Bioinformatics Advance Access originally published online on February 24, 2006
Bioinformatics 2006 22(9):1036-1046; doi:10.1093/bioinformatics/btl048
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The UCSC Known Genes
1 Center for Biomolecular Science and Engineering, School of Engineering, University of California Santa Cruz Santa Cruz, CA 95064, USA
2 Howard Hughes Medical Institute, University of California Santa Cruz Santa Cruz, CA 95064, USA
*To whom correspondence should be addressed.
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ABSTRACT |
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 TOP ABSTRACT INTRODUCTIONMETHODSCROSS REFERENCES TO OTHER...UCSC KNOWN GENE DETAILS...DOWNLOADING SOURCE CODE AND...ANALYSISFUTURE DIRECTIONSCONTACTING USREFERENCES |
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The University of California Santa Cruz (UCSC) Known Genes dataset is constructed by a fully automated process, based on protein data from Swiss-Prot/TrEMBL (UniProt) and the associated mRNA data from Genbank. The detailed steps of this process are described. Extensive cross-references from this dataset to other genomic and proteomic data were constructed. For each known gene, a details page is provided containing rich information about the gene, together with extensive links to other relevant genomic, proteomic and pathway data. As of July 2005, the UCSC Known Genes are available for human, mouse and rat genomes. The Known Genes serves as a foundation to support several key programs: the Genome Browser, Proteome Browser, Gene Sorter and Table Browser offered at the UCSC website. All the associated data files and program source code are also available. They can be accessed at http://genome.ucsc.edu. The genomic coverage of UCSC Known Genes, RefSeq, Ensembl Genes, H-Invitational and CCDS is analyzed. Although UCSC Known Genes offers the highest genomic and CDS coverage among major human and mouse gene sets, more detailed analysis suggests all of them could be further improved.
Contact: fanhsu{at}soe.ucsc.edu
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INTRODUCTION |
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TOPABSTRACTINTRODUCTIONMETHODSCROSS REFERENCES TO OTHER...UCSC KNOWN GENE DETAILS...DOWNLOADING SOURCE CODE AND...ANALYSISFUTURE DIRECTIONSCONTACTING USREFERENCES |
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The UCSC Genome Browser (Kent et al., 2002; Karolichik et al., 2003), which was developed in conjunction with the assembly and publication of the first Human Draft Genome (International Human Genome Sequencing Consortium, 2001), has become a popular website for biomedical communities around the world. The number of its annotation datasets, or tracks, continues to grow each year.
During the earlier stage of Genome Browser development, there were only a few annotation tracks in its Genes and Gene Prediction section. The section included a few gene prediction tracks and a RefSeq Gene track, each having its own limitations. Different gene prediction programs often produce different results. The NCBI RefSeq (Pruitt et al., 2005) offers a high-quality gene set, but because it is produced by an extensive manual curation process it has limitations on its coverage and timeliness of availability. In Addition, direct links between RefSeq genes and Swiss-Prot proteins were not available. Hence we decided to develop an automated process to construct the UCSC Known Genes dataset based on the latest protein data from Swiss-Prot/TrEMBL (Bairoch et al., 2005), now also known as UniProt, and the associated mRNA data from GenBank (Benson et al., 2005).
While there are various different definitions of what constitutes a gene, we chose to limit our gene set to protein coding genes and require each gene be substantiated by at least a transcript (either a GenBank mRNA or a NCBI RefSeq) and a UniProt protein. We relied upon UniProt's comprehensive cross-references between the proteins and their associated GenBank mRNAs to build our initial candidate gene set. Alternative splicing isoforms are included as different entries, as long as they are represented by a UniProt protein and a transcript. The initial candidate gene set is further ranked and processed to select the best representative protein/mRNA for each gene and duplicates with identical CDS structure removed.
The result of this effort is the UCSC Known Genes: a comprehensive gene set based mostly upon experimental data. The set can be built automatically in a relatively short time. Since its first introduction in early 2003, UCSC Known Genes are now available for several assembly releases of three major genomes, human, mouse and rat. As shown in Figure 1, the UCSC Known Genes dataset has also become a central foundation for key genomic and proteomic applications, such as the UCSC Genome Browser, Proteome Browser (Hsu et al., 2005), Gene Sorter (Kent et al., 2005), and Table Browser (Karolchick et al., 2004), offered at the UCSC bioinformatics web site, genome.ucsc.edu. Extensive cross-reference links to other gene-related data available on the web are also compiled and presented for each Known Gene.
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Since the start of our effort, several other gene sets besides RefSeq from NCBI have become available: Ensembl Genes from EMBL-EBI, H-Invitational Gene Database (HInv-DB) of JBIRC and CCDS (the Consensus Coding DNA Sequence) from EBI, NCBI, UCSC and WTSI. We present comparison between UCSC Known Genes and other gene sets in the ANALYSIS section.
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METHODS |
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Raw protein data files are downloaded from UniProt and parsed to create a set of structured relational database tables. A cross-reference table between protein IDs and GenBank mRNA IDs is created from this UniProt data. The existing GenBank mRNA sequences are aligned with their corresponding proteins using BLAT to select the best representative mRNAs for each protein. The resulting protein–mRNA pairs with their mRNA genomic alignments and CDS structures are sorted and filtered to remove redundancy and invalid short sequences. Finally, RefSeq genes having only DNA evidence, which escaped the above process, are added to form the final results as the UCSC Known Genes. More details of this process are described in this section.
A high-level flowchart of the UCSC Known Genes build process is shown in Figure 2. The process consists of the following four sub-processes, as depicted in different colors:
- build protein databases (green)
- get mRNA alignments (red)
- select and prune known genes (blue)
- add DNA-based RefSeq (magenta)
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Protein databases construction
The Swiss-Prot and TrEMBL database flat files are downloaded from UniProt at ftp://us.expasy.org/databases/uniprot/knowledgebase/. These files are parsed into 29 relational tables into the database, swissProt.
Two cross-reference tables, spXref2 and spXref3, are created from the Swiss-Prot/TrEMBL data. The spXref2 table contains rows of the accession and display IDs of proteins and their external reference databases and the accession numbers of the external database entries. The spXref3 table contains rows of accession and display IDs of proteins, their descriptions, division numbers and HUGO gene symbols and gene descriptions if available.
In addition to Swiss-Prot/TrEMBL and HUGO data, cross-reference data to other protein databases, e.g. InterPro, Superfamily, NCBI Taxonomy, pFam and Ensembl are also compiled and stored in the proteins database. Both the swissProt and proteins databases are used during Known Genes data set construction and at run-time to support the UCSC Genome Browser, Proteome Browser, Gene Sorter, and Table Browser.
mRNA alignments
The UCSC Genome Browser has a collection of MySQL genome databases, one for each genome release. For example, hg17 is the genomic database for the May 2004 Human Assembly. The mRNA and RefSeq data in those databases are updated every night by importing data from GenBank (Benson et al., 2005) and then aligned to the base genomes using BLAT (Kent, 2002).
At the beginning of the Known Genes build process, a snapshot of all the mRNA alignments of a genome is filtered to select mRNAs that satisfy a more stringent criterion, i.e. to have at least 40% of its sequence aligned with the genome and having at least 97% sequence identity. For hg17, the filter process removes 14 148 mRNA alignments. These filtered mRNA alignments constitute the initial candidates for UCSC Known Genes. They are converted to genePred format to become UCSC Genome Browser gene tracks.
Selection and pruning known genes
Select best mRNA for each protein
A set of candidate mRNA alignments are selected from the filtered mRNAs that aligned to the genome and have corresponding protein sequences in the Swiss-Prot/TrEMBL data as determined via the cross-reference table spXref2. For hg17, this list contains 74 290 entries.
Each Swiss-Prot/TrEMBL protein may cross-reference multiple mRNAs as its supporting evidence. We tried to pick the best mRNA among all those referenced as the representative mRNA and designate it as a Known Gene. The protein and mRNA sequences in each protein–mRNA pair are aligned using BLAT and then a composite score is calculated for each protein–mRNA pair using the following formula:
Score = mRNA length + date in months since January 1970 * 2 – number of mismatches * 50.
This formula favors longer and newer mRNAs and includes weighted penalty against mismatches of the alignment between the mRNA and base genomes. The relative weights of each term were decided empirically, which seems to work well. We did not conduct a comprehensive search for an optimum combination of the weights.
The mRNA with the highest score is selected as the representative mRNA for the protein. This task is fairly computational intensive because of the number of alignments needed. The UCSC 1000-CPU cluster computer system, KiloKluster, was used to complete this task in 
45 min. For hg17, the result list has 46 485 entries.
Reduce redundancy and duplication
Sometimes multiple proteins cross-refer to a single mRNA. For those cases, we keep only one protein to represent the gene and remove the redundant proteins from the Known Genes set.
A sorted list based on the priority (defined below), transcript length, mRNA date, mRNA ID, protein ID and a computer-generated unique alignment ID is pruned by removing duplicates having identical chromosome number, start and ending positions of coding sequence. Short entries, with total mRNA coding sequence length <50 nucleotides, and questionable protein–mRNA pairs whose coding sequences cover <50% of the corresponding proteins are also removed. For hg17, the resulting table contains 43 597 entries.
Proteins that have corresponding PDB entries are assigned the highest priority. The remaining proteins in Swiss-Prot are then assigned a medium priority. The lowest priority is assigned to TrEMBL entries. This scheme is to ensure that if an mRNA has multiple proteins to choose from, the preference goes to proteins with known 3D structures first, then it would favor Swiss-Prot proteins, which usually have gone through more extensive curation than proteins in TrEMBL.
Add DNA-based RefSeqs
During the initial development of the UCSC Known Genes process, we discovered that several hundred RefSeq genes were not covered by the resulting Known Genes dataset. Further analysis shows that most of the missing RefSeq genes have only DNA evidence without any mRNA evidence. These escaped inclusion owing to the fact that the initial candidates in our process include only mRNA alignments. RefSeq genes without supporting mRNA evidence are added to the set of UCSC Known Genes for completeness. For hg17, there are 741 RefSeq genes without supporting mRNA evidence.
Final loading to Genome database
The finished set of UCSC Known Genes for hg17 has 44 338 entries.
Figure 3 shows the UCSC Known Genes track display together with RefSeq, Ensembl Genes and H-Invitational gene tracks.
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CROSS REFERENCES TO OTHER DATA |
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The UCSC Known Genes have been used as the underpinning base for many other programs and database tables at the UCSC website. Four major cross-reference tables are described here and many more can be found in the database schema specification file, all.joiner.
kgXref
The kgXref table is the central cross-reference table for the UCSC Known Genes. Each record has the following fields:
- kgID, ID of a Known Gene
- mRNA, GenBank accession number of the gene's representative mRNA
- spID, Swiss-Prot/TrEMBL protein accession number
- spDisplayID, Swiss-Prot/TrEMBL display ID
- geneSymbol, HUGO or other gene symbol
- refseq, NCBI RefSeq ID
- proAcc, NCBI protein accession number of the RefSeq
- description, description of the Known Gene.
If a corresponding HUGO (Wain et al., 2004) entry can be found for a Known Gene (via the Swiss-Prot/TrEMBL protein ID), the HUGO gene symbol is used as the gene symbol for this Known Gene and the HUGO RefSeq (if available) is recorded in the refseq field of the kgXref table. If there is no corresponding HUGO entry, but a corresponding RefSeq can be found (via mRNA), the gene symbol of this RefSeq will be used. Otherwise, the representative mRNA accession number is used as the gene symbol.
Not every Known Gene has a corresponding RefSeq. LocusLink (now Entrez) data are downloaded from NCBI and an mrnaRefseq table is constructed to store RefSeq–mRNA pairing info for each RefSeq. If a Known Gene's representative mRNA is referenced by a RefSeq, this RefSeq with its corresponding NCBI protein accession number are recorded in the refseq and protAcc fields of the kgXref table. If the gene has a corresponding entry in HUGO, the gene description from HUGO is adopted as description for the Known Gene; otherwise the description for the corresponding protein from Swiss-Prot/TrEMBL is used.
kgAlias, kgProtAlias and kgSpAlias
Genes and proteins are often referred to by different names, symbols and IDs. To facilitate the various cross-reference and searching requirements of many of our own programs and requests from users, we created three comprehensive alias tables, kgAlias, kgProtAlias and kgSpAlias.
The kgAlias table contains just two columns, the Known Gene ID and gene alias. Each Known Gene may have multiple aliases, such as HUGO gene symbol, alternate names and also withdrawn gene names, with the understanding that some users probably still refer to their favorite genes by their old names. The IDs and gene symbol of the RefSeq entries referred to by Known Genes are also added as part of the aliases. Finally, all gene names associated with a Known Gene protein found in Swiss-Prot/TrEMBL are added as gene aliases as well. Each Known Gene–alias pair forms a separate row in the table. Figure 4a shows the relationship between the kgAlias and kgProtAlias tables to various other gene IDs and protein IDs.
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Similarly, the kgProtAlias table contains three columns, the Known Gene ID, Swiss-Prot/TrEMBL display ID and protein alias. The protein aliases encompass Swiss-Prot/TrEMBL display IDs, accession numbers, secondary accessions, corresponding PDB IDs if available and the NCBI protein accession numbers of the RefSeq entries referenced by Known Genes.
Recently we added the kgSpAlias table to support the UCSC Proteome Browser for easy searching, i.e. the user can enter either a gene or protein ID/name to search for the desired protein. This table contains three columns; the Known Gene ID, Swiss-Prot/TrEMBL accession number and an alias column which contains both the gene aliases and protein aliases from the kgAlias and kgProtAlias tables.
Other Known Genes related cross-reference tables
In addition to the four cross-reference tables described above, there are a few dozen additional cross-reference tables that are directly related to Known Genes. These tables are used by the hgGene program, which produces the Known Gene Details page, and the UCSC Gene Sorter program. Most of these tables are built as part of the Gene Sorter build process. Figure 4b shows a conceptual entity relationship diagram of the data contained in many database tables related to UCSC Known Genes. A complete list of these tables can be found in the schema specification file, all.joiner, or via the UCSC Table Browser. For example, clicking the ‘Filter’ button of the UCSC Table Browser shows the following list of 46 tables that are cross linked to UCSC Known Genes:
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The definition of each table can be found in individual .sql files at our download server (see the ‘Downloading source code and data’ section for details).
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UCSC KNOWN GENE DETAILS PAGE |
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Besides being displayed as one of the major tracks in the Genome Browser main display, each UCSC Known Gene also has an accompanying details page. This details page contains rich and comprehensive information about the chosen gene together with extensive links to other additional web resources. Major sections of this page are shown in Figure 5a–d.
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At the top of the details page, Figure 5a, the gene symbol and its description are shown, followed by the representative mRNA and the protein ID. If a corresponding RefSeq entry exists for this gene, the RefSeq summary is also shown. Additional links to other sections of the details page are also shown.
A Quick Links section contains links to the following:
- Genome Browser (UCSC)
- Proteome Browser (UCSC)
- Gene Sorter (UCSC)
- Swiss-Prot (ExPASy/SIB)
- Entrez (NCBI)
- PubMed (NCBI/NLM)
- OMIM (NCBI and Johns Hopkins University)
- GeneLynx
- GeneCards (Weizmann Institute of Science)
- CGAP (NCI/NIH)
- Source (Stanford University)
- MGI (Jackson Labs)
Various comments and descriptive text about the protein encoded by this gene are copied here from Swiss-Prot/TrEMBL and shown. A Sequence section provides links to detailed displays of genomic, mRNA and protein sequences.
A microarray expression section, Figure 5b, shows the absolute and relative expression levels of RNAs in various tissues using several different microarrays, e.g. the GNF Gene Expression Atlas 2 data provided by the Genomics Institute of the Novartis Research Foundation (GNF), which contain two replicates each of 79 human tissues run over Affymetrix microarrays (U133A and GNF1H). As is standard with microarray data red indicates over expression in the tissue and green indicates under expression.
The protein domain and structure information section, Figure 5c, shows links to InterPro and Pfam for further domain information. If there are known or predicted 3D structures, small picture stamps of the structures are shown. Clicking on them will bring the user to PDB (Berman et al., 2000) or ModBase (Pieper et al., 2004) for more detailed protein structural information.
As shown in Figure 5d, a Homolog section contains links to other genomes if homologs are found (determined by best protein sequence match using BLASTP). A Gene Ontology section lists the molecular function, biological process and cellular component information about this gene based on data from the Gene Ontology (GO) (Gene Ontology Consortium, 2004) database. An associated mRNA section lists all mRNAs that overlap with the Known Gene for at least 12 bases. A Pathway section shows links to three major pathway databases, KEGG, NCI/BioCarta and BioCyc where available. Finally a section at the end presents a link for the user to learn more about the UCSC Known Genes.
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DOWNLOADING SOURCE CODE AND DATA |
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The UCSC Genome Browser, Proteome Browser, Gene Sorter and Table Browser are all CGI-based web applications written in C and supported by MySQL databases. The entire source code of these programs and many other utilities are available at http://hgdownload.cse.ucsc.edu/admin/jksrc.zip, which are free for academic, government and non-profit organizations but a license is required for commercial use.
The shell script, KGprocess.sh, which was used to automatically generate the knownGene table and many other associated tables, can be found in the source tree directory src/hg/protein/. The processes to produce other downstream database tables used by the Gene Sorter and Proteome Browser are documented in various files, e.g. makeHg17.doc, makeMm5.doc and makeRn3.doc. These documents are found in the source directory src/hg/makeDb/
All the table definitions and actual data of genomic and proteomic databases used by the major applications mentioned above can be downloaded at http://hgdownload.cse.ucsc.edu/downloads.html
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ANALYSIS |
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TOPABSTRACTINTRODUCTIONMETHODSCROSS REFERENCES TO OTHER...UCSC KNOWN GENE DETAILS...DOWNLOADING SOURCE CODE AND...ANALYSISFUTURE DIRECTIONSCONTACTING USREFERENCES |
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The genomic coverage of UCSC Known Genes is compared with other gene sets using the featureBits analysis utility. As shown in Table 1, out of 2 866 216 770 bases of human genome (May 2004 Assembly), 2.293% bases are covered by Known Genes exons (including UTRs), which is 31% more than the RefSeq coverage of 1.746%. In comparison, the Ensembl Gene Predictions track covers 1.985% and the H-Invitational Genes (Imanishi et al., 2004) track covers 2.171% of the total genome. We also included the gene set (CDS only) from the CCDS (Consensus CDS) project (Pruitt,K., Maglott,D., Harrow,J., Diekhans,M., Birney,E., Baertsch,R., Haussler,D., Hubbard,T., Searle,S., Siepel,A., Ostell,J. and Durbin,R., manuscript in preparation, http://www.ncbi.nlm.nih.gov/CCDS/), a highly curated but conservative subset of RefSeq, with a total genomic coverage of 0.704%.
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Similar analysis is performed on the coverage of the coding regions (CDS only) of different gene sets. As shown in Table 2, the CDS regions of RefSeq cover 1.018% of human genome, while UCSC Known Genes and Ensembl genes each offers about 15% more CDS coverage, at 1.183 and 1.182%.
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The number of entries in human UCSC Known Genes track is 44 338, which is almost double of the number for RefSeq, 23 111. Visual examination of the Known Genes track compared with the RefSeq track shows that typically for a given gene locus, the Known Genes often include multiple entries with different exon structures. Compared with a manual curation process, e.g. RefSeq, the current automated Known Genes build process could not differentiate and identify true alternative splicing isoforms at a gene locus among multiple transcripts of slight variations or imperfect exon structures. As part of the data we generate for the UCSC Gene Sorter (Kent et al., 2005), Known Genes are clustered based on their genomic positions and one canonical gene is assigned to each cluster. This result is stored in the knownCanonical table, which has 21 680 entries, comparable with the total number of RefSeq entries of 23 105.
In Table 1, we also used RefSeq as the gold standard and compared it with UCSC Known Genes and other gene sets. In terms of RefSeq bases covered, UCSC Known Genes has the highest overlap at 94.4%. Ensembl Genes overlap coverage with RefSeq is 92.1% and H-Invitational gene set covers 65.1% of RefSeq. When we compared the coding regions only, we found both UCSC Known Genes and Ensembl genes have high coverage on RefSeq CDS regions at 97.6 and 96.5%, as shown in Table 2.
Table 3 shows the comparison of UCSC Known Genes with other gene sets. In terms of the percent genome bases covered, the UCSC Known Genes missed 0.098% when compared with RefSeq bases, 0.221% when compared with Ensembl Gene bases and 0.754% when compared with H-Invitational Gene bases. On the other hand, the Known Genes also offers additional bases, which are not covered by RefSeq (0.645%, 6.5 times higher than the number of RefSeq bases missed by Known Genes), or not covered by Ensembl genes (0.529%, 2.4 times higher) and not by H-Invitational (0.876%). Table 4 shows the comparison of CDS only regions. H-Invitational was not included because of incomplete CDS data. Significantly more CDS regions were covered by Known Genes when compared with RefSeq. When compared with Ensembl Genes, each offers similar amount of unique CDS regions.
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We are pleased to see that our automated process could capture close to 95% of genomic bases of human RefSeq genes. We also noticed the significant difference between UCSC Known Genes and the H-Invitational Genes. Using the Table Browser, we pulled out H-Invitational genes that are not covered by the UCSC Known Genes and found most of them having only mRNA evidence without direct protein evidence. These genes are not expected to show up in UCSC Known Genes because our process requires the existence of a representative protein–mRNA pair of a gene be considered as a UCSC Known Gene candidate. Likewise, out of a total of 57 401 human proteins listed in Swiss-Prot/TrEMBL,
18% (10 462) of them were not included in our initial list of Known Gene candidates because they do not have any external reference to Genbank mRNAs. These findings suggest that all the major human gene lists included in our analysis may not be complete. Similar analyses were done on mouse and rat genomes. The results are listed in Tables 5 and 6, and Tables 7 and 8, respectively. For mouse genome, the Known Genes have comparable RefSeq coverage (95.1%). For rat genome, the RefSeq genomic coverage itself is relatively low (0.523%) and only 75.7% of the RefSeq bases are covered by UCSC Known Genes. This probably owes to the fact that the UCSC Known Genes for rat had not been updated for more than a year and the base genome data of rat (June, 2003) was a relatively incomplete one.
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FUTURE DIRECTIONS |
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TOPABSTRACTINTRODUCTIONMETHODSCROSS REFERENCES TO OTHER...UCSC KNOWN GENE DETAILS...DOWNLOADING SOURCE CODE AND...ANALYSISFUTURE DIRECTIONSCONTACTING USREFERENCES |
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The coverage of the UCSC Known Genes dataset could be further extended. We would like to develop additional processing steps to accomplish this. For example, we could include the large number of Swiss-Prot/TrEMBL proteins, which do not have supporting mRNA evidence listed under the external cross-reference section, as Known Genes candidates. Likewise, we would also like to consider including the full-length cDNAs in other sources, e.g. H-Invitational and MGC, which are not covered by the Swiss-Prot/TrEMBL external cross-references as part of our initial gene candidates dataset.
We would also like to implement additional selection criteria to mimic expert curators' thought processes to eliminate false positives or redundant data to increase the quality of the resulting dataset.
Although the build time, about a day, of the UCSC Known Genes for a genome is relatively short because of process automation and utilization of the UCSC Kilo Kluster, the Known Genes are not updated as frequently as we would like to owing to practical resource constraints and need of time to update and QA the associated downstream annotation data. Nevertheless, we would like to further automate the processing steps of those downstream annotation data so that we can update the UCSC Known Genes and the associated downstream annotations more frequently.
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CONTACTING US |
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TOPABSTRACTINTRODUCTIONMETHODSCROSS REFERENCES TO OTHER...UCSC KNOWN GENE DETAILS...DOWNLOADING SOURCE CODE AND...ANALYSISFUTURE DIRECTIONSCONTACTING USREFERENCES |
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The mailing list genome{at}cse.ucsc.edu provides a forum for questions and discussion about the UCSC Genome Browser, Proteome Browser, Table Browser, Gene Sorter and databases. Users may subscribe to this list at http://www.cse.ucsc.edu/mailman/listinfo/genome. For announcement of new releases and features, a lower volume mailing list, genome-announce{at}soe.ucsc.edu is available. To report problems on accessing the website, servers or mirror sites, or for correspondence inappropriate for the public forum, send email to genome-www{at}cse.ucsc.edu
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Acknowledgments |
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The authors like to thank Swiss-Prot for sharing their high-quality protein data. The authors would also like to thank the many collaborators who have contributed sequence and annotation data to our projects, as well as our users for their feedback and support. This project is funded by National Human Genome Research Institute (NHGRI) and the Howard Hughes Medical Institute (HHMI). Funding to pay the Open Access publication charges for this article was provided by HHMI.
Conflict of Interest: none declared.
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FOOTNOTES |
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Associate Editor: Christos Ouzounis
Received on September 9, 2005; revised on January 23, 2006; accepted on February 7, 2006
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REFERENCES |
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TOPABSTRACTINTRODUCTIONMETHODSCROSS REFERENCES TO OTHER...UCSC KNOWN GENE DETAILS...DOWNLOADING SOURCE CODE AND...ANALYSISFUTURE DIRECTIONSCONTACTING USREFERENCES |
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Bairoch, A., et al. (2005) The Universal Protein Resource (UniProt). Nucl. Acids Res, . 33, D154–D159
Benson, D.A., et al. (2005) GenBank. Nucleic Acids Res, . 33, D34–D38
Berman, H.M., Westbrook, J., Feng, Z., Gilliland, G., Bhat, T.N., Weissig, H., Shindyalov, I.N., Bourne, P.E. (2000) The Protein Data Bank. Nucleic Acids Res, 28, 235–242
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PLoS Biology Imanishi, T., et al. (2004) Integrative annotation of 21,037 human genes validated by full-length cDNA clones. 2, 856–875.
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Pieper, U., Eswar, N., Braberg, H., Madhusudhan, M.S., Davis, F.P., Stuart, A.C., Mirkovic, N., Rossi, A., Marti-Renom, M.A., Fiser, A., Webb, B., Greenblatt, D., Huang, C.C., Ferrin, T.E., Sali, A., et al. (2004) MODBASE, a database of annotated comparative protein structure models, and associated resources. Nucleic Acids. Res, . 32, D217–D222
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