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226 10 Transforming with Traditional Programming Languages

following file that was produced by BioProspector (Liu et al. 2001). Some

parts of the file were omitted to save space.

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**

* BioProspector Search Result *

**

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Read input sequences.

Use following data to represent motif score distribution.

1.950

1.982

[26 similar lines omitted]

2.027

1.943

Null motif score distribution mean: 2.005, standard deviation: 0.052

Look for motifs from the original sequences.

Try #1 2.462 CGTTCCGGAGACCG CGGTCTCCGGAACG 36

Try #2 2.295 CTCGAGGAGCTTGG CCAAGCTCCTCGAG 32

[36 similar lines omitted]

Try #39 2.274 CGCTTCCAGCCCTC GAGGGCTGGAAGCG 32

Try #40 2.516 GAAGTTTCCCGACC GGTCGGGAAACTTC 40

The highest scoring 3 motifs are:

Motif #1:

******************************

[1 line omitted]

Blk1 A G C T Con rCon Deg rDeg

1 0.00 0.21 0.21 0.59 T A T A

2 0.00 0.44 0.50 0.06 C G S S

[10 similar lines omitted]

13 0.44 0.00 0.56 0.00 C G M K

14 0.21 0.59 0.18 0.03 G C G C

Seq #1 seg 1 r998 TCATCCAATCAGAG

Seq #2 seg 1 f91 TCAACCGAACAGAA

[30 similar lines omitted]

Seq #27 seg 1 r343 GGAACCAATCAGCG

Seq #27 seg 2 r261 TCAGCCAATGACCG

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[The other motifs were omitted]

The information produced by BioProspector is not only complex, but the

format is also complex. Furthermore, it is unique to BioProspector. There are

many other motif-finding programs available such as AlignACE (Hughes