RESEARCH ARTICLEOpen Access
Characterization of differential transcript
abundance through time during Nematostella
Rebecca Rae Helm1*, Stefan Siebert1, Sarah Tulin2, Joel Smith2,3and Casey William Dunn1
Background: Nematostella vectensis, a burrowing sea anemone, has become a popular species for the study of
cnidarian development. In previous studies, the expression of a variety of genes has been characterized during N.
vectensis development with in situ mRNA hybridization. This has provided detailed spatial resolution and a
qualitative perspective on changes in expression. However, little is known about broad transcriptome-level patterns
of gene expression through time. Here we examine the expression of N. vectensis genes through the course of
development with quantitative RNA-seq. We provide an overview of changes in the transcriptome through
development, and examine the maternal to zygotic transition, which has been difficult to investigate with other
Results: We measured transcript abundance in N. vectensis with RNA-seq at six time points in development: zygote
(2 hours post fertilization (HPF)), early blastula (7 HPF), mid-blastula (12 HPF), gastrula (24 HPF), planula (5 days post
fertilization (DPF)) and young polyp (10 DPF). The major wave of zygotic expression appears between 7–12 HPF,
though some changes occur between 2–7 HPF. The most dynamic changes in transcript abundance occur
between the late blastula and early gastrula stages. More transcripts are upregulated between the gastrula and
planula than downregulated, and a comparatively lower number of transcripts significantly change between
planula and polyp. Within the maternal to zygotic transition, we identified a subset of maternal factors that
decrease early in development, and likely play a role in suppressing zygotic gene expression. Among the first genes
to be expressed zygotically are genes whose proteins may be involved in the degradation of maternal RNA.
Conclusions: The approach presented here is highly complementary to prior studies on spatial patterns of gene
expression, as it provides a quantitative perspective on a broad set of genes through time but lacks spatial
resolution. In addition to addressing the problems identified above, our work provides an annotated matrix that
other investigators can use to examine genes and developmental events that we do not examine in detail here.
Keywords: Nematostella vectensis, Transcriptome, Gene expression, Maternal to zygotic transition, Development
Nematostella vectensis is a burrowing, estuarine sea
anemone that has been an important model system for
embryonic development in Cnidaria, and was the first
cnidarian to have a draft genome sequence available .
Mature N. vectensis liberate gametes into the water.
Cleavage begins roughly two hours after fertilization,
with gastrulation occurring roughly 20 hours post
fertilization (HPF) at 18°C . Embryos develop into
swimming planula larvae. After variable time in the
water column (roughly 10 days post fertilization (DPF)),
the planulae metamorphose and settle to the benthos as
There have been extensive studies of gene expression
throughout development in N. vectensis, based largely
on in situ mRNA hybridization [e.g. 2-6]. These studies
have provided detailed pictures of differential spatial ex-
pression, as well as qualitative assessments of changes in
expression through time. In this paper, we complement
these spatial expression studies with quantitative RNA-
* Correspondence: firstname.lastname@example.org
1Ecology and Evolutionary Biology, Brown University, 80 Waterman Street,
Box G-W, Providence, RI 02912, USA
Full list of author information is available at the end of the article
© 2013 Helm et al.; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative
Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and
reproduction in any medium, provided the original work is properly cited.
Helm et al. BMC Genomics 2013, 14:266
seq analyses of whole embryos through time. This ap-
proach provides no spatial resolution, but allows for a
temporal analysis of changes in expression across the
whole embryo for previously annotated genes . High-
throughput quantitative expression studies have been
conducted in a handful of other cnidarian species. Previ-
ous quantitative sequencing work has focused on differ-
ential gene expression between different zootypes within
a colony , the response of developing coral larvae to
temperature stress , and the response to ocean acidifi-
cation . However, none of these studies focus on pat-
terns in gene expression through multiple stages of
We sampled N. vectensis embryos at six time points
through the course of development: 2 HPF, 7 HPF, 12
HPF, 24 HPF, 5 (DPF), and 10 DPF. The interval from
2–7 HPF captures early cleavage events through ap-
proximately 128 cells. 7–12 HPF encompases prawn
chip stages I-V . 12–24 HPF includes the onset of gas-
trulation. The 24 HPF - 5 DPF interval spans develop-
ment from a gastrula to a planula. In the interval from
5–10 DPF the animals develop tentacle buds and settle.
Each of these six time points was sampled from two
replicated spawning events, giving a total of twelve sam-
ples. Expression in each of the twelve samples was then
quantified with RNA-seq. This project design allows us
to characterize broad patterns in expression through
time, as well as address specific questions about tran-
scriptional dynamics over the course of development.
We focus in particular on the maternal to zygotic transi-
tion, with a brief overview of gastrulation.
Results and discussion
Sequencing, mapping, and consistency across replicates
On average, 13.77 million sequence reads passed the
Illumina chastity filter for each sample. We deposited
these reads at the NCBI Sequence Read Archive (Pro-
ject: PRJNA189768). Between 28-69% of these reads
mapped to nuclear ribosomal sequences (18S or 28S) or
the mitochondrial genome, and were not considered in
the statistical analysis. Reads mapped to 23,044 of the
26,514 sequences in the reference (full edited reference:
Additional file 1). Dispersion was low in the edgeR ana-
lyses (ranging from 0.008 to 0.016), indicating low vari-
ation between the replicates even though they were from
two different clutches that were spawned months apart.
The count matrix, along with results of statistical ana-
lyses and other annotations, is available as Additional
file 2. The R code for several example analyses of this
matrix are presented in Additional file 3. These vignettes
can be used as starting points for analyses beyond those
presented here, such as more detailed investigations of
particular time points, genes, or genes with gene ontol-
ogy (GO) annotations of particular interest.
Patterns in transcript abundance through time
To get a broad overview of changes in transcript abun-
dance through time we performed Short Time-series Ex-
pression Miner (STEM) analysis, which categorizes each
transcript according to temporal patterns of expression
(Figure 1). 17,383 transcripts received a STEM profile.
Among the top five expression profiles, are patterns of
both monotonic increase and decrease, as well as peaks
at 3 of the 4 intermediate time points (Figure 1). The
most represented pattern is an increase in abundance
through development (3,497 genes, Figure 1: Profile 39),
which includes transcripts involved in ribosomal func-
tion e.g. JGI transcript ID: 234893, ID: 235818, ID:
236265), wnt-like transcripts (e.g. ID: 106241, ID:
211618, ID: 228651, ID: 230011) several possible Wnt
Peak at 24 hours
Peak at 7 hours
Peak at 12 hours
Peak at 5 days
Figure 1 Selected STEM profiles. The five most abundant patterns
of changes in transcript abundance through time, ranked by
decreasing number of transcripts. Stem pattern 31, which is
discussed in the text, is also shown below the dashed line. The full
set of STEM profiles are shown in Additional file 4. The vertical axis is
relative transcript abundance. The horizontal axis is developmental
time, with the 6 time points arranged consecutively on the
horizontal axis of each plot, from the first time point (2 HPF) on the
left and the last (10 DPF) on the right.
Helm et al. BMC Genomics 2013, 14:266
Page 2 of 10
133025), neurotransmitters and receptors (ID: 10746, ID:
247614) and transcripts with a possible relationship to
muscle structure/function (eg. ID: 125819, ID: 202060,
ID: 211472). A monotonic decrease in transcript abun-
dance is also among the top five most abundant plots
(Figure 1: Profile 8), and these transcripts are discussed
in greater detail below (under Maternal Transcript Deg-
radation). Transcripts within a STEM peak at or after
gastrulation (3,355 genes, Profile 22; 1,370 genes, Profile
24) include those related to laminins, which are possibly
involved in gastrula epithelialization (eg. ID: 187372; ID:
208267, ID: 214923), as well as light sensing rhodopsins
and opsins (eg. ID:189274, ID: 197433, ID: 201968). The
fifth most abundant profile includes transcripts that peak
at 7 hours and decrease over time (980, Figure 1: Profile
32). This category includes cellular growth factors (such
as a possible fibroblast growth factor, ID: 211797), a
kruppel-like factor (ID: 39461), and a possible CCR4
NOT transcription complex member (ID: 122610),
discussed in greater detail in the Maternal to Zygotic
Transcripts with significant changes in abundance
9,456 of the 23,044 reference gene sequences with
mapped reads (41.0%) were found to have differential
gene expression (DGE; we only use this term when the
difference is significant with an adjusted p-value < 0.05)
across at least one of the time intervals examined here
(Figure 2). There are relatively fewer genes with DGE
between the first two time points (2–7 HPF), indicating
that rates of zygotic transcription and selective mRNA
degradation are low in this interval. Relative to this first
interval, there are nearly four-fold more genes with DGE
between 7–12 HPF. Most of these show an increase in
transcript abundance, rather than a decrease. The num-
ber of genes with significant DGE is greater still in the
interval between 12–24 HPF, with an almost equal num-
ber of genes with increased DGE (i.e., transcripts with
significant increases in abundance) and decreased DGE
(i.e., transcripts with significant decreases in abundance).
The number of DGE genes continues to grow between
24 HPF - 5 DPF, with slightly more genes increasing in
abundance than decreasing. Relative to the previous sev-
eral intervals, a drop in the number of DGE genes is
seen in the interval between 5 and 10 DPF, and is com-
parable to the number and proportion of DGE genes
seen between 7–12 HPF.
In order to understand broad changes in functional cat-
egories of genes we performed a gene set enrichment
analysis (Additional file 5). This analysis identified those
GO categories that are overrepresented among genes
with significant changes in expression over a given inter-
val. The number of enriched GO categories in a given
interval also lends some insight into the magnitude of
change occurring in that timeframe.
Between 2–7 HPF 49 GO categories are significantly
enriched. This is the smallest number of enriched cat-
egories of any interval, reflecting the relatively small
number of significant transcriptional changes that occur
between these time points. Many GO categories in this
interval involve cell cycling; this is not surprising, as
early development in many organisms is often character-
ized by rapid, maternally-run cell divisions . Between
7–12 HPF 104 GO categories are enriched. These
include categories related to translation, DNA binding
(including transcription factor activity), and axis specifi-
cation. These categories reflect the onset of zygotic tran-
scription in this interval (see Table 1, and the Timing of
the Onset of Zygotic transcription section below). The
largest number of enriched GO categories is be-
tween 12–24 HPF, which comprises 339 categories.
These include translation, intracellular processes (in-
cluding endoplasmic reticulum targeting and protein
localization), and processes
targeting and function. Between 24 HPF and 5 DPF, 312
categories are enriched, including categories related to
transcription factor activity. Between 5–10 DPF a rela-
tive decrease in the number of enriched categories is ob-
served, with only 87 categories. These include categories
related to receptor activity, calcium binding, and muscle
function, reflecting the development of neuronal and
muscular features of the 10 day old polyps. Ribosomal
activity is enriched in three of the intervals: 7–12 HPF,
12–24 HPF, and 24–5 DPF; this reflects the increasing
translational needs of the embryo after the onset of zyg-
otic transcription, until the interval between the planula
and polyp stages, where these data imply the cellular
ribosomal concentration reaches steady state.
Maternal to zygotic transition
All sexually produced animals must pass control of the
gene regulatory network from maternal factors deposited in
the egg to newly synthesized gene products, synthesized
after the embryo begins developing, of the zygotic genome.
This transition is referred to as the Maternal to Zygotic
Transition (MZT) and has been studied in depth in mam-
mals, insects, fish, amphibians, nematodes, and echino-
melanogaster andthe sea
purpuratus has found that the transition is a combination
of two phases: (1) the elimination of maternal RNAs and
(2) the beginning of transcription from the zygotic genome
. Our data reveal key aspects of both processes in N.
vectensis. First, we narrow the window of timing for embry-
onic transcription from the zygotic genome. Second, we
MZT in Drosophila
Helm et al. BMC Genomics 2013, 14:266
Page 3 of 10
identify transcripts that are present at the earliest time point
and then decrease in abundance, and are likely the first ma-
ternal transcripts to be degraded. Other maternal tran-
scripts may also be degraded, but the change hidden by
concurrent embryonic transcription that masks the decline
in maternal abundance. Third, we identify transcripts that
increase in abundance between early time points, which are
the first genes to be transcribed from the zygotic genome.
Of these, we specifically focus on transcripts whose pattern
of expression peaks early in development.
Timing of the onset of zygotic transcription
Only 260 transcripts show increasing abundance and
DGE between 2–7 HPF, compared to 1,146 transcripts
Figure 2 Differential gene expression during early development of N. vectensis. A) Number of transcripts that are significantly (p < 0.05)
increasing (red), or decreasing (blue) through time within intervals. (B-F) Pairwise comparison Log2-fold-change vs log2CPM (counts per million)
for the five pairwise comparisons between adjacent sampling times. The comparisons are between (B) 2 HPF and 7 HPF, (C) 7 HPF and 12 HPF,
(D) 12 HPF and 24 HPF, (E) 24 HPF and 5 DPF and (F) 5 DPF and 10 DPF. Each point represents an individual transcript, red points indicate
transcripts with significant (adjusted p-value < 0.05) differential expression. Positive log2-fold-change values indicate increased transcript
abundance from the first to the second time point, negative log2-fold-change values indicate decreased transcript abundance from the first to
second time point. Horizontal grey lines indicate 2-fold differences in expression.
Helm et al. BMC Genomics 2013, 14:266
Page 4 of 10
with increased DGE over the 7–12 HPF interval
(Figure 2). These results suggest that the major onset of
zygotic transcription begins between 7–12 HPF at 18°C.
GO enrichment of the 7–12 hour time interval reflects
this transition, and includes many categories related to
early zygotic activity (Table 1).
Maternal transcript degradation
We took two approaches to identifying maternal tran-
scripts that are degraded over time. First, we examined
the transcripts with STEM profile 8, which have a
(Figure 1: Profile 8, Additional file 6; Additional file 4).
Next, we identified those transcripts that decrease sig-
nificantly between 2–7 HPF, regardless of changes in
abundance over later intervals.
With STEM analysis we determined that 10.7% of
mapped transcripts decrease over time (2,474 of 23,044),
representing 15% of transcripts detected in the zygote
(2,474 of 16,385). Of these 2,474 transcripts, 82% de-
creased significantly between at least two time points in
development (2,053 of 2,474). Among them was a pos-
sible N. vectensis homologue of mos2 (JGI N. vectensis
transcript identification number (ID): 189257), which
plays a role in oogenesis in the hydrozoan Clytia
hemisphaerica . Many transcripts associated with
cell totipotency also decrease throughout development
, including vasa domain containing transcripts (ID:
244465; ID: 230331), piwi (ID: 127599), and tudor (ID:
245679; ID: 224903; ID: 121235; ID: 7216).
After gaining general insights into broad patterns of
transcripts with decreasing expression over the course of
development, we next identified transcripts that decrease
significantly between 2–7 HPF, regardless of subsequent
changes. A set of 179 transcripts met this criterion (97
of these also decrease monotonically throughout the
course of development).
Histone modification and rapid cell cycling are two
proposed mechanisms by which expression of the zyg-
otic genome is repressed in early development of
bilaterians, and transcripts associated with these pro-
cesses may be degraded preferentially in cnidarians as
well . Several chromatin remodeling homologues de-
crease significantly from 2–7 HPF. These include a puta-
tive histone modifier BRG1 associated factor (ID:
127783) that has been shown to be essential for the
MZT in mice, . Other putative histone modifiers in-
clude a histone methyltransferase (ID: 116282) and a
possible member of a male specific lethal-like complex
(ID: 113169) . Cell cycle genes include a rootletin-
like transcript (ID: 232574), which is associated with mi-
tosis , and cyclin B-like transcript (ID: 208415). Ma-
ternally loaded RNA of Cyclin B is targeted for
degradation in Xenopus embryos, possibly through an
miRNA pathway . The significant decrease in tran-
script abundance for these genes between 2–7 HPF sug-
gests that maternal repression of zygotic expression in
Table 1 Select GO terms enriched between 7–12 HPF
Rank GO IDOntogeny term Name P-adjusted AnnotatedDecreasing Increasing
structural constituent of ribosome 1.74E-371750 77
7 GO:0006415 Biological
translational termination 1.25E-25540 36
translational elongation9.01E-25 731 43
9 GO:0006614 Biological
SRP-dependent cotranslational protein
targeting to membrane
1.64E-24 540 37
30 GO:0006413 Biological
translational initiation3.63E-131401 42
translation6.57E-13 6787 108
sequence-specific DNA binding transcription
0.0003050592 5878 56
DNA binding 0.0030186091429 4086
axis specification0.01298371 802 15
transcription factor complex 0.02501985860 1967
GO enrichment of select transcripts that are changing in abundance during the major wave of zygotic gene expression. The GO-seq rank is given for each GO
term, as well as the GO ID, and one of three possible GO ontology terms, which describe either the location, function or process the transcripts may be associated
with. A description of the category is listed, the GO-seq p-adjusted value, the number of transcripts annotated within that category, and the relative number of
transcripts whose abundance is decreasing or increasing through the interval.
Helm et al. BMC Genomics 2013, 14:266
Page 5 of 10
N. vectensis shares some conserved features with
bilaterians, and that maternal repression is weakening in
Initiation of zygotic transcription
We next examined the initiation of zygotic transcription,
first by identifying transcripts whose proteins may play a
functional role in the degradation of maternal RNA, and
second, by looking for transcripts that peak in abun-
dance only at the 12 hour time point, and thus may be
specific to the MZT or early embryonic development.
To isolate transcripts whose protein products may
function in maternal RNA degradation, we examined
transcripts that increase significantly either between 2–7
or 7–12 HPF. A smaug homolog (smg; ID: 240079) is
present at low abundance in the zygote, and increases
significantly at 12 HPF. In D. melanogaster Smg is a
transcriptional regulator that binds to maternal tran-
scripts, targeting them for degradation . After D.
melanogaster Smg binds to specific RNA sequences, it
recruits the CCR4/POP2/NOT-deadenylase complex,
which removes the poly(A) tail, thus signaling the RNA
for degradation . Two possible members of the
upregulated between 2–7 HPF (ID: 122631, ID: 104011)
and a third between 7–12 HPF (ID: 195293). Which
recruiting, if any, remains to be determined. However,
these data suggest that the Smg CCR4/NOT transcrip-
tion complex pathway for degradation of maternal RNAs
may be present in cnidarians.
We next examined genes that appear to be expressed
only at the 12 hour time point, and may be involved in
the MZT, or early embryonic development. We isolated
this subset by selecting for only those transcripts that
exhibit significant changes in expression before and after
12 HPF, and that also have a STEM profile of 31 (peak
only at 12 HPF, Figure 1). 42 genes met these criteria.
This subset represents some of the earliest genes to be
transcribed by the zygotic genome that are also specific
to the blastula stage.
Five genes whose homologues are known to interact in
other organisms, which function in body plan formation
and neuronal development, were among these 42 tran-
scripts. These include an achaete-scute homologue (ID:
106438), also known as NvashB, which functions in
proneural patterning in other organisms . In N.
vectensis, the spatial and temporal expression of this
transcript was studied. Expression was first detected via
in situ hybridization in the blastula at the oral pole, with
less staining in the early gastrula, and loss of staining by
mid-gastrula , these results agree with our findings. In
D. melanogaster and humans, Achaete-scute is laterally
inhibited by Hairy and a Hairy-related protein HES-1,
complex Smg maybe
respectively [19,20]. A N. vectensis homologue of hairy
(ID: 242118) also shows increasing expression exclu-
sivelyin the7–12 HPF
homeodomain transcription factor (ID: 246590) known
in D. Melanogaster as Chip, which interacts with
Achaete-scute in proneural prepattern and thorax devel-
opment , is also upregulated only at 12 HPF. Lastly,
we identified two wnt genes in this subset (ID:115036;
ID: 195613), both similar to wnt8 in other organisms.
Wnt8 is among the earliest zygotically-expressed regula-
tory factor in the sea urchin, where it is responsible for
patterning along the animal-vegetal axis [22,23]. Wnt8b
expression in humans and mice is restricted to early
brain development , and in the spider Achaearanea
tepidariorum, wnt8 knockdowns affect expression of
hairy, among others transcripts, and decrease formation
of posterior body regions . While multiple N.
AY725205), the transcripts we identify have not been ex-
amined. The wnt8 transcripts we observe at this time
point are strongly expressed at 12 HPF, with one (wnt8
(ID: 115036)) being expressed almost exclusively at this
time (corresponding to Wnt8a , NCBI ID: AY792510),
and another (wnt8 (ID: 195613)) displaying low level ex-
pression before and after 12 HPF.
Wnt8, Hairy, Chip, and Achaete-scute are known to
interact in other organisms, and play a role in both body
plan patterning and nervous system development. In N.
vectensis it is possible that they play a role in one or
both of these processes. Examining the spatial expres-
sion of more of these genes, as well as conducting func-
tional studies,would further
interval.A LIM class
shed light onthis
The onset of gastrulation
We chose to sample gastrula at several hours past the
initiation of gastrulation , in an attempt to capture
early gastrula gene expression. N. vectensis gastrulation
occurs via invagination , and the formation of a blas-
topore was clearly visible in some embryos.
Of the 2,575 transcripts that increase between 12–24
HPF, 170 have a significant expression peak only at 24
HPF (significantly increasing before and after 24 HPF,
and a STEM profile of 28). These genes include a
homologue of homeobrain (ID: 165614), a transcription
factor that is expressed in brain formation in D.
melanogaster  and the annelid Capitella teleta .
We also identified a possible frizzled family receptor-10
(ID: 168924), which is also significantly up regulated
from 7–12 HPF, and is involved in limb and nervous sys-
tem development in chicks . These transcripts may
be involved in early nervous system and apical organ
formation in N. vectensis.
Helm et al. BMC Genomics 2013, 14:266
Page 6 of 10
The analyses presented here provide a global perspective
on significant changes in gene expression through time
during N. vectensis development. We identify likely ma-
ternal transcripts targeted for degradation, and a subset
of transcripts whose proteins may play a role in targeting
maternal factors, as well as genes among the first to be
transcribed by the N. vectensis embryos, which may play
a role in neuronal development and/or patterning. We
also identified the major wave of zygotic transcription,
which occurs after the 128 cell stage between 7–12 HPF.
The matrix file (Additional file 2), as well as some sug-
gested approaches for its use (Additional file 3) will
allow other investigators to examine temporal changes
in transcripts of particular interest, perform additional
analyses, and examine time points relevant to processes
not directly addressed here. Future applications of RNA-
seq to characterize the transcriptional dynamics of N.
vectensis development will likely benefit by higher tem-
poral resolution. The results presented here will help
guide the selection of additional time points so that
changes in expression can be pinpointed in time more
precisely. In addition, an updated set of transcript pre-
dictions will be essential for more detailed analyses. The
gene predictions provided by the N. vectensis genome
project have been an invaluable resource to the commu-
nity, and enabled many projects (including the one
presented here). There are several properties of the gene
predictions generated for this project that limit their
utility for use in conjunction with new tools, such as
RNA-seq, that were not available at the time the genome
annotations were produced. In particular, the presence
of rRNA in a large number of gene predictions and the
absence of multiple known genes limit the analyses that
can be done with these sequences. An updated set of
gene annotations and transcript predictions, which will
surely benefit from the much deeper transcriptome se-
quencing that is now possible, will be a critical goal for
further work with high-throughput tools for the study of
N. vectensis development and functional genomics.
Spawning and sample collection
Our N. vectensis culture was founded with adults from
Mark Martindale’s laboratory (University of Hawaii). An-
imals were kept in 12 parts per thousand seawater
(Nematostella Medium: NM) at 16°C, fed newly hatched
Artemia twice weekly, and cleaned once a week. Females
were kept in female-only bowls.
A total of two replicates time course were collected in
this study. For each time course, a single pool of em-
bryos, spawned from the same parents at the same time,
was sampled over the course of 10 days. Spawning was
induced by feeding female-only bowls and mixed bowls
(with males and females) oyster, followed by a water
change and placement on a light box attached to a timer
. They were exposed to 8 hours of light, with bowl
water temperatures increasing to above 24°C. After light
exposure, animals were moved to a dimly lit room, and
any eggs spawned overnight were removed. Bowl water
was changed to room temperature 0.2 μm filtered NM.
Females began to spawn approximately two hours after
light exposure ended. Every 30 minutes newly spawned
eggs were moved to small mesh bottom cups in NM
from mixed male/female bowls (which contained sperm),
kept at 18-23°C. Time of fertilization was considered to
be at the time cups were placed in mixed water. Eggs
were allowed to sit in water from mixed bowls for one
hour. NM from the mixed male/female bowls was
changed once over the course of collection. In N.
vectensis, eggs are secreted in a gelatinous matrix, which
must be removed before embryos can be sorted. Em-
bryos were de-jellied by rocking them for 15–30 minutes
in 40 ml NM, with 1.6 g cystine and 12 drops 5M
NaOH. Embryos were rinsed 3 times with 0.2 μm 18°C
filtered NM, divided and placed in 0.2% gelatin coated
dishes. A total of six dishes, one for each time point,
were prepared. Each dish had approximately 500–1000
embryos. These dishes were kept at 18°C for the dur-
ation of development.
We sampled six time points during each replicated
time series. The target sampling points were zygote (2
HPF), early blastula (7 HPF), mid-blastula (12 HPF),
early gastrula (24 HPF), planula (5 DPF), and young
polyp (10 DPF). The exact sampling times had minor
variation, and were 2.50, 7.23, 12.23, 23.60, 125.42, and
240.07 HPF for the first replicate spawning, and 2.55,
7.30, 12.48, 23.63, 125.50, 240.13 HPF for the second
replicate. Prior to sampling for gene expression, any
anomalous or un-cleaved embryos were removed (after 2
HPF), and the remaining embryos were rinsed with 0.2
μm 18°C NM. The fertilization rate was higher than 90%
for both replicates. For each time point, approximately
500–1000 embryos were placed in RNAse-free non-stick
tubes, excess liquid was drawn off, and they were snap
frozen on liquid nitrogen.
mRNA extraction & HiSeq preparation
Messenger RNA was extracted directly from tissue with
Dynabeads from the mRNA Direct Kit (Invitrogen) with
only one round of bead hybridization. mRNA was
treated with Turbo DNase (Ambion) and concentrated
by ethanol precipitation. Re-suspended mRNA samples
were quantified with Qubit. Samples were prepared for
multiplex sequencing using Illumina TruSeq RNA
Sample Prep Kits A and B (part # FC1221002 (kit B),
Lot: 5781467) according to manufacturer's instructions,
with an RNA fragmentation time of 8 minutes at 94°C.
Helm et al. BMC Genomics 2013, 14:266
Page 7 of 10
All twelve samples were sequenced in a single lane on
the Illumina HiSeq 2000 at the Brown Genomics Core
Facility. Reads were single-end 50bp, with a separate
read to sequence the sample index. Reads were de-
multiplexed according to their index sequences with
Casava version 1.8 (Illumina). Reads that did not pass
the Illumina chastity filter were discarded.
Reference and mapping
Filtered transcript predictions from the Joint Genome insti-
tute (JGI) N. vectensis genome project (ftp://ftp.jgi-psf.org/
scripts.Nemve1FilteredModels1.fasta.gz) were used as refer-
ence sequences . The original JGI transcriptome file has
27,273 predicted transcripts, with a mean contig length of
1,092 nucleotides. Some of these transcripts contain frag-
ments of ribosomal RNA sequences, which, due to the high
expression of ribosomal RNA, could complicate analyses of
differential gene expression. We therefore removed tran-
scripts that matched any of the following sequences
according to a blastn search with an e-value less than 1e-
40: 28S (extracted from the N. vectensis genome), 18S
(GenBank: AF254382.1) and the mitochondrial genome (in-
cluding 16S and 12S; GenBank: DQ643835.1). 762 tran-
scripts matched these sequences and were removed, though
some gene predictions that include rRNA fragments that
did not match with these stringent criteria are still present.
The 18S, 28S, and mitochondrial sequences itemized above
were then added to the reference, so that the number of
reads that mapped to these high-abundance sequences
could be quantified. This produced a reference of 26,514
transcripts. The modified reference, including single copies
of the ribosomal sequences and the mitochondrial genome,
is provided as Additional file 1.
Our sequence reads were mapped to the reference
using bowtie 2.2.0 beta3, with the --very-sensitive-local
and -a flags. The --very-sensitive-local increases sensitiv-
ity at the cost of computational resources, while -a
returns all possible mappings for a single read, rather
than just the top mapping. A list of full commands used
can be found in (Additional file 7). Counts were gener-
ated from the bowtie2 map file using an in-house script
(Additional file 8). This script does not count any read
that maps to more than one reference sequences, and
multiple mappings to the same reference sequence are
misassembled transcripts on the derivation of read
reducesthe impact of
Statistical analyses of expression
Statistical analyses were performed with R version
2.15.2. The R code for the analyses are in Additional files
9 and 10. The matrix with raw read counts, normalized
counts, statistical analyses of changes in expression, top
blast hit, and other annotations is in Additional file 2.
This file includes all reads present in the filtered tran-
script predictions from the N. vectensis genome project,
except redundant reads matching our query ribosomal
sequences, which were removed (as discussed in “Refer-
ence and mapping”). One copy each of 18S, 28S and the
mitochondrial genome were manually added after statis-
tical analysis. This matrix can be used to examine genes
and time points not presented in the main text.
We tested the significance of differential expression
between each pair of adjacent sampling time points,
using the R library edgeR version 3.0.4 . Since there
were six sampling time points, there were five intervals
that were tested. Transcripts without read counts or
with very low read counts were filtered out before
performing the test. This filtering strategy aimed at
keeping transcripts with an average read count of at least
1 count per million (CPM) for replicates of a particular
time point (keep <− rowSums(cpm(d) > 1) >= 2). TMM
positional difference between libraries using the function
calcNormFactors . Dispersion was estimated using the
functions estimateCommonDisp and estimateTagewiseDisp.
Testing for differential expression was done using the func-
tion exactTest. P-values were corrected for multiple testing
using p.adjust and Bonferroni correction. We considered
genes with an adjusted p-values below 0.05 as differentially
We refer to an increase or decrease in read counts be-
tween time points for a gene as an increase or decrease
in expression of the gene. We acknowledge that this is a
simplification of terminology, as numerous biological
factors can influence mRNA abundance in cells, includ-
ing mRNA degradation rate, so that read counts are not
a strict measure of expression.
Temporal patterns of expression for individual tran-
scripts were categorized and visualized with Short Time-
series Expression Miner (STEM) version v1.3.8 .
These analyses were performed on normalized count
data, averaged between replicates. Normalized data were
generated by multiplying each count by a normalization
multiplier (generated by dividing 1 million by the
multiple of the library size and a normalization factor
calcNormFactors function)). Data were input with the
Normalized Data option. The data were fit to 50 possible
expression profiles, with the maximum unit change be-
tween two time points set to two. The STEM output is
in Additional file 6, and the profiles it produced are
shown in Additional file 4.
Helm et al. BMC Genomics 2013, 14:266
Page 8 of 10
STEM profiles only measure abundance, and do not
assess significance. To identify genes with a significant
peak of expression at a particular time point, we first
used STEM profiles to identify all genes with a pattern
of interest, then evaluated the significance of the change
in abundance for each transcript, keeping only those
transcripts that met our significance criteria.
The reference was compared with blastx to the metazoan
sequences in the NCBI nr protein database with an e-value
cutoff of 1e-5. Specific GO annotations were then produced
with the blast2go command line interface  and a local
instance of the blast2go database (version b2g_may10). The
blast2GO output was modified to fit the gene2GO format
used by the R package topGO version 2.10.0 (Additional
file 11) . We used functionality of topGO to build
acyclic graphs for the three domains, biological process
(BP), molecular function (MF) and cellular compart-
ment (CC), based on the blast2go annotations. After
building the graphs the complete set of annotations
were exported using the function genesInTerm. The
resulting file (Additional file 12) in combination with
the reference matrix (Additional file 2) allows for the
retrieval and subset analysis of transcripts that received
a particular GO term.
A gene set enrichment analysis was performed using the
R package GOseq version 1.10.1 . Adjusted p-values
from the edgeR analysis and a cutoff of 0.05 were used to
construct a numeric vector of differentially expressed
genes. Annotations in Additional file 12 were loaded as
category mappings. Weightings for each gene, depending
on its length, were obtained using the probability
weighting function nullp and over and underrepresented
GO categories were calculated using the Wallenius
approximation. P-values were adjusted using p.adjust and
the Benjamini and Hochberg method. We considered cat-
egories with an adjusted p-value below 0.05 as enriched.
The complete list of enriched GO terms for each GOseq
analysis are in Additional file 5. The R code for this ana-
lysis can be found in Additional file 10.
Additional file 1: Reference sequences. This file is based on the
genome project transcript predictions (ftp://ftp.jgi-psf.org/pub/JGI_data/
Nemve1FilteredModels1.fasta.gz), as described in the methods. This
reference contains 26,514 sequences, and was used for mapping. This is a
zipped fasta file.
Additional file 2: Data matrix. Matrix file containing the complete
count data, statistics, and annotated results from various analyses (STEM,
GO etc.). Detailed information is provided in the comments that precede
the file header line. This is a zipped text file.
Additional file 3: Example Analyses. Exampleanalysesofthematrixand
Additional file 4: STEM Profiles. Plots of each profile identified by
STEM for our dataset, with associated profile number. A subset of the
most highly represented of these plots is shown in Figure 1. The vertical
axis is relative transcript abundance, and the horizontal axis is relative
developmental time, from the first time point (2 HPF) on the left and the
last (10 DPF) on the right. This is an image file.
Additional file 5: Enriched GO terms in different intervals. The
complete list of enriched GO terms from all performed GOseq analyses
(adjusted p < 0.05). Additional information is provided in the comments
that precede the file header line. This is a text file.
Additional file 6: STEM output. The “Main Gene Table” output of the
STEM program. This is a text file.
Additional file 7: Bowtie2 Commands. The shell calls for Bowtie2
mapping. This is a text file.
Additional file 8: Python program for converting Bowtie2 output to
count data. This Python program takes in a .sam mapping file generated by
bowtie2 and returns the number of reads that map to each transcript. It
discards reads that map to multiple sequences, and reads that map multiple
times to the same sequence are counted only once. This is a text file.
Additional file 9: Matrix generation code. The R code and regular
expressions used to populate the matrix with count data, normalized and
averaged counts, BLAST annotations, STEM profiles, UniProt annotations,
JGI numbers, and KEGG annotations. Statistical analyses were performed
separately (see Additional file 10). This is a text file.
Additional file 10: R code statistical analysis with edgeR and
GOseq. The R code for performing differential expression tests with
edgeR and gene set enrichment analysis with GOseq. This is a text file.
Additional file 11: Blast2GO annotations. The Blast2GO annotations
modified to fit the gene2GO format used by topGO. To generate this file,
the reference was used as a query and BLAST against the nr database
(e-value cutoff of 1e-5). Transcripts were subsequently annotated with
GO terms using the Blast2GO Pipeline. The header row describes the file
contents. This is a text file.
Additional file 12: GO-transcript annotations. Thisfilecontainsthe
HPF: Hours post fertilization; DPF: Days post fertilization; DGE: Differential
gene expression that is significant (adjusted p < 0.05); NM: Nematostella
medium (12 parts per thousand salinity); GO: Gene ontology; ID: JGI
transcript ID; STEM: Short time-series expression miner; MZT: Maternal to
The authors declare that they have no competing interests.
CWD, RRH, ST and JS designed the study; RRH collected samples and
prepared libraries; RRH, SS, and CWD analyzed data, RRH and CWD wrote the
manuscript. All authors read and approved the final manuscript.
This work was supported by seed funds from the Brown-MBL Partnership
and the National Science Foundation Graduate Student Research Fellowship.
Thanks to the Brown Genomics Core facility (supported by National Institute
of Health grant P30RR031153), where Christoph Schorl and Hilary Hartlaub
have been extremely helpful. Infrastructure for data transfer from the
sequencer was supported by the National Science Foundation EPSCoR
Helm et al. BMC Genomics 2013, 14:266
Page 9 of 10
Program under Grant No. 1004057 (Infrastructure to Advance Life Sciences in Download full-text
the Ocean State). Thanks to Lingsheng Dong for preprocessing the data
through basecalling, and to the Brown Center for Computation and
Visualization, where all analyses were conducted. Freya Goetz provided
critical advice on sample preparation, and Nicholas Sinnott-Armstrong
helped optimize read mapping. We thank Mark D. Robinson for advice on
statistical analysis. Three anonymous reviewers provided valuable
suggestions, which greatly enhanced this manuscript. RRH would like to
thank BDS for valuable guidance.
1Ecology and Evolutionary Biology, Brown University, 80 Waterman Street,
Box G-W, Providence, RI 02912, USA.2Eugene Bell Center for Regenerative
Biology and Tissue Engineering, Marine Biological Laboratory, 7 MBL St.,
Woods Hole, MA 02543, USA.3Department of Molecular Biology, Cell Biology,
and Biochemistry, Brown University, 185 Meeting Street, Providence, RI
Received: 3 September 2012 Accepted: 14 March 2013
Published: 19 April 2013
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Cite this article as: Helm et al.: Characterization of differential transcript
abundance through time during Nematostella vectensis development.
BMC Genomics 2013 14:266.
Helm et al. BMC Genomics 2013, 14:266
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