Genome-wide analysis of mammalian promoter
architecture and evolution based upon humanand mouse CAGE data, Supplementary Data
Piero Carninci1, 2* Albin Sandelin1, 3*, Boris Lenhard1, 3*†, Shintaro Katayama1, Kazuro
Shimokawa1, Jasmina Ponjavic1◆, Colin A. M. Semple1, 4, Martin S. Taylor1, 5, Pär G.
Engström3, Martin C. Frith1, 6, Alistair R. R. Forrest6, Wynand B. Alkema3, Sin Lam
Tan7, Charles Plessy2, Rimantas Kodzius1,2, Timothy Ravasi1, 6, 8, Takeya Kasukawa1, 9,
Shiro Fukuda1, Mutsumi Kanamori-Katayama1, Yayoi Kitazume1, Hideya Kawaji1, 9,
Chikatoshi Kai1, Mari Nakamura1, Hideaki Konno1, Kenji Nakano1, 9, Salim
Mottagui-Tabar3§, Peter Arner10, Alessandra Chesi11, Stefano Gustincich11, Francesca
Persichetti12, Harukazu Suzuki1, Sean M. Grimmond6, Christine A. Wells19, Valerio
Orlando13, Claes Wahlestedt3§, Edison T. Liu14, Matthias Harbers15, Jun Kawai1,2,
Vladimir B. Bajic1, 7,16, David A. Hume1, 6*,**, Yoshihide Hayashizaki1, 2, 17, 18***
Affiliations of the authors
1

Genome Exploration Research Group, RIKEN Genomic Sciences Center (GSC),
RIKEN Yokohama Institute, 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama, Kanagawa,
230-0045, Japan
2
Genome Science Laboratory, Discovery Research Institute, RIKEN Wako Institute, 2-1
Hirosawa, Wako, Saitama, 351-0198, Japan
3
Center for Genomics and Bioinformatics, Karolinska Institutet, Berzelius v. 35, S-171
77 Stockholm, Sweden
4
MRC Human Genetics Unit, Western General Hospital, Crewe Road, Edinburgh, EH4
2XU, UK
5
University of Oxford, Roosevelt Drive, Oxford, OX3 7BN, UK
6
ARC Special Research Centre for Functional and Applied Genomics, Institute for
Molecular Bioscience, The University of Queensland, Brisbane Qld, 4072, Australia
7
Knowledge Extraction Laboratory, Institute for Infocomm Research, 21 Heng Mui
Keng Terrace, 119613, Singapore,
8
Department of Bioengineering, University of California San Diego, 9500 Gilman Drive,
0412 La Jolla, CA 92093,
9
Broadband Communication Service Business Unit, Network Service Solution BusinessGroup, NTT Software Corporation, Teisan Kannai Bldg. 209, Yamashita-cho Naka-ku,
Yokohama, Kanagawa, 231-8551, Japan,
10
Department of Medicine, Karolinska Institute, Huddinge University Hospital, S 141
86 Huddinge, Sweden,
11
The Giovanni Armenise-Harvard Foundation Laboratory Sector of Neurobiology
International School for Advanced Studies I.S.A.S.-S.I.S.S.A. AREA Science Park
Padriciano 99, 34012 Trieste Italy,
12
Sector of Neurobiology International School for Advanced Studies I.S.A.S.-S.I.S.S.A.
AREA Science Park Padriciano 99, 34012 Trieste Italy,
13
Dulbecco Telethon Institute, IGB CNR, Epigenetics and Genome Reprograming lab,
Via Pietro Castellino 111, Napoli, 80131, Italy,
14
Genome Institute of Singapore, 60 Biopolis Street #02-01, Singapore 138672,
15
Kabushiki Kaisha Dnaform, 1-3-35, Mita, Minato-ku, Tokyo, 108-0073, Japan,

16South African National Bioinformatics Institute, University of the Western Cape,
Private Bag X17, Bellville, South Africa,
17
Yokohama City University, 1-7-29 Suehiro-cho Tsurumi-ku Yokohama 230-0045
Japan,
18
Graduate School of Comprehensive Human Science, University of Tsukuba, 1-1-1
Tennodai, Tsukuba-shi Ibaraki-ken, 305-8577, Japan. School of Biomolecular andBiomedical Science,
19
The Eskitis Institute for Cell and Molecular Therapies, Griffith University, Nathan
Campus, Kessels Rd, QLD 4111, Australia.
Correspondence footnotes**To whom technical correspondence should be addressed.
D.Hume@imb.uq.edu.au, tel: +61-7-33462073, Fax: +61-7-33462103*** To whom general correspondence should be addressed.
yosihide@gsc.riken.jp, Tel: +81-45-503-9222, Fax: +81-45-503-9215

E-mail:E-mail:Additional footnotes† Current Address: Bergen Center for Computational Science, Unifob AS, University ofBergen, Thormøhlensgate 55, N-5008 Bergen, Norway§ Current address: Scripps Florida, Jupiter, FL 33458, USA.
◆

Current address: MRC Functional Genetics Unit, Department of Physiology, Anatomy
and Genetics, University of Oxford, Oxford OX1 3QX, United Kingdom.

* PC, AS,BL and DAH contributed equally to this work.

This Supplementary On-line Material (SOM) contains supporting information including consideration of
the reproducibility and accuracy of the CAGE technology, additional analysis of the CAGE data set andthe detailed methodological sections.
The Supplementary Online material is also available as a single consolidated file at
http://fantom3.gsc.riken.jp and at www.macrophages.com.

Contents
1. Demonstrations that CAGE technology captures true 5’ ends
1-a. Introduction
1-b. Validity Enrichment of capped molecules
1-c. CAGE mapping positions are non-randomly distributed
1-d. Empirical support for promoter identification by CAGE
1-e. Validation of CAGE based upon known transcription start site bias
1-f. Support for CAGE data from cross-species validation
1-g. Correlation between TCs and active promoters identified by ChIP of transcription
complexes in humans
2. Extended biological findings
2-a. Detailed analysis of exonic promoter activity
2-b.Promoter evolution in mammalian genomes
2-c. Different tissues utilize different promoter types
2-d. Alternative promoters are common in protein coding genes
2-e. Detailed analysis of alternative promoters in the gelsolin gene2-f. An in-depth look: promoting the macrophage-specific transcriptome
3. Methods and resources
3-a. Links to Databases and resources
3-b. Dataset and basic tag analysis
3-c. CAGE libraries preparation
3-d. Rules for the assignment of CAGE tags
3-e.Transformation function correcting for systematic G-addition bias
3-f. Classification of tag clusters
3-g. Analysis of initiator sites of TSSs located in inner exons
3-h. Calculation of over and under-represented features associated with TC shape classes.
3-i. TATA box spacing
3-j. Sp1 over-representation
3-k. Tissue specificity comparisons in PB-class promoters3-l. Analysis of cross-species conservation in mouse and human promoters3-m. Promoter evolution in mammalian genomes
3-n. Analysis of substitutions effects of pyrimidine-purine dinucleotide start sites
3-o. Promoter-based clustering
3-p. Supergroup shape and TATA/CpG island association
3-q. TFBS density of TC super groups
3-r. Definition of alternative promoters with differential expression patterns
3-s. Analysis of 3’ UTR promoters3-t. Analysis of differential expression of 3’ UTRs compared to associated 5’ region
3-u. RACE validation of 3’ UTR transcripts
3-v. Experimental validation of the promoter activity of the 3’-UTR transcripts
3-w. Determination of over-represented and under-represented TFBSs in macrophage-expressedpromoters
3-x. Analysis of Ets and CAGA-site over-representation in macrophage and CNS promoters3-y. Comparison of human liver-containing CTSS conservation
3-z. Calculation of the probability of the observed number of multitag CTSS
4. Supplementary references

1. Demonstrations that CAGE technology captures true 5’ ends
1-a. Introduction
A key issue underlying the analysis of transcription start sites is the level of confidence one can ascribe toindividual CAGE tags. The CAGE technology relies on two independent biochemical events, theextension of reverse transcriptase to the 5-‘ end of the transcript, and the CAP-dependent second strand
synthesis, capture and cloning of a cDNA. If reverse transcriptase fails to generate a complete full length
extension product AND that product is captured by the CAP trapping procedure, the result will be a
product that is less than full length. As noted in the main text, the intent of this project was to identify thetranscription start sites of the largest possible set of protein-coding genes, and also to gain some insight
into the dynamic regulation of TSS use (since CAGE tag frequency in a particular library provides anindication of the level of expression). We therefore chose to sequence a very large set of different libraries
at around 50,000 to 100,000 tags per library. With this sequencing depth, less abundant transcripts,
including the large numbers of non-coding RNAs which represent a major class of rare transcripts, havebeen sampled randomly. Because of the sampling approach, the coverage of rare transcripts issignificantly less than one-fold, and accordingly, there is a strong bias towards singletons in the complete
data set (Table 1). We have focused on clusters supported by two or more tags in detailed analyses
because the majority is supported by independent libraries, and the probability of two tags occurring inthe same location in the number of cases observed by any chance event is infinitesimal（see below).
Several lines of evidence indicate that the overall error rate in the CAGE method is very small(see section
1b-g below) and accordingly that even singleton CAGE tags define genuine 5' ends with a high level ofconfidence. The precise confidence cannot be evaluated without another complete data set, but many
individual singleton CAGE tags can be further validated by considering the enrichment of cappedmolecules, the local sequence context (e.g. Inr sequence), location relative to cDNA and 5'EST sequences,promoter architecture, conservation across species and other supporting evidence.
1-b. Enrichment of capped molecules
We assessed the efficacy of cap selection by examining the enrichment for RNA PolII-derived mRNAs
compared to uncapped ribosomal RNAs in random-primed libraries. When using random priming, mostof the first-strand cDNA is ribosomal RNA, which by visual inspection was estimated to exceed 90%.
Since the cap-trapper procedures enriches for capped RNAs, the decrease of non-cappedribosomal-derived cDNA gives a measure of relative enrichment. We selected 10,000 CAGE tags for bothhuman random primed and mouse random-primed libraries and blasted these sequences against 18,133
ribosomal RNA sequences extracted from the Rfam database (RF00001 5S_rRNA; RF00002
5_8S_rRNA; RF00177 SSU_rRNA_5). The cap-selected random-primed CAGE libraries contain no
more than 2.6% (human) and 1.42% (mouse) ribosomal RNA. Assuming that the ribosomal RNA
constitutes the 90% of the total RNA, the enrichment of non-ribosomal, presumably capped RNA isbetween 333 to 625 fold (calculated with human and mouse data, respectively).
The issue of whether all of the CAGE tags are genuine 5’ ends can be assessed by looking at the
distribution of tags across individual genes. If extension of reverse transcriptase to the 5’-end of thetranscript, and the CAP-dependent second strand synthesis, capture and cloning of a cDNA fails for any
reason due to incomplete extension, or capture of a transcript that has been subjected to nuclease cleavage,
the frequency of tags must decay exponentially from the 3’ end to the 5’ end of the transcript, since eachtruncation reduces the proportion of genuine full length transcripts captured. As shown clearly in theexample of the albumin gene (alb1) in Fig. S2A, as well as Fig. 6A, this is clearly not the case. In thesubset of genes in which we observe apparent start sites over all of the exons, their apparent frequency
does not decay from the 3’ to the 5’ end. Furthermore, there is no correlation between the expression levelof the full-length transcript and the amount of tags in inner exons in a given tissue (see above).
1-c. CAGE mapping positions are non-randomly distributed
These observations above alone argue unequivocally that CAGE specifically captures genuine 5’ termini.
The same point can also be made from purely statistical viewpoint by considering the likelihood offinding the observed number of multitag CTSS (TSS with more than 1 tag exactly aligned at their 5’ ends)
occurring by chance: the associated p-values are below what normal computers can handle without
underflow errors (<10-324) (Table S5A). As an example, the number of CTSS composed of two tags occurs

close to 15 times as often than expected: the ratio is exponentially increased when assessing CTSS withincreasing tag density (Table S5A). This is an argument for the validity of single tags, since multiple tagsin the majority of cases are from more than one library (see below). The high occurrence of multitag
CTSS would be impossible if single CAGE tags had significant random errors.
As a further indication of the reproducibility of CAGE, we assessed the proportion of tag clusters (TCs)with more than one tag that come from distinct libraries. The proportion was 77.83% for the set of 67,660
TCs supported by 2 tags, increasing to over 95% for TCs having more than 3 tags (Table S5B). The
reproducibility is even higher than this if we assess the replication of exactly aligned 5’ ends in distinct
libraries by repeating the analysis on CTSS with more than one tag: the proportion was 87.45% for the setof 203,401 CTSS supported by 2 tags within classified TCs, and only marginally lower (77.89%) for the
total set of 389,412 CTSS supported by 2 tags (Table S5C). The lower number in the second instance arises
because the unclassified set includes rare transcripts, including non-coding RNAs, which have only been
detected in the small number of libraries that were sequenced at greater depth. Hence, in these cases there
is a greater likelihood that 2 tags have been derived from the same library. If we examine CTSS supported
by 5 tags; 99.03% derived from classified TCs have tags from more than one library, and 97.33% derived
from unclassified TCs. These data also show that less abundant CAGE tags do, indeed, derive from lessabundant mRNAs in that they are unlikely to be sampled again in the same library. By extension, the highincidence of singleton CAGE tags is principally a consequence of the strategy used and the depth ofsampling in individual libraries.

1-d. Empirical support for promoter identification by CAGE
Amongst the 20-30,000 protein-coding genes in the mouse genome, relatively few transcription start sites
have been determined experimentally. Two approaches have generally been used, nuclease protection andprimer extension or 5’RACE. Nuclease (RNase or S1) protection assays are relatively insensitive in
detecting minor starting sites, and because the protected fragments are usually detected byautoradiography, it does not commonly provide quantitative information about relative promoter usage.
Additionally, the number of genes for which the TSS has been determined in 250 different RNA samples
is extremely small. Nevertheless, we have undertaken both experimental and published validation of the
CAGE data.
For experimental validation, we analyzed the complex OPRM locus, which shows 36 distinct TSS spreadover intergenic, exonic and intronic regions using 5’ RACE. 5’ RACE is distinct from CAGE in that
RACE takes place only after RNA ligation adds a primer at the 5’ end instead of the cap site. This isobtained after the phosphate group at the 5’-end of non-capped RNA molecules removed by phosphatasetreatment. This biochemical reaction is completely distinct from the CAGE technology and therefore
serves to validate the CAGE-determined TSSs.
The majority of TSS in the OPRM receptor locus (30 sites) are represented by single CAGE tags;
nevertheless, 26 (87%) of these single tags could be validated using RACE even on a single tissue
sample.
To compare the CAGE and other methods such as nuclease protection we randomly chose from theliterature 19 examples of mouse genes in which the TSS had been determined by nuclease protection,
RNase protection and primer extension; in most cases the examples chosen have multiple TSS. Despite
the bp-level inaccuracy of the gel-based methods and the use of different cell lines/tissues, in the majorityof cases CAGE data is wholly consistent with the previous results (Fig. S7A-S shows a set of publishedexamples). The CAGE tags do not, of course, correlate perfectly with the published start sites in everycase. Published data represents start site usage from a single cell type or tissue under a single condition,
whereas CAGE is based upon cumulative data from many different libraries. It has much greater depth ofcoverage than published 5'RACE, and greater sensitivity than nuclease protection for detecting minor
start sites. However, the CAGE tags clearly map to the same regions, and where there is a distinction,
there is no bias towards mapping downstream (i.e. there is no evidence that they are based upon truncated
5' ends).
In many of the published reports, arrays of weaker start sites are apparent in the gel images are noticable(but not annotated/discussed); in these cases the more comprehensive CAGE tag mapping will indicate

both the major start site indicated in the paper as well as the weaker sites. An example studied in detail by
one of the authors (DAH) is the start site of the macrophage-specific CSF-1R promoter. This is a memberof the novel purine-rich subclass of broad promoters (see below). The mouse CSF-1R promoter was
analyzed previously by both primer extension and RNase protection1. Primer extension was based upon
primers designed to hybridize to the longest known cDNA. The RNase protection actually detectedclusters of start sites downstream of those identified by primer extension. Reevaluation of those results by
the CAGE data shows that the RNase protection is precisely congruent with the CAGE information (Fig
S7T).
1-e. Validation of CAGE based upon known transcription start site bias
The strong signal-noise ratio in CAGE-based start site identification is evident a priori from the stringent
mapping of the start sites in TATA-containing promoters. The tight distribution of TATA-associated TSS
is based solely on CAGE tag occurrences and corresponds precisely to the historically defined position
around -30.
As noted in the main text, the actual bp detected as 5' ends by CAGE display a profound bp bias towards
the initiator consensus (PyPu). Further evidence of the non-randomness of the CAGE tags was obtainedby analyzing the distribution of initiation site [-1,+1] dinucleotides for TSS having 1,2 and up to10 ormore CAGE tags separately (note that the -1 position is not part of the tag). Regardless of the level of
CAGE support, each dinucleotide distribution is significantly different from a distribution of 10,000
randomly sampled non-repetitive, non-overlapping genomic dinucleotides (p < 2.2* 10-16 in all cases,
chi-square test). Simply stated, the identified start sites correspond to the initiator sequence. While the
distributions with different level of CAGE support overall are similar (Figure S4A-B), there is a higher
preference to PyPu nucleotides, especially CA, in high-expressed transcripts, while other dinucleotides (inparticular GG) are gradually favored as the CAGE tag count is lowered.
1-f. Support for CAGE data from cross-species validation
Start sites identified by CAGE not only have a non-random sequence preference, they are correlated
across species; the human data set can be considered an independent experimental validation of the mouse
data. We report a strong correlation between the TSS usage in orthologous mouse/human promoters,
both in terms of distribution shape (Fig S3J-K) and even on base pair level (Fig. S4C-H). The
discrepancies in the TSS distribution can be correlated to single nucleotide substitutions (Fig. 4I). Froma different perspective, we can ask what proportion of the CTSS is conserved across species (Table S5D).
For this purpose, we selected a set of mouse and human genes where the promoters can be
unambiguously aligned, and determined the numbers of CTSS that are replicated in liver-derived libraries
across species as a function of tag density in the human CTSS. Liver was chosen for this comparison
because it has been polled to approximately equal depth in the two species. If CAGE accurately detects 5'
ends, detection of any particular 5' end will obey a Poisson distribution, with a frequency determined byrelative abundance and depth of sampling.
This series reached a saturation at around 81.9% for CTSS with 20 tags. If one assumes that thedetection of TSS is purely a function of their relative use to initiate transcription and their sampling
density, then this series should also obey a Poisson distribution, and for singletons sampled to equivalent
depth in two samples, the expected replication rate should be 1-1/e, or 67%. In this case, the support for
singletons across species was 59%, or 72% of the observed asymptote. This is remarkably close to theprediction given the possible caveats. Firstly, the alignment cannot be perfect. To take account ofarbitrary positioning of bp insertions or deletions, we allowed a 1bp slack in the actual 5' end allowed as
identical. Secondly, the sampling depth on any individual promoter across species cannot be precisely
equivalent because it depends on relative abundance and depth of sequencing. The human data comeswith a small number of libraries sequenced to great depth, whereas mouse comes from many libraries
with liver in different states. Finally, on individual broad promoters mouse and human differ in precise
start site dominance; so the frequency of use of weaker initiators is influenced by the "strength" of othersites in the region. The degree of replication can also be seen by examining particular genes in detail. The
highly-conserved tissue-specific brain proteolipid protein 1 gene (Plp1) has a broad CTSS region, whichin humans is dominated to a greater extent than in mouse by one major start site, but the overall profile isstrikingly similar (Fig S7U). The human promoter has been polled to a 3-fold greater extent than in themouse. Out of the few start sites (including singletons) that are not replicated in both species, the majority
have mutations in the initiator sequence.

1-g. Correlation between CTSS and active promoters identified by ChIP of transcription complexes
in humans
If CAGE is identifying active promoters, we would expect the sites to be occupied by transcription
initiation complexes. Chromatin immunoprecipitation linked to genome tiling arrays is clearly not
feasible on the same range of tissues that has been used to generate the mouse and human CAGE data.
However, it is possible to assess the correlation between CAGE and genomic regions occupied by TAF1
(TBP-associated factor 1 of the TFIID, associated with the preinitiation complex) defined using
chromatin immunoprecipitation (ChIP) and hybridized to Nimblegen tiling arrays in a recent study2. We
investigated how many of these sites within the human ENCODE regions had CAGE tags within 100 bp
of inferred TAF1 binding sites. Specifically, the “known sites” and “novel sites” ENCODE track from the
UCSC ENCODE browser in human genome assembly hg17 was examined; the human CAGE data is
mapped to the same assembly. The 100 bp limit was chosen because i) CAGE corresponds to the actual
starts of transcripts, while TAF1 is part of the pre-initiation complex ii) the resolution of ChIP is at best in
the range of 10s of bp and iii) more importantly, the 50-mer Nimblegen probes are placed at 100bp
genomic intervals. In the validation of the ChIP study itself2 using GenBank cDNA, the corresponding
limit was 2.5kb. On this basis, 58.3% of the TAF1 sites were supported by CAGE. Given that this study
only interrogated a single cell line (which has no direct correspondence to the tissue libraries used forCAGE), the agreement is remarkable and similar to the analogous overlap study using cDNA ends
(described in main text).

2. Extended Biological findings
2-a. Detailed analysis of exonic promoter activity
For a given tissue, we counted the CAGE tag density (tags/bp) in the +-100bp of the 5’ edge of the TUs
representative cDNA transcript. Only transcripts with at least 100 tags from the relevant tissue in thisregion were considered. We compared this value to the overall CAGE tag density for the inner exons(tags/bp) of the corresponding cDNAs. Lung, liver and macrophage transcripts from mouse wereanalyzed this way, since these were the tissues in which multiple CAGE Tag libraries were generated andtherefore those in which there were sufficient tags for analysis of a substantial number of TUs. Fig 1bshows that there is no relation between the two variables: hence that there is no correlation between
overall expression level and extent of exonic promoter activity. This finding also supports the view thattranscripts arising from within exons are not attributable to incomplete synthesis or capture of full length
cDNA, since such truncations would have to be proportional in some measure to the abundance of thecorresponding full length transcript.
To determine whether there was any correlation between primary (i.e. 5'end) promoter architecture andinternal exonic promoter activity the 5% most and least extreme cases of exonic promoter activity in innerexons were collected and annotated for each tissue. The SP class of promoter was strongly
over-represented amongst the 5’ promoters of transcripts with high exonic promoter while corresponding
promoters for transcripts with low exonic promoter activity are enriched for the BR class (Fig. S2B-D).
Consistently, transcripts with high exonic promoter activity have significantly fewer CpG islands in theirmajor promoter compared to transcripts with no or little exonic promoter activity (Fisher 2-tail test pvalues: 1.48E-17 (liver), 2.31E-3 lung, 3.03E-5 (macrophages))(Fig. S2E-G). Given that the SP class isalso over-presented amongst tissue-specific promoters, high exonic promoter activity might also be
associated tissue-specific transcription. To test this proposition for liver, lung and macrophages we plotted
the average tag density (mean tags/bp) in inner exons versus the tissue specificity of the full-lengthtranscript. The tissue specificity was assessed empirically by calculating the proportion of the total
number of tags in the major 5’ promoter that had been derived from the tissue of interest (Figure 1D). In
all three tissues studied, high exonic promoter activity is correlated with high tissue-specificity.
2-b.Promoter evolution in mammalian genomes
Comparisons of human with mouse and rat suggest that human promoter sequences are also more slowly
evolving than randomly sampled sequence (Table S2B). In contrast, the comparison with dog shows thathuman promoters, particularly CpG promoters (substitution rate = 0.3259+/-0.0025), have evolved
significantly more rapidly than randomly sampled sequences (substitution rate = 0.3070+/-0.0017), while
the core 200 bp of these promoters evolved close to random sequences (Table S2B). Since this effect isnot seen for mouse versus dog TSSs (Table S2A), it could reflect events in the human lineage since
divergence from dog. This is supported by comparisons between human and chimpanzee, which suggest astriking lack of constraint has been a general feature of the recent evolution of human promoters (Table
S2B). This is consistent with recent findings that human promoters exhibit higher substitution rates(relative to the mutation rate) than mouse promoter regions.
2-c. Different tissues utilize different promoter types
To support the hypothesis that particular tissues preferentially utilize particular promoter architectures, weanalyzed the CAGE data in two other ways. We extracted a subset of library-specific TSS sets, consistingof clusters in which more than 80% of tags (normalized for library size) come from only one tissue (orembryo). Then we determined the observed/expected ratios and the associated P-values for tissue versus
shape category associations (Table S3A). Single start site promoters are strongly overrepresented in thetissue-specific transcripts, with the notable exceptions of the macrophage- and the CNS-specific genes.
Secondly, we analyzed the association of transcribed genes with their gene ontology (GO) terms. We
show (Table S3B) that many specific GO categories are correlated significantly with single peakpromoters, while only a handful of general categories exist where broad peaks are significantly
overrepresented.
2-d. Alternative promoters are common in protein coding genes
Our data shows that previous estimates on alternative promoter usage of TUs were conservative. A
majority of protein-coding genes have at least two alternative promoters; in most cases, these promotersshow differential expression and are therefore likely to be subject to different regulatory mechanisms.
This concept has important implications for array-based gene expression measurements – using only one
type of probe per gene will increase the risk of not observing biologically significant expression patterns,
as in the case of the gelsolin gene (below). The TCs described herein provide the basis for development ofspecific promoter arrays.
2-e. Detailed analysis of alternative promoters in the gelsolin gene
The gelsolin gene (Gsn) contributes to actin filament remodeling (Fig. S6A,B). The Gsn gene has twoalternative promoters (T02F02195C0E and T02F021984A4) potentially producing the same protein
product, and a third alternative promoter, T02F0219C1BB, which directs a distinct 5’UTR encoding not
only a distinct methionine but an N-terminal signal peptide permitting protein secretion. Although the
T02F02195C0E and T02F021984A4 have the same cytoplasmic protein product, they belong to different
TC supergroups. The promoter T02F02195C0E is in the same cluster as the core promoter of the vimentin
gene (Vim), an intermediary filament, and is the dominant form of Gsn expressed in macrophages.
Conversely, the promoter T02F021984A4 is in the same supergroup as the laminin beta 3 gene (Lamb3),
which is a part of the basement laminins, and is the main Gsn promoter in liver cells (Hepa 1-6). We inferthat T02F02195C0E is used in cellular contexts where the Gsn and Vim need to be coexpressed while
T02F021984A4 is used when Gsn and Lamb3 are needed. The secreted protein product of the third
alternative promoter, mainly found in cerebellum and heart libraries, encodes a plasma form of gelsolin,which has a potential role to solubilize actin molecules derived from damaged cells to prevent thrombosis
3.
2-f. An in-depth look: promoting the macrophage-specific transcriptome
The [-1000, +200] region of all 159,075 TCs used in the cluster analysis was extracted, and centered on
the dominant start site within each TC. A comparative analysis of the incidence of TRANSFAC4 predicted
motifs between the 450 macrophage-specific TCs, the 295 LPS-inducible TCs and a random set of TCs
was carried out (40,000). The comparative data was used to calculate an over-representation index(Experimental procedures) for each promoter cluster (Table S6 and Table S7). For themacrophage-specific set (Table S6A, Table S7A), many over-represented sites are motifs recognized bymembers of the Ets transcription factor family, consistent with the known unique architecture of myeloid
promoters5. Such promoters lack TATA box, GC or CCAAT box elements and belong to the broad class ofpromoters. The minimal requirement for a macrophage-specific promoter is multiple Ets sites, one ofwhich must be recognized by the lineage-specific transcription factor PU.1, and the other by another Ets
family member6. We further compared the set of BR type promoters in which the normalized fraction of
CAGE tags comes from either macrophage or a CNS library that has similar properties to all BR-type
promoters, even if they are tissue specific. We specifically looked for core Ets7 and CAGA motifs. The

latter is a motif identified in the CSF-1R promoter and other myeloid promoters5, recognized by the
Ewing sarcoma proteins, a component of the basal transcription machinery (Hume, D. A., in preparation)(Fig. S5B-D). Unlike the 42 CNS-specific ones, the 63 macrophage specific promoters have a highincidence of core Ets sites within 100 bp upstream of TSS, while most CAGA sites cluster downstream ofit. These preferences cannot be explained solely by different nucleotide compositions along the TC region.
This example of a simple characterization of a highly specialized type of core promoters is likely toprovide enough data for the construction of a predictive model for macrophage-specific promoters. We
assume that our data will enable the same for other subcategories of core promoters.
The LPS-inducible set (Tables S6 and S7) is clearly distinguishable from the constitutive set by anover-representation of Rel/NFkappaB motifs, as one might presume given the well-documented roles ofthese factors8. More dramatic is the over-representation of interferon-responsive elements (IRFs), whichhighlights the likely involvement of interferon in a large proportion of the downstream transcriptional
regulation by lipopolysaccharide9.

3. Methods and Resources
3-a. Links to databases and resources
Novel, publically available databases and resources integrating CAGE, ESTs, full-length cDNAs andother genomic elements are described in Table S4. Sequences of tags are made available to thecommunity also through DDBJ at the site http://www.ddbj.nig.ac.jp/whatsnew/050124-e.html.
3-b. Dataset and basic tag analysis
Except for a substantial (4.17 million tags) part of human CAGE set here described for the first time, thedataset production is described elsewhere11, including the detailed mapping conditions for all the CAGE
tags. The process to build the TCs is described in Fig. S8A, and the assignment to transcripts is in Fig.
S8B.
3-c. CAGE libraries preparation
CAGE libraries12 were prepared with a protocol developed based on the described procedures13. Briefly,
the CAGE technology is based on priming the first strand cDNA with an oligo-dT or a random primer,
starting from total RNA and synthesize the first-strand cDNA at high temperature (55-60oC) in presenceof trehalose and sorbitol to increase the full-length cDNA rate even in presence of strong secondary RNA
structure. Then, cap-trapping is performed. Cap-trapping is a method to enrich cDNA/RNA hybrids
through the cap-structure, only when the hybridized cDNA is a full-length one. After chemicalbiotinylation, RNase I (which cleaves only single strand mRNA at any base) is used to remove any
ssRNA linking the biotinylated cap and the double-strand RNA/truncated cDNA. RNA molecules
hybridized with full-length cDNA molecules are left undigested, and are next captured with streptavidin
beads. After several stringent washings of the beads, full-length cDNAs are removed with mild alkali
treatment. After specific addition of a linker, which contains the class-IIs restriction enzyme MmeI site
next to the ligation junction with the 5’ end of cDNAs, the second strand cDNA is synthesized.
Subsequently, the cDNA is cleaved with MmeI, only the initial 20-21 nt of the cDNA are left attached to
the 5’-end linker, while cDNA is removed. After addition of appropriate linkers and cycles of PCR andpurification, restriction-digested double strand sequencing tags are obtained. After formation of
concatamers, these are cloned and sequenced. The whole procedure is described in details
elsewhere13.The sequenced CAGE tags are extracted and aligned to the genome by using BlastN. Only
CAGE tags without base-calling problems (no “N” nucleotides in the sequence) were used for mapping,
and tags mapping on multiple genomic regions (such as tags consisting of repeats) were not used for thecurrent analysis. CAGE alignment to the genome is complicated by the addition of one template-free C(sometimes two Cs) to the end of the first strand cDNA, which results in one (or sometimes two) G’s on
the DNA strand. For this reason, best alignments of at least 18 nt long or better was chosen. The G
addition bias is corrected using the algorithm described below, based on unambiguous mapping cases.
More technical details of mapping are described elsewhere11.
3-d. Rules for the assignment of CAGE tags
The hierarchical structure of CAGE data is explained briefly in this paragraph and the associatedmethodology described in details below. Two or more individual CAGE tags that have identicalsequences (and therefore identical genomic mappings) are grouped into a Representative CAGE tag.
Representative CAGE tags that have exactly the same genomic starting point and strand define a CAGE
tag-defined transcriptional start site (CTSS). CTSSs are grouped into tag clusters (TCs), where themember tags map to the same strand and overlap by at least one bp.
The Tag Cluster (TC) definition is exemplified in Fig S8A. A TC is a cluster of overlapping tags,
spanning from the 5’-end of its 5'-most tag, to the 3' end of its 3'-most tag (1). RIKEN 5'-ESTs and 5’-endof FANTOM3 clones were also used. At first, all of the cDNAs, CAGE tags and ditags were mapped to
the genome using BlastN14. The threshold of the Blast was at least 18 nt match for CAGE tags and at least
16 nt of each side of the ditags (32 nt alignment within a 2.5Mbp of the genome). Only best matches wereconsidered (usually, 19-20bp) and ambiguous cases were not used in the analysis. After the genome
assignment, we further grouped the tags to form TCs. We grouped all tags overlapping with one or morebp (on the same strand) into a single TC. Human TCs are defined by CAGE, 5’-end of Long-5’ SAGE anddbTSS. The representative position (rep. pos.) of a TC defines the location where the largest number ofmapped tags occurs based upon a prioritized source map; CAGE tags have the highest priority, then GIS,
GSC, RIKEN 5’-EST, FANTOM3 clone, Long-5’ SAGE and dbTSS tags have decreasing priority in theorder listed. (1). When the number of tags is equal in more than one position, the rep. pos. is taken as the
5’-end (1, 3). A tag in a TC needs to be overlapping with at least one base of another tag (3), hence, two
adjacent but non-overlapping tags contribute to separate TCs unless both share sequence with a bridging
tag (4). Tags on opposite strands are not considered overlapping and contribute to different TCs (5).
(Fig S8B). Rules to assign CTSSs (CAGE tag starting sites) to mRNA. This protocol is used to associate
CTSS with the transcript that is the presumptive product (i.e. to give the promoter a name associated withthe transcript annotation). In order of priority, the promoter is associated with a transcript annotation
based upon mapping within the first exon and CDS, 3’UTR, any undefined UTR (CDS was not defined),
inside an exon after the first exon and 5’UTR, any CDS, any 3’UTR, any UTR, inside intron, upstream
10bp or less, upstream 100bp or less, far upstream (more than 100bp), and downstream have decreasingpriority.
The detailed rules to assign CAGE TCs to mRNA are as follows. Rule 1: a TC is assigned to an mRNA in
which first exon includes the rep. pos. of the TC. Rule 2: if such mRNA is not found, the TC is assigned
to an mRNA having any exon including the rep. pos. of the TC. Rule 3; when more than one mRNA
complies to rule 1 or 2, the TC is assigned to the one in which 5'UTR (1st priority), CDS (2nd), or 3'UTR(3rd) includes the rep. pos. of the TC. Rule 4: if no such mRNA is found after applying rules 1 and 2, theTC is assigned to an mRNA having any intron including the rep. pos. of the TC. Rule 5: when more than
one mRNA complies with rule 1-4, the TC is assigned to the one in which coding sequence is the longest.
Rule 6: if no such mRNA is found following rules 1, 2 and 4, the TC is assigned to the one which 5’-end
is nearest to the rep. pos. of TC. See examples in Supplementary Fig. S8 Example (1): the TC is assigned
according to rule 1. Example (2): the TC is assigned according to rule 1, 3 & 5. Example (3): the TC is
assigned according to rule 6.
3-e. Transformation function correcting for systematic G-addition bias
The experimental protocol for preparing CAGE uses the MmeI restriction enzyme for separating the
5’-end of the full-length cDNA and a linker sequence (the protocol steps are described in detail in
elsewhere12,13). Because of the template-free activity of the reverse transcriptase used to prepare thecDNA, an additional G nucleotide is often attached to the 5' side of the tag. In cases where the added G
does not map to corresponding genome sequence, the extra G can be confidently classed as added; we callthose cases unambiguous. However, when the first nucleotide in a tag is a G that maps to the genome, wecannot know with certainty if the tag starts with a G or the following nucleotide. In situations where wehave two or more Gs after each other in the genome sequence, and all have one or more tags, theambiguity situation is worse, since for a given position i, tags with an added G will be mapped to theposition i-1.
We estimated how often a G is added in tags whose mappings were unambiguous. We found that a single
G is added in 87.4% of all tags, while the addition of more than one G is rare (1.95% of unambiguous
cases). Since the chance of adding more than one G is small, we constructed a transformation algorithm
based on that either a single G or no G is added to the tag. Each TC used for classifications below was
first subject to the transformation function.
Algorithm: A CTSS is defined by the genome position of the first nucleotide and the strand. CTSSs thatstart with something other than a G and do not have a G directly upstream in the genome are not altered.
Where a CTSS I starts with a mapped G and does not have another CTSS starting with G starting justbefore it, all CTSS following I (given strand) are identified until a CTSS is reached that does not startwith a mapped G. This gives a set of n CTSSs, each harbouring a number of tags. As an example, in thesequence HHHGGGHH (where H is a nucleotide which is not a G), positions 1, 2, 3 and 8 are
non-ambiguous: the number of tags starting in each position is not altered. Positions 4,5,6,7 are
ambiguous and can be partitioned into three different cases: Position 4 marks the start of a series of Gs(start case), while position 7 marks the end (end case). Positions 5 and 6 are inside the series (generalcase).
In such a series, we are interested in two values for each position: the true number of tags N starting in theposition and the number of tags falsely assigned to this CTSS F (= belonging to next CTSS). We areassuming that the number of additions of more than one G is negligible. We traverse each CTSS in theseries from 5' to 3'.
Let X denote the number of tags observed in the CTSS, N denote the corrected count of tags in the CTSS,F denote the number of tags observed in this CTSS that belong to the next CTSS and P denote the chanceof adding a G, assessed to be 0.8935878.
Start case: The start case is special since the number of tags with falsely added Gs can be counted (asthey are not mapping to the genome). Thus, X contains tags that belong to this CTSS but have an extra G
added (denoted A), tags that belong in this CTSS but have no extra G added (denoted U) tags that belong
in the next CTSS (F).
Since we can count the number corresponding to A, we can estimate the true number of tags N starting inthis position:(1)
N= A/P
Since we know that N cannot be larger than X, we let X be the upper bound of N.
The expected number of tags U starting in this position with no false G added is then(2)
U= N*(P-1)
As discussed above, the number of tags in X that have no falsely added G must belong to this CTSS or
the next. We expect that the number of tags in this position without an added G is U. Thus, the number oftags F belonging to the next CTSS is(3)
F= (X-A)-U
This can be expressed as(4)
F= (X-A)-N*(P-1)
We impose two constraints F must be positive and cannot be larger than X-A.
General case: The general case differs from the previous case in that only two types of tags are observedin this CTSS: tags that belong in this CTSS but have no extra G added (=U) , and tags that belong in thenext CTSS (=F). Therefore,(5)
X= F+UThe number of tags A belonging to this CTSS with a falsely added G is observed in the previous CTSS
(due to the definition of CTSS). However, the F value calculated in the previous CTSS can be used as an
estimate for A.
(6)
A= F[previous CTSS]
Given F[previous CTSS], we can estimate N,U and F analogous to equation (1,2,3):(7)
N= A/P(8)
U= N*(P-1)(9)
F= (X)-U
which can be simplified to(10)
F=(x)-N*(1-P)
A is not subtracted from X, since the count A is from the previous CTSS. Again, we impose twoconstraints; F must be positive and cannot be larger than X.
End case: The end case differs from the previous case in that only one type of tag is present: tags thatbelong in this CTSS but have no extra G added (=U), since the number of tags A belonging to this CTSS
that has an added G are observed in the previous CTSS, and the number of tags F belonging to the next
position is zero (since this position does not start with G).
(11)
X=U;
Thus, we only need to calculate N, which simply is the sum(12)
N= F[previous CTSS]+X.
3-f. Classification of tag clusters
Tag clusters (TCs) containing 100 tags or more were classified into four mutually exclusive shapecategories, using four different criteria, which were applied in a specific order. 1) A TC is assigned asingle peak (SP) shape if the distance between the 75 and 25 tag density percentile within a TC is lessthan 4 bp; 2) If the ratio between the first and second tag peak >2 and the TC is not classed as SP, it isclassed as broad with dominant peak (PB); 3) If the TC is not classed as SP or PB and there is one ormore consecutive tag density 5th percentile pairs with a distance exceeding 10bp a TC is classed as bi- or
multimodal (MU); 4) If none of the above apply, the TC is classed as broad (BR).
3-g. Analysis of initiator sites of TSSs located in inner exons
We focused on TUs with more than 10,000 tags mapped, and analyzed those CTSSs situated in their innerexons and containing more than one tag. Since the G addition correction algorithm was designed for TCs
containing a large number of tags, we choose to only analyze unambiguous CTSSs (defined above). For
any such CTSS, we investigated the [-1,+1] position relative to the CTSS, counting nucleotide basecombinations (one count per tag).

3-h. Calculation of over and under-represented features associated with TC shape classes.
To calculate over- or underrepresented features (tissue or transcription starting site sequence) with distinct
TC shape classes two tests were applied: 1) a test to assess whether a TC has shape class (X) of interest;2) a test to assess whether a TC is associated with the feature (Y) of interest. The results of these two tests
are summarized in a contingency table, which contains the number of TCs that failed in both tests, thenumber of TCs that failed in test 1, the number of TCs that failed in test 2 and the number of TCs thatpassed both tests. The p-value for the null hypothesis that both tests are independent can be calculatedwith the Fisher Exact test. A low p-value indicates that the tests are not independent and that feature Y isover or underrepresented in shape class X
3-i. TATA box spacing
The occurrence of TATA-boxes in the -50 to -15 region relative to the most dominant tag peak in SP classpromoters was assessed using the Bucher’s weight matrix 15 and the TFBS Perl module16 for binding site
prediction. Only predictions on the forward strand having a score >75% of maximum were accepted.
3-j. Sp1 over-representation
The (-200,+200) region around every CTSSs in all TCs of each class was extracted. The occurrence of
TATA and Sp1 motifs 17 was assessed using a 80% relative cutoff in the region, non-normalized (one per
CTTS) and intensity-normalized. For each TC, the weight of each individual CTSS was proportional to
the number of tags in it, and sum of all weights in a TC was equal to 100; this accounts for relative
strengths of different CTSSs positions within a TC, while avoiding the domination of pattern by a small
number of TCs with a large number of tags.
3-k. Tissue specificity comparisons in PB-class promoters
We measured the transcriptional specificity of the dominant TSS, which contains the most tags within theTC, and then the specificity of the remaining TSSs in the TC.
A discrepancy would indicate that the dominant TSS and the rest of the promoters are subject to different
mechanisms, creating a “hybrid” promoter: an overlaid SP and BR class promoter. We measured
transcriptional specificity of a set of tags by measuring by the relative entropy (the Kullback-Leibler
distance)18of the RNA library distribution of a sample tag cluster with respect to the RNA library
distribution of all CAGE tags:⎛p ⎞d = ∑ p k log 2 ⎜⎜ k ⎟⎟k⎝ qk ⎠

where k is the number of different libraries (103), q is the discrete probability distribution of RNA
libraries for all tags and p is the discrete probability distribution of RNA libraries in the sample tag cluster.
Only tags from cDNA libraries with more than 10,000 tags were used for the calculation.
We first compared the tissue specificity of the dominant peaks to the tissue specificity of the surroundingtags in the promoter, using Wilcoxon tests on the two vectors of relative entropy values. The dominantpeaks have significantly higher tissue specificity (p< < 2.2*10-16).Secondly, we divided the dominantpeaks into two subsets: those that had a predicted TATA-box in the –40 to –19 region, and those lacking
the TATA box. We then compared the distribution of relative entropy values for the two subsets, as above.
The TATA containing set has a significantly higher tissue specificity (p< 3.823*10-07). This indicates that
the TATA box has a clear effect on the tissue specificity of the dominant TSS.
3-l. Analysis of cross-species conservation in mouse and human promoters
TSSs were derived from mouse and human CAGE TCs composed of 10 or more tags. We analyzed
pairwise alignments from multiple species in the region 1Kb upstream and 200 bp downstream of mouseand human TSS. Promoters were categorized according to their TSS categories (MU, BR, PB and SP) andaccording to whether they possessed a CpG island or a TATA box. In addition we examined a large
number of 1.2Kb alignments ('random' below) randomly sampled from the mouse and human genomes(these were intended to give us a 'base line', near-neutral substitution rate to compare to the promoter
rates). We have estimated evolutionary divergence in the form of nucleotide substitution rates calculatedusing the REV model in PAML 3.1419 for each TSS alignment as in20 . The mean rate and 95%
confidence intervals were calculated for each category of promoters.
3-m. Promoter evolution in mammalian genomes

TSSs were derived from mouse and human CAGE tag clusters composed of 10 or more tags. Alignment
data for TSSs refers to 1.2Kb regions around CAGE TSSs (1Kb upstream and 200bp downstream of theTSS) from mouse (30,898 TSSs derived from >10 tag clusters examined) and human (26,290 TSSs
derived from >10 tag clusters examined). Each region was compared to the aligned, putatively
orthologous regions from various species taken from the UCSC MULTIZ whole genome multiplealignments for the mouse mm5 and human hg17 assemblies21. For the mouse TSSs we estimated rates
from comparisons to aligned rat (Rn), human (Hs) and dog (Cf) sequences, for the human TSSs we madeestimates for comparisons to chimpanzee (Pt), dog (Cf), mouse (Mm) and rat (Rn) sequences. Promoterswere categorized according to their TSS categories (MU, BR, PB, and SP) and according to whether they
possessed a CpG island (taken from UCSC annotation) or a TATA box (predicted using
EMBOSS/TRANSFAC profile within 50bp of the TSS). Two other categories were examined: thosepromoters with TSSs supported by >100 tags (Tables 1A and B) and those supported by <100 tags(‘Low’), as a simple way to examine rate differences between promoters associated with relatively highand low rates of transcription. In addition we examined a large number (23,993 in mouse, 20,605 in
human) of 1.2Kb alignments ('random' below) randomly sampled from the mouse and human genomes(these were intended to give us a 'base line', near-neutral substitution rate to compare to the promoter
rates). We also extracted a large number (17,025 in mouse, 33,407 in human) of 1.2Kb alignments
centered upon start codons to provide a relatively well-conserved set of alignments.
We have estimated evolutionary divergence in the form of nucleotide substitution rates calculated using
the REV model in PAML 3.1419 for each TSS alignment as in20. The mean rate and 95% confidence
intervals were calculated for each category of promoters. All alignments were masked for CpG islands
and simple repeats (based upon UCSC annotation for the human and mouse genomes) before rateestimates were made as such regions are known to evolve by mechanisms other than point mutation. For
each alignment, divergence was measured for the entire upstream and downstream regions as well as in
the 200bp core promoter region immediately upstream of the TSS. To ensure the accuracy of theseestimates alignments with fewer than 100 nucleotides were ignored. Rate estimates are presented with
95% confidence intervals.
3-n Analysis of substitutions effects of pyrimidine-purine dinucleotide start sites
Human-mouse genomic alignments of orthologous TC pairs corresponding to the SP, BR, PB and
MU-classed TCs were analyzed. Using the mouse position as a template, the genomic position, thecorresponding human genomic region was retrieved by using the NET alignment data from the UCSC
genome browser database (assembly MM5 and HG17, respectively). Only unambiguous coordinates wereconsidered. Having defined a corresponding location in the human genome, we identified TCs using thefollowing criteria: (i) TC is within the corresponding region or (ii) overlaps this region totally or partiallywith more than 75% tag coverage. In order to ensure orthologous one-to-one mappings, only those cases
where only one human TC, containing more than 100 tags, fulfilled this criteria were considered. This
resulted in 2890 orthologous promoter pair alignments.
We used a sliding 2bp window [-1, +1] along the mouse and human alignments in the direction of the TC,(which then corresponds to the [-1, +1] positions of a start site). Each such window was analyzed in termsof the tag counts at position +1 using the following metric:(#tags in mouse/#total tags in mouse) /(#tags in human/#total tags in human)
We required that the sum of tag counts in both species exceeded 30 tags, and added a pseudocount of 1 toall counts to avoid division by zero. Mutations were classified with respect to changes on
purine/pyrimidine level.
3-o. Promoter-based clustering
The 127 libraries obtained from mouse CAGE data were analyzed by cluster analysis.
CAGE expression values for 159,620 TCs were measured as log2 (tags per million). We applied theK-means clustering procedure using Euclidean distance as implemented by the Eisen Cluster 3.0
program22. The identification of 70 clusters was decided based investigation of mean square error andapplication of cross-validation methodology. Within each of the 70 supergroups we applied hierarchicalclustering.
CpG rich ubiquitous transcripts are clearly depleted of the AT-rich binding sites (e.g. Forkhead, HMG, andHomeobox), while tightly regulated transcripts have the opposite properties. On a more detailed level,
REL and Ets binding sites are over-represented in macrophage-specific promoters.

3-p. Supergroup shape and TATA/CpG island association
Within a supergroup, we classified a TC as being peaked if the highest tag peak and its two neighboring
nucleotides contained more than 80% of all tags within the TC, or broad otherwise. If the tag count of the
TC was less than 20 it was not classified. CpG island and TATA association for classed TCs were
measured as above.
3-q. TFBS density of TC super groups
For each TC in a supergroup, we analyzed the 300 bp region upstream of the highest CAGE tag peak in
the TC. Familial binding profiles corresponding to major transcription factor classes described in 7 wereused to scan this region using a 70% score cutoff 23. The relative density of sites of a certain class on eachstrand was evaluated by the following metric:(sum of sites within super group/number of TCs with super group)/(total sum of sites within all super groups/total sum of TCs within all super groups)
3-r. Definition of alternative promoters with differential expression patterns
For each TU with multiple TCs, we evaluated the distributions of RNA libraries in the TCs using
pair-wise chi-square tests for independence (P<0.05). The TU was defined as having at least one
differential alternative promoter if at least one pair-wise comparison was significantly different.
3-s. Analysis of 3’ UTR promoters
To seek evidence for specific control of such transcripts, we analyzed 1,327 transcripts that have at least
30 tags mapping to their terminal exons, and at least 300 CAGE tags mapping to their corresponding
full-length transcript. The majority of these transcripts, 770 (58%), have at least 20% of tags mapping in
the most distal 20% of the last exon.
The conservation of the 1kb regions centered around these prominent 3’ UTR TSS was determined based
upon Phastcons scores (based on cross-species comparison between human, chimp, mouse, rat, dog,chicken, fugu, and zebrafish)24 from the UCSC genome browser database25.
3-t. Analysis of differential expression of 3’ UTRs compared to associated 5’ region
Tag counts from the 5' region of representative transcripts (-100 to +100 relative to the cDNA start) werecompared with the tag counts from the 3’ region (the last 20% of the terminal exon). Tag counts were
subdivided depending on tissue origin. In those cases where the total tag count from a certain tissue
exceeded 30, we tested if the tags counts from that tissue in both regions are likely to be drawn from thesame population using Fisher’s exact test, corrected for the number of tests (number of tissues) by the
Bonferroni method.
3-u. RACE validation of 3’ UTR transcripts
RACE was performed by using oligo-capping based on the method described26. Calf Intestinal
Phosphatase (CIP) was used to dephosphorylate truncated RNA from total RNA. Tobacco Acid
Pyrophosphatase (TAP) was used to decap full-length RNA. A RNA oligo was ligated to decapped RNA.
Reverse transcription reaction was performed using random primers (N6). Two nested PCR reactions
were performed using gene specific primers and primers on sequence from ligated RNA oligonucleotide.
The PCR products were cloned into pCR4-TOPO vector and transformed to competent Escherichia coli.
After sequencing, originating DNA sequence was used for BLAST. The generated gff file was imported to
Genome Element Viewer to display the RACE products.
3-v. Experimental validation of the promoter activity of the 3’-UTR transcripts
We selected four putative 3’-end promoters of the genes Aldoc (chr11, 77939144..77939393), Ilk (chr7,
93271482..93271731), Lass2 (chr3, 95318188.. 95318437) and Klhl5 (chr5, 63803784.. 63804033). For
each of them, two distinct regions were cloned into the luciferase pGL3-Basic vector (Promega, accession
number U47295) (Fig. S6E). We selected two regions, a shorter one (from -100 to +10 respect to the
GGG identified starting site), and a larger region (-100 to +120 from the GGG-identified starting site. The
sequences of these constructs were verified by sequencing. HepG2 cells were each cultured in minimum
essential medium (Eagle) with non-essential aminoacid, and 1% non-essential amino acid, 1mM sodium
pyruvate with Earle’s BSS, 10% heat-inactivated fetal bovine serum (FBS), 200 U/ml penicillin and 200
mg/ml streptomycin. The reporter gene vectors (200 ng each) were transfected into either 4x104 HepG2

cells in 96-well assay plates using the Lipofectamine 2000 reagent (Invitrogen). After 24 h of incubation,
the luciferase activity of the reporter gene was measured with the Steady-Glo luciferase assay system(Promega). Experiments were repeated at least three times.

3-w. Determination of over-represented and under-represented TFBSs in macrophage-expressedpromoters
For this purpose we analyzed 450 genes in which the promoter-usage is specifically enriched inmacrophage libraries, and 295 in which promoter usage is specifically enriched in macrophages treatedfor 7 hours with lipopolysaccharide, the time of maximal gene induction in this system. The aim was tofind the TFBSs that appear either over-represented or under-represented in these promoter groups whencompared to mouse promoters in general. As a control group, we randomly selected over 40,000 TSSs outof approximately 160K. Consequently, these 40K TSSs correspond to an ‘average’ mouse promoter.
Segments [-500, +200] relative to the peak of TSSs were selected and all promoter that had 5% or more
ambiguous nucleotides within their sequences were excluded. The test set was therefore 287 and 430
promoters corresponding to induced and constitutively expressed genes, respectively, and 39,090‘average’ mouse promoters. Then, we mapped all available matrix models of TFBSs contained inTransfac (Professional Ver. 7.4) database4. We used minSUM profiles for matrix models parameters, asthese represent optimized thresholds for the core and matrix scores used in Transfac models27. The
thresholds in minSUM are based on optimization that provides the minimum sum of false positive andfalse negative TFBS predictions. We used models of TFBSs from all species with the rationale that
TFBSs known in other species than mouse, if found in the mouse promoters, could still represent realbinding sites. In determination of over-representation we used the method presented in elsewhere28. We
ranked all TFBS mapped to promoters based on their over-representation index (ORI) as definedelsewhere28. If ORI = 1 or is close to this value, then our estimate is that there is no over-representation of
the motif in the target promoter groups. The results for the two promoter sets are summarized in Table S6and S7. These tables also contain the actual probabilities of motifs as found in the target sets and in the
background set obtained as follows:
P = (# of motifs found in the dataset) / (total length of the dataset).
3-x. Analysis of Ets and CAGA-site over-representation in macrophage and CNS promoters
Macrophage-specific (>80% tags in the TC originating from macrophage RNA libraries, normalized in
respect to library sizes) BR-classed promoters were scanned with ETS (SPI-1 matrix from the JASPAR
database with 90% cutoff, essentially capturing the core GGAA motif) and exact CAGA motif. The
spacing of such sites in relation to individual CTSSs in the TC was investigated by plotting the site
distribution in a 400bp window centered on the CTSS. Sites were analyzed in 10bp bins. The sameprocedure was used to investigate CNS-specific BR promoters and all BR promoters.
3-y. Comparison of human liver-containing CTSS conservation
We used the set of aligned promoters as described above (the analysis of substitutions effects ofpyrimidine-purine dinucleotide start sites). We located all CTSS in human containing at least one tagfrom liver libraries. Using the same alignments, we investigated if the position and strand of each such
CTSS was replicated by a liver CTSS in the mouse genome, and analyzed this rate as a function of thenumber of liver tags in the human CTSS. The criterion for “replication” was that the CTSS must be on
same strands and the start position of the CTSSs must match within 1 bp: this is much more conservative
than the commonly applied criteria of any subsequence overlap.
3-z. Calculation of the probability of the observed number of multitag CTSS
We calculated the probability of observing the actual number of CTSS with given tag density by applying
the method described in figure 5 in reference29. The same statistical method also gives the expectedoccurrence. The “cluster width” was set to the minimum possible: 2bp. Since the definition of a CTSS is asingle bp position, the p-values are conservative calculations.

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27.

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Figure S1:Mapping of CAGE starting sites to the genome

Fig. S1. Mapping of CAGE starting sites to the genome.
Partition of the CAGE tags map with respect to mRNAs for mouse (A) and human (B). From left to right, wehave grouped TC with progressively larger numbers of CAGE tags. Categories are indicated on the right. 100~1, tags mapping up to 100 nucleotides upstream than 5'-ends. ~101, tags mapping more than 101
nucleotide upstream of known mRNA sequences. "CAGE" indicates all of the CTSS (one tag sufficient);"clustering" indicates cluster having at least 2 tags per TC deriving from libraries having >1500 tags.

CAGE tags on plus strand

CAGE tags on plus strand

CAGE tags on plus strand

Figure S2(A-G) Assessing exonic promoter activity:
28183

28100

Col3a1 Mouse

800
700
600
500
400
300
200
100
0

694

23

2 7 5 6 2 7 2 83

558
16 54 34 49
4 7 586 19 68 5 13

0

2

10

2

3

20
30
40

40

50
60
2530

Col3a1 Human

2534

6000

6002

Alb1 Mouse

3000

2524

2500
2000

1561

1500
1000
500
0

CAGE tags on plus strand

Fig. S2A.

66

126

200 171

18

34

6634

475

210

133

880

810

226

148

257

61

58

0
500
1000

80

217

667

5

10

1

1

304

33

1500
2000
2500
3000
13900

Alb Human
13893

Fig. S2A. Exonic promoter activity is conserved between species.
The number or CAGE tags mapping to the exons of the Col3a1 and Alb1 genes are shown. Col3a1 is anubiquitously expressed gene with almost no exonic promoter activity in its inner exons in mouse or
human. Conversely, the Alb gene, expressed almost exclusively in liver, show high exonic promoter
activity over all exons in both mouse and human. The large number of CAGE tags in exon12 in mouse
Alb1 are due to a single peak promoter with over 2000 tags which is missing in the corresponding humanexon. The sequence conservation between human and mouse in the proximal promoter to this peak issignificantly smaller than the rest of the exon (data not shown), indicating that this promoter might berodent-specific.

fraction of cases

B) Promoter class distribution for transcripts with high/low
exonic promoter activity (Liver)
0.8

100 %

0.6

high exonic promoter
activity
low exonic promoter
activity

0.4
0.2
0

BR

fraction of cases

fraction of cases

60 %no cpg
cpg

40 %
20 %
high exonic promoter
activity

low exonic promoter
activityF) CpG association of the major 5' promoter for transcripts withhigh/low exonic promoter activity (Lung)
100 %
80 %
60 %

no cpg
cpg

40 %
20 %

BR

0%

MU
PB
SP
Class of major 5' promoter

high exonic promoter
activity
low exonic promoter
activity
BR

MU
PB
SP
class of major 5' promoter

high exonic promoter
activity

low exonic promoter
activityG) CpG association of the major 5' promoter for transcripts withhigh/low exonic promoter activity (macrophage)
100 %
80 %
60 %
40 %
20 %
0%

no cpgcpg

high exonic promoter
activity

low exonic promoter
activity

Fig. S2B-G

Fig. S2B Properties of genes with high/low exonic promoter activity.
The 5% most and least extreme cases of exonic promoter activity were analyzed for liver, lung and macrophagetissues. B-D) Distribution of shape classes of the major promoter of the genes in the two groups E-G) CpG and
TATA-box association of the major promoters of genes in the two groups. Genes with low exonic promoter activity
generally have BR-class promoters associated with CpG islands, while high exonic promoter activity is associatedwith SP-class promoters and lack of CpG islands.

Figure S2(A-G) Assessing exonic promoter activity:

high exonic promoter
activity
low exonic promoter
activityD) Promoter class distribution for transcripts with high/low
exonic promoter activity(Macrophage)
0.5
0.4
0.3
0.2
0.1
0

80 %

0%

MU
PB
SP
Class of major 5' promoter

C) Promoter class distribution for transcripts with high/low
exonic promoter activity (Lung)
0.6
0.5
0.4
0.3
0.2
0.1
0

E) CpG association of the major 5' promoter for transcripts withhigh/low exonic promoter activity (Liver)Figure S3 A-K : Conservation of promoters and TSS shapes over evolution

Fig. S3A-I. Average pairwise nucleotide identities for mouse
TSSs aligned to orthologous sequences. The percentage identity is calculated for each nucleotide of
the mouse sequence, summing all identities across pairwise alignments and dividing by the numberof alignments. Nucleotide coordinates are relative to the mouse sequence, +1 is the TSS reference
position (most common tag position in TC). (A) Shows mouse to human pairwise conservation for
TCs containing greater than 100 tags (dark blue), TCs containing 10 to 100 tags (light blue) and forcomparison, conservation based on UCSC (http://www.genome.ucsc.org/) annotated mouse ATGtranslation start sites (dark green) and randomly selected sites (gray). Trifurcation of the ATGtranslation start site graph highlights the differing levels of first, second and third codon position
conservation. (B-I) Conservation profiles for mouse TSSs aligned with orthologous rat (black),
human (red), dog (blue) and chicken (green) genomic sequences. (B) Shows overall conservation
for all TSSs with 10 or more tags in the TC. (C-F) Categories of TSS based on tag distribution within
the TC. (G) Highlights the peaks and troughs of conservation most noticeable around SP-type
TSSs. (H) Conservation profile for mouse TSSs with matches to the TRANSFAC TATA-box matrix(see Experimental procedures) within 29 to 35 nucleotides upstream of the TSS. (I) Conservation
profile of TSSs with 10 or more tags in the TC but no TATA-box 29 to 35 nucleotides upstream of
the TSS.

Figure S3 A-K : Conservation of promoters and TSS shapes over evolution

J

K

Fig. S3J-K. Core promoter shapes are retained over evolution.
We measured the fraction of retained and changed promoter shapes in orthologous mouse-human promoters using either
four promoter classes (SP, PB, MU, and BR) (J), or two classes (SP and non-SP)(K). All changes are relative to mousepromoters. Summary tables for each analysis are shown in the right panel.

Figure S4 A-H : Initiation site properties and evolutionary changes

0.3

A

G-correction not used

Fraction of total counts

0.25
1 tag
2 tags3 tags4 tags5 tags
6 tags7tags
8tags
9 tags>9 tags
expected fraction

0.2

0.15

0.1

0.05

0
AA

AC

AG

CA

CC

CG

CT

GA

GC

GG

TA

GT

TC

TG

TT

Initiation site usage, broken down by level of TSS CAGE support

0.3

B

AT

G-correction used

Fraction of total counts

0.25
1 tag
2 tags3 tags4 tags5 tags
6 tags7tags
8tags
9 tags>9 tags
expected fraction

0.2

0.15

0.1

0.05

0
AA

AC

AG

AT

CA

CC

CG

CT

GA

GC

GG

GT

TA

TC

TG

TT

Initiation site usage, broken down by level of TSS CAGE support

Figure 4 A-B. Dinucleotide distribution analysis of CTSS with varying CAGE tag support
We analyzed the usage of different [-1, +1] dinucleotides relative to each CTSS in the data set (note thatthe -1 nucleotide is not part of the sequenced tag). We subdivided the cases in respect to how many tags
the CTSS contained into 10 classes (1,2,3 to 9 tags and 10 tags). As an additional reference class, wecollected 10.000 randomly selected start points in the genome (non-overlapping and not part of repetitive
regions). This distribution will correspond to the expected distribution if start sites are random (noise). The
frequency of all possible dinucleotides for the 11 classes is shown as a barplot, with (panel B) or without G
correction (panel A). The dinucleotide distribution is dramatically different from random selection, evenwith single CAGE tag support. We also note that there is a higher preference for INR-like CA dinucleotides
when the transcript has a higher expression (i.e. more tag counts), while AG and GG dinucleotides are
more favored in rarely expressed transcripts. Part of the GG dinucleotides corresponds to the GGG motif(before G correction) we found for the novel 3'UTR transcripts.This is true regardless of whether the
CTSSs are subjected to G correction or not. The difference in dinucleotide use when the tag count is 5 is arounding artifact in the G correction algorithm (which was designed for correcting larger tag counts).
Regardless of this, the overall frequency pattern as a function of number of supporting tags is indicative ofvery low level of noise in the CAGE dataset: otherwise the preference for TSSs supported by one tag(singletons) would be much closer to that expected by chance, and different from the preference of TSSs
supported by two or three tags.

Figure S4 A-H : Initiation site properties and evolutionary changes
Fig. S4C-D Examples of pyrimidine-purine dinucleotides substitutions and effects.
Gallery of barplots of mouse and human orthologous TCs illustrating dinucleotide substitutions andtheir effect on the start site usage. Y-axis indicate the number of CAGE tags starting at given
genomic positions(X axis). Green arrows indicate the transition from a pyrimidine-purine start site to
any other base combination.

C
Ccm1 geneTag cluster T05F0003A1FA6

D
Wasf2 geneTag cluster T04F07D7XFEE

Figure S4 A-H : Initiation site properties and evolutionary changes
E
Pfdn2 geneTag cluster T01F04A379D63

F
Jarid1b geneTagclusterT01F08038B70

Figure S4 A-I : Initiation site properties and evolutionary changes

G
D1Bwg1363 geneTag cluster T01R048684BF

H
Grim19 geneTag cluster T08R041BDDDA

Figure S4 A-I : Initiation site properties and evolutionary changes
Mutation of a purine-purine dinuclotide to...

10e+5
10e+2
1
10e-2
10e-5

no mutation
3410 cases( 67.2 %)pu.pu>pu.pu
640 cases( 12.6 %)pu.pu>pu.py
156 cases( 3.1 %)pu.pu>py.pu
828 cases( 16.3 %)pu.pu>py.py
40 cases( 0.8 %)Mutation of a purine-pyrimidine dinuclotide to...

10e+5
10e+2
1
10e-2
10e-5

no mutation
419 cases( 51.3 %)pu.py>pu.pu
155 cases( 19 %)pu.py>pu.py
90 cases( 11 %)pu.py>py.pu
80 cases( 9.8 %)pu.py>py.py
73 cases( 8.9 %)Mutation of a pyrimidine-pyrimidine dinuclotide to...

10e+5
10e+2
1
10e-2
10e-5

no mutation
1228 cases( 53 %)py.py>pu.pu
42 cases( 1.8 %)py.py>pu.py
78 cases( 3.4 %)py.py>py.pu
695 cases( 30 %)py.py>py.py
275 cases( 11.9 %)Mutation of a pyrimidine-purine dinuclotide to...

10e+5
10e+2
1
10e-2
10e-5

no mutation
9270 cases( 67.8 %)py.pu>pu.pu
1048 cases( 7.7 %)py.pu>pu.py
136 cases( 1 %)py.pu>py.pu
2362 cases( 17.3 %)py.pu>py.py
865 cases( 6.3 %)Fig. S4I Substitution effects on dinucleotides in core
promoters.
Boxplots show the effects of substitutions on initiation sites for all
possible base combinations. Mutations are annotated relative tomouse (i.e. mouse to human). Boxplot generation and Y axis scoreis described in Methods. The four sections correspond to four
different reference dinucleotides (Pu-Pu, Pu-Py, Py-Pu, Py-Py).

Figure S5A-D: Sequence pattern distributions for different classes of promoters

100

30

SP class promoters

20

50

10
0

0

20
30

Predicted Sp1 sites per 1000 sequences

100
100

30

BR class promoters

20
50
10
0

0

10
50
20
100
100

30
30

PB class promoters

20

50
10
0

0

10
50
20
100
100

30
30

MU class promoters

Predicted TATA boxes sites per 1000 sequences

10
50

20

50
10
0

0
10

50

Sp1 sites on + strand

TATA boxes on + strand
TATA boxes on - strand

100

200

100

1

Nucleotide position

100

20
30

200

Fig. S5A Distribution of TATA and SP1 sites for different shape classes of TCs.
The [-200,+200] sequence region was extracted around each CTSS for a given TC. The
density of detected TATA box and Sp1 motifs was obtained by scanning sequences withthe corresponding weight matrix profiles from the JASPAR database. Densities on (+)
strand are plotted on positive half of y-axis and densities on (-) strands on its negative half.
SP-type TCs exhibit a typical localized preference for Sp1 and TATA boxes as previously
described15. BR-type TCs have an increased density of putative Sp1 sites around -50
relative to TSSs, but the spacing is not well defined. PB and MU classes share generalproperties with the BR class, but with a significantly higher incidence of TATA boxes, inaccord with the notion that these are ambiguous or mixed cases. A peak of Sp1 at theposition -4 relative to TSS of broad type of promoters is a secondary effect due to apyrimidine:purine preference at [-1,+1], which matches positions [4,5] of the Sp1 consensus
sequence.

All BR class TCs(5013 sequences)

20

0

20

B

-100

1

100

Macrophage-specific TCs(63 sequences)

200

C

20

0

20

-200

-100

1

100

200

D

CNS-specific TCs(42 sequences)

20

0

20

-200

40

Sites per 100 sequences

40 40

Sites per 100 sequences

40 40

Sites per 100 sequences

40

Figure S5A-D: Sequence pattern distributions for different classes of promoters

-200

-100

1
Nucleotide position

Sp1 sites on + strand
Sp1 sites on - strand
CAGA sites on + strand

100

200

ETS sites on + strand
ETS sites on - strand
CAGA sites on - strand

Fig. S5B-D Macrophage-specific promoters are characterized by high density of core Ets (GGAA)
and CAGA motifs.
The promoter sequence (one per CTSS, relative positions -200 to +200) were aligned at thecorresponding TSS positions. Densities of Sp1 elements, GGAA (core Ets) motifs and CAGA (presumed
Ewing sarcoma protein binding motif) were counted in 10bp bins for (+) and (-) strand separately (SeeMethods). The density of the three types of binding sites are not significantly different between the set of
all BR-type promoters (B) and CNS-specific BR-type promoters (D), the latter being more variable due tosmaller sample size. Macrophage specific promoters (C) show a strong overrepresentation of Ets
elements upstream of TSS, and a less pronounced, but significant overrepresentation of CAGA elements
downstream. (CAGA sites at position -1 are overrepresented in all three cases because CA matches PyPu consensus of the initiator sequence).

Figure S6 A-E:Alternative promoters and transcription start sites in 3' UTRs
Fig. S6A-B Context-driven alternative promoter usage in the Gsn gene.
A) Expression and genomic overview of Gsn (gelsolin) primary three alternative promoters. 1. T02F02195C0E (left red
angle with blue circle in "Tag Cluster" track, potential transcript 7120467P06 in FANTOM3 clone) 2. T02F021984A4 (center,
potential transcript I1C0015L23) 3. T02F0219C1BB (right, potential transcript F430007G01). Colored rows describe theexpression level (TPM normalized) in characteristic four RNA libraries. Red rectangles describe the 'ATG' (translation startsite) containing exons. In case that transcription starts from the promoter 1 and 2, these transcripts do not contain thesignal peptide coding exon. B) Detailed clustering based on the heatmap (Fig. 5) showing alternate Gsn promoter usage.
Each of the clusters display detailed promoter usage for each TSS and the related annotated TUs, deriving from theparental supergroups (66, 39 and 45). 02F02195C0E is in the CAGE expression cluster No. 66, T02F021984A4 is 39,
T02F0219C1BB is 45 respectively. We clip the most similar 6 promoters in each Gsn alternative promoters.
The Gsn gene has two alternative promoters (T02F02195C0E and T02F021984A4) potentially producing the same protein
product, and a third alternative promoter, T02F0219C1BB, which directs a distinct 5'UTR encoding not only a distinct
methionine but an N-terminal signal peptide permitting protein secretion. Although the T02F02195C0E and T02F021984A4
have the same cytoplasmic protein product, they belong to different TC supergroups. The promoter T02F02195C0E is in
the same cluster as the core promoter of the vimentin gene (Vim), an intermediary filament, and is the dominant form of
Gsn expressed in macrophages. Conversely, the promoter T02F021984A4 is in the same supergroup as the laminin beta 3gene (Lamb3), which is a part of the basement laminins, and is the main Gsn promoter in liver cells (Hepa 1-6). We inferthat T02F02195C0E is used in cellular contexts where the Gsn and Vim need to be coexpressed while T02F021984A4 isused when Gsn and Lamb3 are needed. The secreted protein product of the third alternative promoter, mainly found in
cerebellum and heart libraries, encodes a plasma form of gelsolin, which has a potential role to solubilize actin molecules
derived from damaged cells to prevent thrombosis.

Figure S6 A-E:Alternative promoters and transcription start sites in 3' UTRs
Fig S6C Validation and analysis of 3' UTR transcription
RACE validation of the 3' UTR transcription of the A130090K04 (Oprm1 locus).
Genome analysis viewer screenshot of the RACE validation of the 3' UTR transcriptions reveals a large
number of TSSs. The upper panel shows the TCs and their tag support level. The panel below shows thelocation of the 3' UTR of the transcript A130090K04, as well as two shorter cDNAs starting inside the 3'
UTR region. Coding regions are marked in orange, UTRs in grey. The central panelshows that multiple
EST sequences (colored in beige) start in the region corresponding to the 3'UTR. Five sets of RACE
primers designed from the sequences corresponding to the TCs (not shown) produced ~25 discrete race
products over the 3.5 Kb region, suggesting multiple initiation sites within this 3' UTR (bottom panel). The
number of RACE products initiating in each position is indicated.

C

chr10

3458k

3459k

Tag clusters

3460k
1tag

2tag

3461k
1tag

3462k

1tag 1tag

1tag
6tags

cDNA transcripts
AK038257 ( last exon)
AK038911

AK041335

EST sequences

RACE products (number indicate the number of products at the position)
1x
2x
30x
3x
6x
15x
3x
23x
3x
6x
23x
25x
7x
8x
117x
1x
43x
3x
1x
23x
9x
15x
30x
42x
10x

Figure S6 A-E:Alternative promoters and transcription start sites in 3' UTRs

D)

Figure S6D Tissue proportionality of transcripts in 5' and 3' ends of genes.
Numbers of significant discrepancies in tissue distribution between tags from the 5'end of representative transcript vs 3' UTR promoters. Note that the skew in thediscrepancies varies between tissues.

Figure S6 A-E:Alternative promoters and transcription start sites in 3' UTRs

E)
conserved region

-100

5'

Short region
Long region

1

40 90

3'

ca. 110 bp
ca 220 bp
Amplification with P CR from mouse genome
Cloning to reporter gene plasmid andassay of luciferase activity

Figure S6E Reporter gene construct for validation of 3' UTR promoters
The upper part contains a schema of the reporter assay construct. Short regions and long regions
represent two different regions that were subcloned and used for the reporter assay. Bottom, aschematic representation of the vector pGL3-Basic vector.

Figure S7A-U: CAGE validation examples

Fig. S7 A-U Comparison with TSS analysis annotation from literature sources
We randomly selected 19 articles encompassing 70 defined and annotated TSS using
different methods (S1 nuclease protection, RNA protection assay (RPA), RACE andprimer extension). The sites were mapped to the mouse genome using BLAT. In general,
CAGE can confirm most sites defined by any of the methods; however, it is importantto realize that a total correspondence is not expected due to fundamental methodologicalreasons, including i) the experimental setup is different; in particular certain TSS may
be used in the specific cell lines/tissues used, which often do not overlap with ourlibraries. The depth of the CAGE data is many orders of magnitudes higher than the
single experiments reported here, which should capture and quantify many more rarely
used sites, ii) in many cases the method (especially RNase protection assay) does notprovide accurate base pair-level assignment when mapped on the promoter. In fact,
when CAGE data indicates many TSS, gel-based methods often indicate many weaker,
broader start sites, which are not reported in corresponding papers. Promoters areindicated from 5’ to 3’ regardless of the directionality of the gene location in the genome.
The chromosomal location is indicated above each of the panels A-S, and corresponds
to the UCSC mm5 genome assembly. Red bars in the CAGE diagram indicates CAGE
tags on the same strand as those TSS reported in the article in question, while blue bars
indicate tags on the opposite strand.
A) PCMT31. The Protein Carboxyl Methyltransferase start site was mapped with both
RACE and RPA. The RACE overlaps all three main peaks obtained by CAGE, whileboth the RPA points map several bases upstream to both the CAGE and the RACE data
main peaks, suggesting potential problem in determination of length of the protectedfragments. With long exposure of the RPA assay31, minor shorter TSS became evident,
which would correspond to downstream TSSs indicated by CAGE.
B) GHR/BP32. Mouse Growth-Hormone receptor/growth hormone-binding protein 5’
UTR was determined by S1-Nuclease mapping. The main peak is accurately mapped, as
well as the second strongest peak one (originating the shortest transcript), which is one
nucleotide off. However, the correspondence is lower for most upstream TSS, andCAGE identifies more upstream TSS. In reference32, additional weaker TSS are foundbut not annotated by the authors, including an upstream TSS transcript which would
correlate with the CAGE most upstream. The authors used only liver mRNA, which
may explain the absence of some TSSs.
C) RNAse133. Annotation of the gene of ribonuclease gene Rib-1, where there is perfectmatch of the TSS determined by primer extension and the main CAGE peak. CAGE
shows a few other shorter start sites, which are detected but not annotated in the
publication33.
D) LOX34. The mouse lysyl oxidase promoter was determined by RNA protection
assay34. One of the two confirmed cap sites agree with the smallest tag cluster, while theother identifies a minor start but maps in proximity of another broad initiation site. The
paper shows additional minor unannotated sites, which agree with a widespread
distribution of start sites obtained by CAGE.
E) ITG7A35. Alpha-7 Integrin Primer extension and S1 protection show two main
starting sites, which were annotated35. One of them matches perfectly with the CAGE
tags main peak, the other is shorter. CAGE analysis shows other minor starting sites; inthe report35 only myoblasts and myotubes were used.
F) Munc1836. The Munc-18 TSS, as determined by CAGE, is not in agreement with theFigure S7A-U: CAGE validation examples

TSS determined by S1 nuclease assay36. S1 nuclease assay, determined with brain RNA,
seems to identify a longer transcript than CAGE data, with the exception of a minor
CAGE determined TSS.
G) PAX837. CAGE tag coverage of this developmental gene is very low because of its
overall low expression level. The two TSSs lie between two of the four sites determinedby S1 nuclease and primer extension, all within 6 bp in the genome. The longest were
not detected by CAGE. In the37 a cocktail of RNA was used.
H) Tbxas38. The thromboxane synthase gene most upstream TSS matches perfectly the
upstream CAGE tag. The most downstream RPA-determined TSSs map within other
CAGE-TSS, but the match frequently in disagreement by a few bp. CAGE identifies
additional downstream TSSs.
I) G-protein gamma3 subunit39. The two genes Gng3 and Gng3lg map head to head andshare a bidirectional promoter, and Gng3lg (+ strand) has a downstream different core
promoter. The Gng3 RPA mapping starts relatively close (6bp) downstream of the main
CAGE peak. CAGE tags do only partially match the RPA and do poorly correlate withthe primer extension, probably due to fact that only brain and testis were used39. The
presence of TSSs on both strands strongly confirms the bidirectional nature of thispromoter.
J)11beta-hydroxysteroid dehydrogenase 2 (HSD11B2)40. The RPA assay detects a TSS
about 10 bp downstream of the CAGE-detected TSS, however the band in the gel isquite broad and molecular weight markers are not shown. Except for liver, other tissues
used for the RPA (kidney and colon) are not well represented in the CAGE collection.
K) TIMP-441. Timp-4 RPA-determined TSSs differ from the main CAGE peaks, which
are located between the two RPA sites. CAGE did not detect the most upstream
RPA-annotated TSS, which seems more evident in heart (not deeply sampled withCAGE). The width of the bands in the RPA assay are does not enable single nucleotide
resolution.
L) CSF1R1. An additional first exon of CSF-R is not shown, as it is expressed only introphoblasts which were not sampled by CAGE. The shortest TSS of the 1 may not bereal due to a high background signal.
M) Pore forming gene42. CAGE and S1 nuclease protection methodology on CTLL-R8
cell line (not sampled with CAGE) disagree on the position of the TSS by about 5bp.
However, the resolution of the RPA reported is not that high, and seems to suggest aslightly shorter form. CAGE coverage is low in this region because this gene isexpressed exclusively in CTL and NK cells, not samples in our project.
N) Laminin B243. The start sites reported by S1 nuclease protection agrees well with the
CAGE data. Consistent with the broader distribution of CAGE tags, the report
indications of many additional weaker sites.
O) DHFR44. The report indicates that three different promoters exist for the DHFR gene.
CAGE and full-length cDNA data adds another explanatory layer on the historical data.
Promoter II, which is the most 5’ TSS, is actually a TSS for the MSH1 gene, which is on
the opposite strand and forms a sense-antisense pair with DHFR. CAGE and S1
nuclease protection agrees fully on the most used start site (promoter III). The TSSs
defined by CAGE and S1 nuclease protection are not in agreement: these TSSs are
inside the fist exon of the MSH1 gene and are likely rarely used since no EST or cDNA
evidence supports them.
P) DNA cytosine-5 methyltrasferase45. The RACE-defined peak from erythroleukemia

Figure S7A-U: CAGE validation examples

cells (not sampled by CAGE) coincides with a CAGE CTSS with 4 tags. Expression
from other tissues may account for the remaining TSS. Resolution in the original Figure
3 in ref.45 is not high.
Q) Alpha7 integrin35. The two closely located TSS defined by S1 nuclease protection
agrees with the CAGE data; both methods agree on exact position of the dominant peak.
R) Alpha 1 (IV) collagen46. The primer extension-defined TSS is only 1bp off the major
TSS defined by CAGE, which is likely to be the correct site since the initiation site is a
PyPu. The report also indicates several minor start sites around this TSS, fully
consistent with CAGE data.
S) Wnt-147. The cell lines in this study (P19 teratocarcinoma and cell line 3S) are tumor
cell lines that do not correspond to any of the libraries used for CAGE. None of themethods agree on the start sites in this case: it is interesting to note that the report
indicates several minor start sites between the reported TSS. It is possible that all TSSs
reported are real, but are used in different contexts.
The CAGE data is inherently digital, while the signals obtained by RNase protection
assay (the most commonly used experimental method) are analog. It is remarkable howeasy it is to find CAGE evidence for minor bands in autoradiographs that their original
authors probably disregarded as methodological noise. Data from the tissues that were
not sampled by CAGE in this paper shows suggests that their CAGE sampling would be
worthwhile to obtain more accurate representation of TSS usage in those expression
contexts. Of special interest might be future large-scale CAGE sampling of tumor
libraries, which were shown by multiple independent evidence (EST and other) to have
TSS position preferences that differ significantly from normal tissues.

Figure S7A-U: CAGE validation examples
A)
GeneID Pcmt

CAGE

RACE
RNAse Protection

B)
GeneID GhrCAGERNAse Protection

C)
GeneID Rnase1

CAGE

Primer extension

D)
GeneID Lox

CAGE

RNAse Protection

E)
GeneID ITG7ACAGE

S1 nuclease protection

F)
GeneID Stxbp1

CAGE

S1 nuclease protection

G)
GeneID Pax8

CAGE

S1 nuclease protection and primer extension

H)
GeneID Tbxas1

CAGE

RNA protection and RACE

Figure S7A-U: CAGE validation examples
I)
GeneID Bsc12 I
and Gng3(bidirectional
promoter,promoters I and
II direct Bsc12)
II

CAGE (blue bars indicate TSS on strand opposite to historical annotations)

RNAse Protection
Primer extension
CAGE (blue bars indicate TSS on strand opposite to historical annotations)

RNAse Protection
CAGE

III
RNAse Protection

J)
GeneID Hsd11b2

CAGE (blue bars indicate TSS on strand opposite to historical annotations)

RNAse Protection

K)
GeneID Timp-4

CAGE

RACE
RNAse Protection

L)
GeneID Csf1r

CAGE

Primer extension and RNAse protection

M)
GeneID Prf1CAGE

S1 nuclease protection

N)
GeneID LamC1
S1 nuclease protection

Figure S7A-U: CAGE validation examples
O)
GeneID Dhfr

CAGE (blue bars indicate TSS on strand opposite to historical annotations)(three promoters)

S1 nuclease protection

CAGE (blue bars indicate TSS on strand opposite to historical annotations)S1 nuclease protection

CAGE (blue bars indicate TSS on strand opposite to historical annotations)S1 nuclease protection

P)
GeneID Dnmt1

CAGE

RACE

Q)
GeneID Itga7

CAGE

S1 nuclease protection

R)
GeneID Col14a1

CAGE

Primer extension

S)
GeneID Wnt1
PMID

CAGE

S1 nuclease protection
Primer extension

Figure S7A-U: CAGE validation examples

T

GGAGCCAGTGCAACAGACAGGAACGTGTTCATCTGTTCCGTCCTCACAGAACTAGCAGCTGGGAGCCCCGTGCCCAGCCGACTCTCCA

Figure S7T CAGE validation examples
A Comparison of CAGE and RNase protection assay at the Csf1R promoter
We have aligned the CAGE TSS deriving from the promoter (left to right, 5' end to 3' end of the promoter andmRNA sequence). Upper panel: CAGE derived CTSS map (red indicated the major starting site). Bottom:
RNase protection assay autoradiograph image aligned to the corresponding genome position (migration: left toright). Notice that the resolution of RNA protection assay does not necessarily allow fine resolution at thesingle nucleotide level of the TSS. The RNase protection assay image has been sized so that the sequences
over the two major protected clusters correspond to the precise base pairs of the histogram above. Since theelectrophoretic mobility is not linear, the minor protected RNAs between these major protected bands do notalign precisely to the sequence shown.

Figure S7A-U: CAGE validation examples

CAGE tag counts in mouse

Mouse Plp1 promoter (TC id: T0XF078F45A4)

Mouse

gaaacgagCCTGTCTCTTTAAGGGGGTTGGCTGTCAATCAGAAAGCCCTTTTCATTGCAGGAGAAGAGGACAAAGATACTCAGAGAGAAAAAGTAAAGGACAGAAGAAGGAGACTGGAGAGACCAGGATCCTTCCAGCTGAGCAAAGTCAGCCGCAAAACAGACTAGCCAACAGGCTACAATTGGAGTCAGAGTGCCAAAGACATG** * ******* ********************************************************************************** *** ********** **************************** *********** **** *********** * ********************* ***********AAAGCAGGCCTGTCCCTTTAAGGGGGTTGGCTGTCAATCAGAAAGCCCTTTTCATTGCAGGAGAAGAGGACAAAGATACTCAGAGAGAAAAAGTAAAAGACCGAAGAAGGAGGCTGGAGAGACCAGGATCCTTCCAGCTGAACAAAGTCAGCCACAAAGCAGACTAGCCAGCCGGCTACAATTGGAGTCAGAGTCCCAAAGACATG

CAGE tag counts in human

Human

Human Plp1 promoter (TC id: T0XF06212E99)

Figure S7U Example of cross-species replication of TSS: the Plp1 promoter
Alignment of the major Plp1 promoter in mouse (top panel) and human (lower panel), with corresponding
CAGE tag distributions in red. The actual BLASTZ alignment is shown in the central panel. The CAGE tag
peaks in human and mouse are clearly corresponding to each other in both location and relative strength,even at locations with single tags. Changes between human and mouse are correlated with observed
nucleotide substitutions. Due to the difference in tissue sampling, the human promoter has been polled toa 3-fold greater extent than in the mouse.

Fig. S8 Definition of TCs and mRNA assignments of TCs.
Detailed exemplification of the rules used to assign the tags to TCs with examples. The rules for theassignments are described in the Experimental Procedures on line.
Figure S 19

A Definition of a T ag cluster ( TC)
CAGE tag, GIS/GSC
TC (Tag

ditag, RIKEN

5' EST

Cluster)

Representative position of TC

1

2

3

4

5

B. Rules to assign TCs to mRNA

1

TC

mRNA to which T C is assigned

mRNA

Representative position of TC
23

TC-set TypesAllAll

Tag origin

Total TC numbers 736,403

CAGE
Conservative

100.0% 236,498

All

100.0% 594,136

Conservative
100.0% 177,349

Clustering

100.0% 159,075

More than 100
CAGE tags

100.0% 8,242

100.0%CAGE & GIS & GSC & RIKEN 5'-EST & FANTOM3

0.6%

1.7%

0.7%

2.3%

2.5%

35.9%

CAGE & GIS & GSC & RIKEN 5'-EST

0.1%

0.3%

0.1%

0.3%

0.4%

3.1%

CAGE & GIS & GSC & FANTOM3

0.0%

0.0%

0.0%

0.0%

0.0%

0.1%

CAGE & GIS & GSC

0.1%

0.2%

0.1%

0.3%

0.2%

1.2%

CAGE & GIS & RIKEN 5'-EST & FANTOM3

0.2%

0.8%

0.3%

1.0%

1.1%

11.8%

CAGE & GIS & RIKEN 5'-EST

0.1%

0.2%

0.1%

0.2%

0.2%

0.5%

CAGE & GIS & FANTOM3

0.0%

0.0%

0.0%

0.0%

0.0%

0.0%

CAGE & GIS

0.2%

0.6%

0.2%

0.8%

0.6%

0.6%

CAGE & GSC & RIKEN 5'-EST & FANTOM3

0.8%

2.4%

1.0%

3.2%

3.4%

21.8%

CAGE & GSC & RIKEN 5'-EST

0.5%

1.6%

0.6%

2.1%

2.0%

5.0%

CAGE & GSC & FANTOM3

0.0%

0.1%

0.0%

0.1%

0.1%

0.1%

CAGE & GSC

2.2%

6.7%

2.7%

9.0%

5.1%

1.7%

CAGE & RIKEN 5'-EST & FANTOM3

1.2%

3.7%

1.5%

4.9%

3.8%

9.1%

CAGE & RIKEN 5'-EST

1.5%

4.7%

1.9%

6.3%

4.2%

2.2%

CAGE & FANTOM3

0.1%

0.4%

0.1%

0.5%

0.3%

0.1%

CAGE

73.2%

51.8%

90.8%

69.0%

76.0%

6.6%

GIS & GSC & RIKEN 5'-EST & FANTOM3

0.0%

0.0%

GIS & GSC & RIKEN 5'-EST

0.0%

0.0%

GIS & GSC & FANTOM3

0.0%

0.0%

GIS & GSC

0.0%

0.1%

GIS & RIKEN 5'-EST & FANTOM3

0.0%

0.0%

GIS & RIKEN 5'-EST

0.0%

0.0%

GIS & FANTOM3

0.0%

0.0%

GIS

0.4%

0.3%

GSC & RIKEN 5'-EST & FANTOM3

0.1%

0.4%

GSC & RIKEN 5'-EST

0.2%

0.5%

GSC & FANTOM3

0.0%

0.1%

GSC

8.6%

19.2%

RIKEN 5'-EST & FANTOM3

4.4%

1.4%

RIKEN 5'-EST

4.7%

2.8%

FANTOM3

0.9%

0.1%

Table S1. Detailed description of the data sets.
A detailed breakdown of the evidence collected for the identification of mouse TSS. "All" indicates thetotal count of different types of evidence (type of data, left) including at least one tag, while"Conservative" indicates that at least two items of evidence (tags) were found for each position.
Clustering indicates the dataset used for general promoter clustering (as in Fig. 4), consisting of CAGE
tags from libraries having at least 1500 tags and having at least 2 tags per TC.

Type

RegionMm vs Rn

All

Core

0.1126(0.0010) 0.3348(0.0021) 0.3775(0.0027)

Upstream

0.1181(0.0007) 0.3525(0.0015) 0.4028(0.0019)

Downstream

0.1077(0.0009) 0.3175(0.0020) 0.3621(0.0026)

Core

0.1010(0.0040) 0.3243(0.0085) 0.3755(0.0121)

Upstream

0.1169(0.0028) 0.3767(0.0061) 0.4370(0.0077)

Downstream

0.0923(0.0037) 0.3031(0.0084) 0.3498(0.0112)

Core

0.1123(0.0029) 0.3675(0.0069) 0.4073(0.0089)

Upstream

0.1201(0.0020) 0.3902(0.0046) 0.4481(0.0059)

Downstream

0.0925(0.0029) 0.3085(0.0062) 0.3539(0.0081)

Core

0.1063(0.0038) 0.3422(0.0081) 0.3864(0.0105)

Upstream

0.1191(0.0026) 0.3851(0.0057) 0.4405(0.0072)

Downstream

0.0947(0.0033) 0.3221(0.0078) 0.3675(0.0104)

Core

0.1176(0.0044) 0.3419(0.0087) 0.3801(0.0112)

Upstream

0.1274(0.0033) 0.3777(0.0062) 0.4307(0.0080)

Downstream

0.1125(0.0039) 0.3238(0.0076) 0.3706(0.0102)

Core

0.0998(0.0013) 0.3369(0.0031) 0.3817(0.0041)

Upstream

0.1107(0.0009) 0.3697(0.0021) 0.4294(0.0027)

Downstream

0.0901(0.0012) 0.3105(0.0029) 0.3558(0.0041)

Core

0.1150(0.0030) 0.3169(0.0065) 0.3508(0.0079)

Upstream

0.1227(0.0027) 0.3321(0.0049) 0.3729(0.0060)

Downstream

0.1160(0.0029) 0.3115(0.0060) 0.3492(0.0075)

Core

0.1103(0.0019) 0.3473(0.0040) 0.3899(0.0053)

Upstream

0.1212(0.0013) 0.3836(0.0028) 0.4400(0.0035)

Downstream

0.0983(0.0017) 0.3145(0.0037) 0.3602(0.0049)

Core

0.1133(0.0011) 0.3307(0.0025) 0.3736(0.0031)

Upstream

0.1171(0.0009) 0.3414(0.0018) 0.3902(0.0023)

Downstream

0.1107(0.0011) 0.3185(0.0024) 0.3627(0.0030)

Core

0.1280(0.0013) 0.3808(0.0027) 0.4389(0.0036)

Upstream

0.1312(0.0011) 0.3888(0.0021) 0.4486(0.0031)

Downstream

0.0999(0.0012) 0.2779(0.0023) 0.3068(0.0028)

MU

BR

PB

SP

CpG

TATA

High

Low

ATG

Random

Mm vs Hs

Mm vs Cf

0.1609(0.0010) 0.4134(0.0022) 0.4633(0.0022)

Table S2A: Substitution rate estimates for mouse promoters.
Substitution rate estimates (95% confidence intervals in parentheses) based upon mouse-basedalignments for core promoter, upstream and downstream regions flanking mouse TSSs and startcodons (ATG) and randomly sampled mouse genome sequences (Random). Estimates are given for
all promoters considered together (All) and various categories of promoters: the promoter classes
discovered in the CAGE data (MU, BR, PB, SP), those possessing CpG islands (CpG) or TATA
boxes (TATA) and those promoters with TSSs supported by >100 tags (High) and those supported by<100 tags (Low). Comparisons are between the mouse sequence and those from rat (Rn), human(Hs) and dog (Cf). Green background indicates that the promoters' evolution has been significantly
slower than that of randomly sampled sequence (yellow).

T ypeR egionHs vs P t

Hs vs C f

Hs vs Mm

Hs vs R n

A ll

C ore

0.0148(0.0004) 0.2901(0.0024) 0.3263(0.0023) 0.3458(0.0026)

Ups tream

0.0136(0.0003) 0.3085(0.0020) 0.3465(0.0020) 0.3667(0.0022)

Downs tream 0.0146(0.0004) 0.2748(0.0024) 0.3115(0.0023) 0.3314(0.0026)
MU

C ore

0.0146(0.0020) 0.2707(0.0109) 0.3013(0.0104) 0.3245(0.0118)

Ups tream

0.0132(0.0016) 0.3216(0.0090) 0.3503(0.0078) 0.3753(0.0089)

Downs tream 0.0150(0.0023) 0.2471(0.0112) 0.2844(0.0101) 0.3050(0.0117)
BR

C ore

0.0139(0.0010) 0.2956(0.0099) 0.3366(0.0087) 0.3513(0.0094)

Ups tream

0.0126(0.0008) 0.3320(0.0081) 0.3709(0.0071) 0.3875(0.0076)

Downs tream 0.0134(0.0013) 0.2538(0.0103) 0.2993(0.0089) 0.3152(0.0100)
PB

C ore

0.0150(0.0019) 0.2841(0.0116) 0.3262(0.0110) 0.3425(0.0117)

Ups tream

0.0125(0.0013) 0.3311(0.0093) 0.3643(0.0086) 0.3849(0.0093)

Downs tream 0.0143(0.0020) 0.2497(0.0118) 0.2875(0.0101) 0.3048(0.0116)
SP

C ore

0.0183(0.0020) 0.2764(0.0097) 0.3175(0.0098) 0.3350(0.0109)

Ups tream

0.0164(0.0016) 0.3105(0.0081) 0.3453(0.0080) 0.3663(0.0087)

Downs tream 0.0209(0.0023) 0.2702(0.0097) 0.3093(0.0095) 0.3298(0.0108)
C pG

C ore

0.0144(0.0004) 0.3017(0.0032) 0.3328(0.0029) 0.3520(0.0032)

Ups tream

0.0129(0.0003) 0.3259(0.0025) 0.3564(0.0023) 0.3761(0.0025)

Downs tream 0.0138(0.0004) 0.2755(0.0033) 0.3095(0.0029) 0.3270(0.0032)
TATA

C ore

0.0151(0.0016) 0.2600(0.0077) 0.3071(0.0080) 0.3266(0.0089)

Ups tream

0.0146(0.0014) 0.2821(0.0062) 0.3328(0.0066) 0.3546(0.0072)

Downs tream 0.0162(0.0016) 0.2659(0.0077) 0.3114(0.0078) 0.3323(0.0087)
High

C ore

0.0156(0.0009) 0.2822(0.0052) 0.3211(0.0050) 0.3388(0.0055)

Ups tream

0.0138(0.0007) 0.3235(0.0043) 0.3578(0.0039) 0.3783(0.0043)

Downs tream 0.0162(0.0010) 0.2564(0.0053) 0.2964(0.0048) 0.3149(0.0055)
L ow

C ore

0.0146(0.0004) 0.2919(0.0027) 0.3276(0.0026) 0.3476(0.0030)

Ups tream

0.0135(0.0003) 0.3048(0.0022) 0.3437(0.0023) 0.3638(0.0026)

Downs tream 0.0142(0.0004) 0.2790(0.0027) 0.3153(0.0026) 0.3355(0.0029)
ATG

C ore

0.0124(0.0002) 0.2901(0.0019) 0.3467(0.0018) 0.3676(0.0021)

Ups tream

0.0125(0.0002) 0.2936(0.0016) 0.3472(0.0016) 0.3676(0.0018)

Downs tream 0.0110(0.0002) 0.2186(0.0017) 0.2665(0.0017) 0.2791(0.0019)
R andom

0.0132(0.0002) 0.3070(0.0017) 0.3953(0.0022) 0.4188(0.0024)

Table S2B Substitution rate estimates for human promoters.
Substitution rate estimates (95% confidence intervals in parentheses) based upon human-basedalignments for upstream and downstream regions flanking human TSSs and start codons (ATG)
and randomly sampled human genome sequences (Random). Estimates are given for all
promoters considered together (All) and various categories of promoters: the promoter classes
discovered in the CAGE data (MU, BR, PB, SP), those possessing CpG islands (CpG) or TATA
boxes (TATA) and those promoters with TSSs supported by >100 tags (High) and those
supported by <100 tags (Low). Comparisons are between the human sequences and those from
chimp (Pt), dog (Cf), mouse (Mm) and rat (Rn). Green background indicates that the promoters'
evolution has been significantly slower than that of randomly sampled sequence (yellow), while
red indicates significantly more rapid evolution than randomly sampled sequence.

Tissueadiposecns

embryo

liver

lung

macrophage

testis

SP

BR

PB

MU

1.98

0.27

1.58

0.44

P=0.14

P=0.11

P=0.29P=0.47

1.02

0.69

1.22

1.23

P=0.86

P=0.0020

P=0.10P=0.10

4.11

0.00

0.30

0.00

P=1.21e-22 P=6.22e-08 P=0.0099 P=8.096e-05
2.15

0.41

0.71

1.07

P=3.56e-21 P=1.14e-14 P=0.0053 P=0.56
2.41

0.23

1.11

0.58

P=1.37e-10 P=1.42e-08 P=0.61

P=0.049

1.39

0.64

0.89

1.26

P=0.024

P=0.0041

P=0.59P=0.14

4.36

0.00

0.00

0.00

P=0.21

P=0.21

P=7.70e-06 P=0.058

Overrepresented

1e-10 1e-06 0.0001 0.01 1.00

Underrrepresented

1e-10 1e-06 0.0001 0.01 1.00

Table S3A. Over-representation of shape classes within tissue
specific libraries. Observed/expected ratios and the associated Pvalues are shown for each combination of tissue and promoter
shape.

Table S3B Gene Ontology (GO) terms preferentially associated with SP (red rows) and NM type (green rows) promoters.
GO annotation was compared between all 1216 GO-annotated TUs with SP promoters and all 2033 GO-annotated TUs with NM promoters. TUs without GO annotation and a small number of TUs that had
both NM and SP promoters were not used in the comparison. The GO terms had been assigned in the FANTOM3 annotation pipeline. For each of the 735 GO terms that were associated with at least 10 of
the 1216 + 2033 TUs, we carried out a two-sided Fisher's exact test to assess preferential association with the SP as compared to the NM set or vice versa. The resulting p-values were corrected for
multiple testing with the conservative Bonferroni method. Results significant at p < 0.01 are shown.
For example, the majority of genes encoding structural muscle-specific (myofibril) proteins in the observed set (18/19, corrected P-value P=1.7x10-4), polysaccharide binding proteins (20/20, P=1.9x10-6),
and chemokines (12/12, P=5.4x10-3) have strong association with SP-type promoters. Only a handful of general categories exist where broad peaks are significantly overrepresented, including RNA
processing (corrected P=5.0x10-3) and the ubiquitin cycle (P=5.2x10-3), both of which can be considered constitutive cellular processes. A note of caution is required for certain general categories. For
example, this analysis suggests that developmental genes use SP-type promoters. The result is due to highly expressed, event-driven developmental effectors (e.g. Notch1, Socs1, Socs3 or Rtn4),
secreted proteins (Spp1, Ifgbp7), or enzymes involved in specific differentiation processes (e.g. xanthine dehydrogenase), which show strong preference for SP-type promoters. This contrasting information
can be explained by the fact that in our set of highly expressed genes there are few key developmental regulatory transcription factors overlapping CpG islands both in promoters and along much of theirlength17, because their overall expression level is too low and embryonic libraries were not deeply sampled.

Ontology

Cellularcomponent

Molecularfunction

Biologicalprocess

Level Term id
1 GO:0005576
GO:0005615
2
GO:0043227
GO:0005886
3
GO:0043231
GO:0030484
4
GO:0042598
GO:0030016
5
GO:0005792
6 GO:0030017
1 GO:0005198
GO:0030246
GO:0001871
2 GO:0004857
GO:0005102
GO:0005200
GO:0004497
GO:0030247
3
GO:0005125
GO:0001664
GO:0008009
4 GO:0042379
GO:0005539
5 GO:0008201
1 GO:0007275
GO:0009653
GO:0048513
2
GO:0050793
GO:0050874
GO:0007155
GO:0009887
3
GO:0006955
GO:0009607
GO:0007517
GO:0006952
4
GO:0043207
GO:0009613
GO:0001525
GO:0006954
5 GO:0006935
GO:0006464
GO:0006396
6 GO:0006512

Parent term
extracellular region
organelle
membranemembrane-bound organelle
cytoplasm
membrane fraction
muscle fiber
vesicular fraction
myofibrilbindingbinding
enzyme regulator activity
signal transducer activity; binding
structural molecule activity
oxidoreductase activity
pattern binding; carbohydrate binding
receptor binding
receptor binding
cytokine activity
G-protein-coupled receptor binding
polysaccharide binding
glycosaminoglycan binding
development
development
development
physiological processcell communication
morphogenesis; organ development
organismal physiological process
response to stimulusorganogenesis
response to biotic stimulus
response to biotic stimulus
response to stressblood vessel morphogenesis
response to pest, pathogen or parasite
taxis
protein metabolism
RNA metabolism
protein modification

Term
extracellular region
extracellular space
membrane-bound organelle
plasma membrane
intracellular membrane-bound organelle
muscle fiber
vesicular fraction
myofibrilmicrosomesarcomere
structural molecule activity
carbohydrate binding
pattern binding
enzyme inhibitor activity
receptor binding
structural constituent of cytoskeletonmonooxygenase activity
polysaccharide binding
cytokine activity
G-protein-coupled receptor binding
chemokine activity
chemokine receptor binding
glycosaminoglycan binding
heparin binding
development
morphogenesis
organ development
regulation of development
organismal physiological processcell adhesion
organogenesis
immune response
response to biotic stimulus
muscle development
defense response
response to external biotic stimulus
response to pest, pathogen or parasite
angiogenesis
inflammatory response
chemotaxis
protein modification
RNA processing
ubiquitin cycle

Number Number
Fraction
Bonferroniof SP
of BR
Fractionof BR Fisher test corrected
TUs
TUs
of SP TUs
TUs
p-valuep-value
287
248
23.6%
12.2% 8.16e-17
6.00e-14
262
228
21.5%
11.2% 5.56e-15
4.09e-12
413
875
34.0%
43.0% 3.09e-07
0.000227
135
104
11.1%
5.1% 6.31e-10
4.64e-07
413
875
34.0%
43.0% 3.09e-07
0.000227191
1.6%
0.0% 9.22e-08
6.77e-05
27
92.2%
0.4% 5.48e-06
0.00403181
1.5%
0.0% 2.36e-07
0.00017427
92.2%
0.4% 5.48e-06
0.00403171
1.4%
0.0% 6.05e-07
0.0004449364
7.6%
3.1% 1.52e-08
1.12e-05
34
11
2.8%
0.5% 1.98e-07
0.000146
230
1.9%
0.0% 1.34e-10
9.83e-0835
12
2.9%
0.6% 3.16e-07
0.000233
4320
3.5%
1.0% 9.90e-07
0.000728243
2.0%
0.1% 3.89e-08
2.86e-05
204
1.6%
0.2% 4.93e-06
0.003622001.6%
0.0% 2.64e-09
1.94e-06
24
72.0%
0.3% 6.54e-06
0.00481120
1.0%
0.0% 7.30e-06
0.00537120
1.0%
0.0% 7.30e-06
0.00537120
1.0%
0.0% 7.30e-06
0.00537190
1.6%
0.0% 7.11e-09
5.23e-06
160
1.3%
0.0% 1.39e-07
0.000102
189
151
15.5%
7.4% 7.14e-13
5.25e-10
144
101
11.8%
5.0% 2.04e-12
1.50e-09
120
77
9.9%
3.8% 6.13e-12
4.50e-09
34
14
2.8%
0.7% 2.91e-06
0.00214113
80
9.3%
3.9% 1.26e-09
9.23e-07
56
37
4.6%
1.8% 9.65e-06
0.0070912076
9.9%
3.7% 4.85e-12
3.57e-09
69
37
5.7%
1.8% 5.00e-09
3.67e-06
9453
7.7%
2.6% 3.73e-11
2.74e-08
29
92.4%
0.4% 1.44e-06
0.00106
81
41
6.7%
2.0% 4.90e-11
3.60e-08
45
23
3.7%
1.1% 1.50e-06
0.0011041
21
3.4%
1.0% 4.20e-06
0.00308206
1.6%
0.3% 4.98e-05
0.036625
92.1%
0.4% 2.10e-05
0.0155228
1.8%
0.4% 7.89e-05
0.058084
246
6.9%
12.1% 1.43e-06
0.001051475
1.2%
3.7% 6.78e-06
0.0049825
105
2.1%
5.2% 7.02e-06
0.00516

Resource

URL

Contents

CAGE Basic

http://gerg01.gsc.riken.jp/cage/

All basic information about CAGE
tags,

ViewerMouse

http://gerg01.gsc.riken.jp/cage/mm5

Human

http://gerg01.gsc.riken.jp/cage/hg17
http://gerg02.gsc.riken.jp/gev-promoter/gbrowse/

Genomemappings

andtissueinformation

Promoter elements, cDNA, EST and

Element

other data are mapped to respective

ViewergenomeMouse

http://gerg02.gsc.riken.jp/gev-promoter/gbrowse/m
m5

Human

http://gerg02.gsc.riken.jp/gev-promoter/gbrowse/hg
17

Promoter

http://fantom31p.gsc.riken.jp/cage/download/

CAGE

tag

data

and

mapping

information

datasets
Mouse

http://fantom31p.gsc.riken.jp/cage/download/mm5/

Human

http://fantom31p.gsc.riken.jp/cage/download/hg17/

Expression

http://gerg01.gsc.riken.jp/expr_tree/

clustering

tree

with

associated annotations

tree viewer
Mouse

http://gerg01.gsc.riken.jp/expr_tree/mm5/

Human

http://gerg01.gsc.riken.jp/expr_tree/

CAGE analysis

Promoter

http://gerg01.gsc.riken.jp/cage_analysis/

User-friendly interface to CAGE dataon promoter levelviewerMouse

http://gerg01.gsc.riken.jp/cage_analysis/mm5

Human

http://gerg01.gsc.riken.jp/cage_analysis/hg17/
Visualization of genome location,

3D viewer
Mouse

http://gerg01.gsc.riken.jp/expr_3D/mm5/

development
specificity

Alternative

http://gerg01.gsc.riken.jp/altp/

promoter sets

Table S4 Internet links to publicly available resources and datasets.

stagesandtissueCts s size

Obser ved

Expected

observ ed/

numbernumberexpec ted

p-valu e2

1441299707.84

14.85<1E-324

3

60762

6.59

9221.78<1E-324

435026

2.98E-003

1.17E+7<1E-324523479

1.01E-006

2.32E+10<1E-324106720

3.08E-025

2.18E+28<1E-324

25

1259

7.66E-087

7.13E+88<1E-32450

348

7.48E-198

9.28E+198<1E-324100

90<1E-324NA<1E-324

Table S5A Observed and expected number of multitag CTSS in mouse
The probability of the observed number of multitag CTSS(broken up by the
number of tags in the CTSS) by random selection is shown (all p-values are
below the underflow limit of standard computers)
number of ta gs in TC

number of T Cs withtags from > 1 library

number of T cs with tags
from a single libray% reproduci bility

2

52662

14998

77.83

3

23752

1824

92.87

4

12559

611

95.36

5

7870

296

96.38

6

5401

161

98.11

7

3964

116

97.16

8

2992

44

97.55

9

2421

29

98.82

10

1934

27

98.62

15

939

7

98.26

20

535

0

100

50

134

0

100

100

57

0

100

Table S5B Tag cluster reproducibility
Inter-library reproducibility of multitag tag clusters (TCs), broken up by number of tagsin the TC. A TC is considered reproduced if it is composed of tags from more than one
library. Since different CAGE libraries are distinct experiments, TCs that contain tags
from more than one library are experimentally reproducible

number oftags in ctss
2
3
4
5
6
7
8
9
10
15
20
50
100

Ctss from classified tag clusters
number of
number of ctss%
ctss with tags with tags from a reproducibili
from >1
single libraryty
library
41528
5955
87.46
26761
843
96.94
18151
212
98.88
13396
130
99.03
10420
74
99.29
8137
52
99.36
6620
16
99.75
5602
13
99.76
4794
8
99.83
2538
1
99.86
576
1
99.93
325
0
100
89
0
100

number ofctss withtags from >1
library112262
56878
33803
22853
16664
12530
9877
8034
6682
3171
1929
348
90

All ctss
number of ctss
with tags from asingle library%
reproducibility

31867
3884
1223
626
327
205
99
54
38
7
2
0
0

77.89
93.61
96.51
97.33
98.07
98.39
99.01
99.33
99.43
99.78
99.89
100
100

Table S5C CTSS reproducibility
Inter-library reproducibility of multitag CTSS, broken up by number of tags in the
CTSS. A CTSS is considered reproduced if it is composed of tags from more than one
library.

number of liver number of cases number of human liver % human liver ctss thattags in humanin human
ctss validated by mouse are supported by liver ctss
ctss
liver ctss
in mouse
1

20492

12078

58.9

2

8268

5325

64.4

3

4530

3102

68.5

4

2894

2078

71.8

5

2076

1495

72.0

10

654

509

77.8

15

328

262

79.9

20

215

176

81.9

Table S5D Cross-species validation of CTSS
Extent of human liver CTSS supported by mouse liver ctss within alignablepromoters, broken up by number of liver tags in the CTSS. A liver CTSS in humanis considered repreduced if a liver CTSS occurs in mouse with the same strand at
the corresponding aligned position, plus/minus 1 bp

Table S6 Over-representation index of TFBS in macrophage
promotersA) List of TFBS over-represented in the constitutive macrophageset (450 TSSs) compared to background. B) List of TFBS overrepresented in the induced set (295 TSSs) compared to thebackground. TFBS names refer to cognate profile model name.
A more comprehensive list including counts and likelihood isavailable in Table S7
A Constitutive macrophages TC versus 40,000 random
TC comparison having frequencies ≥ 10% and ORI ≥ 1.6
Elk-1
GC box
IRF1
PEA3
PU.1
SOX-9
c-Ets-1
c-Ets-2
B Induced macrophages TC versus 40,000 random TC
comparison having frequencies ≥ 10% and ORI ≥ 1.6
IRF
IRF-7
PU.1
SOX-9
c-Ets-1
c-Ets-2

Table S7. Over-representation and under-representation index of TFBS in macrophage promoters,
detailed view.
A, over-representation/under-representation of TFBS in the constitutively expressed
macrophages promoters. ORI indicates the ratio of over-representation of compared to thecontrol set (40K, consisting of 40,000 randomly selected promoters). B, overrepresentation/under-representation of TFBS in promoters induced in macrophages after 7 hours
from addition of LPS, versus the 40,000 randomly selected promoters. An ORI value >1 indicatesover-representation in the target, while <1 indicates under-representation.

A Constitutive macrophages TC versus 40,000 random TC comparison having frequencies ≥ 10% and ORI ≥ 1.6
TFBS pattern

ORI% in target% in backgroundprobability in TARGET

probability in BACKGROUND+1 c­Ets­1

3.3796+

15.35

8.82

2.5914E­04

1.3346E­04+1 c­Ets­2

3.3325+

13.49

7.64

2.1595E­04

1.1446E­04­1 ABI4

3.3

14.65

8.03

2.4585E­04

1.3577E­04+1 E2F­1/DP­1

2.79

13.72

8.33

2.2259E­04

1.3153E­04+1 ABI4

2.57

11.16

6.86

1.7940E­04

1.1340E­04+1 MAZR

2.36

21.86

13.89

6.3123E­04

4.2068E­04­1 PCF2

2.31

21.86

14.5

3.9203E­04

2.5604E­04­1 c­Ets­12.2200+

13.26

8.87

1.9934E­04

1.3412E­04+1 ELF­1

2.2

20.7

13.93

3.3887E­04

2.2918E­04+1 c­Ets­1 68

2.2

18.14

12.76

3.1229E­04

2.0228E­04+1 PEA3

2.1499+

23.95

16.26

3.7874E­04

2.5947E­04+1 MAZ

2.12

46.51

32.94

1.2193E­03

8.1314E­04­1 CDC5

2.03

11.4

8.12

1.8272E­04

1.2648E­04+1 E12

1.98

11.4

8.02

1.6944E­04

1.2188E­04+1 Nrf2

1.96

10.23

7.47

1.5947E­04

1.1132E­04­1 PEA3

1.9457+

23.02

16.87

3.8538E­04

2.7033E­04­1 ELF­1

1.86

18.6

14.14

3.2226E­04

2.2805E­04­1 c­Ets­2

1.8470+

10.93

8.16

1.6944E­04

1.2283E­04+1 E2F

1.83

64.65

50.18

5.4784E­03

3.8674E­03+1 GC box

1.8215+70

57.05

3.8904E­03

2.6208E­03­1 c­Ets­1 68

1.8

17.21

12.92

2.7575E­04

2.0389E­04+1 Sp­1

1.79

75.58

62.5

7.2558E­03

4.8926E­03­1 GCR1

1.77

26.51

19.78

4.3854E­04

3.3231E­04­1 ETF

1.76

52.33

40.96

2.0498E­03

1.4850E­03­1 Elk­1

1.7514+

44.88

35.67

1.0698E­03

7.6859E­04­1 Hairy

1.75

21.63

16.59

4.1860E­04

3.1188E­04+1 ETF

1.73

58.37

44.52

2.4053E­03

1.8203E­03+1 EGR

1.73

58.6

46.37

2.2259E­03

1.6254E­03­1 PU.1

1.7221+

47.91

38.56

1.0299E­03

7.4294E­04­1 Sp­1

1.72

75.81

61.73

6.4352E­03

4.5977E­03­1 E2F

1.7

60.23

49.72

4.8904E­03

3.4836E­03+1 Sp3

1.7

36.05

28.08

7.1096E­04

5.3839E­04+1 GCR1

1.69

24.19

19.36

4.3522E­04

3.2160E­04­1 E2F­1/DP­1

1.69

11.63

8.53

1.6944E­04

1.3686E­04

1.6705+

43.02

34.78

1.0066E­03

7.4535E­04

1.67

41.63

33.05

9.8007E­04

7.3968E­04+1 PU.1

1.6533+

48.14

38.75

1.0133E­03

7.6132E­04+1 Adf­1

1.65

18.6

13.03

6.5449E­04

5.6668E­04+1 c­Rel

1.64

36.51

29.66

7.1429E­04

5.3550E­04­1 EGR

1.62

51.86

41.37

1.7741E­03

1.3726E­03+1 STRE

1.62

22.09

17.14

3.6545E­04

2.9163E­04­1 GC box

1.6119+

63.26

52.73

2.9967E­03

2.2301E­03+1 Dfd

0.62

50.93

59.37

1.3023E­03

1.7877E­03+1 IRF1

0.6248+

15.81

19.92

2.6578E­04

3.3779E­04­1 dri

0.62

30.23

35.3

5.4485E­04

7.5379E­04+1 FOX

0.62

27.91

36.59

1.1229E­03

1.3834E­03­1 COMP1

0.62

16.28

20.24

2.5249E­04

3.2884E­04­1 TATA

0.61

28.37

34.94

7.5083E­04

9.9744E­04­1 HP1 site factor

0.61

12.79

15.84

1.9269E­04

2.5619E­04­1 Nkx6­2

0.61

26.98

33.26

5.7475E­04

7.7002E­04­1 Cdc5

0.6

36.51

43.96

6.9435E­04

9.5549E­04­1 Cdx­2

0.6

26.28

32.72

4.9169E­04

6.5706E­04­1 CCAAT box

0.6

16.05

20.89

2.7243E­04

3.4952E­04+1 Evi­1

0.6

21.63

27.39

4.3189E­04

5.7037E­04­1 TBP

0.6

36.98

43.93

9.3355E­04

1.3154E­03+1 Nkx6­2

0.6

28.14

33.48

5.5482E­04

7.8259E­04­1 Zen

0.59

41.63

52.77

9.8339E­04

1.3224E­03+1 dri

0.58

27.67

34.67

5.3488E­04

7.3029E­04­1 Nkx2­5

0.58

18.84

23.41

2.8571E­04

3.9393E­04­1 Pbx­1

0.58

47.44

57.26

1.3289E­03

1.8880E­03+1 Elk­1+1 CAC­binding protein

­1 FOXO4

0.57

14.19

18.09

2.2259E­04

3.0508E­04­1 Ovo

0.57

10.7

13.96

1.6279E­04

2.1876E­04+1 Cdx­2

0.55

25.58

33.4

4.8837E­04

6.8435E­04­1 SBF­1

0.54

14.65

19.14

2.4252E­04

3.4196E­04+1 BR­C Z1

0.5

16.28

23.15

3.1894E­04

4.5298E­04+1 NIT2

0.49

13.26

18.54

2.1927E­04

3.2062E­04­1 SOX­9

0.4238+

13.49

19.64

2.0598E­04

3.3377E­04­1 Sox­5

0.4

10.47

15.4

1.4950E­04

2.5286E­04­1 MRF­2

0.33

10.7

17.71

1.6279E­04

3.0150E­04a

B Induced macrophages TC versus 40,000 random TC comparison having frequencies ≥ 10% and ORI ≥ 1.6
TFBS pattern

ORI% in target% in backgroundprobability in TARGET

probability in BACKGROUND­1 IRF8.0819+

16.38

6.18

3.0363E­04

9.9587E­05+1 IRF4.4706+

12.2

6.54

2.5386E­04

1.0595E­04+1 c­Ets­2

3.5717+

13.94

7.64

2.2399E­04

1.1446E­04­1 STATx

3.36

10.8

5.78

1.5431E­04

8.5846E­05­1 c­Ets­12.6228+

13.94

8.87

2.2399E­04

1.3412E­04+1 c­Ets­12.5288+

13.59

8.82

2.1901E­04

1.3346E­04+1 ELF­1

2.4

22.3

13.93

3.4345E­04

2.2918E­04+1 CHOP­C/EBPalpha

2.36

10.45

7.16

1.7422E­04

1.0792E­04

2.2961+

33.45

22.3

6.0727E­04

3.9667E­04­1 Poly A downstream element 2.25

10.1

6.81

1.5431E­04

1.0185E­04+1 c­Ets­1 68

2.17

18.12

12.76

3.0861E­04

2.0228E­04­1 Nrf2

2.14

11.15

7.68

1.6924E­04

1.1490E­04­1 IRF­7

2.0651+

30.66

22.93

6.3216E­04

4.0931E­04­1 CAT8

2.03

11.15

7.95

1.7422E­04

1.2024E­04+1 c­Rel

2.03

39.72

29.66

8.1135E­04

5.3550E­04­1 ELF­1

2.02

19.86

14.14

3.2852E­04

2.2805E­04+1 GCN4

1.97

10.45

7.42

1.6924E­04

1.2111E­04­1 c­Ets­2

1.9407+

11.5

8.16

1.6924E­04

1.2283E­04­1 GCR1

1.82

25.78

19.78

4.6292E­04

3.3231E­04+1 NF­kappaB

1.81

50.17

40.46

1.2842E­03

8.7819E­04

­1 dl

1.8

27.87

20.58

4.4798E­04

3.3724E­04+1 c­Ets­1(p54)

1.79

67.6

52.79

1.8915E­03

1.3507E­03­1 core­binding factor

1.77

11.5

8.54

1.7422E­04

1.3237E­04­1 PCF2

1.76

19.86

14.5

3.2852E­04

2.5604E­04­1 AML1

1.7

24.39

18.66

3.9821E­04

3.0614E­04+1 Osf2

1.7

28.92

22.53

4.9776E­04

3.7664E­04+1 NF­Y

1.69

16.72

13.59

5.6247E­04

4.1048E­04­1 NF­kappaB

1.68

47.04

41.85

1.3639E­03

9.1456E­04+1 NF­AT

1.67

60.63

48.58

2.0458E­03

1.5261E­03­1 c­Rel

1.67

35.54

28.2

6.6700E­04

5.0345E­04+1 Ik­1

1.66

22.3

17.61

3.6834E­04

2.8082E­04+1 PU.1

1.6392+

49.83

38.75

9.7063E­04

7.6132E­04­1 ABI4

1.62

10.45

8.03

1.6924E­04

1.3577E­04+1 HNF­3alpha

0.61

32.75

39.16

7.4664E­04

1.0156E­03­1 BR­C Z1

0.61

21.6

28.06

4.6789E­04

5.9387E­04­1 TBP

0.6

37.28

43.93

9.3081E­04

1.3154E­03+1 Ubx

0.6

35.54

44.11

7.3171E­04

9.8429E­04+1 FOXJ2

0.6

16.03

21.36

3.6336E­04

4.5580E­04­1 LEF1

0.6

16.72

21.85

2.8372E­04

3.6403E­04­1 AGL3

0.59

10.45

13.67

2.2399E­04

2.9244E­04­1 TATA

0.58

28.92

34.94

6.9686E­04

9.9744E­04­1 dri

0.57

26.13

35.3

5.8238E­04

7.5379E­04+1 MAZR

0.57

11.5

13.89

2.8870E­04

4.2068E­04­1 HP1 site factor

0.55

11.85

15.84

1.8915E­04

2.5619E­04­1 SOX­9

0.5472+

15.33

19.64

2.3395E­04

3.3377E­04+1 Cdx­2

0.54

25.09

33.4

4.9278E­04

6.8435E­04­1 Sox­5

0.44

10.8

15.4

1.5928E­04

2.5286E­04­1 MRF­2

0.42

11.85

17.71

1.8915E­04

3.0150E­04+1 IRF­7