GeneLink: a database to facilitate genetic studies of complex traits

BACKGROUND: In contrast to gene-mapping studies of simple Mendelian disorders, genetic analyses of complex traits are far more challenging, and high quality data management systems are often critical to the success of these projects. To minimize the difficulties inherent in complex trait studies, we have developed GeneLink, a Web-accessible, password-protected Sybase database. RESULTS: GeneLink is a powerful tool for complex trait mapping, enabling genotypic data to be easily merged with pedigree and extensive phenotypic data. Specifically designed to facilitate large-scale (multi-center) genetic linkage or association studies, GeneLink securely and efficiently handles large amounts of data and provides additional features to facilitate data analysis by existing software packages and quality control. These include the ability to download chromosome-specific data files containing marker data in map order in various formats appropriate for downstream analyses (e.g., GAS and LINKAGE). Furthermore, an unlimited number of phenotypes (either qualitative or quantitative) can be stored and analyzed. Finally, GeneLink generates several quality assurance reports, including genotyping success rates of specified DNA samples or success and heterozygosity rates for specified markers. CONCLUSIONS: GeneLink has already proven an invaluable tool for complex trait mapping studies and is discussed primarily in the context of our large, multi-center study of hereditary prostate cancer (HPC). GeneLink is freely available at

neuPrint: An open access tool for EM connectomics

Due to advances in electron microscopy and deep learning, it is now practical to reconstruct a connectome, a description of neurons and the chemical synapses between them, for significant volumes of neural tissue. Smaller past reconstructions were primarily used by domain experts, could be handled by downloading data, and performance was not a serious problem. But new and much larger reconstructions upend these assumptions. These networks now contain tens of thousands of neurons and tens of millions of connections, with yet larger reconstructions pending, and are of interest to a large community of non-specialists. Allowing other scientists to make use of this data needs more than publication—it requires new tools that are publicly available, easy to use, and efficiently handle large data. We introduce neuPrint to address these data analysis challenges. Neuprint contains two major components—a web interface and programmer APIs. The web interface is designed to allow any scientist worldwide, using only a browser, to quickly ask and answer typical biological queries about a connectome. The neuPrint APIs allow more computer-savvy scientists to make more complex or higher volume queries. NeuPrint also provides features for assessing reconstruction quality. Internally, neuPrint organizes connectome data as a graph stored in a neo4j database. This gives high performance for typical queries, provides access though a public and well documented query language Cypher, and will extend well to future larger connectomics databases. Our experience is also an experiment in open science. We find a significant fraction of the readers of the article proceed to examine the data directly. In our case preprints worked exactly as intended, with data inquiries and PDF downloads starting immediately after pre-print publication, and little affected by formal publication later. From this we deduce that many readers are more interested in our data than in our analysis of our data, suggesting that data-only papers can be well appreciated and that public data release can speed up the propagation of scientific results by many months. We also find that providing, and keeping, the data available for online access imposes substantial additional costs to connectomics research

A connectome and analysis of the adult Drosophila central brain.

The neural circuits responsible for animal behavior remain largely unknown. We summarize new methods and present the circuitry of a large fraction of the brain of the fruit fly Drosophila melanogaster. Improved methods include new procedures to prepare, image, align, segment, find synapses in, and proofread such large data sets. We define cell types, refine computational compartments, and provide an exhaustive atlas of cell examples and types, many of them novel. We provide detailed circuits consisting of neurons and their chemical synapses for most of the central brain. We make the data public and simplify access, reducing the effort needed to answer circuit questions, and provide procedures linking the neurons defined by our analysis with genetic reagents. Biologically, we examine distributions of connection strengths, neural motifs on different scales, electrical consequences of compartmentalization, and evidence that maximizing packing density is an important criterion in the evolution of the fly's brain

Mapping the Neural Substrates of Behavior.

Assigning behavioral functions to neural structures has long been a central goal in neuroscience and is a necessary first step toward a circuit-level understanding of how the brain generates behavior. Here, we map the neural substrates of locomotion and social behaviors for Drosophila melanogaster using automated machine-vision and machine-learning techniques. From videos of 400,000 flies, we quantified the behavioral effects of activating 2,204 genetically targeted populations of neurons. We combined a novel quantification of anatomy with our behavioral analysis to create brain-behavior correlation maps, which are shared as browsable web pages and interactive software. Based on these maps, we generated hypotheses of regions of the brain causally related to sensory processing, locomotor control, courtship, aggression, and sleep. Our maps directly specify genetic tools to target these regions, which we used to identify a small population of neurons with a role in the control of walking

GeneLink: a database to facilitate genetic studies of complex traits

Abstract Background In contrast to gene-mapping studies of simple Mendelian disorders, genetic analyses of complex traits are far more challenging, and high quality data management systems are often critical to the success of these projects. To minimize the difficulties inherent in complex trait studies, we have developed GeneLink, a Web-accessible, password-protected Sybase database. Results GeneLink is a powerful tool for complex trait mapping, enabling genotypic data to be easily merged with pedigree and extensive phenotypic data. Specifically designed to facilitate large-scale (multi-center) genetic linkage or association studies, GeneLink securely and efficiently handles large amounts of data and provides additional features to facilitate data analysis by existing software packages and quality control. These include the ability to download chromosome-specific data files containing marker data in map order in various formats appropriate for downstream analyses (e.g., GAS and LINKAGE). Furthermore, an unlimited number of phenotypes (either qualitative or quantitative) can be stored and analyzed. Finally, GeneLink generates several quality assurance reports, including genotyping success rates of specified DNA samples or success and heterozygosity rates for specified markers. Conclusions GeneLink has already proven an invaluable tool for complex trait mapping studies and is discussed primarily in the context of our large, multi-center study of hereditary prostate cancer (HPC). GeneLink is freely available at http://research.nhgri.nih.gov/genelink.</p

A connectome and analysis of the adult Drosophila central brain.

The neural circuits responsible for animal behavior remain largely unknown. We summarize new methods and present the circuitry of a large fraction of the brain of the fruit fly Drosophila melanogaster. Improved methods include new procedures to prepare, image, align, segment, find synapses in, and proofread such large data sets. We define cell types, refine computational compartments, and provide an exhaustive atlas of cell examples and types, many of them novel. We provide detailed circuits consisting of neurons and their chemical synapses for most of the central brain. We make the data public and simplify access, reducing the effort needed to answer circuit questions, and provide procedures linking the neurons defined by our analysis with genetic reagents. Biologically, we examine distributions of connection strengths, neural motifs on different scales, electrical consequences of compartmentalization, and evidence that maximizing packing density is an important criterion in the evolution of the fly's brain

A connectome and analysis of the adult Drosophila central brain

The neural circuits responsible for animal behavior remain largely unknown. We summarize new methods and present the circuitry of a large fraction of the brain of the fruit fly Drosophila melanogaster. Improved methods include new procedures to prepare, image, align, segment, find synapses in, and proofread such large data sets. We define cell types, refine computational compartments, and provide an exhaustive atlas of cell examples and types, many of them novel. We provide detailed circuits consisting of neurons and their chemical synapses for most of the central brain. We make the data public and simplify access, reducing the effort needed to answer circuit questions, and provide procedures linking the neurons defined by our analysis with genetic reagents. Biologically, we examine distributions of connection strengths, neural motifs on different scales, electrical consequences of compartmentalization, and evidence that maximizing packing density is an important criterion in the evolution of the fly’s brain