capture, sequencing, and analysis methods over time. We present best practices for applying an ensemble of seven mutation-calling algorithms with scoring and artifact filtering. The dataset created by this analysis includes 3.5 million somatic variants and forms the basis for PanCan Atlas papers. The results have been made available to the research community along with the methods used to generate them. This project is the result of collaboration from a number of institutes and demonstrates how team science drives extremely large genomics projects.

Phylogeny of the bacterial superfamily of Crp-Fnr transcription regulators: exploiting the metabolic spectrum by controlling alternative gene programs.

The Crp-Fnr regulators, named after the first two identified members, are DNA-binding proteins which predominantly function as positive transcription factors, though roles of repressors are also important. Among over 1200 proteins with an N-terminally located nucleotide-binding domain similar to the cyclic adenosine monophosphate (cAMP) receptor protein, the distinctive additional trait of the Crp-Fnr superfamily is a C-terminally located helix-turn-helix motif for DNA binding. From a curated database of 369 family members exhibiting both features, we provide a protein tree of Crp-Fnr proteins according to their phylogenetic relationships. This results in the assembly of the regulators ArcR, CooA, CprK, Crp, Dnr, FixK, Flp, Fnr, FnrN, MalR, NnrR, NtcA, PrfA, and YeiL and their homologs in distinct clusters. Lead members and representatives of these groups are described, placing emphasis on the less well-known regulators and target processes. Several more groups consist of sequence-derived proteins of unknown physiological roles; some of them are tight clusters of highly similar members. The Crp-Fnr regulators stand out in responding to a broad spectrum of intracellular and exogenous signals such as cAMP, anoxia, the redox state, oxidative and nitrosative stress, nitric oxide, carbon monoxide, 2-oxoglutarate, or temperature. To accomplish their roles, Crp-Fnr members have intrinsic sensory modules allowing the binding of allosteric effector molecules, or have prosthetic groups for the interaction with the signal. The regulatory adaptability and structural flexibility represented in the Crp-Fnr scaffold has led to the evolution of an important group of physiologically versatile transcription factors.

Backbone 1H, 13C, and 15N NMR assignments for the Cyanothece 51142 protein cce_0567: a protein associated with nitrogen fixation in the DUF683 family.

Cyanothece 51142 contains a 78-residue protein, cce_0567, that falls into the DUF683 family of proteins associated with nitrogen fixation. Here we report the assignment of most of the main chain and 13C(beta) side chain resonances of the approximately 40 kDa homo-tetramer.

A conserved structural module regulates transcriptional responses to diverse stress signals in bacteria.

A transcriptional response to singlet oxygen in Rhodobacter sphaeroides is controlled by the group IV sigma factor sigma(E) and its cognate anti-sigma ChrR. Crystal structures of the sigma(E)/ChrR complex reveal a modular, two-domain architecture for ChrR. The ChrR N-terminal anti-sigma domain (ASD) binds a Zn(2+) ion, contacts sigma(E), and is sufficient to inhibit sigma(E)-dependent transcription. The ChrR C-terminal domain adopts a cupin fold, can coordinate an additional Zn(2+), and is required for the transcriptional response to singlet oxygen. Structure-based sequence analyses predict that the ASD defines a common structural fold among predicted group IV anti-sigmas. These ASDs are fused to diverse C-terminal domains that are likely involved in responding to specific environmental signals that control the activity of their cognate sigma factor.