AlleleSeq: analysis of allele-specific expression and binding in a network framework

Abstract

To study allele-specific expression (ASE) and binding (ASB), that is, differences between the maternally and paternally derived alleles, we have developed a computational pipeline (AlleleSeq). Our pipeline initially constructs a diploid personal genome sequence (and corresponding personalized gene annotation) using genomic sequence variants (SNPs, indels, and structural variants), and then identifies allele-specific events with significant differences in the number of mapped reads between maternal and paternal alleles. There are many technical challenges in the construction and alignment of reads to a personal diploid genome sequence that we address, for example, bias of reads mapping to the reference allele. We have applied AlleleSeq to variation data for NA12878 from the 1000 Genomes Project as well as matched, deeply sequenced RNA-Seq and ChIP-Seq data sets generated for this purpose. In addition to observing fairly widespread allele-specific behavior within individual functional genomic data sets (including results consistent with X-chromosome inactivation), we can study the interaction between ASE and ASB. Furthermore, we investigate the coordination between ASE and ASB from multiple transcription factors events using a regulatory network framework. Correlation analyses and network motifs show mostly coordinated ASB and ASE.

Full text

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AlleleSeq: analysis of allele-specific expression and
binding in a network framework
JoelRozowsky1,2,8,*,AlexejAbyzov1,2,8,JingWang2,PedroAlves2,DebasishRaha3,ArifHarmanci1,2,JingLeng2,RobertBjornson4,5,
Yong Kong5, Naoki Kitabayashi6, Nitin Bhardwaj1,2, Mark Rubin6, Michael Snyder7and Mark Gerstein1,2,4,*
1Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, USA,2Department of Molecular Biophysics and Biochemistry, Yale
University, New Haven, CT, USA,3Department of Molecular, Cellular, and Developmental Biology, Yale University, New Haven, CT, USA,4Department of Computer
Science, Yale University, New Haven, CT, USA,5Keck Biotechnology Resource Laboratory, Yale University, New Haven, CT, USA,6Department of Pathology and
Laboratory Medicine, Weill Cornell Medical Center, New York, NY, USA and7Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA
8These authors contributed equally to this work
* Corresponding authors. J Rozowsky or M Gerstein, Program in Computational Biology and Bioinformatics, Yale University, 266 Whitney Ave Bass 432, New Haven,
CT 06520, USA. Tel.: þ1 203 432 5405; Fax: þ1 203 432 5175; E-mail: joel.rozowsky@yale.edu or Tel.: þ1 203 432 6105; Fax: þ1 203 432 5175;
E-mail: mark.gerstein@yale.edu
Received 21.12.10; accepted 7.7.11
Tostudyallele-specificexpression(ASE)andbinding(ASB),thatis,differencesbetweenthematernally
and paternally derived alleles, we have developed a computational pipeline (AlleleSeq). Our pipeline
initially constructs a diploid personal genome sequence (and corresponding personalized gene
annotation) using genomic sequence variants (SNPs, indels, and structural variants), and then
identifies allele-specific events with significant differences in the number of mapped reads between
maternal and paternal alleles. There are many technical challenges in the construction and alignment
of reads to a personal diploid genome sequence that we address, for example, bias of reads mapping to
the reference allele. We have applied AlleleSeq to variation data for NA12878 from the 1000 Genomes
Project as well as matched, deeply sequenced RNA-Seq and ChIP-Seq data sets generated for this
purpose. In addition to observing fairly widespread allele-specific behavior within individual
functional genomic data sets (including results consistent with X-chromosome inactivation), we can
study the interaction between ASE and ASB. Furthermore, we investigate the coordination between
ASE and ASB from multiple transcription factors events using a regulatory network framework.
Correlation analyses and network motifs show mostly coordinated ASB and ASE.
Molecular Systems Biology 7: 522; published online 2 August 2011; doi:10.1038/msb.2011.54
Subject Categories: Functional genomics; Computational methods
Keywords: allele-specific; ChIP-Seq; networks; RNA-Seq
Introduction
Duetorapidlyincreasingthroughputanddecreasingcosts,next-
generation short read sequencing is fast replacing array-based
technology for performing functional genomic assays such as
mappinglocationsoftranscriptionfactorbindingordetermining
transcribed sequences in the genome. The initial analyses of
high-throughput functional data using ChIP-Seq (Johnson et al,
2007; Robertson et al, 2007) or RNA-Seq (Mortazavi et al, 2008;
Nagalakshmietal,2008)yieldsimilarresultsthat wereobtained
using tiling array-based methodologies albeit with greater
sensitivity and resolution, that is, binding regions or regions of
transcription. Also, with the developments in sequencing
technologies there have been increasingly larger studies of the
amount of sequence variation across the human population
(The1000GenomesProjectConsortium,2010).Anaturalareaof
recent focus has been looking at the degree of functional
genomic differences across the human population (Gregg et al,
2010a,b; Kasowski et al, 2010; McDaniell et al, 2010; Montgom-
ery et al, 2010; Pickrell et al, 2010). However, in order to
understandpopulation effects it is first useful to characterize the
effects of functional variation within a single individual such as
differences of expression and binding between alleles (i.e.,
allele-specific differences). When comparing functional data
betweenindividualsitisnecessarytoworryaboutnormalization
beforeanycomparisonsareperformed;however,withinasingle
individual there is a natural control of each allele against each
other. By utilizing the actual sequence composition of the
functional genomic sequence reads that overlap a heterozygous
SNP,itispossibletodeterminethesequencesthatoriginatefrom
each allele separately (Degner et al, 2009; McDaniell et al, 2010;
Montgomery et al, 2010; Pickrell et al, 2010; Lalonde et al, 2011).
Thus, it is possible to determine sites where transcription or
transcription factor binding is originating predominately from
one allele, that is, allele-specific expression (ASE) or allele-
specific binding (ASB); however, there are number of technical
issues which make this analysis challenging.
In each of the recently published studies that contained
some level of allele-specific analysis, only one type of
functional genomic assay was performed. A logical question
Molecular Systems Biology 7; Article number 522; doi:10.1038/msb.2011.54
Citation: Molecular Systems Biology 7:522
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is howthese allele-specific events are coupled betweenassays.
At first glance, we expect significant coordination between
binding of different transcription factors and expression of
target genes. This has been previously been studied in a more
limitedmannerusingarray-basedtechnologies(Maynardetal,
2008). Here, we address this question by analyzing a number
of different functional genomic data sets using a pipeline that
we have developed, AlleleSeq, for determining sites showing
allele-specific behavior. For the first time, we analyze allele-
specific behavior for both transcription data using a very
deeply sequenced RNA-Seq data set (B160 million mapped
reads) as well as matching deeply sequenced ChIP-Seq data
sets (B30 to B60 million mapped reads) for a number of
different transcription factors (cFos, cMyc, JunD, Max, NfkB,
and CTCF) as well as polymerases (RNA Polymerase II and
Polymerase III). These experiments were generated for the
lymphoblastoidcelllineGM12878,whichhasalsobeendeeply
sequenced together with both parents (as a trio) as part of the
pilot II phase of The 1000 Genomes Project Consortium (2010).
Thus, for these data sets we have a complete set of
heterozygous variants (SNPs, indels, and structural variations
(SVs)) for the individual NA12878, which can mostly be
phased into maternal and paternal variants by comparing
against the parents sequences. This is important for assessing
the genome-wide amount of allele-specific behavior, which is
severely limited by the number of identified heterozygous
SNPs available (for instance, Montgomery et al (2010) and
Pickrell et al (2010) used HapMap III SNPcalls which are B10-
fold fewer than those available from pilot II of the 1000
Genomes Project). Allele-specific behavior is presumably
occurring also in regions devoid of heterozygous SNPs, where
we cannot distinguish between the alleles.When assessing the
number of comparisons of allele-specific behavior between
transcription factor binding and expression, 10-fold fewer total
numberofheterozygousSNPs would onlyallowfor B100-fold
fewer comparisons between ASB and ASE SNPs to be made.
There are numerous technical hurdles in determining allele-
specific behavior. One might think that it is possible to simply
map the sequenced reads against the reference genome in
order to determine allele differences; however, this introduces
reference biases. Most analyses of human genomic data use
the reference genome sequence for comparison; nevertheless,
when genome scale analysis of allele-specific behavior is
performed we show that it is necessary to align reads against a
diploid sequence for that individual. We deal with this by
constructing a diploid personal genome sequence by using the
variation data (both for SNPs, indels, and SVs) for NA12878
(Mills et al, 2011; The 1000 Genomes Project Consortium,
2010). While the 1000 Genomes Project has created call sets of
sequencevariantsforeachofthedifferentgenomessequenced,
they have not however assembled genome sequences (includ-
ing NA12878)foreach ofthe individualssequenced.Inthefirst
part of our AlleleSeq pipeline, we generate a diploid genome
sequence of maternal and paternal haplotypes by integrating
the phased variation data (SNPs, indels, and SVs) onto the
reference genome sequence. In addition, we filter out genomic
sequences that are likely to correspond to copy number
variants (CNVs) using read-depth analysis (Abyzov et al,
2011). Construction of individual personal reference diploid
sequences, as a first step for functional genomic analysis, will
likely become standard in the near future.
In this paper, we show that ASE of genes as well as novel
transcribedregions,thatis,noveltranscriptionallyactiveregions
(TARs) or transfrags (Kapranov et al, 2002; Rinn et al, 2003;
Bertone et al, 2004), are coordinated with ASB of transcription
factors and other DNA binding proteins located adjacent to the
transcribed region. One can measure how wellASB and ASE are
coordinated, by using a correlation plot of the two. However,
representing the coordination between multiple allele-specific
events is difficult. In order to facilitate this, we show how ASB
for multiple transcription factors is coordinated with ASE of the
target genes or novel TARs by visualizing this behavior using a
simplifying regulatory network. We will see how certain allele-
consistent regulatory motifs are enriched using network
analysis. We will observe that ASB and ASE are not as
coordinated as might have been naively expected and speculate
on potentially complexities of allele-specific regulation.
Results
We start by assembling a set of sequence variants from the 1000
GenomesProjectfortheNA12878individual.Wethengenerated
deeplysequencedChIP-SeqdatasetsforcFos,cMyc,JunD,Max,
and RNA Polymerase II for the GM12878 cell line. We also
created a matching deeply sequenced RNA-Seq data set for the
same cell line. We combined these data sets with previously
published matching data sets for RNA Polymerase II, RNA
PolymeraseIII,NfkB,andCTCF(Kasowskietal,2010;McDaniell
et al, 2010; Raha et al, 2010). We summarize these data sets in
Table I (see Materials and methods for further details).
Table I GM12878 RNA-Seq and ChIP-Seq data sets
DataNumber of reads
(millions)
Number of mapped
reads (millions)
Read length;
sequencing layout
Source
RNA-Seq393.9 164.7 36nt; single end
50nt; single end
50nt; paired end
36nt; single end
36nt; single end
36nt; single end
36nt; single end
36nt; single end
36nt; single end
36nt; single end
36nt; single end
This paper
Pol II ChIP-Seq
Pol III ChIP-Seq
cMyc ChIP-Seq
Max ChIP-Seq
JunD ChIP-Seq
cFos ChIP-Seq
NFkB ChIP-Seq
CTCF ChIP-Seq
128 (33)
12
125
79
133
84
62
46
69.5 (13.2)
7.5
65.5
46.1
72.5
30.4
35.5
26.4
This paper+Raha et al (2010) (shown in parentheses)
Raha et al (2010)
This paper
This paper
This paper
This paper
Kasowski et al (2010)
McDaniell et al (2010)
AlleleSeq: analysis of ASE and ASB in a network framework
J Rozowsky et al
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Determining allele-specific behavior from
functional genomic data alone
Intuitively if one has performed a deeplysequenced functional
genomic experiment such as RNA-Seq or ChIP-Seq from a
single individual, it should be possible to determine allele-
specific behavior solely from the sequences obtained. The first
step in this approach would be to determine the SNPs and
other sequence variants directly from the sequence reads
obtained. This might be true for certain regions sequenced
at great depth; however, since functional genomic data
(e.g., reads from a ChIP-Seq experiment) cover the genome
with greatly varying sequence depth due to the nature of the
functional assay. Thus, the accuracy of SNP (and other
variant) calling from functional genomics datawill necessarily
vary across the genome. Conversely, the average sequencing
depth across the genome for conventional genomic DNA
sequencing is nearlyuniform
to repeated regions and compositional biases).
We find that the accuracy of de novo SNPcalling using reads
from a functional genomic sequencing experiment such as
RNA-Seq using a standard SNP caller package (e.g., SNVmix;
Shah et al, 2009) is not as good as we would need for
determining allele-specific behavior (see Supplementary
Table 1 for the results of de novo SNP calling of heterozygous
SNPs). Any significant amount of miscalling of heterozygous
SNPs will (obviously) lead to ill determined allele-specific
behavior. There are a number of possible explanations for
such miscalls; the very events we would like to find, SNPs
within regions showing ASE could potentially appear as
homozygous using only the RNA-Seq sequence reads. If one
experimentally only obtains sequences from a region that is
expressed on one allele (due to ASE) then there is no way to
know that any base within that region is polymorphic.
Second, RNA editing could also lead to variations in RNA
sequences that are not present at the DNA level. Finally,
sequencingof RNAinvolves additional experimental stepslike
usage of reverse transcriptase that can increase chance of
mis-sequencing.
Obviously, determining short indels from sequenced func-
tional genomic data would be even harder and SVs would be
nearly impossible. Thus, while it might be possible to
determine certain sequence variants from the functional
genomicsequencereads,inordertogenerateacomprehensive
set of polymorphic sites as well as other forms of sequence
variation it is necessary to have an independently determined
set from sequenced genomic DNA (such as from the 1000
Genomes Consortium).
(withsome differences
Building an individual diploid reference genome
for NA12878
It might not seem obvious but for a number of reasons
reconstruction of a diploid personal genome sequence and
using it instead of the reference genome is a critical step
preceding allele-specific analysis. First, using reference gen-
ome introduces biases in read mapping—reads originated
from non-reference allele are more susceptible to mismapping
since,whenalignedtothereferenceallele,theycontainatleast
one mismatch (in case of SNPs) or gap (in case of indels)—the
referencebiaseffect,thatis,bothallelesarenottreatedequally
bydefault.Second,expressionorbindinginregionsofgenome
SV could be misinterpreted as ASE or ASB. For example,
duplication of an allele in the studied genome will double
binding signal for the allele while signal for the allele on
another haplotype will be unchanged. Last, but not least, SNP
calling in the regions of SV is likely to be less precise and
contain more false positives compared with non-SV regions
(The 1000 Genomes Project Consortium, 2010). Thus,
we construct a personal diploid genome of NA12878
(see Materials and methods), by utilizing genomic variations
(see Table II for summary statistics) determined in the
framework of The 1000 Genomes Project Consortium (2010)
and, additionally, SVs determined by sequencing of fosmid
clones (Kidd et al, 2008).
To accomplish this, we have developed a tool—vcf2di-
ploid—for personal genome construction, which constitutes
the first part of the AlelleSeq pipeline (see Figure 1A). The tool
uses as input VCF files with all the SNPs, indels, and SVs
available for an individual of interest and outputs fasta
sequences for each allele for each chromosome, along with
equivalence map files (see Figure 1 and Supplementary Figure
1) that map nucleotide positions between paternal, maternal,
and reference haplotypes. It is important to be able to map
annotation (i.e., genes) from the reference genome to the
personal genome sequences. This is done using chain files,
which facilitate the mapping of annotated regions between
genomes using the liftOver tool (Rhead et al, 2010). This is
particularly important for RNA-Seq where we also build
maternal and paternal versions of the gene annotation
(including, most importantly, splice-junction library) by
mapping the GENCODE annotation (GENCODE 3c annotation
is available from the UCSC Genome Browser; Harrow et al,
2006) onto the personal diploid genome.
The constructed diploid genome of NA12878 was different
in 3962637 (B0.14%) bases from the reference for paternal
and in 3947162 (B0.14%) for maternal alleles. The software
Table II Statistics on variants used to construct personal genome of NA12878
Source Variant typeCountsTotal Phased
(%)
Unphased
(%)
Inconsistent
(%)
Unutilized
Fosmid sequencing
1000 Genomes Project
Deletions
Deletions
Indels
SNPs
33 94
77
89
89
6
8
0 0 (0%)
15 (1%)
37 (0.1%)
1794 (B0%)
152215
0
0
328528
2766607
11
11
Avariantcanbephased(i.e.,unambiguouslyassignedtoapaternalormaternalhaplotype),unphased(i.e.,ambiguouslyassignedtoeitherhaplotype)oritsgenotyping
can be inconsistent with genotyping in parents (e.g., heterozygous deletion in child but homozygous deletion in each parent), which precludes it from phasing. Due to
overlap with other variants some variants are not used for genome construction of NA12878 (column ‘unutilized’).
AlleleSeq: analysis of ASE and ASB in a network framework
J Rozowsky et al
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package to perform personal genome sequence construction
(the vcf2diploid tool and associated source code), the actual
diploid sequence for NA12878, splice-junction sequences and
personalized gene annotation for NA12878 and corresponding
equivalence maps (between the maternal and paternal
sequences as well as the reference genome, NCBI36/hg18)
areavailablefromhttp://alleleseq.gersteinlab.org.Thediploid
sequence for NA12878 is a valuable resource for anyone
performing any sequence-based analysis on this genome. The
GM12878 cell lines are a primary tier one cell line under
detailed investigation by the ENCODE Consortium. It should
be also noted that a constructed personal genome is only as
good and as complete as the variants used in construction. In
light of this, the diploid genome of NA12878 that is presented
here, is not perfect, but we believe it is the best possible
sequence to date since it includes the most comprehensive set
ofvariants.Weintendtoupdatethisassemblyasaresource,as
sequence variants are even more accurately determined.
In order to assess the effect of the differences between the
maternal and paternal sequences compared with using
the reference genome sequence on functional genomic data,
we aligned the reads from the Pol II and CTCF ChIP-Seq data
for GM12878 against each of the three sequences using
BOWTIE (Langmead et al, 2009; see Supplementary Figure
2). In Table III, we comparethePol II reads that align to eachof
the three genome sequences (reference, maternal, and
paternal haplotypes). We observe that by allowing up to two
mismatches more reads (0.3% for paternal and 0.4% for
maternal) align to the correct NA12878 as compared with the
reference genome sequence (NCBI36). The majordifference in
numbers for paternal/maternal and reference haplotypes is
due to reads that map to one haplotype but not the other.
Namely, only about 0.1–0.2% of reads that map to the
reference cannot be mapped to paternal or maternal haplo-
type, while a significantly higher fraction of reads (B0.5%)
map to the paternal or maternal genome and cannot be
mapped to the reference. For paternal and maternal haplo-
types, unmapped reads and reads with different mapping
locations contribute roughly equally to the differences in
overall mapping, presumably mostly due to short indels and
SVs. We also see similar results for the same analysis done to
thereadsforCTCFChIP-Seq(seeSupplementaryTable2).This
demonstrates that it is important to use a correctly assembled
personal genome for aligning reads when performing an allele
specificity analysis.
Similarly, transcription factor binding sites also overlapped
more when aligned to the maternal and paternal genomes of
NA12878, rather than the reference sequence. For this compar-
ison,weusedthesetofindependentlymappedreadsforallthree
genome sequences to determine binding sites using PeakSeq
(Rozowsky et al, 2009), and performed a pair-wise nucleotide
overlapofthebindingsitesbetweenthethreegenomesequences
(Supplementary Table 3). In addition, we observe that the
differences in binding sites, among the three genomes, are
greater than the underlying differences in read mapping.
Determining ASE and ASB
The second part of the AlleleSeq pipeline determines ASE
using RNA-Seq data and ASB using ChIP-Seq data. After the
Filter
Reference genome
& transcriptome
AB
Personal
variants
Personal
halpotype
SVs
IndelsSNPs
vcf2diploid
Reference
Deletion
SNP
Insertion
Genotyping, Phasing, Filtering
Maternal haplotype
Personal genome
& transcriptome
Equivalence map
Equivalence map
Equivalence map
Paternal haplotype
Align reads
to paternal
haplotype
Align reads
to maternal
haplotype
Compare to find
best alignment
Counts over het
SNPs to determine
allele specificity
Filter SNPs in
CNVs using
read-depth
Overlap ASB
SNPs with TF
binding sites
Overlap ASE
SNPs with gene
annotation
Report ASB and ASE
SNPs with significance
in VCF format
Align reads to
paternal splice-
junction library
Counts over het
SNPs to determine
allele specificity
Align reads to
maternal splice-
junction library
Compare to find
best alignment
Reads
GenesBinding sites
T G G A A G A A A C C G T T T ...
T G G A A A C C G T T T ...
T G GA A A G C G T T T ...
T G G A A A G C G T T T ...
A G
TGGAAAGCGAGTT...
Figure 1
require additional pre-processing, that is, filtering, genotyping, and/or phasing. The output is the two (paternal and maternal) haplotypes of personal genome. During the
construction step, the reference genome is represented as an array of nucleotides with each cell representing a single base. Iteratively, the nucleotides in the array are
being modified to reflect personal variations. Once all the variations have been applied, a personal haplotype is constructed by reading through the array.
Simultaneously, equivalence map (MAP-file format—see Supplementary Figure 1) between personal haplotypes and reference genome is being constructed. This can
similarly be done for a personal transcriptome. (B) AlleleSeq pipeline for determining allele-specific binding (ASB) and allele-specific expression (ASE) aligning reads
against the personal diploid genome sequence as well as a diploid-aware gene annotation file (including splice-junction library).
(A) Construction of a personal genome by vcf2diploid tool is made by incorporating personal variants into the reference genome. Personal variants may
AlleleSeq: analysis of ASE and ASB in a network framework
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maternal- and paternal-derived haploid sequences are con-
structed,sequencedreadsarealignedagainstthematernaland
paternalsequences separatelyusingBOWTIE(Langmead etal,
2009). Locations of mapping are determined by selecting the
best alignment to both genome sequences. Read counts for the
maternal and paternal alleles are then generated at each
heterozygous SNP nucleotide positions, and ASE/ASB events
arereportedbyapplyingabinomialtest followedbycorrection
for multiple hypothesis testing. SNP positions that by read-
depth analysis (Abyzov et al, 2011) are determined to be
potentiallyinaCNV(thereaddepthofgenomicDNAreadsina
1-kbwindowaround theSNP iseither o1 or43) areexcluded
(see Materials and methods). We correct for multiple hypoth-
esis testing by estimating the false-discovery rate (FDR) by
explicit simulation of the number of false positives given an
even null background (i.e., no allele-specific events)—see
Figure 1B for a schematic of the second part of the pipeline
(see Materials and methods for further technical details).
We also align reads to the maternal and paternal splice-
junction libraries and determine splice-junction ASE SNPs in
a similar way.
Results for GM12878 RNA-Seq and ChIP-Seq data
We start our study of allele-specific phenomena by first
focusing on analyses of individual events that occur within
singleexperimentaldataset.Wethenanalyzethecoordination
between binding and expression in a pair-wise manner
using direct correlation. Finally, we investigate higher order
coordination of ASB and ASE using a regulatory network
framework that will allow us to study the agreement between
multiple regulatory interactions and target expression simul-
taneously.
We summarize the results of the AlleleSeq pipeline applied
to the RNA-Seq data and ChIP-Seq data for GM12878 in
Table IV. In the upper half of the table, we present the results
for all the autosomes and in the lower half for chromosome X
(with all the transcription factor combined). In the second
column of Table IV, we list the number of genomic elements
(genes, novel TARs, and binding sites) that are accessible for
allele-specific behavior—that is, those that we could have
detected allele-specific activity in. This is the set of genomic
elements that contain at least one heterozygous SNP and are
sequenced at sufficient depth in order to detect allele-specific
activity, see Materials and methods for further details. The
third column shows that number of genomic elements that we
determine to show allele-specific behavior. The fourth column
shows the fraction of genomic elements that are accessible
for allele-specific behavior that do show either ASE or ASB.
Finally for allele-specific events that can be phased we report
those that are maternal or paternal.
We observe that B19.4% of all autosomal GENCODE genes
that are accessible for allele-specific behavior exhibit ASE. We
similarly find that 21.6% of accessible heterozygous SNPs
within splice junctions of genes also show ASE. Similarly, we
findthat9.3%ofautosomallyexpressedaccessiblenovelTARs
show ASE, we expect this number to be lower than genes as
novel TARs correspond to exons of genes. We found that for
genes that contained two or more heterozygous SNP showing
allele-specific behavior, 481% of the time all the SNPs would
showconsistentASEfromthesameallele(significantlygreater
than expectedbychance),someoftheexceptionscouldbedue
to allele-specific alternate splicing. For the transcription
factors, the fraction of accessible autosomal binding sites that
exhibit allele-specific behavior varies between 2% (for cMyc)
and 11% (for Pol II). A possible explanation for the difference
between binding and expression allele specificity is that even
though these ChIP-Seq data sets have been sequence quite
deeply (see Table I), in order to have comparable power with
the RNA-Seq data one would need to sequence an order or
magnitude or two further. The number of overlapping
sequence reads in binding site peaks for ChIP-Seq data
depends on the efficiency of the antibody used and for most
ChIP-Seq data sets we do not have sufficient read depth within
Table III Comparison of read mappings to reference genome and paternal and maternal haplotypes of GM12878
HaplotypeNo. of mapped reads ReferencePaternalMaternal
Equivalently mapped reads in
Reference
Paternal
Maternal
69086591
(+0.3%) 69296783
(+0.4%) 69394995
68942501 (99.79%) 69034357 (99.92%)
69099705 (99.72%) 68942501 (99.49%)
69034357 (99.48%)69099705 (99.58%)
Differently mapped reads in
Reference
Paternal
Maternal
69086591
(+0.3%) 69296783
(+0.4%) 69394995
18248 (0.03%) 18291 (0.03%)
113796 (0.16%) 18248 (0.03%)
18291 (0.03%) 113796 (0.16%)
Unmapped reads in
Reference
Paternal
Maternal
69086591
(+0.3%) 69296783
(+0.4%) 69394995
125842 (0.18%)33943 (0.05%)
83282 (0.12%)336034 (0.48%)
342347 (0.49%)181494 (0.26%)
Chip-Seq reads for Pol II were independently mapped to each haplotype (chromosomes 1–22 and X) and the best unambiguous mapping (no more than two
mismatches) was selected for each read. More reads are mapped to either haplotypes of GM12878 than to the reference genome. The major difference in numbers for
paternal/maternal and reference haplotypes is due to reads that map to one haplotype but not to other. Namely, onlyabout 0.1–0.2% of readsthat map to the reference
cannot be mapped to paternal/maternal haplotype, while a significantly higher fraction B0.5% of reads map to paternal/maternal genome and cannot be mapped to
the reference. Interestingly, for paternal and maternal haplotypes unmapped reads and reads with different mappings contribute roughly equally to the discrepancy in
overall mapping. See Supplementary Table 1 for the results for CTCF.
AlleleSeq: analysis of ASE and ASB in a network framework
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a binding site as comparedwith the readdepth within exons of
highly expressed genes. As expected when restricted to the
autosomes, both ASE for genes and novel TARs and ASB forall
the transcription factors and polymerases are evenly divided
between the maternal and paternal alleles.
In the lower half of Table IV, we present similar results for
chromosome X. Since NA12878 is female there are two copies
of chromosome X. We first observe that almost 80% of the
accessible genes on chromosome X exhibit allele-specific
behaviorand95%oftheseareexpressedonthematernalcopy.
This is consistent with our knowledge of X-chromosome
inactivation (Lyon, 1961; Goto and Monk, 1998) where
one copy of the two copies is shut off. There are four genes
on chromosome X that show ASE on the paternal copy;
however, all of these are located in the pseudo-autosomal
region of chromosome X which is known to escape
X-chromosome inactivation (these include Xist, SLC25A6
and SFRS17A). We observe similar results on chromosome X
for the allele-specific behavior of novel TARs as well as
transcription factor binding where a greater fraction of sites
exhibit allele-specific behavior compared with the autosomes
and almost all are expressed on the maternal copy. It is
interesting to note that most of the novel transcription and
binding that shows paternal allele specificity are also in the
pseudo-autosomal region similar to Xist and possibly have an
associated functional role.
We make available the complete list of SNPs that show ASB
or ASE in VCF format as a resource from our website http://
alleleseq.gersteinlab.org. We imagine that this file may be a
useful resource for researchers interested in focusing on allele-
specific sites in less deeply sequenced functional genomic
experimental data sets. This might be valuable even if the
functional genomic experiment was not performed on the
GM12878 cell line as regions to investigate for allele-specific
behavior.
There are a number of technical issues associated with
determining allele-specific behavior including various biases
that can be introduced as part of the analysis. We investigate
some of these potential effects in detail below:
1. Reference bias: In order to assess whether our pipeline has
some residual bias toward the reference allele versus the
alternate, we can plot the fraction of reads from the
alternative allele for each heterozygous SNP location
sequenced sufficiently deeply. If there were no bias, we
would expect that this distribution would be symmetric
having as many reference allele-specific locations as for the
alternate allele. In Figure 2, we plot the alternative allele
fraction distribution for the RNA-Seq data, Pol II, and the
other transcription factors combined. We first observe that
the overall distributions are fairly symmetric and that the
allele-specific events (indicated in blue) are also sym-
metric—this indicates that there is no residual bias toward
or against the reference allele. In Supplementary Figure 3,
we observe similar distributions for the fractions of reads
from the maternal allele for each heterozygous SNP
location that could be phased and that was sequenced
sufficiently deeply in the appropriate assay.
2. Correlation with SNP quality (genotype likelihood scores):
SNPs determined by The 1000 Genomes Project Consor-
tium (2010) each have a genotype likelihood score.
In Supplementary Figure 4, we plot the distribution of all
heterozygous SNPs and the subset of ASE SNPs versus this
genotype likelihood score. We see a slight enrichment of
ASE SNPs will lower scores; however, the majority of SNPs
from both distributions have the highest possible score. For
comparison, non-synonymous SNPs also show a similar
distribution.
3. Relation to genome duplications (effect of Degner et al,
2009): It has been reported by Degner et al (2009)
Table IV List of ASE and ASB events for each data set (a) only autosomes (b) only chr X
Genomic element Number of elements accessible
for allele-behavior
Number with
ASE or ASB
Fraction with
allele-specific behavior
Maternal Paternal
Autosomes
Genes
Splice junctions
Novel TARs
Binding sites
Pol II
Pol III
CTCF
NfkB
cFos
Max
cMyc
JunD
4829
2556
9238
935
552
860
0.19
0.21
0.09
491
272
386
424
202
363
3187
46
4573
1300
378
943
1542
313
3440.11
0.04
0.10
0.04
0.10
0.06
0.02
0.08
172126
202
443
56
36
55
36
25
178
22
12
24
15
15
207
27
12
22
15
6
Chromosome X
Genes
Novel TARs
Pol II sites
TFs sites combined
94
149
110
259
75
75
48
40
0.80
0.50
0.44
0.15
70
70
47
28
4
1
1
10
The first column indicates the number of elements (genes, novel TARs, splice junctions, or binding sites) that are sequenced at a sufficient depth and containing a
heterozygous SNP in order to be accessible to detect allele-specific behavior, see Materials and methods for further details). The number of elements containing either
ASE or ASB that can be phased are then split into maternal- and paternal-specific counts. We used the GENCODE 3c set of gene annotation and binding sites were
determined using PeakSeq (Rozowsky et al, 2009) with default parameters.
AlleleSeq: analysis of ASE and ASB in a network framework
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that heterozygous sites showing apparent allele-specific
behavior can be caused by regions in the genome that have
been duplicated. Thus, even though there might be a
similar number of reads coming from each allele, only one
of the alleles would have uniquely mapping reads which
would lead to seemingly allele-specific behavior (see
Supplementary Figure 5 for a schematic comparing region
withoutaduplicationtoregionsthathavebeenduplicated).
In order to assess the size of this effect on our results, we
used the Pol II ChIP-Seq reads mapped uniquely and
independently to each of the maternal and paternal
genomes. This is as opposed to the normal mapping
procedure in the AlleleSeq pipeline, where we map
independently to both haplotypes and then select the allele
with the better mapping location. At each heterozygous
SNP location determined to show ASB we computed the
haplotype fraction, the fraction of reads mapped to one
allele over the sum of reads mapped to both alleles (we
choose the haplotype fraction corresponding to the allele
that has the greater fraction, see Supplementary Figure 5).
Forsitesthathavenotbeenduplicatedthehaplotypefraction
shouldhaveavaluecloseto0.5,whileforduplicatedregions
exhibiting the Degner effect the fraction would be close to 1
(where alltheuniquelymapped readswouldcome fromone
allele). In Supplementary Figure 6, we plot the distribution
ofhaplotypefractionsforallPolIIASBsites.Weobservethat
only a minority of the sites (o15%) shows a significant
skew in the haplotype fraction toward one haplotype (a
fraction 40.6). As valid ASB sites that contain additional
proximal sequence variants (such as additional SNPs or
indels) would also exhibit a fraction biased toward one
haplotype, we conclude that this is an upper bound on the
size of this effect and do not consider it significant.
4. Modified binomial test: In order to assess the effect of
aligning reads against the constructed diploid genome
sequence for NA12878 versus using the reference genome
sequence we perform the following analysis. For the RNA-
Seq reads, we also aligned the reads against the reference
genome and determined ASE using an even binomial
distribution (threshold applied in a similar manner). As an
additional comparison, wealso applied the methodologyof
Montgomery et al (2010) where a skewed binomial
distribution is used with the reads aligned against the
reference genome (they composite for the reference bias
induced by mapping to the reference genome by modifying
the binomial distribution). Similar to Figure 2, we plotted
the distribution of all expressed heterozygous SNPs (ASE
SNPs in blue) for these two methods in Supplementary
Figure 7. Using the naive methodology with an even
binomial we see the skew of the ASE SNPs toward the
reference genome which is largely removed using the
modified binomial test. When comparing our set of 5862
ASE SNPs determined using the personal genome we find
that only 69% are shared with those determined using the
naive approach. Using the modified binomial methodology
from Montgomery et al (2010), we see an improvement
(75% in common); however, we still are detecting a
significant number of ASE sites that were missed aligning
to the reference genome and only modifying the binomial
testversususingthecorrectdiploidgenometoalignagainst.
5. Comparison with McDaniell et al, (2010): We have also
compared the ASB sites for the CTCF ChIP-Seq data from
the AlleleSeq pipeline against those from McDaniell et al
(2010). Restricting the comparison with common hetero-
zygousSNPbetweenthetwoanalysis(McDanielletal,2010
used an earlier set of SNP calls for NA12878 from The 1000
Genomes Project Consortium, 2010) we find that greater
using a P-value threshold of 0.01 on their results 485% of
the ASB SNPs are in common.
6. Allele-specific elements using heterozygous indels: The
AlleleSeq pipeline determines allele-specific behavior for
genomic elements (transcribed regions or binding sites)
that contain heterozygous SNPs. It is also possible to
determine allele-specific behavior for genomic elements
that contain a heterozygous indel. In Supplementary
Table 4, we show the results for transcribed exons and
novelTARsaswellbindingsitesforPolIIandCTCFthatcan
be determined to show allele-specific behavior using a
heterozygous indel to distinguish the haplotypes.
Correlation of ASB in binding sites containing
known motifs
In our analysis of ASB events, heterozygous SNPs are initially
only used for distinguishing between the maternal and
Pol II & Pol III
0.00.2 0.40.60.81.0
0
1000
2000
3000
4000
5000
Combined TFs
0.00.20.4 0.60.8 1.0
0
2000
4000
6000
8000
10000
RNA
Frequency
0.00.2 0.40.6 0.81.0
0
2000
4000
6000
8000
10000
14000
Fraction of maternal reads per heterozygous SNP
Figure 2
the reference sequence. We then plotted the distribution of alternative allele fraction for all heterozygous SNPs (significant allele-specific positions are indicated in blue)
for the RNA-Seq, Pol II, and remaining ChIP-Seq data sets combined. We observe that the distribution of all heterozygous SNPs as well as the allele-specific SNP
positions is quite symmetric; and thus, we do not see a significant reference bias.
For each heterozygous SNP location covered at a depth greater than six, we can compute the fraction of reads derived from the alternative allele relative to
AlleleSeq: analysis of ASE and ASB in a network framework
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paternal alleles (presumably allele-specific behavior also
occursingenomicregionsnotcontaining heterozygousSNPs).
However, in some locations the heterozygous SNP might be
the causative reason for the difference in binding between the
alleles, this would most likely occur in ASB sites where the
heterozygous SNP is within a known DNA binding motif for a
transcription factor. In order to see how ASB is correlated with
perturbations toknownDNAbindingmotifs,wecomparedthe
allele that is bound against the allele that matches better with
the known motif. Thus, we first scanned ASB sites for known
motifs, position weight matrices (PWMs) obtained from
TRANSFAC (Matys et al, 2006) and JASPAR (Portales-Casamar
et al, 2010) (see Materials and methods for further details). We
correlated the nucleotide of the allele, which is preferentially
bound with the fitness score of the PWM. We observed a
number of significant correlations between the PWM score for
both alleles and the allele that is bound. The allele that is
bound tends to correspond to the better match to the known
PWM. In particular, we see this for the known cMyc motif
within both cMyc and Max binding sites (see Figure 3). This is
interesting as we observe a correlation between the allele-
specific behavior of cMyc motifs with Max binding sites,
indicating that these transcription factors tend to significantly
regulate the same target genes. We also include in Supple-
mentary Figure 8 additional examples of the correlation
between motif fitness score and the allele being bound for
CTCF binding sites containing CTCF motifs and cMyc motifs
within Pol II binding sites.
Correlation of ASE with protein structural fitness
Some heterozygous SNPs within genes can result in one allele
losing its ability to function as a transcript (i.e., heterozygous
loss of function). Additionally, non-synonymous SNPs within
the protein-coding sequence can cause a difference in the
structure fitness of the protein coded from each allele. We
studied the coordination between these effects and the genes
that show ASE.
We first investigated the overlap between genes that exhibit
ASE with genes that show loss of function on one allele due to
a premature stop codon, a frame-shift or a disruption of an
intron–exon splice site (all caused by heterozygous SNPs). We
find fourcases of genes that showASE as wellas heterozygous
loss of function and in all four cases the allele that is expressed
is opposite to the allele that has lost its ability to code for a
protein. We speculate that in some of these cases the transcript
from the allele suffering from a disruption might be degraded
due to non-sense-mediated decay, which, in turn, might cause
the ASE from the other allele.
SincesomeheterozygousSNPsthatshowASEarewithinthe
protein-coding sequence of genes, it is natural to ask whether
the allele that is expressed could track with allele-dependent
structural changes (for SNPs in non-synonymous positions in
the protein-coding sequence). However, it is not clear that we
expecttofindacorrelationbetween structuralfitnessandASE,
as many of these SNPs are not selected for in the human
population in any case. In order to assess whether the allele
that is expressed (for genes showing ASE) is correlated with
the allele containing mutations deleterious to proteinstructure
we performed the following analysis. We compared the
occurrences of ASE SNPs within genes where the SNP
corresponds to a non-synonymous substitution within the
protein-coding sequence of the gene. Using the tool SIFT (Ng
and Henikoff, 2003), we can compare the preference of the
allele that is expressed with the allele whose amino-acid
sequence shows better structural fitness. We find that 20of the
37 genes that meet these criteria show expression on the allele
that has the protein sequence that has better fitness. While we
find slightly more genes where the allele with better structural
fitness occurson the same allele that is expressed,this result is
not significant.
Correlating ASB with ASE
In the upper panel of Figure 4, we present an example of the
gene SKA3 on chromosome 13 which has multiple hetero-
zygous SNPs within exons which show consistent maternal
ASE which agrees with the maternal ASB of another hetero-
zygous SNP within a Pol II binding site proximal to the 50end
of the gene. In this example, we see coordinated maternal
bindingofPolIIwithexpressionofthegene.Inthelowerpanel
ofFigure4,wepresentasimilarexampleofanoveltranscribed
region on chromosome 4 where we see coordinated paternal
binding of Pol II with the paternal expression of the novelTAR.
ThesetwoexamplesshowhowASBiscoordinatedwith ASE
for a known gene and a novel TAR. To investigate this trend,
we assess to what extent allele-specific behaviors detected
using heterozygous SNPs are coordinated on a genome-wide
scale. In Table V, we tabulate the number of genes and novel
TARs that have evidence for ASE and for proximal ASB. We
also tabulate the total counts of genes that have a proximal
binding site where both the gene and binding are jointly
accessible for detecting allele-specific behavior. We perform a
similar calculation for novel TARs and their proximal sites.
In Table V, we present the tabulated counts for Pol II & Pol III
and CTCF separately from all the other transcription factors
combined. We find a number of genes (74 genes proximal to
Pol II & Pol III sites, 29 genes proximal to CTCF binding sites,
and 44 genes with proximal transcription factor sites other
than CTCF) and novel TARs (55, 8, and 15 for Pol II & Pol III,
6
4
2
0
–6
0
0.2
Fraction of maternal reads per ASB SNP
in cMyc motifs in Max binding sites
0.4 0.60.81
–4
–2
Difference in cMyc motif scores
between maternal and paternal alleles
Figure 3
between the maternal and paternal alleles against the fraction of maternally
derivedreadsforASBSNPsoverlappingmotifswithinbindingsites.Here,weplot
this for ASB SNPs in cMyc motifs that are located within Max binding sites. We
see a strong correlation indicating that the motif with the stronger match tends to
be on the allele that is preferentially bound.
We plot the difference of motif scores (see Materials and methods)
AlleleSeq: analysis of ASE and ASB in a network framework
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Page 9

CTCF, and remaining transcription factors, respectively) in
which we see both ASB proximal to ASE. We separate CTCF
from the remaining transcription factors because of its function
asaninsulator.Whilethesenumbersmightseemrelativelysmall,
they reflect the low chance of having both a proximal binding
site as well as an expressed gene with both of them jointly
accessible for the detection of allele-specific activity. This
underscores the need to sequence deeply and use a compre-
hensively determined set of SNPs or else we would have
significantly fewer genes to be able to compare ASB and ASE.
In order to assess the degree of coordinated allele-specific
behaviorforthe74genesthatexhibitASEthathaveaproximal
ASB Pol II binding site we performed the following analysis.
For each gene, we tabulated the allele-specific behavior of the
most significant ASE SNP versus the most significant ASB SNP
(if there are more than one significant heterozygous allele-
specific SNP present). In Table VI , we tabulate maternal and
paternal ASB of binding sites against maternal and paternal
ASEofproximalgenes(wedothisseparatelyforPolII&PolIII,
CTCF, and the remaining transcription factors combined). We
seethat there is astatisticallysignificant coordination between
ASB of Pol II & Pol III to genes that exhibit ASE (Fisher’s exact
test, P-value¼1.4e?3). Similarly, as seen in Table VI there is
also a statistically significant correlation between the 45 genes
that exhibit ASE with ASB for all the combined transcription
factors excluding CTCF (Fisher’s exact test, P-value¼1.8e?5).
We do not however, see a significant correlation of ASB with
ASE for CTCF which is probably due to its role as an insulator.
Further coordination of allele-specific behavior
As a further way to assess the coordination of allele-specific
events within genes, we combined all the ASE and ASB SNPs
that occurred within a gene (from 2.5kb upstream of the
transcription start site (TSS) to the transcription termination
Table V Association of transcriptional factor binding (for Pol II & Pol II, CTCF, and the other TFs combined) and expression of genes and novel TARs
Number of genes near binding
sites jointly accessible for
allele-specific behavior
ASE genes near
ASB sites
Number of novel TARs near
binding sites jointly accessible
for allele-specific behavior
ASE novel TARs near
ASB sites
Pol II & Pol III
CTCF
Other TFs
3190
1739
7716
74
29
44
4845
99
6758
55
8
15
The association is defined by binding of TFs 2.5kb upstream or downstream of a GENCODE gene or within 2.5kb of a novel TAR. Het SNPs: with heterozygous SNPs.
Scale
chr13:
Pol II
ASE
ASB
SAP18
SKA3
SKA3
MRP63
10 kb
206250002063000020635000 2064000020645000 20650000
Pol II peaks
ASE in known genes
ASB in Pol II peaks
RefSeq genes
Scale
chr4:
Pol II
TARs
ASE
ASB
TLR1
1 kb
3848250038483000 38483500 38484000
Pol II peaks
Novel TARs
ASE in novel TARs
ASB in Pol II peaks
RefSeq genes
Maternal SNPPaternal SNP
Figure 4
are indicated in blue and corresponding maternal SNPs are indicated in red. We also indicate the region of enriched Pol II binding in black. For these two examples, we
see coordinated maternal binding and expression for the known gene and coordinated paternal binding and expression for the novel TAR.
Examples showing ASE and ASB for a gene (SKA3 on chromosome 13) and a novel TAR (on chromosome 4). Paternal SNPs exhibiting either ASE or ASB
Table VI We tabulate the ASB SNPs proximal to genes (within 2.5kb of the TSS
to the TTS of the gene) containing ASE SNPs
Maternal binding Paternal binding
ASB for Pol II and Pol III versus ASE
Maternal expression
Paternal expression
35
7
5
19
ASB for transcription factors combined
(excluding CTCF) versus ASE
Maternal expression
Paternal expression
14
2
6
19
ASB for CTCF versus ASE
Maternal expression
Paternal expression
8
4
10
6
For genes that contain multiple ASB or ASE SNPs, we select the SNP with the
greatest significance for each. We separately tabulate binding sites that exhibit
either maternal or paternal ASB against genes that have maternal or paternal
ASE. We do this separately for Pol II and Pol III, CTCF, and the remaining
transcription factors combined. Using a Fisher’s exact test, we see a significant
coordination between Pol II & Pol II ASB versus ASE (P-value¼1.4e?3) and
between the other TFs showing ASB versus ASE (P-value¼9.3e?5). We do not
observe significant coordination between CTCFASB and proximal ASE, which is
expected given the role of CTCF as an insulator.
AlleleSeq: analysis of ASE and ASB in a network framework
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Page 10

site (TTS) including introns). Using only genes that contained
410 SNPs showing ASE or ASB wecould compute the fraction
ofSNPsthatshowmaternalspecificity.Ideally,ifallSNPswere
perfectly coordinated then the fraction would be either zero or
one. In the first panel of Figure 5, we see the actual
distribution, most genes do show a high degree of coordina-
tion.Underarandomnull(whereeachASEorASBeventcould
be maternal or paternal with equal chance) for the same genes
each with the same number of SNPs, we would expect a null
distribution of maternal fraction computational simulated in
panel two of Figure 5. Using a Kolmolgorov–Smirnov test, we
find significantly more coordination of ASB and ASE SNPs in
genes than compared with the random null expectation
(P-value¼8.45e?8; see the third panel of Figure 5).
Representing ASE and ASB behavior on a
regulatory network
In the previous analysis, we showed that binding and
expression were correlated in a pair-wise manner. Next, we
would like to investigate the coordination of allele-specific
behavior between multiple factors and expression simulta-
neously. This is hard to represent in a two-dimensional
correlation plot; thus, we have developed a simplified
representation of ASE and ASB in a regulatory network
framework. Looking at the occurrences of network motifs
(Milo et al, 2002) in this framework allows us to measure the
coordination of allele-specific behavior between multiple
transcription factors and target expression.
The network shown in Figure 6 represents the regulatory
network of six transcription factors (cMyc, Max, cFos, JunD,
NfkB, and CTCF) and two polymerases (Pol II and Pol III). The
network discretizes the ASB events into allele-specific regula-
tionoftargetgenesandnovelTARsandtheirASE.Theedgesin
the network represent ASB of the TF or polymerase to the
target gene or novel TAR (red edges indicate predominantly
maternal regulation, blue edges indicate paternal regulation,
andgrayedgesindicateallele-specificregulationthatcouldnot
be phased). ASE of the target genes is indicated by the color of
the target gene or novel TAR (red—maternal, blue—paternal,
and gray—unphased allele-specific behavior). Thus, the net-
work contains all information on the allele specificity of the
regulation and the expression of the targets. One can observe
that there is a clear agreement between the allele specificity of
the regulation and the expression of the target. When we
tabulate the maternal/paternal regulation edges with mater-
Fraction of maternal ASE & ASB SNPs per gene
Observed distribution
Number of genes
Null distribution
0.00.20.40.60.81.0
0
5
10
15
20
25
30
0.00.20.4 0.60.81.0
0
5
10
15
20
25
30
Cumulative number of genes
Fraction of maternal ASE & ASB SNPs per gene
0
0.00.20.40.60.81.0
Observed
Null
20
40
60
80
P-value=8.45e–08
Figure 5
All genes that contain 10 or more such SNPs across all our GM12878 data sets are included. Using this set of genes and number of SNP per gene, a null distribution is
generated. The null hypothesis is that each SNP within a gene has an independent 50/50 chance of being maternal or paternally biased. The histograms show the
distribution of maternal fraction across all genes, compared with that for the null distribution. The observed data show a strong tendency toward either zero or one,
indicating that, within agene, the SNPs have astrong tendency to be either mostly maternal or paternal. The lower graph displays the results of aKolmolgorov–Smirnov
test to support the claim that the two distributions are significantly different, with a P-value of 8.45e?8 (maximal difference is indicate with a green line).
We compare the degree of coordination in the maternal or paternal preference of ASB and ASE SNPs within a gene, to that of a random null distribution.
AlleleSeq: analysis of ASE and ASB in a network framework
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Page 11

nal/paternal expressed genes or novel TARs (see first part of
Table VII) we find that they are highly coordinated (P-value
o1e?3, Fisher’s exact test). Furthermore, we can scan the
network for coordinated regulation using multiple-input
motifs (MIMs) and single-input motifs (SIMs) (Milo et al,
2002). MIMs are network motifs where at least two transcrip-
tion factors are regulating the same target gene or novel TAR,
while SIMs are motifs where a single transcription factor
regulates a pair of targets. In the second part of Table VII,
we tabulated the number of MIMs where the pairs of TFs (or
polymerase) exhibit both maternal or both paternal regulation
with the maternal or paternal expression of the target genes
or novel TAR.
We find that for MIMs the regulatory allele-specific behavior
is highly coordinated with the ASE of the target gene or novel
TAR (P-value o1e?3, Fisher’s exact test). As we can see
in Figure 6, most MIMs correspond to the coordinated
regulation of Polymerase II and a transcription factor of
a target gene or novel TAR. In the third part of Table VII,
we count the occurrences of SIMs where a TF (or polymerase)
that exhibits maternal or paternal regulation for each of its
targets, which can each be either maternally or paternally
expressed. We similarly see a significant degree of coordi-
nation of allele-specific expression and regulation in SIMs as
with MIMs.
Discussion
In this paper, we have demonstrated that it in order to assess
the effects of sequence variation on functional genomic data
such as RNA-Seq or ChIP-Seq it is necessary to independently
determine the sequence variants from sequenced genomic
DNAsuchasbyThe1000GenomesProjectConsortium(2010).
TF
Novel TAR
Gene
Maternal
Paternal
Expression
Maternal
Paternal
Binding
Network motifs
MIM
SIM
Max
NfKB
cMyc
Pol II
JunD
cFos
CTCF
Figure 6
triangles, whilethe genes and novel TARs are represented by squares andcircles, respectively. The color of the genes and tars are representative of their allele-specific
expression and the edges from TFs, which represent regulation by TFs, to them likewise; the colors used are pink for maternal, and blue for paternal. As it can be
observed, there is significant agreement between allele-specific regulation and allele-specific binding.
This figure shows a regulatory network of genes and novel TARs that are regulated by TFs in an allele-specific manner. The TFs are represented by green
Table VII Number of transcription factors (or polymerases) that maternally or
paternally regulate GENCODE genes or novel TARs that are maternally
or paternally expressed
Single TFMaternal expressionPaternal expression
Maternal regulation
Paternal regulation
81
31
22
64
Multiple TFs (MIM)Maternal expressionPaternal expression
Both maternal
regulation
Both paternal
regulation
Mixed regulation
400
436
32
Single TF (SIM)Both maternal
expression
Both paternal
expression
Both maternal
regulation
Both paternal
regulation
2840 224
254 1232
We see the maternal regulation is coordinated with maternal expression and
similarly for paternal regulation with paternal expression. We also tabulate the
breakdown of counts for two network motifs, multiple-input motifs (MIMs) and
single-input motifs (SIMs), also see Figure 6. An MIM is where two TFs regulate
the same target gene or novel TAR and an SIM is where one TF regulates two
different targets. We again observe coordinated regulation in these network
motifs. For SIMs, we also observe 1910 cases of the form MP-MP (opposite by
coordinated regulation and expression) and 222 cases of MP-PM (mixed
regulation and expression).
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Page 12

Determining sequence variation from sequenced functional
genomic data directly is problematic especially if it is the same
data that is being used to assess the effects of the variation.
Studying allele-specific behavior is the simplest type of this
analysiswhereitispossibletoutilizethevariationbetweenthe
maternally and paternally derived alleles in order to detect
sites of ASB and ASE.
We have developed a pipeline for first building a personal
genome sequence for an individual using the available
sequence variants in order to construct a sequence containing
both maternal and paternal haplotypes. Other groups (Adey
et al, 2010; Roach et al, 2010; Fan et al, 2011) have also been
developing methods for constructing haplotypes from se-
quence variants with and without trios. In addition, we have
made available tools to enable a user to map annotation
between alleles and the reference sequence from which it was
derived. As more personal functional genomic data becomes
availableconstructingapersonal
will become the standard first step for analyzing the data.
Also the method we have used to construct the personal
genome, by overlaying sequenced variants onto a reference
genome sequence, is more natural than de novo sequence
assembly, given the short sequence reads generated from
next-generation sequence technology.
We observe that ASE and ASB are reasonable common in
the regions we are able to assess allele-specific behavior.
Consistent with X-chromosome in inactivation we observe
that on chromosome X the majority of the binding and
expression occur on the maternally derived copy except for
a couple exceptions in the pseudo-autosomal regions know
to escape X-chromosome inactivation. Unlike earlier studies,
we were able to investigate the correlation between ASB and
ASE. We do see a significant degree of coordination between
the two. It is worthwhile mentioning that not all ASE is
necessary correlated with eQTLs, some might be due to
imprinting or random mono-allelic expression (Gimelbrant
et al, 2007).
Furthermore, by displaying on a regulatory network the
allele-specific regulatory functions of the transcription factors
and polymerases studied, together with the ASE of the target
genes and novel TARs, we can investigate the coordination
between multiple factors regulating shared target genes or
novel TARs. We find that target genes or novel TARs that share
multiple regulatory factors show highly coordinated allele-
specific behavior.
Inthefuture,weimaginethattheapproachespresentedhere
willbe scaledup.The discoveryofpersonalgenomic sequence
variants, such as being done by The 1000 Genomes Project
Consortium (2010) the types of experimental annotation being
performed by The ENCODE Project Consortium (2007) will
merge into a hybrid ‘MyENCODE’ endeavor focusing on
explicit annotation of a personal genome.
genomesequence
Materials and methods
Experimental protocols for data generation
GM12878 cells were obtained from American Type Culture Collection
(Expansion A for GM12878) and cultured by using standard condi-
tions. RNA Pol II (8WG16) and Pol III antibodies were validated
by both immunoprecipitation followed by western blot (IP/western)
and mass spectrometry. Antibodies for cFos, cMyc, JunD, Max, and
NfkB were validated by IP/western.
ChIP-Seq
ChIP DNA and matching input DNA control for each biological
replicate were prepared from 5?107formaldehyde crosslinked
GM12878 cells, except after RNase and Proteinase-K digestion, ChIP
DNA was purified by using QIAquick PCR Purification Kit (Qiagen).
The adapters (Illumina) were ligated to ChIP DNA and sequenced.
Peaks from the unique reads with two mismatches or less were scored
using PeakSeq (Rozowsky et al, 2009) using default parameters.
RNA-Seq
The samples were prepared in accordance with the Illumina RNA
sample preparation protocol (Part #1004898 Rev. A September 2008).
Briefly, mRNAs were fragmented at elevated temperature using
divalent cations and transcribed into cDNA thereby generating a
library of cDNA fragments. RNA-Seq adapters are then ligated to the
blunt ends of the cDNA fragments. The library of cDNA fragments
subsequently underwent a size-selection step in which cDNAs were
first electrophoresed through a 2.5% agarose gel in TAE buffer. Then,
the desired fragment size products (200 or 300bp) were retrieved from
the gel and subjected to PCRamplification using universal primersites
present at the end of the ligated adapters. The library was then
subjected to quality control steps such as verification of fragment
size and concentration measurements using the DNA 1000 Kit
(Agilent Technologies) on an Agilent 2100 Bioanalyzer. All samples
were sequenced using an Illumina Genome Analyzer II (GAII). Since
the experiments were performed over several months as Illumina
introduced advances to the GAII platform, the total number of reads
and the read length vary (see Table I). However, all samples were
prepared following the same protocol.
All the RNA-Seq and ChIP-Seq data were generated as part of The
ENCODE Project Consortium (2007) and are available from the UCSC
GenomeBrowser(Rheadet al,2010). TheCTCFdatasetwaspublished
in McDaniell et al (2010) (GEO accessionGSE19622), the NfkB dataset
was published in Kasowski et al (2010) (GEO accession GSE19485),
the Pol III and a subset of the Pol II reads were published in Raha et al
(2010) (GEO accession numbers GSE19549 and GSE19550). We
sequenced the Pol II ChIP-Seq samples significantly deeper in order
to perform our allele-specific analysis as well as the additional
GM12878ChIP-SeqandRNA-Seqdatasets(GEOaccessionGSE30401).
All the sequence data are also available from http://alleleseq.
gersteinlab.org. A summary of the number total and mapped reads
for these data sets is available in Table I.
Construction of a diploid reference genome
for NA12878
Construction of a personal diploid human genome can be performed,
provided genomic sequence variants (SNPs, indels, and SVs) are
knownwith base-pair resolutionwithrespect to the reference genome.
Information about personal genomic variants can be obtained from
public databases (e.g., dbSNP or Database of Genomic Variants) or
downloaded from projects aimed at the discovery and cataloging
variant, for example, HapMap or the 1000 Genomes Project.
Construction of a diploid genome requires assigning each variant
to one of the two (maternal/paternal) haplotypes or to both personal
haplotypes, that is, variant phasing. Variant phasing can be
accomplished in few ways: (i) by utilizing long reads spanning two
or more variants; (ii) by imputing from genotyped variants in the
population;(iii)bycomparingvariantgenotypesinfamilytrios(father,
mother, and child). The latter one, while in principle simple, is also
very accurate, for example, 89% of SNPs in NA12878 from The 1000
Genomes Project Consortium (2010) could be unambiguously phased.
To construct the personal genome for NA12878, we used fosmid
sequenced deletions (Kidd et al, 2008) and the genomic variants
(SNPs, indels, and deletions) from The 1000 Genomes Project
Consortium (2010), see Table II. We have genotyped the fosmid
AlleleSeq: analysis of ASE and ASB in a network framework
J Rozowsky et al
12 Molecular Systems Biology 2011
& 2011 EMBO and Macmillan Publishers Limited

Page 13

sequenced deletions using a read-depth approach (Abyzov et al, 2011)
(all other variants were genotyped). Subsequently, we have phased all
variants (except SNPs that were already phased) using family trio
genotypes.
The phased variants were incorporated into the reference genome
usingthevcf2diploidtooltoyieldthediploidgenomeforNA12878(see
Figure 1A). Random haplotypes were chosen for heterozygous
variants that could not be phased. Due to the higher chance of SNP
andindelmiscallingandmisgenotypinginSVregions,weincorporated
the SVs before the indels and the SNPs. For the same reason, we
incorporated the indels before the SNPs. However, the vcf2diplod tool
allows variants to be incorporated in any order if desired. During the
construction, if a variant overlaps an already incorporated variant on
the same haplotype (e.g., SNP within breakpoints of a deletion), then
such a variant is not used (see last column in Table II). The fraction of
such variants was very small for NA12878.
Filtering SNPs in CNVs
We started from the human reference genome sequence, version
hg18 (NCBI36). The mitochondrial chromosome, chromosome Y,
alternative haplotypes, and random genomic supercontigs were
excluded from consideration. We considered SNPs for the remaining
23 chromosomes (chromosomes 1–22 and X) only. We additionally
filtered out SNPs in genomic regions with abnormalreaddepth; where
the normalized mapped read depth in symmetrical 2kb window
around each SNP is o1 or 43 (the normalization factor of 2 indicates
thediploidnatureofthehumangenome).WefilteredoutSNPsthatare
more likely to be false positives or may represent duplicatedor deleted
regions which would complicate calling allele-specific behavior.
The allele-specific SNP processing pipeline
The pipeline has four main inputs: one or more collections
of unmapped reads, a set of SNP positions, a personal genome, a set
of known genes, listing transcription starts and stops, and exon
coordinates.
The processing follows these steps. For each logical set of reads:
(1) The reads are trimmed, if necessary, to remove ends that contain
large numbers of errors and filtered to remove any reads containing
N’s. (2) SNP locations are converted to a standardized format that
describes the alleles for all heterozygous SNPs in GM12878, including
parental phasing, if possible. Phasing is possible for all heterozygous
GM12878 SNPs except those in which both parents are also
heterozygous. (3) The filtered reads are mapped, using bowtie, to
the maternal and paternal genomes. Bowtie was invoked with these
flags: –best –strata -v 2 -m 1, which returns only unique hits within a
minimum number of mismatches, up to two. (4) The two sets of
mapped reads are merged into a single set, with each read represented
at most once, using the better mapping from the maternal or paternal
haplotypes. If the two mappings for the same read tie in quality, one is
chosen at random. (5) Using Het-SNP file and the mapped reads, allele
counts are generated for each Het-SNP location. The resulting counts
file contains the number of As, Cs, Gs, and Ts found in reads mapped
over each SNP location. Various other values are also generated for
each Het-SNP location, including reference allele, maternal/paternal
allele(ifdeterminable),majorandminorallele,andabinomialP-value
assuming a 50/50 probability of sampling each of two alleles. (6) In
order to calculate the FDR, we perform an explicit computational
simulation to correct for multiple hypothesis testing. We start with all
the heterozygous SNP locations; for each SNP location, we randomly
assign each mapped read in the data set to either allele. At a given
P-value threshold (using the binomial test), we can determine the
number of false positive allele-specific event calls (from the simulated
data); and thus, we can determine the FDR as the number of false
positive over the total numberof observed positives. We require a FDR
of o10% (which corresponds to a P-value of threshold between 0.004
for cMyc and 0.03 for Pol III). We intentionally apply a relaxed
threshold in order to obtain a decent number of allele-specific events
so as to perform genome-wide correlation analyses between ASB and
ASEbehavior.WhilewecouldapplyastricterFDRthreshold,wefound
that the statistical significance of the Pearson’s correlations is
dependent on both the accuracy (greater accuracy using a stricter
FDR threshold) and the statistical power determined by the number of
observations made (more observations using a more relaxed FDR
threshold). Thus, a strict threshold would increase the accuracy at the
cost of fewer observed allele-specific events. There is a balance
between the accuracy of the observations made as well as the number
of observations made, in order to determine optimal correlation
behavior. We found that the significance of the Pearson’s correlation
between the observed ASB and ASE events was most significant when
the FDR threshold set to B10%.
In Table IV, we present the results for ASE and ASB calls for all the
data sets. The second column is the number of elements (genes, TARs,
or binding sites) that are accessible for the detection of allele-specific
behavior, that is, they contain a heterozygous SNP as well as are
sequenced sufficiently deeply in order for allele-specific activity to
have been detected in that specific data set given the P-value used in
order to obtain a 10% FDR threshold. For the RNA-Seq results 6? was
sufficientlytoobtainthemaximumallowingP-valuethreshold.Forthe
ChIP-Seq data sets, the depth threshold required was 7? for Pol III,
8? for CTCF, JunD, cFos, and NfkB, and 9? for cMyc and Pol II. The
results of applying these thresholds are outputed in the filtered counts
file for each data set.
Usingthe listofgenesandallfilteredcountsfiles,informationabout
allasymmetricHet-SNPsfromanyofthedatasetsaregroupedtogether
by gene. The locations are annotated as being exonic or intronic.
The information about each SNP includes: reference allele; maternal
and paternal genotype; phasing if possible; A, C, G, Tcounts; biased
toward parent allele; q-value (FDR). Supplementary data includes the
list of all allele-specific SNPs (Supplementary Dataset 1) as well as the
list of all genes that show ASE (Supplementary Dataset 2), these are
also available from http://alleleseq.gersteinlab.org/.
Comparison of ASB SNPs with know
transcription factor motifs in binding sites
The motif consensus sequences were generated from the PWMs
(source TRANSFAC, Matys et al, 2006 and JASPAR, Portales-Casamar
et al, 2010). The frequencies of the matrices were normalized if the
original ones were not normalized.
The rules for creating IUPAC consensus sequences for TF motifs are
as follows. A single nucleotide code is used if its frequency is 450%
and at least twice as high as the second most frequent nucleotide.
A double-degenerate code is used if the combined frequencies of two
nucleotides are 475% but each of them is present in o50%. A triple-
degeneratecodeisusedwhereoneofthenucleotidesdoesnotshowup
atallinthesequencesetandnoneoftheaforementionedrulesapplied.
The letter ‘N’ represents all other frequency distributions. We scanned
binding sites using TF PWMs. Genomic sequences defined by
the binding sites are fetched (for both strands). The TF PWMs (and
corresponding consensus motif) were used as queries to search the
genome sequence, with 0 or 1 edit distance. Only those sites
that include allele-specific heterozygous SNP locations that phased
are retained.
Wecomputethedifferenceinbindingstrengthofthemotifsbetween
the maternal and paternal alleles to compare against the fraction of
maternally derived read counts. For the maternal and paternal alleles
at position i:
deltaðmaternal ? paternalÞ ¼ log2ðPðmaternal;iÞ
=PðmaternalÞ?PðpaternalÞ=Pðpaternal;iÞÞ
where P(n, i) is the frequency for allele n at position i in the
PWM (required to be 40.01) and P(n) is the background frequency of
allele n.
In Figure 3, we plot this difference in motif scores, delta (maternal–
paternal) against the fraction of maternally derived reads overlapping
the same heterozygous SNP in the ASB site. In small number of cases
where there are multiple SNPs in the TF motif region, the best one is
chosen where if the maternal read count fraction greater than half
the best is equal to the biggest delta, while if fraction is less than half
the smallest delta is chosen.
AlleleSeq: analysis of ASE and ASB in a network framework
J Rozowsky et al
& 2011 EMBO and Macmillan Publishers LimitedMolecular Systems Biology 2011 13

Page 14

Building an allele-dependent regulatory network
We decided to integrate the expression data for genes and TARs from
the RNA-Seq experiment with the TF binding data from the (cFos,
cMyc, JunD, Max, NfkB, CTCF, Pol II, and III) ChIP-Seq experiments
intoaregulatorynetwork.Inordertoconstructaregulatorynetworkto
determinetheedgebetweenaTFandagenebyassigninganASBevent
to a target ASE gene if it lies within 2.5kb upstream of the annotated
TSS and the TTS. For ASE novel TARs we do not know which strand is
beingexpressed,thusweassociateASBeventsthatoccurwithin2.5kb
of either end of the novel TAR. If it is allele specific then it could
be further classified into paternal, maternal, and unphased. The
‘unphased’ category represents the case where the experiments show
allele specificity but it cannot be phased. After constructing the edges
between the TFs and gene/novel TARs in the network, we overlaid the
gene/novel TAR ASE information onto the nodes. Each gene/novel
TAR was categorized into three categories: paternal, maternal, or
unphased ASE. After constructing the network, we performed a
network motif analysis on it, the results of which are shown in Table
VII. We analyzed the occurrences of MIMs where two TFs regulate the
same gene/novel TAR and SIMs where a single TF regulates two
different gene/novel TARs, taking into account the allele specificity
of the regulation and the expression of the targets. Counting of
occurrences of MIMs and SIMs was performed using Cytoscape (Cline
et al, 2007).
Supplementary information
Supplementary information is available at the Molecular Systems
Biology website (www.nature.com/msb).
Acknowledgements
We acknowledge Suganthi Balasubramanian, Andrea Sboner, and
Lukas Habegger for valuable discussions. We acknowledge support
from the NIH and the ALWilliams Professorship funds.
Author contributions: JR and MG conceived of the study. DR and
NK performed the experiments. JR, AA, JW, PA, AH, JL, RB, YK, and
NB performed the analysis and developed the code. JR, AA, and MG
drafted the manuscript. MR, MS, and MG supervised the study.
Conflict of interest
The authors declare that they have no conflict of interest.
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AlleleSeq: analysis of ASE and ASB in a network framework
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& 2011 EMBO and Macmillan Publishers LimitedMolecular Systems Biology 2011 15