| Run | Spots | Bases | Size | GC content | Published | Access Type |
|---|---|---|---|---|---|---|
SRR10995672 |
5.2M | 1.4Gbp | 657.2M | 48.9% | 2020-03-25 | public |
This run has 2 reads per spot:
| ̅L=138, σ=18.7, 100% | ̅L=138, σ=18.7, 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 |
|---|---|---|---|---|---|---|---|
| SRX7656780 | Illumina | RNA-Seq | TRANSCRIPTOMIC | cDNA | PAIRED |
| Biosample | Sample Description | Organism | Links |
|---|---|---|---|
| SAMN13957905 (SRS6086045) | Cricetulus griseus | PRJNA604220 |
| Bioproject | SRA Study | Title |
|---|---|---|
| PRJNA604220 | SRP246348 | Multiplex secretome engineering enhances recombinant protein production and purity |
Host cell proteins (HCPs) are process-related impurities generated during biotherapeutic protein production. HCPs can be problematic if they pose a significant metabolic demand, degrade product quality, or contaminate the final product. Here, we present an effort to create a “clean” Chinese hamster ovary (CHO) cell by disrupting multiple genes to eliminate HCPs. Using a model of CHO cell protein secretion, we predicted the elimination of unnecessary HCPs could have a non-negligible impact on protein production. We analyzed the total HCP content of 6-protein, 11-protein, and 14-protein knockout clones and characterized their growth in shake flasks and bioreactors. These cell lines exhibited a substantial reduction in total HCP content (40%-70%). We also observed higher productivity and improved growth characteristics, in specific clones. With the reduced HCP content, protein A and ion exchange chromatography more efficiently purified a monoclonal antibody (mAb) produced in these cells during a three-step purification process. Thus, substantial improvements can be made in protein titer and purity through large-scale HCP deletion, providing an avenue to increased quality and affordability of high-value biopharmaceuticals. Overall design: CHO-S cells were subjected to 4 cycles of CRISPR/Cas9-mediated multiplex gene disruption to produce 6-protein (BGN, TIMP1, LGALS3BP, NID1.1, NID1.2, and CTSD), 11-protein (BGN, TIMP1, LGALS3BP, NID1.1, NID1.2, CTSD, Aga, Erp29, Gpr56, Tinagl1, and Lgmn), and 14 protein (BGN, TIMP1, LGALS3BP, NID1.1, NID1.2, CTSD, Aga, Erp29, Gpr56, Tinagl1, Lgmn, Yeats2, Sparc, and Lpl) knockout strains. WT CHO-S and knockout cell lines (6x, 11x, and 14x) were transacted with a plasmid encoding the Rituximab antibody and the zeocin selection gene using Freestyle MAX reagent. The WT CHO-S, 6xKO and 11xKO strains were subjected to RNA-Seq at 3 different time points (4 days, 6 days, and 8 days). Library preparation was performed with Illumina's TruSeq Stranded mRNA Library Prep Kit High Throughput (Catalog ID: RS-122-2103).
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 | 641,762 Kb | AWS | https://sra-pub-run-odp.s3.amazonaws.com/sra/SRR10995672/SRR10995672 | worldwide | anonymous |
| SRA Lite | 371,723 Kb | NCBI | https://sra-downloadb.be-md.ncbi.nlm.nih.gov/sos5/sra-pub-zq-16/SRR010/995/SRR10995672.sralite.1 | worldwide | anonymous |
| AWS | s3://sra-pub-zq-2/SRR10995672/SRR10995672.sralite.1 | s3.us-east-1 | aws identity | ||
| GCP | gs://sra-pub-zq-108/SRR10995672/SRR10995672.zq.1 | gs.us-east1 | gcp identity |
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 | 469,955 Kb | AWS | s3://sra-pub-src-5/SRR10995672/Bio304-day-6_S10_L002_R1_001.fastq.gz.1 | - | Use Cloud Data Delivery |
| fastq | 481,090 Kb | AWS | s3://sra-pub-src-5/SRR10995672/Bio304-day-6_S10_L002_R2_001.fastq.gz.1 | - | 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: 6.91%
- Identified reads: 93.09%
Strong signals
| SuperKingdom | Organism | Rank | %% | Kbp | Coverage |
|---|---|---|---|---|---|
| Eukaryota | Cricetulus griseus | species | 92.7 | 1,335,440 | |
| Eukaryota | Mesocricetus | genus | 0.2 | 2,233 | |
| Bacteria | Bacteria | superkingdom | 0.0 | 647 | |
| Viruses | Macacine betaherpesvirus 3 | species | 0.0 | 178 | |
| Viruses | Gammaretrovirus | genus | 0.0 | 6 | |
| Viruses | Guanarito mammarenavirus | species | 0.0 | 4 | |
| Viruses | Gallid alphaherpesvirus 1 | species | 0.0 | 4 | |
| Viruses | Simplexvirus | genus | 0.0 | 3 | |
| Viruses | Crimean-Congo hemorrhagic fever orthonairovirus | species | 0.0 | 2 |
- 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