| Run | Spots | Bases | Size | GC content | Published | Access Type |
|---|---|---|---|---|---|---|
SRR2567561 |
42.4M | 8.4Gbp | 3.5G | 45% | 2016-01-15 | public |
This run has 2 reads per spot:
| L=99, 100% | L=99, 100% |
- Legend
Technical read Application Read L=4, 100% Length is 4, 100% spots contain this read ̅L=165, σ=92.8, 66% Average length is 165, standard deviation is 92.8, 66% spots contain this read
| Experiment | Library Name | Platform | Strategy | Source | Selection | Layout | Action |
|---|---|---|---|---|---|---|---|
| SRX1309080 | Illumina | RNA-Seq | TRANSCRIPTOMIC | cDNA | PAIRED |
| Biosample | Sample Description | Organism | Links |
|---|---|---|---|
| SAMN04147825 (SRS1102321) | Equus caballus | PRJNA297894 |
| Bioproject | SRA Study | Title |
|---|---|---|
| PRJNA297894 | SRP064530 | The equine embryo influences immune related gene expression in the oviduct |
While the equine oviduct clearly affects early embryo development and while the selective transport of equine embryos through the oviduct indicates a reciprocal interaction, the influence of the embryo on gene expression in the oviduct remains to be determined in the horse. The aim of this study was to examine this by means of RNA sequencing. Four days after ovulation, the oviduct epithelial cells ipsilateral and contralateral to the ovulation side from five cyclic and five pregnant mares were collected. mRNA was extracted and samples were sequenced using the Illumina Hiseq-2000 sequencer. Data analysis was performed with the CLC Genomics software and differentially expressed genes (DEGs) were determined (p-value = 0.05 and absolute fold change = 2). ClueGO was used for functional interpretation. A total of 26,991 genes was identified and 253 genes were found to be upregulated and 108 to be downregulated in the pregnant ipsilateral oviduct, when compared to the cyclic ipsilateral oviduct. Comparison of the ipsilateral and the contralateral oviduct indicated 164 DEGs in pregnant mares and 77 DEGs in cyclic mares. Functional enrichment analysis only detected differences in the comparison of pregnant and cyclic ipsilateral oviducts and showed that the equine embryo affects the expression of immune response related genes in the oviduct, with marked upregulation of interferon associated genes. This research represents the foundation for further assessment of the role of specific genes in the early embryo-maternal dialogue of the horse. Overall design: mRNA profiles of epithelial cells of equine oviducts ipsilateral and contralateral to the ovulation side of pregnant and cyclic mares four days after ovulation were examined, resulting in four different conditions (pregnant ipsilateral, pregnant contralateral, cyclic ipsilateral and cyclic contralateral, consisting of five biological replicates each.
SRA archive data
SRA archive data is normalized by the SRA load process and used by the SRA Toolkit to read and produce formats like FASTQ, SAM, etc. The default toolkit configuration enables it to find and retrieve SRA runs by accession.
Public SRA files are now available from GCP and AWS cloud platforms as well as from NCBI. Access to most data in the cloud requires a user account with the cloud service provider. The user’s account will incur costs for cloud compute or to copy data outside of the specified cloud service region.
| Type | Size | Location | Name | Free Egress | Access Type |
|---|---|---|---|---|---|
| SRA Normalized | 3,420,844 Kb | AWS | https://sra-pub-run-odp.s3.amazonaws.com/sra/SRR2567561/SRR2567561 | worldwide | anonymous |
| SRA Lite | 2,134,587 Kb | NCBI | https://sra-downloadb.be-md.ncbi.nlm.nih.gov/sos5/sra-pub-zq-11/SRR002/567/SRR2567561/SRR2567561.sralite.1 | worldwide | anonymous |
| AWS | s3://sra-pub-zq-5/SRR2567561/SRR2567561.sralite.1 | s3.us-east-1 | aws identity | ||
| GCP | gs://sra-pub-zq-107/SRR2567561/SRR2567561.zq.1 | gs.us-east1 | gcp identity |
Original format
The original files submitted to SRA. These files may require specific software to open, read and interpret data.
| Type | Size | Location | Name | Free Egress | Access Type |
|---|---|---|---|---|---|
| fastq | 2,872,070 Kb | AWS | s3://sra-pub-src-8/SRR2567561/PC5_ATGTCA_L007_R1_001.fastq.gz | - | Use Cloud Data Delivery |
| fastq | 2,904,098 Kb | AWS | s3://sra-pub-src-7/SRR2567561/PC5_ATGTCA_L007_R2_001.fastq.gz | - | Use Cloud Data Delivery |
- Egress and Access: what does it mean?
-
Egress
term used by cloud providers to describe cost (charged to the user) of moving data outside of the storage region Free Egresscolumn indicates where the data can be accessed without an egress charge: - worldwide can be downloaded from anywhere for free
- s3.us-east-1 is free to access from machines running in Amazon's us-east-1 region, access from other regions or transport outside of AWS will require paying egress charges
- gs.US is free to access from machines running in Google’s gs.US region, access from other regions or transport outside of GCP will require paying egress charges
Access Typedescribes whether a cloud service user account is necessary for data access. "anonymous" access means general public access Namecolumn provides either a link to free download location at NCBI or a URL for the cloud provider storage location. Either s3:// for Amazon or gs:// for Google storage
- Why is SRA data in the cloud?
-
In order to support large scale (hyper parallel) data analyses SRA data is now available at GCP and AWS with few caveats:
- SRA data is copied to the cloud from NCBI. There may be a lag between availability from NCBI and from CSP (cloud service providers)
- To access public data user account with the cloud service provider is required. Your account will incur costs for cloud compute and/or to copy data (either archival or results of your comute) outside of the specified cloud service region
- Distribution of protected data is signed by NIH account and requires user to operate in the same region as the data
SRA has also begun to provide access to originally submitted source files:
- not all files have been validated by SRA
- not all files have been copied to cloud locations (recovering it from NCBI tape system takes time ).
- the volume of this type of data a much larger and it is not used as often so we will keep most of it on tape or "cold" storage in cloud. As a result the data may not be available instantly and restore requests will be served on first-come first-served basis and cost of resore may be charged to your user account.
- What is "Cloud Data Delivery"?
-
Please visit SRA Data Delivery service to request Sequence Read Archive (SRA) data to be delivered to an Amazon Web Services (AWS) or Google Cloud Platform (GCP) bucket of your choice.
Taxonomy Analysis
- Unidentified reads: 11.75%
- Identified reads: 88.25%
Strong signals
| SuperKingdom | Organism | Rank | %% | Kbp | Coverage |
|---|---|---|---|---|---|
| Eukaryota | Equus | subgenus | 86.0 | 7,215,957 | |
| Eukaryota | Equus przewalskii | species | 3.3 | 279,056 | |
| Eukaryota | Equus caballus | species | 1.5 | 126,341 | |
| Eukaryota | Equus asinus | species | 0.8 | 69,163 | |
| Bacteria | Bacteria | superkingdom | 0.5 | 44,832 | |
| Eukaryota | Bovidae | family | 0.1 | 4,388 | |
| Eukaryota | Cetacea | order | 0.0 | 3,823 | |
| Eukaryota | Caniformia | suborder | 0.0 | 3,822 | |
| Eukaryota | Rodentia | order | 0.0 | 3,785 |
- How to read results?
Results show distribution of reads mapping to specific taxonomy nodes as a percentage of total reads within the analyzed run. In cases where a read maps to more than one related taxonomy node, the read is reported as originating from the lowest shared taxonomic node. So when a read maps to two species belonging to the same genus, it is assigned at the genus level. Sequence reads from a single organism will map to several taxonomy nodes spanning the organism’s lineage. The number of reads mapping to higher level nodes will typically be greater than those that map to terminal nodes.
STAT results are proportional to the size of sequenced genomes. Given a mixed sample containing several organisms at equal copy number, proportionally more reads originate from the larger genomes. This means that the percentages reported by STAT will reflect genome size and must be considered against the genomic complexity of the sequenced sample.
- How can I query this analysis data?
Please take a look at NCBI SRA Cloud-based Taxonomy Analysis Table