Introduction: RNA sequence analysis can be effectively used to identify aberrant splicing, and tumor suppressor genes are adequate targets considering their loss-of-function mechanisms. Sanger sequencing is the simplest method for RNA sequence analysis; however, because of its insufficient sensitivity in cases with nonsense-mediated mRNA decay (NMD), the use of cultured specimens with NMD inhibition has been recommended, hindering its wide adoption.Method: The results of Sanger sequencing of peripheral blood RNA without NMD inhibition performed on potential splicing variants of tumor suppressor genes were retrospectively reviewed. For negative cases, in which no change was identified in the transcript, the possibility of false negativity caused by NMD was assessed through a review of the up-to-date literature.Results: Eleven potential splice variants of various tumor suppressor genes were reviewed. Six variants were classified as pathogenic or likely pathogenic based on the nullifying effect identified by Sanger RNA sequencing. Four variants remained as variants of uncertain significance because of identified in-frame changes or normal expression of both alleles. The result of one variant was suspected to be a false negative caused by NMD after reviewing a recent study that reported the same variant as causing a nullifying effect on the affected transcript.Conclusion: Although RNA changes found in the majority of cases were expected to undergo NMD by canonical rules, most cases (10/11) were interpretable by Sanger RNA sequencing without NMD inhibition due to incomplete NMD efficiency or allele-specific expression despite highly efficient NMD. RNA sequence analysis is a simple yet powerful tool for pathogenicity classification of germline genetic variants. In the context of germline variant interpretation, the role of RNA sequence analysis is to identify aberrant splicing that leads to the loss of function of an allele via in-frame loss of a critical part of a protein or frameshifting changes. The mRNAs produced by frameshift variants are prone to nonsense-mediated mRNA decay (NMD), a physiological surveillance mechanism  that preWhen a specimen is available, the simplest and most effective method for RNA sequence analysis is the Sanger sequencing of cDNA after reverse transcription PCR (RT-PCR) . Sanger It is widely accepted that the efficiency of NMD is highly variable, depending on the position of premature termination codons (PTC). Most well-known factor of NMD efficiency is defined by the canonical rules, namely, the last exon rule and the 50 nt rule. By these two rules, PTCs in the last exon and those in last 50 nt of the penultimate exon are expected to escape NMD, respectively. However, it turned out that there are many other factors affecting NMD efficiency including random variance . Accordiin silico tools for aberrant splicing prediction, such as SpliceAI  for potential splicing variants found in the DNA sequencing of tumor suppressor genes. For apparently negative cases, in which no change was identified in the transcript, a review of up-to-date literature was performed to determine the recent experimental results for the same variant. This study was performed in accordance with the Declaration of Helsinki and approved by the Institutional Review Board of SMC, Seoul, Korea . The requirement for informed consent was waived due to the retrospective nature of the study, which was approved by the institutional review of IRB.RT-PCR and cDNA sequencing were performed to investigate altered splicing. Peripheral blood samples were drawn into vacuum tubes containing ethylenediaminetetraacetic acid and RNA was extracted from the leukocytes of patients and healthy controls. RNA was extracted by the TRIzol methods and 1 µg of RNA was reverse transcribed into cDNA using the random hexamer RT primer and RevertAid First Strand cDNA Synthesis Kit . cDNA amplification was performed using a GeneAmp PCR System 9700 Thermal Cycler  with target-specific in-house primers . The sizDuring the study period, Sanger RNA sequencing was performed on 11 patients to identify the splicing effects of potential splicing variants. The RNA sequencing results and clinical information of the 11 patients are described in SDHB  was detected by targeted panel sequencing of pheochromocytoma/paraganglioma-related genes. A retrospective review of medical records revealed that this variant was repeatedly detected in many patients in SMC who had paraganglioma and/or pheochromocytoma  with no conflicts. This variant was not observed in the general population (gnomAD v2.1.1) and was located at the 3′-end of exon 6 with a SpliceAI delta score of 0.90. RT-PCR revealed allele-specific expression (ASE), represented as the loss of the mutant allele, presumably caused by aberrant splicing that leads to frameshift or nonsense changes and subsequent highly efficient NMD  detected in two patients with early onset colon cancer  was observed in a patient with breast cancer having family history of breast cancer in mother and maternal aunt. Notably, this variant was classified as ‘Likely Benign (LBV)’ in ClinVar by multiple (5) submitters without conflict in interpretation. However, this variant was predicted to affect splicing (SpliceAI delta score 0.84), and RNA sequencing revealed exon 42 skipping (r.6096_6198del), resulting in a frameshift and premature termination  was observed in a patient who was clinically compatible with Peutz-Jeghers syndrome. According to medical records, the patient’s father and younger brother showed similar symptoms. This variant was predicted to affect splicing (SpliceAI delta score 0.47), and RNA sequencing showed a partial deletion of exon 5 (r.706_734del), which was also predicted to cause premature termination by a frameshift  were observed in a patient who underwent a medical checkup and had a family history of cancer. Notably, this variant was classified as VUS in ClinVar because of the naturally occurring skipping of exons 8 and 9, an in-frame deletion event. RT-PCR electrophoresis showed a smaller band in both the control and the patient, presumably due to a naturally occurring exon-skipping event  was detected in ler band , and RNAler band . This valer band .BRCA1/2 genes from three patients with breast or ovarian cancer . All these 3 variants showed similar predictions from SpliceAI, with loss of the original splice site; however, RNA analysis revealed the creation of a new splice site, resulting in the creation of a longer exon length due to the inclusion of intronic sequences.In-frame insertions of intronic sequences were observed in BRCA1  showed a 9 bp insertion of an intronic sequence .Case 7 involved a patient with bilateral breast cancer and a family history of a younger sister and daughter. RNA analysis of the VUS in BRCA1  showed a 9 bp insertion of an intronic sequence  and gastric cancer (elder brother). RNA analysis of the VUS in BRCA2  showed an 18 bp insertion of an intronic sequence .Case 9 involved a patient with breast cancer who showed triple-negative IHC of tissue specimens. The patient had no family history of breast or ovarian cancer. An RNA study of the VUS in MSH3 , located near the exon-intron boundary (3′-end of exon 17). RNA sequencing was performed because aberrant splicing could not be ruled out (SpliceAI delta score: 0.17). Aberrant splicing was not detected by RNA sequencing and false negativity caused by NMD was ruled out based on the heterozygous SNV observed in the cDNA (Case 10 involved a patient with a mixed germ cell tumor and no family history of cancer. A missense variant was noted in the cDNA .MSH2 (NM_000251.3:c.2635-24A>G). And a spliceAI delta score of 0.69. IHC results for MSH2 in tissue specimens were not available, and an electropherogram of cDNA showed no abnormal findings  and the mother (endometrium). Intronic variants were also observed in findings . Howevernscripts . ConsideDespite its essential role in interpreting potential splice variants, the clinical use of RNA sequence analysis remains limited. In addition to specimen accessibility and stability, additional costs and turnaround times are obstacles to the widespread application of confirmatory RNA sequence analysis of variants found in DNA sequencing. In this study, we described the results and interpretation procedure of 11 cases of Sanger RNA sequencing, including six cases that escalated from VUS to LPV. Considering that some of these variants are repeatedly found at our institute, suggesting allele frequencies higher than those reported in public databases, the results described in this study could be useful references for other clinicians or geneticists who encounter the same variants in the future. A description of how we interpreted the Sanger RNA sequencing results under the effect of NMD could also provide insights for other laboratories.To overcome the effect of NMD, Sanger sequencing of RNA should be performed with NMD inhibitor which requires the process of short-term culture of fibroblasts or mononuclear cells . HoweverASE can be utilized as a surrogate for the degree of NMD for the RNA sequence analysis of an exonic variant, as in Case No. 1 in this study. Distinction between allele dropout and loss of heterozygosity caused by NMD can be achieved by ruling out allele dropout by investigating RNA primer binding sites, as DNA sequencing data are available in the scenario of confirmatory RNA sequencing. More importantly, the primer design process can utilize this information to prevent allele dropout. For confirmatory tests for intronic variants, which constitute the majority of cases in the real world, exonic heterozygous variants can be searched and included as targets of RNA sequence analysis, if present, for ASE-based interpretation of NMD.As in this study, there do exist small proportion of cases in which the Sanger RNA sequencing results cannot be interpreted because of NMD. Considering its cost-effectiveness, Sanger RNA sequencing without NMD inhibition could be performed as a screening test for aberrant splicing identification, after which technically more demanding tests can be performed in the case of indeterminate results, as suggested by .In conclusion, the effect of potential splicing variants was identified in 10 of the 11 variants of tumor suppressor genes by Sanger sequencing of RNA without NMD inhibition. The results described in this study could serve as a useful reference for other clinicians or geneticists who encounter the same variants in the future. Sanger RNA sequencing without NMD inhibition is valuable in terms of cost-effectiveness in clinical settings.


Histones, which make up nucleosomes, undergo various post-translational modifications, such as acetylation, methylation, phosphorylation, and ubiquitylation. In particular, histone methylation serves different cellular functions depending on the location of the amino acid residue undergoing modification, and is tightly regulated by the antagonistic action of histone methyltransferases and demethylases. The SUV39H family of histone methyltransferases (HMTases) are evolutionarily conserved from fission yeast to humans and play an important role in the formation of higher-order chromatin structures called heterochromatin. The SUV39H family HMTases catalyzes the methylation of histone H3 lysine 9 (H3K9), and this modification serves as a binding site for heterochromatin protein 1 (HP1) to form a higher-order chromatin structure. While the regulatory mechanism of this family of enzymes has been extensively studied in various model organisms, Clr4, a fission yeast homologue, has made an important contribution. In this review, we focus on the regulatory mechanisms of the SUV39H family of proteins, in particular, the molecular mechanisms revealed by the studies of the fission yeast Clr4, and discuss their generality in comparison to other HMTases. In eukaryotic cells, DNA is tightly compacted as chromatin, and dynamic changes in chromatin structure have been linked to transcriptional regulation. The basic unit of chromatin is the nucleosome; the histone octamer containing two copies each of histones H2A, H2B, H3, and H4, forms the core, around which ∼147 bp of DNA wraps. The N-terminal tails of histones are subject to post-translational modifications (PTMs), such as acetylation, methylation, phosphorylation, and ubiquitylation ), and the Trithorax-group protein TRX . T. T28]. TSimilar experiments were performed on human SUV39H1, in which the activity of SUV39H1 was examined using trinucleosome substrates synthesized by ligating H3K9me3-containing nucleosomes and H3K9-unmodified nucleosomes in various combinations. As shown for Clr4, SUV39H1 shows a stronger HMTase activity in the presence of the adjacent H3K9me3 nucleosome. Interestingly, SUV39H1 HMTase activity was also enhanced by the addition of H3K9me3 peptide, suggesting that SUV39H1 HMTase activity is allosterically controlled . The reaAs mentioned above, CD is the domain that recognizes H3K9me3, suggesting that it contributes to the read/write mechanism of SUV39H family histone methyltransferases, but the flanking regions of CD have been shown to have roles other than H3K9me3 recognition. In fission yeast, repetitive sequences in the pericentromeric region are transcribed by RNA polymerase II, and the transcribed RNA is converted to double-stranded RNA by RNA-dependent RNA polymerase (Rdp1), which is cleaved by Dicer (Dcr1) to form small RNA molecules (siRNAs). The siRNA is then incorporated into an effector complex, RNA-induced transcriptional silencing (RITS), which targets heterochromatin to promote Clr4 to deposit H3K9me3. This transcription by RNA polymerase II and the introduction of H3K9me3 by Clr4 are coupled, and the whole process is regulated as a feedback loop. Recognition of nascent RNA by the RITS complex is primarily accomplished by base pairing between siRNAs in RITS and transcribed nascent RNA, but it has been suggested that sequence-independent RNA binding by other factors also contributes to the targeting of RITS to the heterochromatin region. For example, Chp1, a component of the RITS complex, has a CD, but there is a cluster of basic amino acid residues in the C-terminal α-helix region, and it has been shown that Chp1 binds to RNA through this CD-flanking region and that the RNA-binding ability of Chp1 is necessary for Chp1 function . InteresDo other members of the SUV39H family of HMTases have similar RNA-binding activity, and is such activity involved in their function? While no obvious RNA silencing mechanism is known to be involved in the formation of pericentromeric heterochromatin in human and mouse cells, as has been demonstrated in fission yeast, it has been reported that satellite repeats are transcribed at the pericentromeric regions . The assDrosophila Su(var)3-9 contains the N-terminal disordered region, which is rich in basic amino acid residues, suggesting that the RNA-binding ability associated with this region may contribute to the targeting of Su(var)3-9 to heterochromatin. Notably, the PRC2 complex containing EZH2 also binds to RNA, but the presence of RNA inhibits H3K27 methylation activity by preventing the binding of the substrate nucleosome [Human and mouse cells express SUV39H2, in addition to SUV39H1, and SUV39H2 has a basic domain enriched in basic amino acid residues at its N-terminal region. This basic domain plays an important role in Suv39h2 heterochromatin binding, and detailed biochemical analyses showed that, like Suv39h1, Suv39h2 binds to single-stranded RNAs derived from major satellite repeats via this basic domain and that Suv39h2 chromatin-binding is RNA-dependent . Interescleosome ,35,36. TDDB1) [DDB1 complex, Raf2 interacts with Cul4, Rik1, and Raf1, suggesting that Raf2 acts as a hub in the CLRC complex [DDB1, CLRC exhibits ubiquitylating activity in vitro [In fission yeast, Clr4 forms a protein complex called the Clr4 methyltransferase complex (CLRC). The CLRC consists of the cullin scaffold protein Cul4, the β-propeller protein Rik1, the WD-40 protein Raf1 (Dos1/Cmc1/Clr8), the replication foci targeting sequence (RFTS)-like domain-containing protein Raf2 (Dos2/Cmc2/Clr7), and the RING-box protein Rbx1 (Pip1), and these CLRC components are all required for heterochromatin silencing ,39,40,41DDB1) ,43. Alth complex . As expein vitro . HoweverPreviously, we performed an in vitro ubiquitylation assay using affinity-purified CLRC and demonstrated that CLRC preferentially ubiquitylates lysine 14 on histone H3 (H3K14). Mass spectrometry analysis of chromatin immunoprecipitated fractions revealed that the H3K14 ubiquitylation (H3K14ub) is tightly associated with H3K9me-enriched heterochromatin in vivo. Importantly, by using K14-ubiquitylated histone H3 as a substrate in the in vitro methyltransferase assay, we further demonstrated that the K14-ubiquitylated H3 promotes the methyltransferase activity of Clr4 . The proPrevious studies have shown that the H3K14-specific histone deacetylase Clr3 is important for heterochromatin formation in fission yeast ,48, but Neurospora crassa, the H3K9 methyltransferase Dim-5 forms a complex with Cul4 and DDB1 called the DCDC, which is required for Dim-5-mediated H3K9me [DDB1 or an unidentified E3 ligase is involved in the ubiquitylation of H3 in heterochromatin will be the subject of future studies.In d H3K9me . In humad H3K9me , and depd H3K9me . In addid H3K9me , and prod H3K9me . It is, d H3K9me  (our unpCrosstalk between histone methylation and ubiquitylation has been demonstrated for other HMTases. For example, the activity of budding yeast Set1, an H3K4 HMTase, requires H2B monoubiquitylation ,55,56,57In this review, we focus on the regulatory mechanisms of the activity of the SUV39H family enzymes, in particular the results obtained in the study of Clr4 in fission yeast. As mentioned above, the activity of the SUV39H family is regulated in several steps: inhibition of enzymatic activity by automethylation, promotion of enzymatic activity by CD’s H3K9me3 recognition on the neighboring nucleosomes, targeting to heterochromatin by intrinsic RNA-binding activity, and crosstalk between histone ubiquitylation and methylation. We hope you will understand that some of these regulatory mechanisms have been revealed, mainly through the analysis of Clr4 in fission yeast. While the details of the individual regulatory mechanisms have been elucidated, and we have discussed their evolutionary conservation, but the key question that remains unanswered is how these regulatory mechanisms are integrated within the cell to control the activity of the enzyme. How is the automethylation of Clr4 and other SUV39H family HMTases regulated in cells? How can Clr4 and SUV39h family HMTases act on target chromatin in the absence of H3K9me3, and how is CD-mediated repression released? How is the RNA-binding activity of Clr4 and SUV39H family HMTases regulated in a way that does not compete with binding to nucleosomal DNA? How is histone H3 ubiquitylation dynamically regulated in the cell? These important questions should be addressed in the future by integrating our knowledge of SUV39H family enzymes from different species.


Sequencing the human genome empowers translational medicine, facilitating transcriptome‐wide molecular diagnosis, pathway biology, and drug repositioning. Initially, microarrays are used to study the bulk transcriptome; but now short‐read RNA sequencing (RNA‐seq) predominates. Positioned as a superior technology, that makes the discovery of novel transcripts routine, most RNA‐seq analyses are in fact modeled on the known transcriptome. Limitations of the RNA‐seq methodology have emerged, while the design of, and the analysis strategies applied to, arrays have matured. An equitable comparison between these technologies is provided, highlighting advantages that modern arrays hold over RNA‐seq. Array protocols more accurately quantify constitutively expressed protein coding genes across tissue replicates, and are more reliable for studying lower expressed genes. Arrays reveal long noncoding RNAs (lncRNA) are neither sparsely nor lower expressed than protein coding genes. Heterogeneous coverage of constitutively expressed genes observed with RNA‐seq, undermines the validity and reproducibility of pathway analyses. The factors driving these observations, many of which are relevant to long‐read or single‐cell sequencing are discussed. As proposed herein, a reappreciation of bulk transcriptomic methods is required, including wider use of the modern high‐density array data—to urgently revise existing anatomical RNA reference atlases and assist with more accurate study of lncRNAs. Transcriptome‐wide molecular profiling of cells or tissue is used for multiple purposes including classification, pathway biology and drug repositioning. Replacement of microarray technology by short‐read RNA sequencing is premised on its superior performance. An equitable comparison reveals that the modern array yields a more reliable representation of the constitutively expressed transcriptome, while RNA‐seq does not accurately represent the long noncoding transcriptome nor pathway‐level modulation. Transcriptomics has led to various tools for stratified medicine by identifying signatures that better match patients to drugs. Typical statistical analyses applied to clinical transcriptomics data includes differential gene expression (DE) based on group mean differences; regression analyses of transcript abundance versus clinical status; and classification approaches. Regression and DE analyses are then used to identify the molecular pathways regulated between groups.High‐throughput profiling technologies are increasingly used to define the molecular repertoire of disease at the (epi)genomic, transcriptomic, proteomic, and metabolomic level.14 Suc although this can be done when applied appropriately. Rather, RNA expression acts as a “biosensor”, integrating environmental, epigenetic, and genetic influences. Modeling transcriptomic data provides information about the activity of proteins in a pathway, for instance, transcription factor status. While protein abundance may not inform on protein activity, transcriptome analysis can identify protein activity across networks and canonical pathways and thus extends beyond a simple surrogate for protein abundance. Each multi‐omic technology captures distinct and shared information, while each also introduces its own specific sources of variance. The recent trend to report that global transcriptomics fails to capture biology identified only by proteomics can be explained by poorer quality transcriptomics, including use of relative count RNA‐seq data, a lack of consideration of temporal relationships between transcription, translation, and proteostasis or the reliability of pathway or network models used to report differences. In general, studying any mismatch between RNA and protein requires a more equitable and balanced view on the limitations of all the laboratory methods employed. Thus, while the trend towards integrating multi‐omic data continues, in a translational medicine setting, a single robust technology may deliver sufficient information and use fewer resources when applied to disease diagnosis or prognosis. For these reasons, transcriptome profiling will remain at the forefront of precision and stratified medicine efforts.Capturing RNA expression is not done merely as a surrogate for measuring protein abundance;45 alt and the persistent overly pessimistic view of arrays. We do not consider 3rd generation (long read) RNA sequencing methods in detail as they are costly with limited throughput, representing basic research tools with very fluid and evolving laboratory and informatics protocols. We do not discuss single‐cell sequencing technologies for similar reasons. Notably, the myriad of distinct single‐cell methods provide fractional (and often 3’ directed) coverage of the transcriptome, dominated by higher abundance genes, and thus provides a biased view of the single‐cell transcriptome. Such limitations affect the validity of any pathway analyses representing an issue shared with 2nd generation short‐read RNA‐seq assays. The reader is directed elsewhere to in‐depth discussions of the biases inherent to single‐cell methods.Herein, we reflect on the methods used for transcriptomics applied to translational medicine studies, including the reliability to cover the expressed transcriptome and the validity of pathway analysis. Processive technologies must ensure good reproducibility with comprehensive and accurate quantification of the transcriptome to ensure down‐stream analysis is reliable. Large‐scale tissue/blood sample acquisition from clinical trials or from biobank initiatives are costly, especially if accompanied by deep clinical phenotyping. We focus on the merits of the two main bulk transcriptome technologies (RNA‐seq and modern arrays) and consider these in light of whether there is limited clinical materials and study costs. In doing so, we seek to rebalance what we consider to be the overly “enthusiastic” introduction of short‐read RNA‐seq,69 and2 Over a decade later, Sanger introduced “chain‐terminating” sequencing, enabling similar results in days. First‐generation sequencers employed a modified version of the Sanger method, permitted parallel running of 96 reactions and ≈200 bases per sample, to be sequenced per hour. This approach is highly accurate and can remain a method of choice for diagnosing rare diseases. Today, Affymetrix array and short‐read RNA‐seq are the two most widely used approaches for gene expression profiling of clinical samples . A biotinylated “sandwich” assay then yields a fluorescent signal in proportion to the concentration of the copy DNA (cDNA)—a facsimile of RNA as a strand‐specific DNA. Early Affymetrix array designs required 5 “chips” per clinical sample to cover the complete draft of the transcriptome. Each probe was designed to measure the 3’ end of transcripts. The drive to unravel the complexity of transcriptome, combined with the availability of the draft genome sequence, led to the development of the “chromosome tiling” array which studied transcription from the entire chromosome or genome. However, these were costly and never intended as a routine tool to quantify transcript abundance.Various array designs emerged in the 1990's, each with various designs and technical capabilities92—wit and provide equitable profiling of the coding and non‐coding transcriptomes. Informatic analysis of this technology involves bringing together the signal from multiple copies of 25‐mer probes, distributed across the array surface. The probe level signal is assembled by combining a minimum of three probes into “probe‐sets”. The exact composition of each probe‐set is defined by reference to a “map” called a chip definition file (CDF); which is routinely configured to exon or transcript level resolution. Regardless of when the array was designed, the design of the CDF can be updated regularly to ensure this process remains accurate. The physical design of the array can also be updated as the transcriptome becomes more complete. The raw data from each array experiment is retained as a binary “CEL file”. For very large‐scale projects, the high throughput lower density “peg” array may be useful, processing clinical samples in 96‐ and 384‐well format using a GeneTitan workstation (Thermofisher Inc). This format provides coverage of the known protein‐coding transcriptome, but with limited resolution, while the equivalent non‐coding array remains to be developed.Modern high‐density arrays, relying on ≈7 million probes to cover the known transcriptome, became available in 201598 and Through cross‐reference with the literature, the minimal signal for genuinely expressed genes appears to vary between 2 and 3 log2 units. By this criteria, low expressed genes essential physiological roles can be detected, for example, for muscle we observe expression of nicotinic acetylcholine receptor RNA from the neuromuscular junction. Identification of physiologically active, but low‐expressed genes, for each tissue type, along with statistical thresholds, is required to define the lower end of the biological signal. Further, a study‐specific probe‐level scan can be used to remove probes with aberrant signals, before probes are combined into transcript level CDF design. This study‐specific approach typically removes ≈20% of probes, while realignment—to check probe specificity—removes a further 15%. Thereafter, greater than 90% of the remaining probe‐sets produce a signal above the empirical threshold of between 2 and 3 log2 units. The signal from each probe on the high‐density arrays can also be scaled by their guanine and cytosine (GC) content  by processing the CEL file. This process makes the signal generated more comparable across genes (further distinguishing this data type from RNA‐seq based relative counts). In the end, about 5 from 7 million probes are typically used to detect ≈500,000 exons or ≈100 000 transcript probe‐sets in each human tissue type, using a Human Transcriptome Array (HTA) 2.0 array. Transcript signals can be further summarized in a targeted manner for statistical analysis; for example, the signal originating only from the untranslated region can be extracted and compared across conditions, identifying regulatory events unseen with conventional gene level analysis. Finally, it should be noted that the quantification of very short transcripts (<30 base pairs (bp)) by an array will not be ideal as such a signal would rely on less than three distinct probes.Modern high density arrays generate a signal ranging from <1 to 16 on the log2 scale.100 Th2.2 An approach developed by Solexa (acquired by Illumina) generates most of the sequencing data deposited on the Sequence Read Archive (SRA) and thus is the focus of this review. Short‐read RNA‐seq requires making a DNA copy of RNA before analysis, after which in situ cluster formation by amplification is tracked. DNA clusters are generated on a flow cell using a modification of PCR, called bridge amplification, where DNA polymerase directs chain elongation from DNA templates using reversible chain‐terminating nucleotides. Each cluster of amplified nucleotides is then “read” – typically via a four‐color system for base calls—to determine sequence information. Short‐read RNA‐seq has several laboratory requirements that array protocols do not, such as initial depletion of the most abundantly expressed genes  which if not removed, dominates most of the sequence reads.Next‐generation sequencing short‐read RNA‐seq emerged in 2008, reflecting advances in several laboratories.105 An scaled to the total number of mapped reads in the sequencing run. Mapping each signal to an individual gene is mostly done using a reference transcriptome/genome. Allocation of multi‐mapping reads is achieved using probabilistic‐based estimates, which remains a source of bias and uncertainty. Counts represent the relative expression of the gene within the cDNA library during a single sequencing run. Longer genes yield more reads, but other factors such as nucleotide content also influence the number of reads counted, rendering the quantification of transcripts a nontrivial challenge. The influence of gene length on data processing can artificially result in significant pathways in down‐stream statistical analysis.Short‐read RNA‐seq “counts” are based on counting reads of 50–200 bases, and gene abundance is inferred from the number of counts that map back to a given gene,109 sc Here, reads are assembled using various complex models, with or without the use of a genome from a related species. While any initial alignment can be quick, the complete process is iterative and may take weeks or months fully evaluate the validity of the modeling choices. Such analyses are not part of the standard transcriptomics service, while the lack of precise reporting of the hundreds of potential experimental options for RNA‐seq limits meaningful replication. Thus, despite the potential capacity for transcript discovery, most short‐read RNA‐seq studies are not designed to report novel transcripts.Laboratories specializing in genomics, carry out complex “de novo” assembly of sequencing data to discover novel transcripts, often in non‐mammalian species without completed reference genomes.113 He3 explicitly reflecting on the “death of the microarray” and these have fueled a persistent inaccurate view of arrays. To re‐examine this topic, it is critical to contrast the most common short‐read RNA‐seq method with the latest modern high‐density array and reflect on empirical performance rather than any hypothetical advantages. Using a variety of published datasets produced from human clinical samples  we present easy‐to‐follow head‐to‐head comparisons. We have reported comparable observations using post‐mortem human brain as well as from snap‐frozen RNA form blood and adipose tissue, elsewhere.Several influential articles compared RNA‐seq with (older) array technology,105 ex these improvements have not led short‐read RNA‐seq to become cost effective compared with the modern array, reflecting longer laboratory and informatics processing times than arrays (Table For a gene profiling technology to be useful for modeling disease biology (pathways or networks), it must capture the complexity of the transcriptome with limited bias, and that bias must be quantifiable. To have cost‐effective clinical utility, it should be reproducible, generate consistent data comparable to a reference, and have a quick turnaround cycle involving robust computational pipelines. Costs of raw data storage should also not be overlooked. Short‐read RNA‐seq produces gigabytes of summarized data per sample, while each modern array raw data file is 70 MB. While short‐read RNA‐seq technology benefits from efforts to make all forms of DNA sequencing more cost‐effective121 th It highlighted a comparison with the now discontinued BeadChip array (probes for ≈13 000 genes) and, unsurprisingly, found that RNA‐seq detected ≈25% more genes. Yet had an old Affymetrix U133+2 array been used; it would have performed as well as RNA‐seq at this time. A detailed comparison was made with the “chromosome tiling” array, reporting that RNA‐seq had less background noise, improved ability to differentiate between transcript isoforms, and greater dynamic range as well as better reproducibility for DE analysis. These comparisons are not terribly meaningful as the “chromosome tiling” array was not designed as a quantitative tool, nor does this comparison accurately reflect the performance of a modern array.A highly influential 2009 review (with > 13 500 citations) provides the basis for the often‐stated superior performance of RNA‐seq.69 It  SEQC utilized the Affymetrix U133+2 array (older array with 3’ biased probes) and compared performance with at the time state of the art RNA‐seq (which was not like‐for‐like in terms of cost). RNA‐seq unsurprisingly discovered novel exon‐exon junctions and transcripts versus what was possible with the older array. SEQC also reported that relative expression results were consistent among different RNA‐seq platforms, but only if specific ad hoc filters were used. Notably, SEQC reported a comparable performance between RNA‐seq and the U133+2 array technology, with the best DE results produced by the U133+2 array . In general, we believe that the conclusions reached in 2014 need to be revisited to accommodate comparison between typical RNA‐seq and modern high‐density array using best practices.An in‐depth comparison of short‐read RNA seq and arrays was made by the Array/Sequencing Quality Control Consortium in 2014 (SEQC/MAQC‐III Consortium or SEQC for short) in a series of publications. SEQC considered sensitivity, transcriptome coverage and DE.122 SE They did note that including genes with very high counts (top 10%) exaggerates any reproducibility calculation . For the remaining transcriptome, technical reproducibility was reported as being R2 = 0.7, similar for biological replicates using the HTA 2.0 array . Unfortunately, many studies still quote erroneous values for reproducibility. As mentioned above, specialized sequencing protocols will probably profile short RNAs (<50 nucleotides) better than arrays. However, independent technical replicates are difficult to locate for this type of RNA‐seq data, and estimating consistency has numerous caveats. In general, discussion of technical reproducibility is rare in the literature. In addition, raw RNA‐seq count data can often be modified—prior to statistical modeling—adding a small positive signal to all values prior to log transformation. When applied to the extremes of signal abundance this can lead to misleading conclusions .The SEQC publications did not discuss technical reproducibility of short‐read RNA‐seq in much detail nor did they study biological replicates.122 Th of the transcriptome? According to the latest release from the Genome Reference Consortium, the human genome (GRCh38.p13) consists of >20 000 protein‐coding genes, >24 500 non‐coding genes, and >15 000 pseudogenes and these collectively give rise to >245000 possible transcripts. While >50% will show low or no level of expression in any given cell type, low‐expressed genes nonetheless serve vital roles in physiology, for example, transcription factors, signaling kinases and receptors. For RNA‐seq, a gene known to be expressed but not detected  in a cDNA library, returns a “zero value”; while a value will be generated for each probed expressed gene, in each sample, using the modern array, down to the level of detection of that platform. Existence of “zero values” across sample rows is an underappreciated problem for RNA‐seq, representing up to 50% of the data file for projects using typical sequencing depths . How those “zero values” are modelled is critical and can impact on estimates of variance within the dataset and thus influence false discovery rate (FDR) calculations. Missing values are problematic for classification and regression models because they modify the sample size being analyzed per gene. Further, if the aim is to test the performance of a multi‐gene classifier, the assessment in any individual sample may be compromised by the stochastic coverage achieved with RNA‐seq. If instead of raw count values, the investigator is provided “transformed” data by a core facility service, the complexity introduced by “zero counts” would be obscured.Does RNA‐seq deliver the promised unbiased coverage70 of  Details of the code used, and input data can be found here https://doi.org/10.5281/zenodo.7430956. We found that between 12 000 and 15 000 genes are consistently detected in all samples from each individual study. Notably, only 8700 protein‐coding genes were expressed in all samples across all studies .To illustrate the constitutively expressed transcriptome for human muscle tissue, we produced plots for five RNA‐seq studies of human muscle tissue.133 Des Figure . Muscle s Figure . Further modern arrays now cover most of the annotated transcriptome, detecting ≈10% more protein‐coding genes than RNA‐seq, in the example we present  and thus are not just a subsample from higher abundant lncRNAs . This hypothetical benefit of RNA‐seq is not encountered in most clinical studies, as the cost of very deep sequencing is prohibitive and deep sequencing introduces other biases, compromising the analysis of the data (see below).Failure to be represented in the cDNA library is the most likely reason RNA‐seq data show poor agreement for lncRNAs across data sets and only a fraction of those detected by array Figure 2A–D. LncFigure 145 Whs Figure . In facts Figure . A genui This aspect of transcriptome coverage also impacts on pathway analysis of single‐cell sequencing, as most methods to date quantify three or four thousand transcripts per cell. Biased in high abundance RNA from metabolic genes, this has implications for the nature of sub‐clusters of cells based on gene expression. In summary, cDNA libraries produced by RNA‐seq protocols appear to have stochastic properties, ensuring genes are missing seemingly at random, such that in practice modern arrays provide broader and less biased—by biotype—coverage of the transcriptome.In contrast, RNA‐seq detected lncRNAs did tend to originate from those which exhibit less variable expression (array data) across muscle samples Figure . This mo148 Th or even up to 1 billion reads may be required. For protein‐coding genes, commonly detected in both RNA‐seq and HTA 2.0 array, the coefficient of variation (CoV) for the RNA‐seq data is greater than observed using the array  is key. Variance is not equally distributed across the rank order of counts (RNA‐seq) or signal (array), and thus power to detect DE modeling will be influenced by gene “abundance”. It is estimated that for RNA sequencing to quantify DE of lower expressed genes to the same degree as modern arrays, 150 million98 or y Figure . This rey Figure , implyin particularly for genes expressed at low levels. However, the opposite has since been reported when RNA‐seq was compared with a modern array, with the HTA 2.0 array detecting ≈25% more DE than RNA‐seq—leading to the discovery of more regulated pathways. The same authors noted that several low‐expressed protein‐coding genes detected by the array were not detected by RNA‐seq. Recent modelling concluded that modern arrays quantify “low signal” genes more accurately, with highly abundant genes being assayed more equivalently. Indeed, DE analysis of abundant genes will generally produce similar conclusions when comparing arrays with RNA‐seq yet this sort of outcome does not satisfy the intended purpose or utility of global transcriptomics. Other recent reports have also concluded that modern high‐density arrays are more sensitive; and this is before any added benefit of using an optimized CDF design. Thus, any claim that RNA‐seq is superior at detecting DE—in a like‐for‐like setting—is probably incorrect, especially for the many genuinely expressed genes, it fails to detect. Systematic differences between the occurrence of zero counts between cases and controls requires special attention in DE analysis of RNA‐seq data, given that zero counts are not synonymous with “not expressed”, and the profound influence zeros (or “adjusted” zeros) have on any estimation of group mean variance.Data generated by modern arrays or short‐read RNA‐seq differs in other substantial ways. Observations presented above indicate that in practice the modern array detects more of the tissue transcriptome, with less bias (biotype and abundance). Greater variation, with RNA‐seq, should convey a disadvantage in detecting DE, yet the SEQC reported that RNA seq outperformed old arrays for drug‐induced DE,137 pa claims that are partly attributable to early inequitable comparisons and claims that now should be retired. In practice, the modern high‐density array offers superior coverage for similar costs, and in the following sections, we cover some of the reasons that underpin these empirical observations , and reassurances that 20–50 million read alignments are sufficient will not cover every situation. Characterizing array performance as an equivalent depth of sequencing is also difficult. For example, ≈65 million reads are reported to match the older 3’ Agilent array and 40 million aligned reads for the old U133+2 array. To match the HTA 2.0 Affymetrix array, the required read depth has been estimated to be >150 million paired‐end reads (ThermoFisher.com). The fact that the HTA 2.0 array provides quantification for >50% more transcripts than 50 million paired‐end reads  and this cDNA library is not a complete representation (“coverage”) of the transcriptome. Even exceptionally deep (and costly) sequencing will only detect a gene if a copy is present in the cDNA library.155 Cos Figure , illustr Failure for a genuinely expressed gene to make it into an RNA‐seq cDNA library results in a “zero count” entry in the data file—providing the exact same profile as a gene that is not expressed. Visual inspection of raw count data reveals that “zero counts” predominate for ≈50% of the genome in any given RNA‐seq study. A small positive value can be added before the transformation of counts to other units, results in obfuscating these “zeros”. This type of standard data manipulation complicates the estimate of variance across the data impacting for example, on permutation‐based FDR calculations. It is challenging to know if the “zero counts” represents a failure in the cDNA library, a genuine lack of expression or insufficient depth of sequencing. For lncRNA, we show that lack of detection almost certainly reflects a failure in the cDNA step, with limited consistency and low coverage across five independent RNA‐seq studies, yet most “RNA‐seq detected lncRNAs” are detected using the array  is not easily defined or varies between replicate studies Figure  then ont82 Faiy Figure .Figure  but reflect other technical deficits in the RNA‐seq process. Heuristics to include genes across fewer than 100% of all samples might help address the question of whether a gene is expressed in a subgroup of patients, yet it then alters the working sample size for data modeling, complicates the definition of “background expression” and the threshold chosen should be gene specific and thus complex to implement.Two further conclusions can be reached if we consider the relationship between the number of samples in a study with zero counts (for each gene) and the average expression . First, when we plot the frequency of zero counts (RNA‐seq), against the relative abundance of protein‐coding genes determined by RNA‐seq or array we can see that many genes even with relatively low counts, for example, 6–50 reads, have consistent signals in all samples  represents most of the RNA in a clinical sample and will account for >80% of all sequencing reads if not removed . Thus, rRNA is usually depleted using hybridization‐based capture approaches prior to making the cDNA library for RNA‐seq (leaving behind <20% of the rRNA). Alternatively, oligo‐dT primers can be used to enrich the cDNA library in polyadenylated (poly‐A) transcripts (avoiding rRNA). Libraries prepared with rRNA depletion can better represent the diversity of RNA molecules , whereas those using poly‐A enrichment primarily detect protein‐coding genes. Depletion protocols for rRNA are in turn sensitive to RNA purification methods. Both of these methods introduce bias, for example poly‐A selection approaches fail to capture the expression of repetitive non‐coding RNA elements—which can be important for defining treatment responses. These two methods also alter gene quantification among commonly detected genes, resulting in ≈50% of genes demonstrating DE from the two cDNA libraries created from the same RNA sample. This result is predictable as many protein‐coding transcripts can be poorly polyadenylated, yet it illustrates that the choice of RNA processing strategy is hugely influential.Sample processing steps impact what is discernible by any transcriptomic technology.160 Ho Further, rRNA is not the only high abundance RNA species, for example, mRNA encoding mitochondrial genes (in highly metabolic tissues) or globin RNA in the case of blood, and the contribution of these genes to total reads is highly variable across samples. The result is that there is large variation in the effective depth of coverage per sample, that is, the sequencing depth for most of the transcriptome. Avoiding poly‐A‐based selection strategies may enable greater inclusion of non‐coding RNA in the cDNA library however the library remains deficient in lncRNA molecules, and this has led to erroneous conclusions regarding cell‐specific patterns of lncRNA expression.As well as the additional costs, time, and variation introduced by the rRNA depletion protocol, cDNA libraries created following rRNA depletion typically require greater depth of sequencing (≈2.5×) to achieve comparable coverage of protein‐coding genes as poly‐A enrichment.163 Fu while more recently others report better success from FFPE (>80% mapping), particularly with large amounts of RNA (1 µg). Often degraded, substantial additional quality control steps are required to remove artifacts from the FFPE RNA‐seq data. Array protocols can reliably profile the transcriptome from FFPE samples, producing results at least comparable to fresh‐frozen tissue analyzed by RNA‐sequencing. Thus, the necessity for RNA selection protocols to remove high abundance RNAs during short‐read RNA‐seq workflows introduces bias and extra time and costs and no current protocol resolves all the identified issues. We recommend that the high‐density array is used with the whole‐transcriptome (WT) reagent kit as it does not require RNA selection prior to amplification or use of PCR and is associated with a broad representation of both coding and noncoding RNA.Further, variation in RNA preparation is apparent when analyzing formalin‐fixed and paraffin‐embedded (FFPE) samples—a storage choice for many clinical histopathology specimens. RNA derived from FFPE samples suffers from degradation and is poorly suited to poly‐A enrichment protocols. Following rRNA depletion, coverage can be limited, with reports of only ≈20% of sequencing reads mapping to coding regions163 wh6 For most RNA‐seq technologies, quantification of transcripts expressed at these lower levels requires amplification of the input RNA and this is accomplished predominantly using PCR, a method that enabled next‐generation sequencing by allowing amplification of adaptor‐ligated cDNA in hours, instead of days. However, not all RNA species are amplified with the same efficiency, and some are lost, while others are selectively amplified. As many mRNAs for signaling proteins or transcription factors are expressed less abundantly than those coding for structural or metabolic proteins, biased amplification impacts on pathway analysis—where the background of “expressed” genes is ill‐defined—see below. Furthermore, any biases introduced by the RNA isolation steps can be magnified with each PCR cycle, and such events are not resolvable using informatics. Errors introduced before or during PCR amplification are detectable using bar‐coding or unique molecular identifiers (UMI). UMI attempt to identify PCR duplicates (multiple reads amplified from the same cDNA molecule) and indicate that the most influential factors for PCR duplication events are RNA input and sequencing depth. Interestingly, UMIs are now being used to assemble “synthetic” long reads from standard short‐read sequencing of the fragmented cDNA library.Each cell expresses a physiologically biased subset of the genome that reflects function. The abundance will also vary, and a cell may express some transcripts at >1000 copies, while many are expressed at <20 copies.168 Fo More commonly in vitro transcription (IVT), based on Eberwine's work on linear RNA amplification, is used instead of PCR and only requires an RNA input of 100 ng. IVT involves cDNA synthesis, first primed with an oligonucleotide containing a T7‐phage promoter recognition site. Following double‐stranded cDNA production, T7 polymerase is added and directs the synthesis of antisense RNA from the cDNA template, yielding cRNA . This approach can amplify input RNA one million‐fold after two cycles, only relying on RNA from a single cell. The amplified aRNA pool is reported to closely resemble the composition of the original input mRNA population, suggesting that the amplification process has limited bias. It also appears that each RNA, especially for lower abundance genes, is more consistent across samples, compared to PCR‐amplified RNA.In contrast, profiling of the transcriptome using arrays can be done without amplification using biotin‐labelled first‐strand cDNA synthesis.97 Mor An IVT approach has been adapted for RNA‐seq and was noted to remove several PCR‐related biases and more closely approximate the original sample composition but it is not routinely used. The more recent adoption of cost‐effective UMIs and 3’ biased sequencing protocols, reduce the influence of PCR artifacts, but cannot then address alternative exon usage (“splicing”)—something RNA‐seq was supposed to deliver. UMI barcoding is also a common strategy for single‐cell and single nucleus sequencing, where transcript signals are estimated at a gene level. Direct RNA sequencing is an alternative and very promising approach for transcript characterization  that remains in development, requiring large amounts of starting materials, limited throughput, and high costs.Thus, it is assumed that library strategies produce an amplified DNA library that faithfully reflects the original RNA input. Any major deviations from this assumption have major implications for cDNA library composition and the validity of downstream informatics analyses.176 An The choice of library preparation kit substantially influences study outcomes; thus, without due consideration, many of these factors can explain the lack of replication of detailed findings across laboratories when using RNA‐seq or when a comparison is made between RNA and proteomics. In our view, the best strategy is to avoid PCR amplification (including any array protocol that uses PCR).In short, there are no good methods to account for the preferential amplification of certain mRNA molecules and the dropout of others when PCR amplification is relied on, as is the case for most RNA‐seq protocols. All solutions that might address the fundamental characteristics of either PCR‐based cDNA library or count‐based sequencing also appear to introduce bias.176 Th7 Indeed, major limitations of the most frequently used RNA‐seq computational tools, applied to clinical sample sizes, have only recently been uncovered, implying that many “unknown unknowns” remain. Arguably more informaticians than ever are working with transcriptomic data; far more than ever worked with array data, which conveys the nature of the challenge ahead for reaching a consensus for the most appropriate informatic solutions for all types of novel transcriptomics.The type of the raw data produced by arrays and sequencing differs fundamentally. While in‐depth discussion of all the implications is beyond the scope of this review, we will explore some of the key differences. Regardless of the method, the raw “signal” must be processed through several steps, including quality control, normalization, quantification/scaling, and summarization of gene expression . These steps enable statistical comparisons for the same RNA across conditions or relative to a clinical phenotype. RNA‐seq analysis needs to implement a model to estimate how each read relates to the transcriptome, then to “count.” In contrast, Affymetrix array probes are manufactured as a fixed 25‐mer DNA sequence (which needs to be checked for specificity). Data processing of modern array data relies on few options, such that there is a good consensus after ≈17 years of use (1998–2015). Analysis of RNA‐seq data has proven far more challenging and 15 years on (2007 to 2022) there is no consensus; and one is unlikely to be reached that serves all types of data.183 In is often portrayed as a gold standard validation tool in transcriptomics. The SEQC transcriptomic quality assurance concluded that two qPCR results were no more reliable or quantitative than two sequencing results; presumably because both rely on PCR. Real‐time qPCR has no standard for RNA input and lacks widely adhered to laboratory standard conditions. It is also often used without regard to the exact RNA sequence (pre‐configured kits) and can produce a signal from a very minor component of a tissue biopsy (a positive or a negative characteristic depending on how the data is being presented). That real‐time qPCR has been put forward as a “gold standard” tool for validating the results of genome‐wide transcriptomics is not, in our view, logical. Not least because when used, only a few of the “best candidates” from the transcript wide profiling are selected. Real‐time qPCR also relies on housekeeping strategies for quantification, distinct from global methods and often does not measure the same sequence as the global method. Carefully considered real‐time qPCR can be useful for qualitatively confirming differential exon usage, but when used to claim validation of new informatic models, transcriptomic identified candidates should be selected from across the range of false discovery rates (FDR) noted in the study to better illustrate the model's true performance.Before discussing the distinct signals produced by arrays and short‐read RNA‐seq, it is worth briefly commenting on laboratory validation methods. Real‐time qPCR185 is7.12 scale. Early in their development the scanners used to process arrays had a limited dynamic range easily leading to signal saturation. In practice, a majority of probe‐sets show a wide dynamic range—at least 7 log2 units or more. For example, across muscles from 191 sedentary fasted adults, the signal for the mitochondrial gene, pyruvate dehydrogenase kinase‐4  staggeringly varies from 141 to 2800 unlogged units. Interestingly, this heterogeneity might explain why PDK4 is frequently identified as differentially expressed in small human studies. Signals from the HTA array probe‐set that detects expression of a contractile protein known to vary greatly across people, Myosin Heavy Chain IIx , ranges from 14 to 7660 unlogged units. This illustrates, contrary to predominant opinion, that modern arrays possess considerable dynamic range. The continuous nature of the signal from modern high‐density arrays enables well‐established statistical methods for DE and regression analysis, with limited raw data processing. The lack of 3’ bias  ) and the more quantitative nature of the GC corrected signal should also help when modelling differential exon usage. On the other hand, the HTA array is not optimal for profiling small RNAs, such as mature miRNAs, as these are shorter than a single probe, and here—with the help of a modified library protocol—sequencing may have some advantages.Arrays produce a continuous signal, for a given RNA, via hybridization of labelled fragments to short 25‐mer DNA probes dedicated to a single RNA sequence. These probes are combined to estimate the abundance of a transcript, and at the transcriptome level, these continuous signals are approximately normally distributed when plotted on a logog2 units100 or Physiology studies, often using small sample sizes, reporting that 50% of genes are differentially regulated, are for this reason, unlikely to be correctly processed. We have noticed this is not uncommon with RNA‐seq studies and speculate it may also reflect the influence and mishandling of zero counts. Pre‐processing and normalization algorithms for arrays, such as MAS5, PLIER, and dChip, are no longer used and the most common method employed for Affymetrix arrays is robust multi‐array averaging (RMA). Packages that implement RMA can to some extent increase low‐level correlative structure across the transcriptome while compressing the dynamic range of the data. Iterative rank‐order normalization (IRON) allows the combination of some of the most favorable features from dChip, MAS5, and RMA—yielding some improvements in performance. The default implementation of IRON is far more time and data‐storage efficient  than RMA (implemented in aroma.affymetrix R package) and is our preferred choice. Using IRON, one can process new samples, without reprocessing all previous samples (something that RMA cannot do). We also prefer IRON as it appears to limit introduction of correlative structure compared with RMA, a factor that can influence network or classification analyses.Array and RNA‐seq data requires “normalization” to be made more comparable across samples, prior to group based statistical analysis and such methods assume that most of the transcriptome remains unchanged across conditions, such that most data acts as a housekeeping signal.191 Ph and it is not advisable in our view—not least because the probe design varies across arrays, use of a gene level identifier can hide substantial differences in the sequence that is being quantified. Methods developed to remove batch‐related “noise” cannot easily distinguish biological from technical variation in routinely collected samples. The large number of technical considerations and a lack of substantial positive control data , means that merging raw data across technologies is not likely to be a valid pursuit. Meta‐analysis of individually processed data is a reasonable alternative.Normalization is applied within a study and to a particular array type. Merging raw data across different technologies remains a risky pursuit, despite some enthusiasm,194 an It is possible to select one expressed ENST per gene (resulting in ≈30 000 rows of data (genes) for a human tissue profile) based on which shows the greatest signal or greatest variation, or some heuristic combination, across your samples or between groups. This remains a stage in array data processing that could be optimized using machine learning. Overall, the narrow suite of analytical options for processing modern arrays ensures that it is much easier to track analysis from the raw data, promoting research transparency, and reproducibility. Thereafter, DE analysis can be carried out using simple ANOVA models combined with correction for multiple testing or utilize methods such as Significance analysis of microarray data (SAMR) that models a false discovery rate. Modest changes in array analysis software have tended to only lead to subtle changes in the outcome of any analysis—except for the issue of accurately defining the “background” reference for pathway or ontology analysis (see below).There are nonetheless some options prior to applying statistical analysis to data generated by the modern array. For example, using an HTA array with a custom CDF typically produces 100 000 rows of data (probe‐set) summarized at the transcript ID level. In the processed data file, each row represents a transcript  but includes recycled data—as an exon can belong to more than one transcript. It is not easy to know a priori which ENST is the more representative transcript for your experiment, and many cells express more than one variant.199 It7.2 Processing raw sequencing data is more complex than for array data, and there are hundreds of potential combinations of methods, with dozens in common use. A recent comparison of 278 different options, including mapping, quantification, and normalization steps, found that the pipeline chosen profoundly impacted on gene expression analysis. Others report that the number of DE genes substantially varies depending on the processing method, from 208 to 9500 DE—with the later challenging the validity of the normalization or modelling of counts. Other fields have recognized limitations of transforming counts to enable statistical analysis. This is particularly problematic in the case of RNA‐seq, where a zero count may reflect a laboratory failure and not the biology of the sample, or failure to sequencing to a sufficient depth (or variation introduced by “effective” depth).Computational methods for adequately processing of RNA‐seq data are still evolving, and the optimal choice may be experiment specific.115 Pr The numerous options introduce bias, yet normalization, and DE algorithms are thought to contribute most to the variability between analyses or across studies. Traditional alignment tools are slow, and more recent methods, that is, Salmon and Kallisto) were introduced to save time and estimate which transcript a read belong to, using graph theory. Salmon adjusts for nucleotide bias, impacting on subsequent statistical analyses to debatable extent . While very informative, Pachter's commentary does not dwell on the 2–4% difference between these two near‐identical methods, a difference that would yield thousands of distinctly quantified transcripts; more than a typical biology‐driven DE signature. In the Salmon paper, >1000 regulated transcripts were found from the simulation analysis when there should be none.The first step maps short reads to a reference annotation of the genome, a process that can be non‐trivial.203 Th differ for lower count genes, that is, a majority of the rows of “data” in an RNA‐seq study . One promising DE strategy, to address the uncertainty regarding which transcript to assign a read too, is to calculate an aggregated p‐value from analysis of each possible option. This strategy has also been applied to ontology analysis, but this may be far more challenging to interpret (see below). Further refinement, aggregating DE analysis across exons has also been explored.Results from traditional alignment tools and pseudo‐alignment tools204 disee ref. ). Furthe and length related bias. Fragments per million reads mapped (FPKM) and the transcripts per million (TPM) are attempts to scale counts data within a sample or if multiplexed using barcodes, within a sequencing lane. Note that multiplexed sequencing assumes that each sample contributes equally to the library prior to sequencing. Introduction of these types of transformations led to expectations that count values would be quantitative, and comparable across labs, or sequencing runs. FPKM, aims to correct counts per gene by length (“counts per kilo base”) and library size  to scale data and allow comparison within sample runs. It does not consider the idea of what we call “effective” sequencing depth (depth after considering variable coverage because of extremely high abundance transcripts). TPM is a related transformation, adjusting the “counts per kilo base” for the total “counts per kilo base” of each sample (million units) yet as we illustrate here . At this point, counts are still not comparable between “genes” as counts require adjustment for GC content118 an Adjusting by the wrong transcript length results in DE under conditions when differential splicing has occurred or as articulated by Stark et al.; “if the main isoform in one condition is half the length of the main isoform expressed in the second condition (but expressed at twice the “level”) it would look as if the gene was not regulated”.A read can be legitimately assigned to more than one transcript from the same gene, and thus it is unknown which transcript “length” to correct for. We illustrate the influence of transcript length by plotting transformed count data (using FPKM and TPM), correcting to the longest and shortest transcripts for each gene. FPKM is systematically influenced by length, and this is a problem as the transcript used may shift between the conditions Figure . The inf209 Ad Global normalization requires a general consistency of RNA expression to be true, but in projects sequencing libraries from clinical tissue samples, reads for very high abundance genes can vary dramatically across samples, greatly altering the fraction of total reads they make up. This means that the effective sequencing depth across samples—for most genes—is not well scaled by total aligned reads. Systematic flaws in scaling strategies could lead to a high proportion of genes being called DE. Recently, graph‐based strategies have explored normalization methods that may not be so dependent on these traditional assumptions. However, these methods, benchmarked by DE metrics, may not be valid for all types of analyses, while attempts to fit the count‐based data to a distribution model, appropriate for DE methods, are less accurate and control FDR less well than desirable. Stark et al. highlight that iterative evaluation and selection of a normalization procedure may be critical to discover the validity of your pipeline, but final choices must not be based on the outcome that fits best with the original hypothesis. Indeed, to avoid such a scenario, the inclusion of two or more independently produced clinical data sets should be incorporated in a single publication, to validate any pipeline choices.Even after conversion of a read into a “scaled” count, the data are not necessarily “normalized” in the traditional sense applied to arrays or protein blots.210 Gl but this is almost never done due to a lack of specialist expertise or perhaps a rush to publish. The increasing number of models, especially those evaluated using simulated data, may never solve the inherent limitations of the RNA‐seq laboratory steps. It has taken nearly a decade to identify limitations of the most utilized RNA‐seq analysis tools (DESeq2 and edgeR)—despite over 65 000 citations—and it may take years for updated solutions to be fully explored. To complicate matters further, RNA‐seq software packages are frequently updated, and the use of different versions affects the results obtained. For example, the choice of pipeline to analyze tumor samples impacts on survival predictions. Lack of reporting software versions and lack of clarity over optional settings for your study, will drive a lack of reproducibility. Thus, specific recommendations for processing RNA‐seq data are not easily made—and this would normally limit the utility of the technology for translational or stratified medicine. Nevertheless, RNA‐seq has become the dominant technology and there is now an urgent and ethical prerogative that these limitations are more widely discussed, not least because alternatives methods exist.Thus, if the analysis of an RNA‐seq data set is to be done thoroughly, many parallel data processing options should be considered,70 but7.3 . Methods applied to array data to calculate DE are well established and, in our experience, appear robust across independent data sets. Linear modelling or logistic regression can be applied to array data and reliably identify genes correlated with a clinical status, even across a variety of types of arrays. Unless filtered to model genes only expressed in all samples, regression modeling of rows of RNA‐seq data would encounter lots of missing data, with the actual sample size (per rows or column) impractical to define. For quantitative network analysis, modeling can utilize databases  or take a direct data‐driven approach. The basic principles of data‐driven quantitative network analysis apply to all OMIC methods, whereby hundreds (or even thousands) of independent samples are required to produce stable network results. Most researchers do not adhere to such criteria, instead favoring methods that are simple to implement yet do not provide reliable estimates of network stability or adjust for multiple correlations. Database driven pathway or gene‐ontology analysis is used in many transcriptomic projects and increasingly to summarize the conclusions of multi‐omic analyses —and often relies on web‐based tools which can be difficult to replicate.Identifying DE genes to then model which pathways are regulated, represents a very common type of OMIC analysis. Most methods used for DE analysis of RNA‐seq data, typically involves small sample sizes, and these same methods do not appear to control the FDR adequately in larger clinical sample cohorts153  do not confirm that the driver for DE is biological—it could still be driven by bias relating to RNA handling, cDNA library production or sample processing order. Most transcriptomic studies include a table or plot of the enriched pathways as a major general summary of the study. A recent survey identified that >80% of published pathway analyses have serious problems, quantifying our earlier concerns. In fact, the assumptions for calculating valid p‐values for enrichment analyses are also known to be violated (anti‐conservative) to some degree. In general, in our experience, pathway FDR values in the 1 × 10−2 to 1 × 10−5 range probably do not reflect genuine differences, but rather defects in the informatics procedures and biases in the laboratory method.Pathway‐based analysis represents an additional “statistical” hurdle beyond DE analysis—one that should confirm that a list of DE genes reflects the biology of the experiment and not one driven by noise or chance.82 Pat transcriptome. This is especially true when you model a targeted proteomics data set and contrast that data with the ontology database or genome, rather than the narrow set of proteins measured. To calculate if a pre‐defined pathway (ontology) is enriched in a DE gene list, the list must be compared with all genes that were possible to measure. The list should not be compared with the entire content of a database or the genome  has been used, yet it is understood that some minimal level of signal may reflect genomic DNA and other artefacts. When data quality is poor, authors often choose thresholds of <1 count on average across samples to define the detected genes and include genes that are not expressed in many of the samples. This approach not only influences the DE analysis, but it renders the definition of the background transcriptome a moving target.It is simple to carry out a “less biased” pathway analysis, but the consequences are usually loss of any significant results because, in our experience, many results are driven by inappropriately defined background79 traFigure  and three RNA‐seq backgrounds and report that each lists exhibit different biases, only some of which reflects the specialized biology of muscle tissue. The HTA array “detectable background” also had some bias, but in this case for only a few biology‐driven metabolic and contractile‐related pathways, while the rest had moderate adjusted p‐values  ontology category in DAVID was used, with the entire BP ontology database (obviously incorrect as it contains genes that are not expressed in the experiment) and three muscle background transcriptomes used to analyse the 729 DE list  is a central determinant of the complexity of the proteome, occurring with >90% of multi‐exon genes.235 Ac There are very few methods for studying AS/AEU using high‐density arrays, with only one making direct use of these exon–exon junction spanning probes, and one which has subsequently been adapted for RNA‐seq. We developed a pipeline to model AEU called iGEMS, building on a statistical model from Robinson et al. iGEMS aimed to reduce false‐positive results and reliance on laborious visual inspection. Studies of AS/AEU in cells with large treatment effects may indicate a method is working, but it does not establish that the method will be generally applicable. Given that many algorithms formulate AEU as an “outlier detection problem”, optimizing the removal of noise, e.g., poorly performing probes prior to signal summarization may also improve existing methods. Thus, in general, AS/AEU modeling of short‐read RNA‐seq and modern array data is challenging and often relies on multiple strategies, including laborious visual inspection of results.Both the HTA 2.0 and the updated version, the Clariom D array, contain probes designed to span individual exons (“junction probes”) in known transcripts, and these were intended to improve the detection of AS of cassette exons.98 The This project (GTEx) reported 23–516 AEU events across two‐way comparisons including 370 AEU events between adipose and muscle tissue. A major conclusion was that tissue specificity was determined primarily by differences in gene expression abundance rather than through differences in AS/AEU. At the same time, using the HTA 2.0 array we identified >1500 AEU events between adipose and muscle . The splicing ratio statistic used by the GTEx project has high uncertainty at lower count thresholds and we noted that the AEU events they reported were only among abundant genes and biased for events related to cassette exons. More recently, the muscle FUSION project studied AS/AEU using RNA‐seq, reporting one isoform relevant to metabolic status. This analysis used a simple ratio method to detect AS/AEE and the event was not replicated in a comparable array analysis.To illustrate the performance of splicing analysis using RNA‐seq versus the HTA array, consider the 2015 Science article, where RNA‐seq was used to profile multiple post‐mortem tissues.144 Th This suggest that short‐read RNA‐seq has limited utility for large‐scale discovery of AS/AEU, and there is no evidence that it offers better resolution than modern arrays. While modeling AS/AEU using arrays provides many candidates, the data will be incomplete and direct long‐read RNA profiling technologies offer a clear advantage. Whether the latter offer reproducible quantification suitable for large‐scale clinical projects remains unknown. There are still opportunities to improve available methods for studying AS/AEU using modern arrays. For example, no method uses the exon‐exon junction spanning probes while properly adjusting the FDR for gene length.While there are several explanations for limited AEU detection using short‐read RNA‐seq, including those mentioned above, Xu et al. identified that, at a comparable sequencing depth, variability in expression is high for features with fewer than 20 mapped reads, which included ≈60% of the exons quantified. Evaluation of ten commonly used tools for differential splicing analysis using RNA‐seq, found minimal overlap in the number of genes detected, ranging from 0 to more than 14 000, depending on the method used.248 Th9 and this confounds the literature with inaccurate claims regarding the anatomy of gene expression. We strongly advise against using any RNA‐seq reference, commonly linked within genomic browsers, as a view on whether a gene is expressed or not in human tissue, particularly GTEx . Together, we resist concluding that short‐read RNA‐seq and arrays are “complementary technologies” and close instead by stating that high‐density modern arrays are a more robust and cost‐effective option for many types of studies, especially when aiming to profile long noncoding RNAs.Reductions in cost coupled with a number of deep‐rooted misconceptions led to 2nd generation short‐read RNA‐seq becoming the method of choice for most bulk transcriptomics, replacing the microarray. While efforts are being made to reduce costs and provide greater throughput for RNA‐seq these do not address fundamental limitations . Indeed, some modification may make matters worse. Fundamental characteristics of the RNA‐seq methodology, can be shown to compromise the validity of downstream statistical analyses, where numerous biases rather than biology drive significant results. Notably, cost‐effective routine RNA‐seq analyses, using reference databases, do not discover novel transcripts, nor many gene‐splicing events. RNA‐seq also generates huge data files (packed with millions of estimated counts of a few abundant genes), representing an incomplete transcriptomic record of a clinical sample and significant long‐term storage costs. Equally we recognize that 3rd generation direct RNA sequencing technologies are powerful tools for building transcriptome databases, studying allele specific transcription and characterizing their post‐transcriptional modifications. These newer methods do not however appear suitable yet to generate cost‐effective quantitative transcriptomes for large translational medicine projects. Studies contrasting the global transcriptome and proteomics need to reflect on the relative count nature of RNA‐seq data, and better integrate the temporal nature of transcription, translation, and proteostasis. Finally, many genomic databases use RNA‐seq data to define the landscape of the human transcriptome144 anThe authors declare no conflict of interest.T.S. and H.H.C. contributed equally to this work. T.S., H.H.C., and J.A.T. drafted the manuscript and completed the literature review. H.H.C. and J.A.T. performed bioinformatics analysis on previously published datasets. All authors critiqued and edited the manuscript.


Our lab has recently discovered that the Cajal body marker protein coilin also participates in miRNA biogenesis and hypothesized that coilin may be influencing miRNA biogenesis through m6A RNA methylation. Here we report that coilin suppression reduces m6A on primary Let7a and miR-21. We also found that coilin suppression reduced the protein expression of hnRNPA2B1 and METTL3. We observed an interaction between coilin and ectopically expressed METTL3 and found that coilin suppression reduced the nucleoplasmic portion of METTL3 and blunted ectopic METTL3 phosphorylation. Finally, coilin suppression disrupted the greater METTL3 complex with cofactors METTL14 and WTAP. Collectively, our work has uncovered a role for coilin in mediating m6A RNA methylation and provides an avenue by which coilin participates in miRNA biogenesis.MicroRNAs (miRNAs) are a class of noncoding RNAs that regulate gene expression. An important step in miRNA biogenesis occurs when primary miRNAs are bound and cleaved by the microprocessor to generate precursor miRNAs. Regulation at this step is essential and one such regulator includes m Summary: Our work highlights a novel role for the Cajal body marker protein, coilin, in m6A RNA methylation by which it influences the expression of METTL3 and participates in microRNA biogenesis. MicroRNAs (miRNAs) are a class of noncoding RNAs that regulate gene expression at the RNA level and play a pivotal role in a wide array of biological processes . The bio6A RNA methylation of pri-miRNAs has recently been uncovered as a novel regulator of miRNA biogenesis  and the reader hnRNPA2B1 modifies and binds pri-miRNAs, respectively  marker protein coilin as a positive regulator of miRNA biogenesis . Canonic6A RNA methylation. We found that coilin suppression results in a reduction of m6A RNA methylation of primary Let7a and miR-21 miRNAs. We also found that coilin suppression reduced the RNA and protein expression of hnRNPA2B1, but induced the RNA expression while reducing the protein expression of METTL3. We observed an interaction between coilin and ectopic METTL3 and found that coilin suppression reduced METTL3 within the nucleoplasm and depressed phosphorylation of ectopic METTL3 at Serine 43. Finally, coilin suppression disrupted the METTL3-METTL14-WTAP complex. Collectively, our work has uncovered a role for coilin in mediating m6A RNA methylation and provides an additional pathway in which coilin participates in overall miRNA biogenesis.Here, we report that in addition to regulating DGCR8 stability, coilin mediates m6A RNA methylation, we examined m6A via dot blot assay using total RNA extracted from HeLa cells  (6A RNA methylation of numerous RNA types within the cell.To determine if coilin has an effect on mLa cells . After town (KD) . While m6A RNA immunoprecipitation (meRIP) coupled with qRT-PCR. For the meRIP assay, our qRT-PCR targets were pri-Let7a and pri-miR-21. Pri-Let7a is a miRNA whose biogenesis our lab has found to be regulated by coilin  revealed that coilin possesses several predicted CD domains and one predicted F-site that could potentially facilitate association with ERK. This is in comparison to the one predicted CD domain of SMN.Collectively, these findings suggest that coilin may interact with METTL3 and help facilitate its phosphorylation. While coilin is not a kinase, it is possible that coilin's nature as a scaffolding protein could aid in bringing METTL3 and its associated kinase within closer proximity, facilitating an efficient phosphorylation. ERK has been recognized as a kinase that phosphorylates METTL3 . ERK hasBecause coilin is a strictly nuclear protein while METTL3 can be found in both the nucleoplasm and cytoplasm , we nextOur previous findings prompted us to extend our analysis of coilin's interaction with METTL3 to the greater METTL3-METTL14-WTAP complex. It is understood that this complex functions with METTL3 as the catalytic component that facilitates methyltransferase activity while METTL14 and WTAP act as cofactors that aid in the specificity of RNA binding and recruitment into nuclear speckles, respectively . To testWe believe that coilin might be influencing the disruption of the METTL3–METTL14 complex through phosphorylation. Inhibiting phosphorylation of METTL3 has been shown to decrease binding affinity to WTAP, which aids in localization of the METTL3 to nuclear speckles and overall nuclear retention . However6A RNA methylation, this work reports a novel finding for coilin in mediating m6A and opens doors for future studies to interrogate how coilin or other CB proteins influence the m6A regulatory network. Collectively, we find that reducing coilin expression results in suppression of the nucleoplasmic portion of METTL3, decreased phosphorylation of ectopic METTL3, decreased METTL3-METTL14 interaction, and suppressed expression of hnRNPA2B1, METTL14, and WTAP. This all summates to a suggestive influence of coilin on the m6A RNA methylation of various RNAs, but specifically pri-Let7a and pri-miR-21 (While we cannot rule out added influences from other CB proteins or possibly the whole CB as mediators in mi-miR-21 .2 incubator at 37°C. All siRNAs were obtained from Integrated DNA Technologies (IDT)  and used with RNAiMax  per the manufacturer's protocol. Negative control siRNAs were supplied by IDT and a description of all other siRNAs can be found below. siRNA transfections were conducted for at least 72 h and, if required, an additional transfection was conducted to extend KD period to 96 or 120 h. DNA transfections in HeLa cells were conducted using FuGene HD  according to the manufacturer's protocol. FLAG-tagged METTL3 plasmid was obtained from Addgene  under plasmid #160250. FLAG-METTL3 transfections were conducted for 24 or 48 h.HeLa and WI-38 cell lines were obtained from the American Type Culture Collection (ATCC). These lines are routinely authenticated by assessing their morphology and nuclear organization (WI-38 cells have very few CBs compared to HeLa cells). Cells were cultured in DMEM media containing 10% fetal calf serum in a 5% CO2. After which, the membrane is briefly rinsed in TBS-T  and then blocked with a solution of 5% non-fat milk in TBS-T for 1 h. After blocking, the membrane is then immunoblotted overnight with m6A antibody (Synaptic Systems) in a solution of 2.5% non-fat milk in TBS-T. To detect dots, the membrane was washed three times for 10 min each with TBS-T and next incubated with a species-specific HRP-conjugated antibody in 2.5% non-fat milk for 1 h followed three more washes in TBS-T and finally a 5 min incubation with SuperSignal West Pico Chemiluminescent substrate (ThermoFisher Scientific). After immunoblotting, the membrane was then incubated methylene blue staining buffer (0.2% methylene blue in 0.4 M sodium acetate and 0.4 M acetic acid) for 30–60 min and washed several times with distilled water to assess total RNA loading. Imaging was done on a ChemiDoc  with QuantityOne software. Adjustments to images were made using the transformation settings on QuantityOne software and applied across the entire image. Dots were quantified using QuantityOne software. Methylene Blue was used to normalize signal intensity of m6A for each dot.HeLa cells were transfected with negative control or coilin siRNA for 96 h. Total RNA was next extracted with TRI-REAGENT (Molecular Research Center), according the manufacturer's protocol. 500 ng of total RNA is collected in a volume of no more than 2 μl in a 1.5 ml tube and incubated at 95°C for 5 min. RNA is next chilled on ice before being dotted onto a BrightStar Plus positively charged nylon membrane (Invitrogen). The membrane is next subjected to a UV cross-linker  at a setting of 120,000 μJ/cm2O) with 3 μg of m6A antibody (Synaptic Systems) for 30 min while rocking at 4°C. Next, 30 μl of 50% Protein G Sepharose 4 Fast Flow beads  is added and incubated for 2 h while rocking at 4°C. The antibody-bead complexes were next washed three times with RNA IP Reaction Buffer before aspirating the beads dry. RNA is eluted from with beads using TRI-REAGENT supplemented with GlycoBlue coprecipitant. Finally, meRIP RNA is subjected to quantitative real-time PCR.HeLa or WI38 cells were transfected with negative control, coilin #1, coilin #2, METTL3, or WRAP53 siRNAs for 72 or 96 h. After which, total RNA was extracted with TRI-REAGENT. 5 μg of total RNA is next incubated in a solution of RNA IP Reaction Buffer  according to the manufacturer‘s protocol. Reactions were set up with 50 ng total RNA in Brilliant II SYBR Green qRT-PCR master mix  using an Agilent MX3000P qRT-PCR system. Oligonucleotides used were obtained from Integrated DNA Technologies . A complete list of primers can be found below. For mRNA analysis, GAPDH served as the normalizer. For meRIP analysis, 18 s rRNA served as the normalizer. The primers used for primary let7a and miR-21 do not overlap with the precursor miRNA sequences, so only the primary transcript is amplified. Amplification rates, Ct values. and dissociation curve analyses of products were determined using MxPro (version 4.01) software. Relative expression was determined using the 2−ΔΔCT method . GraphPaFor coilin and FLAG-METTL3 interactions, HeLa cells were plated in a 100 mm dish, transfected with 3 μg FLAG-METTL3 for 48, and lysed in RIPA buffer  plus protease inhibitor cocktail (ThermoFisher Scientific). For FLAG-METTL3 interactions with METTL14 or DGCR8, HeLa cells were plated in a 60 mm dish, transfected with 500 ng FLAG-METTL3 for 48, and cells were lysed in IP lysis buffer  plus protease inhibitor cocktail. Lysates were sonicated three times with a ThermoFisher Scientific sonic dismembrator (Model 100) for 5 s each using the output setting of 1 and finally centrifuged at 12,000 rpm for 15 min at 4°C. For FLAG-METTL3 transfections, transfected and untransfected lysates were incubated with 20 µl anti-FLAG-M2 affinity agarose beads  for 2 h. For endogenous METTL3 IP, lysates were incubated with 1 μg of METTL3 or IgG for 2 h while rocking at 4°C before adding 30 μl 50% Protein G Sepharose 4 Fast Flow beads  and incubating for 2 h while rocking at 4°C. Immunoprecipitates were washed three times with RIPA or IP lysis buffer followed by analysis via Western blotting. For RNase digestion, after washing immunoprecipitates and aspirating beads dry, beads were resuspended in 40 μl of PBS supplemented with or without 1 μl of RNase A/T1 Cocktail Enzyme Mix (Invitrogen) and incubated at 37°C for 30 min. After incubation, immunoprecipitates were washed twice with PBS followed by analysis via Western blotting.post-hoc statistical analysis and for histogram generation.For standard Western blotting analysis, HeLa or WI38 cells were lysed in RIPA buffer. Lysates were sonicated three times with a Fisher Scientific sonic dismembrator (Model 100) for 5 s each using the output setting of 1 and finally centrifuged at 12,000 rpm for 15 min at 4°C. Lysate (15 μl) was run on a precast 7.5 or 10% Mini-Protean Gel . For nuclear and cytoplasmic extractions, HeLa cells were collected using NE-PER Nuclear and Cytoplasmic Extraction Reagents (ThermoFisher Scientific) according to manufacturer's protocol and samples were run on a 10% Mini-Protean gel. For IP experiments described above, 20 µl of 2X SDS loading buffer was added to aspirated beads. Immunoprecipitates and input lysates were run on a precast 7.5% Mini-Protean Gel . Western transfer and detection were conducted as previously described . A complpost-hoc statistical analysis. Analyses, first, consisted of a Shapiro–Wilk test to determine normality in the collected data from each experiment. Assuming all datasets had a normal distribution and did not violate an assumption for the ANOVA, the data is then subjected to a one-way ANOVA with a Dunnett's multiple comparison test, comparing only KD datasets to control datasets. If the collected data has a normal distribution, but violated an assumption of the ANOVA, the data is then analyzed by paired t-test (parametric t-test). If the collected data does not have a normal distribution and violates an assumption of ANOVA, data is analyzed by the Wilcoxon matched-pairs signed rank test (nonparametric t-test). P values recorded from each of the previously named tests are reported in the figure legends.GraphPad Prism was used for all 10.1242/biolopen.060116_sup1Supplementary informationClick here for additional data file.Table S1. Raw PCR dataClick here for additional data file.