Comparison of stranded and non-stranded RNA-seq transcriptome profiling and investigation of gene overlap
© Zhao et al. 2015
Received: 28 April 2015
Accepted: 24 August 2015
Published: 3 September 2015
While RNA-sequencing (RNA-seq) is becoming a powerful technology in transcriptome profiling, one significant shortcoming of the first-generation RNA-seq protocol is that it does not retain the strand specificity of origin for each transcript. Without strand information it is difficult and sometimes impossible to accurately quantify gene expression levels for genes with overlapping genomic loci that are transcribed from opposite strands. It has recently become possible to retain the strand information by modifying the RNA-seq protocol, known as strand-specific or stranded RNA-seq. Here, we evaluated the advantages of stranded RNA-seq in transcriptome profiling of whole blood RNA samples compared with non-stranded RNA-seq, and investigated the influence of gene overlaps on gene expression profiling results based on practical RNA-seq datasets and also from a theoretical perspective.
Our results demonstrated a substantial impact of stranded RNA-seq on transcriptome profiling and gene expression measurements. As many as 1751 genes in Gencode Release 19 were identified to be differentially expressed when comparing stranded and non-stranded RNA-seq whole blood samples. Antisense and pseudogenes were significantly enriched in differential expression analyses. Because stranded RNA-seq retains strand information of a read, we can resolve read ambiguity in overlapping genes transcribed from opposite strands, which provides a more accurate quantification of gene expression levels compared with traditional non-stranded RNA-seq. In the human genome, it is not uncommon to find genomic loci where both strands encode distinct genes. Among the over 57,800 annotated genes in Gencode release 19, there are an estimated 19 % (about 11,000) of overlapping genes transcribed from the opposite strands. Based on our whole blood mRNA-seq datasets, the fraction of overlapping nucleotide bases on the same and opposite strands were estimated at 2.94 % and 3.1 %, respectively. The corresponding theoretical estimations are 3 % and 3.6 %, well in agreement with our own findings.
Stranded RNA-seq provides a more accurate estimate of transcript expression compared with non-stranded RNA-seq, and is therefore the recommended RNA-seq approach for future mRNA-seq studies.
RNA-sequencing (RNA-seq) is a next-generation sequencing technique that allows an in-depth look into the transcriptome [1–3]. Compared with microarray-based profiling, RNA-seq can detect the expression of low abundance transcripts and subtle changes under different conditions. RNA-seq has a wider dynamic range and avoids some of the technical limitations in a microarray experiment such as varying probe performance, cross-hybridization, limited dynamic range of individual probes, and nonspecific hybridization [4, 5]. RNA-seq is not limited to known transcripts and thus delivers unbiased and unprecedented information about the transcriptome and gene expression levels. With decreasing sequencing cost, RNA-seq is becoming an attractive approach to profile gene expression levels or specific transcript abundance, and to analyze differential gene expression between biological conditions.
While RNA-seq is emerging as a powerful technology in transcriptome profiling, one significant shortcoming of the standard RNA-seq protocol is that it loses the strand of origin information for each transcript. Synthesis of randomly primed double-stranded cDNA followed by the addition of adaptors for next-generation sequencing leads to the loss of information on which strand the original mRNA template is coming from, and without that information it becomes difficult to accurately determine gene expression from overlapping genes , i.e., those genes that have at least partially overlapping genomic coordinates, but are transcribed from opposite strands. Knowing the strand information of the cDNA is essential to determine from which of the overlapping genes the RNA transcript originates.
This new methodology is now emerging as a powerful tool for transcript discovery, genome annotation, and expression profiling [11, 12]. Previous reports demonstrated that data from stranded libraries are more reliable than data from non-stranded libraries and can correctly evaluate the expression of both antisense RNA and other overlapping genes . Maintaining strand orientation also allows identification of antisense expression, an important mediator of gene regulation. The ability to capture the relative abundance of both sense and antisense expression provides insight into regulatory interactions that might otherwise be missed . With the ability to unlock new information on global gene expression, stranded RNA-seq holds the key to a deeper understanding of the transcriptome.
To allow for efficient transcript/gene detection, highly abundant ribosomal RNAs (rRNAs) must be removed from total RNA before sequencing . One standard solution is to enrich for the polyadenylated (polyA) tail attached RNA transcripts (so-called mRNA-Seq) with oligo (dT) primers. Another approach removes rRNA through hybridization capture of rRNA followed by binding to magnetic beads for subtraction. For most transcriptome studies, mRNA-seq is commonly used, as the sequencing depth required is lower when focusing only on the protein coding fraction of the transcriptome. In this paper, we performed a side-by-side comparison of stranded and non-stranded mRNA-seq by sequencing the same samples using both protocols. We investigated and characterized gene overlap in our RNA-seq dataset, as well as performed theoretical analysis of the number of overlapping genes based on genome annotation in Gencode Release 19 . We demonstrate that stranded RNA-seq improves the accuracy of gene quantification, and this is especially critical for accurate gene expression quantification of antisense genes.
Results and discussion
Read mapping and counting
As shown in Fig. 3c, the majority of uniquely mapped reads are counted towards genes in both stranded and non-stranded RNA-seq as expected for mRNA-seq. About 7–8 % of mapped reads do not match to any gene and thus are excluded from gene quantification. The ambiguous reads in Fig. 3c are those reads mapped to overlapping gene regions, either on the same strand or from the opposite strands. To highlight the genomic loci with genes overlapping on the two opposite strands, the read ambiguity in Fig. 3c is zoomed out and shown in Fig. 3d. The read ambiguity in stranded RNA-seq arises only from overlapping genes transcribed from the same strand. In contrast, for non-stranded RNA-seq, the ambiguity arises from both the overlapping genes on the same strand and also from the opposite strands. For the four stranded RNA-seq samples, the read ambiguity is an average of 2.94 % (Fig. 3d and Additional file 1: Table S1), while for the four non-stranded RNA-seq samples it is 6.1 % (Fig. 3d and Additional file 1: Table S1). Compared with non-stranded RNA-seq, the percentage of ambiguous reads in stranded RNA-seq drops by approximately 3.1 %, and this drop roughly represents the magnitude of gene overlap from the two opposite strands. As we demonstrate below, the gene overlap from our RNA-seq dataset is also consistent with our theoretical estimation.
The correlation for gene expression levels among the eight samples studied is plotted in Fig. 3e. The samples are clearly clustered by sequencing protocol, and while the correlation for samples prepared with the same protocol is nearly 1, the correlation for samples sequenced by the two different protocols is around 0.93. The correlation plot in Fig. 3e indicates underlying gene expression profile differences between the stranded and non-stranded RNA-seq methods. The distribution of gene expression in each sample is shown in the boxplot in Fig. 3d (note the y-axis is log2(RPKM)). Overall, the distribution across samples is very similar. The 1st quartile, median, and 3rd quartile are approximately 0.77 RPKM, 3.0 RPKM, and 9.6 RPKM, respectively. The gene expression distribution plot in Fig. 3d is a good reference to evaluate whether gene expression is relatively low, medium, or high.
Theoretical estimate of frequency and magnitude of gene overlap
Genomic loci with longer overlapping genes will produce more transcript reads that cannot be uniquely assigned to either strand when using non-stranded RNA-seq. To further estimate the impact of overlap on gene quantification, we quantified the overlaps at the nucleotide level (Fig. 4b). On average, the estimated overlaps at the same and opposite strands are 3 % and 3.6 %, respectively, and this agrees very well with our practical RNA-seq data. According to our stranded RNA-seq dataset, the read ambiguity in overlapping genes at the same strand is 2.94 % (Fig. 3d and Additional file 1: Table S1), which is very close to the theoretical estimation (Fig. 4b and Additional file 1: Table S3). In Fig. 3d, the opposite strand overlap in our actual RNA-seq dataset is 3.1 %, slightly lower than the theoretical 3.6 % (Fig. 4b). It should be pointed out that the theoretical estimation is based upon the assumption that all genes in the Gencode annotation database are uniformly expressed. In an actual RNA sample, the expression level varies from gene to gene, including genes that are not expressed at all. In addition, with our chosen sequencing protocol, a transcript is not picked up if it does not have a polyA tail at the 3’ end. Still, the theoretical estimation in Fig. 4b explains very well the counting summary for ambiguous reads in Fig. 3d and Additional file 1: Table S1. In practice, the overlap in actual samples may be higher or lower than our theoretical estimation depending upon the gene expression profile in a sample.
The association between differential expression and gene overlapping is gene-type dependent
Next, we explored the association between differential analysis results and sequencing protocol. Every gene (dot) in Fig. 7 is either a DE (colored in red) or Non_DE (non-differential expression, colored in black) gene, and these genes are then further classified into one of two classes (i.e., “No” and “Yes”) based upon whether it overlaps with one or more genes transcribed from opposite strands. The overlap for each gene type is summarized in the last four columns in Table 1. The proportion of gene overlaps for all genes, DE genes, and Non_DE genes are shown in Fig. 8b. For protein coding, antisense and lincRNA gene types, the overlap is significantly higher in DE genes than in Non_DE genes. For instance, 87 % of antisense DE genes are overlapping genes, while only 60 % of antisense genes are overlapping genes in the Non_DE genes. For pseudogenes, no apparent association is observed, and confirmed by statistical test. To accept or reject the null hypothesis that differential expression and gene overlap are independent, the chi-square test was performed for the top four gene categories in Table 1. A contingency table was first prepared from the counts in the last four columns in Table 1, and then the chisq.test R function was called to evaluate the significance of the test. All tests report a P value lower than 2.2E-16, except for pseudogene (P value = 0.96).
As observed in Fig. 8, antisense genes are enriched substantially in differential expression, and this differential expression is strongly associated with gene overlap. The overwhelming majority of antisense DE genes show higher expression in stranded RNA-seq, and their expressions in non-stranded RNA-seq are quite often zero or very low. Antisense transcripts can act as regulatory elements in the regulation of gene expression , and a number of antisense transcripts are related to various human disorders . A proper elucidation of the antisense transcriptome and its quantification will reveal their novel function in regulation of gene expression. Based on these observations, we have shown that the stranded RNA-seq is more effective than non-stranded RNA-seq in properly quantifying expression for antisense genes.
The ENCODE project recently performed a survey of publicly available expression data to identify transcribed pseudogenes and found over 800 pseudogenes with strong evidence of transcription .
Recent studies have shown that some pseudogenes are transcribed and contribute to cancer when dysregulated . In particular, pseudogene transcripts can function as competing endogenous RNAs . However, reliable quantification of pseudogene expression remains a challenging problem for a number of reasons. First, because parent genes and pseudogenes are highly similar in nucleotide sequence, short RNA-seq reads derived from one may align equally well to others. Such reads are fundamentally ambiguous in terms of their origin. Second, some reads may have nearly identical alignment to locations in the gene and pseudogene, and their mapping is often determined by the location with the least error in alignment. This strategy is unreliable and can result in an incorrect assignment of the read . The enrichment of pseudogenes in differential analysis in Fig. 8a is hard to explain because the gene overlap from the opposite strand seems to not be the cause (see Fig. 8b). Of those 365 DE pseudogenes, 90 genes have higher expression in non-stranded RNA-seq, while 275 have higher expression in stranded RNA-seq. Usually the expression level for pseudogenes is not high. For those DE pseudogenes, the average expression is 3.9 RPKM across all eight samples, while for protein coding genes, the average is as high as 31.6 RPKM. We speculated the enrichment for pseudogenes might arise from (1) the read mapping uncertainty in pseudogenes, (2) the lower expression levels for pseudogenes, and (3) the additional bias introduced by sequencing protocols. We checked the read mapping profiles for some pseudogenes (unpublished results), and found that quite often those reads that mapped to pseudogenes have mismatches. Because of the intrinsic uncertainty in read mapping, we should be cautious about the gene quantification and differential analysis results for pseudogenes.
Exemplary differential expression genes
Because those reads in Fig. 10 are not derived from IL24, an obvious question is why so many reads are mapped to the genomic region of IL24. As we know, our current gene annotation is neither complete nor comprehensive, and it is likely that such reads originate from a novel gene at the opposite strand of IL24. We currently do not have a good explanation for these mapped reads. However, the scenario in Fig. 10 has shown that stranded RNA-seq is likely more powerful than non-stranded RNA-seq in detecting potentially novel unannotated transcripts from regions in which there is not a currently annotated gene.
For the scenario in non-stranded RNA-seq in Fig. 11, it does not help if we use a different counting algorithm such as RSEM (RNA-Seq by Expectation-Maximization) . Despite the fact that RSEM is capable of fully handling reads that map ambiguously or fall into the gene overlapping regions, it proportionally distributes ambiguous reads according to the number of unique reads in overlapping genes. If a gene is completely contained within another gene, it has no unique read at all. As a consequence, zero reads are counted to that gene. According to the theoretical calculation above, there are a total of 582 genes completely contained with other genes from opposite strands. In short, the read ambiguity in non-stranded RNA-seq in Fig. 11 cannot be resolved by a purely computational approach, and stranded RNA-seq is required in this scenario to determine correct gene expression.
GAPDH (glyceraldehyde-3-phosphate dehydrogenase) is a well-known housekeeping gene with very high expression in most cell types and tissues. Compared with stranded RNA-seq, its expression in non-stranded RNA-seq is in fact underestimated (Fig. 9). The reason for this underestimation can be easily understood when considering the gene overlap shown in Additional file 1: Figure S3. All of the ambiguous reads in the overlapping region originate only from GAPDH in stranded RNA-seq, thus the expression for GAPDH in stranded RNA-seq is more accurate than non-stranded.
In this paper, we performed a side-by-side comparison of stranded and non-stranded RNA-seq, and investigated the gene overlap both in our practical whole blood RNA-seq dataset and from the theoretical perspective. Our study demonstrates that stranded RNA-seq provides a more accurate estimate of transcript expression compared with non-stranded RNA-seq and is therefore the recommended RNA-seq approach for all future mRNA-seq studies.
We used various freely available open source tools and implemented an in-house pipeline for stranded and non-stranded RNA-seq data analyses (Fig. 2). The details on each step in the data generation and analyses are described below.
Blood sample collection, RNA extraction, and globin mRNA depletion
Peripheral venous blood samples from five healthy male volunteers were collected in PAXgene Blood RNA tubes (PreAnalytiX GmbH, BD Biosciences, Mississauga, ON, Canada). Blood was pooled across subjects to create a single pooled sample. This pooled blood was dispensed into a set of approximately 10-mL aliquots. Total RNA was extracted from four aliquots of pooled blood using the PAXgene Blood RNA Kit (cat# 762164, Qiagen, Chatsworth, CA, USA) according to the manufacturer's protocol. The yield and quality of the isolated RNA were assessed using a NanoDrop8000 Spectrophotometer (Thermo Scientific, Wilmington, DE, USA) and Agilent 2100 Bioanalyzer (Agilent Technologies, Santa Clara, CA, USA), respectively. An aliquot of 1.5 mg of each RNA was further processed with a GlobinClear kit (cat# AM1980, Life Technologies, Carlsbad, CA, USA) to remove globin mRNA. After globin mRNA depletion, the quality and yield of the RNA were assessed again using an Agilent 2100 Bioanalyzer. Six hundred nanograms of RNA (post-GlobinClear) were divided into two 300 ng aliquots, with one aliquot submitted to stranded RNA-seq processing and the second aliquot submitted to non-stranded RNA-seq processing.
cDNA library construction and sequencing
For stranded RNA-seq, cDNA libraries were prepared with a TruSeq stranded mRNA library prep Kit (cat# RS-122-2101, Illumina, San Diego, CA , USA). For non-stranded RNA-seq, cDNA libraries were prepared with a TruSeq RNA sample preparation kit v2 (cat# RS-122-2001, Illumina). The resulting eight libraries were sequenced on a HiSeq 2000 (Illumina) using a paired-end run (2 × 100 bases). A minimum of 60 M reads were generated from each library. The clean raw sequence reads in FASTQ format were analyzed using the pipeline in Fig. 2.
Mapping and counting
The human genome database and gene annotation database were used to map and count sequence reads. Gencode Release 19 was downloaded from http://www.gencodegenes.org/releases/19.html. The reads were mapped to the hg19 reference genome using STAR v2.4.0h . The detail parameters for the STAR run were “--runThreadN 8 --alignSJDBoverhangMin 1 --outReadsUnmapped Fastx --outFilterMismatchNoverLmax 0.05 --outFilterScoreMinOverLread 0.90 --outFilterMatchNminOverLread 0.90 --alignIntronMax 1000000 --outSAMtype BAM SortedByCoordinate”. The mapping was performed on the Pfizer High Performance Computing cluster. The mapping summaries, such as the percentage of reads that were uniquely mapped, multiple mapped, or unmapped, were then collected from the log files of STAR runs (see Results).
To count reads mapped to individual genes in Gencode, the program featureCounts  was used. FeatureCounts assigns a read to a feature (a gene) or labels it as matching to no feature or as ambiguous if it matches more than one feature and it cannot determine which one it is. The parameters in featureCounts run were “-p -T 4 -F GTF -a hg19.gencode.v19.gtf -t exon -g gene_id -s $Strand -B -C --minReadOverlap 60” (note $Strand was set to 0 for non-stranded RNA-seq, and 2 for dUTP second strand marking RNA sequencing protocol). Only uniquely mapped reads are used in the counting step. Like the mapping step above, the counting metrics were collected from the summary file of each featureCounts run. Genes that have expression levels less than 1 CPM were labeled as low expressed. If a gene had zero or low expression across all eight samples, it was omitted from the correlation and differential expression analysis. This filtering step was included to reduce the false positives in the differential analysis .
Differential expression analysis
A counts table was generated by featureCounts and then used for the DE analysis. The differential analysis was performed by R packages edgeR 3.8.5  and Limma/voom 3.22.4 . We compared the stranded versus non-stranded sequencing groups. All genes with a fold change greater than 1.5 and a Benjamini-Hochberg adjusted p-value smaller than 0.05 were reported as DE genes.
Theoretical estimation of gene overlapping at the same and opposite strands
The estimation was performed by R package GenomicFeatures 1.18.3 . First, a transcript database (TxDB) was created from the Gencode annotation in GTF format by calling R function makeTranscriptDbFromGFF. We then extracted all exons from TxDb and grouped them by gene. According to strand information, the genes in each chromosome were divided into two groups. The overlaps at the same and opposite strands were quantified at both gene and nucleotide base levels (see Fig. 4). For each pair of overlapping genes, for example G1 and G2, the lengths for flattened exons were calculated and the short gene was selected for calculating the ratio of overlapping. The histogram and cumulative distribution of overlap were quantified (Fig. 5).
The protocol for the Pfizer Research Support Program to collect blood samples from volunteer donors was approved by the Schulman Associates Institutional Review Board (IRB#201065670; http://www.sairb.com/Pages/home.aspx). Written informed consent was obtained from all volunteer blood donors for the research described and potential publication thereof. A copy of the written consent is available for review by the Editor of this journal. Samples from individuals were coded at the time of collection and then pooled prior to data generation, removing any possible association of analytical measurements with a single donor.
Availability of supporting data and script
All the raw sequencing reads have been submitted to the NCBI Sequence Read Archive and are available under accession SRP056985.
The R script to estimate the gene overlap is attached as Additional file 1: Script 1.
The authors would like to thank Alexander Dobin for valuable assistance with running STAR. We received no funding support from any third party.
Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
- Mortazavi A, Williams BA, McCue K, Schaeffer L, Wold B. Mapping and quantifying mammalian transcriptomes by RNA-seq. Nat Methods. 2008;5(7):621–8.View ArticlePubMedGoogle Scholar
- Wang Z, Gerstein M, Snyder M. RNA-seq: a revolutionary tool for transcriptomics. Nat Rev Genet. 2009;10(1):57–63.PubMed CentralView ArticlePubMedGoogle Scholar
- Mutz KO, Heilkenbrinker A, Lönne M, Walter JG, Stahl F. Transcriptome analysis using next-generation sequencing. Curr Opin Biotechnol. 2013;24(1):22–30.View ArticlePubMedGoogle Scholar
- Malone J, Oliver B. Microarrays, deep sequencing and the true measure of the transcriptome. BMC Biol. 2011;9:34.PubMed CentralView ArticlePubMedGoogle Scholar
- Zhao S, Fung-Leung W-P, Bittner A, Ngo K, Liu X. Comparison of RNA-seq and microarray in transcriptome profiling of activated T cells. PloS ONE. 2014;9(1):e78644.PubMed CentralView ArticlePubMedGoogle Scholar
- Sanna C, Li W-H, Zhang L. Overlapping genes in the human and mouse genomes. BMC Genomics. 2008;9:169.PubMed CentralView ArticlePubMedGoogle Scholar
- Parkhomchuk D, Borodina T, Amstislavskiy V, Banaru M, Hallen L, Krobitsch S, et al. Transcriptome analysis by stranded sequencing of complementary dna. Nucleic Acids Res. 2009;37(18):123.View ArticleGoogle Scholar
- Zhong S, Joung J-G, Zheng Y, Liu B, Shao Y, Xiang JZ, et al. High-throughput illumina stranded rna sequencing library preparation. Cold Spring Harb Protoc. 2011;2011(8):5652.View ArticleGoogle Scholar
- Weissenmayer BA, Prendergast JGD, Lohan AJ, Loftus BJ. Sequencing illustrates the transcriptional response oflegionella pneumophila, during infection and identifies seventy novel small non-coding RNAs. PLoS ONE. 2011;6(3):17570.View ArticleGoogle Scholar
- Levin JZ, Yassour M, Adiconis X, Nusbaum C, Thompson DA, Friedman N, et al. Comprehensive comparative analysis of stranded rna sequencing methods. Nat Methods. 2010;7(9):709–15.PubMed CentralView ArticlePubMedGoogle Scholar
- Sigurgeirsson B, Emanuelsson O, Lundeberg J. Analysis of stranded information using an automated procedure for strand specific RNA sequencing. BMC Genomics. 2014;15:631.PubMed CentralView ArticlePubMedGoogle Scholar
- Mills JD, Kawahara Y, Janitz M. Stranded RNA-seq provides greater resolution of transcriptome profiling. Curr Genomics. 2013;14(3):173–81.PubMed CentralView ArticlePubMedGoogle Scholar
- Zhao W, He X, Hoadley KA, Parker JS, Hayes DN, Perou CM. Comparison of RNA-Seq by poly (A) capture, ribosomal RNA depletion, and DNA microarray for expression profiling. BMC Genomics. 2014;15:419.PubMed CentralView ArticlePubMedGoogle Scholar
- Harrow J, Frankish A, Gonzalez JM, Tapanari E, Diekhans M, Kokocinski F, et al. GENCODE: The reference human genome annotation for the ENCODE Project. Genome Res. 2012;22(9):1760–74.PubMed CentralView ArticlePubMedGoogle Scholar
- Dobin A, Davis CA, Schlesinger F, Drenkow J, Zaleski C, Jha S, et al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics. 2013;29(1):15–21.PubMed CentralView ArticlePubMedGoogle Scholar
- Liao Y, Smyth GK, Shi W. FeatureCounts: an efficient general purpose program for assigning sequence reads to genomic features. Bioinformatics. 2014;30(7):923–30.View ArticlePubMedGoogle Scholar
- Robinson MD, McCarthy DJ, Smyth GK. edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics. 2010;26(1):139–40.PubMed CentralView ArticlePubMedGoogle Scholar
- Law CW, Chen Y, Shi W, Smyth GK. Voom: precision weights unlock linear model analysis tools for RNA-seq read counts. Genome Biol. 2014;15(2):R29.PubMed CentralView ArticlePubMedGoogle Scholar
- Wu P-Y, Phan JH, Wang MD. Assessing the impact of human genome annotation choice on RNA-seq expression estimates. BMC Bioinformatics. 2013;14 Suppl 11:S8.PubMed CentralView ArticlePubMedGoogle Scholar
- Zhao S. Assessment of the impact of using a reference transcriptome in mapping short RNA-seq reads. PLoS ONE. 2014;9(7):e101374.PubMed CentralView ArticlePubMedGoogle Scholar
- Zhao S, Zhang B. A comprehensive evaluation of Ensembl, RefSeq, and UCSC annotations in the context of RNA-seq read mapping and gene quantification. BMC Genomics. 2015;16:97.PubMed CentralView ArticlePubMedGoogle Scholar
- Pruitt KD, Tatusova T, Maglott DR. NCBI reference sequences (RefSeq): a curated non-redundant sequence database of genomes, transcripts and proteins. Nucleic Acids Res. 2007;35(Database):D61–65.PubMed CentralView ArticlePubMedGoogle Scholar
- Hsu F, Kent WJ, Clawson H, Kuhn RM, Diekhans M, Haussler D. The UCSC Known Genes. Bioinformatics. 2006;22(9):1036–46.View ArticlePubMedGoogle Scholar
- Flicek P, Amode MR, Barrell D, Beal K, Billis K, Brent S, et al. Ensembl 2014. Nucleic Acids Res. 2014;42(Database issue):D749–755.PubMed CentralView ArticlePubMedGoogle Scholar
- Gene/Transcript Biotypes in GENCODE. [http://www.gencodegenes.org/gencode_biotypes.html]
- Faghihi MA, Wahlestedt C. Regulatory roles of natural antisense transcripts. Nat Rev Mol Cell Biol. 2009;10:637–43.PubMed CentralView ArticlePubMedGoogle Scholar
- Pei B, Sisu C, Frankish A, Howald C, Habegger L, Mu XJ, et al. The GENCODE pseudogene resource. Genome Biol. 2012;13(9):R51.PubMed CentralView ArticlePubMedGoogle Scholar
- Kalyana-Sundaram S, Kumar-Sinha C, Shankar S, Robinson DR, Wu YM, Cao X, et al. Expressed pseudogenes in the transcriptional landscape of human cancers. Cell. 2012;149(7):1622–34.PubMed CentralView ArticlePubMedGoogle Scholar
- Welch JD, Baran-Gale J, Perou CM, Sethupathy P, Prins JF. Pseudogenes transcribed in breast invasive carcinoma show subtype-specific expression and ceRNA potential. BMC Genomics. 2015;16(1):113.PubMed CentralView ArticlePubMedGoogle Scholar
- Andoh A, Shioya M, Nishida A, Bamba S, Tsujikawa T, Kim-Mitsuyama S, et al. Expression of IL-24, an activator of the JAK1/STAT3/SOCS3 cascade, is enhanced in inflammatory bowel disease. J Immunol. 2009;183(1):687–95.View ArticlePubMedGoogle Scholar
- Human protein atlas. [http://www.proteinatlas.org/ENSG00000105371-ICAM4/tissue]
- Li B, Dewey C. RSEM: accurate transcript quantification from RNA-Seq data with or without a reference genome. BMC bioinformatics. 2011;12(1):323.PubMed CentralView ArticlePubMedGoogle Scholar
- Bourgon R, Gentleman R, Huber W. Independent filtering increases detection power for high-throughput experiments. Proc Natl Acad Sci. 2010;107(21):9546–51.PubMed CentralView ArticlePubMedGoogle Scholar
- Lawrence M, Huber W, Pagès H, Aboyoun P, Carlson M, Gentleman R, et al. Software for Computing and annotating genomic ranges. PLoS Comput Biol. 2013;9(8):e1003118.PubMed CentralView ArticlePubMedGoogle Scholar