CORRECTED 24 MARCH 2006; SEE LAST PAGE
REPORTS
References and Notes
1. B. D. Santer et al., Science 309, 1551 (2005); published
online 11 August 2005 (10.1126/science.1114867).
2. J. K. Angell, J. Clim. 16, 2288 (2003).
3. J. R. Lanzante, S. A. Klein, D. J. Seidel, J. Clim. 16, 241(2003).
4. D. E. Parker et al., Geophys. Res. Lett. 24, 1499 (1997).
5. P. W. Thorne et al., J. Geophys. Res., in press.
6. D. H. Douglass, B. D. Pearson, S. F. Singer, P. C.
Knappenberger, P. J. Michaels, Geophys. Res. Lett. 31,
L13207 (2004).
7. D. J. Gaffen et al., Science 287, 1242 (2000).
8. D. E. Parker, D. I. Cox, Int. J. Climatol. 15, 473(1995).
9. M. Free, D. J. Seidel, J. Geophys. Res. 110, D07101(2005).
10. J. K. Luers, R. E. Eskridge, J. Appl. Meteorol. 34, 1241
(1995).
11. I. Durre, T. C. Peterson, R. S. Vose, J. Clim. 15, 1335
(2002).
12. L. Haimberger, ‘‘Homogenization of radiosonde temperature time series using ERA-40 analysis feedback
information,’’ Tech. Rep. European Center for Medium
Range Weather Forecasting (2005), ERA-40 Project
Report Series 23.
13. D. J. Seidel et al., J. Clim. 17, 2225 (2004).
14. P. R. Krishnaiah, B. Q. Miao, Handbook of Statistics,
P. R. Krishnaiah, C. R. Rao, Eds. (Elsevier, New York,
1988), vol. 7.
15. M. Free et al., Bull. Am. Meteorol. Soc. 83, 891 (2002).
16. W. J. Randel, F. Wu, in preparation.
17. D. J. Seidel, M. Free, J. Wang, J. Geophys. Res. 110,
D09102 (2005).
18. A. Dai, K. E. Trenberth, T. R. Karl, J. Clim. 12, 2451
(1999).

19. S. Chapman, R. S. Lindzen, Atmospheric Tides (D. Reidel,
Norwell, MA, 1970).
20. D. R. Easterling et al., Science 277, 364 (1997).
21. D. J. Gaffen, R. J. Ross, J. Clim. 12, 811 (1999).
22. W. J. Randel et al., Science 285, 1689 (1999).
23. K. N. Liou, T. Sasamori, J. Atmos. Sci. 32, 2166 (1975).
24. R. E. Eskridge et al., Bull. Am. Meteorol. Soc. 76,
1759 (1995).
25. H. Riehl, Tropical Meteorology (McGraw Hill, New
York, 1954).
26. S. C. Sherwood, Geophys. Res. Lett. 27, 3525 (2000).
27. J. R. Christy, R. W. Spencer, W. B. Norris, W. D.
Braswell, D. E. Parker, J. Atmos. Oceanic Technol. 20,
613 (2003).
28. T. Sasamori, J. London, J. Atmos. Sci. 23, 543 (1966).
29. Data files and further information on methods, uncertainty, and interpretation of our results are available assupporting material on Science Online.
30. S.C.S. thanks J. Risbey and K. Braganza for useful discussions. This work was supported by the National
Oceanic and Atmospheric Administration Climate andGlobal Change Program award NA03OAR4310153, andby NSF ATM-0134893.
Supporting Online Materialwww.sciencemag.org/cgi/content/full/1115640/DC1
Methods
SOM Text
Data files
References and Notes

Downloaded from http://science.sciencemag.org/ on May 29, 2017

only LKS stations in NH), are fully consistent with this assumption, particularly for thetropical stations. In the extratropics there are
only four daytime-only stations so the MSU
test is less meaningful, but the two independent estimates do agree within 0.03-C per
decade.
To illustrate the importance of the heating
bias, we have computed its impact dsol on thetrends at LKS stations. The LKS f factors,
unhomogenized trends, and trends adjusted
only for solar heating are given for the middle
troposphere and lower stratosphere in Table 2.
In the stratosphere, our dsol is similar to thetotal adjustments by LKS and others, withtrends moving closer to those from MSU (13).
At the tropical tropopause (of relevance tostratospheric water vapor), dsol is somewhat
smaller than LKS_s. In the troposphere, however, dsol is much larger than previous adjustments. Indeed, the tropical trend with thisadjustment (0.14-C per decade over 1979 to
1997) would be consistent with model simulations driven by observed surface warming,
which was not true previously (1). One independent indication that the solar-adjusted
trends should be more accurate is their consistency across latitude belts: for the period
1979 to 1997, the spread of values fell by
70% in the lower stratosphere and 25% in
the troposphere.
Though this is encouraging, our confidence
in these nighttime trends is still limited giventhat other radiosonde errors have not beenaddressed. SH trends from 1958 to 1997 seemunrealistically high in the troposphere, especially with the dsol adjustment, although thisbelt has by far the worst sampling. Previous
homogenization efforts typically produced
small changes to mean tropospheric trends,
which could mean that other error trends
cancel out dsol in the troposphere. In our judgment, however, such fortuitous cancellation of
independent errors is unlikely compared to thepossibility that most solar artifacts were previously either missed or their removal negatedby other, inaccurate adjustments. To be detected easily, a shift must be large and abrupt,
but dsol was spread out over so many stations(79% of stations during 1979 to 1997 and
90% during 1959 to 1997 experienced DT
trends significant at 95% level), at suchmodest levels, and of sufficient frequency atmany stations that many may have been
undetectable. Most important, jumps in the
difference between daytime and nighttime
monthly means would be detectable at only a
few tropical stations because most lack sufficient nighttime data. In any case, we conclude
that carefully extracted diurnal temperature
variations can be a valuable troubleshooting
diagnostic for climate records, and that theuncertainty in late–20th century radiosonde
trends is large enough to accommodate thereported surface warming.

2 June 2005; accepted 27 July 2005
Published online 11 August 2005;
10.1126/science.1115640
Include this information when citing this paper.

The Transcriptional Landscape ofthe Mammalian Genome
The FANTOM Consortium* and RIKEN Genome Exploration
Research Group and Genome Science Group(Genome Network Project Core Group)*This study describes comprehensive polling of transcription start andtermination sites and analysis of previously unidentified full-length complementary DNAs derived from the mouse genome. We identify the 5¶ and 3¶
boundaries of 181,047 transcripts with extensive variation in transcripts arisingfrom alternative promoter usage, splicing, and polyadenylation. There are
16,247 new mouse protein-coding transcripts, including 5154 encoding
previously unidentified proteins. Genomic mapping of the transcriptome reveals
transcriptional forests, with overlapping transcription on both strands,
separated by deserts in which few transcripts are observed. The data provide
a comprehensive platform for the comparative analysis of mammaliantranscriptional regulation in differentiation and development.
The production of RNA from genomic DNA
is directed by sequences that determine thestart and end of transcripts and splicing into
mature RNAs. We refer to the pattern of transcription control signals, and the transcripts
they generate, as the transcriptional landscape.
To describe the transcriptional landscape ofthe mammalian genome, we combined fulllength cDNA isolation (1) and 5¶- and 3¶-end
sequencing of cloned cDNAs, with new capanalysis gene expression (CAGE) and gene
identification signature (GIS) and gene signature cloning (GSC) ditag technologies forthe identification of RNA and mRNA sequences corresponding to transcription initi-

www.sciencemag.org

SCIENCEVOL 309

ation and termination sites (2, 3). A detailed
description of the data sets generated, mapping
strategies, and depth of coverage of the mousetranscriptome is provided in supporting online
material (SOM) text 1 (Tables 1 and 2). We
have identified paired initiation and termination sites, the boundaries of independenttranscripts, for 181,047 independent transcripts in the transcriptome (Table 3). In
total, we found 1.32 5¶ start sites for each 3¶
end and 1.83 3¶ ends for each 5¶ end (tableS1). Based on these data, the number oftranscripts is at least one order of magnitudelarger than the estimated 22,000 Bgenes[ inthe mouse genome (4) (SOM text 1), and the

2 SEPTEMBER 2005

1559

REPORTS

The FANTOM Consortium:
P. Carninci,. T. Kasukawa, S. Katayama, J. Gough,.
M. C. Frith,. N. Maeda, R. Oyama, T. Ravasi,. B.
Lenhard,. C. Wells,. R. Kodzius, K. Shimokawa, V. B.
Bajic,. S. E. Brenner, S. Batalov, A. R. R. Forrest, M.
Zavolan, M. J. Davis, L. G. Wilming, V. Aidinis, J. E. Allen,
A. Ambesi-Impiombato, R. Apweiler, R. N. Aturaliya, T. L.
Bailey, M. Bansal, L. Baxter, K. W. Beisel, T. Bersano, H.
Bono, A. M. Chalk, K. P. Chiu, V. Choudhary, A.
Christoffels, D. R. Clutterbuck, M. L. Crowe, E. Dalla,
B. P. Dalrymple, B. de Bono, G. Della Gatta, D. di
Bernardo, T. Down, P. Engstrom, M. Fagiolini, G. Faulkner,
C. F. Fletcher, T. Fukushima, M. Furuno, S. Futaki, M.
Gariboldi, P. Georgii-Hemming, T. R. Gingeras, T.
Gojobori, R. E. Green, S. Gustincich, M. Harbers, Y.
Hayashi, T. K. Hensch, N. Hirokawa, D. Hill, L.
Huminiecki, M. Iacono, K. Ikeo, A. Iwama, T. Ishikawa,
M. Jakt, A. Kanapin, M. Katoh, Y. Kawasawa, J. Kelso,
H. Kitamura, H. Kitano, G. Kollias, S. P. T. Krishnan, A.
Kruger, S. K. Kummerfeld, I. V. Kurochkin, L. F.
Lareau, D. Lazarevic, L. Lipovich, J. Liu, S. Liuni, S.
McWilliam, M. Madan Babu, M. Madera, L. Marchionni,
H. Matsuda, S. Matsuzawa, H. Miki, F. Mignone, S.
Miyake, K. Morris, S. Mottagui-Tabar, N. Mulder, N.
Nakano, H. Nakauchi, P. Ng, R. Nilsson, S. Nishiguchi, S.
Nishikawa, F. Nori, O. Ohara, Y. Okazaki, V. Orlando,
K. C. Pang, W. J. Pavan, G. Pavesi, G. Pesole, N.
Petrovsky, S. Piazza, J. Reed, J. F. Reid, B. Z. Ring, M.
Ringwald, B. Rost, Y. Ruan, S. L. Salzberg, A. Sandelin,
C. Schneider, C. Schönbach, K. Sekiguchi, C. A. M. Semple,
S. Seno, L. Sessa, Y. Sheng, Y. Shibata, H. Shimada, K.
Shimada, D. Silva, B. Sinclair, S. Sperling, E. Stupka, K.
Sugiura, R. Sultana, Y. Takenaka, K. Taki, K. Tammoja, S. L.
Tan, S. Tang, M. S. Taylor, J. Tegner, S. A. Teichmann, H. R.
Ueda, E. van Nimwegen, R. Verardo, C. L. Wei, K. Yagi, H.
Yamanishi, E. Zabarovsky, S. Zhu, A. Zimmer, W. Hide,
C. Bult,. S. M. Grimmond, R. D. Teasdale, E. T. Liu,. V.
Brusic, J. Quackenbush,. C. Wahlestedt,. J. S. Mattick,.
D. A. Hume.
RIKEN Genome Exploration Research Group and Genome Science Group (Genome Network Project CoreGroup):
C. Kai, D. Sasaki, Y. Tomaru, S. Fukuda, M. KanamoriKatayama, M. Suzuki,. J. Aoki, T. Arakawa, J. Iida, K.
Imamura, M. Itoh, T. Kato, H. Kawaji, N. Kawagashira,T. Kawashima, M. Kojima, S. Kondo, H. Konno, K.
Nakano, N. Ninomiya, T. Nishio, M. Okada, C. Plessy,
K. Shibata, T. Shiraki, S. Suzuki, M. Tagami, K. Waki,
A. Watahiki, Y. Okamura-Oho, H. Suzuki, J. Kawai.
General Organizer:
Y. Hayashizaki.*Affiliations can be found on Science Online (availableat www.sciencemag.org/cgi/content/full/309/5740/
1559/DC1).
.These authors are core authorship members.
-To whom correspondence should be addressed.
E-mail: yosihide@gsc.riken.jp

1560

The mapping of ends of transcripts can beused to identify the genomic span of the primary transcript. Figure 1A shows length distributions of the predicted genomic regions
spanned by mouse cDNAs showing a bimodal distribution and compares them with
one peak for unspliced and another for
spliced RNAs. At the upper end of the distribution are candidate mega transcripts (transcripts originating from genomic regionsin the order of millions of base pairs). For
example, we located six pairs of genome signature cloning (GSC) ditags to RIKEN clone
ID 9330159J16 and corresponding RIKEN
expressed sequence tags (ESTs). This clone
encodes for a previously unidentified largetranscript that is similar to a protein tyrosine
phosphatase, receptor type D (accession
no. BC086654), the genomic structure ofwhich has not been previously reported (8).
The predicted mRNA is 2475 base pairs inlength but spans a genomic region of 2.2
megabases (Mb).
We previously coined the term transcriptional units (TUs), which groups mRNAs
that share at least one nucleotide and havethe same genomic location and orientation(9). However, TU fusions can join unrelated
and differently annotated transcripts (SOM
text 2). Therefore, we define a transcriptional framework (TK) as grouping transcriptsthat share common expressed regions as well

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large majority of transcriptional units havealternative promoters and polyadenylationsites. The use of genome tiling arrays (5–7)
in humans has also implied that the numberof transcripts encoded by the genome is atleast 10 times as great as the number ofBgenes.[ To extend the mouse data, two
HepG2 CAGE libraries, one constructed withrandom primers and the other with oligo-dTprimers, were combined to produce 1,000,000
CAGE tags. Mapping of these tags to thehuman genome identified the likely promotersand transcriptional starting site (TSS) of manyof the gene models identified by tiling array,also called transfrags (5), and clearly indicatesthat the same level of transcriptional diversity
occurs in humans as in mice (table S2).

Fig. 1. Genome-transcriptome relation. (A) Genome span covered by full-length cDNA and GIS/GSC
ditags shows similar distribution with two main peaks. Ditags mapping follows the same distribution
profile at various mapping thresholds, with a minimum around 2 to 2.5 Mb. Mapping events above thisgenomic span are nonspecific. Count displays the number of events in the size interval. (B) Asymptotic
unit collapse. Due to extensive overlap of the genome, transcripts overlap to the extent that theycollapse to a few GFs. Simulating addition of ditags shows the collapsing rate of the known annotatedgenes into 9976 elements only. Primary transcripts only, GFs identified by GSC ditags only; Ensembl
only, GFs produced by the 3332 Ensembl-only annotated transcripts; total, the total number of GFs.

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as splicing events, TSS, or termination events(SOM text 1).
TKs can be clustered together into transcript forests (TFs), genomic regions thatare transcribed on either strand without
gaps. TFs encompass 62.5% of the genome(table S1) and are separated by regions

devoid of transcription, or transcription deserts. With the inclusion of GSC tags in addition to full-length cDNA and paired EST
sequences, the estimated total number oftranscript forests is 18,461,which will collapse further with increasing depth of coverage (Fig. 1B).

Fig. 2. Transcription originating in 3¶UTRs. (A) For each analyzed exon, the fraction of tags mapped to
10 equally large subsections of the exon was calculated. (Left) CAGE tags mapping to the first exon
are prevalently located in the first part of the exon. (Middle) CAGE tags mapping to internal exons
are uniformly distributed. (Right) Last exons show a distinct overrepresentation of CAGE tags
mapping close to the 3¶ end. (B) Distance to the closest downstream gene for the set of highly
expressed TUs that have extreme tag density in the 3¶ of the terminal exons. Transcript pairs weregrouped into tail-to-head (3¶ exon and downstream TU on same strand) or tail-to-tail (3¶ exon anddownstream TU on opposite strand) configurations. Remaining TUs were used as control groups. For
TUs with strong 3¶ transcriptional activity, the distance to the next TU is significantly smaller than
expected when the gene pair is in a tail-to-tail configuration (P e 0.001107, Wilcoxon test),
suggesting regulatory mechanisms based on natural antisense influencing the downstream gene (26).
Table 1. Data set resources.
Total
RIKEN full-length cDNAs
Public (non-RIKEN) mRNAs
CAGE tags (mouse)
CAGE tags (human)
GIS ditags
GSC ditags
RIKEN 5¶ESTs
RIKEN 3¶ESTs
5¶/3¶EST pairs of RIKEN cDNA

Number oflibraries

102,801
56,00911,567,973
5,992,395
385,7972,079,652
722,642
1,578,610
448,956237

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Safely mapped
100,313
52,1197,151,511
3,106,472
118,594968,201
607,462
907,007277,702

145
24
4
4
266
265
264

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The approach used to isolate full-length
cDNAs, based on library subtraction and previously unidentified 5¶/3¶ end selection before
full-insert sequencing, was weighted toward
identification of representative transcripts.
Nevertheless, 78,393 different splicing variants
were identified, such that 65% of TUs containmultiple splice variants (Table 2), an increase
from our previous estimate (41%) (9). Thisis still expected to be an underestimate, andnew approaches will be necessary for a fullevaluation of exon diversity (10).
Transcript diversity also arises throughalternative termination. Little is known about
sequence motifs that control alternative polyadenylation. We identified 27 motif families
with six or more nucleotides that were statistically overrepresented within 120 base pairs of
the polyadenylation site of individual transcripts in our data set. These motifs represent
candidate modulators of polyadenylation site
for eight unconventional alternative polyadenylation signals (1) (table S3). In addition, we
found a widespread motif family with sequence TTGTTT, which was associated withboth the canonical (AAUAAA and AUUAAA)
and unconventional signals (1, 11).
Gene names of 56,722 transcripts that were
protein coding were assigned according toannotation rules (9, 12). Their encoded protein sequences were combined with the publicly available proteins supported by cDNA
sequences (8). This generated a nonredundant
set of 51,135 proteins with experimental evidence Eisoform protein set (IPS)^, 36,166 ofwhich are complete (complete IPS). By comparison, the mammalian gene collection (http://
mgc.nci.nih.gov) has cloned, as of July 2005,
only È16,700 transcripts (11,514 nonredundant). In the FANTOM3 data set, 16,274 protein sequences are newly described. Their
splice variants were grouped together into
13,313 TKs. For 9002 of these, a previously
known sequence maps to the same TK (locus),but 4311 clusters (5154 different proteins) map
to new TKs (SOM text 3).
There are a total of 32,129 protein-coding
TKs on the genome, of which 19,197 have onlya single protein splice form, although 2525 of
those do have an alternative noncoding splicevariant. The SUPERFAMILY analysis of
structural classification of protein database(SCOP) domain architectures (13) was carriedout for each sequence. Of the 12,932 TKs thatshow variation in splicing, 8365 showed variation in SCOP domain prediction. Of the
12,932 variable TKs, 2392 produce proteins
with different observed contents of InterPro
entries. More than two alternatives were observed in 439 of the 2392 InterPro-variable
TKs. Thus, in the majority of variable loci,splicing controls some aspect of domain content or organization. To seek evidence for suchan impact in specific sets of regulatory proteins, we compared a representative protein set

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REPORTS

Fig. 3. Noncoding
RNA promoters arehighly conserved. (A)Human-mouse conservation of coding andnoncoding RNAs compared with random genome sequence. (Band C) Promoters conservation of noncoding
and coding mRNAevaluated (B) by identity and (C) by alignment. (D) Overlap of
promoters of ncRNAs.
(E and F) Promoters ofcoding mRNAs containa larger fraction of low
complexity and repeats than noncoding
promoters. LINE, longinterspersed nuclear
elements; LTR, long terminal repeats; SINEs,
short interspersed nuclear elements.

(RPS) and a variant protein set (VPS) of
phosphatases and kinases that have been
comprehensively annotated (14) by looking
at domain composition counts (table S4).
These phosphoregulators could be functionally modulated through alteration in their intracellular location. Among the 21 receptor
tyrosine phosphatase loci, we identified 23
variant transcripts from 14 loci with predictedchanges to the subcellular localization andfunction of the encoded peptides. Of these, we
identified two noncatalytic classes: secreted(10) and tethered (3). Furthermore, we identified two catalytic classes that lack the extracellular domains: catalytic only (5) andtethered catalytic (5). Similarly, among the 77
receptor kinase loci, we identified 41 variant
transcripts from 33 loci which encode secreted(16), tethered (10), catalytic only (7), or othertethered catalytic (8) peptides. We then analyzed the membrane organization splicing

1562

variants class within the full set of TUs (tableS5), which revealed 1287 TUs that exhibit
alternative initiation, splicing, and termination, likely to yield variant isoforms of membrane proteins that differ in their cellularlocation.
Of the 102,281 FANTOM3 cDNAs, 34,030
lack any protein-coding sequence (CDS) andare annotated as non–protein coding RNA(ncRNA) (6, 15) (table S1). Many putative
ncRNAs were singletons in the full-length
cDNA set. Among the FANTOM3 cDNA set
there was additional support from ESTs,
CAGE tags, or other cDNA clones overlapping
both the starting and termination sites for
41,025 cDNAs, of which only 3652 werencRNAs. This supported ncRNA set includes
many known ncRNAs (SOM text 4), andmany are dynamically expressed (SOM text5). Following these same criteria, 3012 from8961 cDNAs previously annotated as truncated

2 SEPTEMBER 2005 VOL 309

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CDS were supported as genuine transcripts andare believed to be ncRNA variants of proteincoding cDNAs.
Many ncRNAs appear to start from initiation sites in 3¶ untranslated regions (3¶UTRs)
of protein-coding loci (16). The normalized
distribution of CAGE tags along annotatedexons of known transcripts with more than 300
mapped tags each is shown in Fig. 2A. As
expected, the highest tag density on averageoccurs at the 5¶ end, but there is also a substantial increase of tags in the last one-fifthof the 3¶UTR. Strong evidence of 3¶ end
initiation was correlated with a short intergenic distance when in tail-to-tail orientation
with a neighboring gene (Fig. 2B), suggesting a possible role in an intergenic regulatoryinteraction.
The function of ncRNAs is a matter of debate (17). Some ncRNAs are highly conserved
even in distant species: 1117 out of 2886

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REPORTSREPORTS

Total
Total number of transcripts
RIKEN full-length
Public (non-RIKEN) mRNAs
GFs
Framework clusters
TUs
With proteinsWithout proteins
TK
With proteinsWithout proteins
Splicing patterns

158,807
102,801
56,00625,02731,99244,147
20,92923,21845,142
21,757
23,38578,393

Table 3. Determination of transcripts start/end
accuracy. Two pieces of evidence (cDNA, tags,
ditags, EST, and 5¶-3¶ EST pairs) are required when
TSS/terminations lie inside larger transcripts, andone piece of evidence is required when they extend
or identify new transcripts. Reliable indicates thatboth ends are associated with reliable tag clusters.

Total 5¶/3¶-end
pair sequence5¶/3¶-end pair cluster

Total

Reliable

1,507,122

1,336,397

313,821

181,047

overlap chicken sequences, of which 780 do
not overlap known CDS and 438 do not overlap known mRNAs on either strand, whereas
68 out of 2886 have BLAST-like alignment
tool (BLAT) alignments to the Fugu genome, of which 40 do not overlap known
CDS on either strand. These ncRNAs are atleast as conserved as a reference set of
known ncRNAs (Fig. 3A), contrary to a previous study (17). However, ncRNAs areslightly less conserved on average than 5¶ or
3¶UTRs. In contrast, the promoter regions of
ncRNAs are generally more conserved thanthe promoters of the protein-coding mRNA,
not only between human and mouse but also
down in the evolutionary scale to chicken(Fig. 3, B to F), and they contain bindingsites for known transcription factors (18). We
conclude that the large majority of ncRNAs
that we analyzed display positional conservation across species. In considering function, one might conclude that the act oftranscription from the particular location iseither important or a consequence of genomic structure or sequence (for example,
enhancers such as that of the globin locus
can act as promoters), the transcript may
function through some kind of sequencespecific interaction with the DNA sequence
from which it is derived, or many noncoding

Average
per TU
cluster

Average
per TK
cluster

7.59

7.30

1.20
1.53
2.11
1.00
1.11
2.16
1.04
1.12
3.75

1.15
1.47
2.03
0.96
1.07
2.07
1.00
1.07
3.60

RNAs have other targets but are evolving
rapidly (19, 20).
New databases have been created for cDNA
annotation, expression, and promoter analysis (http://fantom3.gsc.riken.jp/db/ and SOM
text 6). The databases integrate common
gene and tissue ontologies like eVOC mouse
developmental ontologies (21), cross mappedto Edinburgh Mouse Atlas Project (EMAP)
ontology terms (22). These eVOC terms allow analysis standardization of RNA samples used for cDNA and CAGE libraries inboth mouse and human and were included
into the DNA Database of Japan (DDBJ)
data submission (23).
Analysis of the output of FANTOM2 suggested that there were many more transcripts still to be discovered (24). Here, wehave confirmed that the majority of the
mammalian genome is transcribed, commonly from both strands. Such transcriptional
complexity implies caveats in interpretationof microarray experiments (25) and genome
manipulation in mice, because these willcommonly interrupt or interrogate more than
one TK. Although the current overview gives
us an indication of the complexity of the
mammalian transcriptional landscape and a
new set of tools to begin to understandtranscriptional control (for example a verylarge set of promoters that can be ascribedto distinct classes) (16), we also gain insight into the scale of the task that remains.
The ditag data indicate the existence ofvery long transcripts whose isolation andsequencing will require new cloning andsequencing strategies. Although we have isolated and sequenced many putative ncRNAs,
the FANTOM3 collection only contains 40%of those already known. Finally, the focus
has been on polyadenylated mRNAs thatare processed and exported to the cytoplasm.
Recently, Gingeras and colleagues (5) havewww.sciencemag.org

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shown that the set of nonpolyadenylated nuclear RNAs may be very large, and that manysuch transcripts arise from so-called intergenic regions (7). The future can only reveal
additional complexity in the mammaliantranscriptome.
References and Notes
1. P. Carninci et al., Genome Res. 13, 1273 (2003).
2. T. Shiraki et al., Proc. Natl. Acad. Sci. U.S.A. 100,
15776 (2003).
3. P. Ng et al., Nat. Methods 2, 105 (2005).
4. R. H. Waterston et al., Nature 420, 520 (2002).
5. D. Kampa et al., Genome Res. 14, 331 (2004).
6. P. Bertone et al., Science 306, 2242 (2004).
7. J. Cheng et al., Science 308, 1149 (2005).
8. R. L. Strausberg et al., Proc. Natl. Acad. Sci. U.S.A.
99, 16899 (2002).
9. Y. Okazaki et al., Nature 420, 563 (2002).
10. A. Watahiki et al., Nat. Methods 1, 233 (2004).
11. V. Bajic, in preparation.
12. N. Maeda, R. Oyama, in preparation.
13. J. Gough, in preparation.
14. A. R. Forrest et al., Genome Res. 13, 1443 (2003).
15. Materials and methods are available as supporting
material on Science Online.
16. P. Carninci et al., in preparation.
17. J. Wang et al., Nature 431, 1 p following 757;
discussion following 757 (2004).
18. S. Cawley et al., Cell 116, 499 (2004).
19. T. Ravasi, D. A. Hume, in Encyclopedia of Genetics,
Genomics, Proteomics, and Bioinformatics, L. B. Jorde,
P. F. R. Little, M. J. Dunn, S. Subramaniam, Eds. (John
Wiley & Sons, Chichester, UK, in press), part 2.3.
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21. J. Kelso et al., Genome Res. 13, 1222 (2003).
22. R. A. Baldock et al., Neuroinformatics 1, 309 (2003).
23. All sequences (CAGE, and cDNA) are available
through DDBJ to other public databases. The cDNA
clones are available.
24. Y. Okazaki, D. A. Hume, Genome Res. 13, 1267 (2003).
25. E. Marshall, Science 306, 630 (2004).
26. RIKEN Genome Exploration Research Group andGenome Science Group (Genome Network ProjectCore Group) and the FANTOM Consortium, Science309, 1564 (2005).
27. We thank H. Atsui, A. Hasegawa, Y. Hasegawa, K.
Hayashida, H. Himei, F. Hori, T. Iwashita, S. Kanagawa,C. Kawazu, M. Aoki, K. Murakami, M. Murata, H.
Nishida, M. Nishikawa, K. Nomura, M. Ohno, Y. Onodera,
N. Sakazume, H. Sato, Y. Shigemoto, N. Suzuki, Y.
Takeda, Y. Tsujimura, K. Yoshida for discussion, encouragement, and technical assistance. We thank A.
Wada, T. Ogawa, M. Muramatsu, and all the membersof RIKEN Yokohama Research Promotion Division forsupport and encouragement. We also thank the Laboratory of Genome Exploration Research Group for secretarial and technical assistance, Yokohama City
University for providing human samples, and computational resources of the RIKEN Super Combined Cluster(RSCC). This work was mainly supported by Research
Grant for the Genome Network Project from MEXT, the
RIKEN Genome Exploration Research Project from
MEXT (Y.H.), Advanced and Innovational Research
Program in Life Science (Y.H.), National Project on
Protein Structural and Functional Analysis from MEXT
(Y.H.), Presidential Research Grant for Intersystem
Collaboration of RIKEN (P.C. and Y.H.) and a grantfrom the Six Framework Program from the European Commission (P.C.).
Supporting Online Materialwww.sciencemag.org/cgi/content/full/309/5740/1559/
DC1
Materials and Methods
SOM Text
Figs. S1 to S4Tables S1 to S10
References
DDBJ Accession Codes
9 March 2005; accepted 4 August 2005
10.1126/science.1112014

2 SEPTEMBER 2005

1563

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Table 2. Transcript grouping and classification. The extent of splice variation was calculated by excluding
T-cell receptor and immunoglobulin genes from the transcripts. The remaining 144,351 transcripts weregrouped in 43,539 TUs, of which 18,627 (42.8%) consist of single-exon transcripts, 8110 (18.6%) containa single multiexon transcript, and the remaining 16,802 TUs (38.6%) contain at least two spliced
transcripts. Among these TUs, 5862 (34.9%) show no evidence of splice variation, whereas 10,940(65.1%) contain multiple splice forms.

CORRECTIONS & CLARIFICATIONSERRATUM

Post date 24 March 2006

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Reports: “The transcriptional landscape of the mammalian genome,” by The FANTOM
Consortium et al. (2 Sept. 2005, p. 1559). On page 1561, column 3, lines 40-46 should
read: “In the FANTOM3 data set, 11,559 protein sequences are newly described. Their
splice variants were grouped together into 7445 TKs (transcriptional frameworks). For
5453 of these, a previously known sequence maps to the same TK (locus), but 1992
clusters (2222 different proteins) map to new TKs (see SOM text 3).”www.sciencemag.org

SCIENCEERRATUM POST DATE

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The Transcriptional Landscape of the Mammalian Genome
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Science 309 (5740), 1559-1563. [doi: 10.1126/science.1112014]

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