Summary of gene methylation and GC prognosis in the component studies.

1<p>The prognostic outcome was based on disease-free survival (DFS). The brackets displayed the p-value for studies that showed significance. NS: Not significant.</p

Genes differentially methylated in case-control studies of tumour and normal gastric tissue from GC subjects.

1<p>Odds ratio (OR) describes the likelihood of gene methylation observed in tumour compared to normal gastric tissue. Only the genes for which there was a significant difference in methylation frequency between the two groups are displayed (p<0.05). Genes for which there was no significant difference are listed in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0036275#pone.0036275.s003" target="_blank">Table S1</a>.</p

Genes differentially methylated in case-control studies of normal tissue, serum and plasma from gastric cancer and non-cancer subjects.

1<p>Odds ratio (OR) describes the likelihood of gene methylation observed in samples from gastric cancer compared to non-cancer subjects. Only genes in which there were significant differences in methylation between the two groups are displayed (<i>p</i><0.05). Genes for which there was no significant difference are displayed in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0036275#pone.0036275.s004" target="_blank">Table S2</a>.</p

The prospects for Reduced Emissions from Deforestation and Degradation (REDD) in Mesoamerica

The general reluctance of policy makers to include forests in discussions about global warming has changed with the development of measures to Reduce Emissions from Deforestation and Degradation (REDD). Mesoamerica presents a logical starting point to promote REDD due to the extent of its forest, and the relatively advanced state of its forest management institutions and policies. This paper reviews the prospects for REDD in Mesoamerica using PES and other instruments, with emphasis on the effectiveness of REDD measures at reducing emissions, and their effi ciency and fairness. It concludes that in spite of reduced deforestation in the region, the growth of payments to avoid deforestation will be the most important policy change related to REDD in the region in the coming years. However, the magnitude and impact of any payments must not be exaggerated and should be set in context of the overall trends resulting from broader social and economic dynamics

Additional file1: of Finite-size effects in transcript sequencing count distribution: its power-law correction necessarily precedes downstream normalization and comparative analysis

Figure S1. Concentration versus total mapped reads of the dilution data set. Figures S1A to D shows the concentrations of the AGS cell line (12pM, 3pM) and NUGC3 cell line (12pM, 6pM, 3pM, 1.5pM) versus the respective total mapped reads by the 4 mapping methods: Bowtie1, Bowtie2(global), Novoalign and BWA. Regardless of the mapping methods, the sequencing depth (i.e., the total mapped reads) is shown to be linearly proportional to the system size (in terms of transcript concentration) in the logarithmic scale. Overall, the dilution data set attempts to mimic a system of various sizes of finite-size effects. (PNG 371 kb

Additional file 2: of Finite-size effects in transcript sequencing count distribution: its power-law correction necessarily precedes downstream normalization and comparative analysis

Figure S2. Pareto distributions and scatterplots of spike-in background data set. Figure S2A to F show the Pareto plots (left column) and supplementary Figure S2G to L show the scatterplots (right column) of the spike-in background set where each applied normalization methods (i.e., DESeq, RLE, TMM, UQ, CPM and Quantile) are arranged row-wise. Generally speaking, the characteristics of these Pareto plots of the normalized spike-in background set are very comparable to that of Fig. 1A and C, where only a simple intra-sample scaling has been applied. Despite the application of normalization, two characteristics remain unchanged. Firstly, the non-uniform slope values and its decreasing trend from the highest to lowest-count segment indicate that heteroskedasticity among the replicates will remain. Secondly, for those count segment with slope values far from “-1”, their mathematical moments are infinite and hence, large variation among the replicates will be expected for these segments. (PNG 1662 kb

Additional file 6: of Finite-size effects in transcript sequencing count distribution: its power-law correction necessarily precedes downstream normalization and comparative analysis

Figure S5. Medians of Regressed slopes of first-fitted segment versus R2 fit for the full dilution and spike-in datasets. Figure S5A and 5B show the median slope of the first-fitted segments versus the median R2 value of the dilution and the spike-in data set respectively. In both plots, the refined solution space of the optimum points-per-segment (PPS; as indicated besides the data points) is indicated by the error margins defined by the slope of the first highest-count segments from Table 1 like before. Consequently, the optimum PPS value is determined by the largest average R2 value where it is 55 for the dilution set and 10 for the spike-in set. Note that due to the lack of replicates for the spike-in transcripts, only the background of the spike-in set was used for the parameter estimation. (PNG 315 kb

Additional file 5: of Finite-size effects in transcript sequencing count distribution: its power-law correction necessarily precedes downstream normalization and comparative analysis

Table S2. Significant transcripts calls of comparative dilution analysis (AGS versus NUGC3) before and after power-law correction. (DOCX 16 kb

Additional file 8: of Finite-size effects in transcript sequencing count distribution: its power-law correction necessarily precedes downstream normalization and comparative analysis

Figure S4. Medians of Regressed slopes of first-fitted segment versus R2 fit for NUGC3 dilution and spike-in background datasets. Figure S4A and 4B show the median slope of the first-fitted segments versus the median R2 value of the spike-in background set and the NUGC3 dilution set respectively. For the necessary R2 computations, the reference replicate was taken as the replicate with the largest total reads within the data series. In both plots, the refined solution space of the optimum points-per-segment (PPS; as indicated besides the data points) is indicated by the error margins defined by the slope of the first highest-count segments from Table 1. Within this margin, the optimum PPS value is determined by the largest average R2 value where it is 20 for the spike-in background set and 45 for the NUGC3 dilution data sets. (PNG 316 kb

Measurement of fetal fraction in cell-free DNA from maternal plasma using a panel of insertion/deletion polymorphisms

<div><p>Objective</p><p>Cell-free DNA from maternal plasma can be used for non-invasive prenatal testing for aneuploidies and single gene disorders, and also has applications as a biomarker for monitoring high-risk pregnancies, such as those at risk of pre-eclampsia. On average, the fractional cell-free fetal DNA concentration in plasma is approximately 15%, but can vary from less than 4% to greater than 30%. Although quantification of cell-free fetal DNA is straightforward in the case of a male fetus, there is no universal fetal marker; in a female fetus measurement is more challenging. We have developed a panel of multiplexed insertion/deletion polymorphisms that can measure fetal fraction in all pregnancies in a simple, targeted sequencing reaction.</p><p>Methods</p><p>A multiplex panel of primers was designed for 35 indels plus a <i>ZFX/ZFY</i> amplicon. cfDNA was extracted from plasma from 157 pregnant women, and maternal genomic DNA was extracted for 20 of these samples for panel validation. Sixty-one samples from pregnancies with a male fetus were subjected to whole genome sequencing on the Ion Proton sequencing platform, and fetal fraction derived from Y chromosome counts was compared to fetal fraction measured using the indel panel. A total of 157 cell-free DNA samples were sequenced using the indel panel, and informativity was assessed, along with the proportion of fetal DNA.</p><p>Results</p><p>Using gDNA we optimised the indel panel, removing amplicons giving rise to PCR bias. Good correlation was found between fetal fraction using indels and using whole genome sequencing of the Y chromosome (Spearmans r = 0.69). A median of 12 indels were informative per sample. The indel panel was informative in 157/157 cases (mean fetal fraction 14.4% (±0.58%)).</p><p>Conclusions</p><p>Using our targeted next generation sequencing panel we can readily assess the fetal DNA percentage in male and female pregnancies.</p></div