Data were first trimmed using Trimmomatic. We used the rat transcriptome from AceView28 v08, which includes 40,064 unique genes, as reference. In addition, the rat genome UCSC rn4 was used as a reference genome. Reads were aligned to the rat reference genome and AceView transcriptome with TopHat v2.0.4, allowing a maximum of 2 mismatches in the alignment. The default parameter settings were used. Alignment results were then processed using Cufflinks v2.0.2 for gene/isoform assembly and quantification. For 2–3 technical replicates in each sample, average FPKM (Fragment Per Kilobase per Million mapped reads) were calculated. To avoid infinite values, 1 count was added to each gene before log2 transformation.
2. How consistent or quantitative is the data? Can I trust it?
The data are as consistent on each sample. Generally, the samples are pretty tight, because (i) we use 4 biological replicates, which is really the minimum for experiment, (2) For each RNA samples, we sequenced 2-3 times, data were extremely tight.
The data are highly quantitative for any particular gene. However, more consistency was observed when compared expression among samples on particular genes rather than among genes.
3. What are "low expressed", "always expressed" and "invariantly expressed" genes?
Genes that are low expressed in all eleven organs, across all four developmental stages, and in both male and female at a level of FPKM < 1 refer to low expressed genes.
Genes that are expressed in all eleven organs, across all four developmental stages, and in both male and female at a level of FPKM ≥ 1 refer to always (stably) expressed genes.
Genes that (1) are always expressed (2) don't show variance in all tissue types, across all developmental stages, and in both male and female refer to invariantly expressed genes.
4. What are "organ-enriched", "age-dependent" and "sex-dependent" genes?
Genes that compared to any other organs in four developmental stages meets 1) Bonferroni corrected p ≤ 0.05 and 2) fold change ≥ 2 refer to organ-enriched genes.
To evaluate the overall age-dependent differential gene expression in various organs, we used an ANOVA model and applied fold change ≥ plus Bonferroni-adjusted p-value ≤ 0.05. For both sexes in each organ type, we used the following pairs of ages as comparisons: 104 vs. 2 weeks, 21 vs. 2 weeks, 6 vs. 2 weeks, 104 vs. 21 weeks, 104 vs. 6 weeks, and 21 vs. 6 weeks. Genes that meet criteria above refer to age-dependent genes.
In each organ and developmental stage, genes for female/male with a fold change ≥ 2 and p value ≤ 0.05 were considered as female-dominated genes, vice versa. Both female-dominated genes and male-dominated genes are referred to sex-dependent genes.
5. What are age-dependent patterns (e.g. UUU) stand for?
To evaluate the time-course and age-dependent transcriptomic activities across developmental stages, we performed a time-course differential gene expression analysis that compared any two adjacent ages and used the younger age group as the denominator for each of the 11 organs. In each organ, comparisons were made between two adjacent ages, with the younger age as a denominator, i.e., 6- vs. 2-weeks old, 21- vs. 6-weeks old and 104- vs. 21-weeks old. A gene with fold change > 2 was grouped into 'up', and considered as up-regulated during that age bracket. A gene with fold change < 0.5 was grouped into 'down', and the remaining genes were grouped into 'maintain'. Thus, in each organ, a gene was grouped to 1 out of 27 patterns, ranging from up-up-up (UUU), maintain-maintain-maintain (MMM), to down-down-down (DDD). We obtained 27 patterns, including those that increased across all age boundaries, termed "up, up and up" (UUU); those that were similar across all boundaries, termed "maintain, maintain, and maintain" (MMM); and those that decreased across all boundaries, termed "decrease, decrease and decrease" (DDD).
6. Are the data freely available, and what do publish with it?
The data are totally freely available. You are welcome to publish with these data, but we'd like to know, only for the purpose of surveying the utility of the data. Please send us an email if you have found the data to be useful. And please cite our publication that describes this work.
7. How to browse information in Rat BodyMap?
Table view of all 40,064 entries within the RNA-Seq database. 30 entries per page were displayed be default, eligible the value can be changed to 50, 100, 200 per page. A sorting option is supported whereas the user can sort by gene symbol or chromosomal location. In addition, a select option is supported whereas the user can select to view genes with specific expression profile, for example organ-enriched genes, age-dependent genes, sex-dependent genes and so on.
8. How to search information in Rat BodyMap?
You can search in Rat BodyMap in various ways, these are as follows:
Enter an Entrez ID or gene symbol in the search box.
Select a region on the chromosome map, and enter a specific chromosome region in search box.
9. How to perform BLAST homology search using your DNA sequences?
1) We will use the program BLAST (www.ncbi.nlm.nih.gov/BLAST/) from NCBI. 2) Select search programs (blastn or blastx). 3) Select the database you are searching for. 4) Type or paste your sequence into the box below the button Submit Query. Do not add any spaces or characters other than A, C, G or T. Push the Submit Query button. 5) Your results will appear next. You can find more information about performing BLAST in http://www.ncbi.nlm.nih.gov/books/NBK1762/.
10. How to download information in Rat BodyMap?
Download Rat BodyMap in tab delimited text file format in the download page. All data are open access.
11. How to cite Rat BodyMap?
Ying Yu, James C. Fuscoe, Chen Zhao, Chao Guo, Meiwen Jia, Tao Qing, Desmond I. Bannon, Lee Lancashire, Wenjun Bao, Tingting Du, Heng Luo, Zhenqiang Su, Wendell D. Jones, Carrie L. Moland, William S. Branham, Feng Qian, Baitang Ning, Yan Li, Huixiao Hong, Lei Guo, Nan Mei, Tieliu Shi, Kevin Y. Wang, Russell D. Wolfinger, Yuri Nikolsky, Stephen J. Walker, Penelope Duerksen-Hughes, Christopher E. Mason, Weida Tong, Jean Thierry-Mieg, Danielle Thierry-Mieg, Leming Shi, and Charles Wang, 'A Rat Rna-Seq Transcriptomic Bodymap across 11 Organs and 4 Developmental Stages', Nat Commun, 5 (2014).