iDarwin AS 0021 (2021 AD)

Volume 1

Review Article
iDarwin Volume 1, pp. 3-36
Published on February 12, AS 0021 (2021 AD)

Conserved noncoding sequences: Evolving puzzles
Isaac Adeyemi BABARINDE
Department of Biological Sciences
Southern University of Science and Technology
1088 Xueyuan Avenue, Shenzhen, P. R. China, 518055
Email: babarindeia@sustech.edu.cn; iababarinde@gmail.com

Abstract Majority of the molecular evolutionary studies in the pre-genomic era were focused on
the protein-coding parts which account for less than 2% of the human genome and are mostly
evolutionarily conserved. However, the abundance and the properties of noncoding functionalsequences were a puzzle. With the advent of sequencing technologies and the release ofvertebrate genome sequences, it became obvious that certain noncoding parts of the genomes
tend to be evolutionarily conserved over hundreds of millions of years. These regions of the
genome with specific properties, termed conserved noncoding sequences (CNSs), are usually3computationally discovered. Unlike genes with specific transcription units, the definitions of
CNSs are vague and thresholds are often set to delimit the regions under conservations and otherregions that are neutrally evolving. Thought to be regulatory elements, many of these sequences
have been shown to be transcribed and deletions have occasionally had no observable effects.
The puzzles surrounding CNSs are evolving. In this review, I discuss the various definitions of
CNSs and the computational approaches available for discovering them with the accompanied
puzzles. The reported features of the CNSs and the reported functionalities are discussed. I
conclude this review by discussing the current puzzles in the studies of CNSs.
Keywords: Conserved noncoding sequences; computational search; junk; regulatory elements;
gene deserts
Introduction
Numerous studies reported in the pre-genomic eras have suggested that genomic functionalsequences might reside outside the protein-coding regions (Dawson et al. 1995; Hezroni et al.
2017; King and Wilson 1975; Lou et al. 1995; Oeltjen et al. 1997a). If the sequences arefunctionally important, they would not evolve neutrally (Kimura 1983). Rather, obvious
signatures of evolutionary constraints would be expected (Kimura and Ohta 1974). The regions
outside the protein coding parts of the genomes that show detectable signatures of evolutionary
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constraints are referred to as conserved noncoding sequences, abbreviated as CNS (Hardison
2000; Loots et al. 2000) or conserved noncoding elements abbreviated as CNEs (McEwen et al.
2006; Vavouri et al. 2007). Some of the other variants of the name (Table 1) include
ultraconserved elements or UCEs (Bejerano et al. 2004; Siepel et al. 2005), long conserved
noncoding sequences or LCNSs (Sakuraba et al. 2008), highly conserved noncoding regions or
HCNRs (De La Calle-Mustienes et al. 2005). Each of the names is often defined by the percent
identity and/or length thresholds (Table 1). For some studies (Lowe et al. 2011; Siepel et al.
2005), there is no specified constant thresholds. Rather, definition of constraint is based onmodels that are able to distinguish functional regions from neutrally evolving ones (Table 1). In
cases with variable thresholds, the studies used specific models to delimit conserved regions
from neutrally evolving regions. Such models might have no rigid and constant identity and/or
length thresholds. For Siepel et al. (2005), sliding window of 5 bp was used.
Unlike mRNA, lncRNAs or transcribed enhancers, CNSs are not necessarily transcribed
but are rather computationally discovered. Using various computational strategies, conserved
noncoding regions have been reported in numerous taxonomic groups such as mammals(Babarinde andSaitou 2013, 2016; Bejerano et al. 2004; Loots et al. 2000; Mahmoudi Saber andSaitou 2017; Saber et al. 2016), insects (Brody et al. 2020; Glazov et al. 2005), plants(Hettiarachchi et al. 2014; Van de Velde et al. 2016) and other species (Siepel et al. 2005;
Vavouri et al. 2007).
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Table 1: Representative names for the conserved noncoding part of the genomes

6Definitions of CNSs
By definition, any region of the genome that is under evolutionary constraint but does not codefor protein is defined as CNS (Hardison 2000; Kellis et al. 2014). Although the definition isgrammatically simple, the actual identification of the CNSs in a genome is not such a simple
task and different approaches have been made to get the task done. The first task is thedefinition of “coding”. In the pre-genomic era, “coding” was mostly used to represent proteincoding part of the genome (Poon et al. 1978). However, it is clear that not all the parts ofprotein-coding genes code for amino acids. Therefore, intron should be considered as noncoding(Hardison 2000). Furthermore, not all exons code for amino acid. Therefore, UTR might beconsidered noncoding, if the definition of “coding” is protein-coding part of the genome. With
increasing sequencing power, many more noncoding RNAs are now being discovered (Iyer et al.
2015; Zhao et al. 2016). In fact, majority of human genome has been reported to be“biochemically active”, an expression used to describe transcribed regions or regions bound or
modified by proteins or other elements (ENCODE Project Consortium 2012). Although thesetranscripts exist, they mostly do not code for amino acids (Iyer et al. 2015; Zhao et al. 2016),
and so can be referred to as noncoding. For simplicity, this review defines “noncoding” as“genomic regions that do not code for protein”. Therefore, “noncoding” can include intergenic
regions, intronic regions and untranslated regions (UTRs), irrespective of transcription. This
represents a proportion of the genomic sequences that are not translated. Of course, each part of7

the genome is operating under different intensities of evolutionary forces (Figure 1A). CNSs
and UCNEs show conservation level higher than those of the UTR, intronic and intergenic
regions. Comparison of three noncoding sequences shown in Figure 1B shows very little
overlaps among transcribed enhancers, lincRNAs and CNSs.
The second aspect of the definition of CNS implies that the region must be underevolutionary constraints stronger than those operating on neutrally evolving regions (Babarinde
and Saitou 2013). Then the next task is defining neutrally evolving regions. The classic
definition of neutrally evolving regions includes intron and intergenic regions (Kimura 1983).
These are the exact regions where CNSs are located. And the presence of transposable elementsin these regions can make the expectation under neutral evolution much more complicated. A
number of studies (Babarinde and Saitou 2013; Hettiarachchi and Saitou 2016; Saber et al.
2016; Takahashi and Saitou 2012) masked repeats in the genome, effectively shifting the focus
away from transposable elements, which might be functionally important (Hutchins and Pei
2015; Kim et al. 2012; Ramsay et al. 2017).
Figure 1. Functional constraint and genomic overlap of noncoding sequences in humangenome. A. The PhyloP scores were obtained for CNSs (Babarinde and Saitou 2016), UCNEs (Dimitrieva and Bucher2013), transcribed enhancers (Andersson et al. 2014), coding exons, UTRs, lincRNAs, introns and intergenic regions. The
vertical axis represents the proportion of the distribution. B. CNSs, transcribed enhancers and lincRNA exons have small
overlaps. C. Different criteria give imperfect but significant overlaps. Assuming 51.4% unique genomic regions (Hutchins8and Pei 2015), this much overlap is less likely to be obtained by chance (binomial p value < 2.2 × 10 -308). Unless otherwise
stated, the genomic coordinates were obtained from version 96 of Ensembl database. For B and C, the first values were thenumbers of sequences while the second values were the numbers of base pairs covered.

9In identifying genomic regions under evolutionary constraints, statistical tests are often
conducted to separate regions of interest from neutrally evolving regions. Two parameters areoften checked. The first parameter is sequence conservation. These are sometimes estimated aspercent identity (Babarinde and Saitou 2013; Hettiarachchi and Saitou 2016; Kellis et al. 2014;
Takahashi and Saitou 2012). However, conservation score per site can also be calculated byphyloP (Pollard et al. 2010), PhastCons score (Siepel et al. 2005), GERP (Pollard et al. 2010) or
SiPhy (Garber et al. 2009). The second parameter is length. Evolutionary strength is often
estimated as sequence constraint over certain length. For example, UCE (Bejerano et al. 2004)
requires 100% sequence identity over >=200bp between human and mouse. LCNSs (Sakuraba et
al. 2008) have at least 95% identity over >=500bp. The sequence identity thresholds are often
arbitrary. That is why Babarinde and Saitou (2013) proposed thresholds that were informed byprotein-coding gene properties. Examples of the definitions of CNSs are presented in Table 1.
Various definitions of criteria make overlap between regions reported from different studies not
perfect, but still significantly more than random expectations (binomial p value < 2.2 × 10 -308,
Figure 1C). The binomial test was conducted to find the probability of having at least that many
nucleotide overlaps under the assumption that the sequences come from the 51.4% unique partof human genome (Hutchins and Pei 2015).
Digging into the junk: Computational tools for the discovery of CNSs
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The search for CNSs starts with the acquisition of genome sequence and annotation data. The
search can be restricted to certain genomic region (Bulger et al. 1999; Hardison et al. 1997;
Loots et al. 2000; Oeltjen et al. 1997b; Poon et al. 1978) based on gene neighborhood (Bush and
Lahn 2005; Jareborg et al. 1999) or genome wide (McEwen et al. 2006). Many studies(Babarinde and Saitou 2013; Hettiarachchi and Saitou 2016; Saber et al. 2016; Takahashi and
Saitou 2012) use repeat-masked genome sequences as the repeat sequences are usually complex
to analyze. The annotation data are usually used to mask the coding parts of the genome suchthat only the noncoding part of the genome is searched (Babarinde and Saitou 2013). The searchfor conserved regions can be initiated from each nucleotide position or genomic regions.
Genomic position scores such as GERP (Pollard et al. 2010) and phyloP (Pollard et al. 2010) canbe used to identify the conservation level of each genomic position. Other scores such as
phastCons (Pollard et al. 2010) and SiPhy (Garber et al. 2009) can also be used, and are alsoavailable for short genomic regions. The scores for each genomic position can be easily obtained
from UCSC database (Haeussler et al. 2019) or other sources like PhastWeb (Ramani et al.
2019). Using sliding window analyses, regions that are evolving significantly below neutral
expectations can be retrieved. This approach was employed by some previous studies (e.g.
Siepel et al. 2005).
Another approach is to download the alignment files from databases like UCSC (Haeussler
et al. 2019). Using sliding window analyses, regions that satisfy specified percent identity and11length thresholds are then extracted as done by Takahashi and Saitou (2012). Another common
way is to use pairwise searches with local alignment tools such as BLAST (Altschul et al. 1997)
or BLAT (Kent 2002) to identify regions conserved between two species at specified percent
identity and length thresholds. In this approach, the species divergence should be considered toeffectively delimit neutrally evolving sequences from functional ones. If species are
phylogenetically too close, more stringent conditions should be applied (Saber et al. 2016;
Takahashi and Saitou 2012). However, less stringent conditions can be applied if the species are
significantly divergent (McEwen et al. 2006; Siepel et al. 2005; Van Hellemont et al. 2005;
Woolfe et al. 2004). This consideration also applies to overall evolutionary rates of the species
being considered (Babarinde and Saitou 2013; Takahashi and Saitou 2012) as the heterogeneityof evolutionary rates across species has been established (Babarinde and Saitou 2020). Using areference species, regions that are conserved across multiple species can then be retrieved. Thisapproach has been employed in multiple studies (Babarinde and Saitou 2016; Hettiarachchi and
Saitou 2016; Mahmoudi Saber and Saitou 2017; Saber et al. 2016). One major challenge withpairwise search is the false discovery rates of short regions (Kamoun et al. 2013; Lai et al.
2017). To circumvent this issue, STAG-CNS (Lai et al. 2017) was published with the ability toidentify CNSs as short as 9bp. CNEFinder (Ayad et al. 2018) offers the users the opportunity to
input several thresholds to retrieve CNSs. Attempts have been made not only to discover CNSs,

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but also to prepare accessible databases of the discovered CNSs (Dousse et al. 2016; Inoue andSaitou 2020; Persampieri et al. 2008; Woolfe et al. 2007).
Identified features of CNSs
CNSs are known to have genomic distribution that are different from random expectation. First,
they tend to occur in clusters close to certain types of genes (Matsunami et al. 2010; Mignone etal. 2008; Takahashi and Saitou 2012). Their closeness to genes involved in processes such as
developmental, transcription and nervous systems (Babarinde and Saitou 2013) suggests thatthey might regulate the expression of such genes. Compared to lincRNAs and random intergenic
sequences, CNSs found in intergenic regions tend to be located farther from transcription startsites or TSSs (Babarinde and Saitou 2016). This suggests that distance might not be a barrier for
the functionality of CNSs.
In fact, Visel et al. (2009) reported that ChIP-Seq can accurately predict regulatory
elements. Figure 2A shows that the enrichment pattern of p300 gene which marks enhancer
activity (Raisner et al. 2018; Visel et al. 2009) is different between intergenic CNSs andrandomly selected genomic sequences. Specifically, CNSs tend to be located in the peaks ofp300 ChIP-Seq in mouse embryonic forebrain (Visel et al. 2009) suggesting the CNS activities
at this stage. Two things stand out in Figure 2A. First, average ChIP-Seq outside CNSs is higher
than random averages, suggesting that CNSs and ChIP-Seq do not share the same boundary.
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Second, only a subset of CNSs are enriched in mouse embryonic forebrain p300 binding,highlighting CNS tissue specificity as previously reported (Babarinde and Saitou 2016). This
p300 enrichment and enrichment of genes involved in transcription suggest that CNSs might beenriched in certain transcription factor binding motifs (TFBM). Indeed, the overrepresentationof certain motifs has been reported (Babarinde and Saitou 2016). For example, TFBM analyses
using AME (McLeay and Bailey 2010) in MEME Suite (Bailey et al. 2009) with default settings
returned 217 transcription factors from human and mouse full HOCOMOCO database(Kulakovskiy et al. 2018). The results indicated that about 70% of the intergenic CNSs from
Babarinde and Saitou (2016) have NKX2-2 binding motifs. The top 10 most represented
transcription factors are shown in Figure 2B. It is important to note that the number of CNSs
with TFBMs for each transcription factor is significantly higher than the corresponding number
in randomly selected genomic sequences (adjusted p value of Fisher exact test is shown in
Figure 2B). For each TFBM, the enrichment was calculated as the number of CNSs with the
motif divided by the number of randomly selected genomic sequences with the motif. The top
10 enriched TFBMs which include Tfap2b, HEY and Arnt are shown in Figure 2C.
Figure 2. CNS binding motif enrichments
A. CNSs are found at the peak of P300 bound regions. The short reads sequences, obtained from embryonic forebrain (Viselet al. 2009) were aligned with BWA (Li and Durbin 2010). Deeptools (Ramírez et al. 2016) was then used to plot the profile
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and heatmaps of intergenic CNSs with 2kbp
flanking regions. Similar analyses were also
conducted for random sequences of the similar
number and lengths. The top panel represents
the average normalized read count over 20 bp
windows. The horizontal axis in the top andbottom panels are the positions, relative to
CNSs. Each row in the lower panel represents
each genomic region. B. The top 10
transcription factors with the most represented
TFBM in CNSs are presented. Mouse
intergenic CNSs and randomly selectedintergenic sequences were analyzed using
AME package (McLeay and Bailey 2010) in
MEME Suite (Bailey et al. 2009). Human andmouse (HOCOMOCO v11 FULL) motif
database (Kulakovskiy et al. 2018) was used.
For other parameters, default values were used.
C. The enrichment scores of the top 10
TFBMs. Enrichments were computed by
dividing the number of motif-containing CNSs
by the number of motif-containing random
sequences. The colors of the bars in B and C
correspond to the adjusted Fisher exact test pvalues reported in AME.
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Associating a CNS to the closest TSS, Figure 3 shows the enrichment of 1,385 genes withat least four associated CNSs. As previously reported (Elgar and Vavouri 2008; Hettiarachchi
and Saitou 2016; Ishibashi et al. 2012; Matsunami et al. 2010), there is higher enrichment ingenes associated with development. The topmost associated gene ontology terms include headdevelopment, embryonic morphogenesis and others (Figure 3). These gene ontology terms areheavily connected in gene networks (Figure 4) suggesting interconnections in activities.
Figure 3. Gene ontology enrichment analysis of CNS-associated genes
CNSs tend to cluster around genes
associated with development. CNSs inmouse genomes reported by Babarinde
and Saitou (2016) were associated to thegenes with the closest TSS. The top 1385
genes with at least four associated CNSs
were then analyzed using Metascape(Zhou et al. 2019).

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Figure 4. Network analysis of CNS-associated genes
The top 1000 CNS-enriched genes are heavily connected in functions. The network presented was produced with Metascape(Zhou et al. 2019).

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Associated gene enrichment pattern suggests that CNS activities might be specific tocertain stage and tissues. For example, enrichment of genes involved in development andnervous system implies that CNSs might be more active in embryonic brain (Dickel et al. 2018;
Saber et al. 2016). Indeed, we previously found evidence of CNS activities in mouse embryonicbrain (Babarinde and Saitou 2016). The evidence involved ChIP-Seq signal and the conservationin expression. Conversely, genes associated with defense and immunity and response to stimulus
were found to be underrepresented in CNSs (Babarinde and Saitou 2016; Mahmoudi Saber andSaitou 2017). Further, genes expressed in testes or housekeeping genes with ubiquitous
expressions were found to be underrepresented in CNSs (Babarinde and Saitou 2016). Theseobservations reveal the specificity of CNS activities. Another nature of CNSs is the GC content
heterogeneity (Babarinde and Saitou 2013; Hettiarachchi and Saitou 2016). We previously
reported that tetrapod CNSs of different evolutionary ages, associated with recently acquired ormore ancestral functions, tend to have different GC contents (Babarinde and Saitou 2013). Other
studies (Hettiarachchi et al. 2014; Hettiarachchi and Saitou 2016) also showed that theheterogeneity exists also in non-tetrapod species. Interestingly, the GC contents of CNSs tend to
be different from those of the flanking regions (Hettiarachchi and Saitou 2016). This GC content
patterns have been associated with nucleosome occupancy (Dekker 2007; Hettiarachchi and
Saitou 2016; Zhu et al. 2011).

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Another interesting feature of CNSs is the conservation of CNS-TSS distance. The distanceconservation was reported by (Babarinde and Saitou 2016). Comparing CNS-TSS distance inhuman and mouse, the study showed that genes associated with conserved CNS-TSS distance inhuman and mouse tend to have higher expression correlation, suggesting more stable expression
across evolutionary timescales. The evolutionary analyses suggested that proper CNS function isdependent not only on the nucleotide sequences, but also on the genomic location of the CNSs.
Later, Bagadia et al. (2019) further explored this possibility and found that indeed, evolutionary
loss of genomic proximity of CNSs impacts expression dynamics during mammalian brain
development. They reported that CNS-gene proximity interrupted by mechanisms such as
chromosomal rearrangements could cause brain abnormality of germ line origin. These studies
reveal another level of the functionality of CNSs.
Functionality of CNSs
It is known that different genomic regions have different evolutionary rates (Kimura 1983).
Therefore, high cross-species sequence similarity found in genomes could be due to mutation
cold spot or actual evolutionary constraint. However, derived allele frequency analyses showedthat CNSs are under evolutionary constraint (Asthana et al. 2007; Drake et al. 2006; Ishibashi etal. 2012; J. Xie et al. 2018). Therefore, high sequence conservation in CNSs indicates
functionality but does not necessarily indicates what type of functions they perform. However,19

since the discovery of abundant CNSs in vertebrate genomes, many studies have attributed
CNSs to regulatory functions (Fishilevich et al. 2017; Frankel et al. 2010; Ishibashi et al. 2012;
Osterwalder et al. 2018; Sumiyama et al. 2012; Visel et al. 2007). CNSs tend to cluster around
specific genes (Figures 3 and 4), and there is an overrepresentation of certain TFBSs (Figures
2B and C). Furthermore, ChIP-Seq analyses show that many CNSs function as, or at least
overlap, regulatory sequences (Visel et al. 2009). In a previous study (Babarinde and Saitou
2016), we demonstrated that CNSs have substantial signatures of regulatory functions and genes
associated with more CNSs tend to have more stable expression patterns as indicated byexpression correlations between mouse and human. Although many of these studies arecomputationally conducted, a good number of studies have experimentally validated regulatory
functions of CNSs through in vitro enhancer assay. Furthermore, phenotypic effects of the
genomic deletions of some CNSs have been reported. For example, a number of studies (Furniss
et al. 2008; Lettice et al. 2002; Sagai et al. 2005) have attributed abnormal limb deformity andpolydactyly to loss of certain CNSs. Vista (Visel et al. 2007), CONDOR (Woolfe et al. 2007)
and Genehancer (Fishilevich et al. 2017) are databases of CNSs, including those withexperimentally confirmed enhancer activities. CNSs have also been reported to have silencing
functions (Hermann and Heckert 2005; Mahmoudi Saber and Saitou 2017). Consequently, many
CNSs are believed to be involved in regulating proximal gene expression (Babarinde and Saitou
2016; Nelson et al. 2013).
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There are other reported possibilities of CNS functions. For example, Hezroni et al. (2017)
reported that some pseudogenes are conserved across species. The conservation of promoters ofthese pseudogenes is particularly reported to be relatively high. Whether the conserved
sequences of these pseudogenes acquired new functions after pseudogenization or they had
another function before pseudogenization is often not very clear. Also, recent improvement insequencing technologies has made it possible to detect even lowly expressed transcripts. This
has led to the annotation of tens of thousands of lncRNAs (Harrow et al. 2012; Lagarde et al.
2017; Uszczynska-Ratajczak et al. 2018; C. Xie et al. 2014). Low expression level andextremely high tissue and stage specificity of lncRNAs (Mattioli et al. 2019; Necsulea et al.
2014; Talyan et al. 2018) make their detection difficult. Cheaper and better sequencing
technologies however have made it possible to detect more lncRNAs. Therefore, there is apossibility that some of the CNSs might correspond to lncRNAs or other classes of RNAs.
Indeed some ultraconserved elements have been reported to be transcribed in human cancers(Peng et al. 2013). However, there is a little overlap between GENCODE lincRNA exons and
CNSs (Figure 1B). Also, ENCODE project (ENCODE Project Consortium 2012) ascribed some“biochemical functions” to about 80% of the human genome. Some of these functions werelinked to the ChIP-Seq results. Specifically, data on DNA binding, transcription, DNA
accessibility and DNA methylation would give relevant insights about the functions of specificgenomic regions. Thus, overlap with ENCODE data might give insights into other likely
21

functions of CNSs. Finally, roadmap epigenome data (Bernstein et al. 2010) might be valuable
as some histone modification marks have been associated with enhancer activities (Babarinde
and Saitou 2016; Creyghton et al. 2010).
Unexpected switch: No obvious effect of CNS deletion on mutated individualsWith the extremity of CNS conservation at nucleotide level, the evolutionary constraint isbelieved to be extremely high. Therefore, deletion of such sequences is expected to result inseriously affected phenotype. In a surprising turn, no obvious phenotypic change was foundwhen 1.5 Mbp CNS-rich gene desert in chromosome 3 (Figure 5) and another 845 kbp genedesert in chromosome 19 were deleted in mouse (Nóbrega et al. 2004). The 1.5 Mbp region
contains 21 CNSs identified in (Babarinde and Saitou 2016). Similarly, deletion ofultraconserved elements have also been reported to yield viable mice (Ahituv et al. 2007). These
reports were puzzling, considering the level of constraints on CNSs. A number of studies haveshed some lights into the puzzle. First, the laboratory environment under which the mice wereraised might be very different from real environment in which the animals live. This possibility
is reiterated by a Drosophila experiment in which the deletion of an enhancer element led to an
observable phenotype only at a specific temperature range (Frankel et al. 2010).

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Figure 5. UCSC genome view of the mouse region deleted with no obvious phenotype
The genome view, captured from https://www.genome.ucsc.edu/ on 12th September, 2019, shows MMU3 of Nóbrega et al.
(2004) which corresponds to chr3:147287066-148764954 of mm10 build of mouse genome. This region contains 21 CNSs
identified in Babarinde and Saitou (2016).

23

From the population genetics point of view, the controlled environment and the smaller
effective population size could greatly reduce the impact of evolutionary force especially undersmall selection coefficient (Kousathanas et al. 2011; Mueller et al. 2013). Also, it is possible that
there is redundancy in CNS function. Such enhancer redundancy has been reported to provide
phenotypic robustness in mammalian development (Osterwalder et al. 2018). Another thing toconsider is the ability to measure phenotypic changes. This greatly impacts the observablephenotypic changes. For example, Dickel et al. (2018) reported that mice with deletions in eachor pairs of the studied ultraconserved elements were viable and fertile as previously reported(Ahituv et al. 2007; Nóbrega et al. 2004). However, further analyses revealed that in nearly allcases, there were neurological or growth abnormalities (Dickel et al. 2018). These abnormalities
included alterations of neuron populations and structural brain defects. They concluded thatsome of the phenotypic changes induced by deletion of ultraconserved elements are real but
might be too subtle to be discovered in normal laboratory settings (Dickel et al. 2018).
Therefore, inability to discover the phenotypic effect of a CNS might not imply lack of function;
it might just reflect the limited experimental ability to discover such phenotypic effects.
Current puzzles: unanswered questions
While some CNSs have strong phenotypic effects, others have rather subtle effects that are often
difficult to observe. However, not much is known about the characteristic differences of these24classes of CNSs. As numerous properties of CNSs have been enumerated, one of theseproperties might be different depending on the phenotypic effect. Another puzzle that has
attracted less attention is the reason for the length of CNSs. As an enhancer element which binds
a transcription factor, the typical TFBS can be shorter than 10 bp. However, many CNSs spans
more than 100bp (Table 1). What differentiates longer CNSs from shorter CNSs at the functional
level is not yet fully understood despite the understanding of super enhancers (Hnisz et al. 2013;
Pott and Lieb 2015; Whyte et al. 2013). With the rapidly falling sequencing cost, it is now
possible to detect new transcripts with tissue-specific expression. It is important to understandhow overlapping the transcripts and CNSs are, in a hypothetical scenario of monitoring
expressions across all stages and tissues as well as cell types. Finally, there are other epigenomic
signatures (Tsankov et al. 2015; W. Xie et al. 2013) of regulatory elements including transcribedenhancers (Andersson et al. 2014), protein-DNA interaction (Landt et al. 2012; Visel et al. 2009)and histone methylation or acetylation (Barski et al. 2007; Creyghton et al. 2010) investigatedby ChIP-Seq technologies as well as DNA methylation data (Sharifi-Zarchi et al. 2017). It
would be interesting to dissect the activities of these sequences to unravel the functional
dynamics of the sequences as they relate to gene expression regulation.
Conclusion

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The studies of CNSs have undergone dramatic evolution of puzzles. In the pre-genomic eras,
abundance and the nature of CNSs were a puzzle. After the release of multiple genomes, thepuzzles have morphed into those of functions. The inability to observe a phenotypic change
when CNSs were deleted was puzzling. However, new studies are illuminating the puzzle ofseemingly intact phenotypes in mutants despite the extreme conservation of CNSs. Therefore,
the puzzle is gradually evolving to another form. Now, the puzzle is how to extensively link theproperties of these sequences to their functional importance. Although a lot of puzzles have beensolved about CNSs, it appears that the puzzles are not static. The puzzles are evolving and as
they are evolving, so is the biological knowledge of properties and functions of these sequences.
List of abbreviations
BLAST: Basic Local Alignment Search Tool
BLAT: Blast-Like Alignment Tool
ChIP-Seq: Chromatin ImmunoPrecipitation Sequencing
CNEE: Conserved NonExonic Element
CNS: Conserved Noncoding Sequence
CONDOR: COnserved Non-coDing Orthologous Region
ENCODE: ENCyclopedia Of DNA Element
GERP: Genomic Evolutionary Rate Profiling
HCE: Highly Conserved Element
HCNR: Highly Conserved Noncoding Region
HCNS: Highly Conserved Noncoding Sequence
LCNS: Long Conserved Noncoding SequencelncRNA, lincRNA: long noncoding RNA, long intergenic noncoding RNA
RNA, mRNA: RiboNucleic Acid, messenger RNA
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STAG-CNS: Suffix Tree Arbitrary Gene number: Conserved Noncoding Sequence
TFBM: Transcription Factor Binding Motif
TSS: Transcription Start Site
UCE: UltraConserved Element
UCNE: UltraConserved Noncoding Element
UTR: UnTranslated Region

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Publication history of this article
November 18, AS 0020: review article was submitted from Dr. Isaac BABARINDE
to iDarwin (handled by SAITOU Naruya)
December 11, AS 0020: review result (one associate editor and SAITOU reviewed
this manuscript) was sent to author
December 31, AS 0020: revision was sent from author
January 5, AS 0021: this revision was accepted for iDarwin
January 11, AS 0021: first proof of this review article was sent to author
January 15, AS 0021: reply to first proof was sent from author
January 17, AS 0021: second proof of this review article was sent to author
January 31, AS 0021: third proof of this review article was sent to author
February 12, AS 0021: this review article is published in iDarwin

Dr. Isaac Adeyemi BABARINDE
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