| Run | Spots | Bases | Size | GC content | Published | Access Type |
|---|---|---|---|---|---|---|
SRR5945863 |
17.3M | 5.2Gbp | 2.4G | 45.9% | 2018-08-13 | public |
This run has 2 reads per spot:
| L=150, 100% | L=150, 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 |
|---|---|---|---|---|---|---|---|
| SRX3104244 | Illumina | RNA-Seq | TRANSCRIPTOMIC | unspecified | PAIRED |
| Biosample | Sample Description | Organism | Links |
|---|---|---|---|
| SAMN07502053 (SRS2439705) | Lactuca sativa | PRJNA398146 |
| Bioproject | SRA Study | Title |
|---|---|---|
| PRJNA398146 | SRP115709 | Transcriptomic analysis of gibberellin-regulated genes and transcription factors in regulating bolting in lettuce |
Lettuce prefers cool temperature, as high temperature will cause premature bolting, which seriously affects the lettuces’s eating quality and economic value. Thus, exploring the mechanism of bolting and preventing premature bolting is significant for agriculture. Prior to this study, we observed that there was a significant relationship between gibberellin and bolting in lettuce, but we did not explore it in depth. Thus, in this paper, the bolting-sensitive line S39 was selected for morphological, physiological, and transcriptomic comparisons at 37°C. From paraffin section results, we observed that S39 began bolting on the seventh day at 37°C. When bolting, GA3 and GA4 levels in the leaves and the IAA in the stem reached a maximum on the sixth day, and subsequently maintained the high contents. Additionally, when S39 began bolting on the fifth day, GA3 and GA4 increased and subsequently decreased, reaching a maximum on the fourth day in the leaves. Similarly, IAA contents reached a maximum in the stem on the fourth day. The control group did not bolt, as significant changes were not observed in GA3 and GA4 levels in the controls during the observed period. RNA-sequencing data revealed that transcription factors were implicated in regulating bolting in lettuce, suggesting that the high gibberellin contents in the leaves and IAA in the stem promoted bolting. Transcription factors may regulate expression and synthesis of related genes, such as hormones, which potentially regulate bolting in lettuce. Thus, our results lay a foundation for molecular breeding methods for desired planting periods in lettuce.
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 | 2,313,826 Kb | AWS | https://sra-pub-run-odp.s3.amazonaws.com/sra/SRR5945863/SRR5945863 | worldwide | anonymous |
| SRA Lite | 1,266,546 Kb | NCBI | https://sra-downloadb.be-md.ncbi.nlm.nih.gov/sos5/sra-pub-zq-14/SRR005/945/SRR5945863.sralite.1 | worldwide | anonymous |
| AWS | s3://sra-pub-zq-7/SRR5945863/SRR5945863.sralite.1 | s3.us-east-1 | aws identity | ||
| GCP | gs://sra-pub-zq-105/SRR5945863/SRR5945863.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 | 1,578,463 Kb | AWS | s3://sra-pub-src-7/SRR5945863/131458A-CY-2_H7VYNALXX_L1_1.fq.gz | - | Use Cloud Data Delivery |
| fastq | 1,837,973 Kb | AWS | s3://sra-pub-src-6/SRR5945863/131458A-CY-2_H7VYNALXX_L1_2.fq.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: 93.28%
- Identified reads: 6.72%
Strong signals
| SuperKingdom | Organism | Rank | %% | Kbp | Coverage |
|---|---|---|---|---|---|
| Eukaryota | Populus | genus | 2.0 | 105,368 | |
| Eukaryota | Erythranthe guttata | species | 0.7 | 34,867 | |
| Eukaryota | Daucus carota subsp. sativus | subspecies | 0.6 | 33,270 | |
| Eukaryota | Gossypium | genus | 0.6 | 31,162 | |
| Eukaryota | Prunus | genus | 0.5 | 25,243 | |
| Eukaryota | Glycine max | species | 0.3 | 15,962 | |
| Eukaryota | Nicotiana | genus | 0.3 | 13,531 | |
| Eukaryota | Juglans regia | species | 0.2 | 11,100 | |
| Eukaryota | Ipomoea nil | species | 0.2 | 10,938 |
- 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