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Record W2022380179 · doi:10.1371/journal.pone.0089676

Palindromic Sequence Artifacts Generated during Next Generation Sequencing Library Preparation from Historic and Ancient DNA

2014· article· en· W2022380179 on OpenAlexaff
Bastiaan Star, Alexander J. Nederbragt, Marianne H. S. Hansen, Morten Skage, Gregor D. Gilfillan, Ian Bradbury, Christophe Pampoulie, Nils Chr. Stenseth, Kjetill S. Jakobsen, Sissel Jentoft

Bibliographic record

VenuePLoS ONE · 2014
Typearticle
Languageen
FieldEnvironmental Science
TopicEnvironmental DNA in Biodiversity Studies
Canadian institutionsFisheries and Oceans Canada
FundersNorges ForskningsrådUniversitetet i OsloNorwegian Sequencing Centre
KeywordsPalindromeDNA sequencingDNAPalindromic sequenceSequence (biology)Computational biologyBiologyAncient DNAGeneticsGenomeGene

Abstract

fetched live from OpenAlex

Degradation-specific processes and variation in laboratory protocols can bias the DNA sequence composition from samples of ancient or historic origin. Here, we identify a novel artifact in sequences from historic samples of Atlantic cod (Gadus morhua), which forms interrupted palindromes consisting of reverse complementary sequence at the 5' and 3'-ends of sequencing reads. The palindromic sequences themselves have specific properties - the bases at the 5'-end align well to the reference genome, whereas extensive misalignments exists among the bases at the terminal 3'-end. The terminal 3' bases are artificial extensions likely caused by the occurrence of hairpin loops in single stranded DNA (ssDNA), which can be ligated and amplified in particular library creation protocols. We propose that such hairpin loops allow the inclusion of erroneous nucleotides, specifically at the 3'-end of DNA strands, with the 5'-end of the same strand providing the template. We also find these palindromes in previously published ancient DNA (aDNA) datasets, albeit at varying and substantially lower frequencies. This artifact can negatively affect the yield of endogenous DNA in these types of samples and introduces sequence bias.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.113
Threshold uncertainty score0.671

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.082
GPT teacher head0.195
Teacher spread0.113 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations32
Published2014
Admission routes1
Has abstractyes

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