On Bioinformatic Resources

A starting point of curating bioinformatic resources for the public is marked by the establishment of the US National Center for Biotechnology Information (NCBI) in 1988 [1]. One of its many purposes is certainly to echo the initiative of the Human Genome Project (HGP) –– when two landmark reports were published at the same time: ‘‘Mapping and Sequencing the Human Genome’’ by the National Research Council [2] and ‘‘Mapping Our Genes –– The Genome Project: How Big, How Fast?’’ by the US Congress [3]. As HGP is prepared to scale up its sequencing operations about 5 years into the Project, a new discipline – bioinformatics – became inevitable. It emerged originally for processing and sharing genome sequences and was thus known as genome informatics. In the first five-year plan of HGP, it was pointed out that genome informatics is a scientific discipline that encompasses all aspects of genome information acquisition, processing, storage, distribution, analysis, and interpretation [4]. This insightful statement was footnoted by the launch of GenBank at NCBI in 1992, a database of nucleotide sequences, in collaboration with its international partners at the European Molecular Biology Laboratory (EMBL) and the DNA Data Bank of Japan (DDBJ). Now, some 20 years have passed, not only GenBank still prospers but other databases on human genomics and biology have emerged to fill up all data landscapes [5]. Genomics together with many other research fields of life sciences had entered the Era of Large-scale Data Acquisition in the early 90’s. The Era was led by the fast accumulation of human genomic sequences and followed by similar data from other large model organisms [6]. As soon as HGP declared the human genome sequenced in 2001 [7], the HapMap Project [8] was announced to sequence hundreds of human genomes in a diverse population background. Microbial genomics has also been pursued into both metagenomics and pangenomics [9,10]. The big data collection efforts have been extending all the way into its vertical path –– from DNA to RNA to Protein [11,12]. Another expansion of the efforts is horizontally toward biological functions and disease relevance [13–15]. In order to annotate biological functions at different levels of anatomy and physiology –– cell, organ, tissue, and systems –– for biomedical applications, data integration and interpretation become more relevant, and thus the birth of systems biology, which takes account of data from all omics fields and deciphers them in a context of biological networks. In recent years, with strong funding and rapid development, the sequence technology has been advancing significantly, resulting in dramatic cost reduction and explosion of data accumulation. Therefore, a large number of bioinformatic resources become obvious and there is demand for some comprehensive digestions. There are several data types being curated in bioinformatic resources world-wide. The first of them is classified as scale-up omics data, which includes genome-wide data from the vertical path as mentioned above: genome, transcriptome, proteome, and metabolome, just to name a few. Each of these data may have different disease relevance, such as DNA variations for cancer predisposition and protein structures for drug development. The second type of data resources includes data generated by molecular biologists that tend to look at molecular details, such as transcription factors and signal transduction pathways, where interactions among macromolecules and