Identification of "pathologs" (disease-related genes) from the RIKEN mouse cDNA dataset using human curation plus FACTS, a new biological information extraction system
© Silva et al; licensee BioMed Central Ltd. 2004
Received: 10 October 2003
Accepted: 29 April 2004
Published: 29 April 2004
A major goal in the post-genomic era is to identify and characterise disease susceptibility genes and to apply this knowledge to disease prevention and treatment. Rodents and humans have remarkably similar genomes and share closely related biochemical, physiological and pathological pathways. In this work we utilised the latest information on the mouse transcriptome as revealed by the RIKEN FANTOM2 project to identify novel human disease-related candidate genes. We define a new term "patholog" to mean a homolog of a human disease-related gene encoding a product (transcript, anti-sense or protein) potentially relevant to disease. Rather than just focus on Mendelian inheritance, we applied the analysis to all potential pathologs regardless of their inheritance pattern.
Bioinformatic analysis and human curation of 60,770 RIKEN full-length mouse cDNA clones produced 2,578 sequences that showed similarity (70–85% identity) to known human-disease genes. Using a newly developed biological information extraction and annotation tool (FACTS) in parallel with human expert analysis of 17,051 MEDLINE scientific abstracts we identified 182 novel potential pathologs. Of these, 36 were identified by computational tools only, 49 by human expert analysis only and 97 by both methods. These pathologs were related to neoplastic (53%), hereditary (24%), immunological (5%), cardio-vascular (4%), or other (14%), disorders.
Large scale genome projects continue to produce a vast amount of data with potential application to the study of human disease. For this potential to be realised we need intelligent strategies for data categorisation and the ability to link sequence data with relevant literature. This paper demonstrates the power of combining human expert annotation with FACTS, a newly developed bioinformatics tool, to identify novel pathologs from within large-scale mouse transcript datasets.
The majority of common diseases such as cancer, allergy, diabetes or heart disease are characterised by complex genetic traits where genetic and environmental components contribute to disease susceptibility . Unfortunately, our knowledge of genes contributing to the risk of common diseases remains limited. Consequently, a major goal of the post-genomic era is to better identify and characterise disease susceptibility genes and to use this knowledge for improved disease detection, treatment and prevention.
More than 500 genes are conserved across the invertebrate and vertebrate genomes . Because of gene conservation, various organisms including yeast , fruitfly , zebrafish , rat , and mouse  have been used as genetic models for the study of human disease. Whilst the basic housekeeping genes such as those involved in metabolism, intracellular signalling, transcription/translation, DNA replication and repair are highly conserved in eukaryotes making them useful for the study of basic cellular processes and related diseases, these organisms do not share with humans many genes such as those involved in homeostasis, immunity, and cellular interactions . Rodents and humans have remarkably similar genomes and share closely related biochemical, physiological and pathological pathways making the mouse the most important model organism for the study of human disease genetics and development of new treatments. This is reflected in the fact that approximately 80% of all mouse cDNA clones have matches in the human genome .
Genetic manipulations that can be performed in the mouse include point mutations, gene disruptions, insertions, deletions, or chromosomal rearrangements . Random genome-wide mutagenesis can also be used for identification of gene function . Specific genetic manipulations and alterations in the mouse often produce clinical features that are remarkably similar to human disease . For example, targeted mutation of the transferrin receptor-2 gene was shown to induce haemochromatosis in mice . The recent explosion of genomic data has, however, overwhelmed researchers with tens of thousands of novel genes making it difficult to know where to start in order to identify those most relevant to human disease. This has led to the use of comparative genomics as a strategy to identify promising candidates warranting further study from amongst all these novel genes.
Rubin et al.  compiled a list of 289 human disease genes and compared them to the fruitfly genome, finding 177 fruitfly orthologues to human disease genes. A more recent study  focused on identification of a subset of human disease genes that represent good candidates for study in the fruitfly model. Starting from the 929 entries of known human disease genes listed in the OMIM database , they identified 548 fruitfly genes with sequence homology to human disease genes. Of these, 56 genes belonged to well-known signalling pathways (such as BMP, Hedgehog or Notch). These strategies starting from known human disease-related genes are directed at the identification of orthologs in non-human species of known human disease genes.
The FANTOM2 project  focused on the functional annotation of 60770 cDNA RIKEN clones by large-scale, computerised annotation followed by manual curation. Being the most complete picture of the mouse transcriptome to date, the FANTOM2 dataset provides an ideal opportunity for the identification of novel pathologs thereby leading to the identification of novel human disease-related genes or disease-related gene products (transcript, anti-sense or protein), including candidates not listed in the OMIM Morbidmap database.
Recently, FANTOM2 cDNA clones were searched with TBLASTN (e-value: E-50) against a set of human disease-related genes and mouse orthologs were identified for 807 human disease-related genes . Of these, 67 were novel mouse orthologs for known human disease-related genes . However, this BLAST strategy starting from known human disease-related genes and then searching for orthologs in the mouse is only able to identify mouse genes corresponding to already known human disease-related genes. Consequently, for the present study we developed an alternative strategy for gene discovery from the FANTOM2 cDNA dataset aimed at identifying potential novel human disease-related genes. By comparison to previous reports [15, 12, 4], we started from novel mouse transcripts with similarity but not identity to human disease-related genes and then mapped these sequences back to the human genome to identify novel potential human disease-related genes.
The FANTOM2 dataset contains 2578 cDNA clones annotated as "similar to" known genes or proteins, comprising 1114 members of the representative transcript protein set 6.3 (RTPS6.3) FANTOM2 clusters . The sequences of each of these "similar to" clones have 70–85% identity over more than 70% length to known reference protein or gene sequences. By searching the publication abstracts databases PubMed/MEDLINE , we identified 182 mouse mRNA transcripts that we called "potential pathologs" as they had sequence similarity to human disease-related genes or proteins. We defined "patholog" as a non-human gene with homology to a human gene that encodes a product (transcript, anti-sense or proteins) involved in human disease. A disease-related gene has a role in a patho-physiological pathway, or is relevant to the diagnosis or treatment of a human disease. The most common disease classifications to which these potential pathologs corresponded were neoplastic, hereditary, immune, cardiovascular or neurological diseases. Each patholog represents a potential target for creating novel mouse models of human disease.
One of the bottlenecks in the use of genomic data to search for potential disease genes, is the time required to search the literature and assess the significance of the search results. Semi-automated knowledge extraction tools offer the potential to dramatically accelerate this process, albeit at the risk of some loss of information as a result of misqueries and ambiguous data. An important aspect of this project was a comparison of the performance of FACTS, a newly developed semi-automated knowledge extraction tool, against expert human annotators to determine whether in the future it will be feasible to automate the process of disease gene identification.
Identification of novel pathologs
Classification of pathologs by disease
Novel potential "pathologs" classified by type of human disorder and relationship to the disease process.
Cancer related pathologs. Representative disease is shown for each clone. * RTPS6.3 (representative transcript protein set 6.3) cluster representative transcriptional unit (TU) of the FANTOM2 clone set. OMIM status: 1 = gene present in OMIM with a reported disease; 2 = gene present in OMIM with different disease association or without disease; 3 = gene not present in OMIM.
Squamous cell carcinoma antigen 2
Squamous cell carcinoma
Cargo selection protein tip47
Pregnancy-specific beta 1-glycoprotein
Trophoblastic disease, tumour marker
Hereditary renal tumors
Pancreatic secretory granule membrane major glycoprotein gp2 precursor
Chronic lymphocytic leukemia
Diphthamide biosynthesis protein-2
Prognosis of neoplastic diseases
Udp-n-acetyl-alpha-d-galactosamine:polypeptide n-acetylgalactosaminyl Transferase 7
3-oxo-5-alpha-steroid 4-dehydrogenase 1
Ctcl tumor antigen se2-2
T-cell based immunotherapy
Cutaneous malignant melanoma
Ctcl tumor antigen se57-1
T-cell based immunotherapy
Desmoglein 1 precursor
Serine protease desc1 precursor
Squamous cell carcinoma
Reverse transcriptase-like protein
Chronic myelogenous leukemia
Meningioma-expressed antigen 6/11
Meningioma and glioma
Scaffold attachment Factor b
Restricted expression proliferation associated protein 100
Vacuolar proton-atpase subunit atp6h
Proto-oncogene tyrosine-protein kinase fes/fps
Ras-related protein rab-2
Homeobox transcription factor
Smac protein, mitochondrial precursor
Pregnancy-induced growth inhibitor
Transmembrane 4 superfamily, member 5
Human gastric cancer cells
Ca11 protein homolog
Rho guanine nucleotide exchange factor 5
Acute myeloid leukaemia
Cell metastasis and malignant transformation
Hypoxia-inducible protein 2
1-acyl-sn-glycerol-3-phosphate acyltransferase beta
Cancers and inflammation-associated diseases.
C-myc target jpo1
Colon cancer antigen
Tumor protein d54
Death domain of death-associated protein kinase 1
Non-small cell lung cancer
X-ray repair cross-complementing protein 3
Calcium-activated chloride channel-2
Breast cancer, metastasis
Gas-2 related protein on chromosome 22
Central nervous system tumours
Pms1 protein homolog 1
Serologically defined colon cancer antigen 1
Retinoblastoma-associated protein rap140
Colon cancer cell line
Headpin serine proteinase inhibitor
Squamous cell carcinoma
Basal cell and squamous cell carcinomas
Cell cycle checkpoint protein chfr
Capillary thyroid carcinoma
Hepatic leukemia factor
Ubiquitin specific protease
Squamous non-small cell lung carcinoma
P68 RNA helicase
Lipoma preferred partner
Acute myeloid leukaemia
Serine/threonine protein phosphatase 2a, 72/130 kda regulatory subunit B
Megacaryocytic acute leukemia protein, isoform i
Acute megakaryoblastic leukemias
Hepatocellular carcinoma autoantigen
Deleted in lung and esophageal cancer 1
Myc box dependent interacting protein 1
Androgen-induced prostate proliferative shutoff associated protein
Phd finger protein 3
Cub domain containing Protein 1
Human colorectal cancer
Vault poly(adp-ribose) polymerase
Several tumour types
Matrix metalloproteinase-25 precursor
Colon carcinomas or brain tumours
Swi/snf complex 170 kda subunit
Malignant rhabdoid tumours
Prostate cancer overexpressed gene 1
Transcriptional repressor scratch
Small cell lung cancer
Beta-tubulin class iva isotype
Human colon adenocarcinoma
Pituitary tumor-transforming gene 1 protein-interacting protein
Polyamine modulated factor-1
Tumours originating from epithelial tissue
Phosphoprotein enriched in astrocytes 15
Melanoma antigen p15
NUCLEAR MATRIX PROTEIN p84
Vascular and haematopoietic development
Retinoblastoma-binding protein 1
Chronic myelogenous leukemia tumor antigen 66
Leukemias and tumour cell lines
Carbonic anhydrase xii precursor
Cancer tumour cells
Inositol hexakisphosphate kinase 3
Each of the 96 cancer-related potential pathologs was associated with one of the molecular circuits that maintain normal cell proliferation and homeostasis. Defects in these circuits often induce dysregulation of cell growth and apoptosis, or contribute to tissue invasion, metastasis, or angiogenesis. Defects in these pathways are thus central to cancer development . Amongst the pathologs we identified genes encoding proteins involved in the SOS-Ras-Raf-MAPK cascade that has a key role in normal cell growth, and proteins linked with gene expression and cell proliferation, including Wnt-β Catenin, CdC42-Rac-Rho, and the pRb-E2F transcription factors . A number of matrix metalloproteinases and cell adhesion molecules involved in cell invasion and metastasis were also identified.
Pathologs related to hereditary disorders. Representative disease is shown for each clone. * RTPS6.3 (representative transcript protein set 6.3) cluster representative transcriptional unit (TU) of the FANTOM2 clone set. OMIM status: 1 = gene present in OMIM with a reported disease; 2 = gene present in OMIM with different disease association or without disease; 3 = gene not present in OMIM.
Neuronal protein 15.6
14-3-3 protein tau
Methionine synthase reductase
Methionine synthase reductase deficiency
Nadh-ubiquinone oxidoreductase 20 kda subunit, mitochondrial precursor
Mitochondrial complex I deficiency
Selenoprotein n precursor
Congenital muscular dystrophy
Sterol carrier protein 2
Peroxisomal D-hydroxyacyl-CoA dehydrogenase deficiency
Spondyloepiphyseal dysplasia tarda
Tubulin-specific chaperone d
Protoheme ix farnesyltransferase, mitochondrial precursor
Periodic tryptophan Protein 1
Progressive myoclonus epilepsy
Sco2 protein homolog, mitochondrial precursor
Cardioencephalomyopathy and a severe COX deficiency
Ocular albinism type 1
Machado-joseph disease protein 1
Autoimmune polyglandular disease type I
Muscular dystrophy and cardiomyopathy
Ceroid-lipofuscinosis neuronal protein 5
Late infantile neuronal ceroid lipofuscinosis
Caltractin, isoform 2
Barth syndrome and chondrodysplasia punctata
Gdp-fucose transporter 1
Leukocyte adhesion deficiency II
Zinc finger protein 25
Glycogen debranching enzyme
Glycogen storage disease type III
Nuclear localization signal protein absent in velo-cardio-facial patients
X-linked congenital stationary night blindness
Transmembrane protein vezatin
Peroxisome assembly protein 10
Inositol polyphosphate 5-phosphatase ocrl-1
Mitochondrial intermediate peptidase, mitochondrial precursor
Epilepsy holoprosencephaly candidate-1 protein
Progressive myoclonus epilepsy
Monocarboxylate transporter 5
Spinocerebellar ataxia type 7
Lowe oculocerebrorenal syndrome protein
Oculocerebrorenal syndrome of Lowe
Autosomal recessive deafness
Sodium bicarbonate cotransporter isoform 1
Proximal renal tubular acidosis associated with ocular abnormalities
Y+l amino acid transporter 1
Lysinuric protein intolerance
Progeroid type Ehlers-Danlos syndrome
T-box transcription factor tbx22
Cytochrome b5 reductase b5r.2
Pathologs related to other disorders. Representative disease is shown for each clone. *RTPS6.3 (representative transcript protein set 6.3) cluster representative transcriptional unit (TU) of the FANTOM2 clone set. OMIM status: 1 = gene present in OMIM with a reported disease; 2 = gene present in OMIM with different disease association or without disease; 3 = gene not present in OMIM.
Replication protein a 14 kda subunit
Systemic lupus erythematosus
LES and Juvenile RA
Behecet's Disease, sarcoidosis and Vogt-Koyanagi-Harada disease
Golgi complex autoantigen golgin-97
Neuroblast differentiation associated protein ahnak
Systemic lupus erythematosus
Minor histocompatibility antigen ha-1
U1 small nuclear ribonucleoprotein c
Autoimmunity to U1 snrnps
L1 retroposon, orf2 mrna
Ribonuclease p protein subunit p38
Scleroderma autoimmune antigens
Caspase recruitment domain protein 15
Serine/threonine kinase rick
Huntingtin-interacting protein-1 protein interactor
Metabotropic glutamate receptor 2 precursor
Excitatory amino acid transporter 1
Mitochondrial isoleucine trna synthetase
Ras gtpase-activating Protein 1
Neuropathology of ischemia
Lysosomal pro-x carboxypeptidase precursor
Angiotensin converting enzyme
Sumo-1-specific protease 1
Adam 26 precursor
Sex differentiation disorders
Mitochondrial import inner membrane translocase subunit tim9 b
Sickle cell disease
Ribonucleases p/mrp protein subunit pop1
Connective tissue diseases
N-acetyllactosaminide beta-1,6-n-acetylglucosaminyl Transferase
Blood group I gene
Type 2 diabetes
T-cell receptor alpha chain precursor v-j region
Wd-repeat protein 3
Polycystic kidney disease 2-like protein
Vesicular glutamate transporter 2
Amyloid beta precursor-like protein 2
Healing corneal epithelium
Chronic pain, addictive states and brain injury
Human hemolytic anemias
Identification of novel pathologs
Of the 72 potential pathologs known to be mouse orthologs, 33 (45.9%) were related to neoplastic disorders, 23 (31.9%) to hereditary disorders and 16 (22.2%) corresponded to immunological, cardio-vascular, reproductive and other disorders. The majority of the 65 pathologs representing candidate mouse orthologs were related to cancer (37 or 57%). The remaining transcripts were related to the following disease categories: hereditary disorders 13 transcripts (20%), immunological 4 (6%), cardio-vascular 4 (6%), and other disease classification 7 (11%) which included neurological, haematological, reproductive, endocrine, and respiratory disorders.
Classification of candidate orthologs and novel homologs
We also located 45 potential pathologs not representing mouse orthologs of human genes, as there was a better mouse transcript match for the human gene they share sequence homology with. However, they may represent novel mouse homologs as deducted from sequence analysis and conservation of synteny.
These 45 potential pathologs with novel sequences, corresponded to cancer 26 (58%), hereditary disorders 7 (15%), cardiovascular disease 3 (7%) and other diseases 9 (20%). Nine of these targets had short sequences (less than 1000 bp) and might correspond to pseudogenes (based on gene synteny).
Comparison to Online Mendelian Inheritance in Man (OMIM) database entries
Comparison of the OMIM entries (July 2003) with pathologs. "OMIM NDA" stands for pathology entries that are in OMIM, but disease association was not specified, or it was not consistent with the disease specified in PubMed abstracts. "OMIM DA" stands for pathologs that match both OMIM entries and disease association.
Not in OMIM
The mouse is the most important animal model of human disease, hence the importance of the FANTOM project to characterise the mouse transcriptome, complete with functional annotation and human genome mapping. The FANTOM2 cDNA dataset represents the most complete set of mouse transcripts to date, and it was utilised by us to identify potential novel pathologs. The identification of pathologs was assisted by integrating the FANTOM2 mouse data with all scientific literature referenced by PubMed, which is currently the most comprehensive literature source of molecular and clinical data. The problem is that most relevant data in medical literature databases is embedded in the free text and searching by automated methods often results in the loss of information. Therefore, to more thoroughly screen for potential pathologs, we employed two approaches in parallel; one relying on semi-automated sequence analysis and text searching (FACTS) and the other relying on human expert manual searching. The results of this study clearly indicate the importance of using multiple parallel approaches to identify all potential pathologs.
The semi-automated approach detected 133 (73%) of the potential pathologs compared to 146 (80%) using manual search. Interestingly the overlap between the two methods was only 97 (53%), suggesting that both approaches are required for identification of all potential pathologs. Although the semi-automated approach utilises less than one third of the time required by manual searching, three quarters of the hits detected by this system were classified as false positives, only 134 transcripts out of the initial 708 produced by automated search meeting the criteria for potential pathologs. This is not unusual when using computerized systems. Problems were caused by retrieval of irrelevant abstracts, misconstructed queries, queries containing ambiguous gene symbols or synonyms, wrong disease MeSH term associations in the abstracts or because the abstract did not meet the human expert's criteria for a potential patholog.
Several reasons contribute to a better performance using the FACTS system compared to expert annotation. The coverage and specificity of abstract retrieval from MEDLINE depends on how queries are constructed. Manual searches were performed using gene names and symbols from the FANTOM2 database, while FACTS constructed queries from an automated QueryMaker program that extracts gene/protein names, symbols and synonym accessions of their annotation sources (e.g., MGI, SwissProt). This information is integrated according to query rules and then used to perform MEDLINE searches. The FACTS system also combines a MeSH TermMatcher program with a Sentence Splitter system to identify disease associations from MEDLINE abstracts and OMIM morbidmap database (for detailed explanation of the FACTS system see ). These programs enhance the accuracy of automated queries and searches used for the identification of pathologs. The high frequency of false positive hits makes manual curation an important step when using computational screening. Whilst manual searching produces more true positive hits, it is less efficient than the semi-automated approach. Expert analysis identified 49 clones that were missed by the automated system as FACTS-derived results are based on MEDLINE whilst the expert annotators used PubMed for abstract searches.
In previous reports, pathologs in non-human organisms were identified using the OMIM database, the problem with this approach being that it requires the human disease gene to be already known. Our approach produced 93 potential pathologs that were identified through the scientific literature but were not in the OMIM database, suggesting that the true number of pathologs is far higher than those with strictly Mendelian inheritance. Furthermore, given that this study only focused on the subset of "similar to" cDNA clones and did not cover those annotated as "weakly similar to" (see methods) we anticipate that there are many more pathologs in the mouse that are yet to be identified. Our analysis also suggests that the field of disease-related molecular databases is underserved, other than the Mendelian disorders covered by OMIM.
The pathologs identified in this study were selected from a group of FANTOM2 mouse cDNA clones similar to, but not identical to, other known genes. As expected, sequence comparison revealed that the majority of pathologs (137 or 75%) corresponded to the best mouse to human match although many of them (65) remain to be confirmed as orthologs as no function for them has yet been described. We also located an extra 45 potential pathologs that may represent mouse homologs to novel human disease-related genes as deduced from sequence analysis and conservation of synteny. It is likely that at least some of the potential pathologs with function unknown (110) will represent non-functional transcripts, or gene products with different function. Those pathologs that are experimentally validated as orthologs can be used as targets for genetic manipulation and development of mouse models of human disease.
This paper demonstrates the power of combining human expert annotation with FACTS, a newly developed bioinformatics tool, to identify novel pathologs from within large-scale mouse transcript datasets. Those pathologs can be used as targets for genetic manipulation and development of mouse models of human disease. The similarity between mouse and human genomes and their closely-related biochemical, physiological, and pathological pathways makes the mouse an invaluable model organism for the study of human disease.
The FANTOM2 set of full-length mouse cDNA clones contains 60770 sequences. The FANTOM2 clones were functionally annotated using automated computational annotation followed by expert human curation .
Accession numbers in the manuscript refer to FANTOM accessions submitted to the DNA data bank of Japan (DDBJ), or public accessions.
Pre-computed results of sequence similarity comparisons were retrieved from the FANTOM2 database . The method used for detection of sequence similarity has been explained by Okasaki et. al. . Briefly, according to the percentage of DNA sequence identity and the length of the similarity region to known genes the FANTOM2 clones were annotated as: "identical-to" or "homolog", "similar-to", or "weakly-similar-to". "Identical to" (>95%) and "homolog" (85–95%) were clones with more than 85% identity over more than 90% of their length to known genes. "Similar to" were clones with identity of 70–85% over more than 70% of their length to known genes. "Weakly similar to" were clones with identity between 50–70% over more than 70% of their length to known genes.
The clones grouped as "similar to" and "weakly similar to" could represent novel mouse transcripts whose function may be inferred because of their similarity to known proteins. This study focused on the analysis of "similar to" clones, which are referred here to as the "target set". The clones classified as "weakly similar to" require further bioinformatic characterisation and therefore will be matter of a different study. The target set was comprised of 2578 annotated clones, representing a workable size subset for this study. Using the RIKEN clone ID number of each potential human disease related target, we identified the representative transcript from RTPS 6.3  to indicate the FANTOM2 cluster representative transcriptional unit associated with disease (see Tables 2, 3, 4).
Human disease-related genes
We defined "patholog" as a non-human gene with homology to a human gene that encodes a product (transcript, anti-sense or proteins) involved in human disease. In this study, to be classified as a disease-related gene, there must be at least one scientific publication providing evidence linking a gene (or the related protein) to a disease phenotype (such as protein mutation or up/down regulation), to a diagnostic test, or to a disease treatment. In vitro studies using human cells (fresh tissue, cell lines or tumour cell cultures) or clinical studies were all accepted as evidence for a human disease relationship. Scientific publications where experiments were done using non-human organisms or where results were not tested directly in humans were discarded from the analysis.
All potential pathologs from the target set were used for identification of the corresponding human gene by mapping to the human genome sequence. We used a semi-automated and a manual approach for data searching and identification of pathologs. The manual approach involved searching literature abstracts from the PubMed database  using protein names for each clone in the target set, to identify potential human disease relationships. The gene or protein name was searched via the PubMed interface for keyword search and the retrieved abstracts were analysed by medical experts. Queries that returned one or more abstracts and that met the patholog definition criteria were noted: clone ID, clone name, PubMed ID, and disease-relationship were recorded. In the semi-automated approach we used the FACTS (Functional Association/Annotation of cDNA clones from Text/Sequence Sources) system to query MEDLINE abstracts (described in detail by Nagashima et al. . Briefly, we constructed MEDLINE queries from RIKEN cDNA clone annotations using 205 query construction rules and the FACTS MeSH TermMatcher program. Of 2578 similar to annotated clones 1,949 clones had gene names considered informative for MEDLINE abstract searches that were clustered into 639 queries. 522 queries corresponding to 708 clones yielded 17,051 abstracts with 2637 disease MeSH terms. As FACTS extracts both abstract and sequence-derived based information using accession mapping, from the 708 clones we obtained 109 that had 92 disease associations in OMIM Morbimap. From 629 clones without informative names we extracted 47 OMIM Morbidmap associations for 57 clones. In total FACTS provided 27% of all and 36% of informative disease candidate associations. The MEDLINE and OMIM inferred disease associations can be annotated upon registration through a FACTS annotation interface. The interface displays basic clone information (symbol names, protein motifs and RTPS cluster information) and links to tissue expression information in READ  and GNF gene expression atlas  together with the automatically constructed query. The computationally inferred human disease MeSH terms and OMIM Morbidmap titles are listed in a table containing a hyperlinked MEDLINE identifier, MeSH term and check boxes to delete or confirm the MeSH term and assign a confidence value. The confidence values low, medium, high, and unknown indicate whether the MeSH assignment is based on direct (e.g. mutation in gene) or indirect (pathway component in disease gene pathway) evidence. A comment field provided the possibility of entering evidence and decision-supporting comments. Automated results were obtained in 48 hours and manual curation required approximately 60 man hours.
Medical experts performed manual searches of the 2578 target clones through abstract inspection and thereby selected candidate novel mouse pathologs. The time taken to identify the final number of pathologs required approximately 160 man hours.
Classification and interpretation
The results of the manual and the semi-automated approaches were combined in a single final list. Clones on this list were classified into groups in accordance to the physiological system affected by the related disease. Pathologs were subdivided according to the role of the protein in the disease process: pathophysiology, diagnosis, or treatment. Finally, we compared the pathologs identified in this study with entries from the OMIM database to identify pathologs that could be identified by direct searching of the OMIM database.
Identification of mouse known orthologs, ortholog-candidates and novel sequences was based on sequence similarity (FANTOM2 website ) and conservation of synteny based on mouse to human mapping information (NCBI Map viewer ) and RIKEN-genomapper  (July 2003), and reported function (Locus Link  search – july 2003). Mouse sequences with reported human ortholog and known function were classified as "known orthologs", sequences reported as best mouse to human match with unknown function were classified as "ortholog-candidates" and sequences with function unknown that did not correspond to the best mouse to human match were grouped as "novel sequences".
DS is the recipient of a scholarship from the Canberra Hospital Salaried Specialists Private Practice Fund.
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