The emergence and development of computational technology and informatics innovates and complements the research approaches used in the classical molecular biology. Massively parallel signature sequencing approaches, especially genome-wide approaches, become the mainstream experimental approaches, which have generated a large number of biological data. Computational molecular biology is an integration of experimental molecular and genome biology with computational technology. The newly launching journal, Computational Molecular Biology (ISSN 1927-5587) provides a platform for the community in computational molecular and genome biology to disseminate new discoveries in this interdisciplinary field to meet new challenges including raw molecular data generation, data analysis, comparative and evolutionary genomics, and applications of biotechnology by applying the power of computational technology. Keywords Computational molecular biology; Systems biology; Genome and genomics; Experimental molecular and genome biology; Computational technologyComputational molecular biology cracks the mystery of life based on the background knowledge in molecular biology by combining mathematical algorithms and tools in computer science (Brutlag, 1998). The emergence and development of computational technology and informatics innovates and complements the research approaches used in the classical molecular biology. Traditionally computational biology focuses on aspects such as RNA structure prediction and sequence analysis. More recently the huge amount of data generated by high-throughput experimental technology attracts a large number of efforts of bioinformatics approaches made to the related areas including sequence analysis, protein structure analysis, gene expression, non-coding RNAs, statistical genetics, molecular evolution and computer-aided drug design. Especially for the gene microarray technology emerged in the mid-1980s, the merits such as high-throughput, concurrency, micromation and automation have quickly enabled the technology to be applied in areas including drug filtering, new drug development and disease diagnosis (Weinstein et al., 2002). DNA microarray can screen for gene expression of thousands of genes, and detect differences of gene expression among samples quantitatively and qualitatively. The development of gene microarray and the newly developed next-generation technology has produced exponentially a large amount of sequence and digital data, making the related data analysis a bottleneck for biologists. However, the applications of mathematics and statistics principles and the computer programs have helped to solve these problems (Mychaleckyj, 2007). Current molecular biology, including emerging genome biology, involves of multiple disciplines, such as, life science, medicine, pharmacy and chemistry, with the area covering from lower organisms to higher mammalian animals, from prokaryote to eukaryote and other areas such as multi-organism molecular
Soybean [Glycine max] is an important oil and food plant for both humans and animals. Recent development in RNA sequencing (RNA-seq) technology provides a cost effective approach to analyzing transcriptomes of plants at different developmental stages and in responses to different biotic and abiotic challenges. Currently there are over 5 000 RNA-seq datasets in soybean plants publicly available at SRA database in the National Center for Biotechnology Information (NCBI). Such a large number of RNA-seq datasets provide soybean researchers an opportunity as well as a challenge for fully exploring the data to understand soybean biology. A number of research articles have been published on applications of RNA-seq in transcriptome analysis of soybean plants, covering a wide range of topics including growth and development, plant mineral nutrients, responses to environmental stresses, pathogens and pests. In this work we compile and review recent advances of RNA-seq transcriptome analyses including profiling of differential gene expression, gene alternative splicing, and gene regulatory networks in soybean plants, with key findings excerpted from each individual published article.
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