|Run||Spots||Bases||Size||GC content||Published||Access Type|
This run has 2 reads per spot:
|L=26, 100%||L=26, 100%|
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
|PRJNA289962||SRP061185||Single-cell RNA-seq of bone marrow-derived mesenchymal stem cells reveals unique profiles of lineage priming|
The plasticity and immunomodulatory capacity of mesenchymal stem cells (MSCs) have spurred clinical use in recent years. However, clinical outcomes vary and many ascribe inconsistency to the tissue source of MSCs. Yet unconsidered is the extent of heterogeneity of individual MSCs from a given tissue source with respect to differentiation potential and immune regulatory function. Here we use single-cell RNA-seq to assess the transcriptional diversity of murine mesenchymal stem cells derived from bone marrow. We found genes associated with MSC multipotency were expressed at a high level and with consistency between individual cells. However, genes associated with osteogenic, chondrogenic, adipogenic, neurogenic and vascular smooth muscle differentiation were expressed at widely varying levels between individual cells. Further, certain genes associated with immunomodulation were also inconsistent between individual cells. Differences could not be ascribed to cycles of proliferation, culture bias or other cellular process, which might alter transcript expression in a regular or cyclic pattern. These results support and extend the concept of lineage priming of MSCs and emphasize caution for in vivo or clinical use of MSCs, even when immunomodulation is the goal, since multiple mesodermal (and even perhaps ectodermal) outcomes are a possibility. Purification might enable shifting of the probability of a certain outcome, but is unlikely to remove multilineage potential altogether. Overall design: Examination was performed using single-cell RNA-seq of sixteen mouse MSCs (mMSC1-mMSC16), non MSC single cell controls (HL1cm1-HL1cm5), and the population controls (mMSC-PC and HL1cm-PC).
You need SRA Toolkit to operate on SRA runs.
Default toolkit configuration enables it to find and retrieve SRA runs by accession. It also downloads (and cache) only the part of data you really need. For example quality scores represent a majority of data volume and you may not need them if you dump fasta only (versus fastq). Or if you are looking at particular gene you may not need reads aligned to other regions or not aligned at all. Same way if you use GATK with enabled SRA support you need only SRA run accessions to fire your process.
fastq-dump will dump reads in a number of "standard" fastq and fasta formats.
vdb-dump is also capable of producing fasta and fastq (beside other formats). It dumps data much faster then fastq-dump but ordering of reads may be different and it does not produce split-read multi-file output.
Prefetch tool will help you cache all data in advance if you plan to run data analysis in environment where getting data from NCBI at run time is unfeasible.
Read more at SRA Knowledge Base on how to download SRA data using command line utilities.
The sections below show results of analysis run by software which is still in experimental stage. Please use provided results with a boatload of salt and let us know what you think.
-- SRA team
- Unidentified reads: 100%