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revision 1.2, Tue Jan 25 03:02:07 2005 UTC revision 1.9, Sun Aug 14 23:59:03 2005 UTC
# Line 33  Line 33 
33                                  <DataGen pass="2">join('; ', (RandParam('bacteria', 'archaea', 'eukaryote', 'virus', 'environmental'),                                  <DataGen pass="2">join('; ', (RandParam('bacteria', 'archaea', 'eukaryote', 'virus', 'environmental'),
34                                                                                            ListGen('PKVKVKVK', 5), $this->{genus}, $this->{species}))</DataGen>                                                                                            ListGen('PKVKVKVK', 5), $this->{genus}, $this->{species}))</DataGen>
35                          </Field>                          </Field>
36                    <Field name="group-name" type="name-string" relation="GenomeGroups">
37                        <Notes>The group identifies a special grouping of organisms that would be displayed on a particular
38                        page or of particular interest to a research group or web site. A single genome can belong to multiple
39                        such groups or none at all.</Notes>
40                    </Field>
41                    <Field name="complete" type="boolean">
42                        <Notes>This field is TRUE if the genome is believed to be complete, else
43                        FALSE.</Notes>
44                    </Field>
45              </Fields>              </Fields>
46              <Indexes>              <Indexes>
47                  <Index>                  <Index>
# Line 93  Line 102 
102                                          <DataGen>RandChars("ACGT", IntGen(100,400))</DataGen>                                          <DataGen>RandChars("ACGT", IntGen(100,400))</DataGen>
103                                  </Field>                                  </Field>
104                  <Field name="quality-vector" type="text">                  <Field name="quality-vector" type="text">
105                                          <Notes>String describing the quality data for each . Individual values will                      <Notes>String describing the quality data for each base pair. Individual values will
106                                          be separated by periods. The value represents negative exponent of the probability                                          be separated by periods. The value represents negative exponent of the probability
107                                          of error. Thus, for example, a quality of 30 indicates the probability of error is                                          of error. Thus, for example, a quality of 30 indicates the probability of error is
108                                          10^-30. A higher quality number a better chance of a correct match. It is possible                                          10^-30. A higher quality number a better chance of a correct match. It is possible
109                                          that the quality data is known for a sequence. If that is the case, the quality                      that the quality data is not known for a sequence. If that is the case, the quality
110                                          vector will contain the [b]unknown[/b].</Notes>                                          vector will contain the [b]unknown[/b].</Notes>
111                                          <DataGen>unknown</DataGen>                                          <DataGen>unknown</DataGen>
112                                  </Field>                                  </Field>
# Line 114  Line 123 
123                                          <DataGen>RandParam('peg','rna')</DataGen>                                          <DataGen>RandParam('peg','rna')</DataGen>
124                                  </Field>                                  </Field>
125                  <Field name="alias" type="name-string" relation="FeatureAlias">                  <Field name="alias" type="name-string" relation="FeatureAlias">
126                                          <Notes>Alternative name for this feature. feature can have many aliases.</Notes>                      <Notes>Alternative name for this feature. A feature can have many aliases.</Notes>
127                                          <DataGen testCount="3">StringGen('Pgi|99999', 'Puni|XXXXXX', 'PAAAAAA999')</DataGen>                                          <DataGen testCount="3">StringGen('Pgi|99999', 'Puni|XXXXXX', 'PAAAAAA999')</DataGen>
128                                  </Field>                                  </Field>
129                  <Field name="translation" type="text" relation="FeatureTranslation">                  <Field name="translation" type="text" relation="FeatureTranslation">
130                                          <Notes>[i](optional)[/i] A of this feature's residues into character codes, formed by concatenating                      <Notes>[i](optional)[/i] A translation of this feature's residues into character
131                          the pieces of the feature together.</Notes>                      codes, formed by concatenating the pieces of the feature together. For a
132                        protein encoding group, this is the protein characters. For other types
133                        it is the DNA characters.</Notes>
134                                          <DataGen testCount="0"></DataGen>                                          <DataGen testCount="0"></DataGen>
135                                  </Field>                                  </Field>
136                  <Field name="upstream-sequence" type="text" relation="FeatureUpstream">                  <Field name="upstream-sequence" type="text" relation="FeatureUpstream">
# Line 139  Line 150 
150                                          "&amp;Number=" . IntGen(1,99)</DataGen>                                          "&amp;Number=" . IntGen(1,99)</DataGen>
151                                  </Field>                                  </Field>
152              </Fields>              </Fields>
153                <Indexes>
154                    <Index>
155                        <Notes>This index allows the user to find the feature corresponding to
156                        the specified alias name.</Notes>
157                        <IndexFields>
158                            <IndexField name="alias" order="ascending" />
159                        </IndexFields>
160                    </Index>
161                </Indexes>
162          </Entity>          </Entity>
163          <Entity name="Role" keyType="string">          <Entity name="Role" keyType="string">
164              <Notes>A [i]role[/i] describes a biological function that may be fulfilled by a feature.              <Notes>A [i]role[/i] describes a biological function that may be fulfilled by a feature.
# Line 166  Line 186 
186                                  </Field>                                  </Field>
187              </Fields>              </Fields>
188          </Entity>          </Entity>
189          <Entity name="Subsystem" keyType="name-string">          <Entity name="Subsystem" keyType="string">
190              <Notes>A [i]subsystem[/i] is a collection of roles that work together in a cell. Identification of subsystems              <Notes>A [i]subsystem[/i] is a collection of roles that work together in a cell. Identification of subsystems
191              is an important tool for recognizing parallel genetic features in different organisms.</Notes>              is an important tool for recognizing parallel genetic features in different organisms.</Notes>
192          </Entity>          </Entity>
# Line 251  Line 271 
271                                          </Field>                                          </Field>
272                                  </Fields>                                  </Fields>
273                  </Entity>                  </Entity>
274            <Entity name="Coupling" keyType="medium-string">
275                <Notes>A coupling is a relationship between two features. The features are
276                physically close on the contig, and there is evidence that they generally
277                belong together. The key of this entity is formed by combining the coupled
278                feature IDs with a space.</Notes>
279                <Fields>
280                    <Field name="score" type="int">
281                        <Notes>A number based on the set of PCHs (pairs of close homologs). A PCH
282                        indicates that two genes near each other on one genome are very similar to
283                        genes near each other on another genome. The score only counts PCHs for which
284                        the genomes are very different. (In other words, we have a pairing that persists
285                        between different organisms.) A higher score implies a stronger meaning to the
286                        clustering.</Notes>
287                    </Field>
288                </Fields>
289            </Entity>
290            <Entity name="PCH" keyType="string">
291                <Notes>A PCH (physically close homolog) connects a clustering (which is a
292                pair of physically close features on a contig) to a second pair of physically
293                close features that are similar to the first. Essentially, the PCH is a
294                relationship between two clusterings in which the first clustering's features
295                are similar to the second clustering's features. The simplest model for
296                this would be to simply relate clusterings to each other; however, not all
297                physically close pairs qualify as clusterings, so we relate a clustering to
298                a pair of features. The key is the clustering key followed by the IDs
299                of the features in the second pair.</Notes>
300                <Fields>
301                    <Field name="used" type="boolean">
302                        <Notes>TRUE if this PCH is used in scoring the attached clustering,
303                        else FALSE. If a clustering has a PCH for a particular genome and many
304                        similar genomes are present, then a PCH will probably exist for the
305                        similar genomes as well. When this happens, only one of the PCHs will
306                        be scored: the others are considered duplicates of the same evidence.</Notes>
307                    </Field>
308                </Fields>
309            </Entity>
310      </Entities>      </Entities>
311      <Relationships>      <Relationships>
312            <Relationship name="ParticipatesInCoupling" from="Feature" to="Coupling" arity="MM">
313                <Notes>This relationship connects a feature to all the functional couplings
314                in which it participates. A functional coupling is a recognition of the fact
315                that the features are close to each other on a chromosome, and similar
316                features in other genomes also tend to be close.</Notes>
317                <Fields>
318                    <Field name="pos" type="int">
319                        <Notes>Ordinal position of the feature in the coupling. Currently,
320                        this is either "1" or "2".</Notes>
321                    </Field>
322                </Fields>
323                <ToIndex>
324                    <Notes>This index enables the application to view the features of
325                    a coupling in the proper order. The order influences the way the
326                    PCHs are examined.</Notes>
327                    <IndexFields>
328                        <IndexField name="pos" order="ascending" />
329                    </IndexFields>
330                </ToIndex>
331            </Relationship>
332            <Relationship name="IsEvidencedBy" from="Coupling" to="PCH" arity="1M">
333                <Notes>This relationship connects a functional coupling to the physically
334                close homologs (PCHs) which affirm that the coupling is meaningful.</Notes>
335            </Relationship>
336            <Relationship name="UsesAsEvidence" from="PCH" to="Feature" arity="MM">
337                <Notes>This relationship connects a PCH to the features that represent its
338                evidence. Each PCH is connected to a parent coupling that relates two features
339                on a specific genome. The PCH's evidence that the parent coupling is functional
340                is the existence of two physically close features on a different genome that
341                correspond to the features in the coupling. Those features are found on the
342                far side of this relationship.</Notes>
343                <Fields>
344                    <Field name="pos" type="int">
345                        <Notes>Ordinal position of the feature in the coupling that corresponds
346                        to our target feature. There is a one-to-one correspondence between the
347                        features connected to the PCH by this relationship and the features
348                        connected to the PCH's parent coupling. The ordinal position is used
349                        to decode that relationship. Currently, this field is either "1" or
350                        "2".</Notes>
351                    </Field>
352                </Fields>
353                <FromIndex>
354                    <Notes>This index enables the application to view the features of
355                    a PCH in the proper order.</Notes>
356                    <IndexFields>
357                        <IndexField name="pos" order="ascending" />
358                    </IndexFields>
359                </FromIndex>
360            </Relationship>
361          <Relationship name="HasContig" from="Genome" to="Contig" arity="1M">          <Relationship name="HasContig" from="Genome" to="Contig" arity="1M">
362              <Notes>This relationship connects a genome to the contigs that contain the actual genetic              <Notes>This relationship connects a genome to the contigs that contain the actual genetic
363              information.</Notes>              information.</Notes>
# Line 353  Line 458 
458                  </IndexFields>                  </IndexFields>
459              </ToIndex>              </ToIndex>
460          </Relationship>          </Relationship>
         <Relationship name="IsClusteredOnChromosomeWith" from="Feature" to="Feature" arity="MM">  
             <Notes>This relationship is one of two that relate features to each other. It connects  
             features that are physically close to each other on a single chromosome.</Notes>  
             <Fields>  
                 <Field name="score" type="int">  
                         <Notes>The number of co-occurrences in genomes that are not  
                         extremely closely-related.</Notes>  
                 </Field>  
             </Fields>  
         </Relationship>  
461          <Relationship name="IsBidirectionalBestHitOf" from="Feature" to="Feature" arity="MM">          <Relationship name="IsBidirectionalBestHitOf" from="Feature" to="Feature" arity="MM">
462              <Notes>This relationship is one of two that relate features to each other. It              <Notes>This relationship is one of two that relate features to each other. It
463              connects features that are very similar but on separate genomes. A              connects features that are very similar but on separate genomes. A

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