| Run | Spots | Bases | Size | GC content | Published | Access Type |
|---|---|---|---|---|---|---|
SRR13709446 |
34.2M | 10.3Gbp | 3.2G | 42.9% | 2021-02-15 | 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 |
|---|---|---|---|---|---|---|---|
| SRX10098388 | 150 mM NaCl-light-6D-3-V3 | Illumina | RNA-Seq | TRANSCRIPTOMIC | PCR | PAIRED |
Design:
| Biosample | Sample Description | Organism | Links |
|---|---|---|---|
| SAMN17915118 (SRS8255694) | Medicago truncatula | PRJNA701852 |
| Bioproject | SRA Study | Title |
|---|---|---|
| PRJNA701852 | SRP306387 | Comparative transcriptome analysis of salt stress-induced leaf senescence in Medicago truncatula |
Leaves are the most critical portion of forage crops such as alfalfa (Medicago sativa). Leaf senescence caused by environmental stresses significantly impacts the biomass and quality of forages. To understand the molecular mechanisms and identify the key regulator of the salt stress-induced leaf senescence process, we conducted a simple and effective salt stress-induced leaf senescence assay in Medicago truncatula, which was followed by RNA-Seq analysis and physiological and biochemical characterization. By comparing the observed expression data with that derived from dark-induced leaf senescence at different time points, we identified 3001, 3787, and 4419 senescence-associated genes (SAGs) for salt stress-induced leaf senescence at 2, 4, and 6 days, respectively. There were 1546 SAGs shared by darkness and salt stress treatment across the three time points. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses showed that the 1546 SAGs were mainly related to protein and amino acids metabolism, photosynthesis, chlorophyll metabolism, and hormone signaling during leaf senescence. These results clarify SAGs under salt stress in M. truncatula and provide new insights and additional genetic resources for further forage crop breeding.
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 Lite | 2,521,911 Kb | NCBI | https://sra-downloadb.be-md.ncbi.nlm.nih.gov/sos3/sra-pub-zq-24/SRR013/13709/SRR13709446/SRR13709446.lite.1 | worldwide | anonymous |
| AWS | s3://sra-pub-zq-9/SRR13709446/SRR13709446.lite.1 | s3.us-east-1 | aws identity | ||
| GCP | gs://sra-pub-zq-104/SRR13709446/SRR13709446.lite.1 | gs.us-east1 | gcp identity | ||
| SRA Normalized | 3,085,024 Kb | AWS | https://sra-pub-run-odp.s3.amazonaws.com/sra/SRR13709446/SRR13709446 | worldwide | anonymous |
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 | 2,500,014 Kb | AWS | s3://sra-pub-src-6/SRR13709446/150%20mM%20NaCl-light-6D-3-V3-R2.fastq.gz.1 | - | Use Cloud Data Delivery |
| fastq | 2,398,389 Kb | AWS | s3://sra-pub-src-6/SRR13709446/150%20mM%20NaCl-light-6D-3-V3-R1.fastq.gz.1 | - | 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: 37.18%
- Identified reads: 62.82%
Strong signals
| SuperKingdom | Organism | Rank | %% | Kbp | Coverage |
|---|---|---|---|---|---|
| Eukaryota | Medicago truncatula | species | 57.1 | 5,901,277 | |
| Bacteria | Bacteria | superkingdom | 5.3 | 547,051 | |
| Bacteria | Klebsiella pneumoniae | species | 0.2 | 21,865 | 3.9 |
| Eukaryota | Cicer arietinum | species | 0.2 | 16,476 | |
| Bacteria | Pseudomonas | genus | 0.1 | 12,436 | |
| Eukaryota | Bilateria | 0.1 | 10,453 | ||
| Bacteria | Enterococcus faecium | species | 0.1 | 8,526 | 2.9 |
| Eukaryota | Saccharomycetales | order | 0.1 | 5,998 | |
| Bacteria | Stenotrophomonas maltophilia | species | 0.0 | 4,081 | 0.9 |
- 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