Nucleic Acids Research Advance Access originally published online on October 28, 2008
Nucleic Acids Research 2009 37(Database issue):D738-D743; doi:10.1093/nar/gkn758
Nucleic Acids Research, 2009, Vol. 37, Database issue D738-D743
Published by Oxford University Press 2008
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/2.0/uk/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
Pseudofam: the pseudogene families database
Hugo Y. K. Lam1,
Ekta Khurana2,
Gang Fang2,
Philip Cayting2,
Nicholas Carriero3,
Kei-Hoi Cheung3,4,5 and
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 and 5Department of Genetics, Yale University, New Haven, CT 06520, USA
*To whom correspondence should be addressed. Tel: +1 203 432 6105; Fax: +1 360 838 7861; Email: Mark.Gerstein{at}yale.edu
Received August 15, 2008. Revised October 4, 2008. Accepted October 6, 2008.
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ABSTRACT
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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 pseudogenes including query tools, statistical summaries
and sequence alignments. The current version of Pseudofam contains
more than 125 000 pseudogenes 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 PseudoPipe pipeline) to identify pseudogenes.
Each identified pseudogene is assigned to its parent protein
family and subsequently aligned to each other by transferring
the parent domain alignments 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 addition,
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 pseudogenes
in families correlates across different species. Overall, they
highlight the fact that housekeeping families tend to be enriched
with a large number of pseudogenes.
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INTRODUCTION
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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 retrotransposition events (for processed pseudogenes). Since proteins in the same family are believed to share a common ancestor giving rise to the shared domain, association of pseudogenes 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 pseudogenes.
A number of experimental and computational 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 pseudogenes using protein families (5,6). However, no study thus far has systematically formalized the pseudogene relationships and presented an integrated analysis of several eukaryotes using a family approach. To this end, this article 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.
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THE PSEUDOFAM WEB SITE
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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 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 parameters
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.
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ASSIGNING PSEUDOGENES TO FAMILIES
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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 291 000 protein sequences
were retrieved from Ensembl (
http://www.ensembl.org; release
48—December 2007) (
7,
8). Each genome and its associated
protein sequences are run through BLAST (
9,
10) to identify all
genomic regions that share sequence similarity with the given
protein sequences. The proteins are divided into groups of queries,
which are processed concurrently to reduce overall runtime,
while the genomes are used as the BLAST database. The results
are then processed using PseudoPipe (
4) to identify potential
pseudogenes. 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.
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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.
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Our current release of Pseudofam contains 3821 protein families
covering all the protein sequences used as input for identifying
the 125 272 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 transferring their parent domain alignments from the Pfam multiple alignments. Within each family, a pseudogene is first aligned to its parent protein and then to its corresponding 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 similarity 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 for download.
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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.
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DESCRIBING PSEUDOGENE FAMILIES USING ONTOLOGIES
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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 understood. However, more and more findings
have demonstrated pseudogenes, as a gene relic, not only facilitate
evolutionary study, but also exhibit substantial interactions
in the genome. They have been shown to play different roles
in the genome remodeling process, including retrotransposition,
duplication and mutation. Recent studies, for example, have
shown that some of the pseudogenes 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 illustrate 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.
An upper ontology
The ontology in Figure 3 shows an upper ontology depicting the pseudogene family and its relationships. It spans across several domains and involves different domain-specific ontologies, such as the Gene Ontology (GO), Protein 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 creation (e.g. retrotransposition and duplication). The third (in light gray) is a part that describes the secondary aspects 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 transcribed (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.
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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.
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Family relationships
Based on the fundamental relationship between protein family
and pseudogene, our ontology also depicts the structural and
functional relationships tying to a pseudogene 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 pseudogene dataset annotated with SD information
obtained from the Human Segmental Duplication database at
http://eichlerlab.gs.washington.edu/database.html.
While 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:
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 structural domain and function that evolved from a common ancestor. As a result, a family approach allows us to analyze pseudogenes by their functional groups and have a better understanding of their roles in genome rearrangement by relating them to other genomic features.
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KEY STATISTICS
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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 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:
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
distribution. 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 negative 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 identification algorithm or parameters
that may cause the number of pseudogenes identified to fluctuate.
The following sections show a brief analysis based on the key
statistics provided by Pseudofam.
Degree of pseudogenization
Table 1 shows the numbers of protein and pseudogene families in different species and their degree of pseudogenization. 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 pseudogenes. 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 pseudogenes. In contrast, the bestrophin protein family, which has 71 proteins, has not been found to have any pseudogenes.
Correlation of family 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 chimpanzee, 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 evolving under no
or less selection pressure relative to functional genes.
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Table 2. Spearman's rank correlation of protein family sizes (the upper right) and pseudogene family sizes (the lower left) between different species
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Extreme families
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 housekeeping 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 test. We found that the
pseudogene-to-gene ratio for housekeeping 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.
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CONCLUSION
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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 remodeling processes. For example,
both pseudogenes and SDs represent duplicated regions of the
genome; hence, by analyzing the presence of pseudogenes located
in SDs, some precious clues about the generation processes of
pseudogene 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 variations 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 pseudogene families and those located
in SDs, than that of parent genes (0.41).
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SUPPLEMENTARY DATA
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Supplementary Data are available at NAR Online.
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FUNDING
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National Institutes of Health Grant number: 5U54HG004555-02;
AL Williams Professorship funds. Funding for open access charges:
National Institutes of Health.
Conflict of interest statement. None declared.
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ACKNOWLEDGEMENTS
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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.
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ABSTRACT
INTRODUCTION
