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Vol. 12, Issue 2, 272-280, February 2002
Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, Connecticut 06520-8114, USA
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ABSTRACT |
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 TOP
 ABSTRACT
 INTRODUCTION
RESULTS AND DISCUSSION
CONCLUSIONS
METHODS
REFERENCES
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We have developed an initial approach for annotating and surveying pseudogenes in the human genome. We search human genomic DNA for regions that are similar to known protein sequences and contain obvious disablements (i.e., mid-sequence stop codons or frameshifts), while ensuring minimal overlap with annotations of known genes. Pseudogenes can be divided into "processed" and "nonprocessed"; the former are reverse transcribed from mRNA (and therefore have no intron structure), whereas the latter presumably arise from genomic duplications. We annotate putative processed pseudogenes based on whether there is a continuous span of homology that is >70% of the length of the closest matching human protein (i.e., with introns removed), or whether there is evidence of polyadenylation. We have applied our approach to chromosomes 21 and 22, the first parts of the human genome completely sequenced, finding 190 new pseudogene annotations beyond the 264 reported by the sequencing centers. In total, on chromosomes 21 and 22, there are 189 processed pseudogenes, 195 nonprocessed pseudogenes, and, additionally, 70 pseudogenic immunoglobulin gene segments. (Detailed assignments are available at http://bioinfo.mbb.yale.edu/genome/pseudogene or http://genecensus.org/pseudogene.) By extrapolation, we predict that there could be up to ~20,000 pseudogenes in the whole human genome, with a little more than half of them processed. We have determined the main populations and clusters of pseudogenes on chromosomes 21 and 22. There are notable excesses of pseudogenes relative to genes near the centromeres of both chromosomes, indicating the existence of pseudogenic "hot-spots" in the genome. We have looked at the distribution of InterPro families and Gene Ontology (GO) functional categories in our pseudogenes. Overall, the families in both processed and nonprocessed pseudogene populations occur according to a similar power-law distribution as that found for the occurrence of gene families, with a few big families and many small ones. The processed population is, in particular, enriched in highly expressed ribosomal-protein sequences (~20%), which appear fairly evenly distributed across the chromosomes. We compared processed pseudogenes of different evolutionary ages, observing a high degree of similarity between "ancient" and "modern" subpopulations. This may be attributable to the consistently high expression of ribosomal proteins over evolutionary time. Finally, we find that chromosome 22 pseudogene population is dominated by immunoglobulin segments, which have a greater rate of disablement per amino acid than the other pseudogene populations and are also substantially more diverged.
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INTRODUCTION |
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TOP
ABSTRACT
INTRODUCTION
RESULTS AND DISCUSSION
CONCLUSIONS
METHODS
REFERENCES
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Pseudogenes are disabled copies of genes that do
not produce a functional, full-length copy of a protein (Mighell et al.
2000
; Vanin 1985). They are of two types: First, processed pseudogenes result from reverse transcription of messenger RNA transcripts followed
by reintegration into genomic DNA (presumably in germ-line cells) and
subsequent degradation with disablements (premature stop codons and
frameshifts) (Vanin 1985). Second, nonprocessed pseudogenes result from
duplication of a gene, followed by an initial disablement if the
duplicated copy is not "useful" (Mighell et al. 2000). These then
also accumulate further coding disablements.
The extent of the pseudogene population in the human genome is unclear.
Estimates for the number of human genes range from ~22,000 to
~75,000 (Crollius et al. 2000; Ewing and Green 2000; Lander et al.
2001; Venter et al. 2001; Wright et al. 2001). From previous reports,
it is thought that up to 22% of these gene predictions may be
pseudogenic (Lander et al. 2001; Yeh et al. 2001). It is important to
characterize the human processed and nonprocessed pseudogene
populations as their existence interferes with gene identification and
prediction (particularly nonprocessed pseudogenes or individual
pseudogenic exons). They are also an important resource for the study
of the evolution of protein families (see, e.g., studies on the human
olfactory receptor subgenome [e.g. Glusman et al. 2001]).
Here, we have performed a detailed analysis of the pseudogene
populations of human chromosomes 21 and 22, which have been sequenced
contiguously to high quality. This is similar in spirit to previous
surveys we have performed on pseudogenes and other genomic features in
other organisms (Harrison et al. 2001; Gerstein 1997, 1998; Hegyi and
Gerstein 1999). We have examined the main populations and clusters of
pseudogenes for the two chromosomes. Patterns of distribution of both
nonprocessed and processed pseudogenes indicate the existence of
pseudogenic hot-spots in the human genome. In addition, we have
estimated the total numbers and proportions of processed and
nonprocessed pseudogenes in the whole human genome.
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RESULTS AND DISCUSSION |
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TOP
ABSTRACT
INTRODUCTION
RESULTS AND DISCUSSION
CONCLUSIONS
METHODS
REFERENCES
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We annotated both processed and nonprocessed pseudogenes, as described in Figure 1 and the Methods section. The numbers of processed and nonprocessed pseudogenes that we find are summarized in Table 1. As shown in Figure 1, there are 60 Sanger pseudogene annotations in excess of those pseudogenes that we find for chromosome 22, and 20 Riken pseudogene annotations in excess for chromosome 21.
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Processed Pseudogenes on Chromosomes 21 and 22
We find a total of 189 processed pseudogenes (77 on chromosome 21, 112 on chromosome 22) (Table 1). The total for chromosome 22 is a combination of our own annotations and Sanger Center annotations for pseudogenes, whereas the total for chromosome 21 is a combination of our annotations and those obtained from the Riken genome-sequencing center. The number of processed pseudogenes for chromosome 22 relative to chromosome 21 is rather high (proportion = 112/77 ~1.45). When we remove the additional Riken and Sanger Center pseudogenes, the density of processed pseudogenes is still moderately higher for chromosome 22 relative to 21 (proportion = 83/65 ~1.30). The different numbers of processed pseudogenes for the two chromosomes is intriguing and may be related to the accessibility of genomic DNA for reintegration of a processed sequence, with chromosomes with more genes having more accessible genomic DNA because of transcriptional activity.
Non-Processed Pseudogenes on Chromosomes 21 and 22
For the counting of nonprocessed pseudogenes, we set aside the 70 
and 
immunoglobulin gene segments on chromosome 22 as a separate
population. We then have a total of 195 nonprocessed pseudogenes (72 on
chromosome 21, 123 on 22). Considering all annotations, the number of
nonprocessed pseudogenes on chromosome 22 relative to the number on
chromosome 21 is higher (proportion = 123/72 ~1.71). As described
above for processed pseudogenes, when those pseudogenes that arise only
from Riken and Sanger Center annotations are excluded, the ratio of
pseudogene numbers between the two chromosomes is more modest
(proportion ~1.28), reflecting to less of an extent the corresponding
relative gene density between the chromosomes (~2.13-2.24 for all
sets of gene annotations).
Extrapolation to the Whole Human Genome
Based on our numbers of pseudogenes for chromosomes 21 and 22, we can tentatively extrapolate to derive estimates of the pseudogene numbers in the whole human genome.
Using the total number of processed pseudogenes for either chromosome
22 or 21, we estimate the total number of processed pseudogenes in the
human genome (see Table 1 footnote). The predicted ranges are
~8700-9400 (based on chromosome 22 data) and ~6100-6600 (chromosome 21) processed pseudogenes in the whole human genome. (The
lower number in the range arises from using the total human genome size
given by Lander et al. 2001; the higher from the size given by Venter
et al. 2001.)
Also, as for processed pseudogenes, we estimate a predicted range of
~9600-10,400 nonprocessed pseudogenes in the human genome, extrapolating from the chromosome 22 data. Using the gene-poor chromosome 21, a much lower estimate is obtained (~5700-6200). Arguably, we would expect more of a relationship between nonprocessed pseudogene density and gene density than between processed pseudogene density and gene density, as the former type of pseudogenes arises from
duplication of the genomic DNA. One could modify such estimates to
account for lower gene density on chromosome 22 than on other human
chromosomes (Dunham et al. 1999a; Lander et al. 2001; Venter et al.
2001), so the number of nonprocessed pseudogenes in the whole human
genome may be even higher. However, as noted above, disregarding the
and immunoglobulin variable-region gene segments, there does
not seem to be a clear relationship between gene density and
nonprocessed pseudogene density for these chromosomes.
Overlap with Known Sequencing Center (Riken/Sanger) Genes
During our pseudogene annotation procedure, we find that some
potential pseudogenes overlap known genes; this overlap may be due to
sequence alignment artifact or to a real phenomenon of discarded
fragments of disabled protein homology near the extant parts of genes.
As part of our assignment procedure, the allowed percentage of known
gene exons overlapped by our pseudogene annotations is <5% (Methods
section). Known genes are those labeled as "known" in either the
Sanger or Riken annotations, that is, having a previously characterized
genomic structure. For exons of known Sanger genes, this percentage of
overlapped exons is 3.3%, and for Riken known gene exons, it is 2.6%.
Similar levels of this overlap for exons (~3%) are found for all
other (predicted) gene annotations from the Riken and Sanger centers
(Hattori et al. 2000; Dunham et al. 1999a).
Overlap with Genes Predicted by GenomeScan
Genes predicted by the program GenomeScan (Yeh et al.
2001) were studied as a larger and more uniformly predicted set of
genes than the gene annotations available from the Sanger and Riken
centers. We examined the overlap of the pseudogene data sets with genes
predicted by GenomeScan (Table 1). For the
GenomeScan-predicted exons, on 21 and 22, there is only
5.2% and 6.2% overlap, respectively, of exons with pseudogenes.
Main Populations and Clusters of Chromosome 21 and 22 Pseudogenes
We now focus on the main populations and clusters of pseudogenes on
chromosomes 21 and 22. To aid with our characterization of these, we
determined the prevalence of InterPro motifs (Apweiler et al. 2000) in
the pseudogene sets and used these to assign GO functional classes
(Ashburner et al. 2000) for the processed and nonprocessed pseudogene
populations (Methods section).
Power-Law Behavior of the Occurrence of Families for Pseudogenes
We examine the distribution of InterPro protein families in the processed and nonprocessed pseudogenes. For the overall groups of processed pseudogenes, nonprocessed pseudogenes and total pseudogenes, there is a power-law relationship between the number of InterPro families and the size of a family (the number of members in a family) (Fig. 2), if one removes the outliers that are labeled. These outliers are the zinc finger motif, which occurs in multiple copies (of up to 12) in a sequence, and the collagen triple helix repeat, which also occurs multiple times for the same sequence. There is also a point on the plot for the immunoglobulin (Ig) domain (which occurs in the Ig variable-region gene segments). That is, the trend can be fitted to a straight line when plotted on a log-log scale. This relationship has also been observed for genes in eukaryotes and other features of genomes (J. Qian et al. 2001). As described in the
Methods section, we used the InterPro family assignments for the
pseudogenes to assign a GO functional class when possible. However, the
distribution of GO functional classes in the pseudogenes does not show
the same sort of linearity on a log-log plot (data not shown).
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The Largest Group of Processed Pseudogenes Is in the Ribosomal Class
Based on GO function classifications, the most common group of proteins in the processed pseudogenes is the ribosomal proteins, comprising 22% (42 out of 189 from GO classification) (Fig. 3). Over half of these are from the large subunit of the ribosome (60%, 25/42). This is close to the proportion of large subunit ribosomal proteins in the human ribosome (57%), implying that ribosomal proteins are evenly sampled for processed pseudogene formation. As a fraction of the estimates for the overall number of processed pseudogenes in the human genome (Table 1), this means there may be >1800 ribosomal-protein processed pseudogenes in the complete genome. In comparison, for ribosomal protein genes, there is only one actual gene for a ribosomal protein (RPL3) on chromosome 22, and no ribosomal protein gene on chromosome 21 (Uechi et al. 2001). Interestingly, there is a nonprocessed pseudogene for the
ribosomal protein RPL17, on chromosome 22 (named bK440B3.1), although the "live" homolog for this pseudogene is on chromosome 18. The processed pseudogenes for the ribosomal proteins appear to be
somewhat evenly distributed on the chromosomes (Fig.
4), although the sample is
rather small for the two chromosomes surveyed, at the moment.
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Ancient and Modern Processed Pseudogenes
We divided the processed pseudogene population by age into approximately equal-sized groups of ancient and modern processed pseudogenes using the median percentage identity value (which is 79%). This is based on the similarity of the pseudogenes to the closest matching human protein in the Ensembl database (Birney et al. 2001).
Ancient pseudogenes have <79% sequence identity (see Fig.
5) to their closest matching human protein.
The remainder comprise modern pseudogenes. We examined the ancient and
modern processed pseudogene populations for their prevalent GO
functional classes (Methods section). Ancient and modern processed
pseudogenes do not have any overwhelmingly obvious prevalences; the
"ribosomal structural protein" functional class dominates both
sets. Transcription factors and other DNA-binding proteins tend to be
in the ancient category (Fig. 3).
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Main Nonprocessed Pseudogene Populations
We examined the nonprocessed pseudogene populations for chromosomes 21 and 22 for their prevalent functional classes and compared them with the classes for genes predicted using GenomeScan (Yeh et al. 2001). The total number of InterPro motif assignments and
consequently GO-class assignments is much smaller for the nonprocessed
pseudogenes in comparison with the gene totals and those for processed
pseudogenes (Fig. 3). There is little similarity among all three lists
(genes, processed pseudogenes, and nonprocessed pseudogenes for
chromosomes 21 and 22 combined); the "transcription factor"
functional classes occurs in the top five of all three (processed
pseudogenes, nonprocessed pseudogenes, and genes, Fig. 3). The
"receptor" class is common to the top five of both processed and
nonprocessed pseudogenes. The nonprocessed pseudogenes share three of
their top five functional categories with the top five for the
GenomeScan-predicted genes. On a related note, in general,
a large proportion of the nonprocessed pseudogenes are close to a
homolog along the chromosomes: 28% have at least one homolog within
0.5 Mb and 31% have at least one within 1.0 Mb.
Immunoglobulin Gene Segments
There are a total of 70 and immunoglobulin (Ig)
variable-region pseudogenic gene segments (65 , 5 ) in the
chromosome 22 loci for these gene segments. We find only an additional
two () pseudogenic gene segments relative to those already annotated by the Sanger Center (included in this total). Ig variable-region gene
segments have a higher rate of nonsynonymous substitution in the germ
line relative to synonymous substitution (Nei et al. 1997). We examined
the variable-region Ig pseudogenic gene segments for the total number
of disablements detected relative to the closest matching human protein
sequences from the Ensembl database (Birney et al. 2001). We find a
moderately increased rate of disablements per amino acid relative to
the corresponding overall rate in pseudogenic sequences: 3.3% in Ig
segments (106/3253) relative to 2.5% overall (1704/67965). This
difference is statistically significant (the chance that it would arise
randomly is P <0.002, assuming normal distribution
statistics), and is unaffected by removing the five segments. This
increased rate of disablement is consistent with the increased
nonsynonymous substitution rate for Ig variable-region loci referred to
in the literature (Nei et al. 1997). The nonprocessed pseudogene
population on a whole has a slightly higher rate of disablement than
the processed one, 2.6% (837/32843) versus 2.4% (759/31868). The
degree of identity between the Ig pseudogenic gene segments and their
closest matching Ensembl human protein sequences is also much lower on
average (59.2% [+13.9]) than for either processed or nonprocessed
pseudogenes (72.4% (+20.4) and 75.1% (+19.1), respectively); these
latter two categories also have similar-shaped distributions (Fig. 5).
Pseudogenic Hot-Spots
The density of genes or pseudogenes is defined as their number per
interval of DNA. We have illustrated the trends for the largest
interval for which we obtain any meaningful separation along the
chromosomes (Fig. 4). We searched for the most notable differences in
the pseudogene density and the gene density (either processed and
nonprocessed), where they are observed for both the
GenomeScan genes and the Riken/Sanger complete sets of
gene annotations. We find that the most notable increased density for
both processed and nonprocessed pseudogenes relative to the gene
density is near the centromeres (in the first 5 Mb; difference in
density, 
D > 0.10; Fig. 4). The most notable excess in gene density relative to the pseudogene density is at the telomere of
chromosome 21, where there are few processed pseudogenes
(D = 
0.13); this area contains predicted collagen genes and
nonprocessed pseudogenes. It will be interesting to see if regions of
increased pseudogene density in the absence of increased gene density
or pseudogenic hot-spots can be found on a larger scale in the total human genome. In general, pseudogenes in such regions may be more detectable because they take longer to be degraded; this may occur, perhaps, through local variations in DNA duplication rate relative to
the rate of loss of genomic DNA (Petrov 2001).
The G + C content of genomic DNA is related to gene content, with
G+C-rich regions having elevated numbers of genes relative to G+C-poor
regions (Dunham et al. 1999; Lander et al. 2001; Venter et
al. 2001). There does not appear to be any obvious relationship between
pseudogene content and G + C content that can be readily decoupled
from the known link between gene density and G + C content. On
chromosome 21, the most G + C-poor region, which is between 5 and 12 Mb from the start of the sequence, has low G + C (35%) compared with
the rest of the chromosome (43%), and has low pseudogene content as
well as low gene content (Hattori, et al. 2000); on chromosome 22, the most notable G + C-poor region, the 2 Mb closest to the centromere (<40% G + C; Dunham, et al. 1999), has
elevated pseudogene content relative to gene content (Fig. 5).
Evidently, this topic will be amenable to in-depth study with a larger
data set of pseudogenes derived from the whole human genome.
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CONCLUSIONS |
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TOP
ABSTRACT
INTRODUCTION
RESULTS AND DISCUSSION
CONCLUSIONS
METHODS
REFERENCES
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We have derived a procedure for the assessment of processed and nonprocessed pseudogenes in genomic DNA by looking for disabled protein homologies while minimizing the overlap with known genes; using this, we have predicted the pseudogene populations of chromosomes 21 and 22, finding 180 pseudogenes additional to existing available annotations. Also, we have tentatively extrapolated that there are up to ~9000 processed and ~10,000 nonprocessed pseudogenes in the human genome. Up to 6% of annotated exons in these two chromosomes may be pseudogenic. Based on GenomeScan gene predictions, modified totals for the actual number of genes on chromosomes 21 and 22 are given in Table 1.
Other types of protein-related pseudogenes that are not accounted for in the present work are semiprocessed (pseudo)genes (arising from aberrant mRNAs that contain an intron) and pseudogenes that produce transcripts (but not protein chains). Surveys of the literature by the authors indicate, however, that the occurrence of either of these is relatively rare and is not likely to affect gene prediction significantly. From examination of the distribution of pseudogenes along chromosomes 21 and 22, there is some evidence of the existence of pseudogenic hot-spots; this will remain to be confirmed upon examination of the whole human genome. This study serves as preparation for such a whole-genome survey.
The density of pseudogenes relative to genes derived in this study
seems very high (one processed and one nonprocessed pseudogene for
every ~ four genes), with a total of ~390 found in the 70 Mb of
chromosomes 21 and 22, with >97% noncoding DNA. By comparison, a
moderately-sized complement of > ~1100 verified pseudogenes (corresponding to ~19,000 genes) was found in the 100-Mb worm genome
(Harrison et al. 2001), which has ~70% noncoding DNA. Estimates for
the other eukaryote genomes are at present unavailable, although for
the fly genome (which has 120 Mb of euchromatic DNA with ~80% noncoding DNA), a survey by the authors indicates ~100 pseudogenes (P. Harrison and M. Gerstein, submitted). There appears to
be no obvious relationship between the proteome size or genome size or
the amount of noncoding DNA and the number of pseudogenes for worm,
fly, and human. Contributing factors would include the rate of gene
duplication, the occurrence of transposable elements, and the overall
rate of genomic DNA loss (Petrov 2001). Also, for prokaryotes, there
does not appear to be a clear relationship between the amount of
noncoding DNA and the number of pseudogenes. There are two reported
cases of prokaryotic genomes with high proportions of noncoding DNA:
Rickettsia prowazekii has 24% noncoding DNA and 12 pseudogenes (Andersson et al. 1998); whereas Mycobacterium leprae has 51% noncoding DNA, 27% of which is composed of a
population of 1100 pseudogenes (corresponding to a proteome of 1604 coding sequences) (Cole et al. 2001). Evidently, surveys of more
eukaryote and prokaryote genomes are required to give a fuller picture.
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METHODS |
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TOP
ABSTRACT
INTRODUCTION
RESULTS AND DISCUSSION
CONCLUSIONS
METHODS
REFERENCES
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Determination of a Set of Pseudogenes for Human Chromosomes 21 and 22
We developed an initial scheme for identifying pseudogenes in human genomic DNA; this is depicted as a flow diagram in Figure 1. Genomic annotation is an inherently dynamic process in which it is necessary to make use of many different sources of data (represented by ovals in the flow diagram), which are not updated in a concerted fashion. (Detailed files listing our assignments are available at http://bioinfo.mbb.yale.edu/genome/pseudogene and http://genecensus.org/pseudogene.)
Six-Frame BLAST
Using the BLAST alignment package (Altschul et al. 1997) with repeats masked using RepeatMasker (Bedell et
al. 2000), we compared the SWISSPROT database of protein sequences (version 40) (Bairoch and Apweiler 2000) with the complete
available sequences of human chromosomes 21 and 22 in six frames
(Dunham et al. 1999b; Hattori et al. 2000). Chromosome 21 was
downloaded from GenBank on August 22, 2000; chromosome 22 is the May 9, 2000, version). We then took all of the significant protein matches to
the genomic DNA (e-value < 1 × 10-4), and reduced them
for mutual overlap by picking matches in decreasing order of
significance and deleting any matches that overlap substantially with a
picked match (i.e., more than 10 amino acids).
FASTA Realignment
For each match, we then realigned the matching SWISSPROT sequence to the same region of genomic DNA using the FASTA program (Pearson et al. 1997), expanded on either side by the length of the
matching sequence (in nucleotides). From these alignments, we picked
any matching sequences that had more than one disablement (either a
frameshift or a premature stop codon) as a potential pseudogene. At
this stage, these potential sequences were filtered for low complexity
by comparing with the same SWISSPROT database but using masking with
SEG (settings "25 3.0 3.3" and "45 3.4 3.75") (Wootton and
Federhen 1996).
Minimize Overlap with Known Genes
To ensure that we are not considering disablements at the end of alignment subsequences that are artifactual, we examined how these potential pseudogenic sequences overlapped with the known genes on human chromosome 22 (Dunham et al. 1999a). We calculated the position
of the disablement that is nearest the middle of each potential
pseudogenic sequence and the distance (d) from this particular
disablement to the closest end of the sequence. We then determined an
appropriate margin (m) for the ends of the pseudogenic
sequences, which allows us to discard pseudogenes so that the total set
of pseudogenes overlaps only a small proportion (<5%) of the set of
known gene exons. Following this criterion, all pseudogenes were
discarded if d < m, where m = 16
residues. This gave us a total of 3.3% of all known gene exons that
are overlapped by our set of pseudogene predictions.
Merging
At this stage, the pseudogene predictions for chromosome 22 were merged with previous pseudogene predictions provided by the Sanger Center (Dunham et al. 1999b); those for chromosome 21 were merged with
data downloaded from the Riken sequencing Center Web site (Hattori et
al. 2000). Where a predicted pseudogene from the present
data set was duplicated by a Riken or Sanger Center pseudogene, the
Riken/Sanger Center annotation is chosen in preference. The nature of
the overlap of the data sets is shown in box 4 of the flow diagram
(Fig. 1).
Date by Closest Match Ensembl Protein
For each pseudogene sequence, we searched through the most current version of the Ensembl database (http://www.ensembl.org; [Birney et al. 2001]) of human coding sequences to find the closest known human
sequence homolog. If a matching sequence from Ensembl was found (only
84% of Sanger Center annotations have a corresponding Ensembl database
protein; 92% of our own pseudogenes have a match), each matching
Ensembl sequence was then realigned, using the FASTA program (Pearson
et al. 1997), to the region of genomic DNA corresponding to the
pseudogene sequence but expanded on either side by the length of the
Ensembl sequence (in nucleotides). Any pseudogene that was lengthened
as a result of this realignment was replaced with the new
Ensembl-derived sequence, and the new updated list of pseudogenes was
reduced for overlap, as above. We then rechecked through the complete
set of pseudogenes for overlap with known genes as described previously.
Assess for Processing
We inspected the genomic DNA around the potential pseudogenes for any evidence of exon structure from existing Riken or Sanger Center gene and pseudogene parsing, from gene annotations made using the program GenomeScan (Yeh et al. 2001), or from evidence of
exon structure from our own pseudogene analysis. For this third option,
we considered an intron to occur if the gap in the sequence was >126
nt (from inspection of the distribution of intron lengths for known
genes of chromosome 22, only 5% of introns would be shorter than
this). From our visual curation, we made a decision as to whether the
predicted pseudogene fragment was part of a larger exon structure. We
also checked for pseudogenic duplications of any single-exon genes.
For any potential pseudogene that does not have such evidence of exon
structure, we used two lines of evidence for assessing whether it was
processed:
| (1) | We labeled as candidate-processed pseudogenes all those matches that
comprise >70% of the length of the closest-matching human Ensembl or
SWISSPROT database sequence in a continuous segment. This criterion was
used previously by Venter et al. (Venter et al. 2001). We checked over
the list for matches to known single-exon human genes (which, for
example, comprise ~6% of the known genes on chromosome 22), that do
not have any evidence for processing from the analysis for a
polyadenine tail (see below).
|
| We checked the utility of this
criterion for a set of 46 previously identified primate processed
pseudogenes that we collated from Genbank (Benson et al. 2000) in
August 2000. Almost all of these processed pseudogenes (42/46, 91%)
were detected by this criterion.
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| (2) | Recently processed pseudogenes can be identified through the existence
of a polyadenine tail of at least 15-20 nt that is preceded by a
polyadenylation signal (AATAAA), usually about 15-20 nt upstream. We
searched a 1000-nt region that was 3' to the pseudogene homology
segment, with a sliding window of 50 nt for a region of elevated
polyadenine content (>30 nt), and picked the most adenine-rich 50-nt
segment as the most likely candidate. An interval of 1000 nt was used because
of the possible existence of 3'-untranslated regions (3'-UTRs);
90% of 3'-UTRs are of length <942 nt (Makalowski et al. 1996). In
addition, we searched in the same 1000-nt region for candidate AATAAA
polyadenylation signals and checked whether they were upstream to the
candidate polyadenine tail site. When a polyadenylation signal was
found within 50 nt upstream of the candidate polyadenine tail, the
pseudogene is labeled as a "class 1" candidate processed pseudogene
and "class 2" if the signal is found between 51 and 100 nt
upstream. The latter class may arise if there is a genomic DNA
insertion event; in reality, there are very few of them found (eight in
total). All other pseudogenes with a detected candidate polyadenine
tail are labeled as "class 3." This last class (exclusively) only
accounts for 17% of the candidate-processed pseudogenes; their removal
does not change the main results and trends reported in the paper.
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Analysis for Protein Function
Each pseudogene sequence was run through the InterPro sequence
motif assignment package InterProScan (Apweiler et al.
2000). Functional categories were then assigned using the GO
classification (Ashburner et al. 2000) with a list of correspondences between InterPro motifs and GO functional classes that is available from the InterPro Web site (http://www.ebi.ac.uk/interpro). A substantial proportion (~75%) of the pseudogene annotations were assigned to an InterPro motif and ~45% were able to be mapped onto
GO function classifications. The GO categories given by InterPro were
merged into a higher level if they were judged to be too specific, for
example, all "receptors" are merged into one higher GO category.
These proportions are at a level that is within the range of
proportions of proteomes that have automatic reliable functional
assignment in the GeneQuiz database (Hoersch et al. 2000), a well-known
standard of automated functional classification. We did not try to map
pseudogenes that do not have InterPro motifs to the GO classification
because this introduces an extra degree of judgmental bias.
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ACKNOWLEDGMENTS |
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We thank Ru-Fang Yeh and Chris Burge (MIT) for providing GenomeScan gene predictions and Sam Karlin (Stanford) for discussions. M.G. acknowledges support from the NIH Protein Structure Initiative (P50 grant GM62413-01).
The publication costs of this article were defrayed in part by payment of page charges. This article must therefore be hereby marked "advertisement" in accordance with 18 USC section 1734 solely to indicate this fact.
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FOOTNOTES |
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1 Corresponding author.
E-MAIL Mark.Gerstein@yale.edu; FAX (360) 838 7861.
Article and publication are at http://www.genome.org/cgi/doi/10.1101/gr.207102.
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REFERENCES |
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TOP
ABSTRACT
INTRODUCTION
RESULTS AND DISCUSSION
CONCLUSIONS
METHODS
REFERENCES
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an integrated documentation resource for protein families, domains and functional sites.
Bioinformatics
16:
1145-1150[Abstract].Received July 23, 2001; accepted in revised form November 28, 2001.
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 J. L. Rinn, G. Euskirchen, P. Bertone, R. Martone, N. M. Luscombe, S. Hartman, P. M. Harrison, F. K. Nelson, P. Miller, M. Gerstein, S. Weissman, and M. Snyder The transcriptional activity of human Chromosome 22 Genes & Dev., February 15, 2003; 17(4): 529 - 540. [Abstract] [Full Text] [PDF]
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 P. M. Harrison, D. Milburn, Z. Zhang, P. Bertone, and M. Gerstein Identification of pseudogenes in the Drosophila melanogaster genome Nucleic Acids Res., February 1, 2003; 31(3): 1033 - 1037. [Abstract] [Full Text] [PDF]
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J. E. Collins, M. E. Goward, C. G. Cole, L. J. Smink, E. J. Huckle, S. Knowles, J. M. Bye, D. M. Beare, and I. Dunham Reevaluating Human Gene Annotation: A Second-Generation Analysis of Chromosome 22 Genome Res., January 1, 2003; 13(1): 27 - 36. [Abstract] [Full Text] [PDF]
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 S. Karlin, C. Chen, A. J. Gentles, and M. Cleary Associations between human disease genes and overlapping gene groups and multiple amino acid runs PNAS, December 24, 2002; 99(26): 17008 - 17013. [Abstract] [Full Text] [PDF]
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Z. Zhang, P. Harrison, and M. Gerstein Identification and Analysis of Over 2000 Ribosomal Protein Pseudogenes in the Human Genome Genome Res., October 1, 2002; 12(10): 1466 - 1482. [Abstract] [Full Text] [PDF]
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 M. Vallee, F. Guay, D. Beaudry, J. Matte, R. Blouin, J.-P. Laforest, M. Lessard, and M.-F. Palin Effects of Breed, Parity, and Folic Acid Supplement on the Expression of Folate Metabolism Genes in Endometrial and Embryonic Tissues from Sows in Early Pregnancy Biol. Reprod., October 1, 2002; 67(4): 1259 - 1267. [Abstract] [Full Text] [PDF]
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A. M. Roy-Engel, A.-H. Salem, O. O. Oyeniran, L. Deininger, D. J. Hedges, G. E. Kilroy, M. A. Batzer, and P. L. Deininger Active Alu Element "A-Tails": Size Does Matter Genome Res., September 1, 2002; 12(9): 1333 - 1344. [Abstract] [Full Text] [PDF]
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N. Echols, P. Harrison, S. Balasubramanian, N. M. Luscombe, P. Bertone, Z. Zhang, and M. Gerstein Comprehensive analysis of amino acid and nucleotide composition in eukaryotic genomes, comparing genes and pseudogenes Nucleic Acids Res., June 1, 2002; 30(11): 2515 - 2523. [Abstract] [Full Text] [PDF]
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)
g" denotes
"pseudogene." The steps are as follows (described in detail in the
text): (1) Six-frame blast. Comparison of SWISSPROT database to
chromosome 21 and 22 genomic sequences using BLAST
(Altschul et al. 1997
