PLoS BIOLOGY

Integrative Annotation of 21,037 Human Genes
Validated by Full-Length cDNA Clones
Tadashi Imanishi1, Takeshi Itoh1,2, Yutaka Suzuki3,68, Claire O’Donovan4, Satoshi Fukuchi5, Kanako O. Koyanagi6,
Roberto A. Barrero5, Takuro Tamura7,8, Yumi Yamaguchi-Kabata1, Motohiko Tanino1,7, Kei Yura9, Satoru Miyazaki5,
Kazuho Ikeo5, Keiichi Homma5, Arek Kasprzyk4, Tetsuo Nishikawa10,11, Mika Hirakawa12, Jean Thierry-Mieg13,14,
Danielle Thierry-Mieg13,14, Jennifer Ashurst15, Libin Jia16, Mitsuteru Nakao3, Michael A. Thomas17, Nicola Mulder4,
Youla Karavidopoulou4, Lihua Jin5, Sangsoo Kim18, Tomohiro Yasuda11, Boris Lenhard19, Eric Eveno20,21, Yoshiyuki
Suzuki5, Chisato Yamasaki1, Jun-ichi Takeda1, Craig Gough1,7, Phillip Hilton1,7, Yasuyuki Fujii1,7, Hiroaki Sakai1,7,22,
Susumu Tanaka1,7, Clara Amid23, Matthew Bellgard24, Maria de Fatima Bonaldo25, Hidemasa Bono26, Susan K.
Bromberg27, Anthony J. Brookes19, Elspeth Bruford28, Piero Carninci29, Claude Chelala20, Christine Couillault20,21,
Sandro J. de Souza30, Marie-Anne Debily20, Marie-Dominique Devignes31, Inna Dubchak32, Toshinori Endo33, Anne
Estreicher34, Eduardo Eyras15, Kaoru Fukami-Kobayashi35, Gopal R. Gopinath36, Esther Graudens20,21, Yoonsoo Hahn18,
Michael Han23, Ze-Guang Han21,37, Kousuke Hanada5, Hideki Hanaoka1, Erimi Harada1,7, Katsuyuki Hashimoto38, Ursula
Hinz34, Momoki Hirai39, Teruyoshi Hishiki40, Ian Hopkinson41,42, Sandrine Imbeaud20,21, Hidetoshi Inoko1,7,43,
Alexander Kanapin4, Yayoi Kaneko1,7, Takeya Kasukawa26, Janet Kelso44, Paul Kersey4, Reiko Kikuno45, Kouichi
Kimura11, Bernhard Korn46, Vladimir Kuryshev47, Izabela Makalowska48, Takashi Makino5, Shuhei Mano43, Regine
Mariage-Samson20, Jun Mashima5, Hideo Matsuda49, Hans-Werner Mewes23, Shinsei Minoshima50,52, Keiichi Nagai11,
Hideki Nagasaki51, Naoki Nagata1, Rajni Nigam27, Osamu Ogasawara3, Osamu Ohara45, Masafumi Ohtsubo52, Norihiro
Okada53, Toshihisa Okido5, Satoshi Oota35, Motonori Ota54, Toshio Ota22, Tetsuji Otsuki55, Dominique PiatierTonneau20, Annemarie Poustka47, Shuang-Xi Ren21,37, Naruya Saitou56, Katsunaga Sakai5, Shigetaka Sakamoto5,
Ryuichi Sakate39, Ingo Schupp47, Florence Servant4, Stephen Sherry13, Rie Shiba1,7, Nobuyoshi Shimizu52, Mary
Shimoyama27, Andrew J. Simpson30, Bento Soares25, Charles Steward15, Makiko Suwa51, Mami Suzuki5, Aiko
Takahashi1,7, Gen Tamiya1,7,43, Hiroshi Tanaka33, Todd Taylor57, Joseph D. Terwilliger58, Per Unneberg59, Vamsi
Veeramachaneni48, Shinya Watanabe3, Laurens Wilming15, Norikazu Yasuda1,7, Hyang-Sook Yoo18, Marvin Stodolsky60,
Wojciech Makalowski48, Mitiko Go61, Kenta Nakai3, Toshihisa Takagi3, Minoru Kanehisa12, Yoshiyuki Sakaki3,57, John
Quackenbush62, Yasushi Okazaki26, Yoshihide Hayashizaki26, Winston Hide44, Ranajit Chakraborty63, Ken Nishikawa5,
Hideaki Sugawara5, Yoshio Tateno5, Zhu Chen21,37,64, Michio Oishi45, Peter Tonellato65, Rolf Apweiler4, Kousaku
Okubo5,40, Lukas Wagner13, Stefan Wiemann47, Robert L. Strausberg16, Takao Isogai10,66, Charles Auffray20,21, Nobuo
Nomura40, Takashi Gojobori1,5,67*, Sumio Sugano3,40,68
1 Integrated Database Group, Biological Information Research Center, National Institute of Advanced Industrial Science and Technology, Tokyo, Japan, 2 Bioinformatics
Laboratory, Genome Research Department, National Institute of Agrobiological Sciences, Ibaraki, Japan, 3 Human Genome Center, The Institute of Medical Science, The
University of Tokyo, Tokyo, Japan, 4 EMBL Outstation—European Bioinformatics Institute, Wellcome Trust Genome Campus, Cambridge, United Kingdom, 5 Center for
Information Biology and DNA Data Bank of Japan, National Institute of Genetics, Shizuoka, Japan, 6 Nara Institute of Science and Technology, Nara, Japan, 7 Integrated
Database Group, Japan Biological Information Research Center, Japan Biological Informatics Consortium, Tokyo, Japan, 8 BITS Company, Shizuoka, Japan, 9 Quantum
Bioinformatics Group, Center for Promotion of Computational Science and Engineering, Japan Atomic Energy Research Institute, Kyoto, Japan, 10 Reverse Proteomics
Research Institute, Chiba, Japan, 11 Central Research Laboratory, Hitachi, Tokyo, Japan, 12 Bioinformatics Center, Institute for Chemical Research, Kyoto University, Kyoto,
Japan, 13 National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, Maryland, United States of America, 14 Centre
National de la Recherche Scientifique (CNRS), Laboratoire de Physique Mathematique, Montpellier, France, 15 The Wellcome Trust Sanger Institute, Wellcome Trust Genome
Campus, Cambridge, United Kingdom, 16 National Cancer Institute, National Institutes of Health, Bethesda, Maryland, United States of America, 17 Department of Biological
Sciences, Idaho State University, Pocatello, Idaho, United States of America, 18 Korea Research Institute of Bioscience and Biotechnology, Taejeon, Korea, 19 Center for
Genomics and Bioinformatics, Karolinska Institutet, Stockholm, Sweden, 20 Genexpress—CNRS—Functional Genomics and Systemic Biology for Health, Villejuif Cedex,
France, 21 Sino-French Laboratory in Life Sciences and Genomics, Shanghai, China, 22 Tokyo Research Laboratories, Kyowa Hakko Kogyo Company, Tokyo, Japan,
23 MIPS—Institute for Bioinformatics, GSF—National Research Center for Environment and Health, Neuherberg, Germany, 24 Centre for Bioinformatics and BiologicalComputing, School of Information Technology, Murdoch University, Murdoch, Western Australia, Australia, 25 Medical Education and Biomedical Research Facility, Universityof Iowa, Iowa City, Iowa, United States of America, 26 Genome Exploration Research Group, RIKEN Genomic Sciences Center, RIKEN Yokohama Institute, Kanagawa, Japan,
27 Medical College of Wisconsin, Milwaukee, Wisconsin, United States of America, 28 HUGO Gene Nomenclature Committee, University College London, London, United
Kingdom, 29 Genome Science Laboratory, RIKEN, Saitama, Japan, 30 Ludwig Institute of Cancer Research, Sao Paulo, Brazil, 31 CNRS, Vandoeuvre les Nancy, France,
32 Lawrence Berkeley National Laboratory, Berkeley, California, United States of America, 33 Department of Bioinformatics, Medical Research Institute, Tokyo Medical and
Dental University, Tokyo, Japan, 34 Swiss Institute of Bioinformatics, Geneva, Switzerland, 35 Bioresource Information Division, RIKEN BioResource Center, RIKEN Tsukuba
Institute, Ibaraki, Japan, 36 Genome Knowledgebase, Cold Spring Harbor Laboratory, Cold Spring Harbor, New York, United States of America, 37 Chinese National Human
Genome Center at Shanghai, Shanghai, China, 38 Division of Genetic Resources, National Institute of Infectious Diseases, Tokyo, Japan, 39 Graduate School of Frontier
Sciences, Department of Integrated Biosciences, University of Tokyo, Chiba, Japan, 40 Functional Genomics Group, Biological Information Research Center, National Institute

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June 2004 | Volume 2 | Issue 6 | Page 0856

Integrative Annotation of Human Genesof Advanced Industrial Science and Technology, Tokyo, Japan, 41 Department of Primary Care and Population Sciences, Royal Free University College Medical School,
University College London, London, United Kingdom, 42 Clinical and Molecular Genetics Unit, The Institute of Child Health, London, United Kingdom, 43 Department of
Genetic Information, Division of Molecular Life Science, School of Medicine, Tokai University, Kanagawa, Japan, 44 South African National Bioinformatics Institute, Universityof the Western Cape, Bellville, South Africa, 45 Kazusa DNA Research Institute, Chiba, Japan, 46 RZPD Resource Center for Genome Research, Heidelberg, Germany,
47 Molecular Genome Analysis, German Cancer Research Center-DKFZ, Heidelberg, Germany, 48 Pennsylvania State University, University Park, Pennsylvania, United Statesof America, 49 Department of Bioinformatic Engineering, Graduate School of Information Science and Technology, Osaka University, Osaka, Japan, 50 Medical Photobiology
Department, Photon Medical Research Center, Hamamatsu University School of Medicine, Shizuoka, Japan, 51 Computational Biology Research Center, National Institute ofAdvanced Industrial Science and Technology, Tokyo, Japan, 52 Department of Molecular Biology, Keio University School of Medicine, Tokyo, Japan, 53 Department of
Biological Sciences, Graduate School of Bioscience and Biotechnology, Tokyo Institute of Technology, Kanagawa, Japan, 54 Global Scientific Information and Computing
Center, Tokyo Institute of Technology, Tokyo, Japan, 55 Molecular Biology Laboratory, Medicinal Research Laboratories, Taisho Pharmaceutical Company, Saitama, Japan,
56 Department of Population Genetics, National Institute of Genetics, Shizuoka, Japan, 57 Human Genome Research Group, Genomic Sciences Center, RIKEN Yokohama
Institute, Kanagawa, Japan, 58 Columbia University and Columbia Genome Center, New York, New York, United States of America, 59 Department of Biotechnology, RoyalInstitute of Technology, Stockholm, Sweden, 60 Biology Division and Genome Task Group, Office of Biological and Environmental Research, United States Department ofEnergy, Washington, D.C., United States of America, 61 Faculty of Bio-Science, Nagahama Institute of Bio-Science and Technology, Shiga, Japan, 62 Institute for Genomic
Research, Rockville, Maryland, United States of America, 63 Center for Genome Information, Department of Environmental Health, University of Cincinnati, Cincinnati, Ohio,
United States of America, 64 State Key Laboratory of Medical Genomics, Shanghai Institute of Hematology, Rui-Jin Hospital, Shanghai Second Medical University, Shanghai,
China, 65 PointOne Systems, Wauwatosa, Wisconsin, United States of America, 66 Graduate School of Life and Environmental Sciences, University of Tsukuba, Ibaraki, Japan,
67 Department of Genetics, Graduate University for Advanced Studies, Shizuoka, Japan, 68 Department of Medical Genome Sciences, Graduate School of Frontier Sciences,
University of Tokyo, Tokyo, JapanThe human genome sequence defines our inherent biological potential; the realization of the biology encoded thereinrequires knowledge of the function of each gene. Currently, our knowledge in this area is still limited. Several lines ofinvestigation have been used to elucidate the structure and function of the genes in the human genome. Even so, gene
prediction remains a difficult task, as the varieties of transcripts of a gene may vary to a great extent. We thus
performed an exhaustive integrative characterization of 41,118 full-length cDNAs that capture the gene transcripts ascomplete functional cassettes, providing an unequivocal report of structural and functional diversity at the gene level.
Our international collaboration has validated 21,037 human gene candidates by analysis of high-quality full-length
cDNA clones through curation using unified criteria. This led to the identification of 5,155 new gene candidates. It also
manifested the most reliable way to control the quality of the cDNA clones. We have developed a human genedatabase, called the H-Invitational Database (H-InvDB; http://www.h-invitational.jp/). It provides the following:
integrative annotation of human genes, description of gene structures, details of novel alternative splicing isoforms,
non-protein-coding RNAs, functional domains, subcellular localizations, metabolic pathways, predictions of protein
three-dimensional structure, mapping of known single nucleotide polymorphisms (SNPs), identification of polymorphic
microsatellite repeats within human genes, and comparative results with mouse full-length cDNAs. The H-InvDB
analysis has shown that up to 4% of the human genome sequence (National Center for Biotechnology Informationbuild 34 assembly) may contain misassembled or missing regions. We found that 6.5% of the human gene candidates(1,377 loci) did not have a good protein-coding open reading frame, of which 296 loci are strong candidates for nonprotein-coding RNA genes. In addition, among 72,027 uniquely mapped SNPs and insertions/deletions localized withinhuman genes, 13,215 nonsynonymous SNPs, 315 nonsense SNPs, and 452 indels occurred in coding regions. Together
with 25 polymorphic microsatellite repeats present in coding regions, they may alter protein structure, causing
phenotypic effects or resulting in disease. The H-InvDB platform represents a substantial contribution to resources
needed for the exploration of human biology and pathology.

Introduction
The draft sequences of the human, mouse, and rat genomes
are already available (Lander et al. 2001; Marshall 2001;
Venter et al. 2001; Waterston et al. 2002). The next challenge
comes in the understanding of basic human molecularbiology through interpretation of the human genome. To
display biological data optimally we must ﬁrst characterize
the genome in terms of not only its structure but alsofunction and diversity. It is of immediate interest to identify
factors involved in the developmental process of organisms,
non-protein-coding functional RNAs, the regulatory networkof gene expression within tissues and its governance overstates of health, and protein–gene and protein–protein
interactions. In doing so, we must integrate this informationin an easily accessible and intuitive format. The humangenome may encode only 30,000 to 40,000 genes (Lander et al.
2001; Venter et al. 2001), suggesting that complex interdePLoS Biology | http://biology.plosjournals.org

Received December 19, 2003; Accepted April 1, 2004; Published April 20,
2004
DOI: 10.1371/journal.pbio.0020162
Copyright: Ó 2004 Imanishi et al. This is an open-access article distributed
under the terms of the Creative Commons Attribution License, which permits
unrestricted use, distribution, and reproduction in any medium, provided theoriginal work is properly cited.
Abbreviations: 3D, three-dimensional; AS, alternative splicing; CAI, codon
adaptation index; dbSNP, Single Nucleotide Polymorphism Database; DDBJ, DNA
Data Bank of Japan; EC, Enzyme Commission; EMBL, European Molecular Biology
Laboratories; EST, expressed sequence tag; FANTOM, Functional Annotation of
Mouse; FLcDNA, full-length cDNA; FLJ, Full-Length Long Japan; FTHFD, formyltetrahydrofolate dehydrogenase; GO, Gene Ontology; GTOP, Genomes TO Protein
structures and functions database; H-Angel, Human Anatomic Gene Expression
Library; H-Inv or H-Invitational, Human Full-Length cDNA Annotation Invitational;
H-InvDB, H-Invitational Database; iAFLP, introduced amplified fragment length
polymorphism; NCBI, National Center for Biotechnology Information; ncRNAs, nonprotein-coding RNAs; OMIM, Online Mendelian Inheritance in Man; ORF, open
reading frame; PDB, Protein Data Bank; RefSeq, Reference Sequence Collection;
SMO, Similarity, Motif, and ORF; SNP, single nucleotide polymorphism
Academic Editor: Richard Roberts, New England Biolabs*To whom correspondence should be addressed. E-mail: tgojobor@genes.nig.ac.jp

June 2004 | Volume 2 | Issue 6 | Page 0857

Integrative Annotation of Human Genes

pendent gene regulation mechanisms exist to account for thecomplex gene networks that differentiate humans fromlower-order organisms. In organisms with small genomes, it
is relatively straightforward to use direct computational
prediction based upon genomic sequence to identify mostgenes by their long open reading frames (ORFs). However,computational gene prediction from the genomic sequenceof organisms with short exons and long introns can besomewhat error-prone (Ashburner 2000; Reese et al. 2000;
Lander et al. 2001).
Previous efforts to catalogue the human transcriptome
were based on expressed sequence tags (ESTs) used for theidentiﬁcation of new genes (Adams et al. 1991; Auffray et al.
1995; Houlgatte et al. 1995), chromosomal assignment ofgenes (Gieser and Swaroop 1992; Khan et al. 1992; Camargo
et al. 2001), prediction of genes (Nomura et al. 1994), andassessment of gene expression (Okubo et al. 1992). Recently,
Camargo et al. (2001) generated a large collection of ORF
ESTs, and Saha et al. (2002) conducted a large-scale serial
analysis of gene expression patterns to identify novel humangenes. The availability of human full-length transcripts frommany large-scale sequencing projects (Nomura et al. 1994;
Nagase et al. 2001; Wiemann et al. 2001; Yudate 2001; Kikuno
et al. 2002; Strausberg et al. 2002) has provided a unique
opportunity for the comprehensive evaluation of the humantranscriptome through the annotation of a variety of RNAtranscripts. Protein-coding and non-protein-coding sequences, alternative splicing (AS) variants, and sense–antisense
RNA pairs could all be functionally identiﬁed. We thus
designed an international collaborative project to establish
an integrative annotation database of 41,118 human fulllength cDNAs (FLcDNAs). These cDNAs were collected fromsix high-throughput sequencing projects and evaluated at theﬁrst international jamboree, entitled the Human Full-length
cDNA Annotation Invitational (H-Invitational or H-Inv)(Cyranoski 2002). This event was held in Tokyo, Japan, andtook place from August 25 to September 3, 2002.
Efforts which have been made in the same area as the H-Inv
annotation work include the Functional Annotation of Mouse(FANTOM) project (Kawai et al. 2001; Bono et al. 2002;
Okazaki et al. 2002), Flybase (GOC 2001), and the RIKEN
Arabidopsis full-length cDNA project (Seki et al. 2002). In ourown project, great effort has been taken at all levels, not onlyin the annotation of the cDNAs but also in the way the data
can be viewed and queried. These aspects, along with theapplications of our research to disease research, distinguishour project from other similar projects.
This manuscript provides the ﬁrst report by the H-Inv
consortium, showing some of the discoveries made so far andintroducing our new database of the human transcriptome. It
is hoped that this will be the ﬁrst in a long line of publications
announcing discoveries made by the H-Inv consortium. Here
we describe results from our integrative annotation in four
major areas: mapping the transcriptome onto the humangenome, functional annotation, polymorphism in the transcriptome, and evolution of the human transcriptome. We
then introduce our new database of the human transcriptome, the H-Invitational Database (H-InvDB; http://www.hinvitational.jp), which stores all annotation results by the
consortium. Free and unrestricted access to the H-Inv
annotation work is available through the database. Finally,
PLoS Biology | http://biology.plosjournals.org

we summarize our most important ﬁndings thus far in the HInv project in Concluding Remarks.

Results/Discussion
Mapping the Transcriptome onto the Human Genome
Construction of the nonredundant human FLcDNA database. We present the ﬁrst experimentally validated nonredundant transcriptome of human FLcDNAs produced bysix high-throughput cDNA sequencing projects (Ota et al.
1997, 2004; Strausberg et al. 1999; Hu et al. 2000; Wiemann etal. 2001; Yudate 2001; Kikuno et al. 2002) as of July 15, 2002.
The dataset consists of 41,118 cDNAs (H-Inv cDNAs) thatwere derived from 184 diverse cell types and tissues (seeDataset S1). The number of clones, the number of libraries,
major tissue origins, methods, and URLs of cDNA clones foreach cDNA project are summarized in Table 1. H-Inv cDNAs
include 8,324 cDNAs recently identiﬁed by the Full-LengthLong Japan (FLJ) project. The FLJ clones represent about halfof the H-Inv cDNAs (Table 1). The policies for library
selection and the results of initial analysis of the constituent
projects were reported by the participants themselves: theChinese National Human Genome Center (CHGC) (Hu et al.
2000), the Deutsches Krebsforschungszentrum (DKFZ/MIPS)(Wiemann et al. 2001), the Institute of Medical Science at the
University of Tokyo (IMSUT) (Suzuki et al. 1997; Ota et al.
2004), the Kazusa cDNA sequence project of the Kazusa DNA
Research Institute (KDRI) (Hirosawa et al. 1999; Nagase et al.
1999; Suyama et al. 1999; Kikuno et al. 2002), the Helix
Research Institute (HRI) (Yudate et al. 2001), and theMammalian Gene Collection (MGC) (Strausberg et al. 1999;
Moonen et al. 2002), as well as FLJ mentioned earlier (Ota etal. 2004). The variation in tissue origins for library construction among these six groups resulted in rare occurrencesof sequence redundancy among the collections. In a recent
study, the FLJ project has described the complete sequencing
and characterization of 21,243 human cDNAs (Ota et al.
2004). On the other hand, the H-Inv project characterized
cDNAs from this project and six high-throughput cDNA
producers by using a different suite of computational analysis
techniques and an alternative system of functional annotation.
The 41,118 H-Inv cDNAs were mapped on to the humangenome, and 40,140 were considered successfully aligned. The
alignment criterion was that a cDNA was only aligned if it had
both 95% identity and 90% length coverage against the
genome (Figure 1). The mean identity of all the alignments
between 40,140 mapped cDNAs and genomic sequences was
99.6 %, and the mean coverage against the genomic sequence
was 99.6%. In some cases, terminal exons were aligned with
low identity or low coverage. For example, 89% of internalexons have identity of 99.8% or higher, while only 78% and
50% of the ﬁrst and last exons do, respectively. Thesealignments with low identity or low coverage seemed to becaused by the unsuccessful alignments of the repetitive
sequences found in UTR regions and the misalignments of
39 terminal poly-A sequences. Although better alignments
could be obtained for these sequences by improving the
mapping procedure, we concluded that the quality of the
FLcDNAs was high overall.
Due to redundancy and AS within the human transcriptome, these 40,140 cDNAs were clustered to 20,190 loci
June 2004 | Volume 2 | Issue 6 | Page 0858

Integrative Annotation of Human Genes

Table 1. Summary of cDNA Resources
cDNA
Sequence
Provider*Number of
cDNAs(Without
Redundancy)

Numberof
Library
Origins

Major
TissueLibrary
Origins

MethodURL

Reference

CHGC

758 (754)30

Adrenal gland,
hypothalamus,
CD34þ
stem cell
Testis, brain,lymph node

Selecting FLcDNA
clones from EST
libraries

http://www.chgc.
sh.cn/

Hu et al. 2000

http://mips.gsf.de/projects/cdna

Wiemann et al. 2001

http://www.hri.co.
jp/HUNT/Ota et al. 1997, 2004;
Yudate et al. 2001

DKFZ/MIPS 5,555 (5,521)

14

FLJ/HRI

8,066 (8,057)

46

FLJ/IMSUT

12,585 (12,560) 81

FLJ/KDRI

348(342)1KDRI2,000 (2,000)

9MGC/NIH

11,806(11,414)

69

Selecting FLcDNA
clones from 59- and
39- EST libraries
Teratocarcinoma, Oligo-capping method
and selection byplacenta,
one-pass sequences
whole embryo
Brain, testis,
Oligo-capping methodbone marrowand selection by
one-pass sequences
SpleenSelection by
one-pass sequencesBrain
In vitro protein
synthesis andselection by
one-pass sequences
Placenta,
Selecting genelung,candidates fromskin
59-EST libraries

http://cdna.ims.u-tokyo. Suzuki et al. 1997;
ac.jp/
Ota et al. 2004
http://www.kazusa.or.
jp/NEDO/
http://www.kazusa.or.
jp/huge/

http://mgc.nci.nih.gov/

Ota et al. 2004
Hirosawa et al. 1997;
Nagase et al. 1999;
Suyama et al. 1999;
Kikuno et al. 2002
Strausberg et al. 1999

*FLcDNA data were provided for H-Inv project by the FLJ project of NEDO (URL: http://www.nedo.go.jp/bio-e/) and six high-throughput cDNA clone producers Chinese
National Human Genome Center (CHGC), the Deutsches Krebsforschungszentrum (DKFZ/MIPS), Helix Research Institute (HRI), the Institute of Medical Science in the
University of Tokyo (IMSUT), the Kazusa DNA Research Institute (KDRI), and the Mammalian Gene Collection (MGC/NIH).
DOI: 10.1371/journal.pbio.0020162.t001(H-Inv loci). For the remaining 978 unmapped cDNAs, we
conducted cDNA-based clustering, which yielded 847 clusters.
The clusters created had an average of 2.0 cDNAs per locus(Table 2). The average was only 1.2 for unmapped clusters,
probably because many of these genes are encoded byheterochromatic regions of the human genome and show
limited levels of gene expression. The gene density for eachchromosome varied from 0.6 to 19.0 genes/Mb, with anaverage of 6.5 genes/Mb. This distribution of genes over the
genome is far from random. This biased gene localization
concurs with the gene density on chromosomes found insimilar previous reports (Lander et al. 2001; Venter et al.
2001). This indicates that the sampled cDNAs are unbiased
with respect to chromosomal location. Most cDNAs were
mapped only at a single position on the human genome.
However, 1,682 cDNAs could be mapped at multiple positions(with mean values of 98.2% identity and 98.1% coverage).
The multiple matching may be caused by either recent gene
duplication events or artiﬁcial duplication of the humangenome caused by misassembled contigs. In our study we haveselected only the ‘‘best’’ loci for the cDNAs (see Materials andMethods for details).
In total, 21,037 clusters (20,190 mapped and 847
unmapped) were identiﬁed and entered into the H-InvDB.
We assigned H-Inv cluster IDs (e.g., HIX0000001) to the
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clusters and H-Inv cDNA IDs (e.g., HIT000000001) to all
curated cDNAs. A representative sequence was selected fromeach cluster and used for further analyses and annotation.
Comparison of the mapped H-Inv cDNAs with otherannotated datasets. In order to evaluate the H-Inv dataset,
we compared all of the mapped H-Inv cDNAs with theReference Sequence Collection (RefSeq) mRNA database(Pruitt and Maglott 2001) (Figure 2). The RefSeq mRNA
database consists of two types of datasets. These are thecurated mRNAs (accession preﬁx NM and NR) and the modelmRNAs that are provided through automated processing of
the genome annotation (accession preﬁx XM and XR).
From the comparison, we found that 5,155 (26%) of the HInv loci had no counterparts and were unique to the H-Inv.
All of these 5,155 loci are candidates for new human genes,
although non-protein-coding RNAs (ncRNAs) (25%), hypothetical proteins with ORFs less than 150 amino acids (55%),and singletons (91%) were enriched in this category. In fact,
1,340 of these H-Inv-unique loci were questionable andrequire validation by further experiments because they
consist of only single exons, and the 39 termini of these locialign with genomic poly-A sequences. This feature suggests
internal poly-A priming although some occurrences might bebona ﬁde genes. The most reliable set of newly identiﬁed
human genes in our dataset is composed of 1,054 proteinJune 2004 | Volume 2 | Issue 6 | Page 0859

Integrative Annotation of Human Genes

Figure 1. Procedure for Mapping and Clustering the H-Inv cDNAs
The cDNAs were mapped to the genome and clustered into loci. The
remaining unmapped cDNAs were clustered based upon the grouping of signiﬁcantly similar cDNAs.
DOI: 10.1371/journal.pbio.0020162.g001

coding and 179 non-protein-coding genes that have multiple
exons. Therefore, at least 6.1% (1,233/20,190) of the H-Inv
loci could be used to newly validate loci that the RefSeq
datasets do not presently cover. These genes are possibly lessexpressed since the proportion of singletons (H-Inv lociconsisting of a single H-Inv cDNA) was high (84%).
On the other hand, 78% (11,974/15,439) of the curated
RefSeq mRNAs were covered by the H-Inv cDNAs. Theseﬁgures suggest that further extensive sequencing of FLcDNA
clones will be required in order to cover the entire humangene set. Nonetheless, this effort provides a systematic
approach using the H-Inv cDNAs, even though a portion of
the cDNAs have already been utilized in the RefSeq datasets.
It is noteworthy that H-Inv cDNAs overlapped 3,061 (17%)
of RefSeq model mRNAs, supporting this proportion of the
hypothetical RefSeq sequences. These newly conﬁrmed 3,061
loci have a mean number of exons greater than RefSeq modelmRNAs that were not conﬁrmed, but smaller than RefSeq
curated mRNAs. The overlap between H-Inv cDNAs and
RefSeq model mRNAs was smaller than that between H-Inv
cDNAs and RefSeq curated mRNAs. This suggests that thegenes predicted from genome annotation may tend to be lessexpressed than RefSeq curated genes, or that some may be
artifacts. All these results highlight the great importance ofcomprehensive collections of analyzed FLcDNAs for validat-

Table 2. The Clustering Results of Human FLcDNAs onto the Human Genome
Chromosome

Number of Loci

Number of cDNAs

Number of cDNAs/Locus

Number of Loci/Mb

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
X
Y
UNa
Unmapped
Total

1,998
1,408
1,224
809
920
1,027
1,008
761
817
863
1,116
1,014
394
626
693
865
1,110
334
1,210
536
197
480
646
29
105
847
21,037

4,057
2,791
2,455
1,527
1,851
1,912
1,994
1,448
1,630
1,705
2,245
2,071
743
1,363
1,415
1,851
2,245
593
2,378
1,124
379
985
1,173
32
173
978
41,118

2.0
2.0
2.0
1.9
2.0
1.9
2.0
1.9
2.0
2.0
2.0
2.0
1.9
2.2
2.0
2.1
2.0
1.8
2.0
2.1
1.9
2.1
1.8
1.1
1.6
1.2
2.0

8.1
5.8
6.1
4.2
5.1
6.0
6.4
5.2
6.0
6.4
8.3
7.7
3.5
5.9
6.9
9.6
13.6
4.4
19.0
8.4
4.2
9.7
4.2
0.6
–
–
–

a
UN represents contigs that were not mapped onto any chromosome.
DOI: 10.1371/journal.pbio.0020162.t002

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Integrative Annotation of Human Genes

Figure 2. A Comparison of the Mapped H-Inv FLcDNAs and the RefSeq
mRNAs
The mapped H-Inv cDNAs, the RefSeq curated mRNAs (accession
preﬁxes NM and NR), and the RefSeq model mRNAs (accession
preﬁxes XM and XR) provided by the genome annotation process
were clustered based on the genome position. The numbers of locithat were identiﬁed by clustering are shown.
DOI: 10.1371/journal.pbio.0020162.g002

ing gene prediction from genome sequences. This may beespecially true for higher organisms such as humans.
Incomplete parts of the human genome sequences. The
existence of 978 unmapped cDNAs (847 clusters) suggests thatthe human genome sequence (National Center for Biotechnolgy Information [NCBI] build 34 assembly) is not yet
complete. The evidence supporting this statement is twofold.
First, most of those unmapped cDNAs could be partiallymapped to the human genome. Using BLAST, 906 of theunmapped cDNAs (corresponding to 786 clusters) showed atleast one sequence match to the human genome with a bitscore higher than 100. Second, most of the cDNAs could bemapped unambiguously to the mouse genome sequences. A
total of 907 unmapped cDNAs (779 clusters; 92%) could bemapped to the mouse genome with coverage of 90% or
higher. If we adopted less stringent requirements, more
cDNAs could be mapped to the mouse genome. The rest
might be less conserved genes, genes in unﬁnished sections of
the mouse genome, or genes that were lost in the mouse
genome. Based on these observations, we conclude that thehuman genome sequence is not yet complete, leaving some
portions to be sequenced or reassembled.
The proportion of the genome that is incomplete isestimated to be 3.7%–4.0%. The ﬁgure of 4.0% is based
upon the proportion of H-Inv cDNA clusters that could not
be mapped to the genome (847/21,037), while the 3.7%
estimate is based on both H-Inv cDNAs and RefSeq sequences(only NMs). This statistic indicates that a minimum of one outof every 25–27 clusters appears to be unrepresented in thecurrent human genome dataset, in its full form. Possible
PLoS Biology | http://biology.plosjournals.org

reasons for this include unsequenced regions on the humangenome and regions where an error may have occurred
during sequence assembly. If this is the case, this lends
support to the use of cDNA mapping to facilitate thecompletion of whole genome sequences (Kent and Haussler
2001). For example, we can predict the arrangement ofcontigs based on the order of mapped exons. In addition we
can use the sequences of unmapped exons to search for thoseclones that contain unsequenced parts of the genome. The
mapping results of partially mapped cDNAs are thus quite
useful.
Primary structure of genes on the human genome. Using
the H-Inv cDNAs, the precise structures of many humangenes could be identiﬁed based on the results of our cDNA
mapping (Table S1). The median length of last exons (786 bp)
was found to be longer than that of other exons, and the
median length of ﬁrst introns (3,152 bp) longer than that ofother introns. These observed characteristics of human genestructures concur with the previous work using much smaller
datasets (Hawkins 1988; Maroni 1996; Kriventseva andGelfand 1999).
In the human genome, 50% of the sequence is occupied byrepetitive elements (Lander et al. 2001). Repetitive elements
were previously regarded by many as simply ‘‘junk’’ DNA.
However, the contribution of these repetitive stretches togenome evolution has been suggested in recent works(Makalowski 2000; Deininger and Batzer 2002; Sorek et al.
2002; Lorenc and Makalowski 2003). The 21,037 loci ofrepresentative cDNAs were searched for repetitive elements
using the RepeatMasker program. RepeatMasker indicatedthat 9,818 (47%) of the H-Inv cDNAs, including 5,442 coding
hypothetical proteins, contained repetitive sequences. The
existence of Alu repeats in 5% of human cDNAs was reported
previously (Yulug et al. 1995). Our results revealed asigniﬁcant number of repetitive sequences including Alu in
the human transcriptome. Among them, 1,866 cDNAs overlapped repetitive sequences in their ORFs. Moreover, 554 of
1,866 cDNAs had repetitive sequences contained completely
within their ORFs, including 81 cDNAs that were identical orsimilar to known proteins. This may indicate the involvementof repetitive elements in human transcriptome evolution, as
suggested by the presence of Alu repeats in AS exons (Sorek
et al. 2002) and the contribution to protein variability byrepetitive elements in protein-coding regions (Makalowski
2000). We detected 2,254 and 5,427 cDNAs containing
repetitive sequences in their 59 UTR and 39 UTR, respectively.
The positioning of the repetitive elements suggests they playa regulatory role in the control of gene expression (Deininger
and Batzer 2002) (see Table S1 or the H-InvDB for details).
AS transcripts. We wished to investigate the extent towhich the functional diversity of the human proteome isaffected by AS. In order to do this, we searched for potential
AS isoforms in 7,874 loci that were supported by at least two
H-Inv cDNAs. We examined whether or not these cDNAs
represented mutually exclusive AS isoforms, using a combination of computational methods and human curation (seeMaterials and Methods). All AS isoforms that were supported
independently by both methods were deﬁned as the H-Inv AS
dataset. Our analysis showed that 3,181 loci (40 % of the 7,874
loci) encoded 8,553 AS isoforms expressing a total of 18,612
AS exons. On average, 2.7 AS isoforms per locus wereidentiﬁed in these AS-containing loci. This ﬁgure represents
June 2004 | Volume 2 | Issue 6 | Page 0861

Integrative Annotation of Human Genes

half of the AS isoforms predicted by another group (Lander
et al. 2001). Our result highlights the degree to which fulllength sequencing of redundant clones is necessary whencharacterizing the complete human transcriptome. The
relative positions of AS exons on the loci varied: 4,383
isoforms comprising 1,538 loci were 59 terminal AS variants;
5,678 isoforms comprising 1,979 loci were internal AS
variants; and 2,524 isoforms comprising 921 loci were 39
terminal AS variants.
The AS isoforms found in the H-Inv AS dataset havestrikingly diverse functions. Motifs are found over a wide
range of protein sequences. For certain types of subcellulartargeting signals, such as signal peptides, position within theentire protein sequence appears crucial. A total of 3,020 (35%) AS isoforms contained AS exons that overlapped proteincoding sequences. 1,660 out of 3,020 AS isoforms (55%)
harbored AS exons that encoded functional motifs. Additionally, 1,475 loci encoded AS isoforms that had different
subcellular localization signals, and 680 loci had AS isoforms
that had different transmembrane domains. These results
suggest marked functional differentiation between the varying isoforms. If this is the case, it would appear that AS
contributes signiﬁcantly to the functional diversity of thehuman proteome.
As the coverage of the human transcriptome by H-Inv
cDNAs is incomplete, it would be misleading to conjecturethat our dataset comprehensively includes all AS transcripts
from every human gene. However, the current collection is arobust characterization of the existing functional diversity of
the human proteome, and it represents a valuable resource offull-length clones for the characterization of experimentally
determined AS isoforms.
In the cases where three-dimensional (3D) structures could
be assigned to H-Inv cDNA protein products, we haveexamined the possible impact of AS rearrangements on the
3D structure. Our analysis was performed using the Genomes
TO Protein structures and functions database (GTOP)(Kawabata et al. 2002). We found that some of the sequence
regions in which internal exons vary between different
isoforms contained regions encoding SCOP domains (LoConte et al. 2000). This discovery allowed us to perform asimple analysis of the structural effects of AS. Our analysis of
the SCOP domain assignments revealed that the locidisplaying AS are much more likely to contain class c (b–a–b units, a/b) SCOP domains than class d (segregated a and b
regions, aþb) or class g (small) domains.
An example of exon differences between AS isoforms ispresented in Figure 3. The structures shown are those ofproteins in the Brookhaven Protein Data Bank (PDB) (Berman et al. 2000) to which the amino acid sequences of thecorresponding AS isoforms are aligned. Segments of the AS
isoform sequences that are not aligned with the corresponding 3D structure are shown in purple. Figure 3 demonstrates
that exon differences resulting from AS sometimes give rise
to signiﬁcant alternations in 3D structure.

Functional Annotation
We predicted the ORFs of 41,118 H-Inv cDNA sequencesusing a computational approach (see Figure S1), of which
39,091 (95.1%) were protein coding and the remaining 2,027(4.9%) were non-protein-coding. Since the structures andfunctions of protein products from AS isoforms are expected
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Figure 3. An Example of Different Structures Encoded by AS Variants
Exons are presented from the 59 end, with those shared by AS variantsaligned vertically. The AS variants, with accession numbers AK095301
and BC007828, are aligned to the SCOP domain d.136.1.1 andcorresponding PDB structure 1byr. Helices and beta sheets are redand yellow, respectively. Green bars indicate regions aligned to the
PDB structure, while open rectangles represent gaps in the alignments. AK095301 is aligned to the entire PDB structure shown, while
BC007828 is lacking the alignment to the purple segment of thestructure.
DOI: 10.1371/journal.pbio.0020162.g003

to be basically similar, we selected a ‘‘representative transcript’’ from each of the loci (see Figure S2). Then we
identiﬁed 19,660 protein-coding and 1,377 non-proteincoding loci (Table 3). Human curation suggested that a totalof 86 protein-coding transcripts should be deemed questionable transcripts. Once identiﬁed as dubious thesesequences were excluded from further analysis. The remaining representatives from the 19,574 protein-coding loci wereused to deﬁne a set of human proteins (H-Inv proteins). The
tentative functions of the H-Inv proteins were predicted bycomputational methods. Following computational predictions was human curation.
After determination of the H-Inv proteins, we performed astandardized functional annotation as illustrated in Figure 4,
during which we assigned the most suitable data source ID toeach H-Inv protein based on the results of similarity searchand InterProScan. We classiﬁed the 19,574 H-Inv proteinsaccording to the levels of the sequence similarity. Using asystem developed for the human cDNA annotation (seeFigure S2), we classiﬁed the H-Inv proteins into ﬁve
categories (Table 3). Three categories contain translated
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Integrative Annotation of Human Genes

Table 3. Statistics Obtained from the Functional Annotation Results

H-Inv proteins

Non-protein-coding transcripts

Category

Number of Loci

I. Identical to a known human protein
II. Similar to a known protein
III. InterPro domain containing protein
IV. Conserved hypothetical protein
V. Hypothetical protein
Total number of H-Inv proteins
Putative ncRNA
Uncharacterized transcript
Unclassifiable
Hold
Total number of non-protein-coding transcripts

5,074
4,104
2,531
1,706
6,159
19,574
296
675
329
77
1,377
86
21,037

Questionable transcripts
Total number of H-Inv loci
DOI: 10.1371/journal.pbio.0020162.t003

gene products that are related to known proteins: 5,074(25.9%) were deﬁned as identical to a known human protein(Category I proteins); 4,104 (21.0%) were deﬁned as similar toa known protein (Category II proteins); and 2,531 (12.9%) asdomain-containing proteins (Category III proteins). In total,
we were able to assign biological function to 59.9% of H-Inv
proteins by similarity or motif searches. The remaining
proteins, for which no biological functional was inferred,
were annotated as conserved hypothetical proteins (Category
IV proteins; 1,706, 8.7%) if they had a high level of similarity
to other hypothetical proteins in other species, or as
hypothetical proteins (Category V proteins; 6,159, 31.5%) if
they did not.
To predict the functions of hypothetical proteins (Category
IV and V proteins), we used 196 sequence patterns offunctional importance derived from tertiary structures ofprotein modules, termed 3D keynotes (Go 1983; Noguti et al.
1993). Application of the 3D keynotes to the H-Inv proteins

Figure 4. Schematic Diagram of Human Curation for H-Inv Proteins
The diagram illustrates the human curation pipeline to classify H-Inv
proteins into ﬁve similarity categories; Category I , II, III, IV, and V
proteins.
DOI: 10.1371/journal.pbio.0020162.g004
PLoS Biology | http://biology.plosjournals.org

resulted in the prediction of functions in 350 hypotheticalproteins (see Protocol S1).
Features of ORFs deduced from human FLcDNAs. The
mean and median lengths of predicted ORFs were calculated
for the 19,574 H-Inv proteins. These were 1,095 bp and 806
bp, respectively (Table 4). The values obtained were smaller
than those from other eukaryotes, and are inconsistent withestimates reported previously (Shoemaker et al. 2001).
However, as has been seen in the earlier annotation of the
ﬁssion yeast genome (Das et al. 1997), our dataset might
contain stretches which mimic short ORFs. This would lead toa bias in our ORF prediction and result in an erroneous
estimate of the average ORF length. We examined the size
distributions of ORFs from the ﬁve categories, and found thatthe distribution pattern was quite similar across categories.
The exception was Category V, in which short ORFs were
unusually abundant (Figure S3). Judging from the length
distribution of ORFs in the ﬁve categories of H-Inv proteins,the majority of ORFs shorter than 600 bps in Category V
seemed questionable. In order to have a protein dataset that
contains as many sequences to be further analyzed as
possible, we have taken the longest ORFs over 80 aminoacids if no signiﬁcant candidates were detected by thesequence similarity and gene prediction (see Figure S1). The
consequence of this is that Category V appears to containshort questionable ORFs, a certain fraction of which may be
prediction errors. Nevertheless, these ORFs could be true. It
is also possible that those ORFs were in fact translated in vivo
when we curated the cDNAs manually. The existence of many
functional short proteins in the human proteome is alreadyconﬁrmed, and there are 199 known human proteins that are
80 amino acids or shorter in the current Swiss-Prot database.
We think that the H-Inv hypothetical proteins require
experimentally veriﬁcation in the future. Excluding the
hypothetical proteins from the analysis, we obtained mean
and median lengths for the ORFs of 1,368 bp and 1,130 bp,
respectively, which are reasonably close to those for othereukaryotes (Table 4).
Of the 4,104 Category II proteins, 3,948 proteins (96.2%)
were similar to the functionally identiﬁed proteins ofJune 2004 | Volume 2 | Issue 6 | Page 0863

Integrative Annotation of Human Genes

Table 4. The Features of Predicted ORFs

Human—H-Inv datasets (categories I–IV)
Human—all of the H-Inv datasets
Fly
Worm
Budding yeast
Fission yeast
Plant
Bacteria

Number of ORFs

Mean (bp)Median (bp)Percent GC of
Third Codon Position

13,415
19,574
17,878
21,118
6,408
4,968
27,228
4,289

1,368
1,095
1,580
1,327
1,403
1,426
1,269
951

1,130
806
1,212
1,038
1,128
1,161
1,074
834

52.3
52.4
53.9
42.9
40.3
39.7
44.2
51.9

Nonredundant proteome datasets of nonhuman species were obtained from the following URLs: fly (Drosophila melanogaster; http://flybase.bio.indiana.edu/), worm
(Caenorhabditis elegans; http://www.wormbase.org/), budding yeast (Saccharomyces cerevisiae; http://www.pasteur.fr/externe), fission yeast (Schizosaccharomyces pombe;
http://www.sanger.ac.uk/), plant (Arabidopsis thaliana; http://mips.gsf.de/proj/thal/index.html), and bacteria (Escherichia coli K12; http://www.ncbi.nlm.nih.gov/).
DOI: 10.1371/journal.pbio.0020162.t004

mammals (Figure S4). This implies that the predictedfunctions in this study were based on the comparative study
with closely related species, so that the functional assignment
retains a high level of accuracy if we suppose that proteinfunction is more highly conserved in more closely related
species. Moreover, the patterns of codon usage and the codon
adaptation index (CAI; http://biobase.dk/embossdocs/cai.html)
of H-Inv proteins were investigated (Table S2). The results
indicated that the ORF prediction scheme worked equallywell in the ﬁve similarity categories of H-Inv proteins.
Each H-Inv protein in the ﬁve categories was investigatedin relation to the tissue library of origin (Table S3). We foundthat at least 30% of the clones mainly isolated from dermal
connective, muscle, heart, lung, kidney, or bladder tissues
could be classiﬁed as Category I proteins. Hypotheticalproteins (Category V), on the other hand, were abundant inboth endocrine and exocrine tissues. This bias may indicatethat expression in some tissues may not have been studied inenough detail. If this is the case, then there is likely asigniﬁcant gap between our current knowledge of the humanproteome and its true dimensions.
Non-protein-coding genes. Over recent years, ncRNAs havebeen found to play key roles in a variety of biologicalprocesses in addition to their well-known function in protein
synthesis (Moore and Steitz 2002; Storz 2002). Analysis of the
H-Inv cDNA dataset revealed that 6.5% of the transcripts are
possibly non-protein-coding, although the number is muchsmaller than that estimated in mice (Okazaki et al. 2002). We
believe that this difference between the two species is mainly
due to the larger number of mouse libraries that were used
and to a rare-transcript enrichment step that was applied tothese collections.
To identify ncRNAs, we manually annotated 1,377 representative non-protein-coding transcripts, which were classiﬁed into four categories (see Table 3; Figure 5): putative
ncRNAs, uncharacterized transcripts (possible 39 UTR fragments supported by ESTs), unclassiﬁable transcripts (possible
genomic fragments), and hold transcripts (not stringently
mapped onto the human genome). Of these, 296 (19.5%) were
putative ncRNAs with no neighboring transcripts in the close
vicinity (. 5 kb) and supported by ESTs with a poly-A signal
or a poly-A tail, indicating that these may represent genuine
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ncRNA genes. On the other hand, a large fraction of the nonprotein-coding transcripts (675; 44.5%) were classiﬁed as
possible 39 UTRs of genes that were mapped less than 5 kb
upstream. The 5-kb range is an arbitrary distance that wedeﬁned as one of our selection criteria for identifying
ncRNAs. However, authentic non-protein-coding genes
might be located adjacent to other protein-coding genes (asdescribed earlier). Thus, some of the transcripts initiallyannotated as uncharacterized ESTs may correspond toncRNAs when these sequences satisfy the other selection
criteria.
We deﬁned a manual annotation strategy (Figure 5) thatallowed us to select convincing putative ncRNAs with various

Figure 5. The Manual Annotation Flow Chart of ncRNAs
Candidate non-protein-coding genes were compared with the humangenome, ESTs, cDNA 39-end features and the locus genomic
environment. The candidates were then classiﬁed into four categories: hold (cDNAs improperly mapped onto the human genome);
uncharacterized transcripts (transcripts overlapping a sense gene or
located within 5 kb of a neighboring gene with EST support); putative
ncRNAs (multiexon or single exon transcripts supported by ESTs or
39-end features); and unclassiﬁable (possible genomic fragments).
DOI: 10.1371/journal.pbio.0020162.g005
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Integrative Annotation of Human Genes

lines of supporting evidence. These are the following: absenceof a neighboring gene in the close vicinity, overlap withhuman or mouse ESTs, occurrence in the 39 end of cDNA
sequences, as well as overlap with mouse cDNAs. Out of 296
annotated putative ncRNAs, we identiﬁed 47 ncRNAs withconserved RNA secondary structure motifs (Rivas and Eddy
2001), and nearly 60% of these were found expressed in up to
eight human tissues (data not shown), indicating that the
manual curation strategy employed in this study may
facilitate the identiﬁcation of novel non-protein-coding
genes in other species.
The functions of human proteins identiﬁed through ananalysis of domains. Proteins in many cases are composed of
distinct domains each of which corresponds to a speciﬁcfunction. The identiﬁcation and classiﬁcation of functional
domains are necessary to obtain an overview of the wholehuman proteome. In particular, the analysis of functional
domains allows us to elucidate the evolution of the novel
domain architectures of genes that life forms have acquiredin conjunction with environmental changes. The humanproteome deduced from the H-Inv cDNAs was subjected toInterProScan, which assigned functional motifs from the
PROSITE, PRINTS, SMART, Pfam, and ProDom databases(Mulder et al. 2003). A total of 19,574 H-Inv proteins were
examined, and 9,802 of them (50.1%) were assigned at leastone InterPro code that was classiﬁed into either repeats (aregion that is not expected to fold into a globular domain on
its own), domains (an independent structural unit that can befound alone or in conjunction with other domains or
repeats), and/or families (a group of evolutionarily related
proteins that share one or more domains/repeats in common)when compared with those of ﬂy, worm, budding and ﬁssion
yeasts, Arabidopsis thaliana, and Escherichia coli (Table S4).
Moreover, the proteins were classiﬁed according to the Gene
Ontology (GO) codes that were assigned to InterPro entries(Table S5).
Identiﬁcation of human enzymes and metabolic pathways.
One of the most important goals of the functional annotationof human cDNAs is to predict and discover new, previously
uncharacterized enzymes. In addition, revealing their positions in the metabolic pathways helps us understand theunderlying biochemical and physiological roles of these
enzymes in the cells. We thus searched for potential enzymes
among the H-Inv proteins, and mapped them to a database of
known metabolic pathways.
We could assign 656 kinds of potential Enzyme Commission (EC) numbers to 1,892 of the 19,574 H-Inv proteins basedon matches to the InterPro entries and GO assignments andon the similarity to well-characterized Swiss-Prot proteins(see Dataset S2). The number of characterized human
enzymes signiﬁcantly increased through this analysis. The
most abundant enzymes in the H-Inv proteins were protein–tyrosine kinases (EC 2.7.1.112), which is consistent with thelarge number of kinases found in the InterPro assignments.
The other major enzymes were small monomeric GTPase (EC
3.6.1.47), adenosinetriphosphatase (EC 3.6.1.3), phosphoprotein phosphatase (EC 3.1.3.16), ubiquitin thiolesterase (EC
3.1.2.15), and ubiquitin-protein ligase (EC 6.3.2.19). These
enzymes are members of large multigene families that areimportant for the functions of higher organisms. Furthermore, we could assign 726 EC numbers to mouse representative transcripts and proteins (Okazaki et al. 2002), and most of
PLoS Biology | http://biology.plosjournals.org

them appeared to be shared between human and mouse (datanot shown). The high similarity of the enzyme repertoire
between these two species is not surprising if we consider theclose evolutionary relatedness between them. It does, however, indicate the usefulness of the mouse as a modelorganism for studies concerning metabolism.
We then mapped all H-Inv proteins on the metabolic
pathways of the KEGG database, a large collection of
information on enzyme reactions (Kanehisa et al. 2002). In
total, we mapped 963 H-Inv proteins on a total of 1,613
KEGG pathways, of which 641 were based on their EC
number assignments (Figure S5). Those based on EC number
assignments do not necessarily function as they are assigned
because they have yet to be veriﬁed experimentally. However,
if all other enzymes along the same pathway exist in humans,
the functional assignment has a high probability of being
correct. Using this method, we discovered a total of 32 newly
assigned human enzymes from the H-Inv proteins with thesupport of KEGG pathways (Table S6). For example, we
identiﬁed (1) pyridoxamine-phosphate oxidase (EC 1.4.3.5;
AK001397), an enzyme in the ‘‘salvage pathway,’’ the functionof which is the reutilization of the coenzyme pyridoxal-59phosphate (its role in epileptogenesis was recently reported[Bahn et al. 2002]), (2) ATP-hydrolysing 5-oxoprolinase (EC
3.5.2.9; AL096750) that cleaves 5-oxo-L-proline to form Lglutamate (whose deﬁciency is described in the Online
Mendelian Inheritance in Man [OMIM] database[ID=260005]), and (3) N-acetylglucosamine-6-phosphate deacetylase (EC 3.5.1.25; BC018734), which catalyzes N-acetylglucosamine at the second step of its catabolism, the activityof which in human erythrocytes was detected by a biochemical study (Weidanz et al. 1996). Many of the newly identiﬁed
enzymes were supported by currently available experimental
and genomic data. An example is a putative urocanase (EC
4.2.1.49; AK055862) that mapped onto the ‘‘histidine metabolism’’ that urocanic acid catabolises. A 14C Histidine tracer
study unexpectedly revealed that NEUT2 mice deﬁcient in
10-formyltetrahydrofolate dehydrogenase (FTHFD) excreteurocanic acid in the urine and lack urocanase activity in theirhepatic cytosol (Cook 2001). We then found that both the
FTHFD and AK055862 genes were located within the same
NCBI human contig (NT005588) on Chromosome 3. Moreover, the distance between the two genes was consistent withthe genetic deletion of NEUT2 (. 30 kb). We thus assumed
that FTHFD and urocanase might be coincidentally defectivein mice. This analysis could conﬁrm that the AK055862
protein is a true urocanase. This example demonstrates that
this kind of in silico analysis is a powerful method in deﬁning
the functions of proteins.

Polymorphism in the Transcriptome
Sites of potential polymorphism in cDNAs. Due to therapidly increasing accumulation of genetic polymorphism
data, it is necessary to classify the polymorphism data withrespect to gene structure in order to elucidate potential
biological effects (Gaudieri et al. 2000; Sachidanandam et al.
2001; Akey et al. 2002; Bamshad and Wooding 2003). For thispurpose, we examined the relationship between publicly
available polymorphism data and the structure of our H-Inv
cDNA sequences. A total of 4 million single nucleotide
polymorphisms (SNPs) and insertion/deletion length variations (indels) with mapping information from the Single
June 2004 | Volume 2 | Issue 6 | Page 0865

Integrative Annotation of Human Genes

Table 5. The Numbers of SNPs and indels Occurring in the Representative cDNAs

SNPsa

Synonymous
Nonsynonymous
Truncationb
Extensionb
Synonymous SNP at stop codon
Total

Indels59 UTR

Coding Region

39 UTR

10,715(1/569 bp)381(1/15,999 bp)

11,014(1/325 bp)
13,215(1/1,206 bp)
315
43
28
24,679c(1/833 bp)452(1/45,490 bp)

31,852(1/536 bp)
1,364(1/12,553 bp)a
The numbers of SNPs and indels are summarized for representative cDNA sequences which were mapped on the genome. The numbers in parentheses represent thedensities of SNPs and indels.
b
SNPs that cause nonsense mutation or extension of polypeptides were classified assuming that the cDNAs represent original alleles.
cThis figure includes 64 unclassifiable SNPs.
DOI: 10.1371/journal.pbio.0020162.t005

Nucleotide Polymorphism Database (dbSNP; http://
www.ncbi.nlm.nih.gov/SNP/, build 117) (Sherry et al. 1999)
were used for the search. We could identify 72,027 uniquely
mapped SNPs and indels in the representative H-Inv cDNAs
and observed an average SNP density of 1/689 bp. To classify
SNPs and indels with respect to gene structure, the genomic
coordinates of SNPs were converted into the corresponding
nucleotide positions within the mapped cDNAs. The SNPs
and indels were classiﬁed into three categories according totheir positions: 59 UTR, ORF, and 39 UTR (Table 5). The
density of indels was higher in 59 UTRs (1/15,999 bp) and 39
UTRs (1/12,553 bp) than in ORFs (1/45,490 bp). This ispossibly due to different levels of functional constraints. We
also examined the length of indels and found a higher
frequency of indels in those ORFs that had a length divisibleby three and that did not change their reading frames. We
observed that the density of SNPs was higher in both the 59
and 39 UTRs (1/569 bp and 1/536 bp, respectively) than in
ORFs (1/833 bp).
SNPs located in ORFs were classiﬁed as either synonymous,
nonsynonymous, or nonsense substitutions (Table 5). We
identiﬁed 13,215 nonsynonymous SNPs that affect the aminoacid sequence of a gene product. At least 4,998 of thesenonsynonymous SNPs are ‘‘validated’’ SNPs (as deﬁned by
dbSNP). This data can be used to predict SNPs that affectgene function. SNPs that create stop codons can cause
polymorphisms that may critically alter gene function. We

identiﬁed 358 SNPs that caused either a nonsense mutationor an extension of the polypeptide. We classiﬁed these 358
SNPs into these two types based on the alleles of the cDNA.
Most of these SNPs (315/358) were predicted to cause
truncation of the gene products and produce a shorter
polypeptide compared with the alleles of H-Inv cDNAs. For
example, Reissner’s ﬁber glycoprotein I (AK093431) contains
a nonsense SNP that results in the loss of the last 277 aminoacids of the protein, and consequently the loss of athrombospondin type I domain located in its C-terminal
end. This SNP is highly polymorphic in the Japanese
population, the frequencies of G (normal) and T (termination) being 0.43 and 0.57, respectively. As seen in thisexample, the identiﬁcation of SNPs within cDNAs provides
important insights into the potential diversity of the humantranscriptome. Thus, polymorphism data crossreferenced to acomprehensively annotated human transcriptome mightprove to be a valuable tool in the hands of researchersinvestigating genetic diseases.
Sites of microsatellite repeats. Among the 19,442 representative protein-coding cDNAs, we identiﬁed a total of 2,934
di-, tri-, tetra-, and penta-nucleotide microsatellite repeat
motifs (Table 6). Interestingly, 1,090 (37.2%) of these were
found in coding regions, the majority of which (86.9%) were
tri-nucleotide repeats. Di-, tetra-, and penta-nucleotide
repeats made up the greatest proportion of repeats in 59
UTRs and 39 UTRs. Coding regions contained mostly tri-

Table 6. The Numbers of Microsatellite Repeat Motifs That Occurred in the Representative cDNAs
Microsatellite Repeats

59 UTR
Coding region
39 UTR
Total

Di-

Tri-

Tetra-

Penta-Total

162 (50)70 (13)482 (121)
714 (184)

394 (3)947 (10)340 (3)
1,681 (16)117 (4)63 (2)
281 (8)461 (14)21 (1)
10 (0)
47 (1)
78 (2)

694 (58)
1,090 (25)
1,150 (133)
2,934 (216)Microsatellites were defined as those sequences having at least ten repeats for di-nucleotide repeats and at least five repeats for tri-, tetra-, and penta-nucleotide repeats.
Numbers of polymorphic microsatellites inferred by comparisons of cDNA and genomic sequences are shown in parenthesis. See Table S2 for a list of accession numbers forthese cDNAs.
DOI: 10.1371/journal.pbio.0020162.t006

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Integrative Annotation of Human Genes

nucleotide repeats. This result is consistent with the idea thatmicrosatellites are prone to mutations that cause changes in
numbers of repeats. Only tri-nucleotide repeats can conserve
original reading frames when extended or shortened bymutations. A previous study showed that many of the
microsatellite motifs identiﬁed in human genomic sequences,including those in coding regions, are highly polymorphic inhuman populations (Matsuzaka et al. 2001). We found this tobe the case in our study: 36 of the microsatellite repeats we
detected were found to be polymorphic in human populations according to dbSNP records (data not shown). We
identiﬁed 216 microsatellite repeats in 213 genes that showed
contradictory numbers of repeats between cDNA andgenome sequences (see Dataset S3). This ﬁgure includes 25
microsatellites in ORFs that have the potential to alter theprotein sequences. Individual cases need to be veriﬁed byfurther experimental studies, but many of these microsatellites may really be polymorphic in human populations
and have marked phenotypic effects.
There were 382 cDNAs that possessed two or more
microsatellites in their nucleotide sequences. This is illustrated in RBMS1 (BC018951), a cDNA which encodes an RNAbinding motif. This cDNA has four microsatellites, (GGA)7,(GAG)9, (GAG)6, and (GCC)6, in its 59 UTR. These microsatellites are all located at least 98 bp upstream of the startcodon, but they could still have pronounced regulatory
effects on gene expression. Another example is the cDNA
that encodes CAGH3 (AB058719). This cDNA has four
microsatellites, (CAG)8, (CAG)6, (CAG)8, and (CAG)8, all ofwhich are located within the ORF. These microsatellites all
encode stretches of poly-glutamine, which are known to havetranscription factor activity (Gerber et al. 1994) and oftencause neurodegenerative diseases when the number of
repeats exceeds a certain limit. A typical example of adisorder caused by these repeats is Huntington’s disease(Andrew et al. 1993; Duyao et al. 1993; Snell et al. 1993).
We also searched for repeat motifs containing the sameamino acid residue in the encoded protein sequences. We
located a total of 3,869 separate positions where the sameamino acid was repeated at least ﬁve times. The mostfrequent repetitive amino acids are glutamic acid, proline,
serine, alanine, leucine, and glycine. The glutamine repeats ofthis nature were found in 160 different locations.

Evolution of the Human Transcriptome
Beyond the study of individual genes, the comparison ofnumerous complete genome sequences facilitates the elucidation of evolutionary processes of whole gene sets. Moreover, the FLcDNA datasets of humans and mice give us an
opportunity to investigate the genome-wide evolution ofthese two mammals by using the sequences supported byphysical clones. Here we compared our human cDNA
sequences with all proteins available in the public databases.
Focusing on our results, we discuss when and how the humanproteome may have been established during evolution.
Furthermore, the evolution of UTRs is examined throughcomparisons with cDNAs from both primates and rodents.
Conserved and derived protein-coding genes in humans.
An advantage of large-scale cDNA sequencing is that it cangenerate a nearly complete gene set with good evidence fortranscription. The human proteome deduced from the
FLcDNA sequences gives us an opportunity to decipher the
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Figure 6. The Functional Classification of H-Inv Proteins That Are
Homologous to Proteins in Each Taxonomic GroupThe numbers of representative H-Inv cDNAs with sequencehomology to other species’ proteins (E , 105) were calculated. The
cDNAs for which we could not assign any functions were discarded.
Mammalian species were excluded from the ‘‘animal’’ group.
‘‘Eukaryote’’ represents eukaryotic species other than those includedin the mammal, animal, fungi, and plant groups. See also Table S7.
DOI: 10.1371/journal.pbio.0020162.g006

evolution of the entire proteome. Here we compare therepresentative H-Inv cDNAs with the Swiss-Prot and TrEMBL
protein databases using FASTY (Pearson 2000), and we
describe the distributions of the homologs among taxonomic
groups at two different similarity levels. The number ofrepresentative H-Inv cDNAs that have homolog(s) in a given
taxon was counted (Figure S6), and the cDNAs were classiﬁed
into functional categories (Figure 6). These results indicatedthat homologs of the human proteins were probably
conserved much more in the animal kingdom than in theothers at both moderate (E ,1010) and weak (E , 105)similarity levels (see Figure S6). Moreover, human sequences
had as many nonmammalian animal homologs as mammalian
homologs, with seemingly little bias to any one function (seeFigure 6). This suggests that the genetic background of
humans may have already been established in an early stage ofanimal evolution and that many parts of the whole genetic
system have probably been stable throughout animal evolution despite the seemingly drastic morphological differences
between various animal species. This result is consistent with
our previous observation that the distribution of the functional domains is highly conserved among animal species (seeTable S4). The number of homologs may have been inﬂated
by recent gene duplication events within the human lineage.
Hence we counted the number of paralog clusters instead ofcDNAs that had homologs in the databases, and obtained
essentially the same results (Figure S7).
This analysis also revealed a number of potential humanspeciﬁc proteins, which did not have any homologs in thecurrent sequence databases. In this case the creation of
lineage-speciﬁc genes through speciation is not completely
excluded. However, most ORFs with no similarity to known
proteins would not be genuine for the reasons discussed
above. Therefore, the number of ‘‘true’’ human-speciﬁc
proteins is expected to be relatively small.
We conducted further BLASTP searches matching entries
from the Swiss-Prot database against the H-Inv dataset itself.
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Integrative Annotation of Human Genes

As a result, 12,813 (45.3%) of 28,263 vertebrate proteins had
homologs in nonvertebrates at E , 10–30. Taking into accountthat the dataset is relatively small (approximately 12,000
sequences) and as a result may be biased, animal species may
conceivably share a similar protein-coding gene set.
Ohno (1996) proposed that the emergence of a large
number of animal phyla in a short period of time would
endow them with almost identical genomes. These werecollectively referred to as the pananimalia genome. Our data
support Ohno’s hypothesis from the perspective that thebasic gene repertoires of animals are essentially highly similar
among diverse species that have evolved separately since theCambrian explosion. Subsequently, morphological evolution
seems to have been brought about mainly by changes in gene
regulation. The number of transcription regulator homologs
is different between animals and other phyla (Table S7). In
this analysis it was not possible to examine the genes recently
deleted from the human lineage. However, the similarity ofthe proteome sets between distantly related mammals such as
human and mouse (Waterston et al. 2002) suggests that not
many genes have been deleted speciﬁcally from humans sincehumans and mice diverged.
A unique feature of the Animalia proteome is, for example,
the presence of apoptosis regulator homologs, which arefound widely in the animal kingdom, whilst they are rare in
the other phyla (Table S7). Since apoptosis plays animportant role during the development of multicellularanimals, this observation indicates that apoptosis wasestablished independently of both plants and fungi during
the early evolution of multicellularization in the kingdom
Animalia. Likewise, signal transducers and cell-adhesion
proteins are distinctive. In contrast, enzymes, translation
regulators, molecular chaperones, etc. were highly conserved
among all taxonomic groups. These proteins may have playedsuch essential roles that any alterations were eliminated bystrong purifying selection. It is assumed some functions were
presumably derived from ancient endocellular symbionts(mitochondria and chloroplasts) (Martin 2002).
Evolution of untranslated regions. The UTRs of mRNA are
known to be involved in the regulation of gene expression at
the posttranscriptional level through control of translation
efﬁciency (Kozak 1989; Geballe and Morris 1994; Sonenberg
1994), mRNA stability (Zaidi and Malter 1994; McCarthy andKollmus 1995), and mRNA localization (Curtis et al. 1995;
Lithgow et al. 1997). Only a few studies on very limited
datasets have been carried out so far to describe quantitatively either the evolutionary dynamics of mRNA UTRs
(Larizza et al. 2002), or their general structural and compositional features (Pesole et al. 1997). The human transcriptome
presented here along with the murid data obtained mainly
from the FANTOM2 project enables us to stabilize amammalian genome perspective on the subject (Table S8).
A sliding window analysis of UTR sequence identities
between humans and mice revealed a positive correlation
between the number of indels in an untranslated region andthe distance from the coding sequence (Figure 7). Unlike
indels, mismatches are distributed equally along whole
untranslated regions. In other words, indels seem to be lesstolerated in close proximity to a coding sequence, whilesubstitutions are evenly distributed along the untranslated
regions of the mRNAs. This seems to be a general patternobserved similarly in other species (data not shown). Indels in
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Figure 7. Window Analysis of Similarity between Human and Mouse
UTRs
Results for 59 UTRs presented above and for 39 UTRs below. The
whole mRNA sequences were aligned using a semiglobal algorithm as
implemented in the map program (Huang 1994) with the following
parameters: match 10, mismatch 3, gap opening penalty 50, gap
extension penalty 5, and longest penalized gap 10; the terminal gaps
are not penalized at all. A window size of 20 bp was used with a step of
10 bp. The analysis window was moved upstream and downstream ofstart and stop codons, respectively. The normalized score for a given
window is calculated as a fraction of an average score for all UTRs in
a given window over the maximum score observed in all 59 or 39
UTRs, respectively.
DOI: 10.1371/journal.pbio.0020162.g007

UTRs may have been avoided so that the distance between thecoding region and a signal sequence for regulation in the
UTR could be conserved throughout evolution, while purifying selection against substitutions appeared to be relatively
weak.
Untranslated region replacement. A replacement of theentire UTR may lead to drastic changes in gene expression,
especially if a UTR having a posttranscriptional signal isreplaced by another. We compared the evolutionary distancesof UTRs between primate and rodent orthologous sequences.
We based our analysis on the UTR sequence distances that
contradicted the expected phylogenetic tree of relatedness.
We could detect 149 UTR replacements distributed among
different species. Some of the observed replacements may
result from selection of different AS isoforms of a single locus
in different species. This is particularly likely if an AS event
involves an alternative ﬁrst or last exon. It seems that UTR
replacements are more frequent in rodents than in primates,
but the difference is not statistically signiﬁcant at the 5%
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Integrative Annotation of Human Genes

signiﬁcance level (Table S9). We detected a UTR replacementin less than 2% of the analyzed sequences. The evolutionary
consequences could be signiﬁcant because the UTR replacement might result in changes in expression level or the loss ofan mRNA localization signal.

The H-Invitational Database
All the results of the mapping of the FLcDNA sequences
onto the human genome, the clustering of FLcDNA sequences, sequence alignments, detection of AS transcripts,
sequence similarity searches, functional annotation, protein
structure prediction, subcellular localization prediction, SNP
mapping, and evolutionary analysis, as well as the basic
features of FLcDNA sequences, are stored in the H-InvDB(Figure S8). The H-InvDB is a unique database that integrates
annotation of sequences, structure, function, expression, anddiversity of human genes into a single entity. It is useful as aplatform for conducting in silico data mining. The databasehas functions such as a keyword search, a sequence similarity
search, a cDNA search, and a searchable genome browser. It is
hoped that the H-InvDB will become a vital resource in the
support of both basic and applied studies in the ﬁelds of
biology and medicine.
We constructed two kinds of specialized subdatabases
within the H-InvDB. The ﬁrst is the Human Anatomic Gene
Expression Library (H-Angel), a database of expression
patterns that we constructed to obtain a broad outline of
the expression patterns of human genes. We collected gene
expression data from normal and diseased adult humantissues. The results were generated using three methods onseven different platforms. These included iAFLP (Kawamoto
et al. 1999; Sese et al. 2001), DNA arrays (long oligomers, shortoligomers [Haverty et al. 2002], cDNA nylon microarrays[Pietu et al. 1999], and cDNA glass slide microarrays [Arrays/
IMAGE-Genexpress]), and cDNA sequence tags (SAGE [Velculescu et al. 1995; Boon et al. 2002], EST data [Boguski et al.
1993; Kawamoto et al. 2000], and MPSS [Brenner et al. 2000]).
By normalizing levels of gene expression in experiments
conducted with different methods, we determined the gene
expression patterns of 19,276 H-Inv loci in ten major
categories of tissues. This analysis allowed us to clearly
distinguish broadly and evenly expressed housekeeping genes
from those expressed in a more restricted set of tissues(details will be published elsewhere). The H-Angel database
comprises the largest and most comprehensive collection ofgene expression patterns currently available. Also provided isa classiﬁcation of human genes by expression pattern.
The second subdatabase of the H-InvDB is DiseaseInfo
Viewer. This is a database of known and orphan genetic
diseases. We tried to relate H-Inv loci with disease information in two ways. Firstly, 613 H-Inv loci that correspond withknown, characterized disease-related genes were identiﬁed bycreating links to entries in both LocusLink (http://
www.ncbi.nlm.nih.gov/LocusLink/) and OMIM (Hamosh et al.
2002). To explore the possibility that cDNAs encoding
unknown proteins may be related to ‘‘orphan pathologies’’(diseases that have been mapped to chromosomal regions, but
for which associated genes have not yet been described), we
generated a list of H-Inv loci that co-localized with thesecytogenetic regions. The nonredundant orphan disease dataset we created consists of 586 diseases identiﬁed through
OMIM (http://www.ncbi.nlm.nih.gov/Omim/, ver. Jan. 2003),
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with an additional 108 identiﬁed from GenAtlas (http://
www.dsi.univ-paris5.fr/genatlas/, ver. Jan. 2003). Using the
OMIM and GenAtlas databases in conjunction with theannotation results from the H-InvDB may accelerate theprocess of identifying candidate genes for human genetic
diseases.

Concluding Remarks
There are a number of established collections of nonhuman cDNAs, such as those of Drosophila melanogaster (Stapleton
et al. 2002), Danio rerio (Clark et al. 2001), Arabidopsis thaliana(Seki et al. 2002), Plasmodium falciparum (Watanabe et al. 2002),
and Trypansoma cruzi (Urmenyi et al. 1999). The most extensive
collection of mammalian cDNAs so far has been that of the
RIKEN/FANTOM mouse cDNA project (Kawai et al. 2001;
Okazaki et al. 2002). This wealth of information has spurred awide variety of research in the areas of both gene expression
proﬁling (Miki et al. 2001) and protein–protein interactions(Suzuki et al. 2001). The H-InvDB provides an integrativemeans of performing many more such analyses based onhuman cDNAs.
The most important ﬁndings that have resulted from thecDNA annotation are summarized here.
(1) The 41,118 H-Inv cDNAs were found to cluster into
21,037 human gene candidates. Comparison with known andpreviously predicted human gene sets revealed that these
21,037 hypothesized gene clusters contain 5,155 new genecandidates.
(2) The primary structure of 21,037 human gene candidates
was precisely described. For the majority of them we observedthat both ﬁrst introns and last exons tended to be longer thanthe other introns and exons, respectively, implying thepossible existence of intriguing mechanisms of transcriptional control in ﬁrst introns.
(3) We discovered the existence of 847 human genecandidates that could not be convincingly mapped to thehuman genome. This result suggested that up to 3.7%–4.0%
of the human genome sequences (NCBI build 34 assembly)
may be incomplete, containing either unsequenced regions
or regions where sequence assembly has been performed inerror.
(4) Based on H-Inv cDNAs, we were able to deﬁne anexperimentally validated AS dataset. The dataset was composed of 3,181 loci that encoded a total of 8,553 AS isoforms.
In the 55% of ORFs containing AS isoforms, the pattern ofalternative exon usage was found to encode different functional domains at the same loci.
(5) A standardized method of human curation for the H-Inv
cDNAs was created under the tacit consensus of international
collaborations. Using this method, we classiﬁed 19,574 H-Inv
proteins into ﬁve categories based on sequence similarity andstructural information. We were able to assign functionaldeﬁnitions to 9,139 proteins, to locate function- or familydeﬁning InterPro domains in 2,503 further proteins, and to
identify 7,800 transcripts as good candidates for hypotheticalproteins.
(6) A total of 1,892 H-Inv proteins were assigned identities
as one of 656 different EC-numbered enzymes. This enzyme
library includes 32 newly identiﬁed human enzymes on
known metabolic pathway maps and comprises the largest
collection of computationally validated human enzymes.
(7) Based on a variety of supporting evidence, 6.5% of HJune 2004 | Volume 2 | Issue 6 | Page 0869

Integrative Annotation of Human Genes

Inv loci (1,377 loci) do not have a good protein-coding ORF,
of which 296 loci are strong candidates for ncRNA genes.
(8) We identiﬁed and mapped 72,027 SNPs and indels to
unique positions on 16,861 loci. Of these, 13,215 nonsynonymous SNPs, 358 nonsense SNPs, and 452 indels were
found in coding regions and may alter protein sequences,cause phenotypic effects, or be associated with disease. In
addition, we identiﬁed 216 polymorphic microsatellite
repeats on 213 loci, 25 of which were located in coding
regions.
(9) During human proteome analysis, it was suggested thatthe basic gene set of humans might have been established in
the early stage of animal evolution. Our analysis of UTRs
revealed that insertions or deletions near coding regions wererare when compared with substitutions, though in some cases
drastic changes such as UTR replacements occurred.
(10) A consequence of the annotation process and ourrelated research was the development of the H-InvDB tocontain our annotation work. H-InvDB is a comprehensive
database of human FLcDNA annotations that stores all
information produced in this project. As a subdivision of
H-InvDB, we developed two other specialized subdatabases:
H-Angel and DiseaseInfo Viewer. H-Angel is a database ofgene expression patterns for 19,276 loci. DiseaseInfo Viewer
is a database of known disease-related genes and loci colocalized with 694 orphan pathologies. These pathologies
were mapped onto the genome but were not identiﬁed
experimentally.
In the H-Inv project, we collected as many FLcDNAs as
possible and conducted extensive analyses concerning thequality of cDNAs, such as detection of frameshift errors,
retained introns, and internal poly-A priming, under auniﬁed criterion. Although these analyses are still in anelementary state, we store these results in H-InvDB to share
this information with the biological community. We believethat this is an important contribution of our project, because
it will provide a reliable way to control the quality of thecDNA clones. In the future, this information will be useful for
improving the methods of clone library construction.
It has been suggested that the human genome encodes
30,000 to 40,000 genes. In this study we comprehensively
evaluated more than 21,000 human gene candidates (up to
70% of the total). Thus, efforts should be continued by the HInv consortium and others to ‘‘fully’’ characterize the humantranscriptome. For this purpose new technologies should be
implemented that are more sensitive in detecting rarely
expressed genes and AS transcripts. Nevertheless, there areunavoidable limitations for human cDNA collections, suchthe identiﬁcation of embryo-speciﬁc genes, for which otherapproaches should be employed. One alternative is the use of
ab initio predictions from genomic sequences, in conjunction
with expression proﬁling studies, to identify rarely expressed
genes that share structural similarity to known genes. Additionally, a better characterization of cis-regulatory element
units may help to deﬁne the boundary of other genes that areundetected by current gene prediction programs. Anotherarea that remains to be explored is the identiﬁcation ofpotential hidden RNA gene families that may play vital roles,
such as the recently uncovered family of microRNA genes,
which is involved in the regulation of expression of othergenes (for review see Ambros 2001; Moss 2002).
The proteome determination aspects of this project,
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including the identiﬁcation of new enzymes and hypotheticalproteins, should stimulate more focused biochemical studies.
The functional classiﬁcations may allow deﬁnition of subproteomes that are related to different physiological processes. The H-Inv transcriptome based on the deﬁnition of aconsensus proteome (the H-Inv proteins) links both theanalysis of genomic DNA and direct proteome analysis withthe study of expressed mRNA analysis from different tissues,
cells, and disease states. It creates a standard for thecomparison of disease-related alterations of the humanproteome. Moreover, comparison with pathogen proteomes
may yield many possible drug target proteins. Also, theannotation of ncRNAs raises the possibility of novel ‘‘smart’’
therapeutics that could either inhibit or mimic the mechanisms of these RNAs.
The H-Inv project is the ﬁrst ever comprehensive compilation of curated and annotated human FLcDNAs. The
project may lead to a more complete understanding of thehuman transcriptome and, as a result, of the humanproteome. The preceding examples of the importance ofthe H-Inv data in understanding human physiology andevolution represent just a small fraction of the researchpotential of the H-InvDB.
In conclusion, the H-InvDB platform constructed to holdthe results of the comprehensive annotations performed by
our international team of collaborators represents a substantial contribution to resources that are needed for further
exploration of both human biology and pathology.

Materials and Methods
cDNA resources. 41,118 H-Inv cDNAs were sequenced by the
Human Full-Length cDNA Sequencing Project (Ota et al. 1997;
Yudate et al. 2001; Ota et al. 2004) at the Helix Research Institute, theInstitute of Medical Science at the University of Tokyo, and theKazusa DNA Research Institute (20,999 sequences in total); theKazusa cDNA Sequencing Project (Kikuno et al. 2002) at the Kazusa
DNA Research Institute (2,000 sequences); the Mammalian Gene
Collection (Strausberg et al. 1999) at the National Institutes of Healthin the United States (11,806 sequences); the German Human cDNA
Project (Wiemann et al. 2001) coordinated by the Deutsches
Krebsforschungszentrum in Heidelberg (5,555 sequences); and theChinese National Human Genome Center at Shanghai (Hu et al.
2000) (758 sequences).
Mapping human cDNAs to the human genome and the comparisonof the mapped H-Inv cDNAs with other annotated datasets. We havemapped human cDNA sequences to the human genome sequencecorresponding to the NCBI build 34 assembly. The datasets we usedwere a set of 41,118 H-Inv cDNAs and a set of 37,488 human RefSeq
sequences available on 15 July 2002 and on the 1 September 2003,
respectively. All the revisions for H-Inv cDNA sequences until August
2003 were applied in the datasets. Before performing the mapping
procedure, all the repetitive and low-complexity sequences in all thecDNA sequences were masked using RepeatMasker (http://ftp.
genome.washington.edu/RM/RepeatMasker.html) and Repbase 7.5.
Then we used the cross_match program to mask the remaining
vector sequences in each cDNA sequence. Any poly-A tails were also
masked by using a custom-made Perl script. In the ﬁrst step of the
mapping procedure, we conducted BLASTN (ver.2.2.6) searches of all
the sequences against the human genome sequence and extracted thecorresponding genomic regions for each query sequence. Then weused est2genome (EMBOSS package ver.2.7.1) to align each sequence
to the genomic region with a threshold of 95% identity and 90%
coverage. Coverage of each cDNA sequence was calculated excluding
those from the vector and poly-A tails that were masked in theprevious step. If the sequences were mapped to multiple positions on
the human genome, then we selected their best locus based on theidentity, length coverage, and number of exons of those sequences. As
a result, 77,315 sequences (including 40,140 cDNAs from the H-Inv
project) were successfully mapped onto the human genome and wereclustered into 38,587 clusters based on sharing at least 1 bp of anJune 2004 | Volume 2 | Issue 6 | Page 0870

Integrative Annotation of Human Genes
exon on the same chromosome strand. We used all the mapped
sequences, including human RefSeq sequences, to compare theclusters that included H-Inv cDNAs with those that consisted of only
human RefSeq sequences. 20,190 clusters out of 38,587 consisted ofonly H-Inv cDNAs or both H-Inv cDNAs and human RefSeq
sequences. The rest of the clusters consisted of RefSeq sequences
only. All of the mapped cDNAs and the overlap with the RefSeq
sequences can be viewed using G-integra in the H-InvDB (http://
www.jbirc.aist.go.jp/hinv/g-integra/html/). The mapping procedure forall the unmapped cDNAs against the mouse genome was also
performed, using a threshold of 60% identity and 90% coverage.
Clustering of unmapped sequences. The sequences that were notmapped onto the human genome were clustered by a single linkage
clustering method. The similarity search was performed among all
the unmapped sequences. The program used was MegaBLAST
version 2.2.6 (Zhang et al. 2000). As with to the mapping strategy,
some distinctive sequences (repetitive regions, contaminations fromcloning vectors and poly-A tails) were excluded from the queries ofthe similarity search. The similarity was evaluated using the expectedvalue (E-value) between two sequences. Only when the E-value of thetwo sequences was calculated to be 0, did we assume that a signiﬁcantlevel of similarity was detected between the two sequences.
Identiﬁcation of gene structure. In order to identify gene structure,
we used only the representative H-Inv cDNAs. When detecting
repetitive elements in cDNAs, RepeatMasker was conducted in asimilar manner to the previous phase. We used curated cDNAs in
which frameshift errors and remaining introns were removed.
Prediction of ORFs. We predicted ORFs in all 41,118 H-Inv cDNAs,
as illustrated in Figure S1, based on the alignment of similarity
searches by FASTY (Pearson 2000; Mackey et al. 2002) (ver. 3.4t11)
and BLASTX (Altschul et al. 1990) (ver. 2.0.11), and gene prediction
by GeneMark (McIninch et al. 1996) (http://opal.biology.gatech.edu/
GeneMark/) (Table S10). Prior to the prediction of ORFs, we judged if
the sequence had any frameshift errors or remaining introns (seeFigure S1). During ORF prediction, we corrected the aforementioned
sequence irregularities computationally.
Procedure of computational and human annotation. Prior to thehuman curation, we performed two computational automatedannotation processes to select the representative clone for eachlocus and to predict function of H-Inv proteins (see Figure S2). We
then assigned the most suitable data source ID to each H-Inv protein
following a scheme illustrated in Figure S2 and referring to theinformation using newly developed annotation viewers, named SOUP
location viewer, SOUP annotation viewer, and Similarity Motif ORF(SMO) Viewer (Figure S9). Questionable transcripts were determinedby human curation based upon evidence such as the following:
sequences with no similarity to a known protein or domain,
sequences with a very short ORF, cDNAs with only a single exon,and sequences with no EST support. Only 959 (4.9%) of thecomputationally selected 19,574 representative H-Inv proteins had
to be manually corrected. Another 3,142 (16.1%) of the H-Inv
proteins had their functional assignment altered by manual curation.
Assignment of functional motifs. Nonredundant proteome datasets were obtained for ﬂy (http://ﬂybase.bio.indiana.edu/), worm
(http://www.wormbase.org/), budding yeast (http://www.pasteur.fr/
externe), ﬁssion yeast (http://www.sanger.ac.uk/), plant (http://
mips.gsf.de/proj/thal/index.html), and a bacteria (ftp://ftp.ncbi.nih.
gov/genbank/genomes/Bacteria/Escherichia_coli_K12/). The H-Inv
proteins and other nonredundant proteome datasets were assigned
InterPro codes by InterProScan ver. 3.1 (Mulder et al. 2003). The
codes corresponded to families, domains, and repeats. GO terms werealso assigned (see Table S5).
Evolutionary relationship of proteomes. The top 40 InterPro
entries for the human proteome were compared with theirequivalents from the ﬂy, worm, yeasts, plant, and bacteria proteomes(see Table S4).
Protein domains and low-complexity inserted sequences. Folds
were assigned by reverse PSI-BLAST (Altschul et al. 1997) searches of
the amino acid sequences derived from the H-Inv cDNA against the
SCOP database (Lo Conte et al. 2000). Information on protein andgene structures, with the exception of mouse and puffer ﬁsh, wasobtained from the individual genome projects (Blattner et al. 1997;
Kunst et al. 1997; CESC 1998; Adams et al. 2000; AGI 2000; Wood etal. 2002). The data for mouse and puffer ﬁsh were obtained from theEnsembl database (Hubbard et al. 2002).
Subcellular localization. Subcellular localization targeting signals
and transmembrane helices of 40,352 H-Inv proteins were predictedusing the PSORT II (Nakai and Horton 1999), TargetP (Emanuelsson
et al. 2000), TMHMM, and SOSUI (Hirokawa et al. 1998) computer
programs.
PLoS Biology | http://biology.plosjournals.org

UTR sequences. We obtained the UTR sequences from three
primates (Pan troglodytes, chimpanzee; Macaca fascicularis, crab-eating
macaque; and Macaca mulatta, rhesus monkey) and two rodents (Musmusculus, house mouse; and Rattus norvegicus, Norwegian rat) that
corresponded to UTRs from Homo sapiens. In order to do this, we
mapped the cDNAs to the human or mouse genome. The
corresponding rodent cDNAs were determined by using a human–
mouse genome alignment provided by Ensembl. cDNAs of the
primates and rodents were retrieved from the DDBJ/EMBL/GenBank
databases using the cut off date of 15 July 2002. Additionally, we used
the FANTOM2 mouse sequences released on 5 December 2002, and4,063 59 ESTs of chimpanzees (Sakate et al. 2003). Corresponding
UTRs between human and other species were identiﬁed by aligning 59
and 39 ends of the human ORFs. To compare evolutionary distances,
we analyzed 3,061 and 5,277 orthologous groups that consisted of atleast three species’ information for the 59 and 39 UTR sequences,respectively.

Supporting Information
Dataset S1. List of Library Origins of H-Inv cDNAs (182 Libraries)
The dataset consists of 41,118 H-Inv cDNAs that were cloned from
cDNA libraries derived from 182 varieties of cell and tissue.
Found at DOI: 10.1371/journal.pbio.0020162.sd001 (33 KB XLS).
Dataset S2. List of H-Inv Proteins with Potential EC Numbers (1,892
H-Inv Proteins)
The allotted EC numbers are based on the corresponding DNA
databank records, UniProt/Swiss-Prot and TrEMBL records that show
sequence similarity to the proteins, and InterPro records that theproteins hit.
Found at DOI: 10.1371/journal.pbio.0020162.sd002 (247 KB XLS).
Dataset S3. List of Polymorphic Microsatellites Inferred by Comparisons between the H-Inv cDNAs and Genomic Sequences
Found at DOI: 10.1371/journal.pbio.0020162.sd003 (56 KB XLS).
Figure S1. Prediction of ORFs
(A) Schematic diagram for the prediction of ORFs. This diagram
illustrates the ORF prediction method used on all H-Inv cDNAs. The
method was based upon the alignment of similarity searches using
FASTY and BLASTX. Gene prediction was carried out using
GeneMark. Prior to the prediction of ORFs, we judged if a sequence
had any frameshift errors or remaining introns. During ORF
prediction, we corrected those sequence irregularities computationally. Details of how sequence irregularities were predicted aredescribed in (B) and (C).
(B) Schematic diagram for prediction of unspliced introns. This
schematic diagram illustrates the prediction method used forunspliced introns.
(C) Schematic diagram for prediction of frameshift errors. Frameshifterrors were inferred from cDNA–genome pairwise alignment gaps
due to insertion or deletion, exception of multiple of 3 bp, or over 10
bp in either the query cDNA or genome.
(D) The statistics for the predicted frameshifts and unspliced introns.
Found at DOI: 10.1371/journal.pbio.0020162.sg001 (49 KB PDF).
Figure S2. Scheme of Prediction for Functional Annotation(A) Schematic diagram for determining a representative transcriptfor each locus. The procedure of computational autoannotation is
illustrated. Prior to the human curation of the representativetranscript of each H-Inv cluster, we performed computational
autoannotation.
(B) Schematic diagram for functional prediction of H-Inv proteins.
This schematic diagram illustrates the H-Inv autofunctional annotation pipeline that can determine the most appropriate data source
ID, avoiding the following keywords that suggest proteins without
experimental veriﬁcation in the description; (1) hypothetical, (2)similar to, (3) names of cDNA clones (Rik, KIAA, FLJ, DKFZ, HSPC,
MGC, CHGC, and IMAGE) and (4) names of InterPro domain
frequent hitters.
Found at DOI: 10.1371/journal.pbio.0020162.sg002 (34 KB PDF).
Figure S3. Size Distribution of Predicted ORFs
The size distribution of all H-Inv proteins among the ﬁve similarity
categories.
Found at DOI: 10.1371/journal.pbio.0020162.sg003 (24 KB PDF).
June 2004 | Volume 2 | Issue 6 | Page 0871

Integrative Annotation of Human Genes
Figure S4. Features of Category II Proteins
A total of 4,104 H-Inv proteins were classiﬁed as Category II based onsequence similarity to functionally validated proteins. The table andﬁgure show source species of proteins in public databases to which
the Category II proteins were similar.
Found at DOI: 10.1371/journal.pbio.0020162.sg004 (9 KB PDF).
Figure S5. H-Inv KEGG Analysis Results (Images of KEGG Pathways)
The images illustrate the metabolic pathways of KEGG database
based on the EC number assignments to H-Inv proteins.
Found at DOI: 10.1371/journal.pbio.0020162.sg005 (47 KB PDF).
Figure S6. Numbers of Representative H-Inv cDNAs That Are
Homologous to Proteins in Each Taxonomic Group
Two thresholds (E , 105, white bars, and E , 1010, black bars) were
employed. The ‘‘animal’’ group does not include mammalian species.
The ‘‘eukaryote’’ group represents eukaryotic species other than
animals, fungi, and plants.
Found at DOI: 10.1371/journal.pbio.0020162.sg006 (9 KB PDF).
Figure S7. A Functional Classiﬁcation of H-Inv Protein Families That
Have Homologs in Each Taxonomic Group
H-Inv protein families were identiﬁed by clustering H-Inv proteinsusing the single-linkage clustering method. Then, the number ofhomologs for each H-Inv protein family was calculated. Mammalian
species are excluded from the ‘‘animal’’ group. ‘‘eukaryote’’ represents eukaryotic species other than animals, fungi, and plants.
Single-linkage clustering. All of the H-Inv proteins were comparedwith themselves by BLASTP and clustered with the thresholds of Evalues of 1030 and 1050. The numbers of singleton families detected
were 11,890 and 13,938 at the E-value of 1030 and 1050, respectively.
Found at DOI: 10.1371/journal.pbio.0020162.sg007 (49 KB PDF).
Figure S8. A Sample View of the H-Invitational Database (H-InvDB;
http://www.h-invitational.jp/)
A FLcDNA (BC003551) is shown with its detailed annotations, e.g.,
gene structure, functional annotation, ORF predictions, protein
structure prediction by GTOP, etc. The H-InvDB has links to otherinternal databases (red boxes) such as a genome map viewer (Gintegra) and gene expression library (H-Angel). Green boxes showinternal viewers for the results of clustering (Clustering Viewer
showing results by H-Inv, STACK, TIGR, UniGene, etc.), theprediction of subcellular localization (TOPOViewer showing resultsof TMHMM, SOSUI, TargetP, and PsortII), and the disease-related
information (DiseaseInfo Viewer linking to OMIM and GenAtlas).
The H-InvDB also has links to many external public databases (blackboxes), including DDBJ/EMBL/GenBank, RefSeq, UniProt/Swiss-Protand TrEMBL, Genew, InterPro, 3D Keynote, Ensembl, GeneLynx,
LocusLink, PubMed, LIFEdb, dbSNP, GO, and GTOP, and to
homepages by original data producers of FLcDNA clones andsequences (blue boxes), including the Chinese National Human
Genome Center (CHGC), the Deutsches Krebsforschungszentrum(DKFZ/MIPS), Helix Research Institute (HRI), the Institute of Medical
Science at the University of Tokyo (IMSUT), the Kazusa DNA
Research Institute (KDRI), the Mammalian Gene Collection (MGC/
NIH), and the FLJ project.
Found at DOI: 10.1371/journal.pbio.0020162.sg008 (2,650 KB PDF).
Figure S9. H-Inv Annotation Viewers(A) G-integra: A genome mapping viewer.
(B) SOUP Locus annotation viewer.
(C) SOUP cDNA annotation viewer.
(D) SMO Viewer: The similarity, motif, and ORF information viewer.
Found at DOI: 10.1371/journal.pbio.0020162.sg009 (2,022 KB PDF).
Table S1. Gene Structure(A) Gene structure of the cDNAs.
(B) The frequencies and varieties of repetitive sequences found in the
cDNAs. A list of the 20,899 loci representing cDNAs that RepeatMasker showed contained repetitive elements.
(C) The positions (59 UTR, ORF, and 39 UTR) of repetitive sequencesin the protein-coding cDNAs. A total of 1,863 cDNAs contained
repetitive sequences in their ORF, of which 549 had repetitive
sequences within their most probable ORF. Repetitive sequences
appeared in 2,240 and 5,401 cDNAs in their 59 UTRs and 39 UTRs,
respectively.
PLoS Biology | http://biology.plosjournals.org

Found at DOI: 10.1371/journal.pbio.0020162.st001 (20 KB PDF).
Table S2. CAI and Codon Usage(A) CAI was measured for all H-Inv proteins. CAI is a measure ofbiased patterns for synonymous codon usage (http://biobase.dk/
embossdocs/cai.html).
(B) Codon usage in predicted ORFs of H-Inv proteins. Total trinucleotide frequencies (forward strand) for the sequences of eachspecies are shown. Nonredundant proteome datasets for nonhumanspecies were obtained from the following sites: ﬂy (Drosophilamelanogaster; http://ﬂybase.bio.indiana.edu/), worm (Caenorhabditis elegans; http://www.wormbase.org/), budding yeast (Saccharomyces cerevisiae;
http://www.pasteur.fr/externe), ﬁssion yeast (Schizosaccharomyces pombe;
http://www.sanger.ac.uk/), plant (Arabidopsis thaliana; http://mips.gsf.de/proj/thal/index.html), and bacteria (Escherichia coli K12; ftp://ftp.ncbi.
nih.gov/genbank/genomes/Bacteria/Escherichia_coli_K12/).
Found at DOI: 10.1371/journal.pbio.0020162.st002 (20 KB PDF).
Table S3. Tissue Library Origins of H-Inv Proteins
The results of classiﬁcation into ﬁve similarity categories for each of
ten tissue classes.
(A) Numbers of H-Inv proteins.
(B) Histogram.
Found at DOI: 10.1371/journal.pbio.0020162.st003 (10 KB PDF).
Table S4. The InterPro IDs Identiﬁed in H-Inv Proteins
The top 40 InterPro IDs identiﬁed in H-Inv proteins and proteinsfrom other species are listed for all types (A) and for each type offamily, domain, and repeat (B–D). Analyses were conducted byInterPro ver. 3.1. Nonredundant proteome datasets of other species
were obtained from the following sites: ﬂy (Drosophila melanogaster;
http://ﬂybase.bio.indiana.edu/), worm (Caenorhabditis elegans; http://
www.wormbase.org/), budding yeast (Saccharomyces cerevisiae; http://
www.pasteur.fr/externe), ﬁssion yeast (Schizosaccharomyces pombe; http://
www.sanger.ac.uk/), plant (Arabidopsis thaliana; http://mips.gsf.de/proj/
thal/index.html), and bacteria (Escherichia coli K12; ftp://ftp.ncbi.nih.
gov/genbank/genomes/Bacteria/Escherichia_coli_K12/).
Found at DOI: 10.1371/journal.pbio.0020162.st004 (36 KB PDF).
Table S5. GO Term Assignment to H-Inv Proteins(A) Molecular function.
(B) Cellular component.
(C) Biological process.
Found at DOI: 10.1371/journal.pbio.0020162.st005 (74 KB PDF).
Table S6. List of Newly Assigned Human Enzymes (32 H-Inv Proteins)
All these 32 H-Inv proteins were newly assigned enzyme numberswith the support of the KEGG pathway. These enzyme assignments
were previously unrepresented in Homo sapiens.
Found at DOI: 10.1371/journal.pbio.0020162.st006 (33 KB PDF).
Table S7. A Functional Classiﬁcation of Representative H-Inv cDNAs
That Have Homologs in Other Species(See also Figure 6.)
Found at DOI: 10.1371/journal.pbio.0020162.st007 (9 KB PDF).
Table S8. Basic Statistics for UTR Sequences Analyzed
Found at DOI: 10.1371/journal.pbio.0020162.st008 (8 KB PDF).
Table S9. UTR Replacements in Primates and Rodents
One hundred and forty-seven UTR replacements distributed among
different species were detected.
Found at DOI: 10.1371/journal.pbio.0020162.st009 (9 KB PDF).
Table S10. List of the Databases and Software Used in the H-Inv
Project
Found at DOI: 10.1371/journal.pbio.0020162.st010 (31 KB PDF).
Protocol S1. A Detailed Functional Annotation Based on Protein
Modules
Found at DOI: 10.1371/journal.pbio.0020162.sd004 (25 KB PDF).

Acknowledgments
This paper is dedicated to the late Dr. Yoshimasa Kyogoku, theDirector of the Biological Information Research Center, National
June 2004 | Volume 2 | Issue 6 | Page 0872

Integrative Annotation of Human Genes
Institute of Advanced Industrial Science and Technology, who passed
away on February 27, 2003.
The authors express their most sincere gratitude to Drs. David
Lipman, Graham Cameron, Joakim Lundeberg, and Francis Collins
for their support, the Research Association for Biotechnology ofJapan, the International Human Genome Sequencing Consortium,
and the Chromosome 22 Group at the Sanger Institute for providing
sequence and annotation data. We are grateful to T. Hasui, T.
Habara, K. Yamaguchi, H. Kawashima, F. Todokoro, N. Yamamoto, Y.
Makita, R. Aono, Y. Tanada, H. Kubooka, H. Maekawa, Y. Sasayama, T.
Yamamoto, S. Okiyama, K. Nakamura, A. Matsuya, Y. Mimiura, R.
Matsumoto, K. Takabayashi, Y. Hayakawa, H. Zhang, S. Nurimoto, T.
Sugisaki, T. Kawamura, O. Nakano, S. Hosoda, N. Yoshimura, and T.
Endo for their technical support. This research is ﬁnancially
supported by the Ministry of Economy, Trade, and Industry of Japan(METI), the Ministry of Education, Culture, Sports, Science, andTechnology of Japan (MEXT), the Japan Biological Informatics
Consortium (JBIC), the New Energy and Industrial Technology
Development Organization (NEDO), the United States Departmentof Energy, the National Institutes of Health of the United States, theBundesministerium für Bildung und Forschung (BMBF) of Germany,
the European Union through the EURO-IMAGE Consortium (grantBMH4-CT97-2284 coordinated by Charles Auffray), the 863 and 973
Program of the Ministry of Science and Technology of China, and
CNRS of France. The work on Module 3D-keynote is supported by
Grants-in-Aid for Scientiﬁc Research on Priority Areas (C) ‘‘Genome
Information Science’’ to Mitiko Go and Kei Yura, and for ScientiﬁcResearch (B) to MG, from MEXT. KY is also supported by a Grant-inAid for Encouragement of Young Scientists from MEXT. The work on
subcellular localization is supported by a Grant-in-Aid for ScientiﬁcResearch on Priority Areas (C) ‘‘Genome Information Science’’ from
MEXT and the National Project on Protein Structural and Functional
Analyses from the same Ministry.
Conﬂicts of interest. The authors have declared that no conﬂicts ofinterest exist.
Author contributions. The project was conceived and designed by
T. Imanishi, T. Itoh, Y. Suzuki, C. O’Donovan, S. Fukuchi, Y.
Yamaguchi-Kabata, S. Miyazaki, K. Ikeo, A. Kasprzyk, T. Nishikawa,
M. Stodolsky, W. Makalowski, M. Go, K. Nakai, T. Takagi, M. Kanehisa,
Y. Sakaki, J. Quackenbush, Y. Okazaki, Y. Hayashizaki, W. Hide, R.

Chakraborty, K. Nishikawa, H. Sugawara, Y. Tateno, Z. Chen, M.
Oishi, P. Tonellato, R. Apweiler, K. Okubo, L. Wagner, S. Wiemann, R.
L. Strausberg, T. Isogai, C. Auffray, N. Nomura, T. Gojobori, and S.
Sugano.
The data were analyzed by T. Imanishi, T. Itoh, Y. Suzuki, C.
O’Donovan, S. Fukuchi, K. O. Koyanagi, R. A. Barrero, T. Tamura, Y.
Yamaguchi-Kabata, M. Tanino, K. Yura, S. Miyazaki, K. Ikeo, K.
Homma, A. Kasprzyk, T. Nishikawa, M. Hirakawa, J. Thierry-Mieg, D.
Thierry-Mieg, J. Ashurst, L. Jia, M. Nakao, M. A. Thomas, N. Mulder, Y.
Karavidopoulou, L. Jin, S. Kim, T. Yasuda, B. Lenhard, E. Eveno, Y.
Suzuki, C. Yamasaki, J.-I. Takeda, C. Gough, P. Hilton, Y. Fujii, H.
Sakai, S. Tanaka, C. Amid, M. Bellgard, M. de Fatima Bonaldo, H.
Bono, S. K. Bromberg, A. Brookes, E. Bruford, P. Carninci, C. Chelala,
C. Couillault, S. J. De Souza, M.-A. Debily, M.-D. Devignes, I. Dubchak,T. Endo, A. Estreicher, E. Eyras, K. Fukami-Kobayashi, G. Gopinathrao, E. Graudens, Y. Hahn, M. Han, Z.-G. Han, K. Hanada, H.
Hanaoka, E. Harada, K. Hashimoto, U. Hinz, M. Hirai, T. Hishiki, I.
Hopkinson, S. Imbeaud, H. Inoko, A. Kanapin, Y. Kaneko, T.
Kasukawa, J. F. Kelso, P. Kersey, R. Kikuno, K. Kimura, B. Korn, V.
Kuryshev, I. Makalowska, T. Makino, S. Mano, R. Mariage-Samson, J.
Mashima, H. Matsuda, H.-W. Mewes, S. Minoshima, K. Nagai, H.
Nagasaki, N. Nagata, R. Nigam, O. Ogasawara, O. Ohara, M. Ohtsubo,
N. Okada, T. Okido, S. Oota, M. Ota, T. Ota, T. Otsuki, D. PiatierTonneau, A. Poustka, S.-X. Ren, N. Saitou, K. Sakai, S. Sakamoto, R.
Sakate, I. Schupp, F. Servant, S. Sherry, R. Shiba, N. Shimizu, M.
Shimoyama, A. J. Simpson, B. Soares, C. Steward, M. Suwa, M. Suzuki,
A. Takahashi, G. Tamiya, H. Tanaka, T. Taylor, J. D. Terwilliger, P.
Unneberg, V. Veeramachanen, S. Watanabe, L. Wilming, N. Yasuda,
H.-S. Yoo, W. Makalowski, M. Go, K. Nakai, Y. Okazaki, W. Hide, R.
Chakraborty, Z. Chen, P. Tonellato, K. Okubo, L. Wagner, S.
Wiemann, T. Isogai, C. Auffray, N. Nomura, T. Gojobori, and S.
Sugano.
The paper was written by T. Imanishi, T. Itoh, Y. Suzuki, S.
Fukuchi, K. O. Koyanagi, R. A. Barrero, T. Tamura, Y. YamaguchiKabata, M. Tanino, K. Yura, K. Homma, M. Hirakawa, L. Jia, M.
Nakao, B. Lenhard, C. Yamasaki, C. Gough, P. Hilton, Y. Fujii, S.
Tanaka, C. Chelala, M.-D. Devignes, T. Hishiki, I. Hopkinson, W.
Makalowski, K. Nakai, W. Hide, P. Tonellato, C. Auffray, N. Nomura,&
T. Gojobori, and S. Sugano.

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