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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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Data variability is still an issue in sequencing technologies despite the advances in reproducibility and accuracy of these platforms. But the problem does not originate in the sequencing itself, but in the previous steps, before the sample reaches the sequencer.
The first step is collection, followed by preservation and sample preparation for analysis. Most scientists overlook those steps, but not being careful might just be skewing the experiment’s results.
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With the launch of new single-cell sequencing platforms in 2026, the field stands at an exciting inflection point. This article surveys the most impactful advances in the field and discusses how they’re reshaping research in cancer, immunology, and beyond.
Introduction
Single-cell sequencing technologies have undergone remarkable advances over the past decade, transitioning from low-throughput experimental approaches to highly scalable platforms capable of...05-22-2026, 06:42 AM
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