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 sequence-nonspecific
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.
* 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 high-density 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 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 ChIP-chip 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.
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.
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 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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