Detecting transcription of ribosomal protein pseudogenes in diverse human tissues from RNA-seq data

  • Peter Tonner1,

    Affiliated with

    • Vinodh Srinivasasainagendra2,

      Affiliated with

      • Shaojie Zhang1Email author and

        Affiliated with

        • Degui Zhi2Email author

          Affiliated with

          BMC Genomics201213:412

          DOI: 10.1186/1471-2164-13-412

          Received: 12 April 2012

          Accepted: 10 August 2012

          Published: 21 August 2012

          Abstract

          Background

          Ribosomal proteins (RPs) have about 2000 pseudogenes in the human genome. While anecdotal reports for RP pseudogene transcription exists, it is unclear to what extent these pseudogenes are transcribed. The RP pseudogene transcription is difficult to identify in microarrays due to potential cross-hybridization between transcripts from the parent genes and pseudogenes. Recently, transcriptome sequencing (RNA-seq) provides an opportunity to ascertain the transcription of pseudogenes. A challenge for pseudogene expression discovery in RNA-seq data lies in the difficulty to uniquely identify reads mapped to pseudogene regions, which are typically also similar to the parent genes.

          Results

          Here we developed a specialized pipeline for pseudogene transcription discovery. We first construct a “composite genome” that includes the entire human genome sequence as well as mRNA sequences of real ribosomal protein genes. We then map all sequence reads to the composite genome, and only exact matches were retained. Moreover, we restrict our analysis to strictly defined mappable regions and calculate the RPKM values as measurement of pseudogene transcription levels. We report evidences for the transcription of RP pseudogenes in 16 human tissues. By analyzing the Human Body Map 2.0 study RNA-sequencing data using our pipeline, we identified that one ribosomal protein (RP) pseudogene (PGOHUM-249508) is transcribed with RPKM 170 in thyroid. Moreover, three other RP pseudogenes are transcribed with RPKM > 10, a level similar to that of the normal RP genes, in white blood cell, kidney, and testes, respectively. Furthermore, an additional thirteen RP pseudogenes are of RPKM > 5, corresponding to the 20–30 percentile among all genes. Unlike ribosomal protein genes that are constitutively expressed in almost all tissues, RP pseudogenes are differentially expressed, suggesting that they may contribute to tissue-specific biological processes.

          Conclusions

          Using a specialized bioinformatics method, we identified the transcription of ribosomal protein pseudogenes in human tissues using RNA-seq data.

          Keywords

          Ribosomal protein Pseudogene Transcription RNA-seq data

          Background

          Pseudogenes are “fossil” copies of functional genes that have lost their potential as DNA templates for functional products [16]. While the definition of pseudogenes is still somewhat fuzzy, most of them are defined operationally by bioinformatics criteria, e.g., genomic scans of signatures of homology to known genes. Ribosomal protein (RP) pseudogenes represent the largest class of pseudogenes found in the human genome: over 2000 ribosomal protein pseudogenes are identified by bioinformatics scan of genomic sequence [5].

          These pseudogenes are commonly thought to be non-functional due to the lack of promoters and/or the presence of loss of function mutations. Indeed, the vast majority of these pseudogenes either carry dysfunctional mutations such as in-frame stop codons, or lack of proper regulatory sequences, such as promoters, mTOP signals, and first introns [7]. Interestingly, three RP pseudogenes, with 89%-95% sequence identity to their parent (progenitor) RP genes, were found to be transcribed and seem to be functional, by a bioinformatics scan of cDNA and expression sequence tag (EST) databases and confirmation by PCR and Northern blot [8]. A genome-wide bioinformatics scan identified over 2000 potential pseudogenes [5]. Moreover, it was found [9] that the six RP pseudogenes shared at syntenic loci between the human and the mouse genomes are more conserved than other RP pseudogenes.

          However, data were lacking to experimentally validate pseudogene expression. It is unclear from the literature whether the reported cases are merely anecdotal or that pseudogenes do play some cellular roles. This is largely hindered by the lack of methods for the identification of pseudogenes transcription. The traditional method of transcriptome profiling, gene expression microarray, is not sensitive in distinguishing transcripts among very similar gene sequences.

          Recent advancements of next-generation sequencing allow for direct massive transcriptome sequencing (RNA-seq), and thus providing unprecedented insights into all transcribed sequences. For example, RNA-seq has been applied to detect complex transcriptional activities such as alternative splicing [10, 11] and allelic-specific expression [12]. Recently, RNA-seq has been applied to reveal RNA editing events [13]. However, to the best of our knowledge, there were yet no attempts to detect the transcription of pseudogenes in RNA-seq data. The main challenge for pseudogene identification in RNA-seq data is the difficulty of high fidelity read mapping. Because sequences of pseudogenes are highly similar to the sequences of the mRNAs of the parent genes, specialized read mapping methods are required to detect reads unambiguously generated from pseudogenes.

          In this study, we conduct a bioinformatics analysis of pseudogene expression using RNA-sequencing data of 16 human tissues of the Illumina Human Body Map 2.0 project. We first describe our new computational pipeline for detecting pseudogene expression that disentangles sequencing reads of pseudogenes from those of the parent genes, with consideration of possible mismatches due to SNPs and RNA-editing. This is followed by a description of our findings and a discussion of their implications.

          Results

          Illumina Human Body Map 2.0 RNA-seq data

          The Human Body Map 2.0 Project by Illumina generated RNA-seq data for 16 different human tissues (adipose, adrenal, brain, breast, colon, heart, kidney, liver, lung, lymph node, ovary, prostate, skeletal muscle, testes, thyroid, and white blood cells). In our analysis we used the 75 bps single read data, with one lane of HiSeq 2000 data per tissue. Standard mRNA-seq library preps were used to extract poly-A selected mRNAs.

          Discovery of pseudogene transcription in RNA-seq data

          Our primary goal is to detect transcriptional activities of any of the 1709 processed RP pseudogenes. In addition, we also want to provide a preliminary quantification of their level of transcription.

          We developed a novel bioinformatics approach for detecting the subtle signals of pseudogene expression. Briefly, we first compiled a “composite genome” consisting of known RP gene spliced mRNA sequences and the human genome (hg18) [14]. We then mapped RNA-seq reads to the composite genome using Bowtie [15], allowing no mismatches and discarding reads mapped to more than one locus. Thus we ensured that the reads mapped to RP pseudogenes are neither from repetitive regions nor from normal RP genes. On average 89% of the reads aligning to RP pseudogenes can also be mapped to real RP spliced mRNA sequences and are removed when mapped to the composite genome (see Table 1). Furthermore, to remove mapped reads that may be caused by SNPs and RNA-editing, we extended the concept of the mappability (the mappable regions of human genome is called the uniqueome) [16] and masked regions in RP pseudogenes that are duplicated in the composite genome within 4 mismatches over the 75 bps read length. The number of reads we removed from non-unique locations in both the composite genome and hg18 genome can be seen in Table 2. The mappability regions only correspond to the unaltered human genome locations, so all reads mapped to RP gene mRNA sequences in the composite genome are removed during this step. Additionally, the composite genome alignment lacks the reads that mapped both to the unaltered human genome locations and spliced RP gene mRNA sequences as we only retained reads aligning to a single location. With both of these groups of reads removed, the number of reads mapped uniquely in the composite genome is always less than that in the unaltered human genome. Finally, we calculated the transcription levels, as measured by the Reads Per Kilobase per Million reads (RPKM) [11] of all pseudogenes according to the mapped reads in their mappable regions. As a benchmark for normal expression levels, we also aligned reads to an unaltered genome using TopHat and measured FPKM of all RefSeq genes using Cufflinks [17]. The alignment information of reads to the composite genome, and to the unaltered genome (hg18), can be seen in Table 2. Please see Methods for details.
          Table 1

          The number of reads mapped to RefSeq sequences and RP pseudogenes for both the composite genome and the unaltered human genome (hg18) for each tissue

          Tissue

          RP Pseudogenes

          RefSeq Sequences

          Composite genome

          hg18

          Ratio

          Composite genome

          hg18

          Ratio

          Adipose

          708

          22613

          0.03

          22018081

          23753007

          0.93

          Adrenal

          2439

          22280

          0.11

          19436010

          21074345

          0.92

          Brain

          712

          3853

          0.18

          11585759

          12357788

          0.94

          Breast

          1603

          15951

          0.10

          22962845

          23060856

          1.00

          Colon

          1066

          22948

          0.05

          21458813

          21618780

          0.99

          Heart

          562

          2374

          0.24

          12622175

          13502482

          0.93

          Kidney

          1341

          5928

          0.23

          20630500

          22278600

          0.93

          Liver

          328

          2359

          0.14

          16689110

          17958332

          0.93

          Lung

          1128

          11918

          0.09

          28648503

          31274037

          0.92

          Lymph

          1456

          14010

          0.10

          22126747

          23998836

          0.92

          Muscle

          459

          8705

          0.05

          25836676

          27662752

          0.93

          Ovary

          1501

          34608

          0.04

          21842763

          23663484

          0.92

          Prostate

          1280

          14976

          0.09

          30329651

          32822472

          0.92

          Testes

          1885

          19417

          0.10

          23356973

          25123824

          0.93

          Thyroid

          4064

          22250

          0.18

          24800037

          26627946

          0.93

          White Blood Cells

          1095

          14256

          0.08

          25487259

          27812790

          0.92

          Average

          1351

          14902

          0.11

          21864493

          23411895

          0.94

          The ratio is calculated by the number of mapped reads from the composite genome over the number of mapped reads from the unaltered human genome (hg18).

          Table 2

          Statistics for each tissue sample

          Tissue

          Number of reads in the sample

          Number of reads mapped to the composite genome

          Number of reads mapped to hg18

          Number of reads mapped uniquely to the composite genome

          Number of reads mapped uniquely to the hg18

          Adipose

          76,269,225

          39,499,413

          39,759,404

          27,507,408

          30,635,265

          Adrenal

          76,171,569

          39,330,423

          39,681,721

          29,670,249

          32,603,673

          Brain

          64,313,204

          21,022,913

          21,073,103

          15,785,362

          17,187,772

          Breast

          77,195,260

          39,355,808

          39,568,435

          30,955,674

          31,072,723

          Colon

          80,257,757

          39,406,195

          39,735,900

          28,393,105

          28,582,515

          Heart

          76,766,862

          29,030,896

          29,065,063

          17,099,881

          19,366,541

          Kidney

          79,772,393

          41,368,095

          41,488,175

          27,316,562

          30,570,744

          Liver

          77,453,877

          26,692,219

          26,741,073

          18,897,673

          20,715,729

          Lung

          81,255,438

          45,996,752

          46,211,355

          34,401,958

          37,862,617

          Lymph

          81,916,460

          41,826,888

          42,072,585

          30,451,248

          33,277,288

          Muscle

          82,864,636

          46,580,440

          46,725,392

          30,704,302

          34,165,828

          Ovary

          81,003,052

          36,922,138

          37,385,453

          28,184,877

          30,861,571

          Prostate

          83,319,902

          47,601,661

          47,965,443

          36,138,822

          39,533,973

          Testes

          82,044,319

          38,852,709

          39,069,136

          29,115,004

          31,927,737

          Thyroid

          80,246,657

          40,546,781

          40,785,090

          31,137,501

          33,939,339

          White Blood Cells

          82,785,673

          38,860,771

          39,098,752

          28,796,204

          31,784,122

          The number of reads mapped to the composite genome (which includes spliced ribosomal protein gene sequences) and to the unaltered human genome (hg18), and the number of reads overlapped with uniqueome (“mapped uniquely”) for both are shown. For the composite genome, the number of reads aligning to the entire composite genome and the unaltered hg18 human genome are shown.

          Prevalent transcription of RP pseudogenes

          The expression levels of the top seventeen highly expressed ribosomal protein pseudogenes in 16 human tissues are shown in Figure 1 and Table 3 (See Table S1 in Additional file 1 for complete list for all RP pseudogenes). As expected the majority of pseudogenes have no reads aligning to their sequence. Interestingly, there were some pseudogenes with high expression levels. One RP pseudogene (PGOHUM-249508) is transcribed with RPKM 170 in thyroid. Moreover, three additional RP pseudogenes are transcribed with RPKM > 10. Furthermore, thirteen more RP pseudogenes are of RPKM > 5. We describe pseudogenes with an RPKM > 10 as “highly expressed”, with the understanding that they may be only representing the top 10–15 percentile of all 37,681 RefSeq genes in the Human Body Map 2.0 data set, while RPKM > 5 represents the top 20–30 percentile (see Table 4). Below we discuss these cases in detail.
          http://static-content.springer.com/image/art%3A10.1186%2F1471-2164-13-412/MediaObjects/12864_2012_4285_Fig1_HTML.jpg
          Figure 1

          RPKM of RP pseudogenes. See Table S1 in Additional file 1 for the complete list.

          Table 3

          RP pseudogenes expression identified in Human Body Map 2.0 RNA-seq data

          pg-id

          Parent gene

          Location

          Tissue with Max RPKM

          Max RPKM

          Total RPKM

          Tissue specificity

          Reads coverage

          249508

          RPL21

          chr8:134084035-134084502

          Thyroid

          170.3

          170.6

          0.977

          0.976

          237215

          RPL7A

          chr17:6984988-6985635

          White Blood Cells

          17.3

          18.0

          0.881

          0.913

          249146

          RPS24

          chr16:55497947-55498335

          Kidney

          16.5

          17.6

          0.855

          0.693

          239833

          RPS11

          chr12:63076580-63077044

          Testes

          11.3

          12.5

          0.813

          0.970

          236635

          RPL24

          chr9:72021934-72022269

          Colon

          9.7

          9.7

          1.000

          0.358

          248697

          RPL26

          chr16:1953719-1954097

          Lymph

          9.0

          37.9

          0.376

          0.854

          242376

          RPS20

          chr11:77202016-77202370

          Colon

          8.6

          41.6

          0.355

          1.00

          251340

          RPLP1

          chr5:151125656-151125993

          Testes

          7.4

          40.1

          0.339

          0.567

          237777

          RPL7

          chr3:133445060-133445787

          Prostate

          7.3

          8.0

          0.819

          0.371

          234492

          RPL10

          chr19:9791817-9792131

          Brain

          7.2

          10.8

          0.635

          0.121

          248932

          RPL21

          chr16:72904438-72904782

          Ovary

          6.5

          34.3

          0.343

          1.00

          234407

          RPL39

          chr19:58143205-58143330

          Colon

          6.3

          21.3

          0.414

          1.00

          239633

          RPL13

          chr12:6863424-6864038

          Kidney

          6.0

          27.0

          0.365

          0.923

          245936

          RPL6

          chr4:66121772-66122638

          Colon

          5.9

          7.2

          0.746

          0.109

          243590

          RPL32

          chr6:33155206-33155612

          Adrenal

          5.8

          9.2

          0.617

          1.00

          241074

          RPL13A

          chr2:203093361-203093957

          Lung

          5.4

          9.7

          0.571

          0.995

          238877

          RPL11

          chr10:89695235-89695766

          Adipose

          5.2

          20.4

          0.387

          0.847

          Expression levels of pseudogenes with their pseudogene ID (pg-id, prefix ‘PGOHUM00000’ omitted) are measured in terms of RPKM. Only pseudogenes with maximum RPKM > 5 are shown. Tissue specificities are measured by the JS divergence [20]. Read coverage is the ratio of pseudogene exon length covered by uniquely mapped reads to the total pseudogene exon length.

          Table 4

          Table of FPKM expression values of RefSeq genes in 16 human tissues

          Tissue

          FPKM

          Percentile

          Mean

          Max

          Min

          std dev

          % > 1

          % > 2

          % > 5

          % > 10

          % > 15

          Adipose

          10.03

          17437.80

          0

          143.53

          41.07

          32.42

          20.62

          12.64

          8.96

          Colon

          10.42

          17376.90

          0

          167.07

          40.80

          31.61

          19.67

          11.93

          8.37

          Heart

          7.55

          17376.90

          0

          122.87

          36.61

          27.71

          16.09

          9.41

          6.43

          Kidney

          9.97

          16400.20

          0

          156.94

          45.28

          35.93

          22.62

          13.32

          9.23

          Liver

          14.91

          38505.10

          0

          360.25

          33.57

          25.10

          14.90

          8.93

          6.48

          Lung

          16.29

          57096.60

          0

          456.70

          44.59

          35.08

          22.05

          13.61

          9.72

          Lymph

          13.41

          40919.80

          0

          337.50

          45.77

          36.77

          23.25

          13.70

          9.22

          Muscle

          8.80

          16317.70

          0

          123.85

          33.27

          25.97

          16.38

          10.39

          7.55

          Ovary

          13.62

          61099.30

          0

          359.00

          46.27

          37.60

          24.59

          15.05

          10.37

          Prostate

          13.72

          39039.90

          0

          300.91

          47.82

          38.83

          25.36

          15.68

          10.79

          Testes

          12.76

          57096.60

          0

          325.41

          53.34

          43.44

          28.69

          17.85

          12.46

          Thyroid

          12.06

          29030.10

          0

          234.59

          46.04

          37.25

          24.24

          15.12

          10.74

          White Blood Cells

          15.42

          40919.80

          0

          366.35

          39.25

          32.56

          22.65

          14.87

          10.83

          All Tissues

          12.23

          61099.30

          0

          286.98

          42.59

          33.87

          21.62

          13.27

          9.32

          Percentile columns represent the percentage of RefSeq genes in all tissues with FPKM above a given value.

          PGOHUM-249508, an RPL21 pseudogene, is expressed with RPKM = 170 in thyroid (Figure 2). This highest expressed RP pseudogene is located in an intron of the Thyroglobulin (TG) gene. The TG gene is highly and specifically expressed in the same tissue, thyroid, and the gene encodes a glycoprotein that acts as a substrate for the synthesis of thyroxine and triiodothyronine as well as the storage of the inactive forms of thyroid hormone and iodine [18]. The transcription of this pseudogene goes beyond the annotated pseudogene region, but is less than the entire intron region. Although the pseudogene is specifically expressed in the same tissue as TG, the RP coding frame is on the reverse strand of the TG gene. Therefore, it is possible that this pseudogene is on a distinct transcript other than the TG gene. Moreover, according to UCSC genome browser [19], this pseudogene region is only conserved within the primates (between human and the Rhesus monkey), but not in other mammalian and vertebrate lineages. As a side note, the genome browser shows a peculiar conservation pattern between human and the stickleback fish, but it is likely to be an artifact of matching human genomic sequence with the RPL21 gene of stickleback fish.
          http://static-content.springer.com/image/art%3A10.1186%2F1471-2164-13-412/MediaObjects/12864_2012_4285_Fig2_HTML.jpg
          Figure 2

          UCSC browser view of RNA-seq expression of pseudogene PGOHUM-249508 in Thyroid. RPKM = 170, Tissue Specificity = 0.977. Open reading frames (ORFs) in +1, +2, +3, -1, -2, and −3 are annotated.

          Three additional pseudogenes are highly transcribed (RPKM > 10). PGOHUM-237215, an RPL7A pseudogene, is expressed RPKM = 17 in white blood cells. This pseudogene is located in an intergenic region. Also, the transcription unit seems to span a longer region (Figure 3). It is transcribed in a white blood cell specific fashion. PGOHUM-249146, an RPS24 pseudogene, is expressed in kidney. This pseudogene is located in the intronic region of gene SLC12A3 (Figure 4). This gene encodes a renal thiazide-sensitive sodium-chloride cotransporter that is important for electrolyte homeostasis. PGOHUM-239833, an RPS11 pseudogene, is expressed in testes. This pseudogene is located in an intergenic region (Figure 5). The comparison of read coverage with or without uniqueome filtering for these four RP pseudogenes can be been in Figures S1-S4 in Additional file 2.
          http://static-content.springer.com/image/art%3A10.1186%2F1471-2164-13-412/MediaObjects/12864_2012_4285_Fig3_HTML.jpg
          Figure 3

          UCSC browser view RNA-seq expression of pseudogene PGOHUM-237215 in white blood cells. RPKM = 17, Tissue Specificity = 0.881. Open reading frames (ORFs) in +1, +2, +3, -1, -2, and −3 are annotated.

          http://static-content.springer.com/image/art%3A10.1186%2F1471-2164-13-412/MediaObjects/12864_2012_4285_Fig4_HTML.jpg
          Figure 4

          UCSC browser view RNA-seq expression of pseudogene PGOHUM-249146 in kidney. RPKM = 16, Tissue Specificity = 0.855. Open reading frames (ORFs) in +1, +2, +3, -1, -2, and −3 are annotated.

          http://static-content.springer.com/image/art%3A10.1186%2F1471-2164-13-412/MediaObjects/12864_2012_4285_Fig5_HTML.jpg
          Figure 5

          UCSC browser view RNA-seq expression of pseudogene PGOHUM-239833 in testes. RPKM = 11, Tissue Specificity = 0.813. Open reading frames (ORFs) in +1, +2, +3, -1, -2, and −3 are annotated.

          Tissue-specificity of pseudogene transcription

          Many genes are expressed in a tissue-specific fashion. The Human Body Map 2.0 data allow us to study the tissue-specificity of transcriptions of these pseudogenes. We adopt the entropy-based Jensen-Shannon (JS) divergence measure used in [20]. The distributions of tissue-specificity JS divergences of RP pseudogenes versus RP genes are shown in Figure 6. In the Human Body Map 2.0 data set, all RP genes are not transcribed in a tissue specific fashion (JS divergence <0.5 for all RP genes) (Table S2 in Additional file 3). Unlike ribosomal protein genes that are constitutively expressed in almost all tissues, many RP pseudogenes are differentially expressed (Table S1 in Additional file 1). Among the seventeen pseudogenes with RPKM > 5 at some tissue, 8 of them have a JS divergence > 0.5. In fact, all of the top 4 pseudogenes with RPKM > 10 are transcribed in a highly tissue specific fashion (JS divergence > 0.8). These results suggest that these highly expressed RP pseudogenes may contribute to tissue-specific biological processes.
          http://static-content.springer.com/image/art%3A10.1186%2F1471-2164-13-412/MediaObjects/12864_2012_4285_Fig6_HTML.jpg
          Figure 6

          Distribution of tissue specificity, as measured by the JS divergence[20].

          Discussion and conclusions

          In this work, we conducted a bioinformatics analysis of the pseudogenes of ribosomal protein genes in diverse human tissues. Using our specialized pipeline, we identified several cases of pseudogene expression. Most notably, one pseudogene in an intron of the TG gene is extremely highly expressed in thyroid. Moreover, several other pseudogenes are also highly expressed. We found that many pseudogenes are expressed in a tissue-specific fashion. Also, the expression of pseudogenes seems to often go beyond the boundaries of the annotated pseudogenes. Apparently, further experimental investigations will be needed to reveal the biological relevance of these expressions.

          Transcriptome sequencing, RNA-seq, provides an unprecedented opportunity to uncover many complexities of cellular gene expression. A key computational challenge in RNA-seq data analysis is to discern reads among multiple potential sources with similar sequences. In this work we focused on the detection of evidences of pseudogene expression. We used extremely strict read mapping criteria to minimize potential false positives. Admittedly we did not utilize all potential reads, especially at regions with low uniqueness. Further research may consider using looser mapping criteria combined with sophisticated statistical algorithms to take into account the mapping ambiguity.

          The bioinformatics methods presented here may find application in other RNA-seq studies dealing with high similarity in reference sequences. In particular, the same methodology may be able to identify differential expression between other homologous genome regions. Studies in other fields, such as metagenomics, dealing with high similarity DNA sequences may also find benefits from strict alignment and intersection with uniquely mappable locations.

          Methods

          Human tissue samples

          The Human Body Map 2.0 RNA-seq data for 16 human tissue samples were provided by Gary Schroth at Illumina and can be accessible from ArrayExpress (accession no. E-MTAB-513). Reads were 75 base pairs long and came from the following samples: adipose, adrenal, brain, breast, colon, heart, kidney, liver, lung, lymph, muscle, ovary, prostate, testes, thyroid, and white blood cells. The samples were prepared using the Illumina mRNA-seq kit. They were made with a random priming process and are not stranded.

          Software and datasets

          Bowtie version 0.12.7 [15] and TopHat version 1.2.0 [21] were used for the mapping. Cufflinks version 1.0.3 [17] was used for differential expression calculation for RefSeq genes. BEDTools version 2.12.0 [22] was used to analyze alignments. The uniqueome dataset was collected from the Uniqueome supplementary database [16] for human genome (hg18, NCBI Build 36.1) marking genome locations where reads of length 75 bps are unique within 4 mismatches (hg18_uniqueome.unique_starts.base-space.75.4.positive.BED). The 75 bps read length matches the RNA-seq data provided by Illumina. RefSeq genes and DNA sequences of spliced ribosomal protein genes were collected from NCBI (RefSeq database D32-6) [14]. Pseudogene annotations and sequences were downloaded from pseudogene.org [23] database (human pseudogenes build 58). Pseudogenes whose parent genes are ribosomal protein genes were selected, totaling 1788. Among them, 79 were annotated ‘Duplicated’. As we are only interested in processed pseudogenes, our analysis focuses on the remaining 1709 pseudogenes. The human genome sequence (hg18) was collected from NCBI build 36.1.

          Composite genome

          A composite genome index was constructed with Bowtie-build using the sequences of the human genome (hg18, NCBI build 36.1) and NCBI spliced RP gene sequences.

          Alignment

          RNA-seq data for each tissue was aligned using two distinct methodologies – one for pseudogenes and one for real genes. Pseudogene alignment protocol consists of strict alignment (Bowtie, no mismatches, report reads with only one alignment location only) to the composite genome. Real gene alignment protocol consists of strict alignment (Bowtie, no mismatches, single alignment location) to the human genome (hg18, NCBI Build 36.1).

          Uniqueome

          A uniqueome data set [16] was obtained for Build 36.1 marking genome locations where reads of length 75 bps are unique within 4 mismatches. Alignments for all tissues for both real genes and pseudogenes were intersected with the uniqueome dataset for all genome locations (intersectBed from BEDTools [22]). The total number of remaining reads in each alignment was counted. The uniqueome dataset was used to filter out ambiguously mapped reads.

          Comparative expression analysis

          Gene expression values were calculated as reads aligned to gene per kilobase of exon per million mapped reads (RPKM) [11]. The number of reads aligned to all gene exons and additionally aligning in unique locations was counted for each gene. Exon length for genes was calculated as the sum of unique positions as marked by the uniqueome across all gene exons. It is worth noting that RP pseudogenes appear spliced in the human genome and therefore have only a single exon for counting aligned reads and calculating exon length.

          Expression percentiles of RefSeq genes were calculated using TopHat to map reads to the human genome (hg18, NCBI build 36.1) and Cufflinks was used to calculate FPKM values of all 37,681 RefSeq genes. Expression percentiles were calculated for specific tissues and for all datasets combined.

          Gene reads coverage was calculated using the coverageBed program in the BEDTools software suite. Coverage represents the fraction of RP pseudogene exon covered by reads that aligned to unique genome regions.

          Tissue-specificity analysis

          We followed the definition of Jensen-Shannon divergence in [20]. To avoid zero probabilities, all RPKM numbers are added by 10-10.

          Declarations

          Acknowledgements

          We are grateful for Gary Schroth and Illumina for the early sharing of their Human Body Map 2.0 RNA-seq data. This work is partly supported by a UAB NORC pilot grant funded by NIH grant 5P30DK056336.

          Authors’ Affiliations

          (1)
          Department of Electrical Engineering and Computer Science, University of Central Florida
          (2)
          Department of Biostatistics, Section on Statistical Genetics, University of Alabama at Birmingham

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          © Tonner et al.; licensee BioMed Central Ltd. 2012

          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.

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