NetStart - 1.0
Translation start in vertebrate and A. thaliana DNA
The NetStart server produces neural network predictions of translation start in vertebrate and Arabidopsis thaliana nucleotide sequences.
Sequence submission: paste the sequence(s) and/or upload a local file
At most 50 sequences and 1,000,000 nucleotides per submission; each sequence not more than 500,000 nucleotides.
The sequences are kept confidential and will be deleted after processing.
For publication of results, please cite:
Neural network prediction of translation initiation sites in eukaryotes: perspectives for EST and genome analysis.
A.G. Pedersen and H. Nielsen, ISMB: b,226-233,1997.
1. Specify the input sequences
The sequences intended for processing can be input in the following two ways:
- Paste a single sequence (just the nucleotides) or a number of sequences in FASTA format into the upper window of the main server page.
- Select a FASTA file on your local disk, either by typing the file name into the lower window or by browsing the disk.
Both ways can be employed at the same time: all the specified sequence will be processed.
The allowed input alphabet is A, C, G, T, U
and X (unknown); all the other symbols will be converted to X
before processing. T and U are treated as equivalent.
2. Select organism typeDepending on the origin of your input sequences click on either "Vertebrate" or "A. Thaliana". The former is the default.
3. Submit the jobClick on the "Submit" button. The status of your job (either 'queued' or 'running') will be displayed and constantly updated until it terminates and the server output appears in your browser window.
NOTE: At any time during the wait you may enter your e-mail address and simply leave the window. Your job will continue; you will be notified by e-mail when it has terminated. The e-mail message will contain the URL under which the results are stored; they will remain on the server for 24 hours for you to collect them.
DESCRIPTIONEach input sequence will be shown with the predicted translation start site indicated, followed by a table showing the positions and the scores of all instances of "ATG" in the sequence.
In the lines below the sequence the predicted start codon is indicated by the letter "i" (initiation), other instances of "ATG" by the letter "N" (non-start). The dots (".") are place holders for all the other sequence elements.
The scores are always in [0.0, 1.0]; when greater than 0.5 they represent
a probable translation start.
Translation start predictions for 1 vertebrate sequence Name: AT2A6.1 123456789012345678901234567890123456789012345678901234567890 CACGCGTCCGAAGCAAGATGGAGTCAAGTGATCGTTCAAGTCAAGCAAAAGCTTTCGACG AGACAAAAACCGGCGTGAAAGGGCTTGTGGCTTCGGGAATCAAAGAGATTCCAGCCATGT TCCATACACCTCCGGATACTCTAACAAGCCTGAAACAAACAGCACCA .................i.......................................... ........................................................N... ............................................... Pos Score Pred ------------------------ 18 0.821 Yes 117 0.034 -
Neural network prediction of translation initiation sites
in eukaryotes: perspectives for EST and genome analysis.
ISMB: 5, 226-233 1997.
, ISMB: 5, 226-233 1997.
The complete article in PDF.
Translation in eukaryotes does not always start at the first AUG in an mRNA, implying that context information also plays a role. This makes prediction of translation initiation sites a non-trivial task, especially when analysing EST and genome data where the entire mature mRNA sequence is not known. In this paper, we employ artificial neural networks to predict which AUG triplet in an mRNA sequence is the start codon. The trained networks correctly classified 88 % of Arabidopsis and 85 % of vertebrate AUG triplets. We find that our trained neural networks use a combination of local start codon context and global sequence information. Furthermore, analysis of false predictions shows that AUGs in frame with the actual start codon are more frequently selected than out-of-frame AUGs, suggesting that our networks use reading frame detection. A number of conflicts between neural network predictions and database annotations are analysed in detail, leading to identification of possible database errors.