FragGeneScan: predicting genes in short and
Mina Rho1, Haixu Tang1,2and Yuzhen Ye1,*
1School of Informatics and Computing, Indiana University, Bloomington, IN 47408 and2Center for Genomics
and Bioinformatics, Indiana University, Bloomington, IN 47405, USA
Received April 27, 2010; Revised August 2, 2010; Accepted August 6, 2010
The advances of next-generation sequencing tech-
nology have facilitated metagenomics research that
attempts to determine directly the whole collection
of genetic material within an environmental sample
from short reads has become an important yet
challenging problem in annotating metagenomes,
since the assembly of metagenomes is often not
available. Gene predictors developed for whole
genomes (e.g. Glimmer) and recently developed for
metagenomic sequences (e.g. MetaGene) show a
sequencing error rates increase, or as reads get
shorter. We have developed a novel gene prediction
error models and codon usages in a hidden Markov
model to improve the prediction of protein-coding
FragGeneScan was comparable to Glimmer and
MetaGene for complete genomes. But for short
reads, FragGeneScan consistently outperformed
MetaGene (accuracy improved ?62% for reads of
400 bases with 1% sequencing errors, and ?18%
for short reads of 100 bases that are error free).
When applied to metagenomes, FragGeneScan re-
covered substantially more genes than MetaGene
homology search), and many novel genes with no
homologs in current protein sequence database.
Microbes are ubiquitous in nature and co-exist with other
organisms including humans, and play a critical role in
sustaining biological and environmental cycles (1–5). As
such, the analysis of microbial genomes is necessary for a
better understanding of the functionality of the microbes
and their interactions within the microbe community as
well as with the environment or the host (6). Most
microbes, however, are difficult to culture—previous
studies have indicated that <1% of microbes in many
environments can be cultivated (4,7,8). Environmental
sequencing reads can be assigned into specific genomes
and then assembled for further analysis. High-complexity
communities that contain diverse species in a metagenomic
sequencing project (thus low coverage reads for each com-
posite genome), however, make this problem extremely
challenging. In addition, the sequencing reads generated
by next-generation sequencing (NGS) techniques have
sequencing error rates of up to 3%, some of which can
cause frameshifts and thus make the prediction of
protein-coding regions even more difficult (9,10).
Identification of genes is one of the most important
and challenging problems in whole-microbial genome-
sequencing projects (11–13). In metagenomics, gene
finding can provide the opportunity to elucidate the
activities and interactions of genes within an environmen-
tal sample, from which the metabolic and signaling
pathways specific to the environment can be reconstructed
and identified (14). To date, only a few methods have been
developed for gene prediction in metagenomic sequences
(15–19). Most commonly, genes encoded by metagenomes
have been identified by using homology-based methods
such as BLASTX (20,21). Homology searches against
known protein databases, however, cannot be used to
predict novel genes, although discovering new genes is
one of the most important aspects in metagenomics
research. Alternatively, sequence conservation informa-
tion can be utilized for prediction of novel protein-coding
genes (17,22); for example, a Ka/Ks value of ?1 for a
group of similar sequences indicates that these sequences
are under no selective pressure and hence unlikely to code
for proteins. This way, novel families that have multiple
members in a metagenomic dataset can be identified (22).
The other straightforward solution to novel gene predic-
tion in metagenomics is to use feature-based approaches
such as probabilistic models to evaluate the probabilities
of open reading frames (ORFs) being protein-coding
*To whom correspondence should be addressed. Tel: +1 812 855 8562; Fax: +1 812 856 1995; Email: firstname.lastname@example.org
Published online 30 August 2010Nucleic Acids Research, 2010, Vol. 38, No. 20e191
? The Author(s) 2010. Published by Oxford University Press.
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/
by-nc/2.5), which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
regions (16,18,23), in a manner similar to conventional
gene finding methods such as Glimmer and GeneMark
Short read length and sequencing errors are two major
issues that pose significant challenges to gene prediction:
incomplete genes (gene fragments) are difficult to predict,
and sequencing errors may cause frameshifts that further
complicate gene prediction. The average length of genes in
microorganisms is about 950bp (16), which is much longer
than the sequencing reads generated by NGS (27,28).
Different NGS methods now produce sequencing reads
of various length ranging from 35bp (from Illumina/
Solexa Genome Analyzers) to 400bp (from Roche/454 se-
quencers) and have different error profiles (27). Sanger
sequencers produce reads with an error rate of up to
1%, whereas 454 sequencers produce reads with an error
rate of up to 3% (9,10). Illumina Genome Analyzer may
produce reads that have high mismatch rates, especially
when relatively long reads are acquired (e.g. G is mistaken
as T, and in later cycles A, C and G are mistaken as T)
(29). In 454 sequencing reads, sequencing errors tend to
occur in the homopolymer regions, resulting in frequent
insertions and deletions.
ORF-based gene prediction methods are more substan-
tially affected by sequencing errors that cause frameshifts
(9). As a consequence, programs that are currently avail-
able for gene prediction from short reads show a signifi-
cant decrease in their performance as the sequencing error
rate increases. For example, a low sensitivity of 26–43%
was observed with sequencing error rate of 2.8% (9).
We propose a probabilistic model combining sequencing
error models and codon usages (Figure 1) to improve the
accuracy in predicting protein-coding regions from envir-
onmental sequences. In a study of the effect of sequencing
errors on metagenomic gene prediction (9), all tested gene
predictors showed poor performance on short reads that
have sequencing errors. The gene predictors tested include
GeneMark (26), MetaGene (16), MetaGeneAnnotator
(extended from MetaGene aiming to improve gene predic-
tion for whole genomes; 23) and Orphelia (which uses
machine-learning technique with features for codon
usage, di-codon usage and translation initiation sites, and
shows performance comparable with MetaGene for gene
prediction in short reads; 30). Taking advantage of this
benchmark study, here we focus on the comparison of
our program FragGeneScan with Glimmer (24,25) and
MetaGene, since they are commonly used in genomics
and metagenomics studies, respectively (31–35).
It hasbeenshown that
MATERIALS AND METHODS
Compared with classical microbial gene finding methods,
FragGeneScan has two unique features. The first feature is
finding genes fragmented by the boundary of given input
sequences such as sequencing reads. The second feature,
which is more important for gene prediction from reads
generated by the current NGS methods, is correcting
frameshifts caused by indel errors in reads. Even though
a few recently developed methods (MetaGene and
Orphelia) were designed to predict genes from short
reads (thus can predict gene fragments), they do not
provide a solution for predicting genes with frameshifts
and genes in very short reads. We thus developed
FragGeneScan to predict fragmented genes and genes
with frameshifts in addition to the complete genes.
FragGeneScan is built on a hidden Markov model
(HMM) (36), which incorporates codon usage bias,
sequencing error models and start/stop codon patterns
in a unified model. Given a short read (or a complete
genome), the gene prediction problem is to find the best
path of hidden states (see below) that is most likely to
generate the observed nucleotide sequence, which can be
solved by the Viterbi algorithm. FragGeneScan reports
genes if they meet the following three conditions: (i) the
length of the genes is longer than 60bp, (ii) the genes start
in a start state (start codon) or in a match state (internal
region of genes) and (iii) the genes end in a stop state (stop
codon) or in a match state (internal region of genes).
Therefore, FragGeneScan can predict complete genes as
well as partial (fragmented) genes without start and/or
FragGeneScan HMM consists of two-level representa-
tions based on data abstraction (Figure 1). In order to
FragGeneScan considers separate states representing the
gene regions in the forward strand and the reverse strand
of a nucleotide sequence. The model has seven super-states
(denoted as shaded boxes in Figure 1) representing gene
regions (i), start codons (ii) and stop codons (iii) for the
forward (i–iii) and backward (v–vii) strands, and non-
coding regions (iv), respectively. The states for gene
regions consist of six consecutive sets of a match state,
an insertion state and a deletion state, which collectively
correspond to a six-periodic inhomogeneous HMM. This
representation allows using different parameters for each
position in a di-codon (i.e. six nucleotides). Notably, by
states and the match states, this model effectively detects
frameshifts that are caused by indel errors in sequencing.
Considering that complete genomic sequences are unlikely
to contain indel errors, the transition probabilities to in-
sertion and deletion states are set to 0 when applying
FragGeneScan to gene prediction in complete genomic
sequences. Each match state in the gene regions [(i) and
(v) in Figure 1] uses a second-order Markov chain to
model the codon usage. The state for non-coding regions
is based on a first-order Markov chain. Since the prob-
ability of gene regions and non-coding regions are
calculated solely based on the composition of sequences
(which is consistent regardless of the read length and gene
length), our method is more robust when input sequences
are of different lengths (see ‘Results’ section).
FragGeneScan also incorporates the sequence patterns
for each start codon (ATG, GTG and TTG) and stop
codon (TAA, TAG and TGA) in the start and stop
state, respectively. The stop state is modeled by a
e191Nucleic Acids Research, 2010,Vol.38, No. 20PAGE 2 OF 12
probability distribution of the stop codons estimated from
the training set: P(TAG|stop)=0.54, P(TAA|stop)=0.30
and P(TGA|stop)=0.16. The model of the start states, on
the other hand, takes into consideration the sequence
pattern within a window surrounding the start codons.
Notably, although the start codon model does not neces-
sarily help the prediction of genes in short reads without a
start codon, it can improve the gene prediction for
complete genomes, as well as the short reads that
contain a start codon.
Figure 1. The HMM of FragGeneScan with seven super-states. The super-states are denoted as seven shaded boxes representing gene regions:
(i) start codons (ii) and stop codons (iii) for both the forward (i–iii) and backward (v–vii) strands, and non-coding regions (iv). The states for gene
regions (i and vii) consist of six consecutive match states represented by diamonds, insertion states by triangles and deletion states by squares, which
collectively correspond to a six-periodic inhomogeneous HMM.
PAGE 3 OF 12 Nucleic Acids Research, 2010,Vol.38, No. 20e191
Start codons in bacterial genomes are relatively difficult
to predict because several putative start codons are often
present around each of the real ones. In order to achieve
accurate prediction of start codons, the probability of a
start codon in the start states [(ii) and (v) in Figure 1] is
modeled by using a positional weight matrix (PWM) over
63 nucleotides centered on a putative start codon ATG,
GTG or TTG. In accordance with previous findings [A/
T-rich region, Shine–Dalgarno sequence (AGGAG) (37),
and triple-A downstream box] (38–40), the sequence
patterns around the real start codons are different from
those around false start codons that are present upstream
or downstream of the real ones. We thus compute the
following score for each putative start codon based on
its 63nt window (i.e. 61 overlapping trinucleotides).
where trinucleotideirepresents the triplet at position i, and
P(trinucleotidei|PWM) is the probability of observing the
trinucleotide at position i, given the PWM of triplet
frequencies, which was trained by using the same
complete genomic sequences for HMM parameter estima-
tion (see below). Two Gaussian distributions, one for real
start codons and the other for false start codons, were
fitted to the scores computed for a collection of annotated
start codons (Supplementary Figure S1). For a putative
start codon in a read, the probabilities (likelihood) of
observing its 63-bp window given the condition that it is
a real or false start codon, denoted as P(score|real) and
P(score|false), respectively, can then be estimated from the
two fitted Gaussian distributions. We calculate the poster-
ior probability of a start codon being a real one given its
surrounding 63-bp window, from P(score|real) and
P(score|false) by using a naı¨ve Bayesian classifier (41).
Parameter estimation for HMM
A total of 139 complete genomes (collected from the
NCBI website; Supplementary Table S1) were used to
estimate parameters of second-order Markov chains for
all 12 match states in the forward strand [M1–M6 in
Figure 1(i)] and in the reverse strand [M’1–M’6 in
Figure 1(vii)] (see Supplementary Table S2 for a
summary of the training data for different states). The
parameters show linear correlation with GC contents,
Figure S2) was applied to give estimations of parameters
for various GC contents. Note that FragGeneScan does
not need training for gene prediction in individual
genomes or datasets of short reads. Given a dataset of
short reads, FragGeneScan estimates GC contents inde-
pendently for each read and uses the corresponding set of
pre-computed parameters based on the GC content for
gene prediction in that read.
The parameters of emission and transition probabilities
for insertion and deletion states were estimated for differ-
ent sequencing methods with different error rates. The
current version of FragGeneScan contains different sets
of parameters for Sanger, 454 pyrosequencing and
Illumina sequencing. Since the error rates directly affect
the transition probabilities from match states to insertion/
sequencing error rates: 0.5% and 1% for Sanger and
pyrosequencing, respectively. The sequencing reads used
in the estimation were generated using MetaSim (10). If
emission and transition probabilities of HMM are needed
for error rates different from what we provided, they can
be easily obtained and combined into existing models,
which are separate from the gene prediction procedure.
1%and 3% for454
Running time of FragGeneScan
The computational complexity of FragGeneScan is O(n),
where n is the total length of the input genomic sequences.
FragGeneScan is sufficiently fast for predicting genes for
achieving gene predictions for ?2Mb/min on an Intel
Xeon CPU 2GHz. The running time for all the tests
shown in the paper ranges from 2min (simulated reads
from the Escherichia coli genome) to 58minutes (the
Benchmark data sets
A total of nine complete genomes (with various GC
from the NCBI website (http://www.ncbi.nlm.nih.gov/)
(Table 1). (This set of genomes does not overlap with
the genomes we used for training.) To systematically test
FragGeneScan, reads of various lengths (100, 200, 400 and
700bp) and with various sequencing error rates (0–3%)
were simulated from these genomes using MetaSim (10).
For each genome, up to 1-fold coverage of reads was
sampled for each read length and sequencing error rate.
Based on the current estimation of sequencing error rates
(10), Sanger sequencing reads of 700bp were simulated
with the error rates ranging from 0% to 1%, and 454
sequencing reads were simulated with the error rates
ranging from 0% to 3%.
Table 1. Genomes of microbial species that were used to evaluate the
performance of FragGeneScan
Buchnera aphidicola str.
Bacillus subtilis subsp.
subtilis str. 168
Chlorobium tepidum TLS
Escherichia coli str. K-12
Helicobacter pylori J99
str. MIT 9312
e191Nucleic Acids Research, 2010,Vol.38, No. 20PAGE 4 OF 12
Three real metagenomes were used for gene prediction
in metagenomic sequences (Supplementary Table S3).
Two real metagenomes (TS28 and TS50) from the twin
obese and lean study (14) were downloaded from the
MG-RAST website (http://metagenomics.nmpdr.org).
The other real metagenome (SRX007415) from the
rumen microbiota response study was downloaded from
the NCBI website (http://www.ncbi.nlm.nih.gov). These
three metagenomes were BLASTXed against 98% non-
redundant protein sequences from prokaryotic genomes,
plasmids and phages collected from IMG 3.0 (http://img
.jgi.doe.gov) using an E-value cutoff of 1.0e-3 for TS28
and TS50, and 1.0e-1 for SRX007415 (which has shorter
reads), respectively. FragGeneScan gene prediction in
these metagenomes was compared to the similarity
Performance evaluation and comparison
The performance was measured in terms of sensitivity (the
ratio of true positives to all annotated genes) and specifi-
city (the ratio of true positives to all predicted genes). The
accuracy was calculated by averaging the sensitivity and
specificity.The performance of FragGeneScan was
compared to that of Glimmer3 and MetaGene, which
software/glimmer/ and http://metagene.cb.k.u-tokyo.ac
.jp/metagene/download.html, respectively. For training
of Glimmer and MetaGene, we followed the standard pro-
cedures provided by the programs. Given a new genome,
Glimmer uses its internal long-ORF scanner to predict
long genes, which will be used for parameter estimation
for the genome. MetaGene has its model parameters
Implementation and availability
FragGeneScan is implemented in C and Perl. In the
training phase, we built Markov chains for the intergenic
region and match states, and calculated emission and tran-
sition probabilities for the HMM. In the main module, the
program predicts genes after identifying the best path of
the hidden states by using the Viterbi algorithm imple-
mented in C. The package of FragGeneScan includes all
the programs, and does not require any other third party
programs. FragGeneScan is available as open source at
.indiana.edu/FragGeneScan/. All of our predictions are
also available for download at the FragGeneScan website.
We tested and compared FragGeneScan with Glimmer
and MetaGene by using the nine complete genomes
(Table 1). We also tested FragGeneScan on short
reads simulated from the same set of genomes, which
allowed us to systematically evaluate the performance of
FragGeneScan on the reads with various length and error
rates. Finally, we tested and compared the performance of
FragGeneScan with those of MetaGene and homology-
search approach (BLASTX) on three real metagenomic
datasets. The performances were measured in terms of
sensitivity (the ratio of true positives to all annotated
genes), specificity (the ratio of true positives to all pre-
dicted genes) and accuracy (the average of sensitivity
and specificity). A gene fragment prediction is considered
to be a true positive if it is of at least 60 bases (i.e.
encoding 20 amino acids), and overlaps with ?80% of
the true protein-coding region in the read. We also
compared the performances using different overlap
Evaluation on complete genomic sequences
The nine complete genomic sequences of various GC
contents (Table 1) we used are also widely used for
testing gene predictors in previous studies (16,18).
Overall, the accuracy of FragGeneScan is comparable
with MetaGene, and slightly higher than Glimmer
(Table 2). In particular, FragGeneScan and Glimmer
showed higher sensitivity, whereas MetaGene showed
higher specificity on average. (The conclusion remains
the same when a different overlap threshold of 50% was
Supplementary Table S4.)
All three methods consistently showed the highest
accuracyfor Buchnera aphidicola
Table 2. Comparison of the gene prediction performances of different methods in complete genomic sequences
Organisms FragGeneScanGlimmer MetaGene
AccuracySn SpAccuracySn SpAccuracy
PAGE 5 OF 12Nucleic Acids Research, 2010,Vol.38, No. 20e191
accuracy for Wolbachia endosymbiont. The low accuracy
obtained for W. endosymbiont is due to very low
specificities. This might be caused by the fewer number
of genes in W. endosymbiont. However, Glimmer showed
significantly lower specificity compared with the other two
MetaGene use generalized model parameters obtained
by regression across genomes of different GC contents.
But Glimmer uses specific model parameters trained
from the genes in the testing genome. We thus suggest
that more generalized parameter
improve the performance in gene prediction for the cases
when only insufficient data is available for training a
Evaluation on simulated sequencing reads
Table 3 shows the sensitivity, specificity and accuracy of
gene prediction in simulated reads of 100 (Figure 2), 200
and 400bp (Figure 3) with 1% sequencing error rate and
700bp with 0.5% sequencing error rate. Prediction in
longer reads shows higher accuracies with few exceptions
(predictions in the 200bp reads from B. aphidicola and
Perkinsus marinus show higher accuracies than those
in the 400bp reads). Overall, FragGeneScan achieved
21–68% higher accuracies as compared to MetaGene.
We note that FragGeneScan shows consistently high
accuracy ranging from 63% to 89% for all the lengths
we tested (100–700bp); MetaGene, on the other hand,
shows highly varied and lower accuracy ranging from
16% to 52%. Additional results on simulated reads with
different sequencing error rates, and using different
overlap criteria for defining true positive gene prediction,
are listedin Supplementary
(Supplementary Table S7 lists the proportion of simulated
reads that contain annotated genes for each simulated
The performance of FragGeneScan as a function of
read lengths and sequencing error rates is summarized in
Table 4. For the 400 and 700bp reads without sequencing
error, FragGeneScan and MetaGene show comparable
performance with <1% difference. For the shorter reads
Table 3. Gene prediction performance in short reads simulated from complete genomic sequences
Sensitivity SpecificityAccuracy SensitivitySpecificityAccuracy
aReads were simulated with 1% sequencing error rate for lengths of 100, 200 and 400bp, and 0.5% sequencing error rate for length of 700bp,
respectively. The nine genomes are the same as those in Table 2, and were used for testing gene prediction in short reads (16,18).
e191Nucleic Acids Research, 2010,Vol.38, No. 20PAGE 6 OF 12
Figure 2. Gene prediction performance in simulated reads of 100 bases without sequencing error (a) and (b), and with 1% sequencing error (c) and (d). The x-axis denotes the source genomes
from which the short reads were simulated: 1. E. coli; 2. H. pylori; 3. B. subtilis; 4. B. aphidicola; 5. C. tepidum; 6. B. pseudomallei; 7. W. endosymbiont; 8. C. jeikeium; 9. P. marinus. The y-axis
denotes sensitivity in (a) and (c), and specificity in (b) and (d).
PAGE 7 OF 12Nucleic Acids Research, 2010,Vol.38, No. 20 e191
Figure 3. Gene prediction performance in simulated reads of 400 bases without sequencing error (a) and (b) and with 1% sequencing error rate (c) and (d). The x-axis denotes the source
genomes from which the short reads were simulated: 1. E. coli; 2. H. pylori; 3. B. subtilis; 4. B. aphidicola; 5. C. tepidum; 6. B. pseudomallei; 7. W. endosymbiont; 8. C. jeikeium; 9. P. marinus. The
y-axis denotes sensitivity in (a) and (c), and specificity in (b) and (d).
e191Nucleic Acids Research, 2010,Vol.38, No. 20PAGE 8 OF 12
of 200 and 100bps that have no sequencing errors,
however, FragGeneScan outperforms MetaGene signifi-
cantly. In particular, the accuracy of FragGeneScan in
predicting genes in 100bp reads without sequencing
error is only 5% lower than those in longer reads (200
and 400bp); in contrast, MetaGene shows 22% decrease
in accuracy under the same condition. For all the cases
studied with reads of various lengths and sequencing
errors, a consistently better performance (up to 65%)
was observed for FragGeneScan over MetaGene.
Evaluation on real metagenomes
We also tested FragGeneScan on real metagenomes
(Supplementary Table S3): two datasets TS28 and TS50
from the twin obese and lean study (sequenced by 454
sequencers) (14), and one dataset SRX007415 from the
rumen microbiota response study (sequenced by Illumina
sequencers). For these real metagenomes, there are no
standard annotations available for comparison. So we
MetaGene with those predicted by a homology-based
approach (i.e. a read is considered to contain a
protein-coding region if BLASTX finds its homologs in
a protein database). Here, we consider a predicted gene
as a true positive if the predicted gene overlaps with the
entire length of the annotated gene from the BLASTX
search (which, however, may not give the precise
boundaries of the real gene).
For the TS28 dataset, FragGeneScan successfully
predicted 92% of the genes identified by BLASTX
search, whereas MetaGene predicted 47% of the genes
(Table 5). For the TS50 dataset, FragGeneScan predicted
92% of the genes identified by BLASTX, whereas
MetaGene predicted 69% of the genes. Note that
MetaGene predicted significantly fewer genes, proportion-
ally, in the TS28 dataset than in the TS50 dataset (with
respect to BLASTX). In contrast, these ratios are roughly
the same for both datasets for FragGeneScan (?92%).
(The comparison based on 50% gene overlap, i.e. the
gene predicted by FragGeneScan or MetaGene overlaps
with at least half of the BLAST annotated gene, is also
shown in Table 5.)
FragGeneScan also predicted potentially novel genes
that were missed by homology searches, including 28%
(89340 out of 317440) of the putative genes predicted
from the TS50 dataset, and 25% (142007 out of
579362) from the TS28 dataset. We note that BLASTX
searches discovered protein-coding genes in ?74% of the
reads in both datasets (462815 out of 622554 reads in the
TS50 dataset, and 231946 out of 312665 reads in TS28).
Considering that on average 90% of the 200bp simulated
reads (which is similar to the read lengths of TS28 and
TS50 data sets) contain annotated protein-coding genes
(see Supplementary Table S7), and that ?90% of bacterial
genomes encode for proteins (42), the fraction of reads
annotated as protein-coding
searches on these two metagenomes (74%) is rather low.
Our observation indicates the potential application of
ab initio gene predictors such as FragGeneScan in the dis-
covery of novel genes, which may constitute a significant
proportion of protein-coding genes from an environmen-
The reads in SRX007415 are much shorter (of 72bp)
than those in TS28 and TS50. Considering that a
Table 4. Average gene prediction performance in simulated Sanger and 454 reads
Sensitivity SpecificityAccuracy SensitivitySpecificity Accuracy
Table 5. Gene prediction results in metagenomes of TS28 and TS50
from the twin study and SRX007415 from the rumen microbiota
TS28312665 329 50
aThis experiment was carried out to demonstrate how much improve-
ment gene prediction gained by considering indels.
bMetaGene only works with reads that are of at least 100 bases.
PAGE 9 OF 12Nucleic Acids Research, 2010,Vol.38, No. 20 e191
BLASTX search of the original dataset (which has 4.2Gb
nucleotides) would require a drastic amount of CPU
hours, we only carried out gene prediction for a small
subset (2%), which has 1164805 reads, and compared
the results with BLASTX results (MetaGene works only
with input sequences of at least 100bp, thus cannot be
protein-coding genes in 8% of the reads (87431 out of
1164805) with E-values <1.0e-3. When a less stringent
E-value cutoff was applied (1.0e-1), the ratio increased
to 19%. Both ratios are extremely low, which may not
be that surprising—it has been shown that the sensitivity
of similarity search drops significantly when the reads
become shorter (43). But it also indicates that for short
reads, gene prediction based on homology search may
severely underestimate the gene content in an environmen-
tal sample. FragGeneScan predicted 1099193 gene frag-
ments in total, among which 189875 gene fragments were
also predicted by BLASTX (i.e. FragGeneScan predicted
86% of the potential genes obtained by BLASTX using
E-value cutoff 1.0e-1). It is slightly lower than TS28 and
TS50 datasets, which might be caused by the shorter
length of the reads (72bp).
Examples of genes that contain frameshift sequencing
FragGeneScan integrates sequencing error models in its
HMM so that it can predict genes broken by frameshift
sequencing errors. Here, we show two examples of
predicted genes that contain such sequencing errors
(Figure 4) to demonstrate the importance of incorporating
sequencing error models in gene prediction. Figure 4a
shows a gene predicted by FragGeneScan from a
simulated read, in which two frameshifts caused by
sequencing errors were fixed. The read (r19) was simulated
with two insertions of Cs from the E. coli genome
(sequence from 4578113 to 4578339bp) (Figure 4a).
By adding insertion states near the positions of original
sequencing errors—for example, FragGeneScan predicted
the nucleotide sequence ACTA in the simulated read as
ACA, which encodes Threonine, by annotating the T as
an insertion—FragGeneScan predicted a gene (without
fragmentation) that is almost identical to the annotated
gene (see ‘Discussion’ section). Figure 4b shows another
gene predictedina real
FragGeneScan. From the read (E4LJNJL01APZ27) in
the TS28 dataset, FragGeneScan predicted an incomplete
gene with an insertion state (of nucleotide A, highlighted
in Figure 4b). (MetaGene did not predict any gene from
this read.) BLAST search of this predicted gene against
the IMG 3.0 database resulted in a significant match
(YP_002939026 from Eubacterium rectal ATCC 33656
with an E-value of 2e-28), and the alignment of the pre-
dicted protein and the homolog is shown in Figure 4b.
metagenomic read by
Although FragGeneScan was intentionally developed for
gene prediction in short and error-prone reads, it provides
a versatile method to predict genes in complete microbial
genomes, as well as in short reads with or without
sequencing errors. The read lengths and sequencing
error rates profoundly affect the performance of gene pre-
diction methods. Sequencing errors that cause frameshifts
are difficult to be detected by the ORF-based gene finding
approaches such as MetaGene, resulting in cases where
only fragments of true genes, if anything, are identified;
and it may be difficult to interpret the gene fragments.
FragGeneScan was developed to overcome the limitations
of existing methods in addressing these two major issues,
by incorporating sequencing error models into six-periodic
inhomogeneous Markov models. FragGeneScan is robust
with consistently high performance of predicting genes in
reads with widely ranged sequencing error rates.
QLFAYADT EKQVNNALARVNNL QSILAKAFRGELTAQWRAENPDLISGENSAAALLEKIKAERAASGGK
IVRAATR+GI HFRLTGGEPL + + + KK G + +NAVLLAQHAK LKE
Figure 4. Examples of fragmented genes that contain frameshift sequencing errors: a gene predicted from a read simulated from the E. coli genome
starting at position 4578113 (a), and a gene predicted from a metagenomic read from the TS28 dataset (b). The alignments of the nucleotide
sequences are partially shown for clarity. The dotted lines connect the regions of nucleotides (with sequencing errors fixed) and the amino acid(s) that
they encode. The alignment between the predicted protein from the metagenomic read and its homolog identified in IMG protein database is also
e191Nucleic Acids Research, 2010,Vol.38, No. 20PAGE 10 OF 12
FragGeneScan is also less affected by the length of
sequencing reads. FragGeneScan achieved consistently
high gene prediction accuracies in reads of length 100–
700bp, whereas MetaGene showed significant variation
in its performance. The robustness of FragGeneScan
comes from its design principle: FragGeneScan uses only
the probability of emitting a nucleotide at each position
throughout the entire sequence (in contrast, existing gene
prediction methods use statistical parameters such as the
length distribution of genes). This robustness is essential
for predicting genes in the reads generated by different
NGS methods since each sequencing technique generates
reads with different lengths (27).
We used rather stringent criteria that a predicted gene
fragment is of at least 60bp (i.e. 20 amino acids) and has
50–100% overlap with the true gene in the read to be
considered as a true positive. In contrast, Hoff (9) used
a rather loose definition of true positives: predicted genes
that have a BLAT (44) alignment of at least 20 amino
acids with the annotated gene and at least 80% sequence
identity were called true positives. As a result, truncated
gene fragments (due to frameshift-causing sequencing
errors) may not be considered as true positives according
to our criteria (because they are too short), but still may be
considered as true positives according to Hoff’s criteria.
So the accuracy reported in our article for MetaGene may
be lower than the accuracy reported in Hoff’s paper (9).
We want to emphasize that it is important to use stringent
criteria for measuring the performance of gene predictors
in error-prone short reads, as truncated gene fragments
are more difficult to interpret and are less informative.
For gene prediction in Illumina reads (e.g. the
SRX007415 dataset with reads of 72bp), we used the
same parameters as for Sanger reads, considering that
Illumina sequencers do not show high sequencing errors
in the homopolymer regions as 454 sequencers, and
produce reads with overall low sequencing error rate.
We can learn emission and transition probabilities of
HMM for Illumina sequencing when more, longer
Illumina reads (e.g. of 125bp) become available.
The exact amino acids encoded by nucleotide sequences
containing frameshift sequencing errors may be difficult to
predict (for examples, see Figure 4). But these subtle
mistakes in the predicted gene sequences (as long as the
overall genes are predicted correctly) will not considerably
affect many downstream analyses, such as the similarity
search of the predicted genes. We will further explore the
possibility of improving the prediction by incorporating
the quality score of the reads.
Supplementary Data are available at NAR Online.
We thank Chad Burrus for reading the article. We are
grateful to the authors of MetaGene and Glimmer for
providing their programs for comparison.
Funding for open access charge: National Institutes of
Foundation (CAREER award DBI-0845685).
Conflict of interest statement. None declared.
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