Integrating transcriptomic and metabolomic analysis in roots of wild soybean seedlings in response to low-phosphorus stress.

Introduction: Plants undergo divergent adaptations to form different ecotypes when exposed to different habitats. Ecotypes with ecological adaptation advantages are excellent germplasm resources for crop improvement. Methods: his study comprehensively compared the differences in morphology and physiological mechanisms in the roots of two different ecotypes of wild soybean (Glycine soja) seedlings under artificially simulated low-phosphorus (LP) stress. Result: The seedlings of barren-tolerant wild soybean (GS2) suffered less damage than common wild soybean (GS1). GS2 absorbed more phosphorus (P) by increasing root length. In-depth integrated analyses of transcriptomics and metabolomics revealed the formation process of the ecological adaptability of the two different ecotypes wild soybean from the perspective of gene expression and metabolic changes. This study revealed the adaptation process of GS2 from the perspective of the adaptation of structural and molecular metabolism, mainly including: (1) Enhancing the metabolism of phenolic compounds, lignin, and organic acid metabolism could activate unavailable soil P; (2) Up-regulating genes encoding pectinesterase and phospholipase C (PLC) specifically could promote the reuse of structural P; (3) Some factors could reduce the oxidative damage to the membranes caused by LP stress, such as accumulating the metabolites putrescine and ascorbate significantly, up-regulating the genes encoding SQD2 (the key enzyme of sulfolipid substitution of phospholipids) substantially and enhancing the synthesis of secondary antioxidant metabolite anthocyanins and the AsA-GSH cycle; (4) enhancing the uptake of soil P by upregulating inorganic phosphate transporter, acid phosphatase ACP1, and purple acid phosphatase genes; (5) HSFA6b and MYB61 are the key TFs to resist LP stress. Discussion: In general, GS2 could resist LP stress by activating unavailable soil P, reusing plant structural P, rebuilding membrane lipids, and enhancing the antioxidant membrane protection system. Our study provides a new perspective for the study of divergent adaptation of plants.

Gclust: A Parallel Clustering Tool for Microbial Genomic Data.

The accelerating growth of the public microbial genomic data imposes substantial burden on the research community that uses such resources. Building databases for non-redundant reference sequences from massive microbial genomic data based on clustering analysis is essential. However, existing clustering algorithms perform poorly on long genomic sequences. In this article, we present Gclust, a parallel program for clustering complete or draft genomic sequences, where clustering is accelerated with a novel parallelization strategy and a fast sequence comparison algorithm using sparse suffix arrays (SSAs). Moreover, genome identity measures between two sequences are calculated based on their maximal exact matches (MEMs). In this paper, we demonstrate the high speed and clustering quality of Gclust by examining four genome sequence datasets. Gclust is freely available for non-commercial use at https://github.com/niu-lab/gclust. We also introduce a web server for clustering user-uploaded genomes at http://niulab.scgrid.cn/gclust.

gPGA: GPU Accelerated Population Genetics Analyses.

BACKGROUND: The isolation with migration (IM) model is important for studies in population genetics and phylogeography. IM program applies the IM model to genetic data drawn from a pair of closely related populations or species based on Markov chain Monte Carlo (MCMC) simulations of gene genealogies. But computational burden of IM program has placed limits on its application. METHODOLOGY: With strong computational power, Graphics Processing Unit (GPU) has been widely used in many fields. In this article, we present an effective implementation of IM program on one GPU based on Compute Unified Device Architecture (CUDA), which we call gPGA. CONCLUSIONS: Compared with IM program, gPGA can achieve up to 52.30X speedup on one GPU. The evaluation results demonstrate that it allows datasets to be analyzed effectively and rapidly for research on divergence population genetics. The software is freely available with source code at https://github.com/chunbaozhou/gPGA.

Parallel algorithm research on several important open problems in bioinformatics.

High performance computing has opened the door to using bioinformatics and systems biology to explore complex relationships among data, and created the opportunity to tackle very large and involved simulations of biological systems. Many supercomputing centers have jumped on the bandwagon because the opportunities for significant impact in this field is infinite. Development of new algorithms, especially parallel algorithms and software to mine new biological information and to assess different relationships among the members of a large biological data set, is becoming very important. This article presents our work on the design and development of parallel algorithms and software to solve some important open problems arising from bioinformatics, such as structure alignment of RNA sequences, finding new genes, alternative splicing, gene expression clustering and so on. In order to make these parallel software available to a wide audience, the grid computing service interfaces to these software have been deployed in China National Grid (CNGrid). Finally, conclusions and some future research directions are presented.