Hi everyone. What would be the best place to find all the published data? Really appreciated!!!!
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The NCBI's Short Read Archive is a good place to start http://www.ncbi.nlm.nih.gov/sra. You can easily search by experiment type. Be sure to read the documentation on the website as the downloads aren't in an immediately useable format and require conversion (using tools also available on the website).
Pete
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I'm guessing you want the data in FASTQ format? If so, then read this http://www.ncbi.nlm.nih.gov/books/NB...erting_SRA_for. If you want aligned reads in SAM/BAM format then I don't believe the SRA will be much help as my understanding is that it only contains the raw data.
I'd also suggest reading the complete SRA Handbook (http://www.ncbi.nlm.nih.gov/books/NBK47528/). The SRA toolkit can be downloaded from here http://www.ncbi.nlm.nih.gov/Traces/sra/?view=software. All this information, and more, is available on the homepage of the SRA, which I linked to in my previous post.
Pete
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Originally posted by bb420is there any place where i cn get published data for analysis. anything like chip-seq or rna-seq?
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Instructions on how to get the full data set from NCBI trace archives are at the top of the page.
Originally posted by bb420Also, would you know where can i probably get the human genome data for watson's genome(or somebody else's for that matter....i think it is available publicly). Thanks a lot!
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A BioProject is a collection of biological data related to a single initiative, originating from a single organization or from a consortium. A BioProject record provides users a single place to find links to the diverse data types generated for that project
Browser at JCVI: http://huref.jcvi.org/
Paper: http://www.ncbi.nlm.nih.gov/pubmed/17803354
Originally posted by bb420Hi
can anyone please tell me where can i find craig venter's genome data? and also if any sort of detailed analysis(published or otherwise) is available on it
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by GATTACATLove this - good data definitely starts from good input, and poor input can only give relatively poor data. I particularly like the mention of Nanodrop/absorbance based methods for quantification. It's such a toss up if you'll get an accurate reading or what amounts to a randomly generated number, and a lot of library/sequencing related issues can be traced back to poor quant.
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07-01-2026, 11:43 AM -
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