Bovine ncRNAs Are Abundant, Primarily Intergenic, Conserved and Associated with Regulatory Genes

Abstract

It is apparent that non-coding transcripts are a common feature of higher organisms and encode uncharacterized layers of genetic regulation and information. We used public bovine EST data from many developmental stages and tissues, and developed a pipeline for the genome wide identification and annotation of non-coding RNAs (ncRNAs). We have predicted 23,060 bovine ncRNAs, 99% of which are un-annotated, based on known ncRNA databases. Intergenic transcripts accounted for the majority (57%) of the predicted ncRNAs and the occurrence of ncRNAs and genes were only moderately correlated (r = 0.55, p-value,2.2e-16). Many of these intergenic non-coding RNAs mapped close to the 39 or 59 end of thousands of genes and many of these were transcribed from the opposite strand with respect to the closest gene, particularly regulatory-related genes. Conservation analyses showed that these ncRNAs were evolutionarily conserved, and many intergenic ncRNAs proximate to genes contained sequence-specific motifs. Correlation analysis of expression between these intergenic ncRNAs and protein-coding genes using RNA-seq data from a variety of tissues showed significant correlations with many transcripts. These results support the hypothesis that ncRNAs are common, transcribed in a regulated fashion and have regulatory functions. Copyright: ß 2012 Qu, Adelson. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

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Bovine ncRNAs Are Abundant, Primarily Intergenic,
Conserved and Associated with Regulatory Genes
Zhipeng Qu, David L. Adelson*
School of Molecular and Biomedical Science, The University of Adelaide, Adelaide, South Australia, Australia
Abstract
It is apparent that non-coding transcripts are a common feature of higher organisms and encode uncharacterized layers of
genetic regulation and information. We used public bovine EST data from many developmental stages and tissues, and
developed a pipeline for the genome wide identification and annotation of non-coding RNAs (ncRNAs). We have predicted
23,060 bovine ncRNAs, 99% of which are un-annotated, based on known ncRNA databases. Intergenic transcripts accounted
for the majority (57%) of the predicted ncRNAs and the occurrence of ncRNAs and genes were only moderately correlated
(r=0.55, p-value,2.2e-16). Many of these intergenic non-coding RNAs mapped close to the 39 or 59 end of thousands of
genes and many of these were transcribed from the opposite strand with respect to the closest gene, particularly
regulatory-related genes. Conservation analyses showed that these ncRNAs were evolutionarily conserved, and many
intergenic ncRNAs proximate to genes contained sequence-specific motifs. Correlation analysis of expression between these
intergenic ncRNAs and protein-coding genes using RNA-seq data from a variety of tissues showed significant correlations
with many transcripts. These results support the hypothesis that ncRNAs are common, transcribed in a regulated fashion
and have regulatory functions.
Citation: Qu Z, Adelson DL (2012) Bovine ncRNAs Are Abundant, Primarily Intergenic, Conserved and Associated with Regulatory Genes. PLoS ONE 7(8): e42638.
doi:10.1371/journal.pone.0042638
Editor: Leonardo Marin ˜o-Ramı ´rez, National Institutes of Health, United States of America
Received June 9, 2012; Accepted July 11, 2012; Published August 6, 2012
Copyright: ? 2012 Qu, Adelson. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits
unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Funding: This study was funded by the University of Adelaide. ZQ thanks the China Scholarship Council (CSC) for funding. The funders had no role in study
design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing Interests: The authors have declared that no competing interests exist.
* E-mail: david.adelson@adelaide.edu.au
Introduction
As a result of advances in DNA sequencing technologies, a
number of mammalian genomes have been sequenced and
assembled. The impetus for sequencing mammalian genomes is
to use comparative genomics to identify important, evolutionarily
conserved sequences, such as protein-coding genes. While protein-
coding genes are considered the most important elements of the
genome, they only account for a small fraction of the genome
sequence or the mammalian transcriptome. This indicates that the
complexity of the mammalian genome, especially the transcrip-
tome, cannot be interpreted merely according to the central
dogma of molecular biology ‘‘DNA-RNA-protein’’ [1,2,3,4,5]. In
human, only about 1–2% of the entire genome is transcribed as
protein-coding RNAs, while more than half (,57%) of the human
genome is transcribed as ‘‘non-protein-coding’’ RNAs (ncRNAs)
[3]. Furthermore, studies from the FANTOM consortium have
also confirmed that the majority of the mouse genome is
transcribed, commonly from both strands. Most of these
transcripts cannot be annotated as protein-coding RNAs [4].
These findings are evidence of a hidden, non-protein-coding
transcriptome in mammals.
At present there is debate about the true nature of the non-
protein-coding transcriptome. Some believe that most ncRNAs are
‘‘transcriptional noise’’ associated with protein coding genes and
have no function [6]. But this may not be the whole story. Apart
from well-studied small non-protein-coding RNAs, like miRNAs,
siRNAs, snoRNAs and piRNAs, other classes of abundant
functional ncRNAs have been demonstrated in recent studies.
Guttman et al. identified over a thousand highly conserved large
intergenic non-coding RNAs (lincRNAs) in the mouse by
analysing chromatin signatures [7]. Subsequent experimental
analysis confirmed that one of these lincRNAs serves as a
repressor in p53-dependant transcriptional responses [8]. Recent-
ly, another class of long non-coding RNAs was discovered in the
human. Some of these thousand or so long ncRNAs were shown to
have an un-anticipated enhancer-like role in activation of critical
regulators of development and differentiation [9]. Furthermore,
new types of small ncRNAs, like tiRNAs (tiny RNAs) [10], PASRs
(Promoter-Associated Short RNAs) [11], TASRs (Termini-Asso-
ciated Short RNAs) [11], and aTASRs (antisense Termini-
Associated Short RNAs) [12], have been discovered in mammals.
It is now clear that evidence confirms that there are indeed many
functional sequences in the non-protein-coding transcriptome.
To characterize the non-coding transcriptome at genome scale,
we built a computational pipeline to identify non-protein-coding
transcripts from Expressed Sequence Tags (ESTs), which were
originally designed to identify and annotate protein-coding genes.
ESTs have the advantage of being readily available from public
repositories, and are generally far longer than the RNA-seq tags
generated by current high throughput DNA sequencers. The latter
allows confident reconstruction of much longer transcripts. We
used the bovine genome as a starting point for three main reasons:
it has a large number of ESTs sampled from many tissues and
developmental stages, the protein coding gene annotations are
robust and based on thorough comparative genomic analysis and
we had already exhaustively annotated the repetitive component
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of the genome [13]. We were thus able to reconstruct many long
transcripts and unambiguously map them to either protein-coding
genes or non-repetitive, non-protein-coding regions of the
genome. In this report we have identified thousands of non-
coding RNAs (ncRNAs), the vast majority of which were
previously un-annotated. We have also characterized the genomic
distribution of these ncRNAs, compared to protein-coding genes
and carried out conservation analyses to detect evidence of
potential conserved function. Our analyses show that most
ncRNAs were transcribed from clearly conserved genomic regions.
A predominant class of intergenic ncRNAs were transcribed from
the proximate flanking regions of genes, leading us to hypothesize
that they play cis-regulatory roles in the regulation of their
neighbour genes and/or trans-regulatory roles elsewhere in the
genome. Taken together, our findings provide a general view of
the composition, distribution, and conservation of a mammalian
non-protein-coding transcriptome at genomic scale, sampled
across a wide selection of tissues and developmental stages, and
support the idea that most ncRNAs are of potential functional
importance.
Materials and Methods
Databases
All data used in this research were sourced from public
databases. Bovine ESTs were retrieved from dbEST of NCBI [14].
The information from source libraries is shown in Table S1. Two
different bovine repeat databases were used: the first was
developed by Adelson et al. [13]; the other was a custom-built
repetitive protein database generated according to Smith et al.’s
method [15]. The genome assembly of bosTau4 and its
corresponding RefSeq dataset (as of September of 2009) was
downloaded from NCBI. The Swiss-Prot protein reference
database (as of September of 2009) was also obtained from NCBI.
Several known ncRNA databases were used to annotate
ncRNAs. The miRNA database, miRBase release 14, which
included 10,566 mature miRNAs and 10,867 pre-miRNAs, was
obtained frommiRBase (http://www.mirbase.org/)
Rfam9.1, which contained tRNAs, rRNAs, snoRNAs, miRNAs,
and other ncRNA models, was obtained from http://rfam.janelia.
org/ [17]. NONCODE2.0 was obtained from http://www.
noncode.org/ [18].
[16].
Programs used to develop the pipeline of ncRNA
identification
All programs used in the pipeline of ncRNA identification can
be freely accessed from the Internet (Table S2). All of them are
stand-alone versions running under the Linux environment. Perl
was used to link them into a pipeline. All Perl scripts are available
upon request.
Annotation of ncRNAs
Several methods were used to annotate bovine ncRNAs.
Similarity search was used to identify miRNAs from bovine
ncRNAs. Blastn of ncRNAs against both mature miRNA and pre-
miRNA databases was used to find transcripts of significant
similarity to known mature miRNAs (identity .95%, cover-
age=100%) and primary miRNAs (identity .95%, coverage
.95%). Two steps were used to validate tRNAs from bovine
ncRNAs. tRNAscan_SE was used to generate a list of tRNA
candidates [19]. Only the candidates subsequently validated by
Rfam were classified as known tRNAs [17].
The Stand-alone Rfam search was performed by a Perl script
Rfam_scan.pl provided with Rfam [17]. Additionally, BLASTN
against NONCODE2.0 was used to identify long known ncRNAs
and piRNAs [18].
Distribution analysis of ncRNAs
All 23,060 ncRNAs and 24,373 RefSeqs were mapped to the
bosTau4 assembly. The numbers of ncRNAs and RefSeqs in
1 MB non-overlapping bins were counted to determine the density
distribution. The Spearman correlation coefficient between the
densities of ncRNAs and RefSeqs per 1 MB bin across the whole
genome was calculated using the R package (v2.12.0).
Positional bias analysis of intergenic ncRNAs
For each ncRNA, the closest gene model, either upstream or
downstream, was defined as the nearest neighbour. The intergenic
region of two nearby genes was defined as the gene interval.
To maximize the number of intergenic ncRNAs annotated in
this step, the transcription orientations of intergenic ncRNAs were
determined by the union, instead of the intersection of the two
methods used to determine the transcription orientation of ESTs
in the step of cis-NATs (Natural Antisense Transcripts) identifica-
tion.
Functional over-representation of intergenic ncRNAs’
neighbour genes
All neighbour genes with intergenic ncRNAs in 5 kb flanking
upstream or downstream regions were identified. 3,166 unique
genes with intergenic ncRNAs in 59 flanking regions were
identified, and 741 unique genes were identified with intergenic
ncRNAs in 39 flanking regions. The intersection of these two gene
lists resulted in 183 unique genes. The GO (Gene Ontology)
functional annotation and clustering were conducted using
DAVID (Database for Annotation, Visualization and Integrated
Discovery) [20,21]. Over-represented GO terms were filtered to
contain at least 5 genes and FDR (False Discovery Rate),0.05.
Ten control gene lists for 59 and 39 neighbour gene lists were
generated respectively. For each control list for 59 end intergenic
ncRNA, 741 genes were randomly selected from all the genes with
59 intergenic regions. For each control list for 39 end intergenic
ncRNA, 3,166 genes were randomly selected from all the genes
with 39 intergenic regions. All over-represented GO terms ($5
genes and FDR,0.05) were highlighted as yellow in Table S3.
Analysing the sequence conservation of predicted
ncRNAs
Conservation analysis based on phastCons score [22]: The
reference phastCons score files containing the phastCons scores for
multiple alignments of 4 other vertebrate genomes (Dog, May 2005,
canFam2; Human, Mar 2006, hg18; Mouse, July 2007, mm9;
Platypus, Mar 2007, ornAna1) to the reference of cow genome (Oct
2007, bosTau4) were downloaded from UCSC (http://hgdownload.
cse.ucsc.edu/goldenPath/bosTau4/phastCons5way/). Each base in
the EST or RefSeq was assigned a phastCons score according to the
reference files. The bases that were not included in the conserved
elements of the reference files were given phastCons scores of ‘‘0’’.
For a given sequence, the mean phastCons score was calculated by
normalizing the sum of phastCons scores against the length of the
sequence.
Conservation analysis based on GERP++ score [23]: GERP++
is another tool that uses maximum likelihood evolutionary rate
estimation for position-specific scoring. It calculates the RS
(rejected substitution) score based on multiple alignments and a
phylogenetic tree. The 5-way multiple alignment file for cow (the
same species and genome assemblies used for phastCons scores)
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and the corresponding phylogenetic tree were downloaded from
UCSC(http://hgdownload.cse.ucsc.edu/goldenPath/bosTau4/
multiz5way/). A PERL script was created to convert the default
multiple alignment file format into the file format that can be fed
into GERP++. The GERP++ score for each base of bosTau4 was
calculatedusingGERPv2.1
SidowLab/downloads/gerp/index.html). Mean GERP++ scores
were calculated in the same way as mean phastCons scores.
24,000 genomic fragments, which ranged in size from 500 bp to
15,000 bp, were randomly extracted from un-transcribed regions
of bosTau4 as the control dataset. The cumulative frequency for
each dataset was calculated and plotted using the R package.
(http://mendel.stanford.edu/
Identification of sequence specific motifs from intergenic
ncRNAs
Bovine gene expression profiles were generated based on
transcriptome data from 95 samples (92 adult, juvenile and fetal
cattle tissues and 3 cattle cell lines) [24].
FIRE was used to predict sequence motifs from bovine
intergenic ncRNAs [25]. Bovine intergenic ncRNAs located in
5 kb of upstream or downstream gene flanking regions were used
as motif prediction pools. Intergenic ncRNAs were converted as
sense RNAs according to their transcription orientation. The
motif-identification mode was set as ‘‘DNA’’, which means motif
sequence can be predicted from both strands of intergenic
ncRNAs. FIRE was run against 59 end and 39 end intergenic
ncRNAs according to 95 individual gene expression profiles
respectively.
The comparison of predicted RNA sequence motifs against
known DNA motifs was performed using the TOMTOM web
server [26].
Expression correlation analysis based on bovine MPSS
data
The expression profiles of intergenic ncRNAs and bovine
RefSeqs were calculated based on the MPSS (Massively Parallel
Signature Sequencing) tags mapped to the 39 most end of each
transcript [24]. The tag count for each transcript was normalized
according to the library size. Transcripts mapped with less than 3
tags were removed from the expression profile. The MIC score
was generated by MINE based on the expression of intergenic
ncRNA and RefSeq pairs [27]. Only intergenic ncRNAs/RefSeqs
with expression (read counts) in at least 3 libraries were used to
perform expression correlation analysis.
Results
The development of ncRNAs identification pipeline
We identified ncRNAs from bovine ESTs, by developing a
computational pipeline based on public software and Perl scripts
(Figure 1). A total set of 1,517,143 bovine ESTs (as of 30th
September, 2009), extracted from the dbEST of NCBI, was
processed as the input dataset for the pipeline. After quality
control, repeat filtration and EST assembly, we identified 216,095
unique transcripts. We opted for stringent mapping criteria
(coverage $90% and identity $95%) and as a result, 69,099
unique transcripts were unable to be mapped to the BosTau4
assembly and were therefore discarded. Of the mapped sequences,
3,121 were classified as putative cis-NATs, 74 of which were
subsequently manually checked on UCSC genome browser
(Materials S1). The remaining 143,875 mapped unique transcripts
were further analysed to annotate and characterize the bovine
transcriptome.
Of the 143,875 mapped unique transcripts, 87,373 were very
similar to bovine RefSeqs (E-value,1e-3), and 48,773 of them
shared similarity over more than 90% of their length with 14,962
RefSeqs and were denoted as known gene transcripts. Of the
38,600 sequences that shared similarity with RefSeqs over less
than 90% of their length, more than one third (13,035) were un-
spliced.
There were 1,856 transcripts, which we were unable to annotate
based on similarity search against bovine RefSeqs, but were
identified by BLAST in the Swiss-Prot database at the amino acid
level. These sequences may represent novel un-annotated bovine
protein-coding genes that are conserved across taxa.
The resulting set of sequences, filtered with respect to sequence
similarity to repeats, protein-coding transcripts and cis-NATs was
then further scrutinized by checking the length of predicted ORFs
(Open Reading Frames). As a result, 31,586 unique sequences
were removed from the 54,646 ‘‘protein-coding gene filtered
unique transcripts’’ because they contained either long predicted
ORFs ($100 amino acids) or shorter ORFs ($50 amino acids) at
the ends. These ‘‘ORF-containing sequences’’ may include
transcripts from un-annotated, novel protein-coding genes. The
large number of these transcripts raises the possibility that there
are still significant numbers of protein-coding genes in the bovine
genome that remain undiscovered.
As a result of this highly stringent filtering against known
protein-coding genes and the exclusion of ORF containing
transcripts we were left with 23,060 ncRNAs (Table S4), which
accounted for ,15.5% (23,060 out of 143,875) of the mapped
bovine unique transcripts. These ncRNAs were then analysed to
identify previously annotated ncRNAs.
Few well-characterized ncRNAs were identified
The annotation of the 23,060 ncRNAs was carried out using
several different methods (See methods for detailed procedures).
As a result of this effort we determined that only 77 of these
sequences had been previously identified as ncRNAs, either as
miRNAs, snoRNAs, tRNAs, rRNAs, mRNA-like ncRNAs,
piRNAs and other ncRNAs (Materials S1, Table S5 and Table
S6). One additional class of ncRNAs that we identified were cis-
NATs. We identified 74 cis-NATs distributed on 28 different
chromosomes (Materials S1 and Table S7 and Figure S1).
Whilst our results showed that ESTs could be used to identify
ncRNAs by rational and stringent sequence similarity searches, the
vast majority of the ncRNAs we identified could not be annotated
based on previously well-characterized ncRNAs.
Genome-wide distribution of ncRNAs
To understand the distribution of predicted ncRNAs in the
genome, our 23,060 predicted ncRNAs mapped onto BosTau4
were compared to the mapped locations of 24,373 bovine RefSeqs.
Figure 2 shows the density distributions of ncRNAs and RefSeqs in
30 bovine chromosomes (29 autosomes and X). Together with the
relative frequencies of the densities of ncRNAs and RefSeqs,
which are shown in Figure 3, it is obvious that the ‘‘gene poor
regions’’ (with fewer than 10 genes in 1 Mb) are more abundant
than ‘‘ncRNA poor regions’’ (less than 10 ncRNA s in 1 Mb) in
the bovine genome. Furthermore, 288 gene deserts (no gene in
1 Mb) were identified compared to 156 ncRNA deserts (no
ncRNA in 1 Mb). At the other end of the gene density spectrum,
21 regions were found with more than 50 genes/Mb, but no
comparable regions were found for ncRNAs. These results showed
that ncRNAs were more evenly distributed than protein-coding
genes across the genome. A correlation analysis of the densities of
protein-coding genes and ncRNAs per 1 Mb revealed only a
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moderate correlation between these two transcriptome sets at the
whole genome level (r=0.5528816, p,2.2e-16).
We further classified our ncRNAs with respect to neighbour
protein-coding genes to analyse the potential transcriptional
overlap with RefSeq genes. Our classification scheme for ncRNAs
is shown in Figure 4. Excluding 952 ncRNAs mapped to
uncharacterized genomic locations, there were three main types
of ncRNAs based on this classification and their relative
proportions are shown in Figure 5. The majority of the ncRNAs
in our dataset were intergenic transcripts (57% intergenic
compared to 42% intronic). We also noticed that most ncRNAs
were singletons (72.2% out of intergenic, 81.1% out of intronic
and 71.3% out of overlapped ncRNAs respectively)(Table 1). The
data in Table 1 also showed that the vast majority of ncRNAs
(both intergenic and intronic) were apparently unspliced tran-
scripts.
Detailed inspection of overlapped ncRNAs revealed that 98 of
them overlapped with their corresponding genes by less than 50
basepairs; 85 of them at the 39 end, and the rest at the 59 end of
the genes. These ncRNAs may represent unannotated UTRs or 59
and 39 extensions of genes [28], but there is the possibility that
some of them, especially 59 overlapped ncRNAs, were transcribed
as functional ncRNAs, like PASRs, tiRNAs
[10,11,29,30]. Our result did show that there are antisense
transcripts among these overlapped ncRNAs (10 of 85 at 39 end
and 3 of 13 at 59 end).
or uaRNAs
Most ncRNAs were of intergenic origin
Most bovine ncRNAs mapped to intergenic regions (Figure 5).
To get a better understanding of these intergenic ncRNAs, we
plotted the frequency distribution of intergenic ncRNAs as a
function of their distance and transcriptional orientation to the
nearest neighbour genes (Figure 6). About 67.4% (8,500 out of
12,614) of intergenic ncRNAs had a neighbour gene within 20 kb,
with a significant concentration of intergenic ncRNAs in the 5 kb
flanking regions of genes. Beyond 10 kb, the number of intergenic
ncRNAs decreased very gradually as a function of distance. It was
also apparent from Figure 6A that intergenic ncRNAs were more
prevalent at the 39 end of genes than at the 59 end. The intergenic
ncRNAs closest to the 59 end of a gene also tended to be within
Figure 1. Flowchart describing the pipeline for ncRNA identification.
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5 kb of the gene, but this localization was not significantly different
to the control frequency distribution calculated using gene to gene
nearest neighbour distances, where the majority of intergenic
distances were less than 5 kb. We were able to determine
transcriptional orientation of 10,969 of 12,614 intergenic ncRNAs
based on their dbEST annotation. When we compared the
transcriptional orientation of these intergenic ncRNAs to their
closest gene neighbour, we observed that most of them closest to
the 39 end of genes were transcribed from the same strand as the
gene (Figure 6B). There were four times more ncRNAs in the same
transcriptional orientation when they were 39 to the closest gene
(6,296 to 1,433). This difference in transcriptional orientation for
the ncRNAs 59 of the closest gene was also observed, but not to the
same degree (1,931 same to 1,309 reverse). The intergenic
ncRNAs, transcribed from the same strand as the closest gene,
might be extensions of the UTRs produced by alternative
transcription start or termination sites of protein-coding genes,
but many of them were at significant distances from these genes
making this an unlikely possibility.
To determine the likelihood that these intergenic ncRNAs were
potential gene UTRs, we compared them against the annotated
UTR database (including human, mammals and vertebrates) [31].
3,168 of these intergenic ncRNAs were highly similar to 39 UTRs
(E-value,1e-3), while only 198 were highly similar to 59 UTRs (E-
value,1e-3). Together with 2,516 intergenic ncRNAs which are
located in the proximal 1 kb of gene flanking regions (59 end or 39
end), we classified these 4,584 intergenic ncRNAs as UTR-Related
RNAs (Table S4), which are named to differentiate them from
uaRNAs (UTR-associated RNAs), a class of previously annotated
independent ncRNAs transcribed from UTRs [30]. The reason-
ably large number of intergenic ncRNAs transcribed in the
opposite orientation to their nearest gene (1,309 from the 59 end
and 1,433 from the 39 end), raised the possibility that there might
be transcriptional antisense regulation associated with these
elements.
The spatial clustering of all predicted intergenic ncRNAs with
respect to protein coding genes suggested a cis-regulatory
relationship to us. To understand the potential biological
significance of such a relationship, we functionally clustered the
neighbour genes within 5 kb flanking regions of intergenic
ncRNAs according to GO [32]. We found that regulatory genes
were over-represented in the neighbour genes of these intergenic
ncRNAs (Table S3), but the gene count of these over-represented
GO terms was very small, most likely because of the poor
functional annotation of bovine reference genes in GO. The
functional clustering of control gene lists (see methods) indicated
these over-representations were not chance occurrences (Table
S3). When we differentiated the neighbour genes according to the
position of their nearby intergenic ncRNAs, we observed that
positive regulatory genes were over-represented in the neighbour
genes with intergenic ncRNAs in their 59 flanking regions (Table
S3). Assessment of neighbour gene function based on regulatory-
related keywords searching of the subset of 183 genes flanked at
both ends by intergenic ncRNAs revealed that 85 (46.4%) of these
genes were involved in either transcriptional regulation, signal
transduction or encoded domains consistent with these functions.
By comparison, only 8,087 (33.2%) of all 24,373 RefSeq genes
were annotated as regulatory genes based on the same keywords
searching. This indicated that the purely GO-based results were
probably a significant underestimate of the regulatory potential of
Figure 2. Distribution of genes and ncRNAs in the bovine genome. Chromosomes are on the X axis, and sequence coordinates on the Y axis,
with the ‘‘top’’ of the chromosome at the Y axis origin. All cattle autosomes are acrocentric. Each chromosome is represented by two vertical bands,
the left band shows gene number and the right band shows ncRNA number, per 1 Mb bin. The legend shows the band colour coding for numbers
per 1 Mb bin.
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these neighbour genes. In summary, we hypothesize that our gene-
proximate intergenic ncRNAs are potentially cis-regulatory and
tend to regulate regulatory genes. Confirmation of this hypothesis
will have to await specific, functional perturbation experiments,
but is consistent with published data from small numbers of
intergenic ncRNAs.
Evolutionary conservation of bovine ncRNAs
To assess whether ncRNAs were under selective constraint, we
used two different methods to assess the degree of sequence
conservation as shown in Figure 7. Figure 7A shows the degree of
conservation based on phastCons score; ncRNAs were clearly
conserved compared to control sequences, which were selected at
random from un-transcribed regions of the bovine genome, but
were less conserved compared to protein-coding genes. When we
compared the degree of sequence conservation between intergenic
and intronic ncRNAs according to phastCons score (Figure 7B),
intergenic ncRNAs were more conserved than intronic ones. When
we further refined this to assess the sequence conservation of
intergenic ncRNAs according to their relationships with protein-
coding genes, we observed that intergenic ncRNAs closest to the 39
end of genes were more conserved than those closest to the 59 end of
genes. And when we took into the consideration the transcriptional
orientation of these ncRNAs with respect to their closest gene, the
‘‘sense’’ intergenic ncRNAs, which are transcribed from the same
strand as their neighbour genes, were more conserved than the
‘‘antisense’’ intergenic ncRNAs, regardless of whether they were
closest to the 59 or 39 end of protein-coding genes (Figure 7C).
We were able to confirm these observations regarding the
conservation level of ncRNAs using GERP++ [23], based on a
different statistical model. If we only consider the sequences that
were under a substitution deficit (positive score), the conservation
level of ncRNAs was between protein-coding genes and un-
transcribed genomic fragments, which was consistent with the
phastCons result. Nearly 40% of ncRNAs had a substitution
deficit, compared to ,80% of protein-coding genes and less than
20% of un-transcribed genomic fragments. On the other hand, for
sequences that showed a substitution surplus (negative score), the
divergence level of ncRNAs was more pronounced than for
Figure 3. Probability densities of genes and ncRNAs per 1 Mb bin. NcRNAs have similar genomic densities compared to protein coding
genes, but with fewer extreme density regions. The colour coding is consistent with Figure 2.
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protein-coding genes and un-transcribed genomic fragments
(Figure 7D). The results of the GERP++ score for the intergenic
and intronic ncRNAs, as well as the different intergenic classes
were also consistent with their respective phastCons score results
(Figure 7E and Figure 7F).
When we removed all UTR-related RNAs from 23,060
ncRNAs, the remaining sequences still showed clear conservation
compared to un-transcribed control fragments (Figure S2). The
highly conserved UTR-related RNAs is consistent with these being
part of poorly annotated UTRs or independent transcripts from
UTRs, as UTRs across different species are often well conserved
(Figure S2).
Figure 4. Classification of ncRNAs in relation to protein-coding genes. (A) The entire EST is transcribed from an intergenic region, regardless
of the transcription orientation. (B) The entire EST is transcribed from an intron, regardless of the transcription orientation. (C) Single-overlapped
ncRNA: EST partially overlapped with a gene; Double-overlapped ncRNA: Both ends of the EST overlapped with two genes and spanned an intergenic
region; Single-included ncRNA: The gene was fully included inside the EST; Included-overlapped ncRNA: One gene was fully included within the
ncRNA, and the ncRNA spanned the intergenic region and overlapped with a neighbour gene; Double-included ncRNA: More than one genes were
fully included within the EST.
doi:10.1371/journal.pone.0042638.g004
Figure 5. Relative abundance of the three main classifications
of ncRNAs. Almost 60% of ncRNAs are long intergenic non-coding
RNAs (intergenic ncRNAs).
doi:10.1371/journal.pone.0042638.g005
Table 1. Summary of transcriptional redundancy and splicing
information of three types of ncRNAs.
Class of ncRNAsNumber
Singleton Unspliced
CountFractionCount Fraction
Intergenic12,6149,113 72.2% 9,85278.1%
Intronic 9,337 7,57181.1%8,085 86.6%
Overlapped 157112 71.3%80 51.0%
–Single-overlapped138 9669.6% 78 56.5%
–Double-overlapped22 100%00
–Single-included109 90%110%
–Included-overlapped22100%00
–Double-included53 60%1 20%
– denotes subclass of Overlapped.
doi:10.1371/journal.pone.0042638.t001
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Identification of sequence motifs from intergenic ncRNAs
Based on the gene expression profiles generated from 95 bovine
transcriptome libraries, we identified 21 sequence specific motifs
from 59 intergenic ncRNAs and 29 from 39 intergenic ncRNAs
(Table S8, A & B). By comparison against known DNA motif
databasesusing TOMTOM,
‘‘160_1_5END’’ from59
end
‘‘086_1_3END’’ from 39 end intergenic ncRNAs, showed
significant similarity against known DNA motifs ‘‘ste11’’ and
‘‘ARF’’ respectively (p-value,1e-04 and FDR,0.05) (Figure 8
and Table S8). It is interesting to note that the number of ‘‘sense’’
sequence motifs of ‘‘ste11’’ (the motif is the same as the intergenic
ncRNA strand) is almost equal to the number of ‘‘antisense’’
‘‘ste11’’ motifs (the motif is complementary to the intergenic
ncRNA strand) (Table S8, A & B). 3 other motifs from 59
intergenic ncRNAs and 4 from 39 intergenic ncRNAs also showed
strong similarity (p-value,1e-04, FDR,0.5) against known DNA
motifs (Figure S3 and Figure S4). The numbers of ‘‘sense’’ and
‘‘antisense’’ sequence sites in intergenic ncRNAs are almost equal
for most of the identified motifs (Table S8, A & B and Figure S5).
After we removed all UTR-related RNAs from the 5 kb
intergenic ncRNAs and re-ran the motif identification procedure
with the same expression profiles and parameters, we still found 15
and 17 motifs from the remaining 59 and 39 intergenic ncRNAs.
However, all of these novel 32 motifs were different to the 50
originally identified motifs (Table S8, C & D). Only one novel 39
motif (136-1, [ACT]AG[AC]CATA[AGT]) showed similarity
with a known DNA motif FOXL1, which was also the best hit
for an originally identified 39 end motif (119_1_3END, [AC-
T]AAA[CT]ATA[GT]).
wefound
intergenic
that
ncRNAs
2 motifs,
and
Expression correlation and functional significance
Most of the identified intergenic ncRNAs reported from other
species were directly or indirectly involved in gene regulatory
networks. To understand whether there are correlations between
the expression of intergenic ncRNAs and corresponding neigh-
bour genes, we identified all intergenic ncRNA and neighbour
gene pairs with expression in at least one library based on the 95
bovine MPSS transcriptome data. Globally, there was no clear
correlation between the expression of intergenic ncRNAs and
corresponding neighbour genes no matter whether intergenic
ncRNAs were at the 59 end or 39 end of the genes (Figure 9).
Because many intergenic ncRNAs containing sequence motifs are
also close to regulatory genes, we checked the expression of these
‘‘motif and regulatory’’ intergenic ncRNAs across different
libraries (Figure S6). Some of these intergenic ncRNAs showed
negative expression correlation with neighbour genes. One of
these intergenic ncRNAs is the antisense transcript of protein-
coding gene ‘‘ZNFX1’’ (Figure S6). In human, the antisense
transcript of ‘‘ZNFX1’’ has been annotated as ‘‘ZNFX1-AS1’’ [33].
This antisense transcript in bovine might be the homolog of the
human ‘‘ZNFX1-AS1’’. This bovine ‘‘ZNFX1-AS1’’ does not show
high sequence conservation with 4 different human transcript
variants (Figure S7). It is also the host transcript of two possible
snoRNAs (SNORD12 and SNORD12B), which is consistent with
human ‘‘ZNFX1-AS1’’ (Figure S8) [33].
To understand the associations between the expression of
intergenic ncRNAs with other protein-coding genes, we used
MINE (Maximal Information-based Nonparametric Exploration)
to analyse the correlations between each intergenic ncRNA and all
RefSeq genes [27]. For most intergenic ncRNAs detected by the
RNA-seq data (191 out of 389 at 59 end and 1,678 out of 2,673 at
39 end), we identified significantly associated protein-coding genes
based on MIC (Maximal Information Coefficient) score, with
FDR#0.05 after multiple testing (Table S9), and many of these
showed significant associations with multiple protein-coding genes
in terms of their expression, with 35 out of 191 59 intergenic
ncRNAs and 425 of 1,678 39 end intergenic ncRNAs correlated
Figure 6. Positional bias distribution of ncRNAs with respect to neighbour genes. (A) Relative frequencies of ncRNAs with respect to the
distance from neighbour genes. 100 kb adjacent to TSS or TTS of genes is shown in these plots. 39 END means the ncRNA is located in the 39 flanking
region of its neighbour gene. 59 END means the ncRNA is located in the 59 flanking region of its neighbour gene. ‘‘Gene intervals’’ refers to the
intergenic region of two adjacent genes. (B) Relative frequencies of ncRNAs from neighbour genes partitioned with respect to transcription
orientation. The internal boxes represent the zoom in view of the relative frequencies from 5 kb to 20 kb.
doi:10.1371/journal.pone.0042638.g006
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Figure 7. Sequence conservation analysis of ncRNAs. (A, B & C) are based on phastCons score. (D, E & F) are based on GERP++ score. The
control line is based on a similar number of randomly selected non-transcribed genomic regions. A & D – ncRNAs compared to RefSeqs, B & E –
intergenic ncRNAs compared to intronic and C& F – 59 vs 39 ncRNAs and transcriptional orientation with respect to nearest neighbour genes.
doi:10.1371/journal.pone.0042638.g007
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with their neighbour genes (Table S9). 78 of the 191 59 intergenic
ncRNAs and 1,124 of the 1,678 39 end intergenic ncRNAs were
UTR-related RNAs.
Discussion
Identification of ncRNAs
While increasing numbers of studies have confirmed that
ncRNAs possess significant regulatory functions in different
biological pathways, their computational identification can be very
challenging. One current approach is to identify ncRNA based on
homology searches, such as sequence-based, profile HMM and
structure enhanced methods [34,35,36]. Compared to these
methods, our pipeline for ncRNA identification has two advantages
[37]. First, our ncRNAs were identified from transcriptome data.
Most homology-search-based methods use the entire genome
sequence as the starting point, so it is not obvious if the ncRNAs
identified by these methods are transcribed functional elements.
Normally, further experiments are required to validate the
expression of these functional elements. Second, most of the
homology search methods are based on multi-alignments or taking
known ncRNAs as a training set, so the output generated by these
programstendstoidentifyonlyconservedncRNAs.Conservationof
ncRNAs is not as obvious as mRNAs. Some ncRNAs, like miRNAs,
are indeed under strong selective constraint, but more ncRNAs,
especially long ncRNAs, seem to be less conserved than protein-
coding RNAs. By using stringent filters in our pipeline, we
effectively removed the protein-coding transcripts, and identified
different kinds of ncRNAs, which were not restricted to conserved
ncRNAs. For the time being we have ignored ncRNA transcribed
from repetitive elements, mostly retrotransposons, because it is
virtually impossible to map such sequences to a unique genomic
location and conservation scores for such sequences are only
available for ancestral retrotransposon insertions. However retro-
transposon ncRNAs may also be functional, as previous investiga-
tors have shown that transcripts of retrotransposon origin are
differentially regulated during development [38].
The existence of well-characterized ncRNAs in our ncRNA
dataset indicated that our pipeline was effective but also illustrated
how few ncRNAs were conserved on the basis of sequence
similarity. To avoid false positives, we relied on stringent criteria.
For example, when mapping transcripts to the genome, only
transcripts mapped with more than 90% coverage and greater
than 95% identity were kept for further analyses. This explains
why approximately 32% of the unique transcripts were classified
as ‘‘un-mapped’’ transcripts. These criteria ensured that we
removed contaminating and error rich sequences. Subsequently,
when filtering protein-coding genes using BLAST, transcripts with
hits (E-value,1e-5), regardless of coverage or percent identity in
bovine RefSeq or Swiss-Prot databases, were discarded. This
ensured that un-annotated distant paralogs or pseudogenes along
with protein-coding ESTs were removed from our ncRNA set.
As a result, our pipeline provides a tool to mine the abundance
of ESTs, which were originally used to identify protein-coding
genes. Many studies have confirmed that ESTs can be used to
detect ncRNAs. The most important evidence is the FANTOM
ncRNA dataset, which are mRNA-like ncRNAs identified from
mouse cDNAs [4]. NcRNAs identified from ESTs have also been
reported in other organisms [39,40]. Recently, a class of human
long ncRNAs with enhancer-like function was identified from
GENCODE annotation that, in part, relied on ESTs mapped to
non-protein-coding regions [9]. Because our analyses were based
on such stringent criteria, it is quite likely that our results represent
a conservatively low estimate of the number of long ncRNAs in a
mammalian transcriptome.
The genome-wide distribution of ncRNAs
According to previous RNA-seq and tiling-array studies, more
reads can be mapped to intronic than intergenic regions [5]. In
contrast, our data showed that there were more intergenic than
intronic ncRNAs in the bovine non-protein-coding transcriptome.
Introns are known to be rich sources of both small and long
ncRNA transcripts [41], but the larger number of conserved
intergenic ncRNAs that we identified indicated that there might be
Figure 8. Two sequence motifs from intergenic ncRNAs with significant similarity against known DNA motifs. For each comparison,
the upper one is the known DNA motif, and the lower one is the intergenic ncRNA sequence motif.
doi:10.1371/journal.pone.0042638.g008
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more functional regulatory transcripts embedded in the intergenic
regions of bovine genomes.
Previous research has shown that many ncRNAs are expressed
in tissue-specific fashion or are restricted to certain developmental
stages [42,43,44], which would likely manifest as singletons in the
pooled tissue, normalized EST libraries that account for almost all
of the bovine ESTs we analysed. Furthermore, the prevalence of
unspliced transcripts (Table 1) was also reported in ncRNAs by
Khachane et al. in a dataset of functional long ncRNAs [45]. These
features may explain that why ncRNAs are not as easily detected
as protein-coding genes in many situations.
The genome-wide map of ncRNA distribution in bovine
demonstrates that ncRNAs are more evenly spread throughout
the genome than protein-coding genes. This may mean that
ncRNAs have evolved differently to protein-coding genes, which
can form gene-rich regions by gene duplication [46]. This might
also partially explain the poor conservation of ncRNAs. The
different genomic distributions of ncRNA compared to genes is
reflected in the moderate correlation between the densities of
ncRNAs and protein-coding genes, indicating that many ncRNAs
may function as remote regulatory elements rather than regulating
their neighbour genes in some proximity based fashion. Previously,
ncRNAs have been experimentally demonstrated to regulate gene
expression by influencing the transcription process or chromatin
structure in trans-acting fashion [47,48,49]. Some of these newly
discovered enhancer-like long ncRNAs activate distant genes
rather than surrounding ones, at distances in excess of 300 kb [9].
The moderate correlation of ncRNA density with gene density is
also reflected in the fact that most bovine intergenic ncRNAs were
transcribed from regions near protein-coding genes, especially from
the 39 end. This distribution bias has been observed previously in
RNA-seq and tiling array expression experiments [4,29,50]. Our
results however, were based on long reads from most tissues and
developmental stages and were therefore unlikely to result from
short, ragged ends of run-on transcripts. Furthermore, while many
of these transcripts were found very near to genes, significant
numbers were also found thousands to tens of thousands of base
pairs away. Even in the UTR-related RNAs that we classified, there
are still a proportion (492 of 4,584) transcribed from the antisense
strand of protein-coding genes. Therefore, most of the intergenic
Figure 9. Scatter plot for the log10 ratio of expressions of intergenic ncRNAs and corresponding neighbour genes. Dots were binned
into 80*80 hexagons across the plot area. Different colours represent the dot count in each bin. A represents the expression of 59 end UTR-related
RNAs and neighbour genes. B represents the expression of 59 end intergenic ncRNAs with UTR-related RNAs removed and neighbour genes. C
represents the expression of 39 end UTR-related RNAs and neighbour genes, and D represent the expression of 39 end intergenic ncRNAs with UTR-
related RNAs removed and corresponding neighbour genes.
doi:10.1371/journal.pone.0042638.g009
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ncRNAs, which were transcribed from both strands near protein-
coding genes were inconsistent with trivial explanations such as
transcriptional noise or mis-annotated UTRs. We therefore need to
consider that these gene proximate intergenic ncRNAs may
function as either cis-regulatory elements of their neighbour genes
or as trans-acting regulatory sequences. Previous studies have
confirmed that there are functional ncRNAs transcribed from the
promoter, transcription start and terminal regions of protein-coding
genes in sense orientation [10,11]. Evidence for antisense ncRNAs
comes from a recent study, using tSMS (true Single Molecule
Sequencing)technology[12,29]. Inthis study,a novelRNAcopying
mechanism was proposed, capable of producing antisense poly(U)
small RNAs from the transcription start or terminal regions of
genes, confirming that some human ESTs result from this process
[12]. This is consistent with our results, where a significant fraction
of the gene-proximate antisensencRNAs were mappedvery close to
the 39 ends of genes. However, while the functional significance of
such antisense transcripts is unknown, this copying mechanism does
not explain the significant fraction of gene proximate ncRNAs
originating fromthe antisensestrandmuchfurther away from the 39
ends of genes. Even for the intergenic ncRNAs close to 39 end
neighbour protein-coding genes, in the same transcriptional
orientation, which might be transcribed from potential un-
characterized UTRs, there is also the possibility that they are
independent functional transcripts, which have been observed
mostly in human, mouse and fly genomes, and classified as uaRNAs
[30]. On balance it is difficult to come up with a reasonable,
consistent and trivial explanation for the occurrence of non-coding
transcripts such as our ncRNAs leading us to conclude that they
have a biological purpose.
Conservation level of ncRNAs
The vast majority of the ncRNAs we have identified did not
have detectable sequence similarity with well-annotated ncRNAs.
However, in general, the conservation analysis of bovine ncRNAs
based on phastCons and GERP++ score showed that ncRNAs
were less conserved than protein-coding genes, while still
exhibiting strong selection signatures. Our result was consistent
with previous studies, which demonstrated that ncRNAs might
experience different selective constraints compared to protein-
coding genes [7,9,51]. Our result was also consistent with the
possibility that ncRNAs might represent different ncRNA catego-
ries, each manifesting different levels of sequence conservation.
We observed that intergenic ncRNAs were slightly more
conserved than intronic ones. This finding indicated that there
might be more functional elements transcribed from the intergenic
regions of the genome, such as recently discovered novel ncRNAs,
including uaRNAs, PASRs, lincRNAs and enhancer-like RNAs,
identified from intergenic regions [7,9,10,11,30].
Sequence specific motifs identified from intergenic
ncRNAs
Previous studies have reported that there are small or long
ncRNAs transcribed from gene regulatory elements, like promoter
regions. A report from Hans et al. showed that there are ncRNAs
transcribed from promoter regions, which were named promoter-
associated RNAs [52]. These promoter-associated RNAs function
as recognition motifs to direct epigenetic silencing complexes to
the promoter regions of target genes. Promoter-associated RNAs
can also interact with transcription factor recognition sites to form
DNA:RNA triplexes, which then interact with the rDNA
promoter, mediating recruitment of DNMT3b and silencing
rRNA genes by epigenetic regulation [53]. The location of these 59
end bovine intergenic ncRNAs with respect to their corresponding
neighbour genes and the existence of common sequence motifs
indicate that these sequence motifs from intergenic ncRNAs may
function as recognition sites for RNA-binding proteins, which
form an RNA-protein complex to modulate target gene expres-
sion. Some sequence motifs from our 59 end intergenic ncRNAs
showed strong similarity with known DNA motifs and the almost
equal numbers of sense and antisense motifs distributed in these
transcribed 59 end intergenic ncRNAs indicated that they might
be compatible with different regulatory models. Both the sense and
antisense sequence motifs could bind with known DNA motifs to
form DNA:RNA triplexes that regulate gene expression as above.
Alternatively, it could also be the transcription of the intergenic
ncRNAs themselves that interferes with the binding of transcrip-
tion factors to target sites in promoter regions. It has been reported
that sequence motifs are widely distributed in the 39 UTRs of
protein-coding genes. They tend to be recognition sites of RNA-
binding proteins or target sites of miRNAs, which play important
function in mRNA stability or degradation [54]. The existence of
sequence motifs in intergenic ncRNAs indicates that a similar
regulatory system may also involve non-coding RNAs.
Expression correlation and functional significance
The poor expression correlation between intergenic ncRNAs
and their neighbour genes does not mean that they lack functional
significance. There are three arguments that support this view.
First the observed dynamic range of MPSS tag abundance for
intergenic ncRNAs was very similar to that of RefSeq tags. This
implies that similar levels or types of regulation exist for intergenic
ncRNAs and mRNAs. Second, the bovine MPSS expression
profiles we analysed were generated from multiple sources,
including different tissues/cell lines, different developmental stages
and different sexes [24]. Studies have confirmed that intergenic
ncRNAs tend to be expressed in tissue-specific or development-
specific ways [55,56]. Intergenic ncRNAs in different tissues or
developmental stages may be either repressed or activated. This
will make the expression correlation fuzzy and unpredictable when
these stages are pooled for analysis. Third, intergenic ncRNAs
might represent a wide spectrum of functional non-coding RNAs.
Different classes of ncRNAs use different mechanisms to regulate
gene expression. Some intergenic ncRNAs that are cis-regulators
might have strong correlations with their neighbour genes. While
intergenic ncRNAs functioning in trans might show poor
correlation with their neighbour genes. The MIC scores for each
intergenic ncRNA with all RefSeqs confirmed that many
intergenic ncRNAs showed strong correlations with a number of
non-neighbour protein-coding genes, which indicated that inter-
genic ncRNAs might have multiple targets and be involved in
multiple gene-regulation networks. In human, mouse and zebra-
fish, studies based on RNA-seq have also shown that there is no
strong expression correlation between intergenic ncRNAs and
neighbour genes at the global level [55,56].
In conclusion, we have demonstrated that EST data sets can be
useful for identifying ncRNAs or ncRNA precursors. Genomic
distribution and conservation analysis of ncRNAs suggested that
these transcripts were not of trivial origin and most originated
from genomic regions exhibiting signatures of negative selection or
conservation. Our results support the view that most ncRNAs are
functional in the context of the regulon hypothesis [57] and that
further studies should be aimed at validating this experimentally.
Finally we speculate that some of the gene proximate ncRNAs we
have identified may act as cis-regulatory gene expression elements
of regulatory genes through some as yet unknown mechanism(s),
but that most of them may be trans-acting.
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Supporting Information
Materials S1
(DOCX)
Supporting results and methods.
Figure
pipeline. The top line denotes three sequentially distributed
gene models, in which arrows represent the direction of
transcription.
(TIF)
S1
Classification of cis-NATs identified by
Figure
removed UTR-related RNAs. ‘‘URTs’’ represent ‘‘UTR-
related RNAs’’, which include 4,584 intergenic ncRNAs.
(TIF)
S2
Most ncRNAs are still conserved after
Figure S3
ncRNAs with strong similarity against known DNA
motifs. For each comparison, the upper motif is the known
DNA motif, and the lower one is the sequence motif from
intergenic ncRNA.
(TIF)
Three sequence motifs from 59 intergenic
Figure S4
ncRNAs with strong similarity against known DNA
motifs. For each comparison, the upper motif is the known
DNA motif, and the lower one is the sequence motif from
intergenic ncRNA.
(TIF)
Four sequence motifs from 39 intergenic
Figure S5
nic ncRNAs tend to have equal numbers of sense and
antisense target sites. The target site means the sequence
region of the motif in its host intergenic ncRNA.
(TIF)
The sequence motifs identified from interge-
Figure S6
ry’’ intergenic ncRNAs and corresponding neighbour
genes across different libraries. The ‘‘motif and regulatory’’
represents intergenic ncRNA with motif(s) and regulatory
neighbour gene. The dots linked with coloured line represent
the expresion of one intergenic ncRNA and its neighbour gene
across different libraries. A represents 59 end UTR-related RNAs.
B represent 59 end intergenic ncRNAs with UTR-related RNAs
removed. C represent 39 end UTR-related RNAs, and D represent
intergenic ncRNAs with UTR-related RNAs removed.
(TIF)
Expression profiles of ‘‘motif and regulato-
Figure S7
like’’ ncRNA and four different human ‘‘ZNFX1-AS1’’
transcript variants.
(PDF)
Sequence alignment of bovine ‘‘ZNFX1-AS1-
Figure S8
like’’ intergenic ncRNA. The genomic location of bovine
‘‘ZNFX1-AS1-like’’ intergenic ncRNA and corresponding protein-
coding gene ‘‘ZNFX1’’ is shown in A. The zoomed in view of
‘‘ZNFX1-AS1-like’’ ncRNA is shown in B.
(TIF)
Genomic overview of bovine ‘‘ZNFX1-AS1-
Table S1
table contains a detailed description of bovine EST libraries
downloaded from NCBI.
(XLSX)
Library information of all bovine ESTs. This
Table S2
pipeline.
(DOCX)
Summary of the programs used in the
Table S3
bour genes of intergenic ncRNAs. This table contains the
over-represented GO terms for the 59 end and 39 end neighbour
genes as well as 10 control gene sets for each end.
(XLSX)
Functional over-representation of the neigh-
Table S4
ncRNAs. This excel table contains two sheets: The first one is
the genomic coordinate file with PSL format and based on genome
assembly bosTau4; the second one is the annotation for the
intergenic ncRNAs.
(XLSX)
Genome coordinates of predicted bovine
Table S5
(DOCX)
Summary of annotated known ncRNAs.
Table S6
NONCODE2.0. This excel table contains ncRNA annotation
based on Rfam and NONCODE2.0.
(XLSB)
Known ncRNAs identified by Rfam and
Table S7
table contains all the known cis-NATs that were identified from
bovine ESTs.
(XLSX)
Summary of identified cis-NATs. This excel
Table S8
intergenic ncRNAs. This excel table contains 4 sheets: motifs
identified from all 59 end intergenic ncRNAs with neighbour genes
in less than 5 kb distance; motifs identified from all 39 end
intergenic ncRNAs with neighbour genes in less than 5 kb
distance; motifs identified from 59 end intergenic ncRNAs with
UTR-related RNAs removed; motifs identified from 39 end
intergenic ncRNAs with UTR-related RNAs removed.
(XLSX)
Summary of the motifs identified from
Table S9
with 59 end intergenic ncRNAs and 39 end intergenic
ncRNAs. This excel table contains results of genome wide MINE
correlation analysis for the 59 end and 39 end intergenic ncRNAs.
(XLSX)
Summary of significantly correlated genes
Acknowledgments
The authors wish to thank Dan Kortschak, Udaya DeSilva and Jerry
Taylor for valuable discussions and critical reading of drafts.
Author Contributions
Conceived and designed the experiments: DLA. Performed the experi-
ments: ZQ. Analyzed the data: ZQ DLA. Wrote the paper: ZQ DLA.
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The Bovine Non-Coding Transcriptome
PLoS ONE | www.plosone.org 14August 2012 | Volume 7 | Issue 8 | e42638