| Run | Spots | Bases | Size | GC content | Published | Access Type |
|---|---|---|---|---|---|---|
ERR983184 |
21.7M | 4.4Gbp | 3.2G | 37.6% | 2015-08-15 | public |
This run has 2 reads per spot:
| L=101, 100% | L=101, 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
| Name | Value |
|---|---|
| ENA-FIRST-PUBLIC | 2015-08-14 |
| ENA-LAST-UPDATE | 2018-11-16 |
| Experiment | Library Name | Platform | Strategy | Source | Selection | Layout | Action |
|---|---|---|---|---|---|---|---|
| ERX1064399 | LIB4703 | Illumina | RNA-Seq | TRANSCRIPTOMIC | unspecified | PAIRED |
Design:
| Biosample | Sample Description | Organism |
|---|---|---|
| SAMEA3505169 (ERS812318) | M. persicae genotype F: dissected heads ~100 per biological replica | Myzus persicae |
| Bioproject | SRA Study | Title |
|---|---|---|
| PRJEB9912 | ERP011063 | Aphid RNA-seq to identify putative effectors |
Background Aphids cause a substantial amount of damage and therefore yield loss to world-wide agriculture. Aphids interact with their hosts at a molecular level using molecules secreted in their saliva (effectorss). In this study we used a multi-disciplinary approach of RNA sequencing of dissected aphid material and LC-MS analysis of aphid saliva in order to identify effector candidates from generalist Myzus persicae (genotypes O, J and F), a specialist species M. cerasi and the cereal specialist Rhopalosiphum padi. Results Proteomic analysis of aphid saliva identified a total of 204 proteins. RNA sequencing and the use of differential expression analysis between heads (contains salivary glands) and bodies (without nymphs and without heads) identified transcripts which were upregulated in “heads”, those encoding signal peptides and no transmembrane domain were considered putative effectors. The majority of candidate effectors had sequences with high similarity only to other aphid sequences, indicating aphid specific evolution. De novo transcriptomes were subjected to comparative analysis to identify conserved/core aphid effectors and putative pioneer effectors. The “conserved” effectors consisted of a number of sequences predicted to be involved in the detoxification of plant defence responses and previously identified effector candidates (Bos et al., 2010), including 50 hypothetical Acyrthosiphon pisum salivary gland sequences (Carolan et al., 2011). Several of these sequences were identified as being under positive selection pressure, including previously identified Macrosiphum euphorbiae effector Me10 (Atamian et al., 2013) which was also within the top 120 expressing transcripts in all species. 43 putative pioneer effectors were identified, some of which may be species or genus specific. Conclusion A combined proteomic and bioinformatic approach identified putative effectors from three species of aphids. Clustering analysis was used to identify conserved groups of putative effectors that may be a per-requisite for plant-aphid interactions.
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 | 3,081,382 Kb | AWS | https://sra-pub-run-odp.s3.amazonaws.com/sra/ERR983184/ERR983184 | worldwide | anonymous |
| SRA Lite | 1,115,476 Kb | NCBI | https://sra-downloadb.be-md.ncbi.nlm.nih.gov/sos5/sra-pub-zq-11/ERR000/983/ERR983184/ERR983184.sralite.1 | worldwide | anonymous |
| AWS | s3://sra-pub-zq-5/ERR983184/ERR983184.sralite.1 | s3.us-east-1 | aws identity | ||
| GCP | gs://sra-pub-zq-107/ERR983184/ERR983184.zq.1 | gs.us-east1 | gcp identity |
- 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: 60.68%
- Identified reads: 39.32%
Strong signals
| SuperKingdom | Organism | Rank | %% | Kbp | Coverage |
|---|---|---|---|---|---|
| Eukaryota | Acyrthosiphon pisum | species | 25.0 | 1,095,553 | |
| Eukaryota | Diuraphis noxia | species | 13.8 | 601,909 | |
| Eukaryota | Brassica napus | species | 0.3 | 13,543 | |
| Bacteria | Candidatus Regiella insecticola 5.15 | 0.1 | 4,986 | ||
| Bacteria | Buchnera aphidicola (Myzus persicae) | 0.0 | 1,326 | 2.1 |
- 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