Internal IS for travel agency

The task of the work is to design and implement an information system for a travel agency. The system is supposed to assist at business of a small travel agency. Editing of capacities, booking and selling, voucher printing and sale statistics are all problems to deal with. Application is designed only for inside use in a travel agency and for its contract partners, it's not intended as a booking software for clients. There are some accountant information and cost items in system because of this focus. Coffers control can be managed thru the application interface and user management is of course also part of this application. The system allows mass import of data due to a lot of data which have to be inserted into it

Additional file 6: Figure S5. of Genetic features of red and green junglefowls and relationship with Indonesian native chickens Sumatera and Kedu Hitam

Distribution of gene ontology (GO) slim terms associated with non-synonymous single nucleotide polymorphism (SNP) containing genes, which are common in each breed. GO terms were categorized by biological process, cellular component, and molecular function, as indicated. Abbreviations are defined in Fig. 2. (PDF 89 kb

Mouse Oocyte Methylomes at Base Resolution Reveal Genome-Wide Accumulation of Non-CpG Methylation and Role of DNA Methyltransferases

<div><p>DNA methylation is an epigenetic modification that plays a crucial role in normal mammalian development, retrotransposon silencing, and cellular reprogramming. Although methylation mainly occurs on the cytosine in a CG site, non-CG methylation is prevalent in pluripotent stem cells, brain, and oocytes. We previously identified non-CG methylation in several CG-rich regions in mouse germinal vesicle oocytes (GVOs), but the overall distribution of non-CG methylation and the enzymes responsible for this modification are unknown. Using amplification-free whole-genome bisulfite sequencing, which can be used with minute amounts of DNA, we constructed the base-resolution methylome maps of GVOs, non-growing oocytes (NGOs), and mutant GVOs lacking the DNA methyltransferase Dnmt1, Dnmt3a, Dnmt3b, or Dnmt3L. We found that nearly two-thirds of all methylcytosines occur in a non-CG context in GVOs. The distribution of non-CG methylation closely resembled that of CG methylation throughout the genome and showed clear enrichment in gene bodies. Compared to NGOs, GVOs were over four times more methylated at non-CG sites, indicating that non-CG methylation accumulates during oocyte growth. Lack of Dnmt3a or Dnmt3L resulted in a global reduction in both CG and non-CG methylation, showing that non-CG methylation depends on the Dnmt3a-Dnmt3L complex. Dnmt3b was dispensable. Of note, lack of Dnmt1 resulted in a slight decrease in CG methylation, suggesting that this maintenance enzyme plays a role in non-dividing oocytes. Dnmt1 may act on CG sites that remain hemimethylated in the <i>de novo</i> methylation process. Our results provide a basis for understanding the mechanisms and significance of non-CG methylation in mammalian oocytes.</p></div

Biological significance of female and male-specific PGC-expressed genes.

<p><b>(A)</b> GO enrichment analysis of FSGs and MSGs. The most highly enriched biological processes based on their respective gene counts are shown (Fisher’s exact test: cut-off < 0.1). <b>(B)</b> Pathway analysis of all transcript lists in female and male PGCs. The lists indicate enriched pathways observed among female and male transcripts, as determined using DAVID (Fisher’s exact test: cut-off <0.1). Sex-specific pathways are highlighted in pink and blue, which denote female- and male-specific, respectively.</p

Abundant non-CG methylation in GVOs.

<p>(A) Proportions of mCs in contexts of CG, CHG, and CHH. Data for mCs at positions covered at least 4× on the same strand were used, and those with more than 100× coverage were excluded. (B) Levels of methylation at CG and non-CG sites. Non-CG sites were further divided into different tri- (CHG and CHH) and di-nucleotide sequences (CA, CT, and CC). (C) Bases neighboring the highly methylated (≥30%) non-CG sites.</p

Single-cell transcriptome analysis of E13.5 female and male PGCs.

<p><b>(A)</b> Bi-dimensional <b>PCA</b> of E13.5 female and male single-PGC gene expression patterns. The first principal component (PC1) captures 4.93% of the gene expression variability and the second principal component (PC2) captures 4.25%. The red and blue spheres represent single female (n = 67) and male (n = 77) PGCs, respectively. <b>(B)</b> Hierarchical clustering analysis of transcriptome data sets (FSG: 651, MSG: 428) of female and male single PGCs. <b>(C)</b> Heat map of the relative expression of apoptotic process-related genes (n = 15). <b>(D)</b> Heat map of the relative expression of meiotic and mitotic cell cycle-related genes (n = 26). <b>(E)</b> Relation between apoptosis and meiotic and mitotic cell cycle in female PGCs. The numbers indicate the number of PGCs.</p

DNA methylation and histone modifications in female- and male-specific PGC-expressed genes.

<p><b>(A)</b> Average methylation levels of TSS and the shore values (±5 kb of the TSS) in female- and male-specific PGC-expressed genes. Our previous methylation data of PGCs were obtained from the DDBJ database [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0144836#pone.0144836.ref012" target="_blank">12</a>]. <b>(B)</b> Occupancy of H3K4me3- and H3K27me3-bound TSS and TTS shore regions (±5 kb of the TSS and TTS) in female- and male-specific PGC-expressed genes. <b>(C)</b> Proportion of genes showing histone-modification patterns. The number of genes classified into each histone-modification pattern is shown. <b>(D)</b> Three typical histone-modification patterns. The red regions indicate ChIP/input enrichment-peak regions that were calculated using the DROMPA peak-calling program.</p

Relationship between CG methylation and non-CG methylation in GVOs.

<p>(A) Levels of CG methylation and non-CG methylation across the entire chromosome 4 in a non-overlapping sliding window of 50 kb. The two strands were separately analyzed for non-CG methylation. (B) Correlation between the levels of CG methylation and non-CG methylation in 10-kb non-overlapping sliding windows across the genome was indicated using Spearman's rank correlation coefficient. (C) Effect of CG on non-CG methylation at positions immediately upstream and downstream of mC sites. Blue, orange, and gray bars indicate the levels of non-CG methylation around CG sites with at least 10× coverage with methylation levels of 80–100%, 40–60%, and 0–20%, respectively. (D) Levels of CG methylation and non-CG methylation relative to gene structure. The upstream and downstream regions (10-kb each) were split into 10 non-overlapping windows to determine the methylation levels. The intragenic or coding regions were divided into 20 small windows for methylation analysis.</p

Identification of female and male PGC-specific-expressed genes.

<p><b>(A)</b> Top: scatter plot of female and male PGC samples. The green lines indicate a 2-FC in the average expression levels between the 2 samples. Bottom: volcano plot of differentially expressed genes in female and male PGC samples. Red spheres indicate statistically significant genes. FSGs (651) and MSGs (428). <b>(B)</b> Top 20 differentially expressed genes in female- and male-specific PGCs. Expression levels are shown in log2 values. The genes are ranked in the order of decreasing log FC values. <b>(C)</b> Pie chart showing the composition and quantity of each reference gene type in the UCSC Genome Browser and Ingenuity Pathway analysis tool for FSGs and MSGs.</p

Sequencing and mapping summary.

<p>Sequencing and mapping summary.</p