Echinobase: an expanding resource for echinoderm genomic information

Echinobase, a web accessible information system of diverse genomics and biological data for the echinoderm clade, grew out of SpBase, the first echinoderm genome project for sea urchin, Strongylocentrotus purpuratus. Sea urchins and their relatives are utilitarian research models in fields ranging from marine biology to developmental biology and gene regulatory systems. Echinobase is a user-friendly web interface that links an array of biological data that would otherwise have been tedious and frustrating for researchers to extract and organize. The system hosts a powerful gene search engine, genomics browser and other bioinformatics tools to investigate genomics and high throughput data. The Echinobase information system now serves genomic information for eight echinoderm species: S. purpuratus, Strongylocentrotus fransciscanus, Allocentrotus fragilis, Lytechinus variegatus, Patiria miniata, Parastichopus parvimensis and Ophiothrix spiculata, Eucidaris tribuloides. Herein lies a description of the web information system, genomics data types and content hosted by Echinobase.org. The goal of Echinobase is to connect genomic information to various experimental data and accelerate the research in field of molecular biology, developmental process, gene regulatory networks and more recently engineering biological systems0. Database URL:http://www.echinobase.or

Echinobase: an expanding resource for echinoderm genomic information

Echinobase, a web accessible information system of diverse genomics and biological data for the echinoderm clade, grew out of SpBase, the first echinoderm genome project for sea urchin, Strongylocentrotus purpuratus. Sea urchins and their relatives are utilitarian research models in fields ranging from marine biology to developmental biology and gene regulatory systems. Echinobase is a user-friendly web interface that links an array of biological data that would otherwise have been tedious and frustrating for researchers to extract and organize. The system hosts a powerful gene search engine, genomics browser and other bioinformatics tools to investigate genomics and high throughput data. The Echinobase information system now serves genomic information for eight echinoderm species: S. purpuratus, Strongylocentrotus fransciscanus, Allocentrotus fragilis, Lytechinus variegatus, Patiria miniata, Parastichopus parvimensis and Ophiothrix spiculata, Eucidaris tribuloides. Herein lies a description of the web information system, genomics data types and content hosted by Echinobase.org. The goal of Echinobase is to connect genomic information to various experimental data and accelerate the research in field of molecular biology, developmental process, gene regulatory networks and more recently engineering biological systems0. Database URL:http://www.echinobase.or

Cost-Effective Cloud Computing: A Case Study Using the Comparative Genomics Tool, Roundup

Background Comparative genomics resources, such as ortholog detection tools and repositories are rapidly increasing in scale and complexity. Cloud computing is an emerging technological paradigm that enables researchers to dynamically build a dedicated virtual cluster and may represent a valuable alternative for large computational tools in bioinformatics. In the present manuscript, we optimize the computation of a large-scale comparative genomics resource—Roundup—using cloud computing, describe the proper operating principles required to achieve computational efficiency on the cloud, and detail important procedures for improving cost-effectiveness to ensure maximal computation at minimal costs. Methods Utilizing the comparative genomics tool, Roundup, as a case study, we computed orthologs among 902 fully sequenced genomes on Amazon's Elastic Compute Cloud. For managing the ortholog processes, we designed a strategy to deploy the web service, Elastic MapReduce, and maximize the use of the cloud while simultaneously minimizing costs. Specifically, we created a model to estimate cloud runtime based on the size and complexity of the genomes being compared that determines in advance the optimal order of the jobs to be submitted. Results We computed orthologous relationships for 245,323 genome-to-genome comparisons on Amazon's computing cloud, a computation that required just over 200 hours and cost $8,000 USD, at least 40% less than expected under a strategy in which genome comparisons were submitted to the cloud randomly with respect to runtime. Our cost savings projections were based on a model that not only demonstrates the optimal strategy for deploying RSD to the cloud, but also finds the optimal cluster size to minimize waste and maximize usage. Our cost-reduction model is readily adaptable for other comparative genomics tools and potentially of significant benefit to labs seeking to take advantage of the cloud as an alternative to local computing infrastructure

Developmental Effector Gene Regulation: Multiplexed Strategies for Functional Analysis

The staggering complexity of the genome controls for developmental processes is revealed through massively parallel cis-regulatory analysis using new methods of perturbation and readout. The choice of combinations of these new methods is tailored to the system, question and resources at hand. Our focus is on issues that include the necessity or sufficiency of given cis-regulatory modules, cis-regulatory function in the normal spatial genomic context, and easily accessible high throughput and multiplexed analysis methods. In the sea urchin embryonic model, recombineered BACs offer new opportunities for consecutive modes of cis-regulatory analyses that answer these requirements, as we here demonstrate on a diverse suite of previously unstudied sea urchin effector genes expressed in skeletogenic cells. Positively active cis-regulatory modules were located in single Nanostring experiments per BAC containing the gene of interest, by application of our previously reported “barcode” tag vectors of which> 100 can be analyzed at one time. Computational analysis of DNA sequences that drive expression, based on the known skeletogenic regulatory state, then permitted effective identification of functional target site clusters. Deletion of these sub-regions from the parent BACs revealed module necessity, as simultaneous tests of the same regions in short constructs revealed sufficiency. Predicted functional inputs were then confirmed by site mutations, all generated and tested in multiplex formats. There emerged the simple conclusion that each effector gene utilizes a small subset of inputs from the skeletogenic GRN. These inputs may function to only adjust expression levels or in some cases necessary for expression. Since we know the GRN architecture upstream of the effector genes, we could then conceptually isolate and compare the wiring of the effector gene driver sub-circuits and identify the inputs whose removal abolish expression

The Type 2 Diabetes Knowledge Portal: an Open access Genetic Resource Dedicated to Type 2 Diabetes and Related Traits

Associations between human genetic variation and clinical phenotypes have become a foundation of biomedical research. Most repositories of these data seek to be disease-agnostic and therefore lack disease-focused views. The Type 2 Diabetes Knowledge Portal (T2DKP) is a public resource of genetic datasets and genomic annotations dedicated to type 2 diabetes (T2D) and related traits. Here, we seek to make the T2DKP more accessible to prospective users and more useful to existing users. First, we evaluate the T2DKP\u27s comprehensiveness by comparing its datasets with those of other repositories. Second, we describe how researchers unfamiliar with human genetic data can begin using and correctly interpreting them via the T2DKP. Third, we describe how existing users can extend their current workflows to use the full suite of tools offered by the T2DKP. We finally discuss the lessons offered by the T2DKP toward the goal of democratizing access to complex disease genetic results

Proteomics approach to study neuronal regeneration events of the sequenced sea star Patiria miniata

Sea stars have long been recognized for their ability to fully regenerate injured tissues as a consequence of predation or amputation. Indeed the ability to regenerate the nervous system is maintained throughout the lifespan of the animal. Since sea stars are distantly related to the chordates, their neuronal regeneration is of potential interest to biomedical applications in regenerative medicine [1-3]. With the advent of sea star (Patiria miniata) genome sequence and gene models we can now effectively study protein expression in sea star neurons using high-resolution mass spectrometry. Previously, the sparse information on sea star protein sequences limited peptide identification to ca. 15%. This bottleneck was overcome with alignment of peptides to P. miniata genomic sequences. At present, we have over 18,000 peptides identified at identification rates of 60%, results commonly observed in sequenced proteomes. The methods we are developing allow greater sensitivity and unprecedented identification of low abundance proteins in the starfish nervous system This level of sensitivity is crucially important for the study for neuronal regeneration events: Sonic hedgehog protein, Neuromodulin and Piwi-like protein 1 [4] are among the identified proteins and clearly highlight P. miniata as a promising model to understand protein regulation during neuronal regeneration

An Integrated Map of Cell Type-Specific Gene Expression in Pancreatic Islets.

Pancreatic islets consist of multiple cell types that produce hormones required for glucose homeostasis, and islet dysfunction is a major factor in type 1 and type 2 diabetes. Numerous studies have assessed transcription across individual cell types using single-cell assays; however, there is no canonical reference of gene expression in islet cell types that is also easily accessible for researchers to query and use in bioinformatics pipelines. Here we present an integrated map of islet cell type-specific gene expression from 192,203 cells from single-cell RNA sequencing of 65 donors without diabetes, donors who were type 1 diabetes autoantibody positive, donors with type 1 diabetes, and donors with type 2 diabetes from the Human Pancreas Analysis Program. We identified 10 distinct cell types, annotated subpopulations of several cell types, and defined cell type-specific marker genes. We tested differential expression within each cell type across disease states and identified 1,701 genes with significant changes in expression, with most changes observed in β-cells from donors with type 1 diabetes. To facilitate user interaction, we provide several single-cell visualization and reference mapping tools, as well as the open-access analytical pipelines used to create this reference. The results will serve as a valuable resource to investigators studying islet biology

Leveraging type 1 diabetes human genetic and genomic data in the T1D knowledge portal.

To address the challenge of translating genetic discoveries for type 1 diabetes (T1D) into mechanistic insight, we have developed the T1D Knowledge Portal (T1DKP), an open-access resource for hypothesis development and target discovery in T1D