Bioinformatics Lab @ VIPBG

SNPNB 1.01

Introduction

1. Data preprocess
   All source data files are preprocessed and saved in a single intermediate file. Currently, SNPNB can automatically process the data in dbSNP fasta format and the Celera fasta format.
2. Set reference nucleotide frequency values (%)
   Reference nucleotide frequency values (e.g. genome average) can be manually entered or computed from sequence data (fasta format).
3. Analyze SNP neighboring effect
   • Choose to analyze SNPs with unknown or known SNP mutation direction.
   • Set SNP neighboring nucleotide positions or ranges.
   • Count the number for each nucleotide at each position for each type of SNPs.
   • Calculate the frequencies correspondingly.
   • Obtain the proportion biases correspondingly relative to the refercence values.
4. Evaluate the effective SNP size
   • Generate random numbers by a re-sampling strategy based on the size of the whole data (N).
   • Reorganize and sort the random numbers using each SNP as an object index.
   • Process each randomized SNP subset and evaluate the likelihood of bias patterns.
The software design is illustrated in the flowchart. The annotation of the flanking sites of a SNP is shown here.

System Requirements

Operating system: Windows or Linux. It is possible to work in Unix too.
Memory: 512 MB minimum (1 GB is recommended for large size data).
Hard disk: 2 times the size of source data.
Running environment: JRE 1.4 or higher (JRE 1.4 is recommended), and Perl 5.6 or higher ( Perl 5.8 is recommended).
(Installation instructions for JRE 1.4.2 and Perl 5.6/5.8 are available here)

Downolad

Program: Windows: snpnb.zip; Linux or Unix: snpnb.tgz (updated 2005-02-02)

Test data: Human Chr22 dbSNP build 121 (58MB) ;    human dbSNP build 121 (3.8GB) ;    mouse dbSNP build 121 (69MB) ; sample fasta format data with SNP ancestor information (168k)

Fasta format used in dbSNP (6KB); Celera (4KB)

Instruction

1. Download snpnb.zip (for windows) or snpnb.tgz (for Linux or Unix)
2. Uncompress the downloaded file
   Windows: use winzip to uncompress.
   Linux or Unix: type "tar -xzvf snpnb.tgz".
3. Start SNPNB program
   Open a console window. Go to the directory "snpnb" and find the file "snpnb.pl". Type "perl snpnb.pl" to start SNPNB. A program main menu window will popup.
4. Set parameters in the main menu
   1. "Analysis menu"
      There are two options. The user may select one by clicking the checkbox.
      1. Option 1: "Neighboring Effect Analysis "
         Choose to analyze "Unknown Mutation Direction" or "Known Mutation Direction" SNP data. The program sets the default values of "Sites or Ranges" as 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 20, 50, 100, 200, 250. The user may change these values, but it requires the numbers in an increasing order.
      2. Option 2: "Sample Size Evaluation"
         Two parameters are needed to set.
         1) "Sample Size": the program sets the default value 10,000. The user may change it from the drop-down menu or type a specific number there.
         2) "Sites": the program sets the default values as 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 20, 50, 100. The user may change these values, but it requires the numbers in an increasing order.
   2. "Data Files"
      Click "Browse Files" button to select the source files in the dbSNP or Celera fasta format. Hold ''ctl" or "shift" key to choose multiple files at one time.
   3. "Reference Values (%)"
      User may manually input the reference values, select from drop-down menu, or choose reference sequence files to calculate. Currently, SNPNB provides the reference values for the mouse genome (NCBI builder 32) and the human genome (NCBI builder 34).
5. Run and log
   Click "Run" button at main menu window after the parameters are set. The "Running logs" field displays the status because it usually takes hours to analyze the genome-wide data. After it finishes, the program presents the result window automatically. Both main menu window and result window contain "Running logs" field.
6. Display the results
   The results can be displayed by clicking on the buttons. There are two parts "SNP" and "Region" in the left panel of the result window. If "Known Mutation Direction" is selected in the main menu, the user may choose to see the results of "directed" or "undirected" type of SNPs. In the main panel, the user may choose the button "SNP Neighboring Analysis" or "Sample Size Evaluation".
   1. "SNP Neighboring Analysis " :
      There are three options to display results.
      (1) Resutls of "Count" in table.
      (2) Results of "Frequency" in table.
      (3) Results of "Bias" in table or graphics.
   2. "Sample Size Evaluation":
      The results are shown in table or graphics.
7. Close the program

We tested the performance of SNPNB using the mouse and human dbSNP data (build 121) on a Dell PC (CPU 1.3GHz, Memory 2GB, Windows XP, JDK1.4 and Perl5.83) and a Dell Workstation (CPU 2×3.0GHz, Memory 4GB, Redhad Linux WS, JDK1.4 and Perl5.80).

System	Process		Test data
			Mouse (469 445 SNPs)	Human (8 043 656 SNPs)
PC (Windows)	Data preprocess		0 h 2 m 3 s	1 h 35 m 4 s
	Neighboring effect analysis		0 h 24 m 32 s	10 h 36 m 11 s
	Sample size evaluation (Sample size: 50 000) (Repeat: 1000)	Random number generation	4 h 17 m 32 s	4 h 38 m 17 s
		Random number sort	0 h 19 m 15 s	4 h 48 m 45 s
		Sample size evaluation	0 h 59 m 39 s	3 h 43 m 18 s
	Total elapsed time		6 h 3 m 1 s	25 h 21 m 35 s
Workstation (Linux)	Data preprocess		0 h 2 m 51 s	2 h 50 m 25 s
	Neighboring effect analysis		0 h 29 m 18 s	11 h 17 m 50 s
	Sample size evaluation (Sample size: 50 000) (Repeat: 1000)	Random number generation	6 h 9 m 37 s	7 h 14 m 15 s
		Random number sort	0 h 13 m 9 s	3 h 4 m 40 s
		Sample size evaluation	1 h 6 m 9 s	3 h 19 m 16 s
	Total elapsed time		8 h 1 m 4 s	27 h 46 m 26 s

We add some screenshots here to show the major functions of SNPNB. The screenshots were obtained using the human SNP data (build121) downloaded from dbSNP database:

1. Main Menu
2. Result screen 1 -- Neighboring Effect Analysis (count)
3. Result screen 2 -- Neighboring Effect Analysis (frequency)
4. Result screen 3 -- Neighboring Effect Analysis (bias in table)
5. Result screen 4 -- Neighboring Effect Analysis (bias in graphic)
6. Result screen 5 -- SNP Sample Size Evaluation (in table) (updated 2005-02-02)
7. Result screen 6 -- SNP Sample Size Evaluation (in graphic) (updated 2005-02-02)
8. Result screen 7 -- Neighboring Effect Analysis (bias in graphic for known SNP mutation direction data) new!