Additional file 2 of Coordinates and intervals in graph-based reference genomes

Example of gene notation. Example of representation of genes on an alternative locus on GRCh38 (txt). (TXT 4 kb

Additional file 1 of Coordinates and intervals in graph-based reference genomes

Definitions and experiment details. Formal definitions of terms used in the article and details on the section Genes on GRCh38 (pdf). (PDF 181 kb

Posterior probability density of the transcript concentration (number of transcripts per µg total RNA) for the oncogene in two different cervix tumours

<p><b>Copyright information:</b></p><p>Taken from "Genome-wide estimation of transcript concentrations from spotted cDNA microarray data"</p><p>Nucleic Acids Research 2005;33(17):e143-e143.</p><p>Published online 4 Oct 2005</p><p>PMCID:PMC1243803.</p><p>© The Author 2005. Published by Oxford University Press. All rights reserved</p> The mode of this density is the estimated concentration as listed at . There was a significant difference in the concentration between the tumours ( < 0.001, Kolmogorov–Smirnov test). The qRT-PCR data (relative to TBP) were 0.24 for MM14 and 0.023 for MM18, in agreement with our estimates

Transcript concentration (number of transcripts per µg total RNA) for 10 genes in cervix cancer with highest estimated mean concentration

<p><b>Copyright information:</b></p><p>Taken from "Genome-wide estimation of transcript concentrations from spotted cDNA microarray data"</p><p>Nucleic Acids Research 2005;33(17):e143-e143.</p><p>Published online 4 Oct 2005</p><p>PMCID:PMC1243803.</p><p>© The Author 2005. Published by Oxford University Press. All rights reserved</p> Each point represents the estimated value of a single tumour, showing large differences in transcript concentration among the tumours. The within gene range (max–min) varies from 10 (; ) to 100 (; )

Posterior probability densities of transcript concentrations (number of transcripts per µg total RNA) of genes known to be involved in communication (green), growth (orange) and signal transduction (blue) for cancer cell lines () and cervix cancer ()

<p><b>Copyright information:</b></p><p>Taken from "Genome-wide estimation of transcript concentrations from spotted cDNA microarray data"</p><p>Nucleic Acids Research 2005;33(17):e143-e143.</p><p>Published online 4 Oct 2005</p><p>PMCID:PMC1243803.</p><p>© The Author 2005. Published by Oxford University Press. All rights reserved</p> The calculations were based on a pool of 10 cancer cell lines and 12 cervix tumours. The number of genes in each functional group is indicated. Some genes were shared by the groups. The distribution of all 10 157 genes and ESTs is also shown (black). All distributions were skewed to the right and had similar median values

Posterior probability density of transcript concentrations (number of transcripts per µg total RNA) for cancer cell lines (black) and cervix cancer (red)

<p><b>Copyright information:</b></p><p>Taken from "Genome-wide estimation of transcript concentrations from spotted cDNA microarray data"</p><p>Nucleic Acids Research 2005;33(17):e143-e143.</p><p>Published online 4 Oct 2005</p><p>PMCID:PMC1243803.</p><p>© The Author 2005. Published by Oxford University Press. All rights reserved</p> The data of 10 157 genes and ESTs were included, and the calculations were based on a pool of 10 cell lines and 12 cervix tumours. The median value of each distribution is shown as a vertical line and was slightly higher for the cell lines than for cervix cancer. Both distributions were skewed towards higher values, and less abundant transcripts were more frequent than high abundant ones

Distinct DNA methylation profiles in bone and blood of osteoporotic and healthy postmenopausal women

<p>DNA methylation affects expression of associated genes and may contribute to the missing genetic effects from genome-wide association studies of osteoporosis. To improve insight into the mechanisms of postmenopausal osteoporosis, we combined transcript profiling with DNA methylation analyses in bone. RNA and DNA were isolated from 84 bone biopsies of postmenopausal donors varying markedly in bone mineral density (BMD). In all, 2529 CpGs in the top 100 genes most significantly associated with BMD were analyzed. The methylation levels at 63 CpGs differed significantly between healthy and osteoporotic women at 10% false discovery rate (FDR). Five of these CpGs at 5% FDR could explain 14% of BMD variation. To test whether blood DNA methylation reflect the situation in bone (as shown for other tissues), an independent cohort was selected and BMD association was demonstrated in blood for 13 of the 63 CpGs. Four transcripts representing inhibitors of bone metabolism—<i>MEPE, SOST, WIF1</i>, and <i>DKK1</i>—showed correlation to a high number of methylated CpGs, at 5% FDR. Our results link DNA methylation to the genetic influence modifying the skeleton, and the data suggest a complex interaction between CpG methylation and gene regulation. This is the first study in the hitherto largest number of postmenopausal women to demonstrate a strong association among bone CpG methylation, transcript levels, and BMD/fracture. This new insight may have implications for evaluation of osteoporosis stage and susceptibility.</p