• Title/Summary/Keyword: GINet

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Construction of a Computation Web Server for Genome Analysis (제놈 분석용 계산 Web 서버의 구성)

  • Park, Kie-Jung;Lee, Byung-Wook;Park, Yong-Ha
    • Microbiology and Biotechnology Letters
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    • v.24 no.1
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    • pp.132-136
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    • 1996
  • A comutation server is needed to provide analysis programs to Korean biologists, especially genome researchers, on GINet. For each analysis program, we implmented an input form with HTML and a CGI program for interface between an input form and an analysis program with C language on GINet computatin Web server. We made two construction methods of CGI programs for analysis programs, and implemented all CGI programs based on the methods followed by modifying each CGI program for specific processing of each program. On the server ten programs are availabel now, which include most frequently used ones and those developed by our team, and most programs with will be ported or developed by our team will be available on the Web server.

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TFSCAN 검색 프로그램 TFSCAN의 개발

  • Lee, Byung-Uk;Park, Kie-Jung;Kim, Ki-Bong;Park, Wan;Park, Yong-Ha
    • Microbiology and Biotechnology Letters
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    • v.24 no.3
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    • pp.371-375
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    • 1996
  • TFD is a transcription factor database which consists of short functional DNA sequences called as signals and their references. SIGNAL SCAN, developed by Dan S. Prestridge, is used to determine what signals of TFD may exist in a DNA sequence. This program searches TFD database by using a simple algorithm for character string comparison. We developed TFSCAN that aims at searching for signals in an input DNA sequence more efficently than SIGNAL SCAN. Our algorithms consist of two parts, one constructs an automata by scanning sequences of rFD, the other searches for signals through this automata. Searching for signal-related references is radically improved in time by using an indexing method. Usage of TFSCAN is very simple and its output is obvious. We developed and installed a TFSCAN input form and a CGI program in GINet Web server, to use TFSCAN. The algorithm applying automata showed drastical results in improvement of computing time. This approach may apply to recognizing several biological patterns. We have been developing our algorithm to optimize the automata and to search more sensitively for signals.

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