This is an interesting thread - we also had a drop in MiSeq performance (in low complexity libraries) after upgrading to 2.6 and like some of you rolled back to 2.5. I was told there was nothing in the upgrade that might cause this but our runs turned to custard about the time we upgraded. I am staying put in 2.5 for as long as possible.
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Just in case you haven't seen it: The 2.6 version is currently not available due to a "review"
From: https://support.illumina.com/downloa...s_updater.htmlThe recently-released MiSeq Updater v2.6.1.4 has been temporarily removed from our website for review. We will update customers as information becomes available.
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Well it's clearly library specific, though I can't figure out how. I had a successful run last week 75% 7pM 16s amplicons, 20% 8pM truseq genome, 5% phiX. Tried running the problem library again-60% 6pM 16s amplicon, 10% ITS amplicon, 15% 8pM nextera genome, 15% phiX. Again the first read and the indices look great (700k, 86%PF, 16% aligned to phiX). Second read is horrible and only 3% aligned to phiX. This run is higher diversity (though still very low diversity by general Illumina guidelines) and lower clustering than the last successful run.Last edited by thermophile; 02-10-2016, 02:19 PM.Microbial ecologist, running a sequencing core. I have lots of strong opinions on how to survey communities, pretty sure some are even correct.
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This run finally worked on a different machine. FAE is coming out next week to check the temps on both machines and to adjust my machine to match the one that worked.Microbial ecologist, running a sequencing core. I have lots of strong opinions on how to survey communities, pretty sure some are even correct.
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Yet another run failed (after the TCM was replaced). Second level support suggested adding more primers since the TM of the primers (v4 bacteria, aka the ones that most microbiome people use) is low 62-66. This seems to have fixed the issue. I'll see how long this fix lastsMicrobial ecologist, running a sequencing core. I have lots of strong opinions on how to survey communities, pretty sure some are even correct.
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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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