| Run | Spots | Bases | Size | GC content | Published | Access Type |
|---|---|---|---|---|---|---|
SRR8750611 |
19.5M | 5.8Gbp | 2.1G | 49.5% | 2020-02-20 | public |
This run has 2 reads per spot:
| L=150, 100% | L=150, 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 |
|---|---|---|---|---|---|---|---|
| SRX5541545 | Illumina | RNA-Seq | TRANSCRIPTOMIC | cDNA | PAIRED |
| Biosample | Sample Description | Organism | Links |
|---|---|---|---|
| SAMN11165992 (SRS4507221) | Pan paniscus | PRJNA527986 |
| Bioproject | SRA Study | Title |
|---|---|---|
| PRJNA527986 | SRP188822 | Transcriptome map of the human brain at the single-cell resolution |
The human brain is about three times larger than the brain of our closest relatives, chimpanzees and bonobos. However, increased size alone fails to explain cognitive abilities unique to humans. Functional changes acquired in the human lineage are likely mediated by divergent gene expression and cell composition in brain regions. Here we generated a map of the human brain transcriptome by assessing the gene expression levels in 33 distinct regions representing all major brain structures from four adult cognitively healthy human individuals, three chimpanzees, three bonobos, and three rhesus macaques. We further conducted single-nuclei RNA-sequencing (SN-seq) experiment in three of the 33 brain regions in four species: two regions showing excess of the human-specific differences, cingulate anterior cortex and cerebellar gray matter, as well as one region showing no such excess, caudate nucleus. We determined cell type specificity of human-specific differences observed in the bulk tissue analysis, and identified species-specific expression differences directly using SN-seq data. This data resource forms a comprehensive baseline for studies of normal and abnormal human brain function and evolution. Overall design: 422 polyA-selected RNA-seq samples were obtained from 33 brain regions representing all major brain structures from four adult cognitively healthy human individuals, three chimpanzees, three bonobos, and three rhesus macaques
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 | 1,424,054 Kb | NCBI | https://sra-downloadb.be-md.ncbi.nlm.nih.gov/sos5/sra-pub-zq-16/SRR008/750/SRR8750611.sralite.1 | worldwide | anonymous |
| AWS | s3://sra-pub-zq-9/SRR8750611/SRR8750611.sralite.1 | s3.us-east-1 | aws identity | ||
| GCP | gs://sra-pub-zq-109/SRR8750611/SRR8750611.zq.1 | gs.us-east1 | gcp identity | ||
| SRA Normalized | 2,054,423 Kb | AWS | https://sra-pub-run-odp.s3.amazonaws.com/sra/SRR8750611/SRR8750611 | 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 | 1,488,516 Kb | AWS | s3://sra-pub-src-16/SRR8750611/R18000317-BM13-BM13-22_combined_R1.fastq.gz | - | Use Cloud Data Delivery |
| fastq | 1,574,717 Kb | AWS | s3://sra-pub-src-15/SRR8750611/R18000317-BM13-BM13-22_combined_R2.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: 3.88%
- Identified reads: 96.12%
Strong signals
| SuperKingdom | Organism | Rank | %% | Kbp | Coverage |
|---|---|---|---|---|---|
| Eukaryota | Pan | genus | 93.8 | 5,478,278 | |
| Bacteria | Bacteria | superkingdom | 2.3 | 134,180 | |
| Bacteria | Pseudomonas | genus | 0.0 | 632 | |
| Bacteria | Firmicutes | phylum | 0.0 | 296 | |
| Bacteria | Enterobacterales | order | 0.0 | 246 | |
| Bacteria | Betaproteobacteria | class | 0.0 | 238 | |
| Bacteria | Alphaproteobacteria | class | 0.0 | 234 | |
| Viruses | Human mastadenovirus C | species | 0.0 | 11 | |
| Viruses | Human endogenous retroviruses | 0.0 | 3 |
- 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