Identification of "pathologs" (disease-related genes) from the RIKEN mouse cDNA dataset using human curation plus FACTS, a new biological information extraction system

Abstract

Background

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.

Results

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.

Conclusions

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.

Background

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 [1]. 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 [2]. Because of gene conservation, various organisms including yeast [3], fruitfly [4], zebrafish [5], rat [6], and mouse [7] 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 [2]. 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 [2].

Genetic manipulations that can be performed in the mouse include point mutations, gene disruptions, insertions, deletions, or chromosomal rearrangements [8]. Random genome-wide mutagenesis can also be used for identification of gene function [9]. Specific genetic manipulations and alterations in the mouse often produce clinical features that are remarkably similar to human disease [10]. For example, targeted mutation of the transferrin receptor-2 gene was shown to induce haemochromatosis in mice [11]. 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. [12] 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 [4] 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 [13], 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 [14] 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 [15]. Of these, 67 were novel mouse orthologs for known human disease-related genes [15]. 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 [14]. 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 [16], 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.

Results

Identification of novel pathologs

We identified 182 candidate pathologs from amongst 2578 FANTOM2 mouse "similar to" cDNA transcripts (Figure 1). Each of the transcripts representing these targets shows 70–85% identity over more than 70% of its length to a known human disease related gene or protein found by sequence similarity comparisons (see Methods). Of these, 146 were identified by manual and 133 by a semi-automated approach with 97 (53.3%) of targets being detected by both methods. The manual approach uniquely detected 49 (26.9%) human disease-related gene targets and semi-automated approach uniquely detected 36 (19.8%) targets.

Figure 1.

Flow chart of method used for the identification of "pathologs". Obtained from the FANTOM2 dataset, "similar to" clones were analysed using a manual (left) and a semi-automated approach (right) to identify "patholog" genes. HDR clones: clones with Human Disease Relationship.

Classification of pathologs by disease

The 182 "pathologs" were classified by the disease they relate to. The majority of the clones were related to neoplastic disorders (53%), followed by hereditary (24%), immunological (5%), cardio-vascular (4%), and other (14%), disorders (Table 1).

Table 1.

Novel potential "pathologs" classified by type of human disorder and relationship to the disease process.

Disorder	Pathophysiology	Diagnosis	Treatment	Total
Cancer	61	19	16	96
Hereditary	39	4	0	43
Immunological	5	5	0	10
Cardio-vascular	8	0	0	8
Reproductive	6	0	0	6
Other	17	0	2	19
Total	136	28	18	182

Cancer-related pathologs

The cancer-related category comprised the largest number of potential pathologs, with 96 unique targets (Table 2). Cancers represented in this list cover a variety of systems including the central nervous system, gastro-intestinal tract, breast, and prostate, among others. These potential pathologs related to cancer pathophysiology 61 (63.5%), diagnosis 19 (19.8%) and treatment 16 (16.7%).

Table 2.

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.

Disease	FANTOM ID	DDBJ accession	Gene name	Disease relationship	OMIM status
CANCER
Diagnosis
	1110013A16*	AK003650	Squamous cell carcinoma antigen 2	Squamous cell carcinoma	1
	1300012C15*	AK004970	Cargo selection protein tip47	Gynecologic malignancies	2
	1600002M22*	AK005400	Pregnancy-specific beta 1-glycoprotein	Trophoblastic disease, tumour marker	1
	1810011L16*	AK007436	Adamts-9 precursor	Hereditary renal tumors	3
	2310046E09	AK009843	Pancreatic secretory granule membrane major glycoprotein gp2 precursor	Chronic lymphocytic leukemia	2
	2600013I19*	AK011199	Diphthamide biosynthesis protein-2	Prognosis of neoplastic diseases	2
	4631401E18*	AK019470	Udp-n-acetyl-alpha-d-galactosamine:polypeptide n-acetylgalactosaminyl Transferase 7	Colorectal carcinoma	2
	4930435F02	AK019597	3-oxo-5-alpha-steroid 4-dehydrogenase 1	Breast cancer	3
	4932411D20*	AK029960	Ctcl tumor antigen se2-2	T-cell based immunotherapy	3
	5330423N11*	AK077331	Melastatin 2	Cutaneous malignant melanoma	3
	9130023H10*	AK033603	Mlze	Melanoma	3
	9130413M24*	AK078964	Ctcl tumor antigen se57-1	T-cell based immunotherapy	3
	9330156N18*	AK034104	Desmoglein 1 precursor	Paraneoplastic pemphigus	2
	A030012E10*	AK037235	Serine protease desc1 precursor	Squamous cell carcinoma	3
	A230060D07*	AK038754	Reverse transcriptase-like protein	Chronic myelogenous leukemia	3
	A430037M23	AK039975	Dipeptidyl-peptidase iii	Endometrial neoplasms	2
	A630051G17	AK080312	Meningioma-expressed antigen 6/11	Meningioma and glioma	1
	E130307D12*	AK087504	Scaffold attachment Factor b	Breast cancer	1
	G430124K07*	AK090101	Restricted expression proliferation associated protein 100	Lung carcinoma	3
Pathophysiology
	0610006O14	AK002260	Vacuolar proton-atpase subunit atp6h	Melanoma	2
	0610008P16*	AK002360	Hp33 protein	Hepatocellular carcinoma	3
	1110068E08*	AK004405	Ku70-binding protein	Gliomas	3
	1200003O15*	AK004587	Proto-oncogene tyrosine-protein kinase fes/fps	Leukaemia	1
	1500012D09	AK005230	Ras-related protein rab-2	Nasopharyngeal carcinoma	1
	1700001P03*	AK005620	Homeobox transcription factor	Colon cancer	1
	1700006L01		Smac protein, mitochondrial precursor	Multiple myeloma	2
	1700012B18*	AK005892	Pregnancy-induced growth inhibitor	Breast cancer	3
	1700045I19*	AK006700	Hsd-4 protein	Prostate cancer	2
	1810017F10*	AK007525	Beta-casein-like protein	Tumour-associated antigen	3
	2010003F10*	AK008064	Transmembrane 4 superfamily, member 5	Pancreatic cancer	1
	2210006M16*	AK008663	Gasdermin	Human gastric cancer cells	3
	2210007N23	AK019050	Ca11 protein homolog	Gastric carcinogenesis	1
	2210412D05	AK008907	Rho guanine nucleotide exchange factor 5	Acute myeloid leukaemia	1
	2210414K06	AK008928	Nesh	Cell metastasis and malignant transformation	2
	2310016C08*	AK009377	Hypoxia-inducible protein 2	Cervix cancer	3
	2510002J07*	AK010891	1-acyl-sn-glycerol-3-phosphate acyltransferase beta	Cancers and inflammation-associated diseases.	2
	2610002I10	AK011289	C-myc target jpo1	Tumourigenesis	3
	2610005L07	AK011323	Cadherin	Cancer development	1
	2700048G21*	AK012392	Antigen ny-co-8	Colon cancer antigen	3
	2810411G23	AK013085	Tumor protein d54	Breast cancer	1
	2810425C21*	AK013153	Death domain of death-associated protein kinase 1	Non-small cell lung cancer	2
	2810449N18*	AK013316	Sirtuin 6	Thyroid carcinoma	2
	2900009D12	AK013503	Succinate dehydrogenase	Hereditary paraganglioma	1
	4432412E01*	AK014491	X-ray repair cross-complementing protein 3	Bladder-cancer	2
	4631410F01	AK028459	Adamts-12 precursor	Gastric carcinomas	3
	4732440A06*	AK028704	Calcium-activated chloride channel-2	Breast cancer, metastasis	2
	4930500E24*	AK019661	Gas-2 related protein on chromosome 22	Central nervous system tumours	3
	4932436B18*	AK030081	Pms1 protein homolog 1	Prostate cancer	1
	4933405E14*	AK016662	Serologically defined colon cancer antigen 1	Tumour suppressor	3
	4933409E02*	AK016751	Retinoblastoma-associated protein rap140	Colon cancer cell line	3
	5430417G24*	AK030670	Headpin serine proteinase inhibitor	Squamous cell carcinoma	1
	5630400A09*	AK030708	P63 protein	Basal cell and squamous cell carcinomas	1
	5730484M20*	AK077629	Cell cycle checkpoint protein chfr	Lung cancer	1
	6430531D06*	AK032385	Elks	Capillary thyroid carcinoma	1
	6430587E11	AK032541	Copine vii	Breast cancer	1
	6820401K01*	AK033012	Npat	Cancer development	2
	9330137N20	AK079068	Hepatic leukemia factor	Lymphoblastic leukaemia	1
	9330200A01*	AK034492	Ubiquitin specific protease	Squamous non-small cell lung carcinoma	2
	A130080E24*	AK038118	C-myb protein	Colon tumour	3
	A430025E01*	AK039888	P68 RNA helicase	Colorectal tumours	2
	A430075F05	AK040185	Lipoma preferred partner	Acute myeloid leukaemia	1
	A730042E07	AK042943	Serine/threonine protein phosphatase 2a, 72/130 kda regulatory subunit B	Melanoma	2
	A830098D13*	AK044188	Megacaryocytic acute leukemia protein, isoform i	Acute megakaryoblastic leukemias	1
	A930033B01	AK020920	Graf	Hematopoeitic disorders	1
	B130017M24	AK044984	Hepatocellular carcinoma autoantigen	Hepatocellular carcinoma	3
	B230314N17	AK045848	Deleted in lung and esophageal cancer 1	Carcinogenesis	1
	B930026D14*	AK047144	Myc box dependent interacting protein 1	Prostate carcinoma	3
	B930095M03*	AK081171	Frat2	Gastric cancer	1
	C130050F24*	AK048341	Ranbp7/importin 7	Colorectal carcinoma	2
	C130062I06*	AK048462	Fibrillarin	Hepatocellular carcinoma	1
	C230012L01	AK048706	Androgen-induced prostate proliferative shutoff associated protein	Prostate cancer	1
	C630001O15	AK049821	Malt1	MALT lymphoma	1
	D330038I09	AK052361	10-formyltetrahydrofolate dehydrogenase	Tumour cells	2
	E030001H09*	AK086788	Phd finger protein 3	Glioblastoma multiforme	1
	E030027H19*	AK087108	Cub domain containing Protein 1	Human colorectal cancer	3
	E230037B21	AK054229	Vault poly(adp-ribose) polymerase	Several tumour types	3
	F630110I03	AK089273	Matrix metalloproteinase-25 precursor	Colon carcinomas or brain tumours	3
	F730035A01	AK089461	Swi/snf complex 170 kda subunit	Malignant rhabdoid tumours	1
	G630018E19*	AK090207	Prostate cancer overexpressed gene 1	Prostate cancer	1
	G630034H08*	AK090280	Transcriptional repressor scratch	Small cell lung cancer	3
Treatment
	1100001P14	AK075618	Beta-tubulin class iva isotype	Human colon adenocarcinoma	3
	1200012D01*	AK004723	Magic roundabout	Angiogenesis	3
	1810010L20	AK075773	Pituitary tumor-transforming gene 1 protein-interacting protein	Pituitary adenomas	2
	2310039D24*	AK009689	Carboxylesterase	Solid tumours	1
	2600009M07	AK011174	Polyamine modulated factor-1	Antineoplastic activity	3
	2810459H04	AK013366	Thrombospondin	Angiogenesis	1
	3010033I09*	AK019405	Alex1	Tumours originating from epithelial tissue	1
	5730405M22*	AK077421	Phosphoprotein enriched in astrocytes 15	Glioma	2
	6030493E19	AK031701	Melanoma antigen p15	Melanoma	1
	6230424I22*	AK031785	NUCLEAR MATRIX PROTEIN p84	Tumor suppression	2
	A730016J02	AK042696	Acetyltransferase tubedown-1	Vascular and haematopoietic development	3
	B130023J22	AK045057	Greb1b	Breast cancer	3
	C920008O22	AK050594	Retinoblastoma-binding protein 1	Breast cancer	2
	D030050C19	AK083587	Chronic myelogenous leukemia tumor antigen 66	Leukemias and tumour cell lines	1
	D630010E08*	AK052639	Carbonic anhydrase xii precursor	Cancer tumour cells	1
	D830007E07*	AK052857	Inositol hexakisphosphate kinase 3	Ovarian cancer	2

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 [17]. 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 [17]. A number of matrix metalloproteinases and cell adhesion molecules involved in cell invasion and metastasis were also identified.

Hereditary-disease pathologs

Potential pathologs related to hereditary diseases were the second largest group in this study (Table 3). We found 43 transcripts related to hereditary diseases, of which 39 (90.7%) were described to be defective or deleted in hereditary disorders and 4 (9.3%) were related to diagnosis. Defective pathways contributing to the pathogenesis of these diseases included metabolic pathways (e.g. peroxisomal biosynthesis and oxidation, mitochondrial respiratory chain, and phospholipid biosynthesis), cytoskeleton synthesis and organization and ion transport. Interestingly, 11 gene products from this group were previously described in the literature but their function was unknown or putative.

Table 3.

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.

Disease	FANTOM ID	DDBJ accession	Gene name	Disease relationship	OMIM status
HEREDITARY
Diagnosis
	1700026F24	AK006381	Neuronal protein 15.6	Neurogenetic disorders	3
	2300002L19*	AK009012	Chitotriosidase precursor	Gaucher's disease	1
	2700028P07	AK012300	14-3-3 protein tau	Creutzfeldt-Jakob disease	3
	4732420G08*	AK028628	Methionine synthase reductase	Methionine synthase reductase deficiency	1
Pathophysiology
	1010001M04*	AK003132	Nadh-ubiquinone oxidoreductase 20 kda subunit, mitochondrial precursor	Mitochondrial complex I deficiency	1
	1110019I12*	AK003819	Selenoprotein n precursor	Congenital muscular dystrophy	1
	4930414M06	AK005847	Sterol carrier protein 2	Peroxisomal D-hydroxyacyl-CoA dehydrogenase deficiency	1
	1810064C02	AK007951	Sedlin	Spondyloepiphyseal dysplasia tarda	1
	2310057L06	AK075908	Tubulin-specific chaperone d	Retinitis pigmentosa	2
	2410004F01*	AK010385	Protoheme ix farnesyltransferase, mitochondrial precursor	Charcot-marie-tooth disease	1
	2610205J09	AK011891	Periodic tryptophan Protein 1	Progressive myoclonus epilepsy	1
	2900072D10*	AK013765	Sco2 protein homolog, mitochondrial precursor	Cardioencephalomyopathy and a severe COX deficiency	1
	3110031I02*	AK014104	N-wasp protein	Wiskott-Aldrich syndrome	1
	4832440C16	AK029338	Apical-like protein	Ocular albinism type 1	1
	4930430B17	AK076748	Machado-joseph disease protein 1	Machado-joseph disease	1
	5830404H04	AK017896	Protein c21orf2	Autoimmune polyglandular disease type I	1
	6030476O14	AK031666	Myoferlin	Muscular dystrophy and cardiomyopathy	1
	6430516P20	AK032293	Ceroid-lipofuscinosis neuronal protein 5	Late infantile neuronal ceroid lipofuscinosis	1
	6430560A18	AK078275	Caltractin, isoform 2	Barth syndrome and chondrodysplasia punctata	2
	8030487I16	AK033295	Gdp-fucose transporter 1	Leukocyte adhesion deficiency II	1
	9330166I04*	AK034236	Sialidase	Sialidosis	1
	6720416P20*	AK032725	Zinc finger protein 25	MEN2a MEN2b	1
	9630046L06	AK036225	Glycogen debranching enzyme	Glycogen storage disease type III	1
	9930121L06*	AK037126	Artemis protein	Athabascan SCID	3
	A130054J05*	AK037846	Nuclear localization signal protein absent in velo-cardio-facial patients	Velo-cardio-facial syndrome	1
	A230074J06*	AK038912	Nyctalopin	X-linked congenital stationary night blindness	1
	A230090N11*	AK039054	Cyld protein	Cylindromatosis	1
	A630004L17	AK041354	Transmembrane protein vezatin	Deafness	3
	A730020L24	AK042745	Alkyl-dihydroxyacetonephosphate synthase	Zellweger syndrome	1
	A830020B12	AK043682	Peroxisome assembly protein 10	Peroxisome-biogenesis disorders	1
	A930007F16	AK044320	Inositol polyphosphate 5-phosphatase ocrl-1	Lowe syndrome	1
	A930014F04	AK044460	Mitochondrial intermediate peptidase, mitochondrial precursor	Friedreich's ataxia	1
	B230307C21*	AK045712	Epilepsy holoprosencephaly candidate-1 protein	Progressive myoclonus epilepsy	1
	B230311E17	AK045797	Monocarboxylate transporter 5	Mitochondrial myopathies	2
	B430307M20*	AK046679	Ataxin 7	Spinocerebellar ataxia type 7	1
	C130020P08	AK047906	Lowe oculocerebrorenal syndrome protein	Oculocerebrorenal syndrome of Lowe	1
	C330001M22*	AK049106	Ubash3a protein	Autosomal recessive deafness	2
	C330016K18*	AK049248	Sodium bicarbonate cotransporter isoform 1	Proximal renal tubular acidosis associated with ocular abnormalities	1
	C430015N23*	AK049478	Y+l amino acid transporter 1	Lysinuric protein intolerance	1
	D030003E11	AK050684	Beta-1,4-galactosyltransferase 7	Progeroid type Ehlers-Danlos syndrome	1
	E130016P05	AK084312	T-box transcription factor tbx22	Cleft palate	1
	D630003K02*	AK085272	Cytochrome b5 reductase b5r.2	Methemoglobinemia	1
	D630025L11*	AK052697	Chorein	Chorea-acanthocytosis	1

Other pathologs

All other potential pathologs (immunological, neurological, reproductive, cardiovascular, and others) have been summarised in table 4. The immunological disorders-related group comprised 10 transcripts. The majority of them represent genes involved in autoimmune diseases including systemic lupus erythematosus, rheumatoid arthritis, Sjogren's syndrome, sarcoidosis and Crohn's disease. Eight of the transcripts in this group encode proteins that have homology to known autoantigens. For neurological disorders, four potential pathologs related to Alzheimer's or Huntington's disease. Some neurological pathologs were also classified as hereditary disorder pathologs because of their Mendelian inheritance pattern. Six pathologs were related to reproductive disorders, eight to cardiovascular disorders (mainly hypertension), and 15 to other diseases.

Table 4.

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.

Disease	FANTOM ID	DDBJ accession	Gene name	Disease relationship	OMIM status
IMMUNOLOGICAL
Diagnosis
	1500019E10*	AK005285	Replication protein a 14 kda subunit	Systemic lupus erythematosus	1
	1810019E15	AK007546	Dek protein	LES and Juvenile RA	2
	2700059D02*	AK012454	Uveal autoantigen	Behecet's Disease, sarcoidosis and Vogt-Koyanagi-Harada disease	3
	4632415P04*	AK028516	Golgi complex autoantigen golgin-97	Sjogren's syndrome	1
	D030032G01*	AK050903	Neuroblast differentiation associated protein ahnak	Systemic lupus erythematosus	2
Pathophysiology
	A630006E02*	AK041380	Minor histocompatibility antigen ha-1	Graft-versus-host disease	1
	B230303A05*	AK045681	U1 small nuclear ribonucleoprotein c	Autoimmunity to U1 snrnps	1
	B230397K24*	AK046480	L1 retroposon, orf2 mrna	Rheumatoid arthritis	3
	C330006A15*	AK049133	Ribonuclease p protein subunit p38	Scleroderma autoimmune antigens	1
	F830032C23*	AK089843	Caspase recruitment domain protein 15	Crohn's disease	1
NEUROLOGICAL
Pathophysiology
	2210420D18		Serine/threonine kinase rick	Alzheimer's disease	2
	4833420A15	AK014731	Huntingtin-interacting protein-1 protein interactor	Huntington's disease	1
	9330170I02*	AK034263	Metabotropic glutamate receptor 2 precursor	Alzheimer's disease	2
	B230307E07	AK045716	Excitatory amino acid transporter 1	Alzheimer's disease	3
CARDIOVASCULAR
Pathophysiology
	1700127D06*	AK007298	Tisuee kallilrein	Hypertension	1
	2510048K03*	AK011112	Prolylcarboxypeptidase	Essential hypertension	1
	4833405G23	AK029362	Rtp801-like protein	Ischemic diseases	3
	9630044I02	AK036182	Mitochondrial isoleucine trna synthetase	Cardiomyopathy	3
	B430208E24*	AK046620	Ras gtpase-activating Protein 1	Neuropathology of ischemia	2
	D130064D17*	AK051677	Lysosomal pro-x carboxypeptidase precursor	Essential hypertension	3
	E030024D09*	AK087056	Angiotensin converting enzyme	Hypertension	1
	2310063B19*	AK010021	Epoxide hydrolase	Hhypertension	2
REPRODUCTIVE
Pathophysiology
	0610007H07*		Z-protein	Fetal loss	3
	1700010P14*	AK005852	Nyd-sp6	Spermatogenesis	3
	4631410O16*	AK028461	Sumo-1-specific protease 1	Reproduction	1
	4930406H24*	AK029590	Adam 26 precursor	Spermatogenesis	3
	B130010I06*	AK044891	Dmrt2/terra-like protein	Sex differentiation disorders	3
	D030049L20	AK050982	Sperm antigen	Immunologic infertility	1
OTHERS
Pathophysiology
	1110004H01*	AK003421	Mitochondrial import inner membrane translocase subunit tim9 b	Fracture healing	3
	1810015E19	AK007508	Slp-1	Autism	1
	1810015M19*	AK019013	Lw glycoprotein	Sickle cell disease	1
	4932434G09*	AK016547	Ribonucleases p/mrp protein subunit pop1	Connective tissue diseases	3
	5730465G20	AK077598	N-acetyllactosaminide beta-1,6-n-acetylglucosaminyl Transferase	Blood group I gene	3
	9830131G07*	AK036537	Bomapin	Hematopoiesis	2
	9930031F20	AK036967	Vp165	Type 2 diabetes	3
	A130042M24*	AK037730	Mucin	COPD	1
	A730076H11	AK043253	T-cell receptor alpha chain precursor v-j region	Leishmania major	3
	A830007N20	AK043557	Wd-repeat protein 3	Triple-A syndrome	1
	B830002B15*	AK046772	Polycystic kidney disease 2-like protein	Cystic diseases	1
	C230099M23*	AK082743	Vesicular glutamate transporter 2	Schizophrenia	3
	E130216C05	AK087459	Amyloid beta precursor-like protein 2	Healing corneal epithelium	2
Treatment
	5033405N08*	AK017155	Agmatinase	Chronic pain, addictive states and brain injury	3
	A930028N13	AK044634	Ankyrin-2	Human hemolytic anemias	2

Identification of novel pathologs

These 182 transcripts were further analysed to find those that by sequence comparison and conserved gene synteny corresponded to potential new pathologs, classified as "ortholog candidates" or "novel sequences" (Figure 2). We found 137 pathologs that represented the most similar mouse sequences to known human genes. Of these, 72 (52.5%) were found in public databases (NCBI and SPTR) as previously described mouse orthologs and their function is known or inferred. The remaining 65 (47.5%) represent the best mouse to human match by sequence similarity but their function is not known, making them excellent candidate mouse orthologs.

Figure 2.

Flow chart of method used to classify "pathologs". To identify pathologs that correspond to already known mouse orthologs or potential new orthologs, cDNA sequences were compared to known human sequences and conservation of synteny assessed using mouse to human mapping information. If the patholog corresponded to best mouse to human hit the reported function of the gene product was checked. Mouse sequences with reported human ortholog and known function were classified as "known ortholog", sequences reported as best mouse to human hit with unknown function were classified as "ortholog-candidate" and sequences with unknown function that did not correspond to the best mouse to human hit were classified as "novel sequences".

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

Genome-wide studies of pathologs in other organisms [2,4,18] focused on systematic searches for paralogs or orthologs of human disease genes in the respective genomes. In those studies pathologs in different organisms were detected using the OMIM database [19] that contains entries on hereditary human disorders. We were interested, however, in identifying potential pathologs involved in both inherited and non-inherited diseases and consequently elected to use a broader search strategy focusing on sequence analysis combined with key-word searching of literature abstracts based on annotated gene names and MeSH terms. Scientific abstracts listed in PubMed were searched to identify human disease-related genes or proteins related to our dataset of "similar to" FANTOM2 clones. In a comparison between the disease genes listed in OMIM and those detected using PubMed, we found that out of the 182 potential pathologs we identified in this project, 128 (70.3%) were listed in OMIM, but only 89 were listed as having a disease relationship (Table 5). For the remaining 39 pathologs either no disease relationship was listed in OMIM or the disease association listed was not the same as the one found by our manual expert curation. Furthermore, through our search strategy of PubMed abstracts we identified 93 additional potential pathologs not identified through OMIM.

Table 5.

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.

Disease	OMIM NDA	Not in OMIM	OMIM DA	Total
Cancer	26	33	37	96
Hereditary disorders	4	4	35	43
Other	9	17	17	43
Total	39	54	89	182

Discussion

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 [20]). 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.

Conclusions

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.

Methods

FANTOM2 system

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 [14].

Accession numbers

Accession numbers in the manuscript refer to FANTOM accessions submitted to the DNA data bank of Japan (DDBJ), or public accessions.

Sequence analysis

Pre-computed results of sequence similarity comparisons were retrieved from the FANTOM2 database [21]. The method used for detection of sequence similarity has been explained by Okasaki et. al. [14]. 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 [22] 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 [23] 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. [20]. 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 [24] and GNF gene expression atlas [25] 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 [21]) and conservation of synteny based on mouse to human mapping information (NCBI Map viewer [26]) and RIKEN-genomapper [27] (July 2003), and reported function (Locus Link [28] 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".

Authors' contributions

DS, CS, VB, LS, NP carried out the gene annotation and expert curation and drafted the manuscript. TN and CS designed and created the FACTS system. DS and NP participated in the annotation of its entries. All authors read and approved the final manuscript.