Methods for estimating human endogenous retrovirus activities from EST databases-1

<p><b>Copyright information:</b></p><p>Taken from "Methods for estimating human endogenous retrovirus activities from EST databases"</p><p>http://www.biomedcentral.com/1471-2105/8/S2/S11</p><p>BMC Bioinformatics 2007;8(Suppl 2):S11-S11.</p><p>Published online 3 May 2007</p><p>PMCID:PMC1892069.</p><p></p>ture and the simple BLAST approach are compared to the true generating distribution. The HERVs on the x-axis have been sorted according to relative activity in the true generating distribution

Methods for estimating human endogenous retrovirus activities from EST databases-4

<p><b>Copyright information:</b></p><p>Taken from "Methods for estimating human endogenous retrovirus activities from EST databases"</p><p>http://www.biomedcentral.com/1471-2105/8/S2/S11</p><p>BMC Bioinformatics 2007;8(Suppl 2):S11-S11.</p><p>Published online 3 May 2007</p><p>PMCID:PMC1892069.</p><p></p>t. The two darkest gray areas together show the proportion of active HERVs in that group, the lightest gray area shows the proportion of inactive HERVs. The widths of the bars are proportional to the size of the HERV group. We can see that the proportion of active and inactive HERVs varies a lot from group to group

Methods for estimating human endogenous retrovirus activities from EST databases-2

<p><b>Copyright information:</b></p><p>Taken from "Methods for estimating human endogenous retrovirus activities from EST databases"</p><p>http://www.biomedcentral.com/1471-2105/8/S2/S11</p><p>BMC Bioinformatics 2007;8(Suppl 2):S11-S11.</p><p>Published online 3 May 2007</p><p>PMCID:PMC1892069.</p><p></p> the curve presents EST hit intensity along the HERV structure. See Table 1 for more information on this HERV. EST hit areas for other highly active HERVs are shown in Supplementary Fig. 4 in Additional file

Methods for estimating human endogenous retrovirus activities from EST databases-3

<p><b>Copyright information:</b></p><p>Taken from "Methods for estimating human endogenous retrovirus activities from EST databases"</p><p>http://www.biomedcentral.com/1471-2105/8/S2/S11</p><p>BMC Bioinformatics 2007;8(Suppl 2):S11-S11.</p><p>Published online 3 May 2007</p><p>PMCID:PMC1892069.</p><p></p> are plotted separately on the left (random jitter has been added in the age direction). We can see that there is no clear correlation between estimated age and activity. There is a more detailed figure in the Additional file

Methods for estimating human endogenous retrovirus activities from EST databases-0

<p><b>Copyright information:</b></p><p>Taken from "Methods for estimating human endogenous retrovirus activities from EST databases"</p><p>http://www.biomedcentral.com/1471-2105/8/S2/S11</p><p>BMC Bioinformatics 2007;8(Suppl 2):S11-S11.</p><p>Published online 3 May 2007</p><p>PMCID:PMC1892069.</p><p></p>ture shown in the middle. The shaded box is the basic block of the sub-HMM and is repeated length-2 times. It is identical in all sub-HMMs; only the emission distribution of the match state varies between blocks. The emission is either the nucleotide in that position of the HERV sequence or a mismatch. The probabilities for match and mismatch are equal for all blocks. The EEMIT-state emits the low-quality end part

Methods for estimating human endogenous retrovirus activities from EST databases-5

<p><b>Copyright information:</b></p><p>Taken from "Methods for estimating human endogenous retrovirus activities from EST databases"</p><p>http://www.biomedcentral.com/1471-2105/8/S2/S11</p><p>BMC Bioinformatics 2007;8(Suppl 2):S11-S11.</p><p>Published online 3 May 2007</p><p>PMCID:PMC1892069.</p><p></p> mixture model) and results from the complete set of 2450 HERVs (learned using the BLAST approach). The scale of the figure is such that the relative activities for the HERVs sum up to 1 in both x and y dimensions

Data-driven prediction of usefulness of datasets vs. their citation counts.

<p>Manual checks comparing sets for which the two scores differed revealed inconsistent database records for two datasets; the blue arrows point to their corrected locations, which are more in line with the data-driven model. Regions A, B, and C: see text.</p

Relevance network of datasets in the human gene expression atlas; data-driven links from the model (left) and citation links (right).

<p>Left: each dataset was used as a query to retrieve earlier datasets; a link from an earlier dataset to a later one means the earlier dataset is relevant as a partial model of activity in the later dataset. Link width is proportional to the normalized relevance weight (combination weight ; only links with are shown, and datasets without links have been discarded). Right: links are direct (gray) and indirect (purple) citations. Node size is proportional to the estimated influence, <i>i.e.</i>, the total outgoing weight. Colors: tissue types (six meta tissue types <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0113053#pone.0113053-Lukk1" target="_blank">[12]</a>). The node layout was computed from the data-driven network (details in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0113053#s4" target="_blank"><i>Methods</i></a>).</p

Data-driven retrieval outperforms the state of the art of keyword search on the human gene expression atlas [12].

<p>Blue: Traditional precision-recall curve where progressively more datasets are retrieved from left to right. All experiments sharing one or more of the 96 biological categories of the atlas were considered relevant. In keyword retrieval, either the category names (“Keyword: 96 classes”) or the disease annotations (“Keyword: disease”) were used as keywords. All datasets having at least 10 samples were used as query datasets, and the curves are averages over all queries.</p

Validation of the Predictive Toxicogenomics Space with cell culture data

<b>Cell culture for <i>in vitro</i> cytotoxicity predictions</b>. To validate the predictive performance of the PTGS, a set of CMap instances for 38 compounds that were not included in the NCI-60 data set were assessed. MCF7 (ATCC® HTB22™), PC­3 (ATCC® CRL­1435™) and HL­60 (ATCC® CCL­240™) cell lines were obtained directly from American Type Culture Collection (LGC Promochem AB) and maintained at 37 °C with 5 % CO2 in a humidified incubator according to provider’s instructions. Cell number was titrated to ensure that cell proliferation remained in a linear-exponential phase throughout the experiment (1000-2000 cells per well were plated). Each experiment was performed from unique assay ready cells (same passage). Data quality and assay comparability were first verified by replicating the measurements for 36 instances for 16 different compounds already measured in NCI-60. Measurements were carried out at the Institute for Molecular Medicine Finland, FIMM. The authors want to thank Ida Lindenschmidt and the High Throughput Biomedicine unit at FIMM for technical support to cellular high-throughput screening assays.<div><br></div><div>Table 1. Raw data as % of cell viability after compound treatment. Molar concentrations from -8 to -4 on the log10 scale (5 doses) were employed. Columns: Compound, Cell Line, Cell.viability(%) (-8 to -4, log10.conc.).</div><div><br></div><div>Table 2. Calculated GI50 values for control treatments, 16 compounds which have corresponding cytotoxicity data in the NCI-60 DTP database. Columns: Chemical, CellLine, CMapDose (concentration at which CMap profile was measured), GI50.NCI60 (GI50 in the NCI60 DTP database), Batch.NCI60 (batch in the NCI60 database), Batch.FIMM (batch in FIMM dataset), GI50.FIMM (GI50 value in the FIMM dataset).</div><div><br></div><div>Table 3. Calculated GI50 values for test treatments, 38 compounds which have gene expression data in the Connectivity Map database (used to calculate PTGS cytotoxicity virtual GI50 scores). Columns: Chemical, CellLine, CMapDose (concentration at which CMap profile was measured), Batch.FIMM (batch in FIMM dataset), GI50.FIMM (GI50 value in the FIMM dataset).</div><div><br></div