Phenotyping and a genome-wide association study of elite lines of pearl millet.

Pearl millet ( (L.) R. BR.

Data from collection and analysis of RNA sequencing data from pearl millet.

Pearl millet (, also known as ) is a cereal crop that has a C4 photosynthesis system and that can grow and develop seeds even under stressed conditions including drought-stressed, high temperature-stressed and nutrient-poor conditions. In previous studies, transcriptomes of pearl millet were studied by RNA sequencing (RNA-Seq) to understand mechanisms regulating its development and tolerance to such stressed conditions. Here, RNA-Seq reads from 565 pearl millet samples from 25 projects in the NCBI (National Center for Biotechnology Information) BioProject database were collected and mapped to the pearl millet reference genome to obtain read counts and transcripts per million (TPM) for each pearl millet gene.

Metagenomic Analyses of Viruses in the Orchid Mycorrhizal Interaction Using Improved Assemble Tools.

In nature, mycorrhizal association with soil-borne fungi is indispensable for orchid species. Compatible mycorrhizal fungi form endo-mycorrhizal structures in orchid cells, and the fungal structures are digested in orchid cells to be supplied to orchids as nutrition. Because orchid seeds lack the reserves for germination, they keep receiving nutrition through mycorrhizal formation from seed germination until nonphotosynthetic young seedlings develop leaves and become photoautotrophic.

Construction of a de novo assembly pipeline using multiple transcriptome data sets from Cypripedium macranthos (Orchidaceae).

The family Orchidaceae comprises the most species of any monocotyledonous family and has interesting characteristics such as seed germination induced by mycorrhizal fungi and flower morphology that co-adapted with pollinators. In orchid species, genomes have been decoded for only a few horticultural species, and there is little genetic information available. Generally, for species lacking sequenced genomes, gene sequences are predicted by de novo assembly of transcriptome data.