BIOINFORMATICS APPLICATIONS NOTE
Vol. 25 no. 10 2009, pages 1335–1337
Infernal 1.0: inference of RNA alignments
Eric P. Nawrocki, Diana L. Kolbe and Sean R. Eddy∗
HHMI Janelia Farm Research Campus, Ashburn, VA 20147, USA
Received on January 13, 2009; revised on March 11, 2009; accepted on March 14, 2009
Advance Access publication March 23, 2009
Associate Editor: Ivo Hofacker
Summary: INFERNAL builds consensus RNA secondary structure
profiles called covariance models (CMs), and uses them to search
nucleic acid sequence databases for homologous RNAs, or to create
new sequence- and structure-based multiple sequence alignments.
downloadable from http://infernal.janelia.org.
licensed under the GNU GPLv3 and should be portable to any
POSIX-compliant operating system, including Linux and Mac OS/X.
INFERNAL is freely
When searching for homologous structural RNAs in sequence
databases, it is desirable to score both primary sequence and
secondary structure conservation. The most generally useful tools
multiple alignment), and automatically construct an appropriate
homologs in a sequence database (Gautheret and Lambert, 2001;
Huang et al., 2008; Zhang et al., 2005). Stochastic context-free
grammars (SCFGs) provide a natural statistical framework for
combining sequence and (non-pseudoknotted) secondary structure
conservation information in a single consistent scoring system
(Brown, 2000; Durbin et al., 1998; Eddy and Durbin, 1994;
Sakakibara et al., 1994).
of a general SCFG-based approach for RNA database searches
and multiple alignment. INFERNAL builds consensus RNA profiles
called covariance models (CMs), a special case of SCFGs designed
for modeling RNA consensus sequence and structure. It uses CMs
to search nucleic acid sequence databases for homologous RNAs,
or to create new sequence- and structure-based multiple sequence
alignments. One use of INFERNAL is to annotate RNAs in genomes
in conjunction with the Rfam database (Gardner et al., 2009),
which contains hundreds of RNA families. Rfam follows a seed
profile strategy, in which a well-annotated ‘seed’ alignment of
each family is curated, and a CM built from that seed alignment
is used to identify and align additional members of the family.
INFERNAL has been in use since 2002, but 1.0 is the first version
that we consider to be a reasonably complete production tool. It
now includes E-value estimates for the statistical significance of
searches and multiple alignment that allow INFERNALto be deployed
∗To whom correspondence should be addressed.
A CM is built from a Stockholm format multiple sequence
alignment (or single RNA sequence) with consensus secondary
structure annotation marking which positions of the alignment are
single stranded and which are base paired (Eddy, 2009). CMs
assign position-specific scores for the four possible residues at
single-stranded positions, the 16 possible base pairs at paired
positions and for insertions and deletions. These scores are log-odds
scores derived from the observed counts of residues, base pairs,
insertions and deletions in the input alignment, combined with prior
information derived from structural ribosomal RNA alignments.
CM parameterization has been described in more detail elsewhere
(Eddy, 2002, 2009; Eddy and Durbin, 1994; Klein and Eddy, 2003;
Nawrocki and Eddy, 2007).
INFERNAL is composed of several programs that are used in
combination by following four basic steps:
(1) Build a CM from a structural alignment with cmbuild.
(2) Calibrate a CM for homology search with cmcalibrate.
(3) Search databases for putative homologs with cmsearch.
(4) Align putative homologs to a CM with cmalign.
The calibration step is optional and computationally expensive
(4h on a 3.0GHz Intel Xeon for a CM of a typical RNA family
of length 100nt), but is required to obtain E-values that estimate
the statistical significance of hits in a database search. cmcalibrate
will also determine appropriate hidden Markov model (HMM) filter
thresholds for accelerating searches without an appreciable loss of
sensitivity. Each model only needs to be calibrated once.
A published benchmark (independent of our lab) (Freyhult et al.,
2007) and our own internal benchmark used during development
(Nawrocki and Eddy, 2007) both find that INFERNAL and other
CM-based methods are the most sensitive and specific tools for
updated results of our internal benchmark comparing INFERNAL 1.0
with the previous version (0.72) that was benchmarked in Freyhult
et al. (2007), and also to family-pairwise search with BLASTN
(Altschul et al., 1997; Grundy, 1998). INFERNAL’s sensitivity and
specificity have greatly improved, due to mainly three relevant
© Crown Copyright 2009. Reproduced with the permission of the Controller of Her Majesty’s Stationery Office.
E.P.Nawrocki et al.
false positives per Mb searched per query
Sensitivity (fraction of true positives)
Infernal v1.0 default (with filters)
Infernal v1.0 (no filters)
Fig. 1. ROC curves for the benchmark. Plots are shown for the new
INFERNAL 1.0 with and without filters, for the old INFERNAL 0.72 and for
family-pairwise searches (FPS) with blastn. CPU times are total times for
all 51 family searches measured for single execution threads on 3.0 GHz
Intel Xeon processors. The INFERNAL1.0 times do not include time required
for model calibration.
improvements in the implementation (Eddy, 2009): a biased
composition correction to the raw log-odds scores, the use of Inside
log likelihood scores (the summed score of all possible alignments
of the target sequence) in place of CYK scores (the single maximum
likelihood alignment score) and the introduction of approximate
E-value estimates for the scores.
and test sequences from 51 Rfam (release 7) families [details in
(Nawrocki and Eddy, 2007)]. No query sequence is >60% identical
to a test sequence. The 450 total test sequences were embedded
at random positions in a 10Mb ‘pseudogenome’. Previously, we
generated the pseudogenome sequence from a uniform residue
frequency distribution (Nawrocki and Eddy, 2007). Because base
composition biases in the target sequence database cause the most
serious problems in separating significant CM hits from noise,
we improved the realism of the benchmark by generating the
pseudogenome sequence from a 15-state fully connected HMM
trained by Baum–Welch expectation maximization (Durbin et al.,
1998) on genome sequence data from a wide variety of species.
Each of the 51 query alignments was used to build a CM and
search the pseudogenome, a single list of all hits for all families
were collected and ranked, and true and false hits were defined, as
described in Nawrocki and Eddy (2007), producing the ROC curves
in Figure 1.
INFERNAL searches require a large amount of compute time
[our 10Mb benchmark search takes about 30h per model on
average (Fig. 1)]. To alleviate this, INFERNAL 1.0 implements two
rounds of filtering. When appropriate, the HMM filtering technique
described by Weinberg and Ruzzo (2006) is applied first with filter
thresholds configured by cmcalibrate [occasionally a model with
little primary sequence conservation cannot be usefully accelerated
by a primary sequence-based filter as explained in (Eddy, 2009)].
The query-dependent banded (QDB) CYK maximum likelihood
search algorithm is used as a second filter with relatively tight
bands [β =10−7, the β parameter is the subtree length probability
mass excluded by imposing the bands as explained in Nawrocki
and Eddy (2007)]. Any sequence fragments that survive the filters
are searched a final time with the Inside algorithm [again using
QDB, but with looser bands (β =10−15)]. In our benchmark, the
default filters accelerate similarity search by about 30-fold overall,
while sacrificing a small amount of sensitivity (Fig. 1). This makes
version 1.0 substantially faster than 0.72. BLAST is still orders
of magnitude faster, but significantly less sensitive than INFERNAL.
Further acceleration remains a major goal of INFERNALdevelopment.
The computational cost of CM alignment with cmalign has been
a limitation of previous versions of INFERNAL. Version 1.0 now uses
a constrained dynamic programming approach first developed by
pass HMM alignment. This technique offers a dramatic speedup
relative to unconstrained alignment, especially for large RNAs such
as small and large subunit (SSU and LSU, respectively) ribosomal
RNAs, which can now be aligned in roughly 1 and 3s per sequence,
respectively, as opposed to 12min and 3h in previous versions.This
acceleration has facilitated the adoption of INFERNAL by RDP, one
of the main ribosomal RNA databases (Cole et al., 2009).
INFERNAL is now a faster and more sensitive tool for RNA
sequence analysis. Version 1.0’s heuristic acceleration techniques
make some important applications possible on a single desktop
computer in less than an hour, such as searching a prokaryotic
rRNA sequences. Nonetheless, INFERNAL remains computationally
The most expensive programs (cmcalibrate, cmsearch and cmalign)
are implemented in coarse-grained parallel MPI versions which
divide the workload into independent units, each of which is run
on a separate processor.
We thank Goran Ceric for his peerless skill in managing Janelia
Farm’s high-performance computing resources.
Funding: INFERNAL development is supported by the Howard
Hughes Medical Institute. It has been supported in the past by an
NIH NHGRI training grant (T32-HG000045) to E.P.N., an NSF
Graduate Fellowship to D.L.K.; NIH R01-HG01363 and a generous
endowment from Alvin Goldfarb.
Conflict of Interest: none declared.
database search programs. Nucleic Acids Res., 25, 3389–3402.
Brown,M.P. (2000) Small subunit ribosomal RNA modeling using stochastic context-
free grammars. Proc. Int. Conf. Intell. Syst. Mol. Biol., 8, 57–66.
Cole,J.R. et al. (2009) The Ribosomal Database Project: improved alignments and new
tools for rRNA analysis. Nucleic Acids Res., 37, D141–D145.
Durbin,R. et al. (1998) Biological Sequence Analysis: Probabilistic Models of Proteins
and Nucleic Acids. Cambridge University Press, Cambridge, UK.
Eddy,S.R. (2002) A memory-efficient dynamic programming algorithm for optimal
alignment of a sequence to an RNA secondary structure. BMC Bioinformatics,
Eddy,S.R. (2009) The Infernal user’s guide. Available at http://infernal.janelia.org/.
(last accessed date March 27, 2009).
Eddy,S.R. and Durbin,R. (1994) RNA sequence analysis using covariance models.
Nucleic Acids Res., 22, 2079–2088.
Freyhult,E.K. et al. (2007) Exploring genomic dark matter: a critical assessment of the
performance of homology search methods on noncoding RNA. Genome Res., 17,
Gardner,P.P. et al. (2009) Rfam: updates to the RNA families database. Nucleic Acids
Res., 37, D136–D140.
Infernal 1.0 Download full-text
Gautheret,D. and Lambert,A. (2001) Direct RNA motif definition and identification
from multiple sequence alignments using secondary structure profiles. J. Mol. Biol.,
Grundy,W.N. (1998) Homology detection via family pairwise search. J. Comput. Biol.,
Huang,Z. et al. (2008) Fast and accurate search for non-coding RNA pseudoknot
structures in genomes. Bioinformatics, 24, 2281–2287.
Klein,R.J. and Eddy,S.R. (2003) RSEARCH: finding homologs of single structured
RNA sequences. BMC Bioinformatics, 4, 44.
Nawrocki,E.P. and Eddy,S.R. (2007) Query-dependent banding (QDB) for faster RNA
similarity searches. PLoS Comput. Biol., 3, e56.
Sakakibara,Y. et al. (1994) Stochastic context-free grammars for tRNA modeling.
Nucleic Acids Res., 22, 5112–5120.
Weinberg,Z. and Ruzzo,W.L. (2006) Sequence-based heuristics for faster annotation of
non-coding RNA families. Bioinformatics, 22, 35–39.
Zhang,S. et al. (2005) Searching genomes for noncoding RNAusing FastR. IEEE/ACM
Trans. Comput. Biol. Bioinform., 2, 366–379.