We suggest that the overarching conclusions drawn by van Bakel et al.—that there is only spasmodic (not pervasive) low-level transcription of much of the genome, and that much of this transcription has “random character” 
—are the result of a number of debatable aspects of their logic and analysis. These may be summarized as (1) insufficient sequencing depth and breadth and poor transcript assembly, together with the sampling problems that arise as a consequence of the domination of sequence data by highly expressed transcripts; compounded by (2) the dismissal of transcripts derived from introns; (3) a lack of consideration of non-polyadenylated transcripts; (4) an inability to discriminate antisense transcripts; and (5) the questionable assertion that rarer RNAs are not genuine and/or functional transcripts.
1. Sequencing depth, breadth, and assembly. The conclusions of van Bakel et al. about the pervasiveness of transcription were based on transcript read number, not the extent of genomic coverage of the observed transcripts (which is the correct metric), stating “the vast majority of sequence reads in polyA+ samples correspond to known genes and transcripts, arguing against widespread transcription to the extent reported previously”. The former fact does not justify the consequential argument. This also highlights a key caveat of RNA sequencing—i.e., diminishing returns—whereby abundant transcripts constitute the majority of reads, making rare transcripts difficult to find in the absence of normalization approaches. This problem is clearly evidenced in the van Bakel et al. dataset, where ~88% of unique polyA+ sequences mapped to exons of known genes, which comprise just over 2% of the genome. Therefore, the transcription in the remainder of the genome was sampled by only ~12% of the reads.
This insufficient depth of sequencing is illustrated by comparing the rates of discovery for exonic, intronic, and intergenic sequences as sequencing depth increases. Despite continuing to constitute most reads, the area covered by exons quickly moves towards saturation, while the area covered by intronic and intergenic transcripts was found to “keep increasing at roughly constant rates” 
. Thus, the sequence coverage of the vast majority of the genome is not saturated, and potentially includes many novel protein-coding and noncoding transcripts insufficiently sampled at the given read depth.
Underscoring the importance of adequate transcriptome sampling, concurrently published deep sequencing studies, with two to three times greater depth of data from polyA+ RNAs from cultured cells, were still not saturating 
. Nonetheless, and unsurprisingly, the increased sequencing depth led to increased novel transcript discovery, as only 70% of the identified splice junctions were derived from “known genes” in a mouse myoblast cell line 
, compared to 94% reported by van Bakel et al. Re-analysis of transcript assembly at different sequencing depths also suggested, crucially, poor assembly and poor recovery of lowly expressed transcripts at the deepest level of sequencing used by van Bakel et al. 
The lack of sequencing depth in the van Bakel et al. study was exacerbated by the pooling of 10 tissues/cell lines and the use of such a highly complex tissue as brain. Increasing the complexity of the sample dilutes the relative proportion of tissue- and cell type–specific transcripts. Using the brain (170 billion cells) 
, we calculate that a cell type–specific transcript present at ~10 copies per cell (a common level of abundance) in 0.1% of cells would have only a ~50% chance of being detected by any reads at the depth of sequencing utilized by van Bakel et al., let alone of being assembled into a complete transcript (see Text S1
Importantly, because the genomic strand from which individual sequence reads were derived was unknown in their study, the method that van Bakel et al. employed to assemble these reads into transcriptional units required that contigs in the vicinity of known genes be bounded by splice sites or cross a splice site, automatically excluding (i) nearly all 5′ and 3′ UTRs; (ii) deep sequencing reads, other than the splice site, in genes with a single intron (); (iii) transcripts from single exon genes, such as the highly expressed metastasis associated lung adenocarcinoma transcript 1 (MALAT1
) (Figure S5
), and transcripts (not containing a splice site) originating from introns 
; and (iv) perhaps most importantly, any known transcript for which there was no identifiable splice junction in the dataset. This methodology therefore discriminates against lowly expressed transcripts, heavily biasing in favor of common mRNAs.
Figure 2 Poor coverage of single intron transcriptional units (TUs) by van Bakel et al. .
2. Intronic transcripts.
Despite the data showing that 51.4% of the genomic area covered by their human reads aligns with intronic regions, van Bakel et al. presumptively dismissed these sequences as mainly derived from unprocessed pre-mRNAs, due to their “low coverage and ubiquitous character”. Intronic regions, which correspond to more than a third of the genome, are by definition transcribed, and hence must be included in estimates of the amount of transcription across the genome. It is also important to note that many introns are not fixed entities and whether a genomic region is intronic, intergenic, or exonic depends on the cell type and physiological state of the cell. In addition, the number of functional RNAs that may be derived from introns is unknown, although there is considerable evidence that they can produce a diversity of discrete stable RNA products from both the sense and antisense strands 
, including novel RNAs with validated functions (e.g., 
3. Non-polyadenylated RNAs.
The data used by van Bakel et al. to support the conclusion that “dark matter transcripts make up a small fraction of the total sequenced transcript mass” focused on polyadenylated RNA. However, previous transcriptomic analyses showed that over 40% of non-ribosomal transcripts are non-polyadenylated 
, and more recent deep sequencing of total RNA has revealed that over 45% of uniquely mapping sequence reads originate from intronic and intergenic regions 
, compared to only 10% in the polyA+ RNA from equivalent samples examined by van Bakel et al.
4. Antisense and overlapping transcription.
Tiling array, cDNA, EST, and RNA sequencing evidence all indicate that considerable interleaved transcription occurs on both strands 
, with at least 66% of all protein-coding genes in mouse showing evidence of overlapping or antisense transcription 
. However, van Bakel et al. concluded that their data “argue against widespread interleaved transcription of protein-coding genes”. This discrepancy can be explained in large part by the lack of strand information in the RNA sequencing data used by them to assemble transcriptional units (TUs). Indeed, the assembled TUs covered less than 26% of the genome (compared to over 40% spanned by RefSeq genes) and, tellingly, less than 2% of RefSeq annotated 3′UTR sequences. This lack of coverage and strand information resulted in a large underestimate of the extent of antisense and overlapping transcription (Figures S6
), for which functional evidence is also emerging (see e.g., 
5. Discriminating low signal strength from background noise.
The assertion of van Bakel et al. that low sequence coverage (by seqfrags) equates with transcriptional “by-products” and/or “random initiation events” is highly debatable. Such seqfrags might equally, if not more, plausibly reflect stochastic sampling of transcripts that are less expressed, less stable, and more cell specific 
. This is not proof or even evidence of irrelevance. Moreover, van Bakel et al. infer non-functionality of rare transcripts without any biological data, but one cannot expect vast numbers of novel coding and noncoding RNAs to be functionally annotated coincident with their discovery, especially if, as is likely, they have many different functions 
. The yeast GAL10
-ncRNA provides a good example: despite a steady-state expression level of around one transcript per 14 cells, it is functional 
. Similarly, the mammalian HOTTIP RNA plays an important role in epigenetic regulation despite an average expression level of around 0.3 transcripts per cell in expressing tissues 
. Therefore, while expression levels are important, it cannot be assumed a priori that low expression equates to non-functionality.