INTRODUCTION This Web Service implements SpindleP v. 2.0. It predicts whether the input human proteins are likely to be located at the metotic spindle. Four different protein feature combinations are used as input to artificial neural networks. The method is currently under development; an article is in preparation. The contact person is Thomas Skoet Jensen, skot@cbs.dtu.dk. Alongside this Web Service the SpindleP method is also implemented as a traditional click-and-paste WWW server at: http://www.cbs.dtu.dk/services/SpindleP/ WEB SERVICE OPERATION This Web Service is fully asynchronous; the usage is split into the following three operations: 1. runService Input: 'sequencedata' (containing mulitple 'sequence' elements) 'sequence' 'id' unique identifier; 'comment' optional comment 'seq' protein sequence, written using the one letter amino acid code: `acdefghiklmnpqrstvwy' or `ACDEFGHIKLMNPQRSTVWY'. Other letters will be converted to `X' and treated as unknown amino acids. Other symbols, such as whitespace and numbers, will be ignored. Currently, at most 2,000 sequences are allowedper submission. Output: Unique job identifier 2. pollQueue Input: Unique job identifier Output: 'jobstatus' - the status of the job Possible values are QUEUED, ACTIVE, FINISHED, WAITING, REJECTED, UNKNOWN JOBID or QUEUE DOWN 3. fetchResult Input : Unique job identifier of a FINISHED job Output: 'anndata' (containing mulitple annotation elements) 'annsource' 'method' name of the method; 'version' version of the method; 'ann' (array of annotations - one element per input sequence) 'sequence' (standard sequence object, as above) 'id' sequence identifier 'seq' sequence 'annrecords' (array of predicted features for this sequence) 'annrecord' (annotation record) 'feature' feature name 'score' 'nn-score' normalised neural network prediction score, if >0 the protein is predicted to be located at the metotic spindle, the higher the score the more confident the prediction (the standard deviation of the normalised score is 1). 'score' 'p-val' p-value of the prediction, based on the assumption of 0.5 probability of location at the metotic spindle among the input protein sequences. CONTACT Questions concerning the scientific aspects of the SpindleP method should go to Thomas Skoet Jensen, skot@cbs.dtu.dk; technical questions concerning the Web Service should go to Peter Fischer Hallin, pfh@cbs.dtu.dk or Kristoffer Rapacki, rapacki@cbs.dtu.dk.