| Run | Spots | Bases | Size | GC content | Published | Access Type |
|---|---|---|---|---|---|---|
SRR1767928 |
12.4M | 3.7Gbp | 2.4G | 49.6% | 2016-01-21 | public |
This run has 2 reads per spot:
| L=151, 100% | L=151, 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 |
|---|---|---|---|---|---|---|---|
| SRX849199 | Illumina | RNA-Seq | TRANSCRIPTOMIC | cDNA | PAIRED |
| Biosample | Sample Description | Organism | Links |
|---|---|---|---|
| SAMN03289346 (SRS824132) | Raphanus sativus | PRJNA273265 |
| Bioproject | SRA Study | Title |
|---|---|---|
| PRJNA273265 | SRP052703 | Identification of differentially expressed genes related to cytoplasmic male sterility in radish (Raphanus sativus L.) by comparative transcriptome analysis |
Cytoplasmic male sterility (CMS), a typically maternally inherited trait, causes a failure in producing functional pollen. Although the radish CMS has be widely used to produce hybrid varieties in breeding program, the molecular mechanism of CMS in radish is poorly understood. In this study, two radish CMS lines (HYBP-A and YH-A) and their corresponding maintainer lines (HYBP-B and YH-B) were used to identify genes potentially involving in CMS using Illumina pair-end sequencing. A total of 167.86 million clean sequence reads were generated from the eight libraries (two replicates for each line). These reads were eventually assembled into 130,240 unigenes. Of them, 67,173 (51.6%) unigenes were annotated for their function. Comparison of gene expression levels between CMS line and maintainer line revealed 5,893 differentially expressed genes (DEGs) in HYBP, and 3,739 DEGs inYH. There were 990 DEGs commonly identified in both HYBP and YH, with same direction of expression change in two CMS lines relative to their corresponding maintainer lines, which suggested these 990 DEGs is likely related to CMS of radish. The expression levels of 20 DEGs were further confirmed by real-time quantitative PCR (qRT-PCR). Two pathways and eight functional categories exhibited a significant enrichment with DEGs in HYBP, and one pathway and six functional categories were markedly enriched by DEGs in YH. Among these pathways/functional categories, four of them were enriched in both varieties. A series of candidate genes and pathways that may contribute to the CMS will be helpful for increasing our understanding for this trait in radish. Overall design: Two CMS lines and two their corresponding maintainer lines were used. a total of eight samples, i.e. two replicates for each line were used for RNA-seq.
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 | 2,304,980 Kb | AWS | https://sra-pub-run-odp.s3.amazonaws.com/sra/SRR1767928/SRR1767928 | worldwide | anonymous |
| SRA Lite | 941,541 Kb | NCBI | https://sra-downloadb.be-md.ncbi.nlm.nih.gov/sos5/sra-pub-zq-11/SRR001/767/SRR1767928/SRR1767928.sralite.1 | worldwide | anonymous |
| AWS | s3://sra-pub-zq-8/SRR1767928/SRR1767928.sralite.1 | s3.us-east-1 | aws identity | ||
| GCP | gs://sra-pub-zq-107/SRR1767928/SRR1767928.noqual.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 | 4,758,685 Kb | AWS | s3://sra-pub-src-10/SRR1767928/0819-HYBP-B-1_raw_1.fq | - | Use Cloud Data Delivery |
| fastq | 4,758,685 Kb | AWS | s3://sra-pub-src-12/SRR1767928/0819-HYBP-B-1_raw_2.fq | - | 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: 45.23%
- Identified reads: 54.77%
Strong signals
| SuperKingdom | Organism | Rank | %% | Kbp | Coverage |
|---|---|---|---|---|---|
| Eukaryota | Brassica | genus | 37.5 | 1,403,920 | |
| Bacteria | Bacteria | superkingdom | 14.0 | 524,429 | |
| Eukaryota | Brassica napus | species | 1.4 | 53,696 | |
| Eukaryota | Brassica rapa | species | 1.1 | 40,738 | |
| Eukaryota | Brassica oleracea var. oleracea | varietas | 1.0 | 36,042 | |
| Eukaryota | Camelina sativa | species | 0.8 | 31,039 | |
| Eukaryota | Eutrema salsugineum | species | 0.8 | 29,042 | |
| Bacteria | Lactobacillus plantarum 4_3 | 0.5 | 20,218 | 6.2 | |
| Eukaryota | Arabidopsis lyrata subsp. lyrata | subspecies | 0.4 | 15,317 |
- 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