Management, Analyses, and Distribution of the MaizeCODE Data on the Cloud

SciApps: An Automated Platform for Processing and Distribution of Plant Genomics Data

SciApps is an open-source, web-based platform for processing, storing, visualizing, and distributing genomic data and analysis results. Built upon the Tapis (formerly Agave) platform, SciApps brings users TB-scale of data storage via CyVerse Data Store and over one million CPUs via the Extreme Science and Engineering Discovery Environment (XSEDE) resources at Texas Advanced Computing Center (TACC). SciApps provides users ways to chain individual jobs into automated and reproducible workflows in a distributed cloud and provides a management system for data, associated metadata, individual analysis jobs, and multi-step workflows. This chapter provides examples of how to (1) submitting, managing, constructing workflows, (2) using public workflows for Bulked Segregant Analysis (BSA), (3) constructing a Data Analysis Center (DAC), and Data Coordination Center (DCC) for the plant ENCODE project.

BSAseq: an interactive and integrated web-based workflow for identification of causal mutations in bulked F2 populations

SUMMARY

With the advance of next-generation sequencing technologies and reductions in the costs of these techniques, bulked segregant analysis (BSA) has become not only a powerful tool for mapping quantitative trait loci but also a useful way to identify causal gene mutations underlying phenotypes of interest. However, due to the presence of background mutations and errors in sequencing, genotyping, and reference assembly, it is often difficult to distinguish true causal mutations from background mutations. In this study, we developed the BSAseq workflow, which includes an automated bioinformatics analysis pipeline with a probabilistic model for estimating the linked region (the region linked to the causal mutation) and an interactive Shiny web application for visualizing the results. We deeply sequenced a sorghum male-sterile parental line (ms8) to capture the majority of background mutations in our bulked F2 data. We applied the workflow to 11 bulked sorghum F2 populations and 1 rice F2 population and identified the true causal mutation in each population. The workflow is intuitive and straightforward, facilitating its adoption by users without bioinformatics analysis skills. We anticipate that the BSAseq workflow will be broadly applicable to the identification of causal mutations for many phenotypes of interest.

AVAILABILITY AND IMPLEMENTATION

BSAseq is freely available on https://www.sciapps.org/page/bsa.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Single-cell RNA sequencing of developing maize ears facilitates functional analysis and trait candidate gene discovery

Crop productivity depends on activity of meristems that produce optimized plant architectures, including that of the maize ear. A comprehensive understanding of development requires insight into the full diversity of cell types and developmental domains and the gene networks required to specify them. Until now, these were identified primarily by morphology and insights from classical genetics, which are limited by genetic redundancy and pleiotropy. Here, we investigated the transcriptional profiles of 12,525 single cells from developing maize ears. The resulting developmental atlas provides a single-cell RNA sequencing (scRNA-seq) map of an inflorescence. We validated our results by mRNA in situ hybridization and by fluorescence-activated cell sorting (FACS) RNA-seq, and we show how these data may facilitate genetic studies by predicting genetic redundancy, integrating transcriptional networks, and identifying candidate genes associated with crop yield traits.