Graphics Processing Unit-Enhanced Genetic Algorithms for Solving the Temporal Dynamics of Gene Regulatory Networks

ForkJoinPcc Algorithm for Computing the Pcc Matrix in Gene Co-Expression Networks

High-throughput microarrays contain a huge number of genes. Determining the relationships between all these genes is a time-consuming computation. In this paper, the authors provide a parallel algorithm for finding the Pearson’s correlation coefficient between genes measured in the Affymetrix microarrays. The main idea in the proposed algorithm, ForkJoinPcc, mimics the well-known parallel programming model: the fork–join model. The parallel MATLAB APIs have been employed and evaluated on shared or distributed multiprocessing systems. Two performance metrics—the processing and communication times—have been used to assess the performance of the ForkJoinPcc. The experimental results reveal that the ForkJoinPcc algorithm achieves a substantial speedup on the cluster platform of 62× compared with a 3.8× speedup on the multicore platform.

AGTAR: A novel approach for transcriptome assembly and abundance estimation using an adapted genetic algorithm from RNA-seq data

BACKGROUND

Recently, the rapid development of RNA-seq technologies has accelerated transcriptomics research. The accurate identification and quantification of transcripts based on RNA-seq data will facilitate the exploration of various potential biological mechanisms. However, due to the limitations of the current data analysis tools and RNA-seq technologies, full and accurate reconstruction of the transcriptome still faces many challenges.

RESULTS

We developed the adapted genetic algorithm (AGTAR) program, which can reliably assemble transcriptomes and estimate abundance based on RNA-seq data with or without genome annotation files. We defined a new concept, isoform junction abundance, to help enhance the accuracy of isoform identification and quantification. Isoform abundance and isoform junction abundance are estimated by an adapted genetic algorithm. The crossover and mutation probabilities of the algorithm can be adaptively adjusted to effectively prevent premature convergence. Both simulated and real data indicated that AGTAR's comprehensive ability to assemble transcripts is significantly superior to that achievable by the currently widely used tools with similar functions.

CONCLUSIONS

AGTAR is a tool for identifying and quantifying transcripts from RNA-seq data. It has the advantages of higher accuracy and ease of use. The AGTAR package is freely available at https://github.com/v4yuezi/AGTAR.git.