Evolutionary analysis across mammals reveals distinct classes of long non-coding RNAs

RESEARCH Open Access
Evolutionary analysis across mammals
reveals distinct classes of long non-coding
RNAs
Jenny Chen1,2
, Alexander A. Shishkin3
, Xiaopeng Zhu4
, Sabah Kadri1
, Itay Maza5
, Mitchell Guttman3
,
Jacob H. Hanna5
, Aviv Regev1,6
and Manuel Garber4,7*
Abstract
Background: Recent advances in transcriptome sequencing have enabled the discovery of thousands of long
non-coding RNAs (lncRNAs) across many species. Though several lncRNAs have been shown to play important roles
in diverse biological processes, the functions and mechanisms of most lncRNAs remain unknown. Two significant
obstacles lie between transcriptome sequencing and functional characterization of lncRNAs: identifying truly non-coding
genes from de novo reconstructed transcriptomes, and prioritizing the hundreds of resulting putative lncRNAs
for downstream experimental interrogation.
Results: We present slncky, a lncRNA discovery tool that produces a high-quality set of lncRNAs from RNA-sequencing
data and further uses evolutionary constraint to prioritize lncRNAs that are likely to be functionally important. Our
automated filtering pipeline is comparable to manual curation efforts and more sensitive than previously published
computational approaches. Furthermore, we developed a sensitive alignment pipeline for aligning lncRNA loci and
propose new evolutionary metrics relevant for analyzing sequence and transcript evolution. Our analysis reveals that
evolutionary selection acts in several distinct patterns, and uncovers two notable classes of intergenic lncRNAs: one
showing strong purifying selection on RNA sequence and another where constraint is restricted to the regulation but
not the sequence of the transcript.
Conclusion: Our results highlight that lncRNAs are not a homogenous class of molecules but rather a mixture
of multiple functional classes with distinct biological mechanism and/or roles. Our novel comparative methods for
lncRNAs reveals 233 constrained lncRNAs out of tens of thousands of currently annotated transcripts, which we make
available through the slncky Evolution Browser.
Keywords: Long non-coding RNAs, Evolution, Comparative genomics, Molecular evolution, Annotation,
LincRNA, RNA-seq, Transcriptome
Background
Recent advances in transcriptome sequencing have led
to the discovery of thousands of long non-coding RNAs
(lncRNAs), many of which have been shown to play
important roles in diverse biological processes from
development to immunity and their misregulation has
been associated with numerous cancers [1–10]. Given the
importance of lncRNAs in biology and disease, there is
great interest in defining lncRNAs in new experimental
systems, disease models, and even primary cancer samples.
Yet, despite important progress in RNA-Sequencing (RNA-
Seq), the annotation and computational characterization of
lncRNAs from RNA-Seq data remains a major challenge,
with no easily accessible software available to accomplish
either task.
We previously described a widely adopted computa-
tional framework for filtering lncRNAs from RNA-Seq
transcript assemblies based on the presence of evolu-
tionarily conserved protein-coding potential [11–14].
* Correspondence: Manuel.Garber@umassmed.edu
4
Program in Bioinformatics and Integrative Biology, University of
Massachusetts Medical School, Worcester, MA 01655, USA
7
Program in Molecular Biology, University of Massachusetts Medical School,
Worcester, MA 01655, USA
Full list of author information is available at the end of the article
© 2016 Chen et al. Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0
International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and
reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to
the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver
(http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
Chen et al. Genome Biology (2016) 17:19
DOI 10.1186/s13059-016-0880-9

Yet, this approach is limited in both sensitivity and spe-
cificity: (1) it incorrectly classifies bona fide lncRNAs as
protein-coding simply because they are conserved; and
(2) it incorrectly classifies transcripts as lncRNAs when
they are actually extended untranslated regions (UTRs)
of coding genes, pseudogenes, or members of lineage-
specific protein-coding gene family expansions, such as
zinc finger proteins or olfactory genes. Previous lncRNA
cataloging efforts have addressed these issues by incorp-
orating additional filtering criteria along with extensive
manual curation to define meaningful lncRNA catalogs
[12, 13, 15] or by including specialized libraries that
better capture transcript boundaries [14, 16]. While these
approaches have proven to be extremely valuable, they
remain extremely labor-intensive and time-consuming,
even for experienced users.
To address this challenge, we developed slncky, a
method and accessible software package that enables
robust and rapid identification of high-confidence lncRNA
catalogs directly from RNA-Seq transcript assemblies
without reliance on evolutionary measures of coding
potential. slncky goes through several key steps to accur-
ately separate lncRNAs from coding genes, pseudogenes,
and assembly artifacts, while also identifying novel pro-
teins including small peptides. This approach yields a high
confidence lncRNA catalog. Indeed, when applied to
mouse embryonic stem cells, slncky accurately identifies
virtually all well-characterized lncRNAs and performs as
well as previous manually curated catalogs.
Comparative analysis remains an important approach to
assess potential function of a lncRNA without requiring
additional experimental efforts. Despite its importance,
identifying conservation of lncRNAs remains a challenge.
To address this need, slncky incorporates a comparative
analysis pipeline specially designed for the study of
RNA evolution.
Here we demonstrate the utility of slncky by applying
it to a comparative study of the embryonic stem (ES)
cell transcriptome across human, mouse, rat, chim-
panzee, and bonobo, and to previously defined data-
sets consisting of >700 RNA-Seq experiments across
human and mouse. When applying slncky to these
datasets, we discover hundreds of conserved lncRNAs.
Furthermore, our metrics for evaluating transcript
evolution show that there are clear evolutionary prop-
erties that divide lncRNAs into separate classes that
display distinct patterns of selective pressure. In par-
ticular, we identify two notable classes of ‘intergenic’
ancestral lncRNAs (‘lincRNAs’): one showing strong
purifying selection on the RNA sequence and another
showing only conservation of the act of transcription
but with little conservation on the transcript pro-
duced. These results highlight that lncRNAs are not a
homogenous class of molecules but are likely a
mixture of multiple functional classes that may reflect
distinct biological mechanism and/or roles.
Results and Discussion
slncky a software package to identify long non-coding
RNAs
To develop a simple and accessible method to identify
lncRNAs directly from RNA-Seq transcript assemblies,
we created slncky, a method that enables rapid identifi-
cation of high-confidence lncRNA catalogs directly from
an RNA-Seq dataset.
Determining a set of lncRNAs from reconstructed an-
notations involves several steps to ensure that transcripts
represent complete transcriptional units and that they are
unlikely to encode for a protein. Current methods for de-
fining coding potential rely on codon substitution models,
such as PhyloCSF [17] and RNACode [18], which fail in
three important cases: (1) they often incorrectly classify
non-coding RNAs as protein-coding – including TUG1,
MALAT1, and XIST – merely because they are conserved;
(2) they fail to identify lineage specific proteins as coding;
and (3) they erroneously identify non-coding elements
(for example, UTR fragments, intronic reads) as lncRNAs.
Rather than using codon substitution models, slncky
implements a set of sensitive filtering steps to exclude
fragment assemblies, UTR extensions, gene duplications,
and pseudogenes, which are often mischaracterized as
lncRNAs, while also avoiding the exclusion of bona fide
lncRNA transcripts that are excluded simply because they
have high evolutionary conservation.
To achieve this goal, slncky carries out the following
steps (Fig. 1a): (1) slncky removes any transcript that
overlaps (on the same strand) any portion of an anno-
tated protein-coding gene in the same species; (2) slncky
leverages the conservation of coding genes and uses
annotations in related species to further exclude un-
annotated protein-coding genes, or incomplete tran-
scripts that align to UTR sequences (Methods); and (3)
to remove poorly annotated members of species-specific
protein-coding gene expansions, slncky aligns all identi-
fied transcripts to each other and removes any transcript
that shares significant homology with another non-
coding transcript (Methods). The result is a filtered set
of transcripts that retains conserved, non-coding tran-
scripts that may score highly for coding potential, while
excluding up to approximately 25 % of coding or
pseudogenic transcripts normally identified as lncRNAs
by traditional approaches.
After removing reconstructions that are likely gene
fragments, pseudogenes, or members of gene family
expansions, slncky searches for novel or previously un-
annotated coding genes, using a method that is less
confounded by evolutionary conservation than codon
substitution models. Specifically, slncky uses a sensitive
Chen et al. Genome Biology (2016) 17:19 Page 2 of 17

Mouse
Reconstructions
Human
Reconstructions
Mouse
A
B
Reconstructions
Mouse
STEP 1:
Remove transcripts
that overlap annotated
coding genes
STEP 2:
Remove transcripts
that align to syntenic
coding genes
STEP 3:
Remove clusters
of transcripts from gene
duplication families
STEP 4:
Final set of lncRNAs
Frequency
Log10 (RNAcode P-value)
0
0–5–10–15
50
100
Addt’l lncRNAs
identified by slncky
Gene duplications
150
200
Tug1
Cyrano
ZFP ZFP ZFP
Malat1
Coding gene orthologs
lncRNAs
Coding genes
slncky lncRNAs
C
Log10(RRS)
0
1
2
3
4
–2
–1
Log10 (RNAcode P-value)
0–5–10–15
Coding genes
slncky lncRNAs
0
20
40
60
80
100
2400
1800
1200
600
0
Transcripts
Well-characterized
lncRNAs(%)
D
0
4000
8000
12,000
16,000
2120
44
W
ashietletal.
Necsulea
etal.
W
ashietletal.
Necsulea
etal.
slncky
slncky
GENCODE GENCODEV19 V7 V7V12 V12 V7 V12
Transcripts
E
Fig. 1 (See legend on next page.)

alignment pipeline to find orthologous transcripts
(Methods) and analyzes all possible open reading frames
(ORFs) (that is, sequences containing both a start codon, a
stop codon and containing at least 10 amino acids) that
are present in both species. For each ORF, slncky
computes the ratio of non-synonymous to synonymous
mutations (dN/dS) and excludes all annotations with a
significant dN/dS ratio (Methods). By requiring the pres-
ence of a conserved ORF that is transcribed in multiple
species, and by computing the dN/dS ratio across the
entire ORF alignment, slncky is more specific than con-
ventional coding-potential scoring software, which report
all high-scoring segments within an alignment.
Having developed a method to identify lncRNAs directly
from RNA-Seq data, we sought to characterize its
sensitivity and specificity by comparing lncRNAs identified
by slncky to the well-studied set of lncRNAs expressed in
mouse embryonic stem (ES) cells [11]. To do this, we gen-
erated RNA-Seq libraries from pluripotent cells obtained
from three different mouse strains cultured using previ-
ously described growing conditions [19, 20] and used de
novo reconstruction to build transcript models (Methods,
Additional file 1: Table S1). We then applied slncky
to define a set of 408 lncRNAs (Methods, Additional
file 1: Figure S1). Our analysis also identified four tran-
scripts – Apela, Tunar, 1500011K16Rik (LINC00116), and
BC094334 (LINC00094) – that contain conserved ORFs
with high coding potential (Additional file 1: Figure
S2A and 2B).
Several lines of evidence indicate that our identified
set represents bona fide lncRNAs: (1) slncky recovered
all of the 20 functionally characterized lncRNAs that are
expressed in the pluripotent state (Additional file 2),
demonstrating that our stringent approach is still sensi-
tive; (2) Our identified lncRNAs contain chromatin
modifications of active RNA Polymerase II transcription
(K4-K36), exhibiting similar levels as our previous ES
catalogs (approximately 70 %) [11, 21]; (3) lncRNAs
identified by slncky have significantly lower evolution-
ary coding potential scores than protein-coding genes
(P = 1.3 × 10−6
, t-test) (Fig. 1b); (4) slncky does not fil-
ter out known conserved lncRNAs, such as Malat1,
Tug1, Miat, that are often excluded due to significant
coding-potential scores (Additional file 1: Figure S2C);
and (5) our set of lncRNAs have a significantly reduced
ribosome release score (RRS) [22], a measure that accur-
ately predicts coding potential from ribosome profiling
data, than protein-coding genes (73-fold, P <2.2 × 10−16
,
t-test) (Fig. 1c).
Together, these results demonstrate that slncky provides
a simple and robust strategy for identifying lncRNAs from
a de novo transcriptome. Rather than requiring many user-
defined parameters, slncky learns filtering parameters dir-
ectly from the data making it useful across many different
species, including non-model organisms (Methods).
slncky provides greater sensitivity and specificity than
previous lncRNA catalogs
To verify the scalability and overall utility of slncky for
defining lncRNAs across multiple datasets in different
species, we ran slncky on GENCODE’s latest compre-
hensive gene annotation set (V19) totaling 189,020 tran-
scripts, of which 16,482 are annotated as lncRNAs that
do not overlap a coding gene [15]. GENCODE is an
ideal test case because it represents the current gold
standard lncRNA-annotation set, primarily because much
of its content undergoes extensive manual curation. Ap-
plying slncky, we identified 14,722 human lncRNA genes.
Importantly, these include >90 % of the lncRNAs identi-
fied by GENCODE, with only 136 human (0.9 %) anno-
tated protein coding gene, and 83 (0.6 %) annotated
pseudogenes identified as lncRNAs. Transcripts that are
annotated as lncRNAs by GENCODE but not by slncky
include 1,735 (12 %) transcripts that are part of a cluster
of duplicated genes, of which 123 (1 %) aligned to a
known zinc finger protein or olfactory gene. An additional
181 (1 %) transcripts were excluded because they aligned
significantly to an orthologous protein coding gene in
mouse (Fig. 1d).
We then compared our filtering strategy with two
previously published large-scale comparative studies that
were based on GENCODE annotations [23, 24]. For the
set of lncRNAs defined by Washietl et al. [24], slncky
was able to remove 9.6 % (156) of the annotations that
were likely a result of gene duplications and 1.2 % (19)
that aligned significantly to a mouse coding transcript.
In contrast, slncky only removed a handful of transcripts
(<0.1 %) from the Necsulea et al. dataset [23]. Importantly,
(See figure on previous page.)
Fig. 1 slncky sensitively filters lncRNAs from reconstructed RNA-Seq data. a Schematic of slncky’s filtering pipeline. Annotated coding genes are
shown in dark gray, reconstructed transcripts in medium gray, and filtered transcripts in light gray. b Histogram of log10(P values) of coding potential as
evaluated by RNACode (Washietl et al. [18]) for slncky-identified lncRNAs (gray) and coding genes (red). c Scatterplot of log10(P-values) of coding potential
(x-axis) and log10(ribosomal-release scores) (y-axis) of slncky-identified lncRNAs (gray) and coding genes (red). Distributions of ribosomal-release scores
(RRS) are displayed along right side of y-axis. Dotted lines denote one standard deviation above and below the mean of RRS distributions. slncky-identified
lncRNAs have significantly higher coding potential P-values and lower RRS than coding genes. d Comparison of previously published sets of lncRNAs to
slncky results. Number of transcripts also annotated as a lncRNA by slncky (gray), number removed by slncky as gene duplication or coding (light and dark
blue), and number of additional transcripts annotated as a lncRNA by slncky but not the previous pipeline (purple). e Percentage of well-characterized
lncRNAs identified in previously published sets compared to slncky results. Numbers above bars denote absolute number of lncRNAs

slncky was much more sensitive as it identified virtually all
well-characterized lncRNAs (20/21, Methods) compared
to only 20 % (4/21) by these previous reports (Fig. 1e).
Finally, we compared slncky to a recently published pipeline
for filtering reconstructed transcripts from RNA-Seq data,
called PLAR (Hezroni et al. [14]). We found that slncky
and PLAR performed comparably in removing coding gene
orthologs and gene duplications, but slncky remained more
sensitive in recovering well-characterized transcripts (33/36
recovered by slncky compared to 27/36 by PLAR)
(Additional file 1: Figure S3).
Together, our results highlight the power of slncky for
identifying a high-confidence set of lncRNAs by exclud-
ing known artifacts that are often mistaken for lncRNAs.
Furthermore, our results demonstrate that slncky per-
forms as well as manual curation for defining bona fide
lncRNAs and can even identify the challenging cases
that are often missed by curation efforts.
slncky enables detailed studies of lncRNA evolution
Having developed a method to define high-quality
lncRNAs, we sought to study the evolutionary properties
of lncRNAs. While comparative genomics has provided
important insights for studying proteins, enhancers, and
promoters [25–30], relatively little has been done to study
the evolution of lncRNAs. One of the main challenges is
that lncRNAs diverge rapidly, accumulating both base nu-
cleotide substitutions and insertion/deletion (indel) events.
Both of these properties render lncRNAs difficult to align
with conventional aligners and phylogenetic approaches.
To enable evolutionary analysis of lncRNAs, we imple-
mented a computationally efficient and sensitive strategy
to align lncRNAs and characterize their sequence and
transcript evolution (Fig. 2a, Methods). To this end,
slncky identifies the syntenic genomic region for a lncRNA
in the orthologous species. If a transcript exists in a
syntenic region, slncky aligns the two regions using a sen-
sitive seed-based local pairwise aligner [31]. To avoid the
possibility of spurious matches, slncky scores each align-
ment relative to a set of random intergenic regions from
the orthologous genome and only keeps alignments
that score higher than 95 % of the random intergenic
sequences (Methods).
Next, slncky characterizes sequence and transcript
conservation properties of orthologous lncRNAs. slncky
calculates four metrics: (1) A ‘transcript-genome identity’
(TGI) score, defined as the percent of lncRNA base pairs
that align and are identical to a syntenic genomic locus,
to characterize how well the transcript sequence is con-
served across the two species; (2) A ‘transcript-transcript
identity’ (TTI) score, defined as the percent of identical,
aligning base pairs found in the transcribed, exonic
regions of both lncRNAs, to characterize how much of
the transcript is transcribed in both species; (3) A ‘splice
site conservation’ (SSC) score, defined as the percent of
splice sites that are conserved across both lncRNAs, to
characterize conservation of transcript structure; and (4)
An ‘insertion/deletion rate’, defined as the log2 rate of
insertion/deletion events in exonic regions relative to
intronic regions, to provide an alternative measure of
sequence conservation (Fig. 2a).
We tested the performance of slncky’s orthology finding
step by reanalyzing previous studies of lncRNA conserva-
tion across mammals [24] and vertebrates [14, 16, 23]
(Methods). Our approach of aligning the two syntenic loci
rather than just the transcripts increases slncky sensitivity
with very little drop in specificity. In mammals, slncky
successfully identified the vast majority (>95 %, 1,466/
1,521 lncRNAs) of the previously reported orthologous
lncRNAs while also finding an additional 121 pairs
(8.0 %) of homologous human-mouse lncRNAs that
were previously reported as species-specific (Methods).
Similarly, in vertebrates, a four-fold greater evolution-
ary distance, slncky was able to recover 26 of 29 (90 %)
of the previously defined ancestral lncRNAs; the
alignments for the remaining three, although found, are
indistinguishable from alignments that can be randomly
found across syntenic loci and do not pass our signifi-
cance threshold (Methods). Furthermore, slncky identi-
fied an additional three pairs of vertebrate conserved
lncRNAs.
Together, these results demonstrate that slncky provides
an efficient, sensitive, and accessible method for detecting
and characterizing orthologous lncRNAs across any
pair of species, providing an important tool for studying
lncRNA evolution or for prioritizing lncRNAs based on
evolutionary conservation.
Evolutionary analysis reveals multiple lncRNA classes
characterized by distinct signatures
Initial work by us and others incorporating expression
data across species showed that the expression of
lncRNAs is often poorly conserved – with the rate of
transcript expression loss occurring faster than loss of
its genomic sequence identity across species [23, 24].
While these results provided important insights into the
evolution of lncRNAs, these analyses did not fully ex-
plore the properties of the conserved lncRNAs. Having
developed a method to comprehensively identify and
align lncRNAs across species, we sought to further
understand the evolutionary properties of lncRNAs. To do
this, we generated RNA-Seq data from ES cells derived
from three mouse strains (129SvEv, NOD, and castaneous),
rat, and human (Methods). We added additional pub-
lished RNA-Seq data for chimpanzee and bonobo iPS
cells [32] (Additional file 1: Table S1). The gene expres-
sion between species shows a similarly high correlation
to that previously observed for matched tissues across

species (Additional file 1: Figure S4), highlighting the
suitability of this set for comparative analysis.
Applying slncky, we identified 408 mouse, 492 rat, 407
chimpanzee, and 413 human lncRNAs (Additional file 1:
Figure S1, Additional file 3). We found that lncRNAs are
generally expressed only in a single species, despite the
fact that most lncRNA loci can be aligned across species
(Fig. 2b). In all, we found 73 (18 %) lncRNAs that are
expressed in pluripotent cells across all mammals and
are likely to be present prior to the divergence between
rodents and primates (Fig. 2c, Additional file 4).
Like previous catalogs, our lncRNAs fall into different
classes: miRNA host genes, snoRNA host genes, diver-
gently expressed lncRNAs that are transcribed in the
opposite orientation of a coding gene with which they
share a promoter (Methods), and a remaining set of
‘intergenic’ lncRNAs (lincRNAs). Interestingly, we found
that these classes have distinct patterns of sequence and
transcript evolution.
These classes exhibit modest, but distinct, differences
in transcript-genome identity (TGI), and striking differ-
ences in transcript-transcript identity (TTI) (Fig. 3a).
While the loci of miRNA host genes can readily be aligned
between species (that is, have similar TGI identity), their
transcript structure have diverged tremendously, with
8.5 % median TTI across humans and mouse. lncRNAs
divergently transcribed within 500 base pairs of a coding
gene have also diverged rapidly in TTI, except for se-
quence transcribed near the promoter. For these genes,
TTI is generally confined to the first exon. snoRNA host
transcripts are very well conserved in both sequence and
transcript structure, though we find an excess of indel
Mouse
Human
A
B C
STEP 1:
Find syntenic region
using liftOver
STEP 2:
Align syntenic region
using lastz and
reduced gap penalties
STEP 3:
Examine alignment at
lncRNA and calculate
evolutionary metrics
Gene X Gene YlncRNA
?
Mouse
MouseAlignment
to genome Rat
MouseRat
Chimp
rm1 naïve
rm1 primed
nod naïve
nod primed
cast naïve
cast primed
Rat primed
Rat naïve
Human
Human
Chimp
Bonobo
Rat
Human
Chimp
Bonobo
Human
Gene X Gene YlncRNA
Mouse
lncRNA
Human
genome
Transcript–Genome
Identity (TGI)
Splice site
conservation (SSC)
Insertion / Deletion
rate (IDR)
Mouse
lncRNA
Human
lncRNA
Transcript–Transcript
Identity (TTI)
# conserved
splice sites
Total splice sites
% indels in exons
% indels in introns
0 100
Sequence
identity (%)
Gene
expression
–3 1.5
log10 (FPKM)
356 rm1
nod
cast
407
407
350
492
124
(30%)
207
(51%)
73
(18%)
408
402
408
413
log2 ( )
0.6
10
1
Mouse
Fig. 2 slncky’s orthology pipeline discovers a small set of pluripotent lncRNAs conserved across mammals. a Schematic of slncky’s orthology
pipeline and metrics for measuring sequence and transcript evolution. b Top: Sequence identity of each lncRNA loci when aligned to syntenic
region of every other species. In the species of origin, sequence identity is 100 % (red); if no sytenic region exists, sequence identity is set at 0 %
(blue). Bottom: expression level of every lncRNA loci across studied species. Heatmap colors represent globally-scaled log10(FPKM) values with
log10(0) set to −3. log10(FPKM) values were floored at −3 (blue) and 1.5 (red). The majority of lncRNAs are alignable to syntenic regions of other
species but not expressed. c Number of lncRNAs found within each species and at each ancestral node (inferred by parsimony). Substitutions per
100 bp are given for each branch. Conservation of lncRNA transcription dramatically falls off even between closely-related species humans
and chimpanzees

events in exons (1.2-fold more) as compared to introns
(Fig. 3b). Finally, intergenic lncRNAs (lincRNAs) also have
conserved transcript structure but a 1.5-fold reduction in
exonic indel events compared to snoRNA hosts (Fig. 3b),
despite comparable intronic indel rates (Additional file 1:
Figure S5), suggesting that they undergo different selective
pressure than host genes. Most of the pluripotent-
expressed, well-characterized lncRNAs are found in this
class of lincRNAs, which displays high TTI and splice site
conservation (SSC). Two notable exceptions to the class
of lincRNAs are FIRRE and TSIX, which have very poor
TTI (5 % and 0.1 %, respectively). Both lincRNAs have
been previously reported as ‘conserved in synteny’ only
[14, 33], possibly indicating that they may belong to a
different class of lincRNAs. In addition to distinct differ-
ences in conservation of transcript structure, we found
that the turnover of transcription differ across lncRNA
classes: the majority of miRNA host and snoRNA host
genes show conserved transcription across mammals
(95 % and 87 %, respectively), whereas only a small per-
centage of divergent and intergenic genes show conserved
transcription (22 % and 7 %, respectively, Fig. 3c).
We note that some lncRNAs have been proposed to
have dual functions and our evolutionary metrics allow
us to further explore this possibility. For example, GAS5
is a known snoRNA host gene and has also been
a b
c
Fig. 3 Metrics of sequence and transcript evolution reveal distinct classes of lncRNAs. a Left: Schematic representing alignment signatures found
for miRNA host, divergent, snoRNA host, and intergenic lncRNAs. Alignments of identical base pairs transcribed in both species (that is, transcript-transcript
identity) is shown in light red while alignments of identical base pairs transcribed only in top species (that is, transcript-genome identity) is shown in light
blue. Right: Median transcript-transcript (TTI) (dotted lines) and transcript-genome identity (TGI) (solid lines) from mouse-human alignments of first three
exons of miRNA host (orange), divergent (blue), snoRNA host (purple), and intergenic (green) lncRNAs. Each class of lncRNAs displays distinct patterns of
TTI. b Boxplots of TGI and TTI, barplot of splice site conservation, and boxplot of insertion/deletion rate (IDR). c Number of lncRNAs in each class in mouse
(left), human (middle), and conserved across all studied species (right). Each lncRNA class has individual turnover rates, with miRNA and snoRNA host
genes highly conserved in transcription across mammals, and divergent and intergenic lncRNAs evolving much faster

reported to function as a RNA gene [34]. Interestingly,
we found that GAS5 has the typical signature of a
snoRNA host, with higher indel rates at exons relative to
its intronic regions (1.4-fold higher) (Fig. 3b, Additional
file 4), suggesting that GAS5, if truly functional as a
non-coding gene, likely acts through a different mechan-
ism than other intergenic lncRNAs.
We further note that these distinct signatures of evolu-
tion are robust enough to identify incorrectly annotated
transcripts. For example, based on current annotations,
LINC-PINT is an ‘intergenic’ lncRNA as the closest an-
notated coding gene, MKLN1, begins approximately
184 kb downstream [35]. However, its transcriptional
conservation pattern is typical of a divergent transcript,
with transcriptional identity confined only to its first
exon. Closer inspection of expression data from our and
other tissues [36] revealed that in fact, an unannotated,
alternative transcriptional start site of MKLN1 begins
less than 200 base pairs downstream, consistent with
LINC-PINT’s divergent alignment profile (Additional file
1: Figure S6).
We next sought to extend our evolutionary analysis
to larger catalogs of mouse and human lncRNAs
[15, 23, 24, 37]. Altogether, we searched for candidate
orthologs across 251,786 human and 25,335 mouse tran-
scripts corresponding to 56,280 and 15,508 unique lncRNA
loci (Fig. 4a) using default parameters of slncky. miRNA
hosts, divergent lncRNAs, and snoRNA host genes show
the same distinct evolutionary patterns that we observed in
pluripotent cells (Fig. 4b and c). Additionally, we found
that miRNA hosts that harbor miRNAs inside exonic re-
gions (for example, H19 [38]) show a distinct conservation
pattern reminiscent of lincRNAs (high TTI and SSC),
but without indel-constrained exons (Additional file 1:
Figure S7), consistent with the functional importance
of their exonic sequence.
In contrast to our previous analysis in matched pluripo-
tent cells, we found that the majority of the 1,861 candidate
orthologous intergenic lncRNAs identified from syntenic
locations in human and mouse have low TTI (<30 %) and
no conserved splice sites (approximately 61 %). Several lines
of evidence suggest that the majority of these poorly
aligning pairs may not be true orthologs but instead may be
transcripts at syntenic loci in different cell types or tran-
scriptional noise. First, applying our orthology-finding pipe-
line to randomly shuffled transcripts resulted in a similar
proportion of syntenic transcripts with low TTI and zero
conserved splice sites (Fig. 4d). Second, though poor align-
ment metrics could be the result of incomplete reconstruc-
tions of lowly expressed lincRNAs, when we performed a
similar analysis on a FPKM-matched set of reconstructed
coding transcripts, orthologous pairs have both high TTI
and high SSC (Additional file 1: Figure S8A). Third, incorp-
orating human and mouse expression data and limiting the
orthology search to only lncRNAs expressed in matched
tissues drastically reduced the number of poorly aligning
lncRNAs (Additional file 1: Figure S8B).
Taken together, we conclude that the majority of syn-
tenic pairs we find are unrelated transcripts that have
been annotated independently in human and mouse,
perhaps in very different cell types, and which have no
ancestral relationship. It is notable however that we
found 39 pairs of human-mouse candidate orthologs
that have low TTI, yet have at least one conserved splice
site. This is surprising, because under the null hypoth-
esis that these set of orthologs occupy a syntenic loci
mostly by chance, we expect no pairs of orthologs to have
an orthologous (conserved) donor/acceptor site (Methods).
These 39 transcripts are reminiscent of lincRNA FIRRE,
which has similarly low TTI but has one conserved splice
site (out of 12). The fact that a set of lincRNAs are likely
ancestral but with exonic sequence that has diverged rap-
idly points to a different class of lincRNAs with a very low
purifying selective pressure on most of transcribed bases.
To investigate whether there are (at least) two distinct
classes of lincRNAs, we first sought to reduce the number
of possible spurious lincRNA orthologous pairs by either
requiring transcript-transcript identity >60 %, which
controls the false discovery rate at 10 % (Additional file 1:
Figure S8C), or by requiring at least one conserved splice
sites. We excluded the eight intergenic transcripts that
contain a conserved ORF between human and mouse with
a significant dN/dS ratio and significant coding potential
score because they may encode for small proteins
(Additional file 1: Table S2). Using these criteria, we found
232 pairs of human-mouse lincRNAs orthologs with a
conservation profile similar to that found in the pluripo-
tent analysis (Additional file 1: Figure S9), but with a
bimodal TTI distribution (Fig. 4e). Modeling the TTI
distribution as two Gaussians, we find 186 (80.1 %)
lincRNAs with high TTI (mean 65.5 % ∓ 7.1 %) and 46
(19.8 %) with low TTI (mean 15.6 % ∓ 11.7 %). This
further suggests that selection may operate in two distinct
ways: for the majority of lincRNAs, it acts on the full RNA
transcript, preserving the transcript sequence, while for a
small subset of lincRNAs, the lincRNA sequence may be
under positive selection, or perhaps only the act of tran-
scription may be under selective constraint. With the goal
of aiding in the study of these human-mouse conserved
lincRNAs, we built an easily accessible application avail-
able at https://scripts.mit.edu/~jjenny as a resource for
visually exploring the alignment and conservation proper-
ties of these lincRNAs.
Finally, we sought to understand properties of lincR-
NAs that explain their conservation or rapid turnover by
investigating promoter conservation (Fig. 5). Within
our pluripotent-expressed lincRNAs (Fig. 5a), we found
that mammalian-conserved lincRNA promoters have

conservation scores comparable to protein coding genes,
consistent with previous reports [11, 12], while species-
specific lincRNA promoters are indistinguishable from
neutral evolution of random intergenic genomic sequence.
Conservation also extends to the promoter structure, as
we found clear enrichment for CpG islands in conserved
lincRNAs, despite comparable CG content (approximately
48 %) to that of species-specific lincRNA promoters,
further suggesting strong selection on their transcriptional
control. In contrast, we found that conservation is nega-
tively correlated with repeat content in lincRNA promoters,
and that a significant fraction (30.6 %, P = 1.65 × 10−3
,
Fisher’s exact test) of species-specific lincRNA promoters
contain species-specific endoretroviral K (ERVK) repeat
element that appear to be driving transcription. This re-
peat element is enriched only in promoters of lincRNAs
expressed in pluripotent and testis cells (Additional file 1:
Table S3), consistent with previous observations that
repeat elements are transcribed in ES and germline tissues
and silenced in differentiated tissues. We observe that for
60.7 % of rodent-specific lincRNAs (that is, mouse or
mouse and rat expressed lincRNAs), the time of ERVK
integration on the evolutionary tree corresponds exactly
with the evolutionary pattern of lincRNA transcription,
providing strong evidence that the ERVK element is a
primary driver for the origin of the lincRNA. We found
corroborating trends of promoter conservation when
examining the larger set of lincRNAs from our combined
set of annotations (Fig. 5b). Importantly, we found no
statistical difference in promoter conservation between
high and low TTI lincRNA orthologs, suggesting selection
for transcription even with poorly aligning orthologs.
Together, these results highlight the power of evolu-
tionary analysis to identify distinct functional classes of
lncRNAs and to reveal distinct features of these classes.
In particular, we found 232 intergenic lncRNAs that
A D
E
B
Exon number
Transcript-transcript identity (TTI)
Exon number Exon number
miRNA host Divergent snoRNA host
4,035 (2.1% conserved)
6,526 (1.2%)
7,859 (1.5%)
13,241 (1.9%)
2,331 (6.3% conserved)
8,892 (3.7%)
6,074 (2.6%)
2,653 (8.0%)
8,130 (1.6%)
46,418 (0.8%)MiTranscriptome
Cabili, et al.
GENCODE
UCSC
Necsulea, et al.
RefSeq
NA
NA
Human Mouse
Identity(%)
Splicesiteconservation(SSC)FrequencyFrequency
1 2 31 2 3
0.6
0.4
0.2
0.0
1 2 3
Transcript-genome identity Transcript-transcript identity
Divergent snoRNA hostmiRNA host
C
**
Transcript-genome
identity (TGI)
0.0
0.6
0.8
0.2
0.4
**
Transcript-transcript
identity (TTI)
0.0
0.6
0.8
0.2
0.4
**
**
–1.5
1.0
–1.0
0.0
1.5
–0.5
0.5
Insertion / deletion
rate (IDR)
*
Splice site
conservation (SSC)
0.0
0.6
0.8
0.2
0.4 *
*
0.0 0.2 0.4 0.6 0.8
0.0
0.2
0.4
0.6
0.8
1.0
0
35
70
4
8
12
15
18
22
26
29
32
36
40
43
46
50
54
57
Counts
AK022898LOC728743
AK026502
TUG1
CYRANO
SETD5–AS1
CRNDE
FIRRE
LINC00673
0.0
400
300
200
100
0
1500
1000
500
0
0.2 0.4 0.6 0.8 1.0
Transcript-transcript identity Splice site conservation
0.0 0.2 0.4 0.6 0.8 1.0
All Shuffled
Candidate lincRNA orthologs
TTI > 60% SSC > 0
Filtered lincRNA orthologs (TTI > 60% or SSC > 0)
Fig. 4 Combined catalog search of lncRNA orthologs recapitulates distinct lncRNA classes. a Existing lncRNA catalogs were combined for large-scale
search of lncRNA orthologs between human (left) and mouse (right). Barplot shows number of transcripts contributed from each source. b Mean
transcript-transcript (TTI) (solid line) and transcript-genome identity (TGI) (dotted line) across first three exons of miRNA host (orange), divergent (blue),
and snoRNA host (purple) genes, recapitulating signatures from smaller search of lncRNA orthologs expressed in pluripotent cells. Error bars represent
standard error of the mean. c Boxplots of TGI and TTI, barplot of splice site conservation (SSC), and boxplot of indel rate (IDR) of each lncRNA class.
Insertion/deletion rates were not calculated for miRNA host lncRNAs because not enough exonic segments aligned to accurately calculate rate.
Two-sample t-test was used to test for significance for all figures, except one-sample t-test was used to test if mean of indel rate is significantly deviated
from 0. ** denotes P <0.01 and * denotes P <0.05. d Histograms of TTI (left) and SSC (right) of candidate intergenic lncRNA (lincRNAs) orthologs (solid bars)
from combined search as compared to shuffled transcripts (hashed bars). Even among alignments of shuffled transcripts, we found many poorly-aligning
orthologs, suggesting they are artifactual from large number of initial transcripts. e Binned scatterplot of TTI and SSC of filtered lincRNA
orthologs (TTI >60 % or SSC >0), enriched for true ancestral orthologs. Distribution of filtered TTI is shown on top, along x-axis. Overlaid
on scatterplot are data points for lncRNA orthologs found in analysis of pluripotent cells

appear to be under selective constraint for and may play
important roles in biology. We note that the majority of
lncRNAs appear to be species-specific, raising questions
about whether most of these transcripts are simply
byproducts of transcription, with no important biological
function. Alternatively, these lncRNA functions may be
highly redundant or easily replaceable, in which case
evolutionary turnover could be explained by a stochastic
evolutionary process where redundant lincRNAs are
fixed randomly along the evolutionary tree.
Conclusion
While interest in lncRNAs has exploded, there is still
relatively little known about the functions of lncRNAs
and much skepticism about what these large number of
transcripts mean. The main challenge is that the number
of functionally characterized lncRNAs remains a tiny
fraction of the total number of lncRNAs that have been
annotated. The significant effort required for functional
characterization of a single lncRNA compared to its
annotation has impeded the functional characterization
of the large catalogs of lncRNAs. Accordingly, liberal
cataloging efforts have led to a plethora of transcripts
defined as lncRNAs that are rarely transcribed or artifacts
of transcript assembly, thereby preventing experimental
progress. slncky provides an important and conservative
approach for defining lncRNAs that enriches for bona fide
lncRNAs. While slncky will not necessarily capture every
A B
−5
0
ns
ns
ns
ns
ns
ns
5
10
0
20
40
60
70
10
30
50
0
20
60
100
40
80
0
20
40
60
10
30
50
0
50
100
129SvEv
Mouse
Human
cast
Rat
Coding
Mouse
Human
(highTTI)
Huamn
(lowTTI)
Coding
**
***
***
***
***
***
***
ns
***
CpGisland(%)Conservation(SiPhy)Repeatcontent(%)
CpGisland(%)Conservation(SiPhy)Repeatcontent(%)
−5
0
5
10
15
Conserved
to:
Conserved
to:
Pluripotent-expressed
lincRNA promoters
Combined
lincRNAs promoters
Fig. 5 Conserved lncRNA promoters display strong selection for transcriptional control. a In each plot, each bar from left to right represents
lncRNAs from pluripotent analysis that increase in conservation: 129SvEv-specific, cast-specific, expressed across all mouse subspecies, expressed in
mouse and rat, expressed in all mammalian species, and finally, expressed coding genes. Top: Promoter conservation in SiPhy scores (0 represents neutral
evolution). Middle: Percent of promoters harboring CpG island. Bottom: Percent of promoter base pairs that belong to repeat element. b Same promoter
metrics as A for mouse-specific and human-mouse conserved lncRNAs from combined lncRNA catalogs, and coding genes. Human-mouse conserved
orthologs are split between those with low TTI and high TTI. *** denotes P <0.001; ** denotes P <0.01; * denotes P <0.05 (t-test)

single lncRNA nor will it provide the longest list of
possible lncRNAs, it provides a method to define high
confidence annotation of lncRNAs from any RNA-Seq
dataset. This approach will enable meaningful experimental
characterization of lncRNAs, making it easier to reconcile
the large numbers of defined lncRNAs with the functional
roles of these lncRNAs, and providing a consistent standard
for evaluating bona fide lncRNAs.
Evolutionary conservation has long been a confusing
feature of lncRNAs. While it is clear that lncRNAs are
enriched for conserved sequences, their high levels of
sequence divergence make them a challenge to study.
While most lncRNAs do not appear to be conserved
across mammals, it is currently unclear whether these
lineage-specific lncRNA play important roles in lineage-
specific biology. It is possible that many lncRNAs have
‘functional orthologs’: genes with similar function but no
ancestral relationship. Importantly, evidence of functional
orthology was recently reported for XIST. Although XIST
is not found in marsupials, an opossum lncRNA called
RSX was shown to have similar function. While RSX is
capable of silencing the X chromosome in mouse, it shares
no ancestral relationship with XIST [39]. We note that
functional orthology cannot be studied with the methods
presented here and future work will be needed to explore
how many lncRNAs might play such lineage-specific
roles or to what extend non-homologous lncRNAs carry
similar function.
We demonstrated that lncRNAs can be categorized
into distinct sets based on their evolutionary properties.
Most notably, we found two sets of conserved intergenic
lncRNAs: one that shows signs of purifying selection at
the sequence level, and one that shows selection only for
transcription. It will be fascinating to determine whether
these two sets of lincRNA also correlate with functional
differences. While we defined classes based on conserva-
tion, there are likely many other classes of lncRNAs that
cannot be defined by conservation alone. We anticipate
that as more cell types and tissues are explored, these anno-
tation and evolutionary approaches will be even more valu-
able and enable more detailed studies of lncRNA biology.
Methods
slncky
A stringent pipeline for filtering for lncRNAs
slncky filters for lncRNAs in three simple steps. First,
slncky filters out reconstructed transcripts that overlap
coding genes or ‘mapped-coding’ genes on the same
strand, in any amount.
After this step, slncky chooses a canonical isoform to
represent overlapping transcripts. To do this, slncky
clusters all transcripts with any amount of exonic over-
lap into one cluster, and chooses the longest transcript
as the canonical isoform.
Next, slncky searches for gene duplication events (for
example, zinc finger protein or olfactory gene expan-
sions) by aligning each transcript to every other putative
lncRNA transcript using lastz with default parameters
[31]. slncky then aligns each transcript to shuffled inter-
genic regions to find a null distribution of alignment
scores, repeating this procedure 200 times in order to
estimate an empirical P-value. Any alignment with a P-
value lower than 0.05 is considered significant. Sets of
putative lncRNAs transcript that share significant hom-
ology are then merged, creating larger “duplication clus-
ters”. These transcripts do not necessarily share similarity
to a protein-coding gene, though slncky will check and re-
port homology to known ZFPs and olfactory genes.
slncky’s default parameters, which we used in all analyses
reported (−−min_cluster_size 2), notes and removes any
duplication cluster containing two or more transcripts.
Finally, slncky removes any transcript that aligns to a
syntenic coding gene in another species. (Human and
mouse annotations are provided, though users can de-
fine their own). First, slncky learns a positive distribution
by aligning all the transcripts removed in the first filter-
ing step, which we know overlap coding genes, to their
syntenic coding gene. slncky builds an empirical positive
score distribution from these alignments. To align genes
slncky first uses liftOver (−−minMatch = 0.1) [40] to de-
termine the syntenic loci in the comparing genome and
lastz [31] to perform the alignment across the syntenic
region. Using the empirical distribution, slncky learns an
exonic identity threshold that has an empirical P value
of 0.05. slncky repeats the alignment procedure on the
putative lncRNAs to syntenic coding genes and filters
out any transcripts that align at a higher score than this
threshold, even if alignments occur only in UTR or in-
tronic regions. In this way, slncky removes unannotated
coding genes, pseudogenes, as well use UTR or intronic
fragments from incomplete transcript assemblies. To re-
duce computational cost, whenever more than 250
coding-overlapping genes were filtered out from the first
step, only a random subset of 250 transcripts is used to
build the positive distribution.
Flagging potentially coding ‘lncRNAs’
To find conserved lncRNAs that potentially harbor novel,
unannotated protein, slncky aligns putative lncRNAs to syn-
tenic non-coding transcripts in a comparing species, using
a sensitive non-coding alignment strategy described below.
slncky then crawls through each significant alignment and
reports back any aligned open reading frame (ORF) longer
than 30 base pairs. Only ORFs that do not contain a frame
shift inducing indel in either species are reported. The start
codon is defined as ‘ATG’ and stop codons are defined as
‘TAA’, ‘TAG’, or ‘TGA’. slncky further calculates the
ratio of non-synonymous to synonymous substitutions

(dN/dS ratio). We calculated an empirical P value for each
dN/dS ratio by aligning 50,000 random intergenic regions
and repeating the ORF finding procedure. Because the
distribution of dN/dS ratio is dependent on ORF length
(Additional file 1 1: Figure S2), we binned ORF lengths by
5 base pair windows and assigned an empirical P value if
we had at least 100 random ORFs within that bin. P values
were corrected for multiple hypothesis testing. For long
ORFs, for which less than 100 length-matched random
ORFs existed, we kept all alignments with dN/dS ratios <1.
A sensitive method for aligning orthologous lncRNAs
In searching for conserved lncRNA orthologs, slncky first
defines the syntenic region of the comparing genome with
liftOver (−minMatch = 0.1 –multiple = Y) [40]. If a non-
coding transcript exists in the syntenic region, slncky then
aligns the area 150,000 base pairs upstream to 150,000 base
pairs downstream of two syntenic regions. We choose
150,000 base pairs as a general heuristic that is likely to
include an easily-alignable coding transcript up- and down-
stream of the lncRNA, which helps lastz to find a positively
scoring alignment. Importantly, we also found that
lncRNAs could only be aligned with a reduced gap-open
penalty (−−gap = 25,040) because of many small insertions
that appear to be well-tolerated by lncRNA transcripts.
To ensure we are not reporting alignments that may
occur at random (driven mostly by repetitive elements),
we align each lncRNA to shuffled intergenic regions to
establish a null distribution and determine the empirical
5 % threshold for determining significant alignment
scores. Because of our inclusion of flanking regions, it is
possible to have a significant alignment in which only the
flanking regions align but not the lncRNA transcripts.
slncky reports these transcripts since it is possible that
they are ‘syntologs’ and carry out orthologous functions
but have evolved to a point where they no longer align.
Data collection
Pluripotent cell lines and growth conditions
Naïve 2i/LIF media for mouse and rat (rodent) naïve
pluripotent cells was assembled as follows: 500 mL of
N2B27 media was generated by including: 240 mL DMEM/
F12 (Biological Industries – custom-made), 240 mL Neuro-
basal (Invitrogen; 21103), 5 mL N2 supplement (Invitrogen;
17502048), 5 mL B27 supplement (Invitrogen; 17504044),
1 mM glutamine (Invitrogen), 1 % non-essential amino
acids (Invitrogen), 0.1 mM β-mercaptoethanol (Sigma),
penicillin-streptomycin (Invitrogen), and 5 mg/mL BSA
(Sigma). Naïve conditions for murine embryonic stem cells
(ESCs) included 10 μg recombinant human LIF (Peprotech)
and small-molecule inhibitors CHIR99021 (CH, 1 μM-
Axon Medchem) and PD0325901 (PD, 0.75 μM - TOCRIS)
referred to as naïve 2i/LIF conditions. Naïve rodent
cells were expanded on fibronectin coated plates (Sigma
Aldrich). Primed (EpiSC) N2B27 media for murine and rat
cells (EpiSCs) contained 8 ng/mL recombinant human
bFGF (Peprotech Asia), 20 ng/mL recombinant human
Activin (Peprotech), and 1 % Knockout serum replacement
(KSR- Invitrogen). Primed rodent cells were expanded on
matrigel (BD Biosciences).
129SvEv (Taconic farms) male primed epiblast stem
cell (EpiSC) line was derived from E6.5 embryos previ-
ously described in [41]. 129SvEv naïve ESCs were de-
rived from E3.5 blastocysts. NOD naïve ESC and primed
EpiSC lines were previously embryo-derived generated
and described in [42]. castaneous ESC line was derived
from E3.5 in naive 2i/LIF conditions and rendered into a
primed cell line by passaging over eight times into
primed conditions [43, 44]. Rat naïve iPSC lines were
previously described in [44]. Briefly, rat tail tip derived
fibroblasts were infected with a DOX inducible
STEMCA-OKSM lentiviral reprogramming vector and
M2rtTa lentivirus in 2i/LIF conditions. Established cell
lines were maintained on irradiated MEF cells in 2i/LIF
independent of DOX. Simultaneously, primed rat
pluripotent cells were generated by transferring the rat
naïve iPSC cells into primed EpiSC medium for more
than eight passages before analysis was conducted. Naïve
human C1 iPSC lines were derived and expanded on
irradiated DR4 feeder cells as previously described [19].
RNA-Sequencing
RNA-Seq libraries were prepared as described in [45].
Briefly, 10 μg of total RNA was polyA selected twice using
Oligo(dT)25 beads (Life Technologies) and NEB oligo(dT)
binding buffer. PolyA-selected RNA was fragmented,
repaired, and cleaned using Zymo RNA concentrator-5 kit.
A total of 30 ng of polyA-selected RNA per sample were
used to make RNA-Seq libraries. An adapter was ligated to
RNA, RNA was reverse transcribed, and a second adapter
was ligated on cDNA. Illumina indexes were introduced
during nine cycles of PCR using NEB Q5 Master Mix.
Samples were sequenced 100-index-100 on HiSeq2500.
Filtering
Filtering pluripotent lncRNAs from four mammalian species
Transcripts were reconstructed from RNA-Sequencing
data using Scripture (v3.1, −-coverage = 0.2) [11] and
multi-exonic transcripts were filtered using slncky with
default parameters. Annotations of coding genes were
downloaded from UCSC (‘coding’ genes from track
UCSC Genes, table kgTxInfo) [46] and RefSeq [47].
Mapped coding genes were downloaded from UCSC
Transmap database (track UCSC Genes, table transMa-
pAlnUcscGenes) [46]. For the mouse genome, we also
included any blat-aligned human coding gene (track
UCSC Genes, table blastHg18KG) [46]. As expected, the
majority of reconstructed transcripts overlapped an

annotated coding or mapped coding gene at >95 %
(Additional file 1: Figure S2). In the next step, slncky
aligned each putative lncRNA to every other putative
lncRNA to detect duplications of species-specific gene
families. Across mouse, rat, and human transcriptomes,
we found large clusters (15+ genes) of transcripts sharing
significant sequence similarity with each other that also
aligned to either zinc finger proteins or olfactory proteins.
For unclear reasons, but likely due to the draft status of
the assembly which results in collapsed repetitive se-
quence, we did not find any large clusters of duplicated
genes in the chimpanzee genome, and instead found five
small clusters of paralogs (Additional file 1: Figure S1).
Finally, slncky aligned the remaining transcripts to syn-
tenic coding genes. For mouse and chimp transcripts,
we aligned to syntenic human coding genes and for rat
and human transcripts, we aligned to syntenic mouse
coding genes. The learned transcript similarity threshold
for each pair of comparing species varied as a function
of distance between species: the empirical threshold for
calling a significant human-chimp alignment was 29.8 %
sequence similarity while for human-mouse alignments
it was approximately 14 % (Additional file 1: Figure S1).
Single exon lncRNAs
Transcript reconstruction software tends to report thou-
sands of single exon transcripts existing in a RNA-Seq
library. Previous work suggests that the vast majority of
these transcripts are results from incomplete UTR re-
construction, processed pseudogenes, very low expressed
regions, and DNA contamination [14]). Although slncky
filters a great number of these artifacts, we find that
especially for single exon transcripts, many spurious recon-
structions remain. For this reason, when analyzing single
exon genes, we only focused on single-exon lncRNAs
that are conserved across species.
Verification of filtered lncRNAs
We first verified slncky’s lncRNA annotations by applying
the filtering pipeline to our own generated RNA-Seq data
and comparing the resulting lncRNA set with other
computational and experimental methods, detailed below.
Chromatin modifications
Raw reads from ChIP-Sequencing experiments for
H3K4me3 and H3K4me36 histone modifications in mouse
embryonic stem cells (E14) were downloaded from [48]
(GSE36114). Reads were mapped to mouse genome (mm9)
using Bowtie (v0.12.7) [49] with default parameters. Peaks
were called as previously described [50].
Coding potential
We scored coding potential of mouse lncRNAs using
RNACODE (v0.3) [18] with default parameters using
multiple sequence alignments of 29 vertebrate genomes
from the mouse perspective [29].
Ribosome release scores
Ribosome profiling data of mouse ES cells (E14) was
downloaded from [51] (GSE30839). Ribosome release
scores (RRS) were calculated as described in [22] using
the RRS Program provided by the Guttman Lab.
Functionally characterized lncRNAs
To test the sensitivity of lncRNA filtering pipelines, we
derived a list of well-characterized lncRNAs. To do this,
we first took the intersection of annotated non-coding
transcripts from UCSC [46], RefSeq [47], and GENCODE
[52]. We then removed any lncRNA with a generically
assigned name (for example, LINC00028 or LOC728716)
as well as generically named snoRNA and miRNA host
genes (for example, SNHG8 or MIR4697HG). Finally, we
performed a literature search on the remaining lncRNAs,
and kept only those that were specifically experimentally
interrogated rather than reported from a large-scale
screen. This list of well-characterized lncRNAs is available
in Additional file 2.
Reanalysis of previously published lncRNA sets
We compared slncky’s annotation of lncRNAs to three
different human lncRNA sets: GENCODE V19 ‘Long
non-coding RNA gene’ set [52], a set reported by [23]
based, in part, on GENCODE V7 annotations, and a set
reported by [24] based on GENCODE V12 annotations.
For all three comparisons, we first downloaded the ap-
propriate version of GENCODE’s ‘Comprehensive gene’
annotations and applied slncky using default parameters.
For comparison to [23] and [24] we further scored expres-
sion of GENCODE annotations on the original RNA-Seq
data used [53] using Cufflinks v2.1.1 [54] with default
parameters and only compared robustly expressed (FPKM
>10) lncRNAs.
Evolutionary study of LncRNAs
Reanalysis of previous studies of lncRNA conservation
We downloaded lncRNA annotations and ortholog ta-
bles derived from [23] and applied slncky’s orthology
pipeline to mouse and human lncRNAs using default
parameters. We compared the human-mouse orthologs
discovered by slncky to the list of transcripts that were
defined by [23] to be ancestral to all Eutherians. We
used downloaded FPKM tables to filter the additional
orthologs discovered by slncky for pairs in which both
transcripts are expressed in corresponding tissues.
To assess the ability of slncky to discover lncRNAs of
a further evolutionary distance than mouse and human,
we downloaded lncRNA and ortholog annotations from
[16] and applied slncky using more relaxed parameters

(−−minMatch 0.01, −-pad 500000) to search for human-
zebrafish and mouse-zebrafish lncRNA orthologs. Note
that in both analyses, lncRNA annotations were not fil-
tered by slncky’s filtering pipeline prior to the ortholog
search so that our results could be directly comparable
with the original publication.
Annotating orthologous lncRNAs in pluripotent mammalian
cells
We applied slncky to our pluripotent RNA-Seq data to
conduct an evolutionary analysis of lncRNAs across mul-
tiple mammalian species. We first searched for ortholo-
gous lncRNAs in a pairwise manner between every
possible pair of species. Because the reconstruction soft-
ware we used does not report lowly expressed transcripts
that do not pass a significance threshold, and because we
removed single-exons from our filtering step, we devised a
method to rescue orthologous transcripts that may have
been removed in those steps. For each lncRNA, if no
orthologous lncRNA was detected by slncky, we went back
to the original RNA-Seq data and forced reconstruction of
lowly-expressed and/or single-exon transcripts in the syn-
tenic region. We then re-aligned the lncRNA with these
newly reconstructed transcripts and added the transcript
to our lncRNA set when a significant alignment was found.
We kept only pairs of conserved lncRNAs where a signifi-
cant alignment was found in both reciprocal searches (for
example, mouse-to-human and human-to-mouse).
Next, given pairs of lncRNA orthologs across all spe-
cies, we created ortholog groups by greedily linking
ortholog pairs. For example, given pairs {A,B} and {B,C},
we assigned {A,B,C} to one orthologous group, even if
paring {A,C} did not exist. Finally, we used Fitch’s
algorithm [55] to recursively reconstruct the most
parsimonious presence/absence phylogenetic tree for
each lncRNA and determine the last common ancestor
(LCA) in which each lncRNA appeared. In the event a sin-
gle LCA could not be determined by parsimony, we chose
the most recent ancestor as the LCA in order to have
conservative conservation estimates. For example, if a
lncRNA was found in mouse and rat, but missing in
human and chimp, we assigned the LCA to be at the
rodent root, rather than at the mammalian root with
a loss event at primates.
Annotating matched low expression coding genes
We tested our ability to detect conservation of lowly
expressed transcripts by using our pipeline to reconstruct
lowly-expressed coding genes known to be conserved
across our tested species. We binned the set of intergenic
lncRNAs by increments of 0.1 log10(FPKM), and sampled
a set of 162 coding genes that matched in log10(FPKM)
distribution in mouse ES cells. We then applied slncky’s
orthology-finding module to the de novo reconstructions
of these coding genes from our generated RNA-Seq data.
Repeating the same analysis as described above., we
assigned the last common ancestor (LCA) of each
coding gene. We were able to correctly assign the
human-mouse ancestor as the LCA for 134 of 162
(83 %) coding genes, providing confidence that we
are able to sensitively detect orthologs of lncRNAs,
even though they are lowly expressed.
Combined catalog analysis
We downloaded human and mouse lncRNA annota-
tions, where they existed, from RefSeq [47, 23], UCSC
[46], GENCODE (v19 and vM1) [52, 12], and MiTran-
scriptome [36]. We filtered lncRNAs and searched for
orthologs using slncky with default parameters. For over-
lapping isoforms that belong to the same gene, we chose
one canonical ortholog pair that had the highest number
of conserved splice sites and/or highest transcript-
transcript identity. miRNA host and snoRNA host genes
were annotated using Ensembl annotations of miRNAs
and snoRNAs [56]. Divergent genes were annotated based
on distance and orientation of closest UCSC or RefSeq-
annotated coding gene. Orthologous lncRNAs were classi-
fied as a miRNA host, divergent, or snoRNA host if the
transcript was annotated as such in both species. All other
lncRNAs were classified as intergenic.
An orthology search was conducted on shuffled
transcripts by collapsing overlapping isoforms to a ca-
nonical gene as described above, and shuffling to an
intergenic location (that is, not overlapping an anno-
tated coding gene) using shuffleBed utility [57]. We
then carried out the orthology search and alignment
exactly as described for lncRNAs. To empirically esti-
mate the expected number of conserved splice sites
across shuffled orthologs, we took each pair of true
lncRNA orthologs and reshuffled splice sites within
the loci such that it was correctly located at donor/
acceptor sites (GT, AG), and re-evaluated number of
conserved splice sites.
We used distributions resulting from our shuffled
orthology search to filter and remove spurious hits from
our set of candidate lincRNA orthologs. We then fitted
two Gaussians to the resulting transcript-transcript
identity using the mixtools package for R and default
parameters [58]. Convergence was reached after 31
iterations of EM and final log-likelihood was 146.64.
Each ortholog pair was assigned to a Gaussian based on
posterior probability cutoff of 50 %.
Promoter properties
We defined promoters to be the 500 base pairs up-
stream of the lincRNA’s transcription start site (TSS).
We calculated several genomic properties of this re-
gion as follows:

SiPhy
We calculated average SiPhy score across promoter region
as previously described [59] using 29 mammals’ alignment
from mouse perspective [29].
CpG islands
For the analysis of CpG islands, we used annotations
provided by the UCSC Genome Browser (assembly mm9,
track CpG Islands, table cpgIslandExt).
Repeat elements
We intersected promoter regions with annotations from
RepeatMasker [60] and calculated the number of base
pairs of a lincRNA promoter belonging to a repeat
element as well as percentage of lincRNA promoters
harboring each class of repeat element. We then re-
peated this analysis with random intergenic regions,
matched in size and GC content. To find statistically
significant deviations in repeat content, we used Fisher’s
exact test to compare the proportion of species-specific
lincRNA promoters containing each repeat element to the
proportion of random, GC-matched intergenic regions
containing the same element. We reported any repeat
element that deviated from random, intergenic regions
with a P value <0.005 (corrected for number of repeat types
we tested).
Data availability
Raw and processed RNA-Seq data are available under
GEO accession GSE64818: http://www.ncbi.nlm.nih.
gov/geo/query/acc.cgi?acc=GSE64818
A database of conserved lncRNAs discovered in this
analysis is available at https://scripts.mit.edu/~jjenny
Software availability
slncky (http://slncky.github.io) was developed in Python
2.0 and is freely available as source code distributed
under the MIT License. slncky was tested on Linux and
Mac OS X. The version used in this manuscript is available
from DOI: 10.5281/zenodo.44628 (https://zenodo.org/
badge/latestdoi/19958/slncky/slncky).
Additional files
Additional File 1: Supplementary figures and tables. (PDF 3.85 MB)
Additional file 2: Curated list of well-characterized lncRNAs.
(XLSX 52 kb)
Additional file 3: Bed file of lncRNAs discovered from mouse
(mm9), human (hg19), chimp/bonobo (panTro4), and rat (rn5).
(XLSX 229 kb)
Additional file 4: Excel file of evolutionary metrics of all lncRNAs
found to be conserved to the human/chimp/rat/mouse ancestor.
(XLSX 19 kb)
Abbreviations
ES: embryonic stem; ESC: embryonic stem cell; FPKM: fragments per
kilobase of transcript per million reads mapped; ORF: open reading frame;
RNA-Seq: RNA-Sequencing; RRS: ribosomal release score; lincRNA: long
intergenic non-coding RNA; lncRNA: long non-coding RNA; SSC: splice site
conservation; TGI: transcript-genome identity; TTI: transcript-transcript
identity; UTR: untranslated region.
Competing interests
The authors declare that they have no competing interests.
Authors’ contributions
JC participated in the design and coordination of the study, carried out all
computational analysis and software development of slncky and slncky
Evolutionary Browser, and wrote the manuscript. AS carried out RNA-Sequencing.
XZ and SK participated in development of supporting software. IM and
JH participated in deriving cell lines. M Guttman and AR participated in
writing the manuscript. MG conceived of the study, participated in its
design and coordination, and wrote the manuscript. All authors read
and approved the final manuscript.
Acknowledgements
We thank Leslie Gaffney for artwork and advise on figures. JC was supported
by an NHGRI training grant and by the Jan and Ruby Krouwer Fellowship
Fund. MG was supported by DARPA grants D12AP00004 and D13AP00074.
AR and MG were also supported by the CEGS 1P50HG006193. AR is supported
by the Howard Hughes Medical Institute. JHH is supported by Ilana and Pascal
Mantoux; the New York Stem Cell Foundation and is a New York Stem Cell
Foundation - Robertson Investigator. We thank the Garber, Lander, and Regev
laboratory members for helpful discussions.
Author details
1
Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA. 2
Division of
Health Sciences and Technology, Massachusetts Institute of Technology,
Cambridge, MA 02140, USA. 3
Division of Biology and Biological Engineering,
California Institute of Technology, Cambridge, MA 02140, USA. 4
Program in
Bioinformatics and Integrative Biology, University of Massachusetts Medical
School, Worcester, MA 01655, USA. 5
Department of Molecular Genetics,
Weizmann Institute of Science, Rehovot 76100, Israel. 6
Howard Hughes
Medical Institute, Department of Biology, Massachusetts Institute of
Technology, Cambridge, MA 02140, USA. 7
Program in Molecular Biology,
University of Massachusetts Medical School, Worcester, MA 01655, USA.
Received: 26 October 2015 Accepted: 14 January 2016
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