MiCA : A Web-Based Tool for the Microbial Community Analysis Based on Terminal-Restriction Fragment Length Polymorphisms ( T-RFLP )

A web-based tool, Microbial Community Analysis (MiCA), was developed to facilitate the analysis of microbial community structures and compositions based on terminal-restriction fragment length polymorphisms (T-RFLP) of 16S rRNA genes. MiCA addresses the needs for high-throughput and automated analytical tools for microbial structures. It also provides an intuitive web interface that is accessible to all researchers throughout the world. To facilitate in silico PCR amplification, we have developed two computational tools. The first program employs an integer-encoding scheme that effectively scans through a large number of sequences in the database and permits a specified number of mismatches. The number of base pairs between the 5’ and 3’ primers and the proximal restriction sites are reported. Multiple digest with at most three enzymes is also permissible. The second program performs the community profile analysis by selecting the subset of microbial populations that could be present in a community based on empirical T-RFLP data. The algorithm estimates the relative abundance of populations and yields plausible communities whose profiles are consistent with the empirical data. MiCA is currently accessible at http://hermes.campus. uidaho.edu/. INTRODUCTION High-throughput and automated analyses for microbial structures based on terminal-restriction fragment length polymorphisms (T-RFLP) are hindered by the lack of computational tools [1, 7, 8]. The aim of this research was to develop a suite of web-based tools that enables researchers to perform several tasks, these include: (a) in silico PCR amplification and restriction of sequences found in public databases; (b) automatic retrieval of data and archival storage in a relational database; (c) comparison of multiple T-RFLP profiles obtained from a single sample using different primer-enzyme combinations; and (d) analysis of T-RFLP data and clustering of samples based on similarities and differences. To address these needs, we have developed Microbial Community Analysis (MiCA), a web-based suite of analytical tools that employs a relational database to store sequences and two computational tools to facilitate the analyses of microbial community structure based on the empirical data from T-RFLP. The first program utilizes an integer-encoding scheme and performs in silico PCR amplification by searching for gene sequences with homology to the specified primers. Restriction sites are identified within the gene fragment delimited by the primers. Unlike the traditional character-based search algorithms [11, 12, 13, 14], the integer-encoding scheme effectively reduces the search complexity and permits mismatches within each search without compromise to the performance and accuracy [7, 8]. The second program performs the community profile analysis by selecting the subset of microbial populations that could be present in a community based on empirical T-RFLP data with one or more primer-enzyme combinations. It groups those microbes by fragment length; for examples, microbial populations with identical 5’ and 3’ fragment lengths are organized into groups. The algorithm then uses the peak area data to generate equations that describe the relative abundances of the microbial populations. It estimates the relative abundance of populations and yields plausible communities whose profiles are consistent with the empirical data [8]. For a single primer-enzyme combination the number of populations within each group can be large. However, including data from additional primer-enzyme combinations reduces this program since the inferred community can only include populations that are common to groups defined by all the primer-enzyme combinations used. The ability of the algorithm to define the ‘true’ community structure is limited by the number of sequences in the database that is employed since populations whose sequences are not in the database will not be included as possible members of a community. However, T-RFLP doesn’t offer a quantitative view of the native community structure [1, 2]. Therefore, we are currently investigating an approach for inferring community profiles with dynamic programming and Bayesian statistics, which will provide statistical significance for each inferred community. DESIGN OF MICA The data and computer resources available via the Internet offer an important resource in improving our understanding of biological and evolutionary processes. The World Wide Web has increasingly become a primary means of communicating scientific information. It merges the techniques of networked information and hypertext to make an easy but powerful global information system. The potential of such an approach is beginning to change the fundamental way in which basic science is conducted. With the explosion of sequence and structural information available to researchers, the biological study increasingly relies on the Internet to effectively access and exchange information [15, 16, 17]. In addition, navigation on the Web requires minimum knowledge of computers, which makes it an ideal platform for the development of MiCA. The objective of MiCA is to design a web portal that serves as a one-stop web information and research resource for anyone who is working on the bioinformatics and related fields. The design focuses on employing the cutting-edge web technology that provides an intuitive and straightforward interface. Users are not required to install any additional software their own machines other than a web browser. With the convenience of the Internet and web technology, research are able to access MiCA from anywhere at anytime. MiCA currently operates on a desktop computer running Linux. The web server is powered by Apache, which has been fully configured and optimized for optimal security and performance. We have developed several PHP scripts that establish the connection between the web pages and database. The PHP scripts are used to forward the T-RFLP digest and amplification parameters such as the primers and restriction enzymes. The MiCA database contains the most commonly used forward and reverse primers, and restriction enzymes. Users can select desired primers and enzymes from the drop-down lists or submit their own sequences for digest. In addition, users may also specify the number of mismatches allowed within a digest. Allowing mismatches increases the breadth and sensitivity of the digest. The sequence data is stored on a relational database. There are currently 16,277 16S rRNA gene sequences in the database. These sequences are retrieved from the Michigan State University RDP (Ribosomal Database Project) II database at http://rdp.cme. msu.edu/html/. In addition, we have gathered the most commonly used primers and restriction enzymes. There are currently 9 forward primers, 12 reverse primers, and 27 restriction enzymes. We are currently expanding with several subsets of database. In the future searches can be performed within a specified subset of sequences and primers. In addition, to better assist researchers using the tools, we have constructed an online help system that provides some detailed information and instructions about the web site. The online help system is the essential reference for researchers who wish to better utilize the MiCA web site. It contains some brief tutorials on selecting the primers and enzymes. The MiCA online help system runs on a separate window, which doesn’t interfere with any tasks running on the web site. The following figures show the MiCA query and output pages. Figure 1: The web page list all available primers and enzymes in drop-down menus. Users may also submit their own sequences for digest. Mismatches are permitted to increase the breadth and sensitivity of the search. We have implemented several sorting options, which allow users to customize the output. The output is available in the CSV (Comma Separated Values) and plain-text formats. CSV file format allows data to be easily transferred into spreadsheet or database programs. The format shown here is the plain-text format. Figure 2: The MiCA online help system lists the essential information on utilizing the web site and tools. It also provides brief tutorials on selected subjects. PCR SEARCH ALGORITHM We have developed a program that performs in silico amplification by searching for gene sequences with homology to the specified primers, after which, restriction sites are identified within the gene fragment delimited by the primers. The number of base pairs between the 5’ and 3’ primers and the proximal restrictions are reported [7, 8]. To effectively scan through a large number of sequences, we developed a new encoding scheme that transforms nucleotide characters into integer-coded sequences. Unlike the traditional character-based approaches [11, 12, 13, 14], the search algorithm developed represents sequences as four integers. This approach exploits the fact that there only four possible bases; namely adenine, guanine, thymine or uracil, and cytosine for DNA or RNA [15]. The bit streams in each integer reflect the relative occurrences of a nucleotide in the sequence. The bit pattern ‘1’ signals the existence of such a nucleotide at a given location while ‘0’ signals the absence. On most modern 32-bit computer processors, a sequence of 32 nucleotides can be reduced into four integers (representing four bases). In addition, since nucleotides are individually represented, ambiguities in the sequence can be fully resolved and encoded. This compact representation not only permits an efficient and accurate search, but also reduces the spatial requirement and complexity of the search algorithm regardless the number of ambiguities in the sequence. Shown below are a hypothetical forward primer and a small section of sequence to illustrate how the transformation and search are performed. Figure 3: The top sequence is the hypothetical target DNA and the bottom one is the pr