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Gene co-expression networks comprise one type of valuable biological networks. Many methods and tools have been published to construct gene co-expression networks; however, most of these tools and methods are inconvenient and time consuming for large datasets. We have developed a user-friendly, accelerated and optimized tool for constructing gene co-expression networks that can fully harness the parallel nature of GPU (Graphic Processing Unit) architectures. Genetic entropies were exploited to filter out genes with no or small expression changes in the raw data preprocessing step. Pearson correlation coefficients were then calculated. After that, we normalized these coefficients and employed the False Discovery Rate to control the multiple tests. At last, modules identification was conducted to construct the co-expression networks. All of these calculations were implemented on a GPU. We also compressed the coefficient matrix to save space. We compared the performance of the GPU implementation with those of multi-core CPU implementations with 16 CPU threads, single-thread C/C++ implementation and single-thread R implementation. Our results show that GPU implementation largely outperforms single-thread C/C++ implementation and single-thread R implementation, and GPU implementation outperforms multi-core CPU implementation when the number of genes increases. With the test dataset containing 16,000 genes and 590 individuals, we can achieve greater than 63 times the speed using a GPU implementation compared with a single-thread R implementation when 50 percent of genes were filtered out and about 80 times the speed when no genes were filtered out.

The genus Echinochloa (Poaceae) includes numerous problematic weeds that cause the reduction of crop yield worldwide. To date, DNA sequence information is still limited in the genus Echinochloa. In this study, we completed the entire chloroplast genomes of two Echinochloa species (Echinochloa oryzicola and Echinochloa crus-galli) based on high-throughput sequencing data from their fresh green leaves. The two Echinochloa chloroplast genomes are 139,891 and 139,800 base pairs in length, respectively, and contain 131 protein-coding genes, 79 indels and 466 substitutions helpful for discrimination of the two species. The divergence between the genus Echinochloa and Panicum occurred about 21.6 million years ago, whereas the divergence between E. oryzicola and E. crus-galli chloroplast genes occurred about 3.3 million years ago. The two reported Echinochloa chloroplast genome sequences contribute to better understanding of the diversification of this genus.