Genomic evidence for non-random endemic populations of decaying exons from mammalian genes
© Morais and Harrison; licensee BioMed Central Ltd. 2009
Received: 16 February 2009
Accepted: 13 July 2009
Published: 13 July 2009
Functional diversification of genes in mammalian genomes is engendered by a number of processes, e.g., gene duplication and alternative splicing. Gene duplication is classically discussed as leading to neofunctionalization (generation of new functions), subfunctionalization (generation of a varied function), or pseudogenization (loss of the gene and its function).
Here, we focus on the process of pseudogenization, but specifically for individual exons from genes. It is at present unclear to what extent pseudogenization of individual exon duplications affects gene evolution, i.e., is it a random phenomenon, or is it associated with specific types of genes and encoded proteins, and positions in gene structures? We gathered genomic evidence for pseudogenic exons (ΨEs, i.e., exons disabled by frameshifts and premature stop codons), to examine for significant trends in their distribution across four mammalian genomes (specifically human, cow, mouse and rat). Across these four genomes, we observed a consistent population of ΨEs, associated with 0.4–1.0% of genes. These ΨE populations exhibit codon substitution patterns that are typical of an endemic population of decaying sequences. In human, ΨEs have significant over-representation for functional categories related to 'ion binding' and 'nucleic-acid binding', compared to duplicated exons in general. Also, ΨEs tend to be associated with some protein domains that are abundant generally, e.g., Zinc-finger and immunoglobulin protein domains, but not others, e.g., EGF-like domains. Positionally, ΨEs are also significantly associated with the 5' end of genes, but despite this, individual stop codons are positioned so that there is significant avoidance of potential targeting to nonsense-mediated decay. In human, ΨEs are often associated with alternative splicing (in 22 out of 284 genes with ΨEs in their milieu), and can have different parts of their sequence differentially spliced in alternative transcripts. Some unusual cases of ΨEs embedded within 5' and 3' non-coding exons are observed.
Our results indicate the types of genes that harbour ΨEs, and demonstrate that ΨEs have non-random distribution within gene structures. These ΨEs may function in gene regulation through generation of transcribed pseudogenes, or regulatory alternate transcripts.
Natural selection acts on phenotypes arising from a vast range of genomic variations: chromosomal and segmental duplications, local duplications, and smaller insertions, deletions and nucleotide substitutions. Local duplication arises not only for whole genes or multiples of genes, but also for pieces of genes and for individual exons.
A pseudogene (ΨG), in the case of protein-coding genes, is a copy of a gene that has symptoms of protein-coding deficiency [1–6]. Symptoms of protein-coding deficiency include: (i) coding-sequence disablements (frame-shifts and premature stop codons); (ii) neutral codon substitution patterns (that yield values of Ka/Ks, the ratio of non-synonymous to synonymous codon substitutions of ~1.0); (iii) protein domain truncations ; (iv) mutation of deeply-conserved residue positions essential for protein function or structural integrity . Processed pseudogenes are made by reverse transcription and re-integration into the genome, and have been extensively studied elsewhere [1–6]. Non-processed pseudogenes can arise after local or segmental gene duplication, and subsequent loss of protein-coding ability through mutation. A similar situation can arise within an individual gene structure: one or more exons can become duplicated within the vicinity of a gene. Such partial gene duplications may then lose coding ability, becoming pseudogenic exons (ΨEs), in a similar way.
Here, we have gathered genomic evidence for the distribution of pseudogenic exons (ΨEs) in the chromosomal milieu of annotated genes of four mammals with high-coverage genome assemblies and extensive transcriptional validation (human, cow, mouse and rat). Such ΨEs can have a functional role. For example, recently it has been described that ΨEs with stop codons that are alternatively spliced can target messenger RNAs to nonsense-mediated decay (NMD), in a way that causes changes in expression levels for other transcripts from the gene . In our analysis, we define ΨEs specifically using coding-sequence disruptions (i.e., frameshifts and premature stop codons). We find a non-random distribution of ΨEs in each mammalian genome, associated with certain subtypes of genes and positions within genes.
Results and discussion
We focused on four mammalian genome assemblies with high (>7X) coverage (human, cow, mouse and rat), to analyze the extent of the occurrence of ΨEs. We examined for significant trends in the distribution of ΨEs for a variety of properties. In particular, we focussed on assessing the peculiarities of the ΨEs in comparison to the general population of duplicated exons. We analyzed the following: (i) divergence from designated parent exons; (ii) association with protein families; (iii) association with Gene Ontology functional categories; (iv) position of ΨEs with respect to the intron-exon structure of the gene; (v) participation in alternative splicing, and (vi) coding-sequence selection pressures, as judged by Ka/Ks values.
Summary of the annotations
- 5' half
- 3' half
- Opposite strand
Number of ΨEs that would lead to NMD targeting
- Orthologs and the Gene Order test
(i) Divergence from designated parent exons
(ii) Association with protein families
Some gene families spawn large numbers of pseudogenes. Examples include olfactory receptors , ribosomal-protein genes , ABC transporters , and heat shock proteins . We noted previously that the gene families with the most non-processed pseudogenes tend to be involved in some form of interaction with the environment , e.g. through roles in immunity , chemosensation [1, 15], or small-molecule transport . Such gene families can also be linked to recent segmental duplications in mammals . Here, we examined which are the most common protein domain families in the ΨE and DE data sets (Additional File 2). These numbers indicate the number of exons with at least one copy of each protein domain considered. Exons containing zinc-finger domains and immunoglobulin-like domains are consistently in the top five most abundant for both ΨEs and DEs. Genes for zinc-finger-containing (ZFC) proteins have undergone lineage-specific expansions over the course of mammalian evolution, so decaying ZFC exons are an expected consequence of this, and could perform regulatory roles as part of transcribed pseudogenes . Transcribed pseudogenes have recently been shown to regulate the expression of homologous genes through the formation of small, interfering RNAs [18, 19]. Immunoglobulin-like domains are used in many proteins that are involved in various aspects of immunity, and have been previously noted to generate large numbers of pseudogenes . The most notable difference between ΨEs and DEs in general, is that ΨEs rarely arise that contain EGF-like (epidermal growth factor-like) domains, whereas these exons are consistently abundant, generally (significant difference, P < 0.05, binomial statistics; Additional File 2). EGF-like domains have expanded greatly in number over the course of mammalian evolution, and are found (with a small number of exceptions) either in the extracellular part of transmembrane proteins or in secreted proteins [20, 21].
(iii) Association with Gene Ontology functional categories
Most common Gene Ontology functional categories †
All genes (Total = 31524)
ΨEs (Total = 284)
DEs (Total = 1341)
GO:0005515, protein binding (5864)
GO:0043167, ion binding (92)*§
GO:0005515, protein binding (372)
GO:0043167, ion binding (3861)
GO:0003676, nucleic acid binding (74)*§
GO:0043167, ion binding (349)*
GO:0003676, nucleic acid binding (3251)
GO:0005515, protein binding (34)
GO:0003676, nucleic acid binding (176)
GO:0016787, hydrolase activity (2053)
GO:0016740, transferase activity (18)
GO:0016787, hydrolase activity (105)
GO:0000166, nucleotide binding (1992)
GO:0004872, receptor activity (13)
GO:0004872, receptor activity (84)
GO:0004872, receptor activity (1765)
GO:0000166, nucleotide binding (12)
GO:0000166, nucleotide binding (57)
GO:0016740, transferase activity (1631)
GO:0016491, oxidoreductase activity (11)§
GO:0016740, transferase activity (36)
GO:0016491, oxidoreductase activity (723)
GO:0016787, hydrolase activity (10)
GO:0030246, carbohydrate binding (35)*
GO:0015075, ion transporter activity (541)
GO:0030246, carbohydrate binding (6)
GO:0005201, extracellular matrix structural constituent (22)*
GO:0008289, lipid binding (420)
GO:0046906, tetrapyrrole binding (3)
GO:0004857, enzyme inhibitor activity (21)
All genes (Total = 28390)
ΨEs (Total = 209)
DEs (Total = 1079)
GO:0005515, protein binding (5553)
GO:0043167, ion binding (55)*
GO:0005515, protein binding (374)*
GO:0043167, ion binding (3672)
GO:0003676, nucleic acid binding (43)*
GO:0043167, ion binding (321)*
GO:0003676, nucleic acid binding (3382)
GO:0004872, receptor activity (27)§
GO:0003676, nucleic acid binding (173)
GO:0004872, receptor activity (2779)
GO:0016787, hydrolase activity (16)
GO:0016787, hydrolase activity (114)
GO:0016787, hydrolase activity (2260)
GO:0005515, protein binding (14)
GO:0004872, extracellular matrix (109)
GO:0000166, nucleotide binding (2061)
GO:0016491, oxidoreductase activity (9)
GO:0000166, nucleotide binding receptor activity (91)
GO:0016740, transferase activity (1805)
GO:0004857, enzyme inhibitor activity (7)
GO:0016491, oxidoreductase activity (911)
GO:0000166, nucleotide binding (7)
GO:0016740, transferase activity (51)
GO:0015075, ion transporter activity (598)
GO:0046906, tetrapyrrole binding (6)
GO:0030246, carbohydrate binding (41)*
GO:0008289, lipid binding (401)
GO:0016740, transferase activity (5)
GO:0005201, structural constituent (40)*
GO:0016491, oxidoreductase activity (25)
All genes (Total = 27302)
ΨEs (Total = 218)
DEs (Total = 993)
GO:0005515, protein binding (2732)
GO:0043167, ion binding (23)
GO:0043167, ion binding (158)*
GO:0043167, ion binding (2238)
GO:0016740, transferase activity (16)§
GO:0005515, protein binding (155)*
GO:0004872, receptor activity (2063)
GO:0003676, nucleic acid binding (15)
GO:0003676, nucleic acid binding (74)
GO:0003676, nucleic acid binding (1720)
GO:0004872, receptor activity (14)
GO:0016787, hydrolase activity (62)
GO:0000166, nucleotide binding (1406)
GO:0005515, protein binding (12)
GO:0000166, nucleotide binding (46)
GO:0016787, hydrolase activity (1331)
GO:0016787, hydrolase activity (12)
GO:0004872, receptor activity (37)
GO:0016740, transferase activity (1179)
GO:0000166, nucleotide binding (10)
GO:0016740, transferase activity (29)
GO:0016491, oxidoreductase activity (594)
GO:0016491, oxidoreductase activity (6)
GO:0016491, oxidoreductase activity (16)
GO:0015075, ion transporter activity (392)
GO:0046906, tetrapyrrole binding (5)
GO:0030246, carbohydrate binding (16)
GO:0003735, structural constituent of ribosome (284)
GO:0030246, carbohydrate binding (4)
GO:0005201, extracellular matrix structural constituent (14)*
The top ten human DEs and ΨEs GO terms do not differ greatly from each other, in each of the species studied. However, each organism has distinct significant over-representations of GO terms. In the human genome, 'Ion binding' and 'Nucleic acid binding' are significantly over-represented in ΨEs, compared to DEs (Table 2). This overrepresentation appears to be chiefly due to ZFC transcription factors, which are obviously candidates for regulation through unproductive splicing and translation, or through the formation of regulatory transcribed pseudogenes. In mouse, 'receptor activity' is significantly over-represented in ΨEs compared to DEs, and 'transferase activity' in rat. These indicate that different types of gene have undergone pseudogenic exon formation in recent evolutionary time in each of these three organisms.
(iv) Position of ΨEs with respect to the intron-exon structure of the annotated gene
Position of ΨEs in related with their parents
Number of ΨE 5' to parent
Number of ΨE beyond the 5' end of the gene
Number of ΨE 3' to parent
Number of ΨE beyond the 3' end of the gene
A key issue in examining the distribution of stop codons in ΨEs, is whether they would produce transcripts that are susceptible to nonsense-mediated decay (NMD). We examined for individual stop codons in the ΨEs that would lead to NMD targeting (Table 1). The number of such stop codons in ΨEs that would lead to NMD is significantly smaller than what is expected by chance (P < 0.01, using χ2 test), in human and cow, but not in the two rodent genomes. The expected distribution in this case, is calculated from the total size of the gene introns divided appropriately, given the position of the stop codons in each ΨE. This indicates a selection pressure in human and cow, against the positioning of individual stop codons in ΨEs in places that would cause NMD. It has been shown that alternative splicing can be coupled to NMD to regulate the expression of other transcripts from a gene . This mechanism has been dubbed regulated unproductive splicing and translation . There may therefore be a selection pressure against placement of stop-codon-bearing exons in some genes, so that they are not affected by this mechanism.
We curated on the human ΨE data, to search for unexpected positional distributions in genes. In human, forty-five ΨEs were found embedded in an untranslated region (UTR). These UTR-embedded ΨEs are not highly conserved. Only eight of them are also found in chimp and rhesus (four in each species), and none of them are shared by the three primate species simultaneously. None of the embedded ΨEs is conserved in a non-primate species (cow, dog, mouse or rat). This is despite syntenic conservation of 28 out of the 45 genes in a non-primate species involved in the embedding, when manually compared in the UCSC Genome browser . It is possible that these UTR-embedded ΨEs are remnants of overlapping gene arrangements. The manner of overlap for overlapping gene pairs changes very dynamically over evolution; for example, only 95 out of 255 human overlapping gene pairs were reported to be conserved as overlapping pairs in the mouse genome .
(v) Participation in alternative splicing
Alternative splice products containing premature stop codons can be degraded through nonsense-mediated decay (NMD), and consequently cause altered expression of protein-coding transcripts through changes in abundance of splicing factors . We examined whether any ΨEs have been annotated as part of alternative splicings. To do this, we cross-referenced the ASD alternative splicing database  'splicing event' annotation, with our ΨE list from the human genome. Of the human 284 genes that harbour a ΨE in their genomic milieu, 101 are present in the ASD alternative splicing database. Out of these, we found 22 genes (entailing 59 transcripts) with evidence of transcription of a ΨE as an alternative exon. Analyzing the alternatively-spliced forms in detail, we found four cases of an unusual topology of splicing (Additional File 3). These four human ΨEs can be differentially spliced in a topologically novel manner, in which one portion of a ΨE is recruited in one splice form, while a different portion of it can take part in another splice form (Additional File 3).
(vi) Ka/Ks analysis
We gathered genomic evidence to assess for non-random distribution of pseudogenic exons (ΨEs) in four mammalian genomes. We observed endemic populations of decaying exons consistently across genomes, arising for up to ~1% of genes. These ΨEs were defined using coding-sequence disablements (frameshifts and premature stop codons). Of course, other pseudogenic exons may exist (such as those arising from initial disablement of splicing signals); however, such pseudogenic exons would be likely to acquire coding-sequence disablements rapidly, soon after their initial disablement.
The pseudogenic exons (ΨEs) are longer than duplicated exons in general, are associated with genes encoding specific protein domain families, such as zinc-finger-containing proteins, and are noticeably lacking for genes containing domains that are otherwise abundant, such as EGF-like domains. The ΨEs also demonstrate species-specific over-representation of GO functional categories relative to duplicated exons in general; for example, in human, GO functional categories for 'ion-binding' and 'nucleic acid binding' are significantly over-represented, compared to duplicated exons generally. The ΨE populations indicate the sorts of genes that have undergone exon decay in recent mammalian evolution (recent enough, and in large enough amounts, for them not to be deleted from the genomic DNA). We find statistical evidence for selection pressure on avoidance of stop codon placements in ΨEs that would lead to nonsense-mediate decay. In addition, we find some interesting positionings of ΨEs in gene structures, such as embedding in UTRs, or partial alternative splicing. The ΨE populations are a potential resource for the formation of transcribed pseudogenes, which can function in the regulation of homologous genes through formation of small, interfering RNAs [18, 19, 30]. They may also be involved in alternative transcripts that have a regulatory function . The annotated ΨEs that we have analysed will be a fertile source for study using large-scale micro-array expression techniques for these two potential regulatory functions. Also, the ΨE data sets will be useful for further gene evolution study in mammals. The data are available from the authors at http://biology.mcgill.ca/faculty/harrison/.
The genome sequences and annotation files of four mammals analyzed in this paper (human, mouse, rat, and cow) were downloaded from the Ensemble Web site http://www.ensembl.org, in February 2007. The genome assemblies are: human = Homo_sapiens.NCBI36.43; mouse = Mus_musculus.NCBIM36.43; rat = Rattus_norvegicus.RGSC3.4.43; cow = Bos_taurus.Btau_3.1.43. These genomes were chosen, because: (i) the genome assemblies are to high (>7X) coverage, and (ii) >85% of the gene annotations in these genomes have complete transcription validation. To identify the duplicated exons we compared each exon of each gene against the whole protein sequence of the same gene using BLASTp (e-value ≤ 10-4) . Exon definitions were taken directly from the genome annotations. To detect ΨEs, each exon was compared with the whole genomic DNA of the same gene plus a 5000-nucleotides (nt) buffer, 5' and 3' of the gene (Figure 1). The vast majority (>85%) of the introns of mammalian protein-coding genes are <5000 nt in length. As is illustrated with the data from the cow genome in Additional File 4, the number of ΨEs that are detected, has only a small dependence on the size of this buffer. We used protein-level sequence alignment to detect ΨEs throughout the paper; this is so that we can exploit the signal of protein coding sequence that is still in these sequences to detect them in the genomic DNA.
Identification of Duplicated Exons (DEs) and Pseudogenic Exons (ΨEs)
(1) Exon boundaries
The positioning of exon boundaries in encoded protein sequence was deduced and extracted from Ensembl Genbank-style annotation files, downloaded from http://www.ensembl.org. The positioning was then used to map the exact location of an exon BLAST match .
(2) Homology detection
Each exon (amino acid sequence) was compared against its whole protein sequence using BLASTp to find duplicated exons with similarities with e-value ≤ 10-4. For ΨEs, each exon was compared (using tBLASTn , with e-value as above) against the genomic milieu of the encoding gene, which is defined as the genomic DNA of the gene (including introns), plus 5000 nucleotides, 5' and 3' of the gene.
(3) Fastx/y and Genewise realignments
After filtering for overlapping each tBLASTn match was realigned using FASTX/Y, as previously described [2, 15, 32]. The FASTX/Y  program allows longer alignments and also allows the identification of stop codon and frame-shifts in ΨEs. To confirm that the disablements of FASTX/Y were not an artifact we also aligned the ΨEs with GeneWise . Only ΨEs confirmed by both methods were kept in our analyses.
ΨEs were filtered to remove olfactory receptors (ORs) and other single exon genes, since it is difficult to classify them as processed or duplicated pseudogenic exons . Each match was compared with the Interpro http://www.ebi.ac.uk/interpro/, Gene Ontology (GO ) descriptions and Ensembl http://www.ensembl.org protein family descriptions. If a ΨE was annotated in at least one of those databanks as an OR or other single-exon gene, it was removed from the analysis. To confirm the presence of stop-codons each ΨEs was realigned against its translated parent using bl2seq ; the output was parsed so that stop codons outside of a margin of 10 amino acids at the ends of the aligned subsequences were adjudged to be verified.
The information about orthologs was extracted from the Biomart query system in the Ensembl database. As a further filter for the ortholog assignments, we performed a 'local gene order' test . We compared the chromosomal milieu of genes bearing ΨEs, with the milieus of their orthologs, as follows. After identifying the ortholog of the gene containing the ΨE (step (5) above), we took a window (Wgenes) of 9 genes in either direction (the gene bearing the ΨE plus 4 genes 5' and 4 genes 3' of it) and 'BLASTed' against the equivalent 9 genes for the ortholog. We focused on the human genome; therefore, this local gene order test was performed for the human data vs. cow, dog, chimp, rhesus, mouse and rat genomes. The number of significant matches (BLASTp e-value ≤ 10-4, sequence identity >45%, and match ≥ 0.6 length of both orthologs) between the milieu of the two considered species was investigated. We allowed up to three gaps in total within the Wgenes windows.
(6) NMD targeting
To analyse for potential NMD targeting, we disregarded any ΨE located beyond the 5' and 3' UTRs, and also ΨEs located after the real stop codon, since they would not lead to nonsense-mediated decay (NMD). Then, we mapped the position of each stop codon in every ΨE to see if they are in a NMD area. If a stop codon would be located more than 55 nucleotide 5' to the last exon-exon junction in a transcript containing the ΨE, then the ΨE was labeled as within a putative NMD target.
Gene Ontology (GO ) functional categories, Ensembl protein family and pfam protein family descriptions, where retrieved using the Biomart tool . GO functional category enrichment analyses of DEs and ΨEs were performed using FatiGo database .
Alternative splicing (AS)
We checked whether exons are alternatively spliced, by counting up the number of splice forms that a gene produces, and labeling the exons as constitutive if they appear in all splice forms, and alternative otherwise. We cross-referenced the coordinates of each ΨE with every event of alternative splicing annotation in the ASD database database .
Analysis of Ka/Ks values
The program codeml of the PAML package  was used to calculate the maximum-likelihood Ka/Ks values of designated parent exons compared to ΨEs and DEs. The input codon alignments were generated using the PAL2NAL program . Only pairs of sequence with ≥ 70% of identity and ≥ 40 amino acids long were used in this analysis as the reliability of Ka/Ks analysis falls rapidly below this threshold .
This research was supported by grants from the National Science and Engineering Research Council of Canada, and from McGill University.
- Harrison PM, Gerstein M: Studying genomes through the aeons: protein families, pseudogenes and proteome evolution. J Mol Biol. 2002, 318 (5): 1155-1174. 10.1016/S0022-2836(02)00109-2.View ArticlePubMedGoogle Scholar
- Harrison PM, Zheng D, Zhang Z, Carriero N, Gerstein M: Transcribed processed pseudogenes in the human genome: an intermediate form of expressed retrosequence lacking protein-coding ability. Nucleic acids research. 2005, 33 (8): 2374-2383. 10.1093/nar/gki531.PubMed CentralView ArticlePubMedGoogle Scholar
- Zhang Z, Harrison PM, Liu Y, Gerstein M: Millions of years of evolution preserved: a comprehensive catalog of the processed pseudogenes in the human genome. Genome research. 2003, 13 (12): 2541-2558. 10.1101/gr.1429003.PubMed CentralView ArticlePubMedGoogle Scholar
- Zhang ZL, Harrison PM, Gerstein M: Digging deep for ancient relics: a survey of protein motifs in the intergenic sequences of four eukaryotic genomes. J Mol Biol. 2002, 323 (5): 811-822. 10.1016/S0022-2836(02)01035-5.View ArticlePubMedGoogle Scholar
- Balasubramanian S, Harrison P, Hegyi H, Bertone P, Luscombe N, et al: SNPs on human chromosomes 21 and 22 – analysis in terms of protein features and pseudogenes. Pharmacogenomics. 2002, 3 (3): 393-402. 10.1517/14622422.214.171.1243.View ArticlePubMedGoogle Scholar
- Harrison PM, Carriero N, Liu Y, Gerstein M: A "polyORFomic" analysis of prokaryote genomes using disabled-homology filtering reveals conserved but undiscovered short ORFs. J Mol Biol. 2003, 333 (5): 885-892. 10.1016/j.jmb.2003.09.016.View ArticlePubMedGoogle Scholar
- Lareau LF, Inada M, Green RE, Wengrod JC, Brenner SE: Unproductive splicing of SR genes associated with highly conserved and ultraconserved DNA elements. Nature. 2007, 446 (7138): 926-929. 10.1038/nature05676.View ArticlePubMedGoogle Scholar
- Resch AM, Carmel L, Marino-Ramirez L, Ogurtsov AY, Shabalina SA, et al: Widespread positive selection in synonymous sites of mammalian genes. Molecular biology and evolution. 2007, 24 (8): 1821-1831. 10.1093/molbev/msm100.PubMed CentralView ArticlePubMedGoogle Scholar
- Zhang Z, Gerstein M: Patterns of nucleotide substitution, insertion and deletion in the human genome inferred from pseudogenes. Nucleic acids research. 2003, 31 (18): 5338-5348. 10.1093/nar/gkg745.PubMed CentralView ArticlePubMedGoogle Scholar
- Gilad Y, Man O, Paabo S, Lancet D: Human specific loss of olfactory receptor genes. Proceedings of the National Academy of Sciences of the United States of America. 2003, 100 (6): 3324-3327. 10.1073/pnas.0535697100.PubMed CentralView ArticlePubMedGoogle Scholar
- Zhang Z, Harrison P, Gerstein M: Identification and analysis of over 2000 ribosomal protein pseudogenes in the human genome. Genome research. 2002, 12 (10): 1466-1482. 10.1101/gr.331902.PubMed CentralView ArticlePubMedGoogle Scholar
- Liu Y, Harrison PM, Kunin V, Gerstein M: Comprehensive analysis of pseudogenes in prokaryotes: widespread gene decay and failure of putative horizontally transferred genes. Genome biology. 2004, 5 (9): R64-10.1186/gb-2004-5-9-r64.PubMed CentralView ArticlePubMedGoogle Scholar
- Brocchieri L, Conway de Macario E, Macario AJ: hsp70 genes in the human genome: Conservation and differentiation patterns predict a wide array of overlapping and specialized functions. BMC evolutionary biology. 2008, 8: 19-10.1186/1471-2148-8-19.PubMed CentralView ArticlePubMedGoogle Scholar
- Harrison PM, Hegyi H, Balasubramanian S, Luscombe NM, Bertone P, et al: Molecular fossils in the human genome: identification and analysis of the pseudogenes in chromosomes 21 and 22. Genome research. 2002, 12 (2): 272-280. 10.1101/gr.207102.PubMed CentralView ArticlePubMedGoogle Scholar
- Harrison P, Kumar A, Lan N, Echols N, Snyder M, et al: A small reservoir of disabled ORFs in the yeast genome and its implications for the dynamics of proteome evolution. J Mol Biol. 2002, 316 (3): 409-419. 10.1006/jmbi.2001.5343.View ArticlePubMedGoogle Scholar
- Bailey JA, Gu Z, Clark RA, Reinert K, Samonte RV, et al: Recent segmental duplications in the human genome. Science. 2002, 297 (5583): 1003-1007. 10.1126/science.1072047.View ArticlePubMedGoogle Scholar
- Hamilton AT, Huntley S, Tran-Gyamfi M, Baggott DM, Gordon L, et al: Evolutionary expansion and divergence in the ZNF91 subfamily of primate-specific zinc finger genes. Genome Res. 2006, 16: 584-594. 10.1101/gr.4843906.PubMed CentralView ArticlePubMedGoogle Scholar
- Tam OH, Aravin AA, Stein P, Girard A, Murchison EP, et al: Pseudogene-derived small interfering RNAs regulate gene expression in mouse oocytes. Nature. 2008, 453 (7194): 534-538. 10.1038/nature06904.PubMed CentralView ArticlePubMedGoogle Scholar
- Watanabe T, Totoki Y, Toyoda A, Kaneda M, Kuramochi-Miyagawa S, et al: Endogenous siRNAs from naturally formed dsRNAs regulate transcripts in mouse oocytes. Nature. 2008, 453 (7194): 539-543. 10.1038/nature06908.View ArticlePubMedGoogle Scholar
- Lohmeyer M, Harrison PM, Kannan S, DeSantis M, O'Reilly NJ, et al: Chemical synthesis, structural modeling, and biological activity of the epidermal growth factor-like domain of human cripto. Biochemistry. 1997, 36 (13): 3837-3845. 10.1021/bi961542p.View ArticlePubMedGoogle Scholar
- Doolittle RF: Stein and Moore Award address. Reconstructing history with amino acid sequences. Protein Sci. 1992, 1 (2): 191-200.PubMed CentralView ArticlePubMedGoogle Scholar
- Al-Shahrour F, Minguez P, Vaquerizas JM, Conde L, Dopazo J: BABELOMICS: a suite of web tools for functional annotation and analysis of groups of genes in high-throughput experiments. Nucleic acids research. 2005, W460-464. 10.1093/nar/gki456. 33 Web ServerGoogle Scholar
- Zhang XH, Chasin LA: Comparison of multiple vertebrate genomes reveals the birth and evolution of human exons. Proceedings of the National Academy of Sciences of the United States of America. 2006, 103 (36): 13427-13432. 10.1073/pnas.0603042103.PubMed CentralView ArticlePubMedGoogle Scholar
- Modrek B, Lee CJ: Alternative splicing in the human, mouse and rat genomes is associated with an increased frequency of exon creation and/or loss. Nat Genet. 2003, 34 (2): 177-180. 10.1038/ng1159.View ArticlePubMedGoogle Scholar
- Lareau LF, Brooks AN, Soergel DA, Meng Q, Brenner SE: The coupling of alternative splicing and nonsense-mediated mRNA decay. Adv Exp Med Biol. 2007, 623: 190-211.View ArticlePubMedGoogle Scholar
- Karolchik D, Kuhn RM, Baertsch R, Barber GP, Clawson H: The UCSC Genome Browser Database: 2008 update. Nucleic acids research. 2008, D773-779. 36 DatabaseGoogle Scholar
- Veeramachaneni V, Makalowski W, Galdzicki M, Sood R, Makalowska I: Mammalian overlapping genes: the comparative perspective. Genome research. 2004, 14 (2): 280-286. 10.1101/gr.1590904.PubMed CentralView ArticlePubMedGoogle Scholar
- Stamm S, Riethoven JJ, Le Texier V, Gopalakrishnan C, Kumanduri V: ASD: a bioinformatics resource on alternative splicing. Nucleic acids research. 2006, D46-55. 10.1093/nar/gkj031. 34 DatabaseGoogle Scholar
- Juretic N, Hoen DR, Huynh ML, Harrison PM, Bureau TE: The evolutionary fate of MULE-mediated duplications of host gene fragments in rice. Genome research. 2005, 15 (9): 1292-1297. 10.1101/gr.4064205.PubMed CentralView ArticlePubMedGoogle Scholar
- Korneev SA, Park JH, O'Shea M: Neuronal expression of neural nitric oxide synthase (nNOS) protein is suppressed by an antisense RNA transcribed from an NOS pseudogene. J Neurosci. 1999, 19 (18): 7711-7720.PubMedGoogle Scholar
- Altschul SF, Madden TL, Schaffer AA, Zhang J, Zhang Z, et al: Gapped BLAST and PSI-BLAST: a new generation of protein database search programs. Nucleic acids research. 1997, 25 (17): 3389-3402. 10.1093/nar/25.17.3389.PubMed CentralView ArticlePubMedGoogle Scholar
- Harrison P, Yu Z: Frame disruptions in human mRNA transcripts, and their relationship with splicing and protein structures. BMC genomics. 2007, 8: 371-10.1186/1471-2164-8-371.PubMed CentralView ArticlePubMedGoogle Scholar
- Pearson WR: Using the FASTA program to search protein and DNA sequence databases. Methods Mol Biol. 1994, 24: 307-331.PubMedGoogle Scholar
- Birney E, Clamp M, Durbin R: GeneWise and Genomewise. Genome research. 2004, 14 (5): 988-995. 10.1101/gr.1865504.PubMed CentralView ArticlePubMedGoogle Scholar
- Zheng D, Zhang Z, Harrison PM, Karro J, Carriero N, et al: Integrated pseudogene annotation for human chromosome 22: evidence for transcription. J Mol Biol. 2005, 349 (1): 27-45. 10.1016/j.jmb.2005.02.072.View ArticlePubMedGoogle Scholar
- Harris MA, Clark J, Ireland A, Lomax J, Ashburner M: The Gene Ontology (GO) database and informatics resource. Nucleic acids research. 2004, D258-261. 32 DatabaseGoogle Scholar
- Tatusova TA, Madden TL: BLAST 2 Sequences, a new tool for comparing protein and nucleotide sequences. FEMS Microbiol Lett. 1999, 174 (2): 247-250. 10.1111/j.1574-6968.1999.tb13575.x.View ArticlePubMedGoogle Scholar
- Yu Z, Morais D, Ivanga M, Harrison PM: Analysis of the role of retrotransposition in gene evolution in vertebrates. BMC Bioinformatics. 2007, 8: 308-10.1186/1471-2105-8-308.PubMed CentralView ArticlePubMedGoogle Scholar
- Durinck S, Moreau Y, Kasprzyk A, Davis S, De Moor B, et al: BioMart and Bioconductor: a powerful link between biological databases and microarray data analysis. Bioinformatics. 2005, 21 (16): 3439-3440. 10.1093/bioinformatics/bti525.View ArticlePubMedGoogle Scholar
- Yang Z: PAML: a program package for phylogenetic analysis by maximum likelihood. Comput Appl Biosci. 1997, 13 (5): 555-556.PubMedGoogle Scholar
- Suyama M, Torrents D, Bork P: PAL2NAL: robust conversion of protein sequence alignments into the corresponding codon alignments. Nucleic acids research. 2006, W609-612. 10.1093/nar/gkl315. 34 Web ServerGoogle Scholar
- Letunic I, Copley RR, Bork P: Common exon duplication in animals and its role in alternative splicing. Hum Mol Genet. 2002, 11 (13): 1561-1567. 10.1093/hmg/11.13.1561.View ArticlePubMedGoogle Scholar
This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.