Evidence-Based Immunology and Infectious Disease

How GenBank, Databases Speed Vaccine, Drug Developmentā€”and Precision Medicine

Published Online: April 22, 2014
Surabhi Dangi-Garimella, PhD
The hunt for an AIDS vaccine has lasted 30 years, with many failures.1 The fact that the human immunodeficiency virus-1 (HIV-1) virus continuously adapts and mutates results in broad genetic diversity and constantly changing antigen targets. Additionally, the viral envelope glycoprotein, which is primarily responsible for promoting viral entry into the host cell, has conformational flexibility along with numerous structural features that together help protect the virus from the humoral immune system.

Vaccine development for HIV-1 has focused on generating broadly neutralizing antibodies, which are based on structural elucidation of the viral envelope via sequencing studies. The current strategies use bioinformatics approaches that involve isolating indi-vidual B cells from broadly reactive sera (25% of HIV-positive individuals make relatively broadly reactive neutralizing

antibodies) and cloning potent and broadly neutralizing antibodies from the B cells.2

This work would be impossible without GenBank, an all-inclusive, opensource database initiated by the National Center for Biotechnology Information (NCBI). GenBank includes nucleotide sequences for more than 280,000 species and the supporting bibliographies, with submissions from individual laboratories as well as large-scale sequencing projects. Additionally, sequences from issued patents are submitted by the US Patent and Trademark Office.3 Despite the open access to this database, researchers all over the world have actively contributed to building up the resource, realizing the vast potential of this knowledge-sharing database. The information either goes to GenBank or is submitted through its European counterpart, the European Bioinformatics Institute (EBI) or its Japanese counterpart, the DNA Data Bank of Japan (DDJB).4 All the leading journals need researchers to submit their sequences to GenBank and cite the corresponding access number in the published article.

The new sequences can be directly submitted to EBI, DDJB, or GenBank and the 3 databases are synchronized daily for easy access to all the information on all 3 databases. The data is virtually in real time, with minimal delay in access to the latest data, free of cost. Other commonly used nucleotide databases include the European Molecular Biology Laboratory (EMBL; EBI is run by EMBL), SwissProt, PROSITE, and Human Genome Database (GDB).5 Taken together, these databases are essentially a bioinformatics tool that helps integrate biological information with computational software. The information gained can be applied to understand disease etiology (in terms of mutations in genes and proteins) and individual variables, and ultimately aid drug development.

According to the National Institutes of Health Biomedical Information Science and Technology Initiative, bioinformatics is defined as “research, development, or application of computational tools and approaches for expanding the use of biological, medical, behavioral, or health data, including those to acquire, store, organize, archive, analyze, or visualize such data.”6

Development of GenBank

Initially called the Los Alamos Sequence Database, this resource was conceptualized in 1979 by Walter Goad, a nuclear physicist and a pioneer in bioinformatics at Los Alamos National Laboratory (LANL).7 GenBank followed in 1982 with funding from the National Institutes of Health, the National Science Foundation, and the Departments of Energy and Defense. LANL collaborated with various bioinformatics and technology companies for sequence data management and to promote open access communications. By 1992, GenBank transitioned to being managed by the National Center for Biotechnology information (NCBI).8

Submissions to the database include original mRNA sequences, prokaryotic and eukaryotic genes, rRNA, viral sequences, transposons, microsatellite sequences, pseudogenes, cloning vectors, noncoding RNAs, and microbial genome sequences. Following a submission (using the Web-based BankIt or Sequin programs), the GenBank staff reviews the documents for originality and then assigns an accession number to the sequence, followed by quality assurance checks (vector contamination, adequate translation of coding regions, correct taxonomy, correct bibliographic citation) and release to the public database.3,8

How Are Researchers Utilizing This Database?

BLAST (Basic Local Alignment Search Tool) software, a product of GenBank, allows for querying sequence similarities by directly entering their sequence of interest, without the need for the gene name or its synonyms.4 An orphan (unknown) or de novo nucleotide sequence, which may have been cloned in a laboratory, can gain perspective following a BLAST search and a match with another, better-characterized sequence in the database. Further, by adding restrictions to the BLAST search, only specific regions of the genome (such as gene-coding regions) can be examined instead of the 3 billion bases.4 BLAST can also translate a DNA sequence to a protein, which can then be used to search a protein database.BLAST, which was developed at NCBI, works only with big chunks of nucleotide sequences, and not with shorter reads, according to Santosh Mishra, PhD, director of Bioinformatics and codirector of the Collaborative Genomics Center at the Vaccine and Gene Therapy Institute (VGTI) of Florida. Mishra, who worked as a postdoctoral research associate with Goad at LANL, was actively involved in developing GenBank.

His work contributed to the generation of the “flat file” format, and he also worked on improving the query-response time of the search engine. Additionally, he initiated the “feature table” in GenBank—the documentation within that helps GenBank, EMBL, and DDJB exchange data on a daily basis. According to Mishra, the STAR aligner, developed at Cold Spring Harbor, works better with reference sequences, while Trinity, developed at the Broad Institute in Cambridge, Massachusetts, is useful for de novo sequences. (The Broad Institute made news recently with its work on identifying gene mutations that prevent diabetes in adults who have known risk factors, such as obesity.)

Advantages and Disadvantages of the GenBank Platform

The biggest single advantage of GenBank is the open-access format, which allows for a centralized repository in a uniform format. The tremendous amount of data generated by laboratories (such as from microarrays and microRNA arrays) cannot be published in a research article. However, the data, tagged and uploaded on GenBank, can be linked to the journals’ websites and the links can be provided in the print versions of the articles as well.4

On the flip side, the biggest advantage of being an open-access platform is also the biggest disadvantage of the software. There’s always the probability of scientists registering faulty genetic sequences on the website, which will not be caught unless they are peer reviewed. Despite the incorporation of several quality control mechanisms into the system, reuse of the data by other scientists alone can help discover glitches in the existing data. Additionally, GenBank encourages its users to submit feedback and update records, which unfortunately is not a very proactive process.4

Bioinformatics and Pharmacogenomics in Drug Discovery/Development

Accelerating the drug development process saves costs for the pharmaceutical industry, especially with the way the industry functions today. The company that discovers or invents a new chemical entity, which could metamorphose into a new drug candidate, can squeeze the maximum profit out of the drug before the patent expires and competitors catch on. Essentially, companies jump at every opportunity to accelerate any aspect of the discovery/development process. Resources like the GenBank and EBI are data mines that can speed up the entire process in the following ways:

Target identification. Drug candidates can be identified (following a high-throughput screen of chemical libraries) and developed only after a “druggable target” is discovered for a disease condition. Typically, about 1 in 1000 synthesized compounds will progress to the clinic, and only 1 in 10 drugs undergoing clinical trials reaches the market.9 Optimizing/validating a target is essential due to the prohibitively high cost of conducting trials, and the potential targets for drug discovery are increasing exponentially.10 By mining and storing information from huge data sets, like the human genome sequence, the nucleotide sequence of the target proteins has become readily available, as has the potential to identify new targets. This can exponentially increase the content of the drug pipelines of pharmaceutical companies.10

Target validation. Establishing a robust association between a likely target and the disease, to confirm that target modulation translates into a beneficial therapeutic outcome, would not only validate the drug development process, but also help absorb the risks associated with clinical trial failure of the molecule being developed.10

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