Vol. 27 no. 18 2011, pages 2518–2528
Comparative analysis of RNA-Seq alignment algorithms and the
RNA-Seq unified mapper (RUM)
Gregory R. Grant1,2,3,∗, Michael H. Farkas4, Angel D. Pizarro2, Nicholas F. Lahens5,
Jonathan Schug3, Brian P. Brunk1, Christian J. Stoeckert1,3, John B. Hogenesch1,2,5
and Eric A. Pierce4,∗
1Penn Center for Bioinformatics,2Institute for Translational Medicine and Therapeutics,3Department of Genetics,
4F.M. Kirby Center for Molecular Ophthalmology and5Department of Pharmacology, University of Pennsylvania
School of Medicine, Philadelphia, PA 19104, USA
Associate editor: Ivo Hofacker
Advance Access publication July 19, 2011
Motivation: A critical task in high-throughput sequencing is aligning
millions of short reads to a reference genome. Alignment is especially
complicated for RNA sequencing (RNA-Seq) because of RNA
splicing. A number of RNA-Seq algorithms are available, and claim
to align reads with high accuracy and efficiency while detecting
splice junctions. RNA-Seq data are discrete in nature; therefore,
with reasonable gene models and comparative metrics RNA-Seq
data can be simulated to sufficient accuracy to enable meaningful
benchmarking of alignment algorithms. The exercise to rigorously
compare all viable published RNA-Seq algorithms has not been
Results: We developed an RNA-Seq simulator that models the
main impediments to RNA alignment, including alternative splicing,
insertions, deletions, substitutions, sequencing errors and intron
signal. We used this simulator to measure the accuracy and
robustness of available algorithms at the base and junction
levels. Additionally, we used reverse transcription-–polymerase chain
reaction (RT–PCR) and Sanger sequencing to validate the ability
of the algorithms to detect novel transcript features such as novel
exons and alternative splicing in RNA-Seq data from mouse retina.
A pipeline based on BLAT was developed to explore the performance
of established tools for this problem, and to compare it to the
recently developed methods. This pipeline, the RNA-Seq Unified
Mapper (RUM), performs comparably to the best current aligners
and provides an advantageous combination of accuracy, speed and
Availability: The RUM pipeline is distributed via the Amazon
Cloud and for computing clusters using the Sun Grid Engine
Contact: email@example.com; firstname.lastname@example.org
described in the article are deposited at GEO, accession GSE26248.
Received on March 21, 2011; revised on July 5, 2011; accepted on
July 7, 2011
∗To whom correspondence should be addressed.
The ongoing high-throughput sequencing (HTS) revolution in
Being a sequence based technology, alignment algorithms are
critical for most applications. Genome alignment algorithms such
as Bowtie and BWA rely on Burrows–Wheeler indexing for very
fast genome alignment, but they have difficulties with transcriptome
alignment due to splicing, RNA editing and variations from the
reference such as substitutions, insertions and deletions (Burrows
and Wheeler, 1994; Langmead et al., 2009; Li and Durbin, 2009).
Additional complications arise from poorly annotated genomes, or
from samples with significant polymorphisms from the sequenced
organism or from aberrant splicing found in cells with mutations in
components of the spliceosome (Meyerson et al., 2010).
There are a number of programs available for RNA-Seq
alignment. Table 1 gives a breakdown of the alignment algorithms
used in a random sample of 130 papers listed on PubMed that have
‘RNA-Seq’ in the abstract (see Supplementary Table 1 for detailed
information). The most commonly cited algorithm is ELAND,
which is part of the analysis pipeline bundled by Illumina with its
must satisfy three basic criteria: (i) it must align single reads across
are available that satisfy these three criteria: TopHat (Trapnell et al.,
SpliceMap (Au et al., 2010) and Soap/Soapals (Li et al., 2009). We
further desire algorithms be as robust as possible to polymorphisms
and sequencing error. Based on our analyses, described below, only
Further, none of the published algorithms attempt to map against
both a genome and a transcriptome and to merge the results into
one alignment. As will be shown below, there is an advantage to
merging genome and transcriptome alignments to achieve better
disambiguation, in particular for reads that extend into introns.
In order to evaluate the accuracy of the various RNA-Seq
alignment algorithms, we developed an RNA-Seq simulator that
produces paired-end sequence reads with configurable rates for
substitutions, indels, novel splice forms, intron signal and random
error, including a decrease of the quality in the tails of the
reads, as is typically observed in Illumina data. RNA-Seq data
© The Author 2011. Published by Oxford University Press. All rights reserved. For Permissions, please email: email@example.com
RNA-seq alignment algorithms and RUM
Table 1. Algorithms used in a random sample of RNA-Seq publications
Algorithm No. of times used
Complete information regarding this literature is provided in Supplementary Table 1.
are discrete in nature; therefore, as long as good gene models
are available, it is possible to simulate RNA-Seq data that is
sufficiently realistic to allow for meaningful benchmarking of
with polymorphisms, alternative splice forms, partial retention of
introns, and which follows an error model reflective of Illumina
data. We also require there be no bias toward any particular
set of gene annotations. As far as we are aware, there is no
published RNA-Seq simulation software available. However, there
are a few simulators available online, e.g. FLUX (flux.sammeth.net,
Howard and Heber, 2010), (USeq, useq.sourceforge.net), (simNGS,
www.ebi.ac.uk/goldman-srv/simNGS/).However, none of them
satisfy the specific requirements for our benchmarking goals.
In particular, we require strict control over the sources of
polymorphisms: indels, SNPs, errors and alternate splicing. And
we require detailed logging. Neither FLUX, USeq nor simNGS
provide these capabilities. To meet the necessary criteria, we
developed a framework called Benchmarker for Evaluating the
Effectiveness of RNA-Seq Software (BEERS) (Fig. 1). The BEERS
simulator uses information from a filtered set of the annotated
genes from 11 different annotation efforts, to generate simulated
sequence read pairs with characteristics similar to those observed in
Illumina sequence reads. The details are given in the Section 2 and
To evaluate RNA-Seq alignment, we developed a set of metrics
to compare an inferred alignment to the true alignment of a BEERS
dataset. Accuracy is evaluated on the level of the individual bases
and splice junction calls. Neither metric alone indicates which
method is superior and it is not clear how to define a single metric
that would. For example, BFAST achieves a very high base-wise
accuracy, because it handles polymorphisms well and therefore
rarely fails to align a read. However, BFASTdoes not make junction
Fig. 1. BEERS simulator workflow. Genes are chosen at random from a
master pool, polymorphisms and novel splice forms are introduced, and then
reads are generated in a six step cycle, as shown.
calls and the accuracy at or near splice junctions is quite low,
consistent with its original purpose of DNA resequencing (Homer
et al., 2009). In contrast, GSNAP, MapSplice and RUM (described
junction detection and so should be preferable overall.
Alignment of transcriptome sequences is not a new problem, as
mapping EST’s to the genome has been an informatics challenge
alignment of ESTs are available, the most popular of which is BLAT
(Blast LikeAlignmentTool) (Kent, 2002). BLAThas been criticized
as inappropriate for short read lengths and is viewed by many as too
slow for mapping tens of millions of reads (Dimon et al., 2010).
However, as computational resources have become cheaper, and
BLAT can efficiently map short reads across exon–exon junctions
and identify novel splice junctions (Fig. 2); however, BLAT does
not take advantage of related query sequences, such as those from
paired-end reads, so it does not, without modification, satisfy the
three criteria necessary for an RNA-Seq aligner. BLATalso requires
significant post-processing to decrease the false positive rate at both
the base and junction levels. But, with code wrappers to handle
G.R.Grant et al.
Fig. 2. Gapped alignment using BLAT. BLAT alignments (segments in
black) of a mouse retina 108 base read that spans three exon/exon junctions.
The second junction is unannotated, according to the USCS annotation track
shown in blue.
Fig. 3. The RUM workflow. Reads are first mapped with Bowtie against
the genome and transcriptome. This information is merged and non-mappers
are sent to BLAT. BLAT and Bowtie mappings are merged for the final
alignments. Features are quantified and coverage and junction files are
these issues, our benchmarking shows that this solution is at least as
and speed. In what follows we will refer to this method as the RNA-
Seq Unified Mapper (RUM) (Fig. 3). RUM is implemented as a
three-stage pipeline that takes advantage of the speed of Burrows–
Wheeler based algorithms, sensitivity of BLAT, information coming
from paired-end sequencing and information from both genome
and transcriptome alignments. The pipeline first aligns reads with
Bowtie to a reference genome and to a reference transcriptome, and
then applies BLAT to the reads Bowtie could not align. Significant
complexity arises in post-processing the BLAT output to reduce the
number of false alignments, to utilize paired-end information and to
merge the information from the various mappings. The details are
given in the Section 2 and Supplementary Material.
In addition to the simulated data analysis, we validated the
RUM pipeline empirically, using data from RNA-Seq analyses
of mouse retina, and compared with the other algorithms. Most
algorithms were able to accurately identify novel splice variants,
including splicing events detected novel junctions with low read
depth. However, TopHat performed very poorly on this dataset.
To compare methods and to evaluate the accuracy of options and parameter
for Evaluating the Effectiveness of RNA-Seq Software (BEERS) consisting
of a data simulation engine and a set of comparative metrics for measuring
the accuracy of an inferred alignment (Fig. 1). In order not to bias for or
against any particular set of gene models, 11 different sets of annotation
gene models with 1720769 exons and 1052525 introns.These models were
filtered to remove most of the junctions that had uncharacterized splice
signals, most of which came from the OtherRefSeq track (Supplementary
Table 4), resulting in 538991 final gene models with <0.0003 of the
splice signals being uncharacterized. The characterized splice signals are
as follows: GTAG, GCAG, GCTG, GCAA, GCGG, GTTG, GTAA, ATAC,
ATAA, ATAG and ATAT. In the first step, in a simulation, a number of the
538991 gene models are chosen at random, with a default of N =30000.
This is done in order to not bias toward any particular set of gene models.
in exons, where the number of alternate forms per gene is a parameter with
a default of two. The percentage of signal coming from alternate splice
forms is a parameter with a default of 20%. Polymorphisms (indels and
substitutions) are introduced into the exons, according to independent rates.
Agene quantification file (generated in our case from wild-type mouse retina
This file is further used to determine the distribution of intronic signal, so
that preferential intron inclusion can be simulated. Reads are then produced
by choosing a gene at random, possibly leaving in an intron, choosing a
fragment of normally distributed length, introducing random base and tail
error, and then reporting the M bases of the fragment from either end, where
M is the read length. Random base error is set according to one parameter
and tail error is set according to three parameters: percent of low-quality
tails; length of the low-quality tail; and quality of the low-quality tail. The
reads generated are reported to a fasta file. The true coordinates of each real
and the true junctions spanned are reported to text files. The set of gene
models used, the alternate splice forms and the polymorphisms are reported
to log files. See Supplementary Material for code availability.
Datasets with 10000000 paired-end 100-base reads were generated for
each of two types of data, one with low polymorphism and error rate (Test 1)
and another with moderate polymorphism and error rate (Test 2); details
are given in Supplementary Material. The human polymorphism rate is
roughly one base in 10000 (Sachidanandam et al., 2000) and the error rate
for a clean run of an Illumina machine is less than one base in 200. So
Test 1 was designed with those specifications. Model organisms should be
reasonably well represented by this case. Test 2 allowed for quite a bit more
polymorphism and error, with fairly low-quality tails, which should present
more than the average challenge to alignment. Datasets were generated
in triplicate to assess the variability of the accuracy metrics. An example
of simulated data with intron signal and a two base deletion is shown in
Supplementary Figure 1. Three basic metrics were calculated to compare
the inferred alignment to the true alignment. The most straightforward is the
percent of bases which map uniquely, and to the right location. A second
natural metric is the percent of correct junction calls.
In the first (base-wise) metric, some misalignment of indels must be
measured as accurate. Suppose, for example, that the reference sequence is
‘CCCACCC’ and that in the sample being sequenced it is ‘CCCAACCC’
due to an insertion. Logically, there is no way an alignment algorithm
could determine which ‘A’ was inserted and in fact it does not make
RNA-seq alignment algorithms and RUM
Fig. 4. This illustrates a hypothetical case where it is difficult to resolve the
transcriptome and genome mappings. (A) Shows how a read aligns to the
genome. It spans an exon and erroneously extends one or two bases on either
side into the intron. (B) Shows how the same read maps to the transcriptome.
In this case, the few terminal bases map to the adjacent exons. (C) Shown
in red is the alignment of the paired-end read, which has aligned to the
intron. Even if all bases of all alignments are identities, if all we had was
the information in (A) and (B) we would likely preference the transcriptome
alignment (B). If we have the information in (C) then we would preference
the genome mapping in (A) on the right, but it becomes a difficult judgment
on the left, given that the retention of selective introns and partial introns is
sense biologically to consider it an insertion instead of a duplication. Our
benchmarking metrics, therefore, judge an algorithm as correct on this
insertion as long as it chooses one or the other of these two possibilities,
and a general strategy is employed to handle all cases of indel ambiguity.
A second natural metric is the percent of correct junction calls. This
is complicated, however, by the many different ways algorithms report
junctions. Some of the algorithms being evaluated do not map reads across
junctions at all, so they essentially have an FP rate of 0% and an FN rate of
100%.Among the algorithms that do map across junctions, some of them go
algorithms report all junctions but attach various scores to them, leaving it
to the user to decide which to consider. If reads are aligned across junctions
by an aligner, but no extra processing is done by the aligner to report a
special junctions file, then we simply use as junctions the gaps indicated
by an N in the CIGAR string of the SAM record. On the other hand, if the
program produces a final set of junctions that are supposed to be the most
reliable, then that set was used for benchmarking. If junctions were attached
with scores, we adjusted the scores as best as possible to achieve the best
performance. Once a final set of junctions is determined, the false-positive
of transcripts used for the simulation. The false-negative rate is the percent
of junctions in the database that are crossed by at least one read, but which
are not represented in the set of junctions inferred by the algorithm.
The RUM workflow is given in Figure 3. Bowtie is first run against the
genome. A read which is contained entirely in one exon, except for a few
bases that align to an adjacent exon, will often be erroneously aligned by
Bowtie to the start of the intron. Bowtie is therefore also run against a given
transcriptome. The genome and transcriptome alignments are compared for
consistency and in most cases the transcriptome alignment is preferred,
unless there is a paired-end read that indicates to do otherwise. However,
determining which alignment to preference is not always straightforward
and in any set of merging rules there will be ambiguous cases.
Consider the read alignment in Figure 4. There are three exons, one in the
(B) shows the transcriptome alignment. The correct alignment is uncertain
for the few bases on each side that ambiguously align to both the intron and
the adjacent exons. Three natural merging strategies arise: (i) preference the
transcriptome mapping, (ii) preference the genome mapping or (iii) truncate
the alignment and do not report the ambiguous bases. The RUM pipeline
preferences the transcriptome alignment in this case. If, however, the paired-
end maps as shown in (C), in red, then the genome mapping would be
Fig. 5. Afalse positive BLAT alignment of a 120 base read of mouse retina.
The merging rules are guided by a number of cases, which are given
in detail in Supplementary Material with a brief description given here.
Information between two mapping is joined when possible. So for example,
if one mapping aligns the forward read and another the reverse, and they
are consistent with being ends of the same fragment, then the two single-
end alignments are merged to give the paired-end alignment. If a read
(or read pair) has a unique alignment to the transcriptome and a unique
alignment to the genome, but the two alignments disagree, then, if they
agree on a sufficiently long overlap, just that overlap is reported; otherwise
the read is considered a non-unique mapper. In general, RUM tries to resolve
ambiguities that are minor by either giving preference to the transcriptome
alignment or by just reporting a subalignment consisting of the common
spans where both alignments agree.
In the third stage of the RUM pipeline, reads that were not able to be
aligned by Bowtie are aligned to the reference genome using BLAT (Kent,
low complexity sequence or because of partial homology to other locations.
Inspection of the false positives gives rise to a number of filters which
achieve alignments with an apparently low occurrence of false positives.
We then validated and refined these filters using simulated benchmarks. For
being low complexity sequence (i.e. containing short repeated elements).
However, we do not want to filter out low complexity sequence, because
they often represent real signal. Instead we identify the low complexity reads
and require more stringent alignment parameters for them. Once filtered and
can involve junctions, so the rules are somewhat more complex. Details are
given in the Supplementary Material.
Afile of unique aligners and another file of non-unique aligners is output.
These are human readable and contain basic alignment information for
each read (pair). Also output is a SAM file with all alignments unique
and non-unique. Depth-of-coverage files are generated from the final set
of unique and non-unique aligners, which give the number of reads mapping
to each genome location. A feature quantification file is generated that
assigns quantified values to genes, exons, introns and junctions using the
RPKM measure (Bullard et al., 2010). However, two quantified values are
generated, one assuming no non-unique mappers map to the feature, and
do map to the feature. RPKM values are normalized for feature length and
number of reads mapped, and so are appropriate to use for comparisons
between samples, as long as expression is reasonably well balanced. If data
are unbalanced, the data can be normalized, as described in Supplementary
Material. The pipeline does not try to adjust for this effect, however, and
assumes such normalization, if necessary, will take place downstream.
(15 bases or more, by default). A bed file is produced with the junctions
that have known splice signals and uniquely mapping reads with at least
eight bases on each side of the junction. Increasing this beyond eight bases
does not significantly affect the FP rate but does start to affect the FN rate
(Supplementary Fig. 2). Another bed file is generated with all junctions.
Junctions are colored by whether they have known signal, whether they
exist in the supplied transcript database and whether or not the signal is
canonical. A spreadsheet is also produced that breaks down the different
kinds of evidence in separate columns.
G.R.Grant et al.
RUM is also enabled for strand-specific mapping, variable length reads
and DNA mapping.
complexity filter mdust. RUM requires 64 bit operating systems with at least
6 GB of RAM to handle genomes as large as mouse or human, and is best
run on a cluster or multicore machine, while genomes such as Drosophila
can be run on single processor 32 bit systems with 4GB of RAM. A typical
mouse or human alignment of 10 million read pairs requires ∼200GB of
temporary disk space and ∼20GB for the final output files, uncompressed.
A 100 million read dataset, as is typical for one lane from a HiSeq machine,
requires ∼500GB of temp space. RUM is also enabled for compute clusters
that use the SUN Grid engine. RUM and the simulator are available as open
source under the standard GNU agreement to academic institutions.
The RUM pipeline installs on a stand-alone machine or on a cluster
running the SUN Grid Engine. In order to distribute software that requires
massive compute, a new paradigm is emerging called cloud computing. In
Web services). We developed RUM and optimized its use onAWS using the
‘high memory, quadruple extra large instance’, which provides eight virtual
cores and 68.4GB of RAM. Using this instance, a single paired-end lane
(25 million, 120bp reads) is mapped to a mammalian genome in ∼5–6h.
However, this is dependent on read quality. With lower quality reads, fewer
reads will be aligned by Bowtie and more by BLAT, increasing the run time
several fold in the worst case.
Cloud, are provided at cbil.upenn.edu/RUM/.
Implementation, availability and cloud distribution
Animal research was approved by the Institutional Animal Care and Use
Committee at the University of Pennsylvania. Five micrograms of total RNA
from neural retinas of 2-month-old C57BL/6J mice was used to prepare a
cDNA library. The library was generated using the Illumina mRNA-Seq
Sample Prep Kit, with an average insert size of 350bp (±25bp) (Illumina,
of a flow cell on a GenomeAnalyzer IIx, with 120bp paired-end reads. Base
calls were generated using the CASAVAv1.6 (Illumina) software, and output
unfiltered and unaligned in fasta format. These sequence reads are deposited
at GEO, accession GSE26248.
Reverse transcription polymerase chain reaction (RT–PCR) was performed
from total RNA using primers designed to flank the region of interest, and
the products electrophoresed on a 2% agarose gel. Bands were excised and
sequenced on an ABI 3730xl DNAAnalyzer (ABI, Carlsbad, CA, USA).
RT-PCR and sequence validation
To evaluate the performance of RNA-Seq aligners, we used BEERS
to generate two simulated datasets. For the initial test, designated
Test 1, we generated data from 30000 mouse build mm9 transcript
models with low indel (0.0005), substitution (0.001) and error
frequency (0.005), with no tail error and with only 20% of the signal
indel (0.0025), substitution (0.005) and error (0.01) frequency, with
25% of the trailing 10 bases having 50% error and 35% of the signal
coming from novel splice forms. The gene models include gene
Simulated data and comparison to other methods
families and highly repetitive intron signal. With 100 base paired-
end reads, the non-uniqueness issue affects only 2–3% of reads
on average. Datasets, each of 10 million pairs of 100 base reads,
were generated in triplicate for each of the two tests. Replication in
triplicate allows for assessment of the variability of the metrics. See
the Supplementary Material for availability of the simulated data.
The reads in the two simulated datasets described above were
aligned using RUM, TopHat, BWA, NovoAlign, Soap/Soapals,
MapSplice, SpliceMap, GSNAP and BFAST. Additionally, to
evaluate the contributions to accuracy of the transcript database to
the RUM pipeline, we ran RUM against the genome without the
benefit of the transcript database. We also evaluated Bowtie against
the genome alone, against the transcriptome alone and a merging
of those two alignments. We also evaluated the BLAT module from
the RUM pipeline as a stand-alone aligner. As shown in Figure 6,
Bowtie alone has relatively low accuracy with regard to alignment.
BLAT provides more accurate alignment, and the combination of
the two in RUM is better. Similarly, Bowtie and BLAT alone have
high false positive and negative rates, respectively, while their
combined use in RUM provides much lower false positive and
negative rates. We attempted to optimize the performance of each
algorithm. The parameters and processing used are given in the
Supplementary Material. Some of these algorithms were included
in these comparisons because of their common use in practice, even
though they do not satisfy all three of the basic requirements for
RNA-Seq alignment stated above.
The base-by-base and splice junction accuracies for the analyses
of the two tests are given in Figure 6, and Supplementary Table 2.
As shown in Figure 6, GSNAP, RUM and MapSplice achieved the
most accurate alignment and junction detection on the data in Test
1. RUM and GSNAP also did better on the more complex data in
Test 2, with high base-wise and splice junction accuracy. The lower
accuracy of SpliceMap, SOAP/SOAPals and TopHat is exacerbated
by indels (Fig. 7), with decreased robustness of these algorithms
evident by comparing Test 1 to Test 2.
Figure 8 shows a region with five splice junctions where BLAT-
and the BLAT-based algorithms RUM and RUM-Genome properly
resolve several of the junctions, as compared with the other
algorithms. In contrast, BFAST achieves a high base-wise accuracy,
but it does not attempt to make junction calls and in fact has
very low accuracy near junctions. Of the three most accurate
algorithms, GSNAP, RUM and MapSplice, RUM has the lowest
false positive rate on junctions and the junction calls most robust
to polymorphisms. Algorithms varied considerably in their false
junction calls by the sum FP + FN indicates GSNAP and RUM to
be the most accurate overall.
As the number of reads per lane increases upwards to 100
concern. MapSplice cannot, as of yet, be parallelized and on a 94
million read retina dataset, it required 16 days to process, while
generating over 2.5 TB of temporary files. In contrast, GSNAP and
RUM are both designed for parallel processing and put a much
lower demand on the mass storage device. But, GSNAP requires
significantly more computational resources: 5 days to process the
retina dataset using 300 processors, compared with RUM which
required 50 processors and ∼2 days (Fig. 9). Based on these data,
we believe that RUM is currently the most attractive option for
RNA-Seq alignment of such large datasets.
RNA-seq alignment algorithms and RUM
Fig. 6. Accuracy statistics for analyses of simulated datasets. (A and B) Simulated dataset 1. (C and D) Simulated dataset 2. Test 1 has low polymorphism
and error rates, while Test 2 has moderate polymorphism and error rates. In (A) and (C), the bars show the base-wise accuracy (the percent of bases that
aligned and to the right location). (B) and (D) Show the accuracy of the junction calls, dark bars show the false positive (FP) rate and light bars show the
false negative (FN) rate. The algorithms are sorted in (A) and (C) by accuracy and in (B) and (D) by the sum of the FP and FN rates. Results are mean ±
SEM over the three replicate simulated datasets for each test. There is a considerable drop-off in accuracy seen in Test 2 for the algorithms that do not align
across indels (SpliceMap, TopHat and Bowtie). The base-wise accuracy and the FP and FN rates on junction calls are taken in conjunction to determine the
overall effectiveness of an algorithm. Based on these results, we conclude that GSNAP, MapSplice and RUM are the ones that are most viable for RNA-Seq
G.R.Grant et al.
Table 2. The detection rates for RUM novel junctions, by algorithm
Fig. 7. Representative coverage plots demonstrating the effect of a two base
deletion on alignments with the algorithms indicated. Reads were aligned
using RUM, the individual BLAT and Bowtie components of RUM, and 10
currently available alignment algorithms. The TRUTH coverage plot (top)
represents the true alignment of the reads containing the two-base deletion
(arrow). RUM and several other algorithms were able to correctly align these
reads. Note thatTopHat, SpliceMap, Bowtie and Soap, which do not identify
indels, fail to accurately align reads to these regions.
at the bottom of the accuracy list, with a dramatic decrease seen
between Test 1 and Test 2. For example, TopHat’s overall base-wise
accuracy went from 90.86% to 63.67%, while RUM’s accuracy only
went from 95.19% to 90.39%. In Test 2, RUM achieved the lowest
FP + FN rate on junctions with a FP of 1.41% and a FN of 2.48%.
In contrast, TopHat’s FP rate is 6.62% and FN rate is 25.46%.
Fig. 8. Comparison of accuracies near junctions on BEERS-generated
data. The true junctions are shown in black at the bottom of the figure.
Reads mapping to the region of the simulated annotation track (bottom)
were aligned using RUM, the individual components of RUM and the 10
currently available alignment algorithms indicated. The TRUTH coverage
plot represents the true alignment of the simulated reads. There are five
characteristic splice junction sites (1–5) that indicate varying accuracy of
the alignment algorithms. BLAT- and the BLAT-based algorithms RUM
and RUM-Genome provide the most accurate resolution of the depicted
junctions. GSNAP detects the five junctions, and also displays inaccurate
alignment of reads in the intron near junction #2.
For each read that crosses a junction, an algorithm either calls
it correctly or not. This allows us to calculate the sensitivity and
positive predictive value (PPV) at the individual read level, which
is shown in Figure 10. The PPV is ∼65% in all cases, while
MapSplice and RUM have the highest sensitivity, with RUM being
the algorithm more robust to polymorphisms, in this case.
HTS offers the unprecedented ability to identify novel splice forms,
both alternative and aberrant. From empiric RNA-Seq data, we have
observed a large number of unannotated splicing events, a majority
of which are expressed at a low level compared with annotated
variants. We set out to assess the rates at which these represent
true and biologically replicable events, and to compare the ability
Analysis of a real RNA-Seq library
RNA-seq alignment algorithms and RUM
Fig. 9. The sensitivity and positive predictive value (PPV) at the individual read level. MapSplice and RUM have the highest overall sensitivity,
while all algorithms have PPV ∼65%.
Fig. 10. Processor time required for analysis of simulated datasets. The processor time required for each of the algorithms tested to analyze the first (A) and
second (B) simulated datasets is shown. Data are mean ± SEM. The values from which these graphs are derived are shown in Supplementary Table 2.
Algorithms were run on 64 bit Linux Debian with 2.6GHz processors.
of different RNA-Seq alignment algorithms to detect them. To do
this, we prepared an RNA-Seq library from mouse retinal RNA, and
sequenced it using four channels of an Illumina Genome Analyzer
IIx flow cell to generate 94 million paired-end 120bp reads. We
analyzed these RNA-Seq data using RUM, GSNAP, MapSplice,
SpliceMap, Soapals and TopHat, and performed RT–PCR and
Sanger sequencing validation studies on biologically independent
RNAsamples to assess how reliably RUM and the other algorithms
detected novel junctions.
Of the 94 million sequence reads in the mouse retina RNA-
Seq dataset, ∼41 million cross exon–exon junctions, including
35435507 reads that aligned cleanly (uniquely with at least eight
bases on each side, read depth ≥2) to 172521 known junctions.
An additional 290203 reads aligned cleanly across 47078 novel
junctions with characterized splice signals. ‘Novel’ here means
that the junctions are not represented in any of the 11 annotation
tracks for the mouse genome currently available from the UCSC
genome browser. Many of the novel junctions that were detected
fell into three canonical categories: (i) skipping of annotated exons
(6001; 12.75%); (ii) inclusion of novel exons in known genes (3207;
6.81%); and (iii) alternate 5?and 3?splice sites (≤50 bases from
known site, 3802; 8.08%).
We selected 25 examples randomly, from each of the three
categories described above, for validation in independent retinal
RNA samples. For each category, this includes five cases present
in ‘high’abundance (>33% of reads crossing the novel junction(s),
as compared with the annotated junction(s)) and 20 cases present in
‘low’ abundance (<10% of reads crossing the novel junction(s) as
compared with the annotated junction(s)). In total, these 75 splicing
events involve 100 novel junctions, since the novel exon inclusion
events involve two junctions for each example. RT–PCR verified the
presence of transcript variants with the novel junctions in 81% of
verification of the predicted novel junctions was achieved for 55%
G.R.Grant et al.
of these cases. Of the high abundance cases, 100% PCR validated
and 95% sequence validated. The detection rates for these novel
junctions by GSNAP, MapSplice, SpliceMap, Soapals and TopHat
are also listed in Table 2 and detailed in Supplementary Table 3.
Sequence-verified examples from each of the three novel junction
categories are shown in Figure 11 (the full list is available at
www.cbil.upenn.edu/RUM/validation). In the first example shown
in Figure 11A, five RNA-Seq reads detected a novel junction
between exons 29 and 31 of the Usp32 gene, compared with 525
and 349 reads that detected the 5?and 3?ends of the annotated
exon 30, respectively. RT–PCR and Sanger sequencing confirmed
end of the 7th and final exon of Bcl9. This novel junction removes
36 bases and 12 amino acids in frame from the coding sequence.
RT–PCR and sequencing confirmed the presence of the mRNAwith
this novel junction. In Figure 11C, an abundant novel exon was
detected between exons 50 and 51 of Mll2 gene. In addition to
detection by the RUM junction track, this exon is also evident in
the coverage plot, shown in red. The 48bp novel exon is predicted
to add 16 amino acids in frame to the Mll2 protein. The presence
of this novel transcript in the retina was confirmed by RT–PCR and
sequencing. In Figure 11D, 17 and 6 reads, respectively, detected a
novel exon between exons 2 and 3 of the Gtf2a1 gene, compared
with 682 reads for the known exon–exon junction. RT–PCR and
sequencing validated the expression of the novel Gtf2a1 transcript
containing this 81bp exon. This novel exon is located at the 5?end
reading frame. In contrast to the novel exon in Mll2, this exon was
detected by the identification of novel junctions, and is not evident
from the coverage plot (Fig. 11D).
Robustness of the alignment process to novel splice forms and
sequence polymorphisms is a key to wide application of RNA-Seq.
Therefore, it is important to test alignment systems with datasets
that have varying degrees of such effects, and for which the truth
regarding correct alignment is known. BEERS simulates RNA-Seq
partial retention of introns and sequence error. Of these kinds of
and Heber, 2010). The ability to simulate alternate splice forms
that aim to annotate the transcriptome or to reconstruct full splice
Trapnell et al., 2010).
We used two configurations of the BEERS parameters, referred
to as Test 1 and Test 2, to evaluate 14 alignment algorithms.
These simulation analyses indicate that BLAT offers a powerful
tool for RNA-Seq alignment that has not been fully explored for
RNA-Seq analysis, and as such we have added necessary filters
and a paired-end parser to implement this approach in RUM. The
analyses performed using the simulated data showed that among
the appropriate RNA-Seq alignment algorithms, RUM, GSNAP
and MapSplice provide reasonably accurate and robust alignment.
Although computing resources are expanding, compute time is still
a relevant issue for the analysis of large RNA-Seq datasets. This
is underscored by the dramatic increase in the number of reads per
lane now generated with the Illumina Hi-Seq and ABI Solid 5500
instruments. All the viable and most accurate alignment algorithms
require significant computing resources. For example, none of
them can handle a 100 million read dataset on one processor in
reasonable time. Therefore, the number of processor hours required
for each of the algorithms, as shown in Figure 9, tells only part
of the story. What is more important is the number of real hours
required for running analyses using the desired alignment software
on a reasonably sized compute cluster or multi-processor machine.
MapSplice, as yet, cannot be parallelized, and therefore is the least
convenient for 100 million read datasets. We use RUM on the High-
Performance Computing Facility at the Penn Genome Frontiers
Institute consisting of a 400 node cluster of 64 bit Linux machines
each with 2.8GHz quad processors and 16GB of RAM, managed
with the Sun Grid Engine. Using 50 nodes on this cluster, RUM
can process clean mouse or human RNA-Seq reads at a rate of ∼2–
3 million read pairs per hour. The run time, however, depends on
the error and polymorphism rate, with BLAT taking roughly twice
as long on the second simulated data as compared with the first
(Fig. 9 and Supplementary Table 2). For small genomes such as
microorganisms, run time is considerably faster. Since powerful
have taken advantage of the availability of cloud computing to make
RUM universally available through the Amazon Elastic Compute
Cloud (Amazon EC2: aws.amazon.com/ec2). More generally, RUM
should run on any Unix system, and simple installation scripts will
is designed to work well with default settings in all situations.
When applied to an RNA-Seq dataset from mouse retina, RUM
detected 47078 novel splice junctions with a read depth of ≥2.
To explore the reliability of detection for these novel events, we
used RT–PCR and Sanger sequencing to validate 75 of them with a
focus on the less abundant cases in order to achieve a lower bound
on the true occurrence of such novel splicing. We were able to
empirically validate 81% of a subset of selected novel junctions
in independent RNA samples, indicating the accurate identification
of novel junctions by RUM. We believe that the true accuracy
of RNA-Seq and RUM are higher than indicated by the RT–PCR
and sequencing validation studies we performed due to technical
reasons. For example, it is possible that some transcript variants
produced by the novel junctions detected by RUM are present at
too low a concentration to be detected on agarose gels following
RT–PCR. Further, low abundance transcripts detected by RT–PCR
are more difficult to isolate for sequencing. We have also found that
RUM works well with RNA-Seq data from other species, including
human, zebrafish and microorganisms (data not shown).
Perhaps the most important output of RNA-Seq analyses is the
identification of novel transcript variants and novel transcripts.
Indeed, RNA-Seq data are already being used to improve annotation
of the human and mouse transcriptomes (Werner, 2010). The
ability to accurately detect the complete complement of transcripts
expressed in a given cell or tissue type is especially important
for identification of genes, which harbor mutations that cause
inherited disorders, and for accurate genetic diagnostic testing
of patients with these disorders. A pertinent example of this is
the recent identification of a novel, retina-specific isoform of
the Bardet-Biedl syndrome 8 (BBS8) gene. Mutations in BBS8
typically cause a multi-system cilia disorder characterized by cystic
renal disease, polydactyly, mental retardation, retinal degeneration,
RNA-seq alignment algorithms and RUM
Fig. 11. Validation of novel splice junctions detected by RUM. Exon junctions detected by RUM are displayed as a track using the UCSC Genome Browser.
The reads with annotated junctions are displayed in blue; reads with novel junctions are shown in green. The depth of uniquely mapped sequence reads is
shown in the Coverage Plot in red. The BLAT aligned Sanger sequenced reads from RT–PCR products are shown in black under the coverage plot.Annotated
Ensemble and UCSC genes are indicated at the bottom of the images. (A) RUM aligned five RNA-seq reads cleanly across a putative novel junction between
exons 29 and 31 of the Usp32 gene, compared with 525 and 349 reads that detected the 5?and 3?ends of annotated exon 30, respectively. RT–PCR and Sanger
sequencing in independent biological samples confirmed the presence of the mRNA lacking exon 30. (B) The 47 reads aligned to a putative novel alternate
splice junction at the 5?end of 7th and final exon of Bcl9, while 144 reads aligned to the known junction. The novel junction removes 36 bases, and 12 amino
acids in frame from the coding sequence. RT–PCR and Sanger sequencing in independent biological samples confirmed the presence of the mRNA with this
novel junction. (C)An abundant putative novel exon was detected between exons 50 and 51 of Mll2 gene. In addition to detection by the RUM junction track,
this exon is also evident in the coverage plot. The 48-bp novel exon is predicted to add 16 amino acids in frame to the Mll2 protein. RT–PCR and Sanger
sequencing in independent biological samples confirmed the presence of this novel transcript. (D)Alow abundance putative novel exon was detected between
exons 2 and 3 of the Gtf2a1 gene. RT–PCR and Sanger sequencing in independent biological samples validated the expression of the novel Gtf2a1 transcript
containing this 81bp exon.
G.R.Grant et al. Download full-text
obesity, gonadal malformations, diabetes and situs inversus (Ansley
et al., 2003; Badano et al., 2006). In contrast, mutations in the
retina-specific isoform of BBS8, which was not annotated in the
human genome, have recently been identified to cause the retina-
specific disorder retinitis pigmentosa (RP) (Riazuddin et al., 2010).
The retina-specific isoform of Bbs8, including exon 2a, was readily
detected by RUM (Supplementary Fig. 2).
Several of the novel junctions detected in the retina RNA-
Seq dataset and validated in these studies also demonstrate the
importance of complete characterization of transcriptomes. For
example, BCL9 is a component of the Wnt signaling cascade, and
is aberrantly expressed in several malignancies. It is hypothesized
that deregulation of BCL9 is an important contributing factor to
tumor progression (Mani et al., 2009). The variation in splicing
of exon 7 of Bcl9 detected in our studies could be relevant to
protein function. As a further example, the novel isoform of Mll2
identified in these studies may also have biologic importance,
given the known role of the Mll2 protein in histone methylation
and regulation of gene expression (Andreu-Vieyra et al., 2010)
(Demers et al., 2007). In addition, mutations in MLL2 were recently
identified to cause Kabuki syndrome, a form of congenital mental
retardation syndrome characterized by post-natal dwarfism, peculiar
facies characterized by long palpebral fissures with eversion of
the lateral third of the lower eyelids (reminiscent of the makeup
of actors of Kabuki, a Japanese traditional theatrical form) and
other features (Ng et al., 2010; Niikawa et al., 1981). A complete
knowledge of the isoforms of MLL2 expressed in different tissues
will be important for investigations of the genetics and pathogenesis
of Kabuki syndrome. This idea also applies to other genes and other
to the analysis of RNA-Seq and other HTS data.
We thank Lifeng Tian (UPenn), Michael Hughes (Yale), Elisabetta
Manduchi (UPenn), Mark Consugar (UPenn),Wei Li (UPenn), John
Brestelli (UPenn) andAaron Goodman (UPenn) for discussions and
Funding: National Institutes of Health (EY020902 and EY12910 to
E.A.P.; F32 EY020747 to M.H.F.); Foundation Fighting Blindness,
USA; Rosanne Silbermann Foundation; Penn Genome Frontiers
Institute; Institute for Translational Medicine and Therapeutics;
EuPath DB Project.
Conflict of Interest: none declared.
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