dbNSFP: A Lightweight Database of Human
Nonsynonymous SNPs and Their Functional Predictions
Xiaoming Liu,?Xueqiu Jian, and Eric Boerwinkle
Human Genetics Center, School of Public Health, The University of Texas Health Science Center at Houston, Houston, Texas
Communicated by George Patrinos
Received 23 February 2011; accepted revised manuscript 13 April 2011.
Published online 21 April 2011 in Wiley Online Library (www.wiley.com/humanmutation). DOI 10.1002/humu.21517
ABSTRACT: With the advance of sequencing technologies,
whole exome sequencing has increasingly been used to
identify mutations that cause human diseases, especially
rare Mendelian diseases. Among the analysis steps,
functional prediction (of being deleterious) plays an
important role in filtering or prioritizing nonsynonymous
SNP (NS) for further analysis. Unfortunately, different
prediction algorithms use different information and each
has its own strength and weakness. It has been suggested
that investigators should use predictions from multiple
algorithms instead of relying on a single one. However,
querying predictions from different databases/Web-servers
for different algorithms is both tedious and time consum-
ing, especially when dealing with a huge number of NSs
identified by exome sequencing. To facilitate the process,
we developed dbNSFP (database for nonsynonymous
SNPs’ functional predictions). It compiles prediction scores
from four new and popular algorithms (SIFT, Polyphen2,
LRT, and MutationTaster), along with a conservation score
(PhyloP) and other related information, for every potential
NS in the human genome (a total of 75,931,005). It is the
first integrated database of functional predictions from
multiple algorithms for the comprehensive collection of
human NSs. dbNSFP is freely available for download at
Hum Mutat 32:894–899, 2011. & 2011 Wiley-Liss, Inc.
KEY WORDS: dbNSFP; functional prediction; database;
SIFT; Polyphen2; LRT; MutationTaster; PhyloP
A nonsynonymous SNP (NS) is a single nucleotide variant that
causes an amino acid change of its corresponding protein. Because
of their direct link to protein changes, NSs are believed to be the
major contributor to heritable human diseases among all types of
variants in the human genome. As evidence, NSs constitute more
than half of the entries of disease-causing genetic changes in the
Human Gene Mutation Database (HGMD) [Stenson et al., 2009].
Based on current technologies, it is too costly and time-
consuming to investigate the functional effect of every NS
experimentally. Fortunately, there are algorithms available to help
to predict the potential of a NS being functional or deleterious.
Functional prediction algorithms for NS have been developed in
recent years with the advance of phylogenetics, structure biology,
bioinformatics and population genetics (see reviews by [Horner
et al., 2010; Karchin, 2009; Ng and Henikoff, 2006; Thusberg and
Vihinen, 2009]). Usually, the algorithms output a score measuring
how likely an NS is deleterious, along with its binary prediction.
Functional predictions play an important role in sequencing based
genotype–phenotype association analyses. Prediction scores have
been used to weight different NSs in order to increase the power of
detecting genes influencing a trait [Price et al., 2010]. More
importantly, functional prediction has become an indispensable
step in identifying genes causing rare Mendelian diseases using an
exome-sequencing approach [Cooper et al., 2010; Ng et al.,
2010a,b,c]. Functional predictions or conservation information
are used to filter or prioritize the novel NSs for further analysis or
confirmation [Ng et al., 2010]. Because different algorithms use
different information and are based on different training data,
each has its own strength and weakness. It has been suggested to
use the outputs from multiple algorithms to make a more reliable
prediction for a NS (e.g., by using consensus prediction or
majority vote) [Chun and Fay, 2009].
Because querying predictions from different databases/Web-
servers for different algorithms is both tedious and time
consuming, we developed dbNSFP (database for nonsynonymous
SNPs’ functional predictions) to facilitate the process. We first
compiled a collection of all possible NSs in the human genome (a
total of 75,931,005) based on the annotation of the Consensus
Coding Sequence (CCDS) project [Pruitt et al., 2009]. We next
collected their corresponding prediction scores from four new and
popular prediction algorithms (SIFT [Kumar et al., 2009],
Polyphen2 [Adzhubei et al., 2010], LRT [Chun and Fay, 2009],
and MutationTaster [Schwarz et al., 2010]). We also added other
related information including a conservation score (PhyloP)
[Siepel et al., 2006], degenerate type, and corresponding codons
and genes. The dbNSFP is the first known integrated database of
functional predictions from multiple algorithms for the compre-
hensive collection of human NSs.
Data Sources and Processing
The genes and their corresponding codons were determined
based on CCDS version 20090327. This is the latest version based
on the human reference sequence build hg18. Although the
current human reference sequence is build hg19, many important
human sequence resources are based on hg18, including current
& 2011 WILEY-LISS, INC.
Contract grant sponsor: The National Institutes of Health; Contract grant numbers:
RC2-HL02419-01; RC2 HL103010-01; 1U01HG005728-01.
Additional Supporting Information may be found in the online version of this article.
?Correspondence to: Xiaoming Liu, Human Genetics Center, School of Public
Health, The University of Texas Health Science Center at Houston, 1200 Herman
Pressler Drive, E529, Houston, Texas 77030. E-mail: email@example.com
exome capture chips and the 1,000 genomes pilot data. To
facilitate NS queries based on hg19, we used the liftOver tool from
the UCSC Genome Browser [Rhead et al., 2010] to convert the
coordinates of NSs to hg19. Only 561 NSs out of 75,931,005
(0.0007%) were not converted successfully.
The PhyloP scores were extracted from the placental subset of the
precomputed phyloP44way scores [Pollard et al., 2010] provided by
the UCSC Genome Browser (see details at http://hgdownload.
cse.ucsc.edu/goldenPath/hg18/phyloP44way/). The original score is
presented as phyloPori5?log10P or log10P, if the site is more
conserved than neutral (phyloPori40) or less conserved than neutral
(phyloPorio0), respectively. P is a two-sided P-value based on a
likelihood ratio test. To make it easier to compare with the
prediction scores, we rescaled it to a new score as phyloPnew5
1?0.5?10?phyloPoriif phyloPori40 or phyloPnew50.5?10phyloPoriif
phyloPorio0 to mimic a one-sided P-value. The new score ranges
from 0 to 1 and a larger score signifies higher conservation. We used
phyloPnew40.95 as a rule to predict conserved site [Siepel et al.,
2006]. That is, the prediction is ‘‘C(onserved)’’ if phyloPnew40.95;
otherwise, the prediction is ‘‘N(on-conserved).’’
Original SIFTscores were provided by ANNOVAR [Wang et al.,
2010], which were originally from a local database format of SIFT
4.0.3. The original SIFT scores (SIFTori) range from 0 to 1. If a
score is smaller than 0.05 the corresponding NS is predicted as
‘‘D(amaging)’’; otherwise it is predicted as ‘‘T(olerated).’’ We
defined a new score SIFTnew51?SIFTori. The new score still
ranges from 0 to 1 and a larger score means more likely to be
deleterious. Correspondingly, if a new score is larger than 0.95, the
prediction is ‘‘D’’; otherwise, it is ‘‘T.’’
Original LRT scores (LRTori) were downloaded from the LRT
LRTori is a two-sided P-value of the likelihood ratio test of
codon constraint. Each LRTori is associated with an estimated
nonsynonymous-to-synonymous-rate ratio (o) and an amino
acid alignment of 31 species at the test codon. If the codon is more
constrained than neutral, oo1; otherwise, oZ1. We defined our
if oZ1 to mimic a one-sided p-value. The score ranges from 0 to
1 and a larger score signifies that the codon is more constrained or
a NS is more likely to be deleterious. LRT predictions were derived
using the rules summarized in Supp. Figure S1, which is similar to
those of Chun and Fay . In short, (1) a predicted
D(eleterious) NS needs to fulfill three requirements: (i) from a
codon defined by LRT as significantly constrained (LRTorio0.001
and oo1), (ii) from a site with Z10 eutherian mammals
alignments, and (iii) the alternative AA is not presented in any of
the eutherian mammals; (2) a predicted N(eutral) NS needs to
fulfill either of the two requirements: (i) the alternative AA is
presented in at least one of the eutherian mammals, or (ii) from
a codon defined by LRT as not significantly constrained
(LRToriZ0.001 or oZ1) and with Z10 eutherian mammals
alignments; (3) otherwise, the NS is reported as U(nknown). More
details of the LRT predictions can be found in the Discussion section.
Polyphen2 scores were manually queried and downloaded as
?500 batches from its batch query server (http://genetics.bwh.
harvard.edu/pph2/bgi.shtml). The default query settings (Classifier
model: HumDiv, Transcipts: Canonical, Annotations: Missense)
were used except with genome assembly NCBI36/hg18 instead of
GRCh37/hg19. We used pph2_prob as our Polyphen2 score, which
is the classifier probability of the alternative allele being
deleterious. The score ranges from 0 to 1, and the corresponding
prediction is ‘‘probably damaging’’ (coded as ‘‘D’’) if it is larger
than 0.85; ‘‘possibly damaging’’ (coded as ‘‘P’’) if it is between 0.85
and 0.15 and ‘‘benign’’ (coded as ‘‘B’’) if it is smaller than 0.15.
Throughout this article, we regard ‘‘D’’ as its category for
MutationTaster scores were queried from its Webserver (http://
www.mutationtaster.org/) using its batch query Perl scripts for
each of the 75,931,005 NSs. The input file needs an ENSEMBL
transcript ID and a sequence snippet (the immediate upstream
and downstream sequence from the query site) for each SNP. We
first used ANNOVAR to annotate the NSs using ENSEMBL
database to get the corresponding ENSEMBL transcript ID for
each NS. If there is more than one transcript ID, initially the first
ID was used to build an input file for the first round query. If it
turned out that transcript ID is not in the MutationTaster’s
database (as reported in its output file), we then used the second
ID to build an input file for the second round of query. Snippets
were built using the FASTA files of the human reference sequence
downloaded from the UCSC Genome Browser. For the first round
query, we used 12 upstream and 12 downstream nucleotides from
the query site to build the snippet. If the output file reported that
the snippet is not unique, then 100 upstream and 100 downstream
nucleotides from the query site were used to build a new snippet
for a second round query. MutationTater has four types of
‘‘polymorphism,’’ and ‘‘polymorphism_automatic,’’ which we
coded as ‘‘A,’’ ‘‘D,’’ ‘‘N,’’ and ‘‘P,’’ respectively. Among them, ‘‘D’’
and ‘‘N’’ are determined by the prediction algorithm, whereas ‘‘A’’
and ‘‘P’’ are determined by external information (see the
Discussion section for details). We regard ‘‘A’’ and ‘‘D’’ as
categories for deleterious NSs. MutationTaster also reports a
P_value for each prediction, which represents the probability that
the prediction is true. We defined our MutationTaster score using
the following rule: if the prediction is ‘‘disease_causing’’ or
‘‘disease_causing_automatic,’’ score5P_value; if the prediction is
‘‘polymorphism’’ or ‘‘polymorphism_automatic’’, score51?P_value.
The resulting score ranges from 0 to 1 and a larger score means
more likely to be deleterious.
For various reasons, an algorithm may not output a prediction
or score for a NS, thus regarded as missing data and reported it as
‘‘NA.’’ In dbNSFP, we reported imputed scores for missing scores
(see Missing Data and Imputation). Therefore, a prediction of
‘‘NA’’ can be used as an indicator to determine whether the
corresponding score is imputed or not. Rarely, probably due to
different gene annotations for the same NS, an algorithm may
report a different reference AA or alternative AA as to those
defined by CCDS. In that case, we considered the predictions and
scores (if reported) as missing data. On the other hand, for a small
number of NSs, CCDS may define more than one reference AA or
alternative AA for the same nucleotide site change, due to
different annotations for the same gene. We treated those
definitions as different NSs. In another words, we defined a NS
as a unique combination of chromosome, position, reference
nucleotide allele, alternative nucleotide allele, reference AA allele,
and alternative AA allele.
In the current version, each NS links to the following entries:
chromosome number, physical position on the chromosome as to
hg18 (1-based coordinate), reference nucleotide allele (as on the 1
strand), alternative nucleotide allele (as on the 1 strand),
reference AA, alternative AA, physical position on the chromo-
some as to hg19 (1-based coordinate), gene name, gene Entrez ID,
CCDS ID, reference codon, position on the codon (1, 2, or 3),
HUMAN MUTATION, Vol. 32, No. 8, 894–899, 2011
degenerate type (0, 2, or 3), AA position as to the protein, coding
sequence (CDS) strand (1 or ?), estimated nonsynonymous-to-
synonymous-rate ratio (o, reported by LRT), PhyloP score,
PhyloP prediction, SIFT score, SIFT prediction, Polyphen2 score,
Polyphen2 prediction, LRTscore, LRT prediction, MutationTaster
score, MutationTaster prediction. If any of the PhyloP, SIFT,
Polyphen2, LRT, and MutationTaster scores were missing, their
imputed scores (see Missing Data and Imputation) were reported
(but their corresponding predictions will be ‘‘NA’’). There are a
total of 75,931,005 entries for 64,646,969 unique NSs, each of
which is defined as a unique combination of physical position and
alternative allele. This is due to the fact that the same NS may be
annotated by CCDS multiple times for different forms of the same
gene. The numbers of nonmissing entries of NS, hg19 position,
PhyloP score, SIFT score, Polyphen2 score, LRT score, and
MutationTaster score per chromosome are summarized in Table 1.
Because all scores (PhyloP, SIFT, Polyphen2, LRT, and
MutationTaster) were scaled to [0, 1] with a larger score
corresponding to higher conservation (PhyloP and LRT) or more
likely to be deleterious (SIFT, Polyphen2, and MutationTaster), we
can easily compare their distributions. Figure 1 shows the histograms
of their total scores in different score ranges. An interesting
observation is that all five methods show some form of bimodal
distribution, with the majority of the scores clustered around 1 and a
small proportion of the scores clustered around 0. The predictions
from Polyphen2 and MutationTaster are more balanced, which have
about 30% of the scores in range [0, 0.1), whereas the remaining
three only have less than 7% scores in that range. Correspondingly,
SIFT, LRT, and MutationTaster all predict more than half of the total
NSs being deleterious, whereas Polyphen2 predicts that slightly less
than half of the NSs are deleterious (Table 2). The proportion of
pairwise agreement between methods on whether aN NS is
deleterious is around 60–70%, with the highest agreement between
the LRT and MutationTaster (77.19%) (Table 3). The Spearman’s
rank correlation coefficients (RCCs) between scores of different
Number of Entries in dbNSFP
Chromosome NShg19 PhyloPSIFT Polyphen2LRT MutationTaster
aNSs with the same position and alternative allele were counted only once.
Distributions of PhyloP, SIFT, Polyphen2, LRT, and
Summary of Predictions
Right Triangle) and Spearman’s Rank Correlation Coefficients
(Lower Left Triangle)
Pairwise Prediction Agreement Percentages (Upper
MethodPhyloP SIFTPolyphen2 LRT MutationTaster
HUMAN MUTATION, Vol. 32, No. 8, 894–899, 2011
methods show low to moderate positive correlations, with the
highest RCC between LRTand MutationTaster being 0.62 (Table 3).
We also calculated the Pearson’s correlation coefficients (data not
shown) for each pair of the methods. They are all smaller than their
corresponding RCCs, which suggests stronger nonlinear correlation
between those scores.
Missing Data and Imputation
The numbers of missing data of PhyloP, SIFT, Polyphen2, LRT,
and MutationTaster per chromosome can be derived from Table 1.
Among them, PhyloP has the lowest missing data rate (0.004%).
As for the four prediction methods, MutationTaster has the lowest
missing data rate (5.2%), followed by LRT (7.7%), SIFT (10.7%),
and Polyphen2 (12.5%). This is partially because SIFT and
Polyphen2 do not provide prediction scores for stop-gain
(a mutation that changes an AA codon to a stop codon) or
stop-loss (a mutation that changes a stop codon to an AA codon)
and LRT does not provide prediction scores for stop-loss.
An advantage of having multiple prediction scores for the same
NS is that it facilitates better imputation for missing scores. Some
statistical methods for detecting association between (groups) of
rare variants and phenotypes (e.g., VT test) [Price et al., 2010] use
prediction scores to weight NSs to increase their detecting power. If
scores are missing, typically they are imputed using the average of
the nonmissing scores (of the same algorithm) in the sample. As we
observed in Figure 1, the prediction scores are bimodal distributed.
Therefore, using the average score to impute may not be a good
choice. Although there are only low to moderate correlations
between different types of scores, it is still possible to make full use
of those relationships to obtain better imputation scores.
We chose the program BPCAfill [Oba et al., 2003] to impute the
missing scores in dbNSFP, because of its higher accuracy and faster
computation comparing to other methods [Aittokallio, 2010].
Although BPCAfill is originally designed for imputing missing
expression data for microarray analyses, it can be applied to any
data set with correlated columns or rows. To evaluate the
performance, (1) we compiled a subset of dbNSFP, in which all
five scores are NOTmissing; (2) for each missing pattern observed
in dbNSFP, we randomly picked the same number of NSs as those
with the missing pattern in dbNSFP, and marked their correspond-
ing scores as missing (e.g., dbNSFP has 334,421 NSs with LRTand
MutationTaster scores missing while all other three scores are
present; we randomly picked 334,421 NSs from the subset and
marked their LRT and MutationTaster scores as missing); and
(3) after BPCAfill imputed the missing scores, we compared the
imputed scores with the corresponding original scores. Figure 2
shows the histograms of the error (imputed score minus original
score) distribution from two different imputation strategies:
imputing with BPCAfill or with the average scores of the same
algorithm. Imputation errors for each algorithm with different
missing patterns are summarized in Supp. Table S1. Comparing the
imputation accuracies for different prediction scores over all
missing patterns, LRT has the smallest mean squared error (MSE)
of 0.035, followed by SIFT (0.054) and PhyloP (0.066), whereas
MutationTaster and Polyphen2 have the largest MSEs of 0.133 and
0.142, respectively (Supp. Table S1). A t-test of the difference
between mean error and 0 shows that imputations with both
BPCAfill and the average scores were unbiased (data not shown),
with average errors equal to ?6.25?10?6
?3.6?10?5(average score), respectively. However, the distribu-
tion of the errors using BPCAfill had a significantly smaller
standard deviation than the other strategy (0.303 comparing to
0.341, P-valueo10?14, F-test, one tailed). Therefore, we concluded
that BPCAfill can produce significantly better imputation scores
than simply imputing with the average score.
Companion Search Program
A companion search program written in Java (search_dbNSFP)
is freely available for download along with dbNSFP. It can run
crossplatform on a wide range of computers, as long as a proper
Java Runtime Environment is installed. It enables a simple search
for a NS, a chromosome position or a gene. It runs on a command
line with a basic format ‘‘java search_dbNSFP -i infile -o outfile,’’
where infile and outfile are the user designated input file name
and output file name, respectively. The input file contains one or
more lines, with each line representing a query. The query formats
for NS, chromosome position and gene are presented in Table 4.
The output file contains all NSs that match the query information.
For example, the output of a gene query will contain all NSs in
that gene, and that of a genome position query will contain all NSs
on that position. If there is more than one result for a NS query
due to different annotations (see Data Sources and Processing), all
Distributions of imputation errors by using BPCAfill (A) or using the average score of the same algorithm (B).
HUMAN MUTATION, Vol. 32, No. 8, 894–899, 2011
results will be reported. By default, the program searches all
chromosomes with the positions according to the human genome
reference sequence hg18. Users can specify both the chromosomes
to search for and the reference sequence version by using the format
‘‘java search_dbNSFP [-v reference_version] [-c chromosome_list]
-i infile -o outfile,’’ where the contents in [ ] are optional.
reference_version can be either hg18 or hg19. chromosome_list is a
list of chromosome number (1, 2,y, X, Y) separated by commas
without space, for example ‘‘1,3,22,X.’’ If there are some queries that
do not have matched NS, those failed queries will be listed in the
system output (command line environment).
dbNSFP is a database for all potential NSs (as to the reference
sequence) in the human genome and their functional predictions.
It was developed for two main purposes. The first is to facilitate the
SNP filtering/prioritizing step in studies of mapping rare
Mendelian disease genes using the exome-sequencing approach.
The second is to facilitate the imputation of missing scores that are
required in the weighted sum test (e.g., VT test) [Price et al., 2010]
for detecting association between phenotypes and groups of rare
variants. A companion search program is provided for fast local
queries. For the current version, we attempted to keep the database
simple and lightweight to ensure storage and running efficiency.
Except for five prediction/conservation scores (PhyloP, SIFT,
Polyphen2, LRT, and MutationTaster) for each NS, the database
only contains information of its corresponding genomic position,
gene, codon, among others. Future development may include
expanding the collection of NSs from other genome databases,
incorporating the results from other prediction algorithms that do
not provide genome position searches, such as PhD-SNP
[Capriotti et al., 2006] and PANTHER [Thomas et al., 2003],
and building a fully functional Webserver for on-line database
query. We plan to keep updating the database to have newer CCDS
versions, whenever most of the public available human genome
sequence data shift to a new human genome reference sequence.
Some cautions are needed when compiling and interpreting the
search results from dbNSFP. First, the NSs collected in dbNSFP
were based on the annotations of CCDS, which defines consensus
CDSs from multiple genome databases. Therefore, CCDS by no
means includes the complete set of human CDSs, but rather
includes a set of well-annotated CDSs. It is quite possible that some
NSs defined by other databases are not contained in dbNSFP.
Second, the NSs in dbNSFP are defined as the alternative alleles
against the reference alleles of the human reference sequence.
However, the reference allele is neither necessary the ‘‘wild-type’’
allele, nor the common allele in all human populations. Some
other information about the alternative allele, for example, its
frequency in populations or sampled individuals, may help to
determine its potential to be deleterious. Third, for various
reasons, some scores are missing for some NSs and the imputed
scores may not be reliable. For example, scores for stop-gain NSs
are not available from SIFT and Polyphen2. Although imputed
scores for stop-gain NSs are provided, some researchers may prefer
to assign a score based on their knowledge of the protein. For
instance, a stop-gain near the beginning of the protein is more
likely to be deleterious than one near the end of the protein.
Fourth, there may be multiple entries in dbNSFP that match the
same NS query. This is primarily due to the fact that CCDS
contains different annotations for different forms of the same gene.
Our rules for LRT predictions (Supp. Fig. S1) are similar to
those of Chun and Fay , but be aware that our predictions
may not be the same as those reported by the LRT Webserver
(http://www.genetics.wustl.edu/jflab/lrt_query.html). The major
difference is that the Webserver predicts a NS as either
D(eleterious) or N(eutral) according to LRToriand o, regardless
of the quality of the underlying amino acid alignment. Chun and
Fay  suggested that if the amino acid alignment has less
than 10 eutherian mammals, the prediction power is low.
Therefore, we add a new prediction category U(nknown)
specifically for the low-power situation. As a result, our LRT
predictions for D(eleterious) and N(eutral) are more stringent
than those provided by the Webserver.
Because the predictions of LRT and MutationTaster are not
entirely dependent on the prediction scores, discrepancy between a
score and its corresponding prediction may occur. As to LRT, the
discrepancies come from N(eutral) predictions with high scores (i.e.,
the codon is highly constrained or a NS is likely to be deleterious).
This is because of one prediction rule: if the alternative AA allele is
observed in at least one eutherian mammal, the NS is predicted as
N(eutral), regardless of the score it associated with (Supp. Fig. S1).
The discrepancy cases of MutationTaster come from the two
‘‘automatic’’ predictions, that is, ‘‘disease_causing_automatic’’ (‘‘A’’)
and ‘‘polymorphism_automatic’’ (‘‘P’’). An NS will be predicted to
be ‘‘A’’ if it causes nonsense-mediated decay (i.e., a stop-gain); and
an NS will be predicted to be ‘‘P’’ if all three genotypes of the
reference/alternative alleles are observed in the HapMap data [The
International HapMap Consortium, 2010]. Both types of prediction
are not dependent on the prediction scores at all, although the scores
are still reported.
As to the missing data imputation, our results clearly showed that
BPCAfill can obtain more accurate imputation compared to simply
using the average score of the same algorithm (column-average).
This is easy to understand because BPCAfill borrows information
from the correlated scores of different algorithm while column-
average imputation does not use such information. To further
evaluate the performance of BPCAfill, we also compared its results
to the simple imputation using the average of the nonmissing scores
of the other algorithms for the same NS (row-average). For example,
if an NS misses a SIFT score while all other four scores (PhyloP,
Polyphen2, LRT, MutationTaster) are available, we impute the SIFT
score using the average of the other four scores. The results showed
that although the row-average imputation’s mean absolute error is
smaller than that of the column-average, it is still larger than that of
BPCAfill (data not shown). It is also shown that the row-average
imputation is significantly biased and has a significantly larger
variance than BPCAfill (data not shown). The superior performance
of BPCAfill compared to the row-average can be explained by the
nonlinear correlation between the scores of different algorithms and
the fact that BPCAfill also borrows information from the correlation
between NSs while row-average imputation does not.
Format of Queries for Input File
NS chr pos ref alt
chr pos ref alt refAA altAA
Y 140855 A C
Y 140855 A C M L
aSeparated by tab or space. chr: chromosome number; pos: position on chromosome;
ref: reference allele; alt: alternative allele; refAA: reference amino acid; altAA:
alternative amino acid; gene_name: gene name; gene_id: gene Entrez ID; CCDS_id:
HUMAN MUTATION, Vol. 32, No. 8, 894–899, 2011
Collecting predictions and scores from multiple algorithms has Download full-text
many advantages. Besides providing better imputation for missing
data, as we mentioned in the introduction, it also helps to get more
accurate functional predictions for NS. A simple way to
incorporate multiple predictions is to rank each NS according to
the number of algorithms that predict it as deleterious. The larger
the number, the higher the confidence that it is truly deleterious.
A better solution is to develop a unified score (or a score of scores)
based on training data sets and infer predictions from it. Besides
NS, other types of variants may also contribute to diseases.
However, the functional prediction methods of those variants are
typically less mature than those of NS, but never the less they still
provide valuable information of those variants. For example, a
recent study [Waldman et al., 2010] showed that codon bias is
significantly correlated with gene expression levels in human.
Therefore, some synonymous changes are more likely to have
strong impact on gene expression and lead to phenotypic effects.
Other information that can be used to prioritize variants including
the prediction of promoter regions, linkage disequilibrium with
known genome-wide association study (GWAS) hits, gene
expression profile of disease related tissues, known biochemical
pathways, known or predicted gene interaction networks, among
others. In recent years, some Webservers have been developed to
integrate searches or results from multiple bioinformatics tools, for
example, the SeattleSeq Annotation server, F-SNP [Lee and
Shatkay, 2008], SNPLogic [Pico et al., 2009], SNPit [Shen et al.,
2009], and pfSNP [Wang et al., 2011]. As the field moves from
whole exome sequencing to whole genome sequencing, more
efforts are needed in these nonprotein coding domains.
We thank Bing Yu and Dr. Maja Barbalic for testing the database and its
companion search program. We thank Dr. Taylor J. Maxwell and Dr. Peng
Wei for their valuable comments during the process of the work, and Sara
Barton for her help on polishing the writing. We also want to thank the
comments and suggestions from the anonymous reviewers. Especially, one
reviewer’s comment helped us to identify a bug hidden in the
preprocessing of the prediction scores.
Adzhubei IA, Schmidt S, Peshkin L, Ramensky VE, Gerasimova A, Bork P,
Kondrashov AS, Sunyaev SR. 2010. A method and server for predicting
damaging missense mutations. Nat Methods 7:248–249.
Aittokallio T. 2010. Dealing with missing values in large-scale studies: microarray
data imputation and beyond. Brief Bioinform 11:253–264.
Capriotti E, Calabrese R, Casadio R. 2006. Predicting the insurgence of human
genetic diseases associated to single point protein mutations with support vector
machines and evolutionary information. Bioinformatics 22:2729–2734.
Chun S, Fay JC. 2009. Identification of deleterious mutations within three human
genomes. Genome Res 19:1553–1561.
Cooper GM, Goode DL, Ng SB, Sidow A, Bamshad MJ, Shendure J, Nickerson DA.
2010. Single-nucleotide evolutionary constraint scores highlight disease-causing
mutations. Nat Methods 7:250–251.
Horner DS, Pavesi G, Castrignano ` T, Meo PDD, Liuni S, Sammeth M, Picardi E,
Pesole G. 2010. Bioinformatics approaches for genomics and post genomics
applications of next-generation sequencing. Brief Bioinform 11:181–197.
Karchin R. 2009. Next generation tools for the annotation of human SNPs. Brief
Kumar P, Henikoff S, Ng PC. 2009. Predicting the effects of coding non-synonymous
variants on protein function using the sift algorithm. Nat Protoc 4:1073–1081.
Lee PH, Shatkay H. 2008. F-SNP: computationally predicted functional SNPs for
disease association studies. Nucleic Acids Res 36:D820–D824.
Ng PC, Henikoff S. 2006. Predicting the effects of amino acid substitutions on
protein function. Annu Rev Genomics Hum Genet 7:61–80.
Ng SB, Bigham AW, Buckingham KJ, Hannibal MC, McMillin MJ, Gildersleeve HI,
Beck AE, Tabor HK, Cooper GM, Mefford HC, Lee C, Turner EH, Smith JD,
Rieder MJ, Yoshiura KI, Matsumoto N, Ohta T, Niikawa N, Nickerson DA,
Bamshad MJ, Shendure J. 2010a. Exome sequencing identifies MLL2 mutations
as a cause of Kabuki syndrome. Nat Genet 42:790–793.
Ng SB, Buckingham KJ, Lee C, Bigham AW, Tabor HK, Dent KM, Huff CD,
Shannon PT, Jabs EW, Nickerson DA, Shendure J, Bamshad MJ. 2010b. Exome
sequencing identifies the cause of a Mendelian disorder. Nat Genet 42:30–35.
Ng SB, Nickerson DA, Bamshad MJ, Shendure J. 2010c. Massively parallel sequencing
and rare disease. Hum Mol Genet 19:R119–R124.
Oba S, Sato M, Takemasa I, Monden M, Matsubara K, Ishii S. 2003. A Bayesian missing
value estimation method for gene expression profile data. Bioinformatics
Pico AR, Smirnov IV, Chang JS, Yeh RF, Wiemels JL, Wiencke JK, Tihan T,
Conklin BR, Wrensch M. 2009. SNPLogic: an interactive single nucleotide
polymorphism selection, annotation, and prioritization system. Nucleic Acids
Pollard KS, Hubisz MJ, Rosenbloom KR, Siepel A. 2010. Detection of nonneutral
substitution rates on mammalian phylogenies. Genome Res 20:110–121.
Price AL, Kryukov GV, de Bakker PIW, Purcell SM, Staples J, Wei LJ, Sunyaev SR.
2010. Pooled association tests for rare variants in exon-resequencing studies. Am
J Hum Genet 86:832–838.
Pruitt KD, Harrow J, Harte RA, Wallin C, Diekhans M, Maglott DR, Searle S,
Farrell CM, Loveland JE, Ruef BJ, Hart E, Suner MM, Landrum MJ, Aken B,
Ayling S, Baertsch R, Fernandez-Banet J, Cherry JL, Curwen V, Dicuccio M,
Kellis M, Lee J, Lin MF, Schuster M, Shkeda A, Amid C, Brown G, Dukhanina O,
Frankish A, Hart J, Maidak BL, Mudge J, Murphy MR, Murphy T, Rajan J,
Rajput B, Riddick LD, Snow C, Steward C, Webb D, Weber JA, Wilming L,
Wu W, Birney E, Haussler D, Hubbard T, Ostell J, Durbin R, Lipman D. 2009.
The consensus coding sequence (CCDS) project: identifying a common protein-
coding gene set for the human and mouse genomes. Genome Res 19:1316–1323.
Rhead B, Karolchik D, Kuhn RM, Hinrichs AS, Zweig AS, Fujita PA, Diekhans M,
Smith KE, Rosenbloom KR, Raney BJ, Pohl A, Pheasant M, Meyer LR,
Learned K, Hsu F, Hillman-Jackson J, Harte RA, Giardine B, Dreszer TR,
Clawson H, Barber GP, Haussler D, Kent WJ. 2010. The UCSC genome browser
database: update 2010. Nucleic Acids Res 38:D613–D619.
Schwarz JM, Ro ¨delsperger C, Schuelke M, Seelow D. 2010. MutationTaster evaluates
disease-causing potential of sequence alterations. Nat Methods 7:575–576.
Shen TH, Carlson CS, Tarczy-Hornoch P. 2009. SNPit: a federated data integration
system for the purpose of functional SNP annotation. Comput Methods
Programs Biomed 95:181–189.
Siepel A, Pollard K, Haussler D. 2006. New methods for detecting lineage-specific
selection. In: Proceedings of the 10th international conference on research in
computational molecular biology (RECOMB 2006), p 190–205.
Stenson PD, Mort M, Ball EV, Howells K, Phillips AD, Thomas NS, Cooper DN.
2009. The Human Gene Mutation Database: 2008 update. Genome Med 1:13.
The International HapMap Consortium. 2010. Integrating common and rare genetic
variation in diverse human populations. Nature 467:52–58.
Thomas PD, Campbell MJ, Kejariwal A, Mi H, Karlak B, Daverman R, Diemer K,
Muruganujan A, Narechania A. 2003. PANTHER: a library of protein families
and subfamilies indexed by function. Genome Res 13:2129–2141.
Thusberg J, Vihinen M. 2009. Pathogenic or not? And if so, then how? Studying the
effects of missense mutations using bioinformatics methods. Hum Mutat
Waldman YY, Tuller T, Shlomi T, Sharan R, Ruppin E. 2010. Translation efficiency in
humans: tissue specificity, global optimization and differences between
developmental stages. Nucleic Acids Res 38:2964–2974.
Wang J, Ronaghi M, Chong SS, Lee CGL. 2011. pfSNP: an integrated potentially
functional SNP resource that facilitates hypotheses generation through knowl-
edge syntheses. Hum Mutat 32:19–24.
Wang K, Li M, Hakonarson H. 2010. ANNOVAR: functional annotation of genetic
variants from high-throughput sequencing data. Nucleic Acids Res 38:e164.
HUMAN MUTATION, Vol. 32, No. 8, 894–899, 2011