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Figure 1. The overlap between Ensembl/GENCODE, RefSeq and UniProtKB genes. The number of genes classified as coding in ... Figure 1. The overlap between.

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Presentation on theme: "Figure 1. The overlap between Ensembl/GENCODE, RefSeq and UniProtKB genes. The number of genes classified as coding in ... Figure 1. The overlap between."— Presentation transcript:

1 Figure 1. The overlap between Ensembl/GENCODE, RefSeq and UniProtKB genes. The number of genes classified as coding in ... Figure 1. The overlap between Ensembl/GENCODE, RefSeq and UniProtKB genes. The number of genes classified as coding in each of the three reference databases and the intersection between them. The number of genes in the intersection of A is variable because RefSeq and Ensembl/GENCODE disagree on gene boundaries for a number of genes. For three subsets of genes, we show the percentage of coding genes annotated as antisense, pseudogene or read-through in another database. Unless provided in the caption above, the following copyright applies to the content of this slide: © The Author(s) Published by Oxford University Press on behalf of Nucleic Acids Research.This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License ( which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact Nucleic Acids Res, Volume 46, Issue 14, 30 June 2018, Pages 7070–7084, The content of this slide may be subject to copyright: please see the slide notes for details.

2 Figure 2. Maximum transcript expression of potential non-coding genes and likely coding genes. The percentage of genes ... Figure 2. Maximum transcript expression of potential non-coding genes and likely coding genes. The percentage of genes in seven different maximum TPM bins. Maximum TPM comes from the 36 tissues of the Human Protein Atlas RNAseq experiments. Tissue distribution shown for the likely coding genes (LCG Intersection) as well as potential non-coding genes annotated by all three reference sets (PNC Intersection) and by just one or two sets of annotators (PNC Subsets). Unless provided in the caption above, the following copyright applies to the content of this slide: © The Author(s) Published by Oxford University Press on behalf of Nucleic Acids Research.This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License ( which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact Nucleic Acids Res, Volume 46, Issue 14, 30 June 2018, Pages 7070–7084, The content of this slide may be subject to copyright: please see the slide notes for details.

3 Figure 3. The relation between peptides in proteomics experiments and transcript expression. (A) The percentage of ... Figure 3. The relation between peptides in proteomics experiments and transcript expression. (A) The percentage of genes for which peptides are detected in PeptideAtlas across nine different bins. The bins are based on the number of tissues in which the transcripts are detected with a TPM of >1 in the Human Protein Atlas RNAseq experiments. (B) The percentage of genes for which peptides are detected in PeptideAtlas divided across seven bins of maximum TPM for each gene taken from the 36 tissues of the Human Protein Atlas RNAseq experiments. (C) The percentage of genes for which peptides are detected for those genes that have RNAseq expression in at least 10 tissues with a TPM of 10 or more. In each case the percentage of genes for the likely coding genes (LCG Intersection, blue bars) as well as potential non-coding genes annotated by all three reference sets (PNC Intersection, yellow) and by just one or two sets of annotators (PNC Subsets, green). Unless provided in the caption above, the following copyright applies to the content of this slide: © The Author(s) Published by Oxford University Press on behalf of Nucleic Acids Research.This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License ( which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact Nucleic Acids Res, Volume 46, Issue 14, 30 June 2018, Pages 7070–7084, The content of this slide may be subject to copyright: please see the slide notes for details.

4 Figure 4. Whole gene gains and losses for likely coding and potential non-coding gene in GENCODE v12. The percentage of ... Figure 4. Whole gene gains and losses for likely coding and potential non-coding gene in GENCODE v12. The percentage of genes that have undergone gene gain/loss (purple), whole gene gain (orange), whole gene loss (red) or homozygous gene loss (blue) in at least one of the five different analyses. Potential non-coding genes present in both GENCODE v12 and v24 undergo a similar proportion of gene gain and loss to GENCODE v12 genes that have since been reclassified as not coding. Unless provided in the caption above, the following copyright applies to the content of this slide: © The Author(s) Published by Oxford University Press on behalf of Nucleic Acids Research.This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License ( which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact Nucleic Acids Res, Volume 46, Issue 14, 30 June 2018, Pages 7070–7084, The content of this slide may be subject to copyright: please see the slide notes for details.

5 Figure 5. Genomic variation in likely coding genes and possible non-coding genes. Percentage high impact variants ... Figure 5. Genomic variation in likely coding genes and possible non-coding genes. Percentage high impact variants (yellow) and non-synonymous/synonymous ratios (blue) for known coding genes (likely coding genes with peptide evidence, see text) and for possible non-coding genes (PNC) from the intersection of the three sets (Intersect) or annotated by two or fewer reference sets (Subsets). Read-through genes were removed when calculating variants because they always overlap known coding genes. The darker colours show the values for common variants and the lighter shades show the values for rare variants. 95% confidence intervals are shown. Unless provided in the caption above, the following copyright applies to the content of this slide: © The Author(s) Published by Oxford University Press on behalf of Nucleic Acids Research.This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License ( which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact Nucleic Acids Res, Volume 46, Issue 14, 30 June 2018, Pages 7070–7084, The content of this slide may be subject to copyright: please see the slide notes for details.

6 Figure 6. Genes with the highest proportion of high impact and non-synonymous variants. In (A), the percentage of high ... Figure 6. Genes with the highest proportion of high impact and non-synonymous variants. In (A), the percentage of high impact variants (yellow) and non-synonymous/synonymous ratios (blue) for the 15 genes with the highest rate of common non-synonymous variants. Minimum 30 common variants per gene. The darker colors show the values for common variants and the lighter shades show the values for rare variants. In (B), the alignment between human CRIPAK gene product and primate homologues annotated as CRIPAK in UniProtKB. There is very little evidence of conservation. Unless provided in the caption above, the following copyright applies to the content of this slide: © The Author(s) Published by Oxford University Press on behalf of Nucleic Acids Research.This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License ( which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact Nucleic Acids Res, Volume 46, Issue 14, 30 June 2018, Pages 7070–7084, The content of this slide may be subject to copyright: please see the slide notes for details.

7 Figure 7. GLRA4 loss of trans-membrane helix and genetic variation
Figure 7. GLRA4 loss of trans-membrane helix and genetic variation. (A) The cryo-EM structure of the GLRA1 kinase ... Figure 7. GLRA4 loss of trans-membrane helix and genetic variation. (A) The cryo-EM structure of the GLRA1 kinase domain from Danio rerio (PDB code: 3JAD), which is 80% sequence identical to human GLRA4. In GLRA4, the premature stop codon would lead to the loss of the dark orange trans-membrane helices in the figure (one of which is marked with a red arrow). From the point of view of the pore, this would mean the loss of five of the twenty helices, albeit the helices which are furthest away from the inside of the pore. This would almost certainly destabilize the pore, and would probably have considerable effect on the function. It would also leave the C-terminals of the protein on the cytoplasmic side instead of the extra-cellular side. (B) The percentage of high impact variants (yellow) and non-synonymous/synonymous ratios (blue) for the GLRA gene family. The percentage of high impact variants and non-synonymous/synonymous ratios for GLRA4 from Exac are marked as "Exac". The darker colors show the values for common variants and the lighter shades show the values for rare variants. GLRA4 does not have the same variation pattern as the other four genes. Unless provided in the caption above, the following copyright applies to the content of this slide: © The Author(s) Published by Oxford University Press on behalf of Nucleic Acids Research.This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License ( which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact Nucleic Acids Res, Volume 46, Issue 14, 30 June 2018, Pages 7070–7084, The content of this slide may be subject to copyright: please see the slide notes for details.

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