| Run | Spots | Bases | Size | GC content | Published | Access Type |
|---|---|---|---|---|---|---|
SRR4431159 |
1.4M | 697.8Mbp | 489.7M | 63.3% | 2017-10-19 | public |
This run has 2 reads per spot:
| L=251, 100% | L=251, 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 |
|---|---|---|---|---|---|---|---|
| SRX2250261 | Illumina | WGS | GENOMIC | PCR | PAIRED |
| Biosample | Sample Description | Organism |
|---|---|---|
| SAMN05892026 (SRS1750233) | Ralstonia solanacearum |
| Bioproject | SRA Study | Title |
|---|---|---|
| PRJNA347535 | SRP091690 | Genome sequencing and analysis of Ralstonia solanacearum phylotype I strains FJAT-91, FJAT-452 and FJAT-462 isolated from tomato, eggplant, and chilli pepper in China |
Ralstonia solanacearum is an extremely destructive pathogen able to cause disease in a wide range of host plants. Here we report the draft genome sequences of the strains FJAT-91, FJAT-452 and FJAT-462, isolated from tomato, eggplant, and chilli pepper, respectively, in China. In addition to the genome annotation, we performed a search for type-III secreted effectors in these strains, providing a detailed annotation of their presence and distinctive features compared to the effector repertoire of the reference phylotype I strain (GMI1000). In this analysis, we found that each strain has unique effector repertoires, encoding both strain-specific effector variants and variations shared between all three strains. Our study, based on strains isolated from different host within the same geographical location, provides insight into effector repertoires sufficient to cause disease in different hosts, and may contribute to the identification of host specificity determinants for R. solanacearum.
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 |
|---|---|---|---|---|---|
| run | 478,215 Kb | GCP | gs://sra-pub-crun-5/SRR4431159/SRR4431159.1 | - | Use Cloud Data Delivery |
| AWS | https://sra-pub-run-odp.s3.amazonaws.com/sra/SRR4431159/SRR4431159 | worldwide | anonymous | ||
| NCBI | https://sra-downloadb.be-md.ncbi.nlm.nih.gov/sos1/sra-pub-run-2/SRR4431159/SRR4431159.1 | 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 | 307,335 Kb | GCP | gs://sra-pub-src-7/SRR4431159/FJAT-452_1.fastq.gz | - | Use Cloud Data Delivery |
| AWS | s3://sra-pub-src-7/SRR4431159/FJAT-452_1.fastq.gz | - | Use Cloud Data Delivery | ||
| fastq | 340,069 Kb | GCP | gs://sra-pub-src-6/SRR4431159/FJAT-452_2.fastq.gz | - | Use Cloud Data Delivery |
| AWS | s3://sra-pub-src-6/SRR4431159/FJAT-452_2.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: 0.92%
- Identified reads: 99.08%
Strong signals
| SuperKingdom | Organism | Rank | %% | Kbp | Coverage |
|---|---|---|---|---|---|
| Bacteria | Ralstonia solanacearum FJAT-1458 | 98.6 | 687,732 | 124.2 | |
| Bacteria | Ralstonia solanacearum FQY_4 | 0.9 | 6,123 | 1.1 | |
| Bacteria | Ralstonia solanacearum Rs-09-161 | 0.9 | 6,066 | 1.1 | |
| Bacteria | Ralstonia solanacearum Rs-10-244 | 0.8 | 5,735 | 1.0 | |
| Bacteria | Ralstonia solanacearum Y45 | 0.5 | 3,365 | 0.6 | |
| Bacteria | Ralstonia solanacearum CMR15 | 0.4 | 2,835 | 0.5 | |
| Bacteria | Ralstonia solanacearum GMI1000 | 0.3 | 2,253 | 0.4 | |
| Bacteria | Ralstonia solanacearum PSI07 | 0.3 | 2,067 | 0.4 |
- 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
Assembly
You can also check contigs assembled from kmers of this run:
- contigs: SRR4431159.contigs.fasta (5,191 Kb)
- How to read contig names?
Contig name pattern is {{CN_FR%_cov_SC_len_SL}}
where the terms are:- CN - contig number. Sequential numeric id to separate names even if the rest are identical.
- FR - fraction of reads. Fraction of reads of this run which aligns to this contig.
- SC - coverage. Average number of reads overlapping each base of the contig.
- SL - contig length.
- How its built?
-
All reads are split into 32-mers which later overlapped to build contigs. The second pass is used to align full reads to contigs to build coverage graph and low coverage contigs are thrown away. Same way too short contigs are trown away even with overabundant coverage typical to seqencing primers. The rest is sorted by coverage and presented to you.
The "unidentified" contigs are build only from reads which taxonomy analyses can not match. You can try to paste these sequences to ORF finder to find potential proteins and than check it in smartBlast to see if you can recognoize what this "dark matter" actually is.