DTU Health Tech
Department of Health Technology
This link is for the general contact of the DTU Health Tech institute.
If you need help with the bioinformatics programs, see the "Getting Help" section below the program.
Sequence submission: paste the sequence(s) and/or upload a local file
Restrictions
At most 50 sequences and 1,500,000 nucleotides in total per submission.
Confidentiality
The sequences are kept confidential and will be deleted after processing.
CITATIONS
For publication of results, please cite:
Promoter 2.0: for the recognition of PolII promoter sequences.
Steen Knudsen
Bioinformatics 15, 356-361, 1999.
Position
where 'Position' is a position in the sequence, 'Score' is the prediction score for a transcription start site occurring within 100 base pairs upstream from that position and 'Likelihood' is a descriptive label associated with that score. The scores are always positive numbers; they are labelled as follows:
below 0.5 |
ignored
0.5 - 0.8 |
Marginal prediction
|
0.8 - 1.0 |
Medium likely prediction
|
above 1.0 |
Highly likely prediction
| |
Consult the performance notes for comments on the prediction scores.
The input sequence will be included in the output, preceeding the predictions
if "Full output" has been selected.
Promoter 2.0 Prediction Results INPUT SEQUENCE: >gi_209811_gb_J01917_ADRCG Adenovirus type 2, complete genome. CATCATCATAATATACCTTATTTTGGATTGAAGCCAATATGATAATGAGGGGGTGGAGTT TGTGACGTGGCGCGGGGCGTGGGAACGGGGCGGGTGACGTAGTAGTGTGGCGGAAGTGTG ATGTTGCAAGTGTGGCGGAACACATGTAAGCGCCGGATGTGGTAAAAGTGACGTTTTTGG TGTGCGCCGGTGTATACGGGAAGTGACAATTTTCGCGCGGTTTTAGGCGGATGTTGTAGT AAATTTGGGCGTAACCAAGTAATGTTTGGCCATTTTCGCGGGAAAACTGAATAAGAGGAA GTGAAATCTGAATAATTCTGTGTTACTCATAGCGCGTAATATTTGTCTAGGGCCGCGGGG ACTTTGACCGTTTACGTGGAGACTCGCCCAGGTGTTTTTCTCAGGTGTTTTCCGCGTTCC GGGTCAAAGTTGGCGTTTTATTATTATAGTCAGCTGACGCGCAGTGTATTTATACCCGGT GAGTTCCTCAAGAGGCCACTCTTGAGTGCCAGCGAGTAGAGTTTTCTCCTCCGAGCCGCT CCGACACCGGGACTGAAAATGAGACATATTATCTGCCACGGAGGTGTTATTACCGAAGAA ATGGCCGCCAGTCTTTTGGACCAGCTGATCGAAGAGGTACTGGCTGATAATCTTCCACCT CCTAGCCATTTTGAACCACCTACCCTTCACGAACTGTATGATTTAGACGTGACGGCCCCC GAAGATCCCAACGAGGAGGCGGTTTCGCAGATTTTTCCCGAGTCTGTAATGTTGGCGGTG CAGGAAGGGATTGACTTATTCACTTTTCCGCCGGCGCCCGGTTCTCCGGAGCCGCCTCAC CTTTCCCGGCAGCCCGAGCAGCCGGAGCAGAGAGCCTTGGGTCCGGTTTCTATGCCAAAC CTTGTGCCGGAGGTGATCGATCTTACCTGCCACGAGGCTGGCTTTCCACCCAGTGACGAC GAGGATGAAGAGGGTGAGGAGTTTGTGTTAGATTATGTGGAGCACCCCGGGCACGGTTGC AGGTCTTGTCATTATCACCGGAGGAATACGGGGGACCCAGATATTATGTGTTCGCTTTGC TATATGAGGACCTGTGGCATGTTTGTCTACAGTAAGTGAAAATTATGGGCAGTCGGTGAT AGAGTGGTGGGTTTGGTGTGGTAATTTTTTTTTAATTTTTACAGTTTTGTGGTTTAAAGA PREDICTED TRANSCRIPTION START SITES: gi_209811_gb_J01917_ADRCG Adenovirus type 2, complete genome., 1200 nucleotides Position Score Likelihood 600 1.063 Highly likely prediction
For a favorable comparison of this software to other promoter prediction software, see:
Eukaryotic promoter recognition.
J.W. Fickett and A.G. Hatzigeorgiou.
Genome Res. 7(9), 861-878, 1997.
Motivation: a new approach to the prediction of eukaryotic Pol II
promoters from DNA sequence takes advantage of a combination of elements
similar to neural networks and genetic algorithms to recognize a set of
discrete subpatterns with variable separation as one pattern, a promoter. The
neural networks use as input a small window of DNA sequence, as well as the
output of other neural networks. Through the use of genetic algorithms, the
weights in the neural networks are optimized to maximally discriminate between
promoters and non-promoters.
Results: after several thousand generations of optimization, the
algorithm was able to discriminate between vertebrate promoter and non-promoter
sequences in a test set with a correlation coefficient of 0.63. In addition,
all five known transcription start sites on the plus strand of the complete
Adenovirus genome were within 161 bp of 35 predicted transcription start
sites. On standardized test sets consisting of human genomic DNA, the
performance of Promoter 2.0 compares well with other software developed for the
same purpose.
If you need help regarding technical issues (e.g. errors or missing results) contact Technical Support. Please include the name of the service and version (e.g. NetPhos-4.0) and the options you have selected. If the error occurs after the job has started running, please include the JOB ID (the long code that you see while the job is running).
If you have scientific questions (e.g. how the method works or how to interpret results), contact Correspondence.
Correspondence:
Technical Support: