The Chromosome-Centric Human Proteome Project (C-HPP 1 ) is currently in a mature state with a rough benchmark of 85% of the human proteome covered throughout different tissue/cell types 2 . The remaining portions of the uncovered proteome are challenging because of two issues. First, there are missing proteins never detected by mass spectrometry (MS) or antibodies (Ab)-based experiments at the trustable level of confidence 3 . Second, some proteins were sometimes detected in a certain type of biospecimen carrying no clear function. Although the segregation of the unexplained proteins into two groups is obviously convenient, missing and uPE1 proteins from tissues or cells appear similar due to the equally low level of mRNA expression. Moreover, according to NextProt releases, missing proteins sometimes pass into the uPE1 category, and such transfer confers the ultimate task of the current phase of C-HPP. We hypothesized, that missing and uPE1 proteins might not be so different from the viewpoint of C-HPP mass-spectrometry pillar, particularly if specific biospecimens are analyzed.
For the human chromosome 18 (Chr18, the Russian C-HPP consortium 4 ), the baseline metrics to the beginning of 2020 were 265 protein-coding genes (PE1-PE4), among which protein evidence of the level PE1 was available for 252, 13 proteins were missing and 10 had a uPE1 status. This list of 23 proteins (10 PE2, 12 PE3 and just one PE4) became our targets for the neXt-MP50 and CP50 challenges 2 . To capture highlighted targets in this article we relied upon the new sequencing technology of the Oxford Nanopore enhanced by further targeted data mining for the missing and uPE1
proteins.
The absence of some proteins could be reasonably attributed to alterations in the primary structure 5 , when proteotypic peptides can accidentally fall into the splice junctions, or carry nonsynonymous polymorphisms, affecting the peptide retention time and mass-to-charge ratio in LC-MS/MS analysis. Thus, the increased accuracy of transcriptome data became ultimately essential, despite many studies already published on the chromosome-centric transcriptome to proteome mapping, including those from our group [6] [7] [8] , from the Spanish Proteome society 9 , reports on Chr9 10 and Chr17 11 , and also the report on the transcriptome-to-translatome-to-proteome contribution from the Chinese consortium 12 . Inspired by C-HPP work tasks a corpus of bioinformatics tools 13, 14 and databases 15, 16 was developed to manage the transcriptomes. However, little attention was given to the question of the reliability of the underlying transcriptome data. In most cases, the transcriptome was analyzed by a single method-e.g., RNA-Seq-and at best validated by a second method-e.g., by quantitative PCR. For example, SOLiD technology was applied for the whole-transcriptome analysis of HepG2 cell lines and liver tissues 17 (these biomaterials were proposed by the Russian C-HPP Roadmap 4 ) and validated by only 45 selected mRNAs by droplet digital PCR (ddPCR). Some other data included Illumina GaII x and SOLiD profiling of Chr18 7 but should be considered as outdated due to the novelties in the next-generation sequencing (NGS) platforms.
Validating the transcriptome versus the proteome by quantitatively correlating gene product abundance 18, 19 was an attractive idea. The Chr18 consortium (Russia) investigated such an approach in detail. In capturing the transcriptome to proteome relationships everyone get used to general arguments referring to NGS platform dependence of experiments, gene-to-gene difference in effectiveness of transcription, the rate of protein synthesis and degradation and so on 20, 21 , binding all these claims to the whole genome, not to a single chromosome. Contrastively to the whole genome assumptions, in the report 22 the chromosome-centric transcriptome to proteome correlations were analyzed. The mRNAs levels were determined independently by quantitative PCR (qPCR) of Chr18 genes, and then supported by the next-generation sequencing on Illumina and SOLiD platforms and further by the shotgun MS data.
Actually, studies in the field of Chr18 transcriptome profiling and targeted proteome mapping in liver tissue and HepG2 cells 6 revealed a poor correlation between transcriptome and proteome data. Radko et al. 8 investigated to which extent the targeted PCR-based transcriptome mining can contribute to the problem of the missing proteins of human Chr18. A summary of these chromosome-centric efforts revealed the unexpectedly low quantitative correlation, with no satisfactory explanation 5 .
To create at least some ground for the analysis of missing and uPE1 species, we reanalyzed the transcriptome of Chr18 to gain more accurate data and assess the level of errors in such data by comparing the results from different platforms of transcriptome quantitation. We recruited the gold-standard method of quantitative PCR (RT-PCR and ddPCR) together with well established HiSeq/Illumina. In addition to these methods, the recently emerged sequencing method articulated itself as the Oxford Nanopore Technology (ONT) was recruited here for C-HPP by using an ONT MinION sequencer 23 , the low-cost portable sequencing machine. The technology produces lengthy reads up to 10 4 nucleotides advantageously to the Illumina platform, which could obtain reads of 50to 300-nucleotides long. However, the disadvantage of ONT is that long reads contain errors at the rate of approximately 3-5 lost or misread sites per 100 sequenced nucleotides. ONT sequencing was characterized in genomics by reading up to 70 thousand nucleotides; at transcript level the read length was naturally limited by the mRNA length and quality. For the first time, this technology was applied to the human transcriptome 23 to analyze seven human cell lines and a set of tissue samples, including that of the human liver. For the LC2/ad cell line, the ONT performance was compared to a short-read RNA-seq data with a pretty good correlation (r p =0.88) between the methods Authors 23 thoroughly compared the expression levels of selected genes and found a significant correlation (r p =0.82) between ONT and qPCR, suggesting the ONT MinION is suitable for the quantitative assessment of the human transcriptome.
Herein, by analysis of 3 samples of healthy livers and hepatocyte-related HepG2 cells, we pursued a double task. First, we compared the three methods of transcript identification and quantification to understand, taking the Chr18 transcriptome as an example, the extent to which the quantitative profiles are consistent within the same biological sample. This question must be answered before explaining the poor transcriptome-proteome correlation. Second, we probed an approach to select promising targets from the set of genes encoding missing and uPE1 proteins by mapping them on the short (Illumina) and long (ONT) reads to explore the occurrence of mutations and/or splice junctions within those coding transcript sequences, which could affect protein detection.
Human liver samples and HepG2 cells HepG2 cells (ATCC HB-8065, ATCC, USA) were grown to approximately 80% confluence and harvested. The cells were washed 3 times with PBS, counted using an EVE automated cell counter (NanoEntek, South Korea), pelleted by centrifugation, and kept in liquid nitrogen until further use.
For transcriptome profiling with qPCR, total RNA was isolated from liver tissue samples and HepG2 cells using the RNeasy Mini Kit (Qiagen, Germany) according to the manufacturer's protocol. The on-column DNase digestion step was performed using the RNAse-Free DNase Set (Qiagen, Germany). The isolated total RNA was quantified using a Qubit 4 fluorometer and the Qubit RNA HS Assay Kit (Thermo Fisher Scientific, USA), and the RNA quality was assessed using a Bioanalyzer 2100 System (Agilent Technologies, USA). The RIN numbers for all preparations of total RNA were 7.5 or higher. Synthesis of cDNA was carried out using the AffinityScript qPCR cDNA Synthesis Kit and random primers (Agilent Technologies, USA) according to the manufacturer's recommendations. The cDNA samples were stored at -20°C until further use.
The amount of each transcript encoded on Chr18 was assessed by measuring the number of copies of pertinent cDNA in the cDNA preparation derived from total RNA. qPCR was conducted in two formats-droplet digital PCR (ddPCR; 49 transcripts) and PCR in real time (PCR-rt; 226 transcripts)-employing the earlier designed set of primers and probes 22, 24 , with minor exceptions. While ddPCR was performed as described previously 22, 24 , the transcriptome profiling by PCR-rt was carried out using the ΔCt method 25 .
To calculate the copy number of a transcript per cell, the copy number per PCR probe was normalized by dividing it by the amount of total RNA in the PCR probe (200 ng). The number of transcripts per nanogram of total RNA was brought to the copy numbers per cell based on the amount of total RNA in hepatocytes and HepG2 cells, reported to equal 40 pg/cell 26 .
Total RNA was isolated using the Extract RNA kit (Eurogen, Russia). RNA quality was evaluated using the Bioanalyzer 2100 System (Agilent Technologies). The RIN numbers varied from 7.3 to 9.1. Clustering and sequencing were carried out using the Illumina HiSeq 2500 system (2 lanes per 8 samples) according to the manufacturer's protocols (Denature and Dilute Libraries Guide; Sequencing in Rapid Run Mode). For each replicate, we derived from 32 to 59 million reads.
The derived fastq files were analyzed by FastQC and then were processed by Trimmomatic. The read mapping and expression quantification were carried out employing STAR 2.7 (splice-aware mapping to genome), bowtie2 (mapping to transcripts), RSEM 1.3 (quantifications of the reads), and Salmon (quasi-mapping and quantification) software packages. The genome GRCh38.p12 assembly (Ensembl release 97) was used as a reference. Finally, we compared the results obtained with STAR-RSEM, bowtie2-RSEM and Salmon, calculated the Spearman/Pearson correlation coefficients and created clustering dendrograms. The distance between samples/pipelines (i.e., dissimilarity rate) was evaluated as 1-corr.coeff. To create dendrograms, we used the complete linkage hierarchical clustering method.
The sequencing data obtained in this study is available at NCBI Sequence Read Archive (BioProject ID PRJNA635536).
Total RNA was isolated and characterized as for qPCR analysis. The extraction of mRNA from the total RNA preparations was conducted using the Dynabeads™ mRNA Purification Kit (Thermo Fisher Scientific, USA) following the manufacturer's recommendations. The mRNA preparations were immediately frozen and stored at -80°C until nanopore sequencing.
Nanopore sequencing was carried out using the MinION sequencer (ONT, UK) with 2.17 28 . The overall statistics of alignment mapping was produced using the "samtools stats" command, and the quantitative data were further collected by executing the program Salmon. 0.12/1.1.0 with the command line options "quant -p 8 -noErrorModel" 29 .
The MS/MS dataset from the Mann's group was downloaded from the PRIDE databank (project accession number PXD005141) and searched by the engines embedded into the SearchGUI/PeptideShaker platform 30 and by MaxQuant 31 using the same parameters, as previously specified 32 .
The peak lists obtained from MS/MS spectra were identified using OMSSA 
To characterize the transcription of genes encoding uPE1 and missing proteins (MP), we performed an analysis using the Oxford Nanopore system. In total, 1.5 million reads were obtained for liver cells, and more than 80% of these reads were successfully mapped to the human transcriptome version v32. Data for the same samples were collected using the orthogonal platforms Illumina (hereafter also called HiSeq) and quantitative PCR.
The transcripts of the uPE1 proteins were expected to be detected at more than the transcripts encoding the missing proteins. However, the experimental data indicated the opposite: the level of protein transcription in both groups was comparably low. We compared the values of the expression level of the studied group of genes (MP+uPE1) with the data obtained using alternative methods (Supporting Information   Table S1 ). We confirmed, using both Illumina HiSeq and quantitative PCR, that the transcripts are expressed at an extremely low level, close to the detection limit. Thus, regarding the transcriptome of the studied biomaterials-that is, the liver and HepG2 cells-the genes encoding missing and uPE1 proteins were similar in the expression level and could be analyzed together.
The use of the EMBL-EBI's gene ExpressionAtlas 38 Confirmation of the above observations was found in ProteinAtlas. In the liver tissue and HepG2 cells, the level of transcription of the uPE1 coding genes and missing proteins was comparatively low; however, in at least 1-2 tissues, the uPE1 proteins were expressed pronouncedly at the level of several dozen TPMs. An interesting observation was the TMEM241 gene, which, according to the data of RNA-Seq from ProteinAtlas, demonstrated little expression in the HepG2 cell line at the level of 5 TPM, producing zero values in all the samples by both sequencing methods used herein but was detected as low as 2.5 copies of cDNA per cell using ddPCR.
The pronounced differences between uPE1 and missing proteins were observed in the NextProt portal and the associated PeptideAtlas resource: the uPE1s were usually highly populated using unique natural peptides, with little or no missing proteins.
Notably, one missing protein (CTAGE1) from the number of missing proteins does not have unique proteotypic peptides during tryptic hydrolysis. Two other proteins demonstrated one proteotypic peptide each: the ANKRD30B protein peptide was found in prostate cancer samples 39 , and the proteotypic peptide for HSBP1L1 was found in HepG2 cell samples 32 .
Comparison of the NextProt releases from January and July 2020 revealed the transfers of proteins encoded by human Chr18. The SMIM21 protein has moved from the missing protein category to the uPE1 category. SMIM21 is characterized as "expression level 3" according to ProteinAtlas data, at a high level of normalized expression of mRNA NX=5.0 in the brain cancer line. Another protein, encoded by the C18orf65 gene, changed its category from PE2 to PE5, leaving the ranks of the missing proteins of chromosome 18. in chemical hepatocyte injury 42 , prioritizing this target to investigate the genetic aspects of liver diseases. In the July 2020 version of NextProt, this protein was transferred from the PE2 (e.g. missing proteins) to PE1 category, with the functional annotation at the "silver" level. To our knowledge, this transfer is not yet confirmed by mass spectrometric data in accordance with the criteria of version 3.0 of the guidelines 43 , at the same time MS-based support was delivered by top-down proteomics 44 in HeLa cells.
So, at the time of this writing, the updated version of NextProt included 9 missing genes and 11 genes characterized as uPE1 from Chr18.
A special case is presented by the KLHL14 (uPE1) gene, which, according to different methods, was not expressed in all biosamples, except for the HepG2 sample, where its expression level measured by quantitative PCR was reported as 116 copies per cell. The expression level of the housekeeping gene ATP5F1A was comparable -324 copies per one HepG2 cell on an average.
To rely on transcriptome data for the analysis of MP and uPE1 proteins, all of which exhibit low transcription levels, it is necessary to confirm the reliability of the basic data. In view of this assumption we considered the datasets in two ways:
comparison of the list of highly expressed genes -"Stakhanovite genes" 45 and deciphering a degree of correlation between analytical methods. "Stakhanovite genes" are named after the Soviet coal miner Alexey Stakhanov renowned for his outstandingly hard everyday work.
Missing and uPE1 proteins encoded by the human Chr18 are characterized by low expression of corresponding genes in the liver and HepG2 samples. To estimate the contribution of error in the sample preparation and measuring methods, we started not from the low expressed transcripts, but, oppositely, from the extremely high abundant ones produced by "Stakhanovite genes" (Fig. 1) . The choice of significantly expressed genes is justified, as estimations obtained from highly expressed species are prone to noise, thus providing a minimal level of errors.
Supporting Information Table S2 shows five of the leading "Stakhanovite genes" for each sample studied, exhibiting the highest levels of expression as revealed by ONT MinION, Illumina HiSeq, and qPCR analysis. These 5 topmost genes are responsible for a substantial part of the total mRNA abundance. For example, in the case of ONT MinION sequencing, more than 95% of the total mRNA abundance was attributed to the corresponding "Stakhanovite genes" in both HepG2 cell and liver samples. In this case, the RPL17 gene encoding 60S ribosomal protein L17-a component of the ribosomal complex-leads the list of "Stakhanovite genes" for HepG2 cells. The gene encoding transthyretin, TTR, was expectedly most highly expressed in the liver, which secretes blood plasma ingredients. In addition to RPL17, other housekeeping genes were revealed by ONT sequencing: MYL12A and MYL12B encoding chains of the motor protein myosin; ATP5F1A encoding the ATP-synthase subunit alpha responsible for the energy generation in mitochondria; and CYB5A encoding cytochrome b5, the membrane-bound electron carrier. Among these genes, 4 top genes were common for HepG2 cells and liver tissue, showing a considerable level of qualitative concordance between the biological samples of different origin.
Illumina HiSeq sequencing also revealed some level of qualitative concordance among "Stakhanovite genes" for HepG2 cells and liver tissue samples: TTR and RPL17
were expressed in all liver samples and HepG2 cells, NDUFV2 as well as MYL12A were present in HepG2 and at least in two of three liver samples ( Fig. 1 
Information Table S2 ). The similar level of qualitative concordance among "Stakhanovite genes" was found between the liver samples studied. For qPCR analysis, a relatively high level of matching was observed for the subsets of "Stakhanovite genes" among liver tissue samples: a coincidence of 4 of 5 or even 5 of 5 among the lists of highly expressed genes. Surprisingly, the level of concordance between the subsets of "Stakhanovites" revealed by two sequencing platforms, ONT MinION and Illumina HiSeq, was rather moderate: only 3 items were shared between HepG2 cells and the liver sample; TTR, RPL17, MYL12A and CYB5A were among them. A similar situation was observed for the liver sample when the ONT MinION or Illumina HiSeq data were compared with the qPCR data: only 3 of 5 top genes coincided. When the qPCR data were compared with the HiSeq dataset for the liver tissue samples of donors #3 and #5, only 2 matching hits were identified (out of 5 selected "Stakhanovite genes"). Moreover, for HepG2 cells, no concordance was found between the subset of "Stakhanovite genes" revealed by qPCR and those revealed by either the ONT MinION or Illumina HiSeq sequencing (Supporting Information Table S2 ).
Tanglegram of the most heavily expressed "Stakhanovite" genes with (a) different methods and one sample (human liver) and (b) by the same method, applied for different biospecimens (liver versus HepG2 cell line). Dendrograms were obtained using differences between the estimations of the expression levels as measured by qPCR, Illumina HiSeq and ONT methods and scaled for convenient representation. * indicates the most highly expressed genes. Dendrograms were built using Ward's clustering and the Euclid distance between log2transformed data. The Dendextend package 46 was used to draw tanglegrams and compute entanglement.
The leaves of the dendrograms in Fig. 1 correspond to the highly expressed genes of Chr18 (topmost 13 expressed genes were taken for this analysis, each showing itself at least once in the Supporting Information Table S2 ). Clustering was performed based on the distance metrics, which correspond to the difference in the values of gene expression; therefore, the closer were leaves at the tree, the more similar were the estimations of expression (either FPKM, copies per cell or TPM). Fig. 1A represents the results of the analysis using two different methods for one and the same liver sample. The leaves were severely mixed between clusters, which were visually observed as crosses between edges connecting two dendrograms. Among the 10 probed genes, only two pairs preserved their closeness in both dendrograms: MAPRE2 neighboring AFG3L2 and subcluster MYL12 subunits A and B. No such neighboring was observed when comparing different samples by a single method, the ONT (Fig. 1B) .
The assessment of entanglement as a measure of concordance between dendrograms produced similarly low values of 0.31 and 0.28 for Fig. 1A and 1B, respectively. Thus, working within a single, not-so-large chromosome, even for the case of highly expressed genes, the relationship between the quantitative estimations of the expression level was highly discordant. Although this finding may be expected for the samples of normal liver and HepG2 cells derived from hepatoblastoma, it was rather surprising to find such disparity in the results acquired from the same sample analyzed using different methods.
Different methods of transcriptome analysis may give different results 7, 17, 47 .
At the genome scale, the frequency of RNA-Seq transcripts provides clues for MS identification of the corresponding protein 48 Table S1 and Fig. S1 ). Furthermore, 14 transcripts were not found in HepG2 cells but were observed in a liver sample of at least one donor, and 10 transcripts were present in HepG2 cells but not in a liver sample of at least one donor (Supporting information Table   S1 ). Despite the high overall correlation of the Chr18 transcript abundances between different liver samples, as it was evaluated by qPCR (Supporting information Fig. S2.1 , panels D to F), for several genes we revealed quite dissimilar expression patterns (Supporting information Fig. S3 ). As it was expected, the most pronounced variations in the expression levels were observed at low transcript abundances, where the expression levels varied by more than a factor of 4. By contrast, the abundance of many Chr18 transcripts in HepG2 cells differed from that in the liver tenfold, a hundredfold, and sometimes a thousand-fold for both low-and high-abundant transcripts (Supporting information Fig. S3, panel C) . Thus, although HepG2 cells are sometimes considered a model for hepatic biotransformation studies 42 Compared with the short-read sequencing such as Illumina, the long-read ONT sequencing is a relatively new technology. It should be considered as orthogonal to HiSeq, and different bioinformatics analyses are eventually required to treat the raw data.
Although the LAST and BWA algorithms (originally developed for the mapping of short reads 49, 50 ) were also employed for ONT data processing 23 , the minimap2 tool 51 is nowadays widely used for long noisy read mapping.
Thus, we prepared three datasets from the orthogonal methods and performed correlation analysis supplied with a cluster dendrogram (Fig. 2 , Supporting Information Table S3 ). Table S2 ) for the expression values, which were the square root transformed to reduce the dominant effect of highly expressed genes. The dendrogram was created based on the Spearman rank correlation coefficients (hierarchical clustering using Ward's D2 method).
In this study, we also included our previously obtained results 24 , which were derived using Illumina GaII x and Applied Biosystems SOLiD platforms for the HepG2 cell line (denoted as Hep13 in Fig. 2 ; originating from a different source than the HepG2 cells used in the current study) and from the pooled sample of three human livers (denoted as Liv13).
As can be seen from the dendrogram (Fig. 2) , the gene expression profiles are clustered into four groups. The division occurs at the level of the methodology: the first cluster was attributed to quantitative PCR method, while other three clusters accommodate the results of next-generation sequencing. Furthermore, to a lesser extent, in the case of qPCR, a separation occurred according to the type of biological material. In the case of sequencing, a separation occurred according to both the biomaterial type and cDNA/mRNA library preparation method. These findings are presented more clearly in the Supporting information Table S3 , which includes data on technical replicates and various RNA-Seq data processing pipelines. At the same time, clusters 2 and 3 combined the data from various platforms-Illumina GaII x , AB SOLiD, and ONT MinION. These libraries were prepared by polyA mRNA isolation using magnetic microbeads. Finally, cluster 4 included data on HepG2 and liver samples prepared using polyA amplification.
One possible reason for this discrepancy between library prep techniques may be a difference in RNA integrity. Low initial RNA integrity as well as an improper sample preparation procedure induces a shift in the density read coverage towards the 3′-tail of transcripts when using polyA isolation/amplification methods. Thus, we evaluated the differences in the distribution of 5′-to-3′ read coverage density and fortunately found no significant differences between cluster 4 and clusters 2/3 (i.e., between polyA mRNA isolation using the microbead technique and polyA amplification using MINT reverse transcriptase). The data in Supporting information Table S3 provides information on how fragile are dependencies between samples, sample preparation methods, bioanalytical platforms and bioinformatics pipeline of transcriptome data processing. Such dependencies are usually ignored at whole-genome level. The chromosome-centric current phase of discovery of the missing and uPE1 proteins appeals to re-think the paradigm at the transcriptome level.
With several exceptions, the Spearman (rank) and Pearson correlation coefficients were concordant. The highest Spearman's rank correlation (r = 0.70-0.97) was observed for the qPCR methods (cluster 1). Within this cluster, there were two biomaterialsspecific subclusters-within which the data were year dependent as Liv13 segregated from Liv20. In general, the observed correlation pattern reflected that qPCR works much better with low-expression genes than with RNA-Seq mainly because of the stochastic noise and discrete nature of gene expression values derived with RNA-Seq (i.e., read counts).
Notably, different clustering methods produced different results: preferred grouping was observed either by biological material or method. However, the close relationship of the technical replicates and bioinformatics pipelines remained invariant in the dendrograms. Nonetheless, we did not observe a perfect match between the results obtained using various genome annotations (RefSeq/Ensembl/Gencode) and data processing protocols (STAR+RSEM, bowtie2+RSEM, Salmon), as well as between technical replications: the Spearman correlation coefficient between various data processing pipelines or technical replicates ranging from 60% to 97%.
We observed a large variability among gene expression values derived by RT-qPCR, Illumina and ONT sequencing from the same biological sample. However, the present work was not intended to clarify the underlying reasons for this variability, but we aimed to highlight that a relatively high correlation between various datasets derived by whole-transcriptome analysis comes primarily from the large dynamic range of transcript abundances. Despite the sampling was confined by a particular chromosome (Chr18, 265 genes), the dynamic range of transcript abundances was still fairly large.
Nonetheless, the general correlations may decline (compare the values of correlation coefficients in Fig. 4 with those of 0.92 or 0.9, reported for 18000+ genes by Tyakht et al. 17 or van Deft et al. 52 , respectively). For a more limited set of genes-even if these genes are highly expressed such as in the case of "Stakhanovite genes"-obvious disagreement occurred in the levels of their expression measured by the orthogonal methods (tanglegram in Fig. 1A , see also Supporting Information Table S2 ). The dendrogram topologies (the branching pattern relationship among individual genes) were quite different for the RT-qPCR and Illumina methods (Fig. 1B) . For a set of lowabundance transcripts, one should expect a more profound "noise effect"-the apparent well-known fact from whole-transcriptome analysis. Additionally, the number of lowabundance transcripts included in further analysis is heavily affected by TPM and FPKM threshold values set for ONT and Illumina methods, respectively. For example, taking the TPM/FPKM threshold values as ≥ 0.1 resulted in ~60% of Chr18 transcripts detected by all three methods (Supporting Information Fig. S1 ), while setting the Illumina's FPKM threshold as > 0.1 would result in less than 50% of such transcripts.
Because we learned from the Expression Atlas (EMBL-EBI) generally and from our own data particularly, that the target missing and uPE1 proteins were present in low copies, we focused ourselves on the presence of a corresponding gene product in all samples, detected by all methods employed. The lower is the level of expression, the more nonrandom would be such an observation. Therefore, in the low-copy range, correlation analysis should be replaced by the unraveling of rarely observed events. This enabled us to perform a simple selection of priorities for the MP/uPE1 array as follows.
From the compendium of the ONT-detectable genes, we selected those that exhibited nonzero values of TPM in both types of biomaterials-i.e., the liver and HepG2. Two missing proteins and four proteins with the uPE1 status were obtained ( Table 5 ). The following information was taken from the neXtProt in Table 5 : number of amino-acid residues of the proteoform designated as Iso1 (canonical sequence, often the longest one), number of variant records reported for the given protein as single-amino acid polymorphisms, and number of isoforms (assuming that these could be splice forms or processed forms). Table 1 Table 1 . Selected genes of human chromosome 18. First, two genes were annotated as encoding the missing proteins (MPs), while four other genes were attributed to the category of functionally uncharacterized but translated to the protein level with high evidence (uPE1). The neXtProt knowledgebase was used to obtain information about the gene status, number of amino acid residues in the sequence of the isoform 1 (the longest one), single amino acid polymorphisms (SAPs) according to the dbSNP and COSMIC datasets, and the known splice forms (Iso1, Iso2, Iso3). Table 1 enabled us to select interesting cases of the HSBP1L1 missing protein and C18orf21, the uPE1 protein. Compared with other proteins, these two proteins were covered by ONT reads at modest but detectable levels. By contrast, for example, the serpin-like protein HMSD was poorly represented by the ONT-derived transcripts and undetected by the HiSeq NGS platform. Because of negligible detection, the uPE1 proteins KLHL14 and TMEM200C were discarded. Notably, KLHL14 showed an unexpected finding: the qPCR estimation was 116 copies per cell, including it in a list of "Stakhanovite genes" (Fig. 1) .
As of January 2020, the heat shock factor-binding protein (HSBP1L1) was still missing, probably because of its tiny size of only 75 amino acid residues. Additionally, HSBP1L1 was constitutively expressed in the liver and HepG2 cells at the level of approximately 6-7 transcripts per million (roughly corresponding to 1 cDNA copy per cell). The values of FPKM were at the level of 3.5 for liv.d1, and, remarkably, this protein was also detectable in other samples, viz. liv.d3 and liv.d5.
The second selected candidate was UPF10711 protein, which is encoded with C18orf21 gene. According to the neXtProt record Q32NC0, this protein was characterized by two isoforms, Iso1 (220 aa) and Iso2 (first 88 aa of Iso1 were missing from Iso2), or three computationally mapped potential isoforms. The ONT approach 
In Fig. 3 , two proteotypic peptides for the HSBP1L1 protein are shown, the first comprising 24 amino acid residues (Pept-24) and the second 13 a.a.r. (Pept-13) . The longer peptide was identified previously 32 to encounter 20 y and b fragment ions in its spectra obtained in HepG2 cells. When loss-less fractionation was performed in that study for deeper proteome coverage, the deamidated version of Pept-24 was registered in LNCaP cells, with lower coverage by only 10 y-and b-ions. The intensity of the precursor ion in HepG2 was 2 times higher than the signal captured in LNCaP cells.
Thus, HSBP1L1 identification by a pair of unique peptides is possible, but not in LNCaP or any of the other 11 cell lines probed earlier 32 .
The sequence of Pept-24 is split between exons 1 and 2 of the HSBP1L1 gene ( Fig. 3) , with the first two amino acids encoded at the tail of exon 1, while the rest of the peptide occupies the whole length of exon 2. The inspection of the splice region in Integrated Genome Viewer 53 has shown no abnormalities that can course the variations in the sequence of this proteotypic peptide.
The second putative peptide of HSBP1L1 is located in exon 3. Fig. 3 provides an enlarged image of the corresponding genome region. The amino acid sequence encoded by exon 3 runs in a bottom line of the cartoon, with Pept-13 starting with "DLMV…" (the second-half part of the exon-spanning sequence). The two blocks laying over the sequence data correspond to the ONT and HiSeq reads obtained for the liver (donor 1) and HepG2 cells, respectively.
A noticeable finding depicted in Fig. 3 is G>T substitution at the position of the genome indicated by the vertical red rectangle. The observed G>T nonsynonymous SNP (rs2298645) has no clinical relevance and is characterized by ~81% of the overall population frequency as reported by various databases (1000 Genomes, ExAC, ALFA). Homozygous substitution rs2298645 results in the disappearance of trypsin cleavage site and forming a longer peptide NVNDLMVQAGIENSIK (Pept-16).
It is important to note that the substitution appears to be homozygous, and it causes the substitution of lysine (K) to asparagine (N) in the 55 position of HSBP1L1 sequence (K55N). In turn, the K55N substitution leads to the disappearance of the cleavage site, thus making the second proteotypic peptide of HSBP1L1 longer by 3 additional amino acid residues from the N-terminus (Pept-16).
We tried to search the MS/MS repositories for the aforementioned peptides -Pept- was extended with an additional sequence of HSBP1L1 carrying K55N substitution.
With a modified sequence database, the search was implemented using The hits for Pept-24 (uniquely corresponding to the missing C9JCN9 protein) were detected by MaxQuant at an FDR of 1% for both peptides and proteins 32 according to PeptideAtlas entry. The precursor intensity of Pept-24 in HepG2 was 2.0010 6 compared with a lower signal 0.9810 6 for LNCaP. Thus, by using Pept-24 as a control, we located the HepG2 and LNCaP spectra as a source of evidence for HSBP1L1. In our searches by MaxQuant Pept-24 was absent from most cell lines under study (including LNCaP) except for HepG2 and RKO (Table 2 ).
At the same time, the second peptide Pept-13 was missing from the datasets 32 for the cell lines and from our proprietary collection of MS/MS data 39 . In contrast to our expectations, the mutant version of the peptide (Pept-16) was also missing in these datasets.
Therefore, although HSBP1L1 was assigned to PE1 ("protein evidence at protein level") since July 2020, we failed to reach the MS-based confirmation for this protein without using protein-specific enrichment methods, at least in the datasets of selected cell With identyPROT search engine 37 the two hits were observed in the APMS dataset 54 obtained after fishing of proteins with the protein of coronavirus. The first hit Pept-24 occurred in the raw file labelled as COV2_APMS_5_4. The second peptide of 11 a.a.r. (GPEAPGGRALR) was located upstream to the Pept-24 and was identified in raw file, COV2_APMS_5_3, but not in COV2_APMS_5_4 (Fig. 4) .
The well-identified Pept-24 (charge 3+) scored e-value 10 -8 . For the second peptide the e-value was much weaker 10 -2 , failing to pass the FDR < 0.01 filter. There were no any signs of the Pept-13 and Pept-16, the former one derived from the transcriptome analysis (Fig. 3) . In summary with other data from MaxQuant, the evidence of only the peptides of the first half of HSBP1L1 sequence were visible in several datasets and absent from the second half of the sequence, could indicate, that not the full-length protein, but the nonsense mediated decay product of HSBP1L1 (K7ENV5) was expressed, comprising only 45 a.a.r. In support of this thesis the confident identification in GPMdb referenced not only to the full-length sequence ENSP00000414236, but also referencing to the truncated version of the protein with identifier ENSP00000467108. The GPEAPGGRALR peptide (spectra B at Fig. 4 ) could have problems in identification due to the peculiarities of its sequence. First the positive charge could be 1+. Secondly, the b-ions could be localized at the part of the spectra with low m/z values as the peptide is glycine-rich, thus producing the low-molecular weight fragment masses.
Thirdly, the missed cleavage is present at the C-termini, while for instance the Pept-24 contains no missed-cleavages despite its severe length (spectra A at Fig. 4 ).
The information on selected set of proteins from dozen of cell lines pulled out by deep proteome survey 32 or by the affinity purification 54 was summarized in Table 2 . The unique peptides with posterior error probabilities are shown. The C18orf21 being the uPE1 protein occurred more frequently, however mostly identified by a single peptide.
The high abundance of KLHL14 in HepG2 as was shown by qPCR analysis (Table 1) was confirmed at the proteomic level with at least 4 peptides matching the current C-HPP guidelines.
Other target, the missing protein TMEM241, was absent from the datasets processed in this article (Table 2 ), In fact, in GPMdb/PRIDE also exist other datasets (e.g. PXD002815, PXD001333 and PXD014017), which were not used in this work, as experiments in Table 2 were quite illustrative that neither deep fractionation nor affinity purification can capture the second peptide for the missing HSBP1L1. The detection of this protein and other proteins no matter whether they were missing or uPE1 in most cases was limited by the presence of just a single peptide with the sufficiently high scoring, while the second peptides was observed as a rule as a statistically unjustified finding ( Table 2 ). The signs of the target peptides were also lacking from our proprietary dataset PXD019263, acquired by applying 2D-LC-MS/MS to the HepG2 lysate [Vavilov et al., 2020, submitted]. Therefore we can presumably assume for the Chr18 that the missing and uPE1 proteins share similarity because both types of proteins are successfully escaping the requirement of 2 peptides of 9 aa with FDR < 0.01 per protein (Table 2) . Table 2 . Occurrence of ONT-selected missing and uPE1 proteins across cell lines 32 , and SARS-CoV affinity purification 54 .
*PEP = posterior error probability, **only peptides with PEP<0.01 are shown for Q9P2G3 protein, Stk*** -Stukalov et al., 2020
The data in Table 2 exemplifies the one-hit-wonders identifications of the HSBP1L1 and HSMD missing proteins. Surprisingly such problem was also shared by the studied uPE1s across cell lines (with an exception of KLHL14 confirmed by identification of 7 peptides in HepG2 cells only). It seems virtually impossible to gain the protein by MS-based technology in compliance with C-HPP Guidelines 3.0. At the same, cases listed in Table 2 provide the ground for the deeper inspection of the massspectrometry data enabling the chromosome-centric paradigm to limit analysis to just few valuable artifacts, likewise ascribed HSBP1L1 and C18orf21.
The expression of approximately 50%-60% genes (depending on the biospecimen type) was detected simultaneously by qPCR, Illumina and Oxford Nanopore Technology (ONT)-based transcriptome sequencing (Supporting information Fig. S1 ).
The most sensitive method of qPCR delivered the expression of 64% of the Chr18 genes in the liver tissue, and much more (77%) in HepG2. In total, 92% of the Chr18 coverage was achieved at the transcriptome level by compiling the data from different methods and only two types of biospecimens.
In the present work, we appended the ONT as the independent "third vote" to the previous pair of well-established methods such as qPCR and Illumina sequencing for transcriptomic research. We examined this in detail on an example of HSBP1L1 gene as a promising target for investigation. Our choice was further confirmed, because in the July 2020 version of NeXtProt HSBP1L1 has left the pool of missing proteins and gained the respectful PE1 status.
By analyzing jointly the missing and uPE1 proteins, we distinguished C18orf21 to study its function in the HepG2 cell line in the frame of CP50 challenge. The level of transcription of the gene is elevated up to 10 times in HepG2 cells compared with that in the liver (see Table 1 ). Another uPE1 protein -KLHL14demonstrated an even more striking mRNA expression profile, comprising 168 mRNA copies in a single HepG2 cell determined by RT-PCR, designating it a "Stakhanovite gene", in contrast to the lack of expression in all three individual liver tissues.
We showed significant differences in the transcriptomic results obtained using different experimental methods: for many genes, they reached the order of magnitude. If we consider qPCR as the gold standard, RNA-Seq-based approaches may represent a reliable method only for qualitative evaluation of gene expression, whereas quantitative assessment may be significantly biased because of differences in sample preparation protocols and data processing pipelines. It was illustrated that, in connection with the problem of the detection of low-abundant transcripts, there was also the problem of approaching an actual picture of gene expression at the transcriptome level, unbiased by the sample preparation and data treatment procedures.
In this study, we showed the limitations of the correlation analysis of quantitative omics data, particularly, its strong dependency on the choice of correlation/clustering method. Spearman's rank correlation analysis results are more susceptible to inaccuracies in the RNA-seq analysis of lowly expressed genes, but Pearson's correlations are very prone to outliers for highly expressed genes. In any case, a great random component contributes to the results.
Finally, using the combination of short-read sequencing by Illumina HiSeq and long reads from ONT MinION, we have partially eliminated the suspicion that the problem of Chr18 missing proteins is associated with the choice of proteotypic peptides that are located in genomic regions containing nonsynonymous SNPs or nonannotated tissue-specific splice junctions. At the same time, we observed that, in the liver/HepG2 cells, the level of expression and read coverage of investigated missing proteins were comparable to those of other genes annotated as having protein evidence 1.
In conclusion, we illustrated the method that consists of obtaining the general chromosome-centric picture of the transcriptome datasets from different sources, followed by the analysis of highly expressed genes and transcripts for the functionally uncharacterized and missing proteins, with further verification of the proteotypic peptides of missing and uPE1 proteins by mapping onto the RNA-seq short and long reads. and Oxford Nanopore Technology, MinION (b) for the liver sample from donor #1.