Functional Genomics, Genetics, and Bioinformatics

Biology has become the land of the “-omics,” including genomics [1], transcriptomics [2, 3], epigenomics [4], proteomics [5], lipidomics [6, 7], and metabolomics [8]. Each of these “-omics” generates a huge amount of high-throughput data, and it is a challenge both to analyze these data and to further investigate the function of specific molecules. Though more genomes have been completed due to the rapid development of sequencing technology [9], we cannot understand the information contained within a genome until we mine out its implicated functions including downstream transcription, translation, epigeneticsmodulation, andmetabolic pathways. In this special issue, we mainly focus on functional “-omics” and bioinformatics. The Peer-reviewed papers are collected in the special issue.They are approximately divided into three areas: bioinformatics, functional genomics, and functional genetics.The majority of the papers are purely bioinformatics related papers. We define bioinformatics papers as those using computational tools or developing methods to analyze functional “-omics” data without using wet labs. Two papers fell into the category of functional gen-omics, which is focused on using whole genome level wet-lab technology to find important molecules and investigate their potential functions. Five papers are considered as functional genetics papers. Functional genetics is a broad concept here and these papers are concentrated on studying themolecular functions andmechanisms of individual molecules using wet-lab experimental approaches. Bioinformatics. In the bioinformatics papers, four papers deal with transcriptomics data. F. Wang et al. developed a novel approach for coexpression analysis of E2F1-3 andMYC target genes in chronic myelogenous leukemia (CML); they found a significant difference in the coexpression patterns of those candidate target genes between the normal and the CML groups. It is challenging to analyze the quantity of image data on gene expression. A. Shlemov et al. developed a method called 2D singular spectrum analysis (2D-SSA) for application to 2D and 3D datasets of embryo images related to gene expression; it turned out to work pretty well. J. Li et al. characterized putative cis-regulatory elements (CREs) associated withmalemeiocyte-expressed genes using in silico tools.They found that the upstream regions (1 kb) of the top 50 genes preferentially expressed in Arabidopsis meiocytes possessed conserved motifs, which were potential binding sites of transcription factors. NAGNAG alternative splicing plays an important role in biological processes and represents a highly adaptable system for posttranslational regulation of gene function. Interestingly, X. Sun et al. identified about 31 NAGNAG alternative splicing sites that were identified in human large intergenic noncoding RNAs (lincRNAs). Three papers are focused on the deification of new gene family members and gene evolution. Conotoxins are small disulfide-rich neurotoxic peptides, which can bind to ion channels with very high specificity and regulate their activities. H. Ding et al. developed a novel method called iCTXType, which is a sequence-based predictor that can be used to Hindawi Publishing Corporation