| Run | Spots | Bases | Size | GC content | Published | Access Type |
|---|---|---|---|---|---|---|
SRR4280495 |
15.0M | 3.0Gbp | 2.0G | 46.9% | 2017-10-18 | 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
| Experiment | Library Name | Platform | Strategy | Source | Selection | Layout | Action |
|---|---|---|---|---|---|---|---|
| SRX2182170 | GLQ-17b | Illumina | RNA-Seq | TRANSCRIPTOMIC | cDNA | PAIRED |
Design:
| Biosample | Sample Description | Organism | Links |
|---|---|---|---|
| SAMN05792577 (SRS1706102) | not collected | Python bivittatus | PRJNA343735 |
| Bioproject | SRA Study | Title |
|---|---|---|
| PRJNA343735 | SRP090227 | Transcriptome Analysis of Burmese Python in Response to Digestion and Identification of the Major Digestive Enzymes |
Background:The exceptional and extreme feeding behaviour makes the Burmese python a unique and interesting model to study physiological remodelling and metabolic adaptation in response to feeding after prolonged starvation. With outset in specific hypotheses based on in vivo physiological responses, we use transcriptome sequencing of five visceral organs and three digestive stages to unravel the patterns of changes in the gene expression of Burmese python upon ingestion of a large meal. We first used the combined data to perform a de novo assembly of the transcriptome. We supplemented with a proteomic survey of enzymes in the gastric juice, stomach secretome and plasma during digestion assisted by our transcriptome sequence database.Results:We constructed a high quality transcriptome with 34,423 transcripts of which 19,713 (57%) were annotated. Among highly expressed genes (FPKM>100 in one tissue) we found differential expression for 43 genes in heart, 206 genes in liver, 114 genes in stomach, 89 genes in pancreas and 158 genes in intestine. We interrogated the function of these genes in order to test previous hypothesis on the response to feeding. We also used the transcriptome to identify 314 secreted proteins in the python gastric juice.Conclusions:We provide comprehensive transcriptome data of multiple organs and various digestive time of Burmese python and address specific hypothesis on certain pathways known to related digestion process. We also identify, for the first time, stomach-related proteins from a digesting individual and thereby demonstrate that the sensitivity of modern LC-MS/MS equipment allows the identification of gastric juice proteins that are present during digestion thereby providing novel insight into the digestion mechanism.
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 | 775,906 Kb | NCBI | https://sra-downloadb.be-md.ncbi.nlm.nih.gov/sos5/sra-pub-zq-14/SRR004/280/SRR4280495.sralite.1 | worldwide | anonymous |
| AWS | s3://sra-pub-zq-2/SRR4280495/SRR4280495.sralite.1 | s3.us-east-1 | aws identity | ||
| GCP | gs://sra-pub-zq-103/SRR4280495/SRR4280495.zq.1 | gs.us-east1 | gcp identity | ||
| SRA Normalized | 1,945,698 Kb | AWS | https://sra-pub-run-odp.s3.amazonaws.com/sra/SRR4280495/SRR4280495 | 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 | 1,196,933 Kb | AWS | s3://sra-pub-src-4/SRR4280495/110902_SN132_B_L002_R1_GLQ-17b.fastq.gz | - | Use Cloud Data Delivery |
| fastq | 1,233,555 Kb | AWS | s3://sra-pub-src-8/SRR4280495/110902_SN132_B_L002_R2_GLQ-17b.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: 7.06%
- Identified reads: 92.94%
Strong signals
| SuperKingdom | Organism | Rank | %% | Kbp | Coverage |
|---|---|---|---|---|---|
| Eukaryota | Python bivittatus | species | 92.4 | 2,806,655 | |
| Eukaryota | Protobothrops mucrosquamatus | species | 0.3 | 9,203 | |
| Eukaryota | Colubridae | family | 0.1 | 2,382 | |
| Eukaryota | Eutheria | 0.1 | 2,275 | ||
| Bacteria | Proteobacteria | phylum | 0.0 | 1,173 | |
| Bacteria | Terrabacteria group | 0.0 | 305 |
- 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