What are an internal and external exons?

What are an internal and external exons?

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I read the book: Essential Genetics and Genomics It has a table summarizing the properties of the "typical" human gene:

It has a gene featureSize of internal exon, what internal means in this context?

When I searched for it I found mentions about external exons, but found no definition.

I strongly suspect they mean:

External exons are the first exon right after the 5' untranslated region and the last exon just before the 3' untranslated region and all the other exons are internal exons.

As turned out this was almost right, but had a mistake which is wonderfully pointed out in the accepted answer.

Yes, the internal exons are those that aren't at the ends, which are often referred to as terminal exons1.

However, exons are sequences of nucleotides that are incorporated into the mature mRNA - i.e. they don't have to be (entirely) protein coding.

It is probably simplest to think of exons as being the transcribed regions that are not introns - i.e. they are the sequences that don't get spliced out during transcript maturation.

In other words, the 5'UTR is typically§ part of the first exon and the 3' UTR is typically§ part of the last exon.

§Note: I say typically, because sometimes introns are found within the UTRs2,3.


1: Bolisetty, M. T., & Beemon, K. L. (2012). Splicing of internal large exons is defined by novel cis-acting sequence elements. Nucleic acids research, 40(18), 9244-9254.

2: Eden, E., & Brunak, S. (2004). Analysis and recognition of 5' UTR intron splice sites in human pre‐mRNA. Nucleic acids research, 32(3), 1131-1142.

3: Paolantoni, C., Ricciardi, S., De Paolis, V., Okenwa, C., Catalanotto, C., Ciotti, M. T.,… & Giorgi, C. (2018). Arc 3' UTR splicing leads to dual and antagonistic effects in fine-tuning arc expression upon BDNF signaling. Frontiers in molecular neuroscience, 11, 145.

Difference Between Internal and External Environment

The sum total of life-sustaining fluid, metabolic activities inside a living organism and surrounding objects outside its body cells, conditions, or impacts, ecology, air, water, minerals etc form the internal and the external environments of an organism.

The internal life-sustaining fluid that allows sustaining exchanges and encompassing the cells, form the internal environment. The internal environment is important for normal cell function. The surrounding environment in which a living organism lives forms the external environment.

The association between the internal and external environments of a living organism is quite significant. While talking about unicellular organisms like Amoeba, algae and Paramecium, all the things to the inner side of its cell sheath comprise the internal environment.

Process controls: internal and external

This month, we’ll examine some of the types of controls frequently employed in molecular diagnostic assays—both their varieties and their utilities. Controls are by nature the materials or reactions incorporated in or added to a reaction or one of a group of reactions, which should provide a predetermined result. When the expected result for a control is obtained, an aspect or aspects of the test are confirmed as working within some specification more importantly, when a control does not give the expected result, the test in question is shown to not meet expected performance. By clever use of multiple types of controls, it’s possible for the laboratorian to not only confirm appropriate test performance, but in the event of a test failure, to quickly identify which of many possible steps has not performed correctly. Such information is invaluable in troubleshooting to identify the root cause(s) of assay failure and corrective actions to be taken to re-establish proper performance.

When considering controls, it is important to examine exactly which steps of the MDx process they evaluate, and which they do not. Failure to do so can lead to erroneous conclusions as to the meaning of apparently passing (or failing) controls. A rule of thumb would be that the control material which most closely approximates a real target, in all features, will give the most complete validation. Any differences between nature or format of control material and real assay target should be thought of as potential places for the control to give misleading results if the differences are not consciously considered.

Different assay methods and platforms can have different types of controls. For simplicity let’s start by considering the case of a hypothetical, real-time probe-based quantitative PCR reaction for a viral target. Most of the controls we will consider in this context are generalizable to other assay types and platforms we’ll follow up on this with a few other platform or assay type-specific examples.

Negative controls. Probably the easiest to obtain and interpret, as the name implies, they are a sample which does not contain the assay target, and which should provide a negative / no amplification signal. Simplest examples are use of dH2O as a PCR reaction template when used in this form, downstream of sample extraction, this is best considered a “negative amplification control.” An expected, negative result here demonstrates there’s no contamination or spurious amplification occurring arising from the basic PCR “master mix.” A more informative negative control would, however, consist of a true blank sample matrix, processed through the sample extraction method, in parallel with the test samples. An expected negative result here would also help to validate for the test run in question that contamination or spurious amplification is not arising from something in the sample matrix or arising during the extraction process. An even more complete (but somewhat harder to produce and validate) negative control might consist of a target negative human tissue culture sample, processed through extraction. While this would add a level of certainty that endogenous host (human) nucleic acids don’t cause unexpected positive signals, it can be reasonably argued that this shouldn’t be a variable attribute, and if non-positivity of the assay in the presence of host DNA has been effectively shown in the initial assay validation, it does not need to be proven again each assay run. Either of these latter two options would be best considered as a “negative process control.” Note that some assays may employ both a negative amplification control and a negative process control doing so would allow for almost immediate identification of extraction step contaminations.

Positive controls. These come in a wider range of forms than negative controls. In the context of our current example, starting from the controls evaluating the least steps in the assay to those evaluating the most, these can include:

Internal controls. These are most commonly in the form of a predetermined template and matching primers (and probe, in our present model) which are included in the reaction “master mix” for all reactions. By necessity, this requires an assay system capable of multiplexing at least to the level of two targets (assay target and internal control). While care must be taken in the assay design and validation to ensure that such a control does not appreciably reduce sensitivity of the assay for its target, and that the internal control- specific primers do not somehow lead to spurious false positive results for the assay target, the internal control can be one of the most useful controls available. A positive internal control signal not only demonstrates proper activity of the polymerase, shared master mix components, appropriate cycling conditions, and function of the detector system it also uniquely has the ability to highlight sample-specific PCR inhibition. Arising from the carryover of any of a number of potentially PCR-disrupting substances such as heme, chelating agents, or SPS during extraction, PCR inhibition is not infrequently encountered in the clinical laboratory and, unless tested for, can lead to false negative results. The importance of this form of control cannot be overstated, particularly if the specimens are of certain types prone to inhibition (e.g., stool samples).

Where multiplexing is not possible in the platform used, a slightly less useful alternative to the internal control is to split a test sample into paired aliquots post-extraction, running one as the specimen test, and spiking a PCR positive control template (see below) into the paired reaction. While this fails to evaluate the actual individual test reaction and brings risks of template contamination, it still maintains the capacity to evaluate a specimen for inhibitory substances through failure of the spiked sample to amplify. (A template / primer / probe set not identical with the assay target can also be used for this function, and avoids the template contamination risk.)

The actual CT (CP) value of the internal control (or paired reaction cousin) can also be useful. Significant variation of this from its normal value toward higher values can indicate the presence of incomplete inhibition in a test sample, or a partial loss of function in an assay. It’s worth noting that this can apply to any real-time assay format, even those for which the primary target is qualitative in nature, and would indicate a loss in Limit of Detection (LoD).

PCR positive controls. The next level up in our example system would be a PCR positive control, analogous to our negative amplification control. Usually, this consists of a simple DNA template material for the assay target, added to its own dedicated PCR reaction run in parallel with the clinical samples on a test. A positive test result in this reaction validates the function not only of the shared master mix components tested by an internal control, but also uniquely shows the function of target-specific primers and probes. Upstream processes such as extraction are not tested by this form of control.

Quantitative standards. Most frequently these represent a special case of PCR positive control, and consist of a set of PCR positive controls of known concentrations. Taken as a set, they form the basis for a standard curve, as described in our prior installment on quantitative assays. Occasionally, one finds these formulated as intact assay targets (e.g., titred virus, in our current example) in sample matrix this allows them to also serve as an extraction control (see below), and allow the quantitation standard to take into direct account possible variations in extraction efficiency across a range of target concentrations.

Extraction controls. These are a standardized sample of real, or closely simulated, assay target. For our current case of a viral detection assay, this could be titred live virus for a more easily handled substitute this can be replaced by chemically inactivated and stabilized virus. A further variation on this can exist in the form of assay-specific target nucleic acids (not complete organisms), artificially encapsidated in something similar to a live organism. Both of these sorts of materials are available from commercial suppliers of assay standard materials, for many common assay targets. Regardless of the exact form used, a positive extraction control signal validates extraction processes, and all downstream steps (amplification, detection, etc.). Depending on how an assay workflow is designed, the line between extraction controls and process controls can be blurred and functions combined.

External controls. Usually in the form of real, known positive (or negative) clinical samples, to be treated in parallel with test specimens, external controls represent the most complete control type which can be used. The difficulty in obtaining or using them lies in the term “known” effectively, these samples must have been reliably evaluated by an alternate gold standard reference method to the assay. The availability and ease of such an alternate reference method, and reliability / uniformity of suitable external control material, can vary greatly across platforms and assay types in question.

The above represent some of the most frequently encountered control types in a quantitative real-time PCR for pathogen detection. Most of these control types exist identically in other assay types and settings with minor changes. Some of the more common other types of controls seen in alternate assay settings include:

Hybridization controls. In an assay with an array-based detection system, a defined labeled oligonucleotide may be included in the hybridization buffer. Complementary in sequence to one of the array spots, this control allows for evaluation of hybridization, wash, and detection steps of the assay separate from all upstream processes such as extraction and amplification.

RNA specific controls. The intrinsic low chemical stability of RNAs and ubiquitous nature of RNases make RNA-based assays particularly susceptible to template degradation, leading to false negative results. The types of controls discussed above cannot address whether such sample decay has occurred prior to a specimen being received for some particular types of RNA samples, this question may be assessed post-extraction through instrumental analysis of parameters such as the RIN (RNA Integrity Number, beyond the scope of our present inquiry). Other controls can, however, be used to confirm that whatever RNAs were intact at the time of specimen receipt are being successfully recovered and detected. These can take the form of RNA-based extraction controls, RNA-based process controls, and/or RNA-based external controls—simply RNA analogues of the DNA versions discussed above.

In the common context of an RT-PCR-based assay where the first post-extraction step is reverse transcription of RNA to DNA, it is not essential that all system controls be RNA-based, as long as at least one form of included control validates the reverse transcription (RT) step. Because of the real potential for RNA control materials to decay and lead to apparent assay failures, some assay developers prefer to use more stable DNA-based control materials for RT-PCR assays. This simplifies assay kit handling and storage conditions and can avoid spurious control failures. At a minimum, at least one RNA-based control (extraction control, process control, or amplification control) is essential to confirm performance of the RT step, and possibly preceding steps depending on control form. The formulation of this RNA control relates to where it can be used. For example, an in vitro transcript RNA species can be a good RT-PCR positive amplification control when added to samples post-extraction (or at least, after extraction steps designed to inactivate RNases, usually, the initial lysis step). Adding this same “naked” RNA species into raw samples pre-extraction would be a poor control, as it’s likely to be rapidly degraded. Encapsidated RNAs or live or inactivated RNAs (such as RNA bacteriophage) can, however, be added directly to raw samples and serve as extraction or process controls.

Sequence controls. For assays such as genetic polymorphism (SNP) analysis, control templates for each of the analyzed genotypes are generally run to assure appropriate classification and discrimination between very similar targets.

The above has summarized (and hopefully explained the utilities of) several of the more common forms of molecular assay controls. For a given specific assay or platform there may be variations on or specific additions to the types discussed here, but in all cases an understanding of the exact nature of the control—what it tests and what it assumes—will help in evaluating its results. Readers whose interest has been piqued by this topic and looking for a much more technical and detailed discussion of MDx controls from a regulatory perspective are recommended to look up CLSI guidelines specific to the assay class of interest.

John Brunstein, PhD, a member of the MLO Editorial Advisory Board, is President and CSO of British Columbia-based PathoID, Inc.


Cirexon detection by a de novo algorithm

As an initial step to study circRNA internal structure, we developed a de novo algorithm for cirexon detection, named CIRI-AS. To circumvent undesired splicing signals from linear transcripts, this algorithm primarily focuses on BSJ read pairs peculiar to circRNAs provided by CIRI 9 and uses split alignments of the BSJ read pairs provided by BWA-MEM 21 as the major indicator of splicing events within circRNA (Fig. 1 and Supplementary Fig. 1). Briefly, CIRI-AS compares local alignment position and strand of each neighbouring segment pair within a BSJ read and its paired read, to recognize forward-spliced junctions (FSJs). Splicing acceptor of one upstream FSJ/BSJ and splicing donor of another downstream FSJ/BSJ with reasonable relative positions may indicate two ends of a cirexon. For each candidate cirexon, BSJ read pair coverage, sequencing depth variation and additional splice junctions indicated by non-BSJ reads are taken into account, to determine whether the candidate itself or in-between discontinuous segments are included in the circRNA. Details about cirexon detection algorithm are described in Methods.

(a) The workflow of cirexon and AS detection. BSJ, back-spliced junction FSJ, forward-spliced junction. (b) BSJ read pairs are peculiar to circRNAs and are thus used to identify splice junctions, indicating boundaries of cirexons. Red and blue bold lines indicate BSJ read pairs, which are connected by black dashed lines. Curved black lines indicate the FSJs present within circRNAs. (c) All possible routes are constructed using identified FSJs and cirexons to detect AS events within circRNAs. Red and grey nodes indicate alternatively spliced and constitutive cirexons, respectively. Dashed lines and numbers represent FSJs and supporting read pairs, respectively, in which grey numbers indicated that the FSJs are involved in multiple routes and supporting read pairs for each route are summed.

We evaluated the efficiency of this new algorithm on cirexon detection using simulated circRNA-containing transcriptomic data. CIRI simulator 9 was adapted to generate such data sets with various sequencing depths, insert length distributions and read lengths. As shown in Supplementary Fig. 2A,B, CIRI-AS had great performance on moderately and deeply sequenced circRNAs in the vast majority of simulated data sets. For example, sensitivity for circRNA with sequencing depth of 25-fold ranged from 72 to 79% in all four 100-bp libraries with different insert length distributions (Supplementary Fig. 2A). It has to be mentioned that the sensitivity above was calculated based on the whole detection pipeline and the loss of sensitivity indeed resulted from both circRNA and cirexon detection. For circRNAs successfully detected by CIRI, 80 ∼ 86% of cirexons could be predicted by CIRI-AS in the same data sets. In most cases, library insert length and sequencing read length did not affect the performance of CIRI-AS. However, CIRI-AS showed relatively low sensitivity for short read lengths, especially when sequencing depth was limited (Supplementary Fig. 2B).

To explore the internal structure of circRNAs, we applied CIRI-AS to deeply sequenced data sets generated in this and previous studies including transcriptomic data on human cell lines of HeLa, HEK293 and Hs68. In total, 212 Gb data based on RiboMinus RNA of samples with and without RNase R treatment for these three cell lines were used. In each data set, 3.9 × 10 3 ∼ 5.5 × 10 4 cirexons were detected (Fig. 2a and Supplementary Table 1). As RNase R specifically digests linear RNAs with free 3′ends and thus efficiently enriches circRNAs, samples treated by RNase R tend to generate deeper sequencing depths for circRNAs compared with those without RNase R treatment and thus facilitate cirexon detection. In four such RNase R-treated samples, we detected 3.3 × 10 4 ∼ 5.5 × 10 4 cirexons, corresponding to 1.4 × 10 4 ∼ 3.0 × 10 4 BSJs. Although most of the cirexons are known linear mRNA exons, 5.6 ∼ 16.8% of them are from intronic or intergenic regions. In addition, >10% of detected circRNAs in each sample contain such ICFs, consistent with our previous study 9 . We next selected the largest data set from Hs68 with RNase R treatment for a rarefaction analysis of detected cirexons and corresponding BSJs. In contrast with a near saturation of BSJ detection, identified cirexons showed a steady increase along with the accumulation of sequencing bases (Fig. 2b), which indicated that comprehensive identification of cirexons necessitates much more sequencing data compared with BSJ detection.

(a) BSJs, cirexons and ICFs detected in the three samples treated by RNase R. (b) Rarefaction analysis of detected cirexons, as well as corresponding BSJs and alternatively spliced cirexons in Hs68. (c) Overlap of cirexon detection of CIRI-AS using 100 bp paired-end reads and long reads. The bottom circular plots indicate two circRNAs and their cirexons validated by long-read sequencing (grey and orange spiral lines). Inner coloured solid-line curves indicate the cirexons detected by CIRI-AS, whereas the dashed-line curves represent the cirexons missed by CIRI-AS. (d) Percentage of circRNAs (≥20 BSJ reads) containing four types of alternatively spliced cirexons in the three samples: ES, alternative 3′-splicing site, alternative 5′-splicing site and IR. (e) Comparison of Ψ value for ES events between circRNA and mRNA in HeLa and HEK293 samples. (fj) An experimentally validated example of alternatively spliced cirexons: ES and alternative 5′-splicing site within circRNA chr1:231,090,079|231,097,049. (f) Cirexons and splice junctions, as well as sequencing depth and corresponding BSJ reads, within the circRNA detected by CIRI-AS in RNase R-treated sample of HeLa cells. (g) Exons and splice junctions, as well as sequencing depth and corresponding sequencing read pairs, in poly(A)-selected sample of HeLa cells. (h) Positions of outward-facing primers and critical splice junctions on the three circular transcipts sharing the same BSJs. (i) The amplified fragments by RT–PCR corresponding to the three circular transcripts. (j) Sequencing chromatograms across the critical splice junctions of the PCR products.

To validate the cirexons discovered by our method, in particular for their boundaries, we generated paired-end 250 bp RNA-seq data for the HeLa sample with an estimated insert length of 450 bp. Using this new long-read data set, we reconstructed the complete sequence of circRNAs and used them to compare with cirexons detected by CIRI-AS on the PE100 RNA-seq data of the same sample. Among the 173 circRNAs detected in both data sets, 350 out of 385 cirexons present in the long-read data could be predicted by CIRI-AS (Fig. 2c and Supplementary Fig. 3), demonstrating the high reliability of discovered cirexons. However, the remaining 35 cirexons ( ∼ 9% of 385) found in the long-read data were not detected by CIRI-AS (Fig. 2c). Scrutiny of these cirexons revealed that most of them were from very low abundant circRNAs and the rareness of BSJ reads largely accounted for the inability of detection (Supplementary Fig. 4). For example, CIRI-AS found the two cirexons flanking the circular junction of circRNA chr13:111,857,636|111,870,227 by recognizing its only three BSJ read pairs, but missed the cirexon in between (Supplementary Fig. 4A).

AS events detection and validation within circRNAs

Besides the cirexons discovered by both methods and those only by the long-read data, there were 60 cirexons exclusively predicted by CIRI-AS (Fig. 2c). These predictions and the corresponding FSJs could not be ruled out by applying higher thresholds such as supporting read count and mapping quality, suggesting they are not false-positive predictions by CIRI-AS. A manual scrutiny of the BSJ reads of these cirexon predictions revealed that most of them were alternatively spliced cirexons, which were only present in some of the circular transcripts but absent in others sharing the same BSJs (Supplementary Fig. 5). For example, CIRI-AS predicted a 50-bp ICF within the circRNA chr9:134,312,007|134,314,448 and there were 14 and 15 supporting reads of FSJs for the 3′- and 5′-end of this cirexon, most of which had no other alignment along the human genome and showed high mapping qualities (Supplementary Fig. 5D). Although the circular transcript with skipped ICF was captured by both methods, the transcript containing this ICF was only found in the deep sequencing PE100 data.

To comprehensively identify various types of AS events in circRNAs, we developed a new algorithm based on reconstructing cirexon routes as well as clustering alternatively spliced cirexons, which was implemented in CIRI-AS. For example, three routes representing three potential circular transcripts contain a cirexon 2a, its equivalent cirexon 2b or neither, respectively (Fig. 1c). As there is a transcript splicing both cirexon 2a and cirexon 2b out, it can be classified as an ES event, while simultaneously cirexon 2a and cirexon 2b can have alternative 5′- or 3′-splicing site. Details about cirexon detection algorithm are also included in Methods.

We then used the above algorithm to detect AS events in samples of Hs68, HeLa and HEK293 treated by RNase R. Strikingly, all four types of AS events could be found within circRNAs in all of the three samples (Supplementary Tables 2–4 and Supplementary Fig. 6). For example, ES is the most common AS type in circRNAs and we found skipped cirexons within 735, 428 and 583 circRNAs of the three cell lines, respectively, which account for 2.7 ∼ 4.3% of total circRNAs found in these samples. A3SS and A5SS are also major circular AS types, both of which occur in >1% of detected circRNAs. Our results further revealed that A3SS has a higher frequency than A5SS in all of three cell lines (1.8% versus 1.2%, 1.4% versus 1.1% and 2.1% versus 1.4%), which is similar to AS events in mRNAs reported previously 22 . Of the four AS types, IR is the rarest one, accounting for 0.1 ∼ 0.3% of total circRNAs.

It has to be mentioned that rarefaction analysis on Hs68 sequencing data showed that detection of alternatively spliced cirexons was far from saturation (Fig. 2b), suggesting that rare circular AS isoforms remain to be sequenced or discovered and thus the frequencies of AS events calculated based on all detected circRNAs were likely to be underestimated. To better estimate frequencies of AS events, especially for relatively abundant circRNAs that may undertake biological functions, we set a simple cutoff for circRNAs using BSJ read count, which was often used as a rough estimate of sequencing depth or abundance of circRNA in previous reports 8,23,24 . When such cutoff was set to 20, we got 1,262, 1,952 and 3,910 circRNA loci in HeLa, HEK293 and Hs68 cell lines, respectively, of which a much higher proportion had alternatively spliced isoforms (Fig. 2d). For example, 19.2, 15.3 and 12.2% of the circRNA loci in the three cell lines have ES isoforms, respectively, and even the rarest AS-type IR occurred in >1% of the total circRNAs. Such high frequencies strongly suggest that AS events not only occur in mRNAs but also are prevalent in circRNAs. We also observed ten examples of AS events with altered density of microRNA binding sites in circRNAs, which suggest their potential roles in gene regulation (Supplementary Fig. 7 and Supplementary Table 5).

To further verify that these predicted AS events take place in circRNAs, we performed experimental validation for randomly selected 25 circRNA loci in HeLa samples (Supplementary Table 6). Each of these circRNAs was predicted to contain at least one type of alternatively spliced cirexons. We designed outward-facing primers to amplify fragments containing BSJ and alternatively spliced cirexons, and performed Sanger sequencing on the products. As a result, 22 loci of them, including 12 loci for ES (Supplementary Fig. 8), seven loci for A3SS and A5SS (Supplementary Figs 9 and 10), and three loci for IR (Supplementary Fig. 11), were successfully validated. As no corresponding products were amplified from negative control of total DNA or poly(A)-selected samples, these validations proved the reliability of our predictions and also provided solid evidence for widespread occurrence of AS events within circRNAs. To further confirm the resistance of predicted circular AS isoforms to RNase R, we performed quantitative reverse transcriptase–PCR (qRT–PCR) for nine AS isoforms in four circRNA loci and found that all of them were significantly more resistant compared with linear mRNA (Supplementary Fig. 12). In addition, some AS events detected by CIRI-AS could be further confirmed by long-read sequencing data (Supplementary Fig. 13). Unlike 100-bp short reads, these long reads could record the full length of the AS isoforms, including both BSJ and all cirexons. Interestingly, we also found that some of the validated alternatively spliced events are circRNA specific when comparing them with mRNA AS events in the corresponding poly(A)-selected sample. For example, three circular isoforms were detected in chr1:231,090,079|231,097,049 of gene TTC13 (Fig. 2f,h–j), whereas no AS event was present in the corresponding mRNA transcript (Fig. 2g). In detail, although exon 2, exon 3 and exon 5 of TTC13 were found in all of the mRNA and circRNA isoforms, exon 4 and a cirexon with alternative 5′-splicing site of exon 4 are only present in the 382- and 304-bp circRNA isoform, respectively.

Estimation of relative abundance for AS events in circRNAs

We next asked whether the above distinction between AS patterns of circRNAs and mRNAs is occasional or pervasive. The value of ‘percentage spliced in’ (Ψ), which was used to represent relative abundance of AS event in previous studies on mRNA 13 , provided an appropriate indicator for comparison between circRNA and mRNA AS events. In CIRI-AS, Ψ value was estimated based on specific type of AS event and corresponding supporting read count of related FSJs. We also adopted a correction method by assessing the influence of library insert length distribution on the abundance estimation to eliminate bias caused by ‘anchored’ position of BSJ reads. Details about relative abundance estimation of AS in circRNA are included in Methods.

To test the accuracy of Ψ estimation in our approach, we simulated transcriptomic data containing skipped cirexons in circRNAs with various sequencing depths and relative abundances. As expected, the Ψ estimation simply based on relative reads density showed a slight bias towards underestimation in all data sets with a simulated Ψ value of 0.25, 0.5 and 0.75 (Supplementary Fig. 14A). As the estimation was based on BSJ read pairs, which represent only a subset of sequencing reads of circRNAs, the bias it caused is systematic and cannot be improved by merely increasing sequencing depth (Supplementary Fig. 14B). However, after taking library insert length distribution into consideration and making corresponding corrections, this systematic bias was effectively eliminated and our algorithm showed a better performance for Ψ estimation. We also used qRT–PCR to quantify relative abundance of circRNA AS isoforms that we detected in HeLa cells (Supplementary Table 7). As shown in Supplementary Fig. 15, relative abundances estimated by CIRI-AS and qRT–PCR were highly consistent with each other for all nine AS isoforms, which further demonstrated the reliability of CIRI-AS on relative abundance estimation.

We next pursued our investigation on relative abundance of ES in HeLa and HEK293 cell lines. The values of Ψ were estimated for all detected skipped cirexons in both of the cell lines using the above algorithm and were compared with the expression of corresponding mRNA transcripts in poly(A)-selected samples. The result showed a dramatic difference on splicing efficiency of skipped exon between circRNA and mRNA (Fig. 2e). We applied statistical tests based on a β-binomial model and found that 78.5% of the ES events have a significantly different Ψ value in the two types of RNA (P-values <0.05), of which more than half had a much larger Ψ value (ΔΨ >0.4) in circRNAs compared with the corresponding mRNAs in at least one cell line. A large proportion of alternatively skipped exons in circRNAs were never or seldom expressed in the corresponding mRNAs. A hierarchical clustering based on Euclidean distances of the Ψ values showed that ES within circRNAs of different cell lines have more similar relative abundances compared with that in the corresponding mRNAs of the same cell line (Fig. 2e), which suggest that AS events within circRNAs might be under regulation independent to mRNA splicing.

Among these with higher relative abundance in circRNAs compared with mRNAs in HeLa and HEK293, 54% could be classified as ICFs that were never found in mature mRNAs transcripts, whereas the remaining 46% were mainly from known transcripts annotated as ‘protein coding’, ‘processed transcript’ or ‘nonsense-mediated decay’ (NMD) in Gencode (Supplementary Fig. 16). NMD pathway can specifically degrade mRNA transcripts with premature stop codons 25 . We found that the cirexons with much higher Ψ values (ΔΨ >0.4) in circRNAs than in mRNAs were more likely to contain premature stop codons than the remaining alternatively skipped cirexons (64.5% versus 35.0%). To explore whether the above distinct expression pattern between circRNA and mRNA results from mRNA-specific decay pathway, we also analysed a transcriptome data set of mouse immune dendritic cells with 10-min labelling pulse of 4-thiouridine, which is highly enriched with newly transcribed RNA including NMD transcripts 26 . By applying CIRI-AS on the 4-thiouridine-labelled data, we observed a subset of skipped cirexons still showed significantly higher relative abundance in circRNA than the corresponding mRNA (Supplementary Fig. 17).

It has been reported that several splicing factors and RNA-binding proteins are involved in regulating the biogenesis of circRNAs, such as MBNL 1 , QKI 27 , heterogeneous nuclear ribonucleoprotein and SR proteins 28 . To further explore the potential mechanism of the independently regulated AS events, we investigated binding site density of known splicing factors and RNA-binding proteins in skipped/constitutive cirexons and skipped/constitutive mRNA exons, as well as their adjacent regions using existing algorithms 29,30 . As shown in Fig. 3a and Supplementary Fig. 18, most binding sites are significantly enriched in skipped mRNA exons or skipped cirexons compared with the corresponding constitutive exons and randomly selected annotated exons (also see Supplementary Table 8 and Supplementary Fig. 19 Mann–Whitney U-test: P-values <0.05 after false discovery rate (FDR) correction). Interestingly, the regulatory factors with enriched binding sites in skipped cirexons are distinct from those in skipped mRNA exons. For example, binding sites of QKI, Tra2β and heterogeneous nuclear ribonucleoprotein-U are significantly more enriched in skipped cirexons compared with skipped mRNA exons, whereas other seven splicing factors showed an opposite trend (Fig. 3b Mann–Whitney U-test: P-values <0.05 after FDR correction). Consistent with a recent study, in which the production of over one-third of abundant circRNAs were found to be regulated by the splicing factor QKI 27 , our analyses indicate that AS events in circRNAs may also have their own preference on regulatory factors. Taken together, the above results suggest an independent regulation of circRNA biogenesis or decay may account for the distinct expression pattern of alternative spliced exons between circRNAs and mRNAs.

(a) Binding site density of splicing factors in skipped/constitutive cirexons, skipped/constitutive mRNA exons and randomly selected annotated exons, as well as their adjacent regions. The average binding density was calculated as the percentage of binding sites in the whole length of the sequence for prediction and shown in the heatmap after Z-score normalization. (b) Comparison for the above density between skipped cirexons and skipped mRNA exons with the corresponding statistical significances of Mann–Whitney U-test. *P-value <0.05 **P-value <0.01 ***P-value <0.001.

Localization and characterization of IRs in circRNAs

It was reported that intron-retaining circRNAs may function as nucleus-localized transcription activators 10 . To systematically investigate the localization preference of alternatively spliced circRNAs, we applied CIRI-AS to both nucleus and cytosol poly(A)-/RiboMinus RNA-transcriptomic data in ENCODE including three cancer cell lines (HeLa-S3, HepG2 and K562) and four non-cancer lines (H1-hESC, GM12878, HUVEC and NHEK). We first compared the percentage of alternatively spliced circRNAs between the two cellular localizations and found that all four AS events in circRNAs have a preference towards nucleus localization in the seven cell lines (Fig. 4a and Supplementary Fig. 20 paired t-tests: P-values <0.05). For example, HepG2 cytosol and nucleus data set have comparable data sizes (24.5 Gb versus 30.1 Gb), but much more proportion of alternatively spliced circRNAs were detected in the nucleus data set. By contrast, mRNAs showed no localization preference for three types of AS events including SE, A3SS and A5SS in the corresponding poly(A)+ transcriptomic data (Fig. 4b), although higher proportion of IR events in mRNA was detected in the nucleus (Supplementary Fig. 21 paired t-test: P-value <0.01). Indeed, it has been reported in previous studies 31,32 that mRNAs containing retained introns locate in both the nucleus and cytosol, but more frequently in the nucleus. However, circRNA IR showed a more extreme tendency, that is, all of the 23 retained introns detected in the seven cell lines come from the nucleus (Supplementary Fig. 21 and Supplementary Table 9). A further survey on the cytosol transcriptomic data confirmed the existence of the 23 corresponding circular isoforms with these introns spliced out. For example, one isoform of gene CLK1 containing exon 8 and exon 9 is observed in both the nucleus and cytosol, whereas the other isoform with intron 8 retained only exists in the nucleus and these IR events could not be detected in poly(A)-selected data sets of the nucleus or cytosol according to our analysis and Cufflinks 33 (Supplementary Fig. 22).

(ab) Four types of AS events are enriched in the nucleus. Transcriptomic data sets (poly(A)− and poly(A)+) from cytoplasm and nucleus RNA samples of seven cell lines in ENCODE were used to predict AS events. (a) Percentage of alternatively spliced cirexons in all detected cirexons within circRNA. (b) Percentage of alternatively spliced exons in all detected exons of mRNA. (c) Overlap of IR, ES and alternative 3′- or 5′-splicing site in HeLa, HEK293 and Hs68 samples treated by RNase R. (d) Comparison of retained intron length with other intron length within circRNA, as well as those in mRNA. (e) Sorted Ψ values of IR and other AS events in HeLa, HEK293 and Hs68. (f) An experimentally validated example of alternatively spliced cirexons: IR within circRNA chr10:105,197,772|105,198,565. Cirexons and splice junctions, as well as sequencing depth and corresponding BSJ read pairs, within the circRNA detected by CIRI-AS in RNase R-treated sample of HeLa cells. Exons and splice junctions, as well as sequencing depth and corresponding sequencing reads, in poly(A)-selected sample of HeLa cells. Different outward primers were designed and RT–PCR was performed separately for each isoform. Sanger sequencing was used to validate one BSJ junction and two FSJ junctions.

We next focused on IR events detected in the samples of HeLa, HEK293 and Hs68 treated by RNase R. Although IR is the rarest AS type in circRNAs, we found that >20% (19 out of 93) of retained introns are shared in at least two cell lines and 4 of them exist in all of the 3 cell lines (Fig. 4c), which strongly suggest they are not random products of mis-splicing events. To further characterize IR in circRNAs, we investigated their lengths and relative abundances. Retained intron lengths were about two orders of magnitude smaller compared with other spliced introns in circRNAs and with those in linear transcripts (Mann–Whitney U-test, P-value <0.001), but showed no significant difference with mRNA retained introns (Fig. 4d). As to relative abundance, the circular transcripts containing retained introns tended to have lower expression levels compared with corresponding alternatively spliced transcripts with the intron spliced out in the same sample (Fig. 4e). Similar with skipped exons peculiar to circRNAs, we also detected and validated retained introns within circRNAs but not present in corresponding mRNAs (Fig. 4f and Supplementary Fig. 11C).

Tissue- and developmental stage-specific circRNA AS pattern

To study AS events not only in cultured cell lines but also in different tissues and developmental stages, we applied our algorithms to 103 transcriptome data sets from 62 samples of D. melanogaster including developmental stages such as embryo, larva, pupa and adult, as well as various tissues and cell lines 24 . The detection of circRNAs indicated that much fewer circRNAs exist in fruit fly compared with that in human. For example, we found 268 ∼ 705 circRNAs in each cultured cell line of fruit fly, whereas 2,532 ∼ 6,606 circRNAs were identified in RiboMinus samples of Hs68, HeLa and HEK293 with comparable data amounts using the same criteria (Supplementary Tables 1 and 10). However, such difference reduced after normalization by gene length and exon number of the two species. Our algorithm detected previously unpredicted structures within circRNAs in fruit fly. For example, 4,481 cirexons were recognized from the top 2,000 most abundant circRNAs in all sequencing data, of which ∼ 21% (939 cirexons) were classified as ICFs according to the latest version of Ensembl annotation for D. melanogaster. Moreover, a further survey for AS events showed that ES, A3SS, A5SS and IR were also prevalent in fruit fly, with 303 circRNAs having multiple isoforms.

We next asked whether these identified AS events within circRNAs were associated with specific tissues or development stages. The relative abundance of alternatively spliced cirexons was calculated and depicted in a heatmap. As shown in Fig. 5a, these cirexons could be clustered into three distinct groups according to their Ψ values in the 62 samples. The first group displayed an extensive distribution in almost all samples and the third group of cirexons sporadically existed in certain samples, whereas the second group specifically expressed in nervous systems such as dissected adult heads and larval/pupal central nervous system (CNS). We thus performed Gene Ontology over-representation test for parental genes of alternatively spliced circRNAs in adult heads and laval/pupal CNS. In addition to the gene sets relating to neural functions revealed in the previous study 24 , our result suggested that the genes of these circRNAs may be involved in other processes and functions, including behaviour and channel activity (Fig. 5b). Principal component analysis based on all alternatively spliced cirexons explained ∼ 32% of the total transcriptional variance in the first two principal components, distinguishing adult heads as the most divergent group. Similarly, a clear separation was also observed in other tissues or cell lines (Fig. 5c and Supplementary Figs 23 and 24). We also investigated AS events in mRNA using three corresponding poly(A)-selected data sets and found a distinct AS pattern compared with circRNAs (Supplementary Fig. 25). Considering that mRNA AS events were extensively reported to control genetic switches related to various developmental and cellular processes 34 , the close association between circRNA AS events and specific samples also suggested their potential roles in regulatory transitions in animals.

(a) Relative abundance of detected AS cirexons in all of the 62 samples. The cirexons can be clustered into three distinct groups according to their Ψ values in the tissues and cell lines. Yellow denotes cirexons with Ψ value equal to 1 and dark brown denotes cirexon with Ψ value equal to zero or no expression/detection. (b) Gene Ontology enrichment analysis for parental genes of alternatively spliced circRNAs in adult heads and laval/pupal CNS. BP, biological process CC, cellular component MF, molecular function. (c) Principal components analysis based on the Ψ values of the AS cirexons in each sample. Each dot represents a sample and its colour corresponds to the sample type on the top of a.

Origins and impacts of new mammalian exons

Mammalian genes are composed of exons, but the evolutionary origins and functions of new internal exons are poorly understood. Here, we analyzed patterns of exon gain using deep cDNA sequencing data from five mammals and one bird, identifying thousands of species- and lineage-specific exons. Most new exons derived from unique rather than repetitive intronic sequence. Unlike exons conserved across mammals, species-specific internal exons were mostly located in 5' UTRs and alternatively spliced. They were associated with upstream intronic deletions, increased nucleosome occupancy, and RNA polymerase II pausing. Genes containing new internal exons had increased gene expression, but only in tissues in which the exon was included. Increased expression correlated with the level of exon inclusion, promoter proximity, and signatures of cotranscriptional splicing. Altogether, these findings suggest that increased splicing at the 5' ends of genes enhances expression and that changes in 5' end splicing alter gene expression between tissues and between species.

Copyright © 2015 The Authors. Published by Elsevier Inc. All rights reserved.


Figure 1. Identification and classification of species-…

Figure 1. Identification and classification of species- and lineage-specific exons

Figure 2. Evolutionarily young exons differ from…

Figure 2. Evolutionarily young exons differ from older exons in many properties

(A) Average-linkage hierarchical…

Figure 3. A variety of genomic changes…

Figure 3. A variety of genomic changes are associated with novel exon splicing

Figure 4. Upstream intronic deletions are associated…

Figure 4. Upstream intronic deletions are associated with increased exonic nucleosome occupancy and transcription pausing

Figure 5. Inclusion of new exons is…

Figure 5. Inclusion of new exons is associated with increased species-specific gene expression changes

Figure 6. Increase in species-specific gene expression…

Figure 6. Increase in species-specific gene expression is associated with a lower ISR (Incomplete Splicing…

A Word From Verywell

Setting up an experiment so that it has sound internal and external validity involves being mindful from the start about factors that can influence each aspect of your research.

It's best to spend extra time designing a structurally sound study that has far-reaching implications rather than to quickly rush through the design phase only to discover problems later on. Only when both internal and external validity are high can strong conclusions be made about your results.


There are two types of cancer: haematological cancer and solid cancer. Haematological cancers are involved with blood and lymph (leukaemia and lymphoma), while solid cancers are the rest. Inside the group of solid cancer, the most frequents are carcinomas (epithelial tissue) and sarcomas (connective tissue: muscle, bone, fat).

The majority of cancers are carcinomas because epithelial tissue are in constant regeneration and cellular division. This tissue covers or defines the surface of organs, cavities and tubes. Another reason is this tissue is more exposed to carcinogens.

So What Is Internal and External? – Conclusion

You should now finally understand how the words internal and external are appropriately employed. Both of these words are used commonly as adjectives. They support the word that follows each of them.

These words are antonyms to each other. The former means being confined to a limited area. The latter on the other hand is not something that has a predetermined or limited scope. In addition to the word internal, its synonyms like “inside,” “interior,” or “inner” are used. Similarly, in the place of external, “outer,” “outside,” or “exterior” are employed.

This pair of terminologies can be used as adjectives for different areas of spoken English. Hopefully, the external and internal definitions and the difference between external and internal have been effectively summarized over the course of this post today.

If you ever need a refresher on the summary of each word, simply head back to our quick reference table for a quick overview.

External and Internal Fertilization

External fertilization usually occurs in aquatic environments where both eggs and sperm are released into the water. After the sperm reaches the egg, fertilization can then take place. Most external fertilization happens during the process of spawning where one or several females release their eggs and the male(s) release sperm in the same area, at the same time. The release of the reproductive material may be triggered by water temperature or the length of daylight. Nearly all fish spawn, as do crustaceans (such as crabs and shrimp), mollusks (such as oysters), squid, and echinoderms (such as sea urchins and sea cucumbers). Pairs of fish that are not broadcast spawners may exhibit courtship behavior. This allows the female to select a particular male. The trigger for egg and sperm release (spawning) causes the egg and sperm to be placed in a small area, enhancing the possibility of fertilization.

Figure (PageIndex<1>): Anemone fish protecting its spawn: An image of an Anemone fish protecting its spawn. The anemone fish utilizes a form of external fertilization.

External fertilization in an aquatic environment protects the eggs from drying out. Broadcast spawning can result in a greater mixture of the genes within a group, leading to higher genetic diversity and a greater chance of species survival in a hostile environment. For sessile aquatic organisms such as sponges, broadcast spawning is the only mechanism for fertilization and colonization of new environments. The presence of the fertilized eggs and developing young in the water provides opportunities for predation, resulting in a loss of offspring. Therefore, millions of eggs must be produced by individuals. The offspring produced through this method must mature rapidly. The survival rate of eggs produced through broadcast spawning is low.

What are external environmental factors?

External environmental factors can be defined as the tangible and intangible factors that are not under the direct control of an organization. For example, government policy is outside the control of most organizations.

External factors are important because even if all of your internal factors are moving smoothly, an external environmental factor can derail you in an instant.

In 2015, the Swiss Franc was unpegged against the Euro and many firms such as AlPari UK were made insolvent or lost hundreds of millions of dollars.

Not all eternal factors are created equally. They’re broadly categorized into micro and macro external environmental factors.

Micro external environmental factors

Micro external factors impact your industry or business directly but may not have an impact on the economy as a whole. Changes in micro factors can affect the day-to-day activities in your business and have an outsized impact on you.

An example of a microenvironmental factor is when government policy changes and you’re required to implement safety procedures for staff.

Micro external environmental factors include:


Are your supplies meeting deadlines or are prices increasing over time? Those are factors that affect you alone and you may be able to rectify by switching suppliers or renegotiating terms.


What are your competitors doing? Have they released new features that make your solution obsolete or have they been able to crack markets you’ve struggled with? It’s important to keep an eye on the competition without copying them wholesale.


What are your customers responding well too? Do they like your latest marketing campaign or was there backlash?

Audi posted an innocent-looking picture on social media that got tons of backlash recently.

People were upset for many reasons but it’s a learning experience for Audi. They couldn’t control the collective backlash but they can do better in the future. This is an external environmental factor that affected them alone.

Customer external factors also touch on demographics and psychographics. A few years ago, the Black Lives Matter movement wasn’t mainstream but now it’s capturing headlines the world over.

The same can be said for the Me-Too movement.

These are shifts in the attitudes of customers and if you’re insensitive to the current realities, it can turn into a PR disaster.

Certain government policy

Governments the world over make policies that affect specific industries all the time. For example, the finance industry is highly regulated.

Restaurants and healthcare facilities are also highly regulated. Car washes and retail clothing stores aren’t regulated nearly as much.

It’s necessary to stay up to date with all the regulations that impact your business or you may find yourself on the wrong end of an audit.

Available talent

If the talent pool for your specific industry is narrow because of the size of your city or increased demand, this will have a big impact on how you do business.

You may have to pay well above the market rate to attract top talent. Conversely, if there’s a glut of workers, you can get great staff almost effortlessly.

Macro environmental factors

Macro environmental factors are more generalized and affect the economy as a whole. A change here will affect your industry and business and every other business or industry.

Like microenvironmental factors, it can force you to change the way you strategize, the way you do marketing, and even the kind of customers you work with.

Demographic shifts

The population you’re targeting may age and no longer be interested and the next generation needs a different type of marketing approach.

Your target audience may be emigrating out of the country. It may be that people as a whole no longer make as much money.

The demographic shifts that can affect your business are many and varied. Your job is to have a deep understanding of those changes and stay in front of them.

Societal changes

Gender stereotypes that were acceptable just 70 years ago, as seen in ads, may get a company sued today.

Society is constantly changing and there’s little you can do about it as an organization. Homosexual relationships were frowned upon 30 years ago but are accepted now.

Smoking was considered normal in the ’20s but now it’s taboo – you won’t even see it in the movies (only villains and Arabs smoke in Hollywood).

These are all societal changes that happen slowly then all at once. Stay abreast of them in a general sense so you won’t be blindsided.

Government policy

Like with microenvironmental factors, government policy can also have a large impact on every business. For example, the tax reform bill passed in 2018 cut corporate taxes and impacted every industry.

Things like environmental regulations, interest rates, etc. will affect you no matter what industry you’re in.

Large technological shifts

Industries that didn’t exist – that couldn’t exist – just twenty years ago are now some of the largest companies in the world.

  • Uber
  • Salesforce
  • Snowflake (largest software IPO ever as of this writing)
  • Slack

Technology has transformed the way we work, communicate, and spend money. Retail stores see a large percentage of their sales from their online shops. Project management has gone to the cloud.

These technological shifts can enhance your business but, at the same time, can leave you behind.

Overall economic performance

There’s no way to sugarcoat this one. If the economy is depressed like what happened during the great recession in 2008 and now with COVID-19, everyone industry feels the pain.

These aren’t things you can foresee but they are things you can plan for. Focus on building a rainy day fund that’ll see your business through the good times and hard times.

Watch the video: Biper and His Friends - Patito Juan (August 2022).