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To compare PacBio long reads with MinION for de novo assembly of bacterial genomes what one should consider for library prep and assembly.
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I am not sure what you are asking for. The PacBio option works: 1 library prep, sequence 1 SMRT-cell & assemble the data - you get a "finished" genome. If playing around with new technology is your goal, then the minion is worth considering. -
If the aim is to compare performance of two platforms. I would use the same sheared and size selected DNA from a bacteria with reference genome to prepare libraries for each platform. Then would sequence them to a certain Mb or Gb (can subsample reads to equal bases if one platform output is higher than the other) and use them for assembly and alignment. Metrics such as N50 (L50), contig number, proportion of data used for assembly, evenness of coverage, gaps and GC biases can be used for comparison.Last edited by nucacidhunter; 06-19-2015, 06:34 PM.Comment
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Thanks for replies. I wonder if I can use similar approach for transcript isoform analysis.Comment
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With mapping and assembly of DNA, to a known reference, it is relatively straightforward to determine which platform is better.
With isoform analysis, you're bringing in a huge number of additional variables; you won't know the correct answer, so you will have very little ability to evaluate which platform is doing a better job.Comment
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I agree with Brian about isoform analysis potential issues. With current knowledge the best option is to access a well annotated fungal species with small genome or C. elegans (to keep sequencing cost down) and prepare full-length dscDNA from mRNA fraction using SMART technology and prepare libraries for both platforms. Transcripts sequences can be used for comparison against annotated regions and genome to compare the coverage of known isoforms and potential new isoform discovery of two platforms.Comment
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Another option may be to get in touch with the Snyder lab and study the same transcriptomes used in their exploration of PacBio long-reads:
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Thank you very much everyone for your posts. I have much clearer idea to plan my Honours project.Comment
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Cancer survival rates have significantly increased in the last few decades in the United States, reaching a combined 70% 5-year survival rate by 2021. Behind this number, there are years of research to find new therapies, drug targets, and early detection methods. But there is one core challenge that keeps slowing down these advances, and it’s about drug resistance.
There is no single reason why many patients don’t respond to treatment as expected. Cancer is...Yesterday, 05:17 AM -
Love 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.07-01-2026, 11:43 AM
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I’m not a sequencing expert. I’m a purification scientist who uses NGS to evaluate workflows my group develops. With this perspective, we think about the sample first and the NGS workflow second. The sequencer is an exceptionally honest reporter, but it can only report on what you give it, so whether you get clean, interpretable data from an NGS workflow is largely determined before you begin.
Here are nine questions we think about, in roughly the order they matter, before...
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