DataSheet4_Gene-centric coverage of the human liver transcriptome: QPCR, Illumina, and Oxford Nanopore RNA-Seq.xlsx

It has been shown that the best coverage of the HepG2 cell line transcriptome encoded by genes of a single chromosome, chromosome 18, is achieved by a combination of two sequencing platforms, Illumina RNA-Seq and Oxford Nanopore Technologies (ONT), using cut-off levels of FPKM > 0 and TPM > 0, respectively. In this study, we investigated the extent to which the combination of these transcriptomic analysis methods makes it possible to achieve a high coverage of the transcriptome encoded by the genes of other human chromosomes. A comparative analysis of transcriptome coverage for various types of biological material was carried out, and the HepG2 cell line transcriptome was compared with the transcriptome of liver tissue cells. In addition, the contribution of variability in the coverage of expressed genes in human transcriptomes to the creation of a draft human transcriptome was evaluated. For human liver tissues, ONT makes an extremely insignificant contribution to the overall coverage of the transcriptome. Thus, to ensure maximum coverage of the liver tissue transcriptome, it is sufficient to apply only one technology: Illumina RNA-Seq (FPKM > 0).</p

DataSheet1_Gene-centric coverage of the human liver transcriptome: QPCR, Illumina, and Oxford Nanopore RNA-Seq.PDF

It has been shown that the best coverage of the HepG2 cell line transcriptome encoded by genes of a single chromosome, chromosome 18, is achieved by a combination of two sequencing platforms, Illumina RNA-Seq and Oxford Nanopore Technologies (ONT), using cut-off levels of FPKM > 0 and TPM > 0, respectively. In this study, we investigated the extent to which the combination of these transcriptomic analysis methods makes it possible to achieve a high coverage of the transcriptome encoded by the genes of other human chromosomes. A comparative analysis of transcriptome coverage for various types of biological material was carried out, and the HepG2 cell line transcriptome was compared with the transcriptome of liver tissue cells. In addition, the contribution of variability in the coverage of expressed genes in human transcriptomes to the creation of a draft human transcriptome was evaluated. For human liver tissues, ONT makes an extremely insignificant contribution to the overall coverage of the transcriptome. Thus, to ensure maximum coverage of the liver tissue transcriptome, it is sufficient to apply only one technology: Illumina RNA-Seq (FPKM > 0).</p

DataSheet3_Gene-centric coverage of the human liver transcriptome: QPCR, Illumina, and Oxford Nanopore RNA-Seq.docx

It has been shown that the best coverage of the HepG2 cell line transcriptome encoded by genes of a single chromosome, chromosome 18, is achieved by a combination of two sequencing platforms, Illumina RNA-Seq and Oxford Nanopore Technologies (ONT), using cut-off levels of FPKM > 0 and TPM > 0, respectively. In this study, we investigated the extent to which the combination of these transcriptomic analysis methods makes it possible to achieve a high coverage of the transcriptome encoded by the genes of other human chromosomes. A comparative analysis of transcriptome coverage for various types of biological material was carried out, and the HepG2 cell line transcriptome was compared with the transcriptome of liver tissue cells. In addition, the contribution of variability in the coverage of expressed genes in human transcriptomes to the creation of a draft human transcriptome was evaluated. For human liver tissues, ONT makes an extremely insignificant contribution to the overall coverage of the transcriptome. Thus, to ensure maximum coverage of the liver tissue transcriptome, it is sufficient to apply only one technology: Illumina RNA-Seq (FPKM > 0).</p

DataSheet2_Gene-centric coverage of the human liver transcriptome: QPCR, Illumina, and Oxford Nanopore RNA-Seq.xlsx

It has been shown that the best coverage of the HepG2 cell line transcriptome encoded by genes of a single chromosome, chromosome 18, is achieved by a combination of two sequencing platforms, Illumina RNA-Seq and Oxford Nanopore Technologies (ONT), using cut-off levels of FPKM > 0 and TPM > 0, respectively. In this study, we investigated the extent to which the combination of these transcriptomic analysis methods makes it possible to achieve a high coverage of the transcriptome encoded by the genes of other human chromosomes. A comparative analysis of transcriptome coverage for various types of biological material was carried out, and the HepG2 cell line transcriptome was compared with the transcriptome of liver tissue cells. In addition, the contribution of variability in the coverage of expressed genes in human transcriptomes to the creation of a draft human transcriptome was evaluated. For human liver tissues, ONT makes an extremely insignificant contribution to the overall coverage of the transcriptome. Thus, to ensure maximum coverage of the liver tissue transcriptome, it is sufficient to apply only one technology: Illumina RNA-Seq (FPKM > 0).</p

Post-translational modifications of FDA-approved plasma biomarkers in glioblastoma samples

<div><p>Liquid chromatography-tandem mass spectrometry was used to analyze plasma proteins of volunteers (control) and patients with glioblastoma multiform (GBM). A database search was pre-set with a variable post-translational modification (PTM): phosphorylation, acetylation or ubiquitination. There were no significant differences between the control and the GBM groups regarding the number of protein identifications, sequence coverage or number of PTMs. However, in GBM plasma, we unambiguously observed a decreased fraction in post-translationally modified peptides identified with high quality. The disease-specific PTM patterns were extracted and mapped to the set of FDA-approved plasma protein markers. Decreases of 46% and 24% in the number of acetylated and ubiquitinated peptides, respectively, were observed in the GBM samples. Significance of capturing disease-associated patterns of protein modifications was envisaged.</p></div

Sequence coverage for various protein PTMs.

<p>Identification by Mascot of ≥ 3 peptides per protein. The adjusted inverse normalized values are displayed as a violin plot, comparing distributions between control and GBM samples. Horizontal bars indicate the mean (dashed line) and median (solid line) values for each group.</p

Enrichment of plasma Alpha-2-HS-glycoprotein (FETUA_HUMAN, 367 aa) sequence coverage with peptides assembled from MS/MS search for phosphorylated peptides.

<p>Enrichment of plasma Alpha-2-HS-glycoprotein (FETUA_HUMAN, 367 aa) sequence coverage with peptides assembled from MS/MS search for phosphorylated peptides.</p

Changes in the fractions of PTM peptides of FDA-approved plasma biomarkers in GBM compared to control.

<p>Changes in the fractions of PTM peptides of FDA-approved plasma biomarkers in GBM compared to control.</p

GBM-negative and GBM-positive patterns’ counted for FDA-approved plasma proteins.

<p>GBM-negative and GBM-positive patterns’ counted for FDA-approved plasma proteins.</p

MS/MS spectra of non-modified (A) and phosphomodified (B) peptide DSSPDSAEDVR (2+) of alpha-2-HS-glycoprotein (FETUA_HUMAN) GBM plasma.

<p>Monoisotopic mass of neutral peptide Mr (calc) 1,592.62. Variable modification S7–Phospho (ST) with neutral loss 97.98. [<i>y</i>(10)] in a non-modified peptide was not detected.</p