Web Service Genome Atlas SOAP

Specified Name (from WSDL): WSGenomeAtlas_3_0_ws1 |
No alternative names Add_annotation_inactiveAdd_annotation_inactiveLog in to add alternative_name
PASSED
Annotations: 10 Total number of annotations from the provider's definition / description document. 1 Total number of annotations submitted by members of BioCatalogue. 6 Total number of annotations sourced from other registries. 3

Overview

Provider:
Center for Biological Sequence Analysis (CBS)

Location:
European Union

Submitter/Source:
pfhallin (over 8 years ago)

Base URL:
http://ws.cbs.dtu.dk/cgi-bin/soap/ws/quasi.cgi

WSDL Location:
http://www.cbs.dtu.dk/ws/GenomeAtlas/GenomeAtlas_3_0_ws1.wsdl (download last cached WSDL file)

Description(s):
from provider's description doc (over 8 years ago)
This Web Service accesses the database records and various tools of the 

GenomeAtlas database v3. The records maintained by this database are synchronized regularly with the Entrez Genome Project (http://www.ncbi.nlm.nih.gov/genomes/lproks.cgi?view=1)

#

# DATABASE LOOK-UP FUNCTIONS # 1. getSeq Get one or more genomic sequences from the Genome Atlas database (update regularly against Entrez Microbial Genomes), providing the genbank accession number. Input: * ‘genbank’ : A genbank accession number Output: * ‘sequencedata’ * ‘sequence’ [array] * ‘id’ : id sequence
* ‘comment’: comment of sequence * ‘seq’ : The DNA sequence of the genome 2. getProt Get the protein sequences encoded by annotated coding regions of GenBank record Input: * ‘genbank’ : A genbank accession number Output: * ‘sequencedata’ * ‘sequence’ [array] * ‘id’ : id sequence
* ‘comment’: comment of sequence * ‘seq’ : The translations of the predicted protein coding genes

  1. getOrfs Get the nucleotide sequences of annotated coding regions of GenBank record Input: * ‘accession’: Ond or more GenBank accession numbers. Output: * ‘contig’ * ‘id’: accession number as provided in input
    * ‘sequencedata’: An array of sequencedata objects * ‘id’ : The identifier of the sequence ( output from GenBank record converter ) * ‘seq’ : Protein coding DNA sequence

  2. queryGenomes Query records of the GenomeAtlas database Input: * ‘search’ : Records can be search by various optional fields (AND separated) All fields except ‘pid’ are surrounded by wildcards. * ‘kingdom’ : bacteria / archaea * ‘phyla’ : Phyla * ‘pid’ : Project id * ‘organism’ : Organism name * ‘genbank’ : Genbank accession number * ‘refseq’ : RefSeq accession number * ‘segment’ : Segment / replicon name (e.g. ‘GENOME[PID]’, ‘Chromosome“’, ‘pVir’ …) * ‘hideMerged’ : yes / no: Hide merged segments (GENOME[PID])

    Output: An array of entries containing: * ‘descriptions’ : A genome atlas database record * ‘entry’ * ‘field’ : The name of the field (e.g. ATCONTENT, NGENES) * ‘description’ : A descriptive text for the field * ‘entry’ : A genome atlas database record * ‘kingdom’ : bacteria / archaea * ‘phyla’ : Phyla * ‘pid’ : Project id * ‘organism’ : Organism name * ‘genbank’ : Genbank accession number * ‘refseq’ : RefSeq accession number * ‘segment’ : Segment / replicon name (e.g. ‘GENOME[PID]’, ‘Chromosome“’, ‘pVir’ …) * ‘properties’ : Returned the calculated gemomic properties of the segment * ‘ATCONTENT’ * ‘NGENES’ * ‘LENGTH’ * ‘BPPRGENE’ * ‘CODING_FRACTION’ * ‘GEOMETRY’ * ‘RNAMMER_TSU_COUNT’ * ‘RNAMMER_SSU_COUNT’ * ‘RNAMMER_LSU_COUNT’ * ‘GLO_DIR_REPEAT’ * ‘GLO_INV_REPEAT’ * ‘SR_PERCENT’ * ‘ANN_TRNA_COUNT’ * ‘TRNA_SCAN_COUNT’ * ‘TRUE_PROTEINS’ * ‘TRUE_PROT_RATIO’ * ‘60_ORIGIN’ * ‘60_TERMINUS’ * ‘ADNACC’ * ‘CURVATURE_AVG’ * ‘ELHASSAN_AVG’ * ‘OLSON_AVG’ * ‘ORNSTEIN_AVG’ * ‘RRRECIEVER_COUNT’ * ‘HISKA_1_COUNT’ * ‘HISKA_2_COUNT’ * ‘HISKA_3_COUNT’ * ‘HISKA_COUNT’ * ‘HWE_HK_COUNT’ * ‘LOC_DIR_REPEAT’ * ‘LOC_EVR_REPEAT’ * ‘LOC_INV_REPEAT’ * ‘LOC_MIR_REPEAT’

  3. getFeatures Get details for all annotated features of a single genbank record Input: * ‘accession’ : Genbank accession number * ‘features’ : Comma separated list of features to be returned (e.g. all or cds,rrna,trna) * ‘keys’ : Comma separated list of keys to be returned (e.g. all or locus_tag,gene,translation)

    Output: 'features': An array of 'feature' elements, containing:
        * 'type'  : feature type, e.g. CDS, rRNA, tRNA
        * 'begin' : lower boundary of annotation 
        * 'end'   : upper boundary of annotation 
        * 'end'   : upper boundary of annotation
        * 'dir'   : Annotation direction + or /
        * 'label' : Acquired from 'gene' annotation
        * 'featurekey' : An array of additional annotation keys provided in the Genbank record
         * 'Key'       : the annotation key, e.g. 'product'  
         * 'Value'     : the annotation value, e.g. '16S ribosomal RNA'  
    
        Please be aware, that begin and end refers to the boundaries of the annotation,
        meaning that if multiple concatenations/junctions are present in the annotation, begin
        end and will only refer to the smallest and largest of those numbers. To get a detailed map
        of the junction, this is found in the 'featurekey' element, having attribute key=coordinates.

#

TOOLS

#

  1. DNApropertyRun Calculates structural and physical properties of the DNA molecule. These properties are used in the DNA Atlas representation on the Genome Atlas web pages. Properties include Intrinsic Curvature, Stacking energy, position preference, various repeats etc. (please see below for documentation). Use operation ‘pollQueue’ to poll the status of the job.

    Input: 
        * 'method'    : Calculation method, specifying which result are to be generated,
                        e.g. 'Intrinsic Curvature' (see documentation below)
        * 'sequence'   
         * 'id'       : Sequence identifier
         * 'seq'      : DNA sequence
    
                       The following DNA properties can be calculated:
    
                       Intrinsic Curvature
                         DNA curvature is calculated using the CURVATURE programme (Bolshoy et al. 1991, Shpigelman 
                         et al. 1993). The term curved DNA here refers to DNA that is intrinsically curved 
                         in solution and can be readily characterised by anomalous migration in acrylamide 
                         gels. There are different models for curved DNA (Sinden et al. 1998), although the 
                         predictions for curvature fragments largerthan a few hundred bp is essentially the 
                         same (Haran et al. 1994). The scale is in arbitrary "Curvature units", which ranges 
                         from 0 (e.g. no curvature) to 1.0, which is the curvature of DNA when wrapped around 
                         the nucleosome. The scale used for this atlas ranges 3 standard deviations around 
                         the mean. 
    
                          * R.R. Sinden and C.E. Pearson and V.N. Potaman and D.W. Ussery DNA: Structure and 
                            Function (1998) 5A:1-141 
    
                          * E.S. Shpigelman and E.N. Trifonov and A. Bolshoy CURVATURE: Software for the Analysis 
                            of Curved DNA. (1993) 9:435-444 
    
                          * T.E. Haran and J.D. Kahn and D.M. Crothers Sequences elements responsible for 
                            DNA curvature (1994) 225:729-738 
    
                          * A. Bolshoy and P. McNamara and R.E. Harrington and E.N. Trifonov Curved DNA Without 
                            A-A - Experimental Estimation of All 16 DNA Wedge Angles (1991) 88:2312-2316 
    
                        Position Preference
                         - a trinucleotide model based on the preferential location 
                         of sequences within nucleosomal core sequences (Satchwell et al. 1986). We use the 
                         magnitude (e.g.absolute values) of the trinucleotide numbers as a measure of DNA 
                         flexibility (Baldi et al. 1996). The trinucleotide values range from essentially 
                         zero (0.003, presumably more flexible), to 0.28 (considered rigid). Since very few 
                         of the trinucleotide have values close to zero (e.g. little preference for nucleosome 
                         positioning), this measureis considered most sensitive towards the low ("flexibity") 
    
    
                          * S.C. Satchwell and H.R. Drew and A.A. Travers Sequence periodicities in chicken 
                            nucleosome core DNA (1986) 191:659-675 
    
                          * P. Baldi and S. Brunak and Y. Chauvin and A. Krogh Naturally occurring nucleosome 
                            positioning signals in human exons and introns. (1996) 263:503-510 
    
                        Stacking Energy
                         Base-stacking energies are from the dinucleotide values provided by (Ornstein et 
                         al. 1978). The scale is in kcal/mol, and the dinucleotide values range from -3.82 
                         kcal/mol (will melt easily) up to a maximum value of -14.59 kcal/mol (which would 
                         require more energy to destack or melt the helix). (All 10 values are listed in the 
                         table below.) A positive peak in base-stacking (i.e., numbers closer to 0) reflectsregions 
                         of the helix which would de-stack or melt more readily. Conversely, minima (larger 
                         negative numbers) in this plot would represent more stable regions of the chromosome. 
                         
                         Dinucleotide melting energies in kcal/mols:
    												
                           (GC).(GC)  -14.59
                           (AC).(GT)  -10.51
                           (TC).(GA)   -9.81
                           (CG).(CG)   -9.61
                           (GG).(CC)   -8.26
                           (AT).(AT)   -6.57
                           (TG).(CA)   -6.57
                           (AG).(CT)   -6.78
                           (AA).(TT)   -5.37
                           (TA).(TA)   -3.82
    
    
                          * R.L. Ornstein and R. Rein and D.L. Breen and R.D. MacElroy An optimized potential 
                            function for the calculation of nucleic acid interaction energies. I. Base stacking 
                            (1978) 17:2341-2360
             
                        Protein Deformability
                         "Protein Induced Deformability" dinucleotide values are from protein induced deformation 
                         of DNA helices as determined by examination of more than a hundred cr et et al. 1997al 
                         structures of DNA/protein complexes (Olson et al. 1998). The dinucleotide values 
                         range from 2.1 (the least deformable dinucleotide), to 12.1 (i.e., the dinucleotide 
                         step (CpG), which is often deformed by proteins). Thus, on this scale, a larger value 
                         reflects a more deformable sequence whilst a smaller value indicates a region where 
                         the DNA helix is less likely to be changed dramatically by proteins. The average 
                         protein deformability value in the entire E. coli K-12 genome is 5.12. 
    
                          * Goffeau et al. The Yeast Genome Directory (1997) 387 (supplement):5-105 
    
                          * W.K. Olson and A.A. Gorin and X.J. Lu and L.M. Hock and V.B. Zhurkin DNA sequence-dependent 
                            deformability deduced from protein-DNA crystal complexes. (1998) 95:11163-11168 
    
                        Propeller twist
                         We use propeller twist as a measure of helix rigidity, since the propeller twist 
                         angles have been shown to be inversely related to rigidity of the DNA helix in crystals 
                         (el Hassan et al. 1996). Thus, a region with high propeller twist would 
                         mean the helix is quite rigid in this area, and similarly regions that are quite 
                         flexible would have a low propeller twist. Propeller twist values were obtained from 
                         cr et et al. 1997allographic data (el et al. 1996), with the exception of the TA 
                         step, which was taken from a theoretical estimate (Gorin et al. 1995). Plots using 
                         other sets of propeller twist dinucleotide values were very similar (data not shown). 
                         The average propeller twist value in the entire E. coli K-12 genome is -12.63 degrees. 
    
                          * Goffeau et al. The Yeast Genome Directory (1997) 387 (supplement):5-105 
    
                          * M.A. el Hassan and C.R. Calladine Propeller-twisting of base-pairs and the conformational 
                            mobility of dinucleotide steps in DNA. (1996) 259:95-103 
    
                          * A.A. Gorin and V.B. Zhurkin and W.K. Olson B-DNA twisting correlates with base-pair 
                            morphology. (1995) 247:34-48 
    
                        DNase I Sensitivity
                         DNase I values are based on experimentally determined trinucleotide values (Brukner 
                         et al. 1995, Brukner et al. 1995). These values are reflectiveof the anisotropic 
                         flexibility or "bendability" of a particular DNAsequence. The trinucleotide values 
                         range from -0.280 (rigid) to +0.194 (very "bendable" towards the major groove). Smoothing 
                         over a large regions, (which is necessary for viewing entire genomes) tends to smooth 
                         out differences in bendability. The average DNase I ("bendability") value in the 
    
                          * I. Brukner and R. Sanchez and D. Suck and S. Pongor Sequence-dependent bending 
                            propensity of DNA as revealed by DNase I: parameters for trinucleotides. (1995) 14:1812-1818 
                            
    
                          * I. Brukner and R. Sanchez and D. Suck and S. Pongor Trinucleotide models for DNA 
                            bending propensity: comparison of models based on DNaseI digestion and nucleosome 
                            packaging data. (1995) 13:309-317
    
                        Palindromic hexamers
                         For a given sequence, any palindrome of 6 nt (e.g., AAATTT) is given a value of 1, 
                         while all bases not included inpalindromic hexamers are given a value of 0 (van et 
                         al. 2003). 
    
                          * van Noort V, Worning P, Ussery DW, Rosche WA, Sinden RR Strand misalignments lead 
                            to quasipalindrome correction (2003) 19:365-9 
    
                        G Content
                         The "G Content" of a given sequence is merely the fraction of G's in a given sequence 
                         (Jensen et al. 1999). It can range from 0(no G's), to 1 (all G's). For a sequence 
                         that is 50% AT content, one would expect roughly 25% G's. 
    
                          * L. J. Jensen and C. Friis and D.W. Ussery Three views of complete chromosomes 
                            (1999) 150:773-777
    
                        A Content
                         The "A Content" of a given sequence is merely the fraction of A's in a given sequence 
                         (Jensen et al. 1999). It can range from 0(no A's), to 1 (all A's). For a sequence 
                         that is 50% AT content, one would expect roughly 25% A's. 
    
                          * L. J. Jensen and C. Friis and D.W. Ussery Three views of complete chromosomes 
                            (1999) 150:773-777
    
                        T Content
                         The "T Content" of a given sequence is merely the fraction of T's in a given sequence 
                         (Jensen et al. 1999). It can range from 0(no T's), to 1 (all T's). For a sequence 
                         that is 50% AT content, one would expect roughly 25% T's. 
    
                          * L. J. Jensen and C. Friis and D.W. Ussery Three views of complete chromosomes 
                            (1999) 150:773-777 
    
                        C Content
                         The "C Content" of a given sequence is merely the fraction of C's in a given sequence 
                         (Jensen et al. 1999). It can range from 0(no C's), to 1 (all C's). For a sequence 
                         that is 50% AT content, one would expect roughly 25% C's. 
    
                          * L. J. Jensen and C. Friis and D.W. Ussery Three views of complete chromosomes 
                            (1999) 150:773-777 
    
                        GC Skew
                         For many genomes there is a strand bias, such that one strand tends to have more 
                         G's, whilst the other strand has more C's.This GC-skew bias can be measured the number 
                         of G's minus the number of C's over a fixed length (e.g. 10,000 bp) of DNA(Jensen 
                         et al. 1999). The values can range from +1 (all G's on the examined sequence, with 
                         all C's on the other strand), to -1(the reverse case - all C's on the examined sequence, 
                         and all G's on the other strand). There is a correlation with GC-skewand the replication 
                         leading and lagging strands. 
    
                          * L. J. Jensen and C. Friis and D.W. Ussery Three views of complete chromosomes 
                            (1999) 150:773-777 
    
                        Percent AT
                         The percent AT is a running average of the AT content, over a given window size. 
                         Typically for a bacterial genomes of about5 Mbp, the window size is 10,000 bp. The 
                         Percent AT can range from 0 (no AT content) to 1 (100% AT). The Percent AT iscorrelated 
                         with other DNA structural features, such that AT rich regions are often more readily 
                         melted, tend to be lessflexible and more rigid, although they can also be readily 
                         compacted chromatin proteins (Pedersen et al. 2000). 
    
                          * A.G. Pedersen and L.J. Jensen and H.H. St\aerfeldt and S. Brunak and D.W. Ussery 
                            A DNA structural atlas of \textitE. coli (2000) 299:907-930 
    
                        AT Skew
                         For some genomes there is also an AT strand bias, such that one strand tends to have 
                         more A's, whilst the other strand hasmore T's. This AT-skew bias is measured as the 
                         number of A's minus the number of T's over a fixed length (e.g. 10,000 bp) ofDNA 
                         (Jensen et al. 1999). The values can range from +1 (all A's on the examined sequence, 
                         with all T's on the other strand), to-1 (the reverse case - all T's on the examined 
                         sequence, and all A's on the other strand). For some genomes, there is acorrelation 
                         with AT-skew and the replication leading and lagging strands. 
    
                          * L. J. Jensen and C. Friis and D.W. Ussery Three views of complete chromosomes 
                            (1999) 150:773-777 
    
                        Global Direct Repeats
                         Global Direct repeats are found by taking the first 100 bp of sequence, and
                         looking for the best match within the whole segment, on the same strand, in the
                         same direction [5' to 3'] (Skovgaard et al. 2002). Values are binned into 10
                         values, and represent the lower end of the best match, and range from 0 (10% or
                         less match) to 9 (at least 90 out of the 100 nucleotides match perfectly).
    
    
                        Global Inverted Repeats
                         Global Direct repeats are found by taking the first 100 bp of sequence, and
                         looking for the best match within the whole segment, on the opposite strand, in
                         the same direction  [5' to 3'] (Skovgaard et al. 2002). Values are binned into
                         10 values, and represent the lower end of the best match and range from 0 (10%
                         or less match) to 9 (at least 90 out of the 100 nucleotides match perfectly).
    
                         * M. Skovgaard and L.J. Jensen and C. Friis and H.H. Staerfeldt,and P. Worning
                         and S. Brunak The Atlas Visualization of Genomewide Information (2002) 33:49-63
    
                        Direct Repeats
                         Local Direct repeats are found by taking a 100 bp sequence window, and looking for 
                         the best match of a 30 bp piece withinthat window, on the same strand, in the same 
                         direction (Jensen et al. 1999). Values can range from 0 (no match at all) to 1(one 
                         or more perfect match within the window). 
    
                          * L. J. Jensen and C. Friis and D.W. Ussery Three views of complete chromosomes 
                            (1999) 150:773-777 
    
                        Everted Repeats
                         Local Everted repeats are found by taking a 100 bp sequence window, and looking for 
                         the best match of a 30 bp piece withinthat window, on the opposite strand, in the 
                         same direction (Jensen et al. 1999). Values can range from 0 (no match at all) to 
                         1(one or more perfect match within the window). 
    
                          * L. J. Jensen and C. Friis and D.W. Ussery Three views of complete chromosomes 
                            (1999) 150:773-777
    
                        Local Inverted Repeats
                         Local Inverted repeats are found by taking a 100 bp sequence window, and looking 
                         for the best match of a 30 bp piece withinthat window, on the opposite strand, in 
                         the opposite direction (Jensen et al. 1999). Values can range from 0 (no match at 
                         all)to 1 (one or more perfect match within the window). 
    
                          * L. J. Jensen and C. Friis and D.W. Ussery Three views of complete chromosomes 
                            (1999) 150:773-777 
    
                        Mirror Repeats
                         Local Mirror repeats are found by taking a 100 bp sequence window, and looking for 
                         the best match of a 30 bp piece withinthat window, on the same strand, in the opposite 
                         direction (Jensen et al. 1999). Values can range from 0 (no match at all) to 1(one 
                         or more perfect match within the window). 
    
                          * L. J. Jensen and C. Friis and D.W. Ussery Three views of complete chromosomes 
                            (1999) 150:773-777 
    
                        Quasi-palindromes
                         "Quasi-palindromes" are short inverted repeats, which are found by taking a 30 bp 
                         piece of sequence, and looking for matcheswith at least 6 out of 7 nt matching, on 
                         the opposite strand, in the opposite direction (van et al. 2003). Values canrange 
                         from 0 (no match at all) to 1 (one or more perfect match within the window). 
    
                          * van Noort V, Worning P, Ussery DW, Rosche WA, Sinden RR Strand misalignments lead 
                            to quasipalindrome correction (2003) 19:365-9 
    
                        Perfect-palindromes
                         "Perfect-palindromes" are short inverted repeats, which are found by taking a 30 
                         bp piece of sequence, and looking forperfect matches of 7 nt or longer, on the opposite 
                         strand, in the opposite direction (van et al. 2003). Values can rangefrom 0 (no match 
                         at all) to 1 (one or more perfect match within the window). 
    
                          * van Noort V, Worning P, Ussery DW, Rosche WA, Sinden RR Strand misalignments lead 
                            to quasipalindrome correction (2003) 19:365-9
    
                        Simple Repeats
                         A "simple repeat" is a region which contains a simple oligonucleotide repeat, like 
                         microsattelites. Simple repeats are foundby looking for tandem repeats of length 
                         R within a 2R-bp window. By using the values 12, 14, 15, 16, and 18 for R, allsimple 
                         repeats of lengths 1 through 9 are calculated, of length of at least 24 bp (Jensen 
                         et al. 1999). Values can range from 0(no match at all) to 1 (one or more perfect 
                         match within the window). 
    
                          * L. J. Jensen and C. Friis and D.W. Ussery Three views of complete chromosomes 
                            (1999) 150:773-777 
                         
                         Current undocumented properties are:
                        AAAA
                        CCCC
                        TTTT
                        GGGG
                        T4 or C4 vs. A4 or G4
                        (Y)10 vs. (R)10
                        (CR)5 vs. (YG)5
                        (CA)3
                        (CG)3
                        (TA)3
                        (TG)3
                        (YR)5
    			    
    Output: 
        * 'jobid'     : The 32 byte identification string of the job
        * 'datetime'  : The last timepoint at which the status of the job has changed
        * 'status'    : Possible values are QUEUED, ACTIVE, FINISHED, WAITING, REJECTED, 
    			                  UNKNOWN JOBID or QUEUE DOWN
        * 'expires'   : Normal expire time is 24hrs. Job results should be downloaded 
                        before that.
  2. DNApropertyFetchResult Retrieves the result from a job submitted using ‘DNApropertyRun’

    Input: 
        * 'jobid'     : The 32 byte identification string of the job
    Output: 
        * 'method'    : Method, as provided in request
        * 'values'    : Calculation results given as a string separated by comma. Each
                        position in the list corresponds to the position in the input 
                        sequence.
  3. trnascanRun Submit the input parapeter(s) and sequence data and returns a job identifier to tRNAscan-SE 1.23 (April 2002)

    Input:
    			* 'kingdom'     : The kingdom of the genomic sequence
    			                  3 kingdoms are available: bac, euk, arc. This is specified
    			                  only once for the sequences in the current job.
    			* 'sequence'    : (A single sequence object containing:)
    			 *  'id'        : The identifier of the sequence
    			 *  'seq'       : The sequence specified as one continous string
    Output:
    			* 'jobid'       : The 32 byte identification string of the job
    			* 'datetime'    : The last timepoint at which the status of the job has changed
    			* 'status'      : Possible values are QUEUED, ACTIVE, FINISHED, WAITING, REJECTED, 
    			                  UNKNOWN JOBID or QUEUE DOWN
  4. trnascanFetchResult Once the status is ‘FINISHED’ the results generated by the Web Service can be retrieved by specifying the jobid;

    Input
    			* 'jobid'       : The 32 byte identification string of the job
    Output
    			* 'annsource'
    			 * 'method'     : The name of the prediction method
        * 'version'    : Version of name of the prediction method
    			* 'ann' (ann object with the following content:) 
    			 * 'sequence' 
         * 'id'        : sequence identifier as uploaded by the user
         * 'seq'       : sequence as uploaded by the user
    			 * 'annrecords'
    			  * 'annrecord'
    			   * 'feature'  : E.g. 'Ala,TGC'
    			   * 'range'
    			    * 'begin'   : begin position of the tRNA gene
    			    * 'end'     : end position of the tRNA gene
    			   * 'score' 
    			    * 'value'   : Cove score
  5. pollQueue [common] Once obtained from ‘runService’, a job identification can be used to poll the status to see if the result is ready for download.

    Input 
    			* 'jobid'       : The 32 byte identification string of the job
    Output
    			* 'jobid'       : The 32 byte identification string of the job
    			* 'datetime'    : The last timepoint at which the status of the job has changed
    			* 'status'      : Possible values are QUEUED, ACTIVE, FINISHED, WAITING, REJECTED, 
                        UNKNOWN JOBID or QUEUE DOWN
  6. aaUsage Calculate the amino acid usage in a genome (proteome) and generates a base64 encoded image (PNG) showing a diagram of this usage. Input * ‘contig’ : Array of genome sequences * ‘id’ : Identifier of the genome * ‘sequencedata’ : Container for one or more sequences (typically a proteome) * ‘sequence’
    * ‘id’ : Id of the protein * ‘seq’ : Protein sequence Output * ‘sequence’ * ‘id’ : Genome identifier, provided in the input * ‘image’ : Image object * ‘comment’ : Description of the image * ‘encoding’ : Encoding of the binary content of the image (base64) * ‘MIMEtype’ : File type (image/png) * ‘content’ : Encoded binary content * ‘aaUsage’ * ‘entry’
    * ‘name’ : Name of the amino acid, e.g. Ala, Val, Leu … * ‘count’ : Number of occurences in the genome * ‘freq’ : Frequency of the amino acid * ‘group’ : Amino acid class: Polar,Aromatic,Sulfur, Aliphatic,Structural,+,-

  7. codonUsage Calculate the codon usage in a genome (orfs) and generates a base64 encoded image (PNG) showing a diagram of this usage. Input * ‘contig’ : Array of genome sequences * ‘id’ : Identifier of the genome * ‘sequencedata’ : Container for one or more sequences (typically a proteome) * ‘sequence’
    * ‘id’ : Id of the protein * ‘seq’ : Protein sequence Output * ‘sequence’ * ‘id’ : Genome identifier, provided in the input * ‘image’ : Image object * ‘comment’ : Description of the image * ‘encoding’ : Encoding of the binary content of the image (base64) * ‘MIMEtype’ : File type (image/png) * ‘content’ : Encoded binary content * ‘aaUsage’ * ‘entry’
    * ‘codon’ : DNA triplet (e.g. ATG …) * ‘freq’ : Frequency of the triplet * ‘count’ : Number of occurrences in each genome * ‘aa’ : Corresponding amino acid

    For more information, please contact Peter F. Hallin: pfh@cbs.dtu.dk, David W. Ussery (dave@cbs.dtu.dk), or Krisoffer Rapacki (rapacki@cbs.dtu.dk)

by pfhallin (almost 8 years ago)

This Web Service accesses the database records and various tools of the CBS GenomeAtlas database v3. The records maintained by this database are synchronized regularly with the Entrez Genome Project (http://www.ncbi.nlm.nih.gov/genomes/lproks.cgi?view=1)

ELIXIR Description(s): Info
No info yet
License(s): Info
No info yet
Cost: Info
No info yet
Usage conditions: Info
No info yet
Contact info: Info
No info yet
How to cite this service: Info
No info yet
Publications about this service: Info
No info yet
Citations of this service: Info
No info yet

Info Members Responsible for this Service (1)

Info Favourited By (0)

No one

Info Similar Services (100)