| Run | Spots | Bases | Size | GC content | Published | Access Type |
|---|---|---|---|---|---|---|
SRR5804498 |
26.3M | 5.3Gbp | 3.7G | 50.3% | 2018-06-05 | 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 |
|---|---|---|---|---|---|---|---|
| SRX2983710 | Illumina | RNA-Seq | TRANSCRIPTOMIC | cDNA | PAIRED |
| Biosample | Sample Description | Organism | Links |
|---|---|---|---|
| SAMN07315688 (SRS2336546) | Gorilla gorilla | PRJNA393104 |
| Bioproject | SRA Study | Title |
|---|---|---|
| PRJNA393104 | SRP111096 | Human-specific features of special gene expression and regulation in eight brain regions |
Despite the substantial efforts made to compile the molecular maps of the human brain, they do not fully inform us of the features unique to humans. Yet, identification of these features might help us to understand both the evolution and nature of the human cognitive traits. Here, we approached this question by analyzing gene expression and H3K27ac chromatin modification data collected in eight brain regions of humans, chimpanzees, gorillas, gibbon and macaques. Integrated analysis of these data revealed existence of 1,851 human-specific transcriptome differences affecting one or multiple brain regions, in contrast to 240 chimpanzee-specific ones. More than a half of these differences represented elevated expression in human hippocampus, while the rest affected microglial functions in neocortex. Analysis of predicted regulatory interactions driving these differences revealed role of transcription factors in species-specific transcriptome changes, while epigenetic modifications were linked to spatial expression differences conserved across species. Overall design: Transcriptome across eight brain regions (dorsolateral prefrontal cortex, ventrolateral prefrontal cortex, anterior cingulate cortex, premotor cortex, primary visual cortex, striatum, hippocampus, cerebellum) of humans, chimpanzees, gorillas and gibbon were measured using RNA-seq.
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 | 1,356,465 Kb | NCBI | https://sra-downloadb.be-md.ncbi.nlm.nih.gov/sos5/sra-pub-zq-14/SRR005/804/SRR5804498.sralite.1 | worldwide | anonymous |
| AWS | s3://sra-pub-zq-4/SRR5804498/SRR5804498.sralite.1 | s3.us-east-1 | aws identity | ||
| GCP | gs://sra-pub-zq-106/SRR5804498/SRR5804498.zq.1 | gs.us-east1 | gcp identity | ||
| SRA Normalized | 3,627,371 Kb | AWS | https://sra-pub-run-odp.s3.amazonaws.com/sra/SRR5804498/SRR5804498 | 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 | 2,146,920 Kb | AWS | s3://sra-pub-src-4/SRR5804498/Gorilla_GON_ACC_2.fastq.gz | - | Use Cloud Data Delivery |
| fastq | 2,129,940 Kb | AWS | s3://sra-pub-src-6/SRR5804498/Gorilla_GON_ACC_1.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: 2.01%
- Identified reads: 97.99%
Strong signals
| SuperKingdom | Organism | Rank | %% | Kbp | Coverage |
|---|---|---|---|---|---|
| Eukaryota | Gorilla gorilla gorilla | subspecies | 94.1 | 5,010,876 | |
| Eukaryota | Pan troglodytes | species | 1.1 | 56,612 | |
| Eukaryota | Pan paniscus | species | 0.3 | 15,546 | |
| Eukaryota | Cercopithecinae | subfamily | 0.2 | 12,276 | |
| Eukaryota | Cetartiodactyla | 0.2 | 10,608 | ||
| Eukaryota | Cebidae | family | 0.2 | 8,980 | |
| Eukaryota | Colobinae | subfamily | 0.2 | 8,423 | |
| Bacteria | Enterobacteriaceae | family | 0.1 | 6,995 | |
| Eukaryota | Chiroptera | order | 0.1 | 5,678 |
- 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