Gerstein
Intergenic Annotation. The Gerstein lab has been a major participant in a number of ENCODE
efforts, focusing on intergenic annotation. We developed an approach to analyze the distribution of
regulatory elements found in many different ChIP-chip experiments (Zhang et al., 2006). We
focused on the overall chromosomal distribution of regulatory elements in the encode regions and
showed that it is highly nonrandom. Our results indicate that these elements are clustered into
regulatory rich ‘islands’ and poor ‘deserts.’ We then performed a multivariate analysis on all the
factors collectively. This grouped the transcription factors into sequence-specific and sequencenonspecific clusters. Following up on this, we developed an approach for integrating the results of
many ChIP-chip experiments to discover new promotors in the human ENCODE regions (Trinklein
et al. 2006). We also have carried out comprehensive pseudogene annotation of the human
genome and cross referenced this annotation with tiling arrays (Zheng et al., 2005, 2007; Harrison
et al., 2005; Zheng et al., 2006; Zheng & Gerstein, 2006, 2007; Zhang et al., 2003). We performed
a related analysis of structured RNAs in intergenic regions, also inter-relating them with tiling array
data (Zhang et al., 2007; Washietl et al., 2007).
Tiling Array Tools. The Gerstein Lab has developed a considerable amount of tools and machinery
for processing tiling arrays. Most of these are described elsewhere in the proposal (e.g. scoring
arrays, sect. C.4). One tool not described elsewhere is BoCaTFBS (Wang et al., 2006). This refines
ChIP-chip hits by considering known motifs (Wang et al., 2006). Traditional computational
algorithms used to identify binding sites, such as consensus sequences (Osada et al., 2004), profile
methods (PWM/PSSM) (Stormo, 2000), and HMMs (Pavlidis et al., 2001; Ellrott et al., 2002)
generate high false-positive rates when applied genome-wide (Wasserman & Sandelin, 2004). Our
method uses a boosted cascade of classifiers -- specifically, alternating decision trees (ADTboost)
(Freud & Schapire, 1999), where ADTboost is a special extension of AdaBoost. We use the known
motifs (e.g. from the TRANSFAC database, Matys et al., 2003) as positives and the results of the
ChIP-chip experiments as negatives. Our method is the first motif finder that explicitly takes into
account the data from ChIP-chip experiments. Moreover, BoCaTFBS differs from most other motif
programs in that (1) it takes into consideration positional dependencies within a given motif and (2)
it uses the negative data (regions where the transcription factor does not bind) in order to refine the
binding site.
Regulatory Networks. The Gerstein lab has done quite a number of analyses on the large-scale
structure of regulatory networks (e.g. Yu & Gerstein, 2006; Luscombe et al., 2004; Yu et al., 2003,
2004) and has developed tools to enable their analysis (Yip et al., 2006; Yu et al., 2004;
networks.gersteinlab.org/tyna ).
Publications
* Zhengdong, Z., Alberto, P., Yutao F., Sherman W., Zhiping W., Chang J., Snyder M, Gerstein
M. Development of approaches for global analysis of regulatory elements. Global analysis of
the genomic distribution and correlation of regulatory elements in the Encode regions.
Genome Research. Genome Res. In press.
* Euskirchen, G., Rozowsky, J., Wei, C.L. Lee, W.H., Zhang, Z, Hartman, S., Emanuelsson, O.,
Stolc, V., Weissman, S., Gerstein, M., Ruan, Y., Snyder, M. (2006). Mapping of
Transcription Factor Binding Regions in Mammalian Cells by ChIP: Comparison of Array
and Sequencing Based Technologies. Genome Res. In press.
* Trinklein ND, Karaöz U, Wu J, Halees A, Force Aldre S, Collins PJ, Zheng D, Zhang Z,
Gerstein M, Snyder M, Myers RM, Weng, Z. Integrated analysis of experimental datasets
reveals many novel promoters in 1% of the human genome. Genome Research., in press.
* S Washietl, JS Pedersen, JO Korbel, AR Gruber, J Hackermuller, J Hertel, M Lindemeyer, K
Reiche, C Stocsits, A Tanzer, C Ucla, C Wyss, SE Antonarakis, F Denoeud, J Lagarde, J
Drenkow, P Kapranov, TR Gingeras, M Snyder, MB Gerstein, A Reymond, IL Hofacker, PF
Stadler . Structured RNAs in the ENCODE Selected Regions of the Human Genome
Genome Research (in press)
* J Du, JS Rozowsky, JO Korbel, ZD Zhang, TE Royce, MH Schultz, M Snyder, M Gerstein
(2006) A supervised hidden markov model framework for efficiently segmenting tiling array
data in transcriptional and chIP-chip experiments: systematically incorporating validated
biological knowledge.Bioinformatics 22: 3016-24.
74
* Zhang Z, Rozowsky J, Lam HYK, Snyder M, Gerstein M. Tilescope: online analysis pipeline for
high-density tiling microarray data GenomeBiology (in press).
* Wang L, Comaniciu D, Snyder M, Gerstein M. BoCaTFBS: a boosted-cascade learner to
refine the binding sites suggested by ChIP-chip experiments. GenomeBiology. Genome Biol
7: R102
Bertone P, Trifonov V, Rozowsky JS, Schubert F, Emanuelsson O, Karro J, Kao MY, Snyder M,
Gerstein M. (2006) Design optimization methods for genomic DNA tiling arrays. Genome
Res. 16: 271-81.
Borneman, AR, Leigh-Bell, J, Yu, H, Bertone, P., Gerstein, M., Snyder,.M (2006) Target Hub
Proteins Serve as Master Regulators of the Complex Transcriptional Network Controlling
Yeast Pseudohyphal Growth. Genes Dev 20: 435-448.
* Rozowsky J, Newburger D, Sayward G, Wu J, Jordan G, Korbel JO, Nagalakshmi U, Yang J,
Zheng D, Guigo R, Gingeras T, Weissman S, Miller P, Snyder M, Gerstein M. The DART
Classification of Unannotated Transcription within the ENCODE Regions: Associating
Transcription with Known and Novel Loci Genome Research (in press)
Thomas E. Royce, Joel S. Rozowsky and Mark B. Gerstein (2007). Assessing the need for
sequence-based normalization in tiling microarray experiments. Bioinformatics (in press).
* Zheng D, Gerstein M. A computational approach for identifying pseudogenes in the ENCODE
regions. Genome Biol 7 Suppl 1: S13.1-10.
* Emanuelsson O, Nagalakshmi U, Zheng D, Rozowsky JS, Urban AE, Du J, Lian Z, Stolc V,
Weissman S, Snyder M, Gerstein M. (2006) Assessing the performance of different highdensity tiling microarray strategies for mapping transcribed regions of the human genome.
Genome Research, in press.
Royce, T.E., Rozowsky, J.S., Bertone, P., Samantac, M., Stolc, V., Weissman, S., Snyder, M.
and Gerstein, M. (2005). Issues in the Analysis of Oligonucleotide Tiling Microarrays. Trend
Genetics 21: 466-475.
H Yu, M Gerstein (2006) Genomic analysis of the hierarchical structure of regulatory networks.
Proc Natl Acad Sci U S A 103: 14724-31.
C4.2. Analysis of the ChIP samples
C4.2a. Comparison of ChIP-chip Platforms and Parameters. At the outset of the
ENCODE pilot phase, both PCR product arrays and high density oligonucleotide (HDO) arrays
were being used by various groups. However, the overall performance and suitability for
genome-scale analyses had not ever been compared. Therefore, we began by comparing PCR
arrays and 390,000 feature arrays prepared by maskless photolithography (e.g. NimbleGen) by
performing ChIP-chip for three yeast factors (Borneman et al., 2006b). We found that the HDO
arrays detected approximately three times more binding events than the PCR arrays while also
showing increased accuracy. We also investigated optimal parameters for mapping binding
sites in mammalian cells using ChIP-chip (Euskirchen et al., 2007). We compared parameters
such as DNA microarray format (oligonucleotide versus PCR), oligonucleotide length,
hybridization conditions, and the use of competitor Cot DNA. Also, methods were devised for
scoring and comparing results. Optimal signal-to-noise, sensitivity and specificity was observed
with HDO arrays relative to PCR product arrays (Figure 2). We observed optimal signals using
oligonucleotides >36 b and the presence of Cot competitor DNA (See Figure 2 for the
oligonucleotide length results). Consistent with the better performance of arrays containing
longer oligonucleotides, we were not able to identify STAT1 targets using Affymetrix arrays
(which uses 25 mers). Target identification as a function of biological replicates was also
determined and revealed that 80-86% of targets were identified with three experimental repeats;
four provides a modest 3-5% increase (Euskirchen et al., 2007). We also did a parallel study as
the part of the ENCODE consortium to compare platforms for the suitability for assaying
transcription (Emanuelsson et al., 2006). Overall, our conclusions were similar to those in
Euskirchen et al. (2007) -- that is, HDO arrays performed better than PCR ones and feature
density was all important. However, for transcript mapping we got better performance with
25mers than 36mers. Thus, the HDO array platform provides a far more robust array system by
all measures than PCR-based arrays, all of which is directly attributable to the large number of
probes available. Since we performed this study Agilent has also begun to manufacture HDO
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arrays. However their highest density array (244K features) and price ($500) does not make
them competitive with the Nimblegen whole genome 10 array set (2.1 million features/array).
Affymetrix arrays are competitive in price ($1500 for one set). However, some of our team
members have found that they are not as effective as NimbleGen arrays when analyzing certain
factors and they cannot be reused (Nimblegen arrays can be used at least twice and new
chemistry that should increase the number of rehybridizations/array is currently being tested by
one of our team members in collaboration with NimbleGen). Thus, at this time Nimblegen
arrays provide the highest sensitivity and the most value of the array platforms
Figure 2 Comparison of ChIP-chip results using different array platforms and
oligonucleotide lengths. Raw signals from each oligonucleotide are plotted. Key: PCR: PCR
product array. 36-36 36 b oligonucledotides arrayed end to end; 50-50: 50 b oligonucledotides
arrayed end to end; a-f validated positive regions - negative control region.
C4.2b Comparison of ChIP-chip with ChIP-Sequencing. We have also compared ChIPchip to ChIP followed by DNA sequence analysis of fragment ends (ChIP-PET; Ng et al., 2006) for
the STAT1 transcription factor (Euskirchen et al., 2007). We found that these two technologies
were comparable in terms of sensitivity and specificity (Figure 3). Seven of 8 targets enriched 4-fold
or more were found with both methods; 4 fold is approximately the threshold at which ChIP targets
reproducibly validate by qPCR (see below). The new 2.1 million feature array design contains more
probes in repeated regions and is expected to identify all 8 (Euskirchen et al 2006). The resolution
of ChIP chip is generally higher (targets can be mapped and validation to within 200 bp (K.S., P.R.,
M. S., unpublished; see also XXX) than ChIP PET, particularly on the less enriched peaks (see
figure 3). At this point, ChIP-PET is also considerably more expensive, even with the new 454
sequencing technologies, than analysis of samples using the NimbleGen 10 array set. However, we
will continue to compare ChIP-chip and ChIP-sequencing during the production phase and always
make sure that we are using the technique that provides the highest quality and most
comprehensive data for the lowest price.
76
Figure 3. Comparison of ChIPchip and ChIP-PET results. Raw signals from each method are
plotted. The concordance of the results for both signals (shown above) and called target regions is
quite high (greater than 90% for the top targets (estimated to be 4 fold enriched).
C5. Bioinformatics - Processing pipeline for target identification and integration of primary
data (ChIP-chip) with secondary validation data.
We have extensive experience with the informatics for carrying out tiling array experiments.
This comprises designing the arrays, developing scoring approaches, comparing platforms,
constructing high-throughput pipelines for the Nimblegen data, and building a special purpose
database for handling the results the experiments.
C5.1. Array Processing Platforms -- ExpressYourself & Tilescope
We have developed two generations of tiling arrays software: ExpressYourself for PCR
arrays (Luscombe et al., 2003; bioinfo.mbb.yale.edu/ExpressYourself) and Tilescope for oligo
arrays (Zhang et al., 2007; tilescope.gersteinlab.org). Tilescope is an automated data processing
pipeline for analyzing data sets generated in experiments using high-density tiling microarrays. The
software performs data normalization, combination of replicate experiments, tile scoring, and
feature identification. Given the modular architecture of the pipeline, new analysis algorithms can be
readily incorporated. Tilescope is capable of handling very large data sets, such as ones generated
by whole genome ChIP-chip experiments, as we developed an efficient new data compression
algorithm to reduce the data size for fast online data transmission. Tilescope is designed with a
graphical user-friendly interface to facilitate a user’s data analysis task, and the results, presented
on a web page, can be downloaded for further analysis.
C5.2. Approaches for Array Scoring and Artifact Correction
Tilescope incorporates a number of scoring and artifact correction approaches we have
developed for tiling arrays. In particular, for Nimblegen arrays, we identified those procedures in the
microarray normalization literature that are transferable to tiling arrays and those that needed to be
modified or replaced (Royce et al., 2005, 2006). One issue is that a small fraction of probes on an
77
array experience significant levels of nucleic acid hybridization; many normalization procedures
assume hybridization to at least half of all probes on an array. Another issue is that tiling arrays for
whole genomes, for example, require many arrays - each of different design. Care must be taken
when normalizing these arrays to one another because the underlying distribution of measured
signals may vary widely among arrays tiling different regions of the genome. We have also worked
extensively on compensating for issues of cross-hybridization. In Royce et al. (2007) we developed
a correction system for non-specific, sequence-based cross hybridization that is readily applicable
to tiling arrays. We have also developed tools for the optimal design of tiling arrays to minimize the
effect of repetitive and specifically cross-hybridizing probes (Bertone et al., 2006). These algorithms
are implemented as a collection of web-based tools, available at tiling.gersteinlab.org. Finally, in
processing large amounts of expression data it is important to be able to discover and correct
various spatial array artifacts. Qian et al. (2003), Kluger et al. (2003), and Yu et al., (2007)
developed a way of measuring and quantifying a common spatial artifact that can induce spurious
correlation of nearby spots on the array.
C5.3. Tools to integrate the list of functional elements -- DART
We have developed DART (DART.gersteinlab.org, Rozowsky et al., 2007) to facilitate the
flexible storage, visualization, and comparison of the growing number of experimentally defined sets
of transcription factor binding sites. DART has been designed to address a number of challenging
issues that arise when attempting to analyze, compare, and store these types of data. The key
aspects of DART include the following: Dealing with heterogeneous datasets, flexibility for storing
different tiling array hit attributes, accommodating new genome builds, and integrated linking to
other web resources for broader visualization and analysis. Furthermore, DART provides machinery
for helping to pick targets for validation.
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