Plant

miRLocator: A Python Implementation and Web Server for Predicting miRNAs from Pre-miRNA Sequences

microRNAs (miRNAs) are short, noncoding regulatory RNAs derived from hairpin precursors (pre-miRNAs). In synergy with experimental approaches, computational approaches have become an invaluable tool for identifying miRNAs at the genome scale. We have …

Opaque-2 Regulates a Complex Gene Network Associated with Cell Differentiation and Storage Functions of Maize Endosperm

Development of the cereal endosperm involves cell differentiation processes that enable nutrient uptake from the maternal plant, accumulation of storage products, and their utilization during germination. However, little is known about the regulatory …

Massive expansion and differential evolution of small heat shock proteins with wheat (Triticum aestivum L.) polyploidization

Wheat (Triticum aestivum), one of the world's most important crops, is facing unprecedented challenges due to global warming. To evaluate the gene resources for heat adaptation in hexaploid wheat, small heat shock proteins (sHSPs), the key plant heat …

Transcriptome Dynamics during Maize Endosperm Development

The endosperm is a major organ of the seed that plays vital roles in determining seed weight and quality. However, genome-wide transcriptome patterns throughout maize endosperm development have not been comprehensively investigated to date. …

RNA sequencing of laser-capture microdissected compartments of the maize kernel identifies regulatory modules associated with endosperm cell differentiation

Endosperm is an absorptive structure that supports embryo development or seedling germination in angiosperms. The endosperm of cereals is a main source of food, feed, and industrial raw materials worldwide. However, the genetic networks that regulate …

Machine learning-based differential network analysis: a study of stress-responsive transcriptomes in Arabidopsis

Machine learning (ML) is an intelligent data mining technique that builds a prediction model based on the learning of prior knowledge to recognize patterns in large-scale data sets. We present an ML-based methodology for transcriptome analysis via …

mlDNA

Machine learning-based differential network analysis

Application of the Gini correlation coefficient to infer regulatory relationships in transcriptome analysis

One of the computational challenges in plant systems biology is to accurately infer transcriptional regulation relationships based on correlation analyses of gene expression patterns. Despite several correlation methods that are applied in biology to …

RSGCC

Gini-based transcriptiome analysis

KGBassembler

Karyotype-based genome assembly for Brassicaceae species

KGBassembler: A Karyotype-based Genome Assembler for Brassicaceae Species

**MOTIVATION**: The Brassicaceae family includes the most important plant model Arabidopsis thaliana and many cruciferous vegetable crops. A number of close relatives of Arabidopsis and economically important Brassica species are being sequenced with …