Nirav Merchant

The iPlant Collaborative: Cyberinfrastructure for Plant Biology

The iPlant Collaborative (iPlant) is a United States National Science Foundation (NSF) funded project that aims to create an innovative, comprehensive, and foundational cyberinfrastructure in support of plant biology research (PSCIC, 2006). iPlant is developing cyberinfrastructure that uniquely enables scientists throughout the diverse fields that comprise plant biology to address Grand Challenges in new ways, to stimulate and facilitate cross-disciplinary research, to promote biology and computer science research interactions, and to train the next generation of scientists on the use of cyberinfrastructure in research and education. Meeting humanitys projected demands for agricultural and forest products and the expectation that natural ecosystems be managed sustainably will require synergies from the application of information technologies. The iPlant cyberinfrastructure design is based on an unprecedented period of research community input, and leverages developments in high-performance computing, data storage, and cyberinfrastructure for the physical sciences. iPlant is an open-source project with application programming interfaces that allow the community to extend the infrastructure to meet its needs. iPlant is sponsoring community-driven workshops addressing specific scientific questions via analysis tool integration and hypothesis testing. These workshops teach researchers how to add bioinformatics tools and/or datasets into the iPlant cyberinfrastructure enabling plant scientists to perform complex analyses on large datasets without the need to master the command-line or high-performance computational services.

The iPlant Collaborative: Cyberinfrastructure for Enabling Data to Discovery for the Life Sciences.

The iPlant Collaborative provides life science research communities access to comprehensive, scalable, and cohesive computational infrastructure for data management; identity management; collaboration tools; and cloud, high-performance, high-throughput computing. iPlant provides training, learning material, and best practice resources to help all researchers make the best use of their data, expand their computational skill set, and effectively manage their data and computation when working as distributed teams. iPlant’s platform permits researchers to easily deposit and share their data and deploy new computational tools and analysis workflows, allowing the broader community to easily use and reuse those data and computational analyses.

Y-chromosome analysis reveals genetic divergence and new founding native lineages in Athapaskan- and Eskimoan-speaking populations

For decades, the peopling of the Americas has been explored through the analysis of uniparentally inherited genetic systems in Native American populations and the comparison of these genetic data with current linguistic groupings. In northern North America, two language families predominate: Eskimo-Aleut and Na-Dene. Although the genetic evidence from nuclear and mtDNA loci suggest that speakers of these language families share a distinct biological origin, this model has not been examined using data from paternally inherited Y chromosomes. To test this hypothesis and elucidate the migration histories of Eskimoan- and Athapaskan-speaking populations, we analyzed Y-chromosomal data from Inuvialuit, Gwich’in, and Tłįchǫ populations living in the Northwest Territories of Canada. Over 100 biallelic markers and 19 chromosome short tandem repeats (STRs) were genotyped to produce a high-resolution dataset of Y chromosomes from these groups. Among these markers is an SNP discovered in the Inuvialuit that differentiates them from other Aboriginal and Native American populations. The data suggest that Canadian Eskimoan- and Athapaskan-speaking populations are genetically distinct from one another and that the formation of these groups was the result of two population expansions that occurred after the initial movement of people into the Americas. In addition, the population history of Athapaskan speakers is complex, with the Tłįchǫ being distinct from other Athapaskan groups. The high-resolution biallelic data also make clear that Y-chromosomal diversity among the first Native Americans was greater than previously recognized.

Machine-learning approaches for classifying haplogroup from Y chromosome STR data.

Genetic variation on the non-recombining portion of the Y chromosome contains information about the ancestry of male lineages. Because of their low rate of mutation, single nucleotide polymorphisms (SNPs) are the markers of choice for unambiguously classifying Y chromosomes into related sets of lineages known as haplogroups, which tend to show geographic structure in many parts of the world. However, performing the large number of SNP genotyping tests needed to properly infer haplogroup status is expensive and time consuming. A novel alternative for assigning a sampled Y chromosome to a haplogroup is presented here. We show that by applying modern machine-learning algorithms we can infer with high accuracy the proper Y chromosome haplogroup of a sample by scoring a relatively small number of Y-linked short tandem repeats (STRs). Learning is based on a diverse ground-truth data set comprising pairs of SNP test results (haplogroup) and corresponding STR scores. We apply several independent machine-learning methods in tandem to learn formal classification functions. The result is an integrated high-throughput analysis system that automatically classifies large numbers of samples into haplogroups in a cost-effective and accurate manner.

Jetstream: a self-provisioned, scalable science and engineering cloud environment

Jetstream will be the first production cloud resource supporting general science and engineering research within the XD ecosystem. In this report we describe the motivation for proposing Jetstream, the configuration of the Jetstream system as funded by the NSF, the team that is implementing Jetstream, and the communities we expect to use this new system. Our hope and plan is that Jetstream, which will become available for production use in 2016, will aid thousands of researchers who need modest amounts of computing power interactively. The implementation of Jetstream should increase the size and disciplinary diversity of the US research community that makes use of the resources of the XD ecosystem.

iPlant atmosphere: a gateway to cloud infrastructure for the plant sciences

The cloud platform complements traditional compute and storage infrastructures by introducing capabilities for efficiently provisioning resources in a self-service, on-demand manner. The new provisioning model promises to accelerate scientific discovery by improving access to customizable and task-specific computing resources. This paradigm is well-suited, especially for those applications tailored to leverage cloud-style of infrastructure capabilities. Adoption of the cloud model has been challenging for many domain scientists and scientific software developers due to the technical expertise required to effectively utilize this infrastructure. Some of the key limitations of cloud infrastructure are: limited integration with institutional authentication and authorization frameworks, lack of frameworks to enable domain-specific configurations for instances, and integration with scientific data repositories alongside existing computational clusters and grid deployments. Specifically designed to address some of these operational barriers towards adoptions by the plant sciences community, the iPlant Collaborative cloud platform, aptly named Atmosphere, is an open-source, robust, configurable gateway that extends established cloud infrastructure to meet the diverse computing needs for the plant science. Atmosphere manages the Virtual Machine (VM) lifecycle while maximizing the utilization of cloud resources for scientific workflows. Thus, Atmosphere allows researchers developing novel analytical tools to deploy them with ease while abstracting the underlying computing infrastructure, at the same time making it relatively easy for the users to access these tools via web browser. Atmosphere also provides a rich extensible Application Programming Interface (APIs) for integration and automation with other services. Since its launch, Atmosphere has seen a wide adoption by the plant sciences community for a broad array of applications that range from image processing to next generation sequence (NGS) analysis and can serve as a template for providing similar capabilities to other domains.

Using the iPlant Collaborative Discovery Environment

The iPlant Collaborative is an academic consortium whose mission is to develop an informatics and social infrastructure to address the “grand challenges” in plant biology. Its cyberinfrastructure supports the computational needs of the research community and facilitates solving major challenges in plant science. The Discovery Environment provides a powerful and rich graphical interface to the iPlant Collaborative cyberinfrastructure by creating an accessible virtual workbench that enables all levels of expertise, ranging from students to traditional biology researchers and computational experts, to explore, analyze, and share their data. By providing access to iPlants robust data‐management system and high‐performance computing resources, the Discovery Environment also creates a unified space in which researchers can access scalable tools. Researchers can use available Applications (Apps) to execute analyses on their data, as well as customize or integrate their own tools to better meet the specific needs of their research. These Apps can also be used in workflows that automate more complicated analyses. This module describes how to use the main features of the Discovery Environment, using bioinformatics workflows for high‐throughput sequence data as examples. Curr. Protoc. Bioinform. 42:1.22.1‐1.22.26.

Genome-wide signatures of male-mediated migration shaping the Indian gene pool

Multiple questions relating to contributions of cultural and demographical factors in the process of human geographical dispersal remain largely unanswered. India, a land of early human settlement and the resulting diversity is a good place to look for some of the answers. In this study, we explored the genetic structure of India using a diverse panel of 78 males genotyped using the GenoChip. Their genome-wide single-nucleotide polymorphism (SNP) diversity was examined in the context of various covariates that influence Indian gene pool. Admixture analysis of genome-wide SNP data showed high proportion of the Southwest Asian component in all of the Indian samples. Hierarchical clustering based on admixture proportions revealed seven distinct clusters correlating to geographical and linguistic affiliations. Convex hull overlay of Y-chromosomal haplogroups on the genome-wide SNP principal component analysis brought out distinct non-overlapping polygons of F*-M89, H*-M69, L1-M27, O2a-M95 and O3a3c1-M117, suggesting a male-mediated migration and expansion of the Indian gene pool. Lack of similar correlation with mitochondrial DNA clades indicated a shared genetic ancestry of females. We suggest that ancient male-mediated migratory events and settlement in various regional niches led to the present day scenario and peopling of India.

Building an environment to facilitate discoveries for plant sciences

The iPlant Collaborative is an NSF-funded cyberinfrastructure (CI) effort directed towards the plant sciences community. This paper enumerates the key concepts, middleware, tools, and extensions that create the unique capabilities of the iPlant Discovery Environment (DE) that provide access to our CI. The DE is a rich web-based application that brings flexible CI capabilities to a wide audience affiliated with the plant sciences, from computational biologists, bioinformaticians, applications developers, to bench biologists. The inherent interdisciplinary nature of plant sciences research produces diverse and complex data products that range from molecular sequences to satellite imagery as part of the discovery life cycle. With the constant creation of novel analysis algorithms, the advent and spread of large data repositories, and the need for collaborative data analysis, marshaling resources to effectively utilize these capabilities necessitates a highly flexible and scalable approach for implementing underlying CI. The iPlant infrastructure simultaneously supports multiple interdisciplinary projects providing essential features found in traditional science gateways as well as highly customized direct access to its underlying frameworks through use of APIs (Application Programming Interfaces). This allows the community to develop de novo applications. This approach allows us to serve broad community needs while providing flexible, secure, and creative utilization of our platform that is based on best practices and that leverages established computational resources.

Aboriginal Australian mitochondrial genome variation – an increased understanding of population antiquity and diversity

Aboriginal Australians represent one of the oldest continuous cultures outside Africa, with evidence indicating that their ancestors arrived in the ancient landmass of Sahul (present-day New Guinea and Australia) ~55 thousand years ago. Genetic studies, though limited, have demonstrated both the uniqueness and antiquity of Aboriginal Australian genomes. We have further resolved known Aboriginal Australian mitochondrial haplogroups and discovered novel indigenous lineages by sequencing the mitogenomes of 127 contemporary Aboriginal Australians. In particular, the more common haplogroups observed in our dataset included M42a, M42c, S, P5 and P12, followed by rarer haplogroups M15, M16, N13, O, P3, P6 and P8. We propose some major phylogenetic rearrangements, such as in haplogroup P where we delinked P4a and P4b and redefined them as P4 (New Guinean) and P11 (Australian), respectively. Haplogroup P2b was identified as a novel clade potentially restricted to Torres Strait Islanders. Nearly all Aboriginal Australian mitochondrial haplogroups detected appear to be ancient, with no evidence of later introgression during the Holocene. Our findings greatly increase knowledge about the geographic distribution and phylogenetic structure of mitochondrial lineages that have survived in contemporary descendants of Australia’s first settlers.