Nucleic Acids Research, 2009, Vol. 37, Database issuePublished online 28 October 2008
Pseudofam: the pseudogene families database
Hugo Y. K. Lam1, Ekta Khurana2, Gang Fang2, Philip Cayting2, Nicholas Carriero3,
Kei-Hoi Cheung3,4,5and Mark B. Gerstein1,2,3,*
1Program in Computational Biology and Bioinformatics,2Department of Molecular Biophysics and Biochemistry,
3Department of Computer Science,4Center for Medical Informatics and5Department of Genetics, Yale University,
New Haven, CT 06520, USA
Received August 15, 2008; Revised October 4, 2008; Accepted October 6, 2008
Pseudofam (http://pseudofam.pseudogene.org) is
a database of pseudogene families based on the
protein families from the Pfam database. It provides
resources for analyzing the family structure of pseu-
dogenes including query tools, statistical summa-
ries and sequence alignments. The current version
of Pseudofam contains more than 125000 pseudo-
genes identified from 10 eukaryotic genomes and
aligned within nearly 3000 families (approximately
one-third of the total families in PfamA). Pseudofam
uses a large-scale parallelized homology search
algorithm (implemented as an extension of the
Each identified pseudogene is assigned to its
parent protein family and subsequently aligned to
each other by transferring the parent domain align-
ments from the Pfam family. Pseudogenes are also
given additional annotation based on an ontology,
reflecting their mode of creation and subsequent
history. In particular, our annotation highlights the
association of pseudogene families with genomic
features, such as segmental duplications. In addi-
tion, pseudogene families are associated with key
statistics, which identify outlier families with an
unusual degree of pseudogenization. The statistics
also show how the number of genes and pseudo-
genes in families correlates across different species.
Overall, they highlight the fact that housekeeping
families tend to be enriched with a large number
The complexity of the eukaryotic genome is characterized
by its large amount of non-protein-coding DNA. This
type of DNA typically lies in intergenic regions and was
regarded as ‘junk’ DNA in the past. However, due to the
recent advancement of genomic technology, it has been
found that intergenic DNA indeed plays an important
role in regulatory function and also provides a basis for
studying the dynamics and evolution of a genome (1).
Among all the intergenic elements, from transcription
factor binding sites to microsatellites, pseudogenes, which
are in effect genetic fossils, are the elements most likely to
record historical aspects of living genes. Pseudogenes not
only capture genes in the past, but also provide precious
clues about genome dynamics, such as gene duplication
events (for duplicated pseudogenes) and retrotransposi-
tion events (for processed pseudogenes). Since proteins
in the same family are believed to share a common ances-
tor giving rise to the shared domain, association of pseu-
dogenes with their parent protein families could reveal the
correlation between the generation of pseudogenes and the
functions of their parents. This correlation otherwise
might not be observable from the study of individual
approaches have been developed to identify and annotate
pseudogenes in eukaryotic genomes (2–4). Also, there are
a few prior studies that have attempted to analyze pseudo-
genes using protein families (5,6). However, no study thus
far has systematically formalized the pseudogene relation-
ships and presented an integrated analysis of several
eukaryotes using a family approach. To this end, this arti-
cle aims to develop a large-scale database of pseudogene
families of eukaryotes that could enable researchers to
analyze pseudogenes and relate them to existing genomic
information in an integrated fashion.
THE PSEUDOFAM WEB SITE
Pseudofam is implemented as an online database, which
is available at http://pseudofam.pseudogene.org. The web
site itself is a thin-client application implemented using
Java on the server side and requires only a web browser
on the client side. It provides tools for researchers to
*To whom correspondence should be addressed. Tel: +1 203 432 6105; Fax: +1 360 838 7861; Email: Mark.Gerstein@yale.edu
? 2008 The Author(s)
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/
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browse and query the pseudogene families. Moreover,
it provides certain useful statistics (described in detail
below), such as the enrichment of parent proteins for
each family and the correlation of different family param-
eters between species. The database is also capable
of interfacing with other related systems, such as the
Ensembl server and the Pfam database. Furthermore,
researchers can download the family data sets, including
the alignment of the sequences, in flat file formats.
ASSIGNING PSEUDOGENES TO FAMILIES
Figure 1 depicts an overview of the generation of
Pseudofam data from the identification of pseudogenes
to the formation of the families. DNA sequences of 10
eukaryotic genomes: human, chimpanzee, dog, mouse,
rat, fruit fly, mosquito, chicken, zebra fish and worm,
together with their over 291000 protein sequences were
retrieved from Ensembl (http://www.ensembl.org; release
48—December 2007) (7,8). Each genome and its asso-
ciated protein sequences are run through BLAST (9,10)
to identify all genomic regions that share sequence simi-
larity with the given protein sequences. The proteins are
divided into groups of queries, which are processed con-
currently to reduce overall runtime, while the genomes are
used as the BLAST database. The results are then pro-
cessed using PseudoPipe (4) to identify potential pseudo-
genes. This analysis pipeline uses tFasty (11) to refine the
BLAST alignments and determine frame shifts and other
disablements. It takes about 3 days of computational time
to complete the identification of pseudogenes in the
human genome with our current configuration.
Our current release of Pseudofam contains 3821 protein
families covering all the protein sequences used as input for
identifying the 125272 pseudogenes. The parent proteins
of the identified pseudogenes belong to 2986 pseudogene
families. Thus, there are 835 protein families not found to
have any pseudogenes. Families for the protein sequences
are constructed by mapping the Ensembl peptide IDs to
the Pfam ID via mappings available at the BioMart server
(http://www.biomart.org/; Ensembl Release 48) (12).
Pseudogenes are assigned to the protein families based
on the assignments of their parent proteins and then
aligned to identify any pseudogene domains by the
mechanics described below.
Figure 2 shows a schematic representation of our
approach in aligning pseudogene domains by transfer-
ring their parent domain alignments from the Pfam multi-
ple alignments. Within each family, a pseudogene is first
aligned to its parent protein and then to its correspond-
ing protein domain retrieved from the Pfam database
(http://pfam.sanger.ac.uk; version 22) (13,14). After the
individual alignments, all the pseudogene domains from
distinct species are then aligned together with their parent
protein domains. This approach of alignment enables us
to accurately align pseudogenes with low levels of similar-
ity and consequently to identify pseudogene domains
that might exhibit low similarity to their parent protein
domains. The resulting pseudogene domain alignment
data provide researchers a means to estimate the mutation
rate of genomic elements that evolve under no or less
selection pressure (15). This alignment data is available
DESCRIBING PSEUDOGENE FAMILIES
With the family data available at Pseudofam, we can
extend our family approach to other potentially related
analyses. Since pseudogenes are nonprotein coding and
have no direct functions, their relationships with other
parts of the genome are often neglected and poorly under-
stood. However, more and more findings have demon-
strated pseudogenes, as a gene relic, not only facilitate
evolutionary study, but also exhibit substantial interac-
tions in the genome. They have been shown to play differ-
ent roles in the genome remodeling process, including
retrotransposition, duplication and mutation. Recent
studies, for example, have shown that some of the pseudo-
genes may have mediated the formation of segmental
duplications (SDs) (16), regulating their parent genes
through RNA interference (17), or even been reactivated
(18,19). As a result, we have developed an ontology
[a formal specification of conceptualization (20)] to illus-
trate pseudogene family relationships. To facilitate further
analysis, we have also formatted our ontology into the
Open Biomedical Ontology (OBO) format and annotated
our data accordingly.
The ontology in Figure 3 shows an upper ontology depict-
ing the pseudogene family and its relationships. It spans
across several domains and involves different domain-
specific ontologies, such as the Gene Ontology (GO), Pro-
tein Ontology (PO/PrO), Sequence Ontology (SO) and
Pseudogene Ontology (see Supplementary Figure S1). It
basically consists of three parts. The first (in blue) is the
core part and family concept that Pseudofam is built
upon. The second (in dark gray) is a part that describes
certain primary aspects of pseudogenes that are fairly
well established, such as their genomic processes of crea-
tion (e.g. retrotransposition and duplication). The third
(in light gray) is a part that describes the secondary aspects
Figure 1. The generation of pseudofam. (1) Identify pseudogenes by
existing proteins of the genome. (2) Map all the parent proteins to
their protein families. (3) Assign the identified pseudogenes to their
parent protein families. (4) Align the pseudogenes in each family to
build the pseudogene families. (5) Calculate the key statistics for the
families and organize the data into the Pseudofam database.
Nucleic Acids Research, 2009,Vol. 37,Database issueD739
of a pseudogene family (e.g. its association with SDs), as
well as terms that are currently in a draft state. These draft
terms include unitary (describing pseudogenes mutated
directly from a parent gene), orphaned (for pseudogenes
whose parent genes were lost after speciation) and tran-
scribed (for apparently active pseudogenes). While the
upper ontology is essentially finished, the full Pseudogene
Ontology is still being developed in collaboration with
a number of other individuals.
Based on the fundamental relationship between protein
family and pseudogene, our ontology also depicts the
structural and functional relationships tying to a pseudo-
gene family. These relationships could aid in further
understanding of various genomic processes. For example,
the co-localization of pseudogenes in a shared synteny
could indicate their formation before speciation (19,21),
and the presence of pseudogenes in SDs could provide
clues about SD formation since both pseudogenes and
SDs represent duplicated regions of the genome (22).
Thus, Pseudofam currently provides the human pseudo-
gene dataset annotated with SD information obtained
from the Human Segmental Duplication database at
the SD relationship derives directly from the pseudogenes
themselves, the family relationship of a pseudogene is
inferred by the protein family relationship of its parent
protein and hence is more indirect. Here, we formalize
this inferred relationship in a first-order logic on which
Pseudofam is built:
!has pseudogene familyðp,fÞ
8p PseudogeneðpÞ^9r ProteinðrÞ^has parent proteinðp,rÞ
In words, for all pseudogene p, if there exists a protein r,
which is a parent protein of p, and there also exists a
protein family f, which contains r, then p has a pseudogene
family f. Even though a pseudogene is nonprotein coding,
this protein family approach of classification gives us a
way to associate domain and function with it. Proteins
in the same family are believed to share a common struc-
tural domain and function that evolved from a common
ancestor. As a result, a family approach allows us to ana-
lyze pseudogenes by their functional groups and have a
better understanding of their roles in genome rearrange-
ment by relating them to other genomic features.
To further facilitate analysis with our family data,
Pseudofam provides key statistics, such as the degree of
pseudogenization and pseudogene-to-gene ratio, for each
family both online and in the datasets for download.
It also provides a tool to correlate different family
Figure 2. The alignment of pseudogene family. Each pseudogene in a family is first aligned to its parent protein. Then, the pseudogene alignment is
aligned with the parent protein domain by transferring the corresponding alignment from the Pfam multiple alignments. At last, all the aligned
pseudogene domains, including their aligned parent protein domains, will be adjusted together to generate the final alignment.
Nucleic Acids Research, 2009, Vol. 37, Databaseissue
parameters between species. To identify outlier families
that have an unusual degree of pseudogenization,
Pseudofam calculates the enrichment of parent proteins
in each family and uses the hypergeometric distribution
to calculate P-value, viz:
Pr K ¼ k
ð Þ ¼ f k;N,m,n
ð Þ ¼
N ? m
n ? k
This formula calculates the probability Pr(K) of having
the observed number of parent proteins k for a given
family with n proteins under the hypergeometric distribu-
tion. Required for the computation is the total number of
proteins N used for identifying the pseudogenes and the
corresponding number of parent proteins m. The P-value
for a positive enrichment is the Pr(K>k) and for a nega-
tive enrichment is the Pr(K<k). This parent protein
approach is preferred over using a random sampling
method to calculate the enrichment of pseudogenes
because it is more computationally efficient and less
susceptible to the changes of the pseudogenes identifica-
tion algorithm or parameters that may cause the number
of pseudogenes identified to fluctuate. The following sec-
tions show a brief analysis based on the key statistics
provided by Pseudofam.
Table 1 shows the numbers of protein and pseudogene
families in different species and their degree of pseudogen-
ization. It indicates that among the species in our study
mammals have a higher percentage (an average of 50%)
of families containing pseudogenes than nonmammals (an
average of 22%). For instance, human has 3486 protein
families of which 1790 (51%) are found to have pseudo-
genes. On the other hand, Drosophila has 2620 protein
families but only 201 (8%) are found to have pseudogenes.
Looking at the families individually shows that certain
families have a high degree of pseudogenization, while
some have no pseudogenes at all. For example, the reverse
transcriptase (RNA-dependent DNA polymerase) family
has 18 out of 22 (82%) proteins found to have associated
Figure 3. The Pseudogene family ontology. An upper ontology that describes the various relationships between a pseudogene family and other
genomic elements. The solid lines represent direct relationships and the dashed lines represent inferred or indirect relationships. The core part is
represented in blue, while the well-established relationships are in dark gray and the secondary aspects of a pseudogene family are in light gray. For
detailed concepts and relationships about pseudogene, see Supplementary Figure S1.
Nucleic Acids Research, 2009,Vol. 37,Database issue D741
pseudogenes. In contrast, the bestrophin protein family,
which has 71 proteins, has not been found to have any
Correlation offamily sizes across species
Since the mammalian genomes have a substantial number
of pseudogene families, they enable us to carry out a more
accurate statistical analysis of the correlation of genes and
pseudogenes. Table 2 shows the Spearman correlation
of the family size between the five mammalian genomes
in our study. It shows that protein family size has an
obviously stronger correlation (?0.81) among species
than pseudogene family size (?0.63). It also shows that
the correlation of pseudogene family size decreases when
the evolutionary distance increases between the species.
For example, human has a correlation of 0.89 with chim-
panzee, but only around 0.58 with dog, mouse and rat.
Similarly, mouse has a correlation of 0.67 with rat, but
only around 0.58 with human, chimpanzee and dog. It
supports the theory that pseudogenes in general are evolv-
ing under no or less selection pressure relative to func-
The enrichment results (see Supplementary Table S1)
show that families with housekeeping proteins, such as
the GAPDH protein (a NAD-binding enzyme involved
in glycolysis and glyconeogenesis), and the ribosomal
protein RPL7A (responsible in mRNA-directed protein
synthesis in all organisms) (14) have significantly more
parent proteins than others. In order to investigate
whether proteins having housekeeping functions tend to
have more pseudogenes than those with nonhousekeeping
functions, we downloaded a total of 575 human house-
keeping genes derived from gene expression profiling
(23,24). We selected all the 197 pseudogene families that
contain both the housekeeping and nonhousekeeping
genes, and tested the pseudogene-to-gene ratio between
these two types of genes using a Wilcoxon signed rank
keeping genes is significantly higher (P-value<0.04) than
for nonhousekeeping genes in such pseudogene families,
especially in processed pseudogenes (P-value<0.01). It
has also been reported previously by Gonclaves et al. (25)
that housekeeping genes generally have more processed
pseudogenes. This could be explained by the relatively
constant expression level of housekeeping genes, which
boosts their chances of being retrotranscribed.
With the tools, statistics and ontology provided by
Pseudofam, we can analyze pseudogenes from a different
perspective and integrate pseudogene families with other
related datasets to better understand the genome remodel-
ing processes. For example, both pseudogenes and SDs
represent duplicated regions of the genome; hence, by ana-
lyzing the presence of pseudogenes located in SDs, some
precious clues about the generation processes of pseudo-
gene and SD formation can be obtained (26). It was
reported recently by Zheng (22) that in humans, SDs are
more enriched with pseudogenes than genes, with 36.8%
pseudogenes located in SDs and 17.8% genes located in
SDs. Since genomic duplications have a destabilizing effect
(26), it makes sense that the SDs are more enriched with
pseudogenes than with genes, because structural varia-
tions in pseudogenes have less impact than in genes.
This trend also reflects in the correlations of pseudogenes
and parent genes of pseudogene families within SDs for
the human genome (see Supplementary Figure S2), where
there is a stronger positive Spearman correlation (0.69)
between the numbers of duplicated pseudogenes in pseu-
dogene families and those located in SDs, than that of
parent genes (0.41).
Supplementary Data are available at NAR Online.
We thank Hongyu Zhao, Rajkumar Sasidharan, Philip
Kim, Joel Rozowsky, Nitin Bhardwaj, Deyou Zheng
and Rebecca Robilotto for their comments on the article,
technical assistance and helpful discussion. We would also
like to extend our thanks for the technical help from Bert
Overduin at Ensembl.
Table 1. Numbers of protein and pseudogene families in different
species out of 9318 PfamA families
Homo sapiens (HS)
Pan troglodytes (PT)
Canis familiaris (CF)
Mus musculus (MM)
Rattus norvegicus (RN)
Anopheles gambiae (AG)
Gallus gallus (GG)
Drosophila melanogaster (DM)
Danio rerio (DR)
Caenorhabditis elegans (CE)
The number of protein families represents the total number of families
that each has at least one protein in the species. The number of pseu-
dogene families is a subset of the previous number representing the
total number of protein families with at least one pseudogene.
Table 2. Spearman’s rank correlation of protein family sizes (the upper
right) and pseudogene family sizes (the lower left) between different
Nucleic Acids Research, 2009, Vol. 37, Databaseissue
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