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Enregistrement W3015352353 · doi:10.17504/protocols.io.7eshjee

Bead-free long fragment LSK109 library preparation v1

2019· preprint· en· W3015352353 sur OpenAlexaff
John D. Tyson

Notice bibliographique

Revuenon disponible
Typepreprint
Langueen
DomaineEnvironmental Science
ThématiqueBacteriophages and microbial interactions
Établissements canadiensUniversity of British Columbia Hospital
Organismes subventionnairesnon disponible
Mots-clésDNAMagnetic beadFragmentation (computing)DNA fragmentationChemistrygenomic DNAComputational biologyChromatographyBiologyNanotechnologyGeneticsMaterials science

Résumé

récupéré en direct d'OpenAlex

Bead-Free LSK109 ligation prep for ultra-long DNA The use of AMPureXP beads for reaction cleanup when targeting Ultra-long DNA reads is not optimal as inter-bead binding results in bead clumping and potential fragmentation as individual beads move around freely in suspension. This is why the original Ultra-read method developed by Josh Quick and Nick Loman used dilution of the Rapid MuA kit and high concentrations of HMW genomic DNA. Saturating the MuA insertion reaction with HMW DNA results in less individual strand fragmentation and produces previously unattainable read lengths. It does not require cleanup or binding and elution on and off magnetic beads and minimized “handling” of the DNA. This therefore mitigates the fragmentation and DNA recovery issue with very long DNA molecules and has allowed the production of individual reads in excess of 1 million bases to be produced. The yield from individual flowcells using this approach is around an order of magnitude less however as there appears to be flowcell surface damage and rapid pore “blockage”. While 1Mb+ reads are amazing to see, they are a very small fraction of the total and it is the read N50 that is important. Targeting 100kb+ reads in volume goes a long way to producing substantial improvements in large genome assemblies particularly when trying to span large repeats or low complexity regions refractory to other sequencing technologies. We have been experimenting with DNA size fractionation / precipitation using PEG/NaCl as mentioned above. This provides a means to remove the requirement for magnetic beads with their associated issues, and allows a higher yield of ultra-long reads to be produced. We have found that the DNA produced from the Phenol/Chloroform prep above provides DNA of a size that requires shearing down into a range to provide a mix of length and yield. Shown below is data from a single flowcell that I ran multiple libraries on looking at the effects of sequential shearing to gradually shorter lengths using a single DNA sample and where aliquots were removed before the next smaller shear was performed. Using a P1000 pipette tip or simple needle shear and PEG/NaCl precipitations, you are able to produce up to 21 % of total sequenced data in 100kb+ reads. Combined with DNaseI flowcell clears, high yields are looking obtainable. It is early days for this protocol so expect changes to come quickly as we try and dial in better size selection and an even higher proportion of 100kb+ reads. At present we are not really seeing much short read depletion and I think this is to do with suboptimal conditions used for the DNA precipitation. Unknown buffer components in the End-Prep and Ligation buffers appear to be throwing off the PEG/NaCl parameters we have identified for size selection so there is still work to be done to tune this and something we are working on. For the 21G and 29G examples below we did do a final PEG/NaCl precipitation of the finished library but didn’t see consistent depletion comparing the two, so still things to be worked out for consistency and to understanding what is happening. As a fall back, assuming little size selection can be implemented during the prep for now, we looked again at the size selection step at the end of the library preparation when eluted into EB buffer (no other salts or other components to throw size selective precipitation off). This is shown below comparing the Circulomics SRE buffer and 9 % PEG8000 (w/v) 1 M NaCl on precipitation and recovery using an equal volume addition to a P1000/26G needle sheared Bead-free LSK109 prep. Take this with a grain of salt at the moment as one used an old MinION flowcell (for Circulomics) and the others (library before selection (control), and PEG/NaCl size selection) were run on flongles so data volume is a little low with long frag blockage. We are seeing the SRE buffer performing better at short fragment removal, but want to investigate what is going on with the upper end and read N50.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,002
score de la tête « metaresearch » (Gemma)0,002
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Expérimental (laboratoire) · Signal consensuel: Expérimental (laboratoire)
GenreSignal candidat: Méthodes · Signal consensuel: Méthodes
Score de désaccord entre enseignants0,039
Score d'incertitude au seuil0,130

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0020,002
Méta-épidémiologie (sens strict)0,0020,002
Méta-épidémiologie (sens large)0,0020,002
Bibliométrie0,0020,002
Études des sciences et des technologies0,0010,001
Communication savante0,0020,001
Science ouverte0,0030,002
Intégrité de la recherche0,0010,004
Charge utile insuffisante (le modèle a refusé de juger)0,0390,069

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,009
Tête enseignante GPT0,240
Écart entre enseignants0,231 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeExpérimental (laboratoire)
Domainenon disponible
GenreMéthodes

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations9
Publié2019
Routes d'admission1
Résumé présentoui

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