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Record W2323760660 · doi:10.1158/1538-7445.am2011-4857

Abstract 4857: Improved technology for ribosomal RNA (rRNA) removal from formalin-fixed paraffin-embedded (FFPE) total RNA

2011· article· en· W2323760660 on OpenAlexaff
Roy Sooknanan, Agnes Radek, John Hitchen, Anupama Khanna

Bibliographic record

VenueCancer Research · 2011
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMolecular Biology Techniques and Applications
Canadian institutionsCégep de Saint-Laurent
Fundersnot available
KeywordsRNARibosomal RNABiologyDNAMolecular biologyComputational biologyRNA editingIntronGeneticsGene

Abstract

fetched live from OpenAlex

Abstract Deep, massively parallel sequencing of cDNA generated from RNA (“RNA-Seq”) is rapidly gaining momentum for transcript profiling, discovery of novel transcripts, and identification of alternative splicing events. Current methods for making sequencer-specific di-tagged DNA fragment libraries for RNA-Seq typically comprise first, preparing rRNA-depleted RNA from total RNA samples that are usually of good quality followed by the synthesis of the di-tagged cDNA sequencing templates. In the past few years however, it has also become evident from microarray and qPCR studies that formalin-fixed paraffin-embedded (FFPE) cancer tissues hold valuable secrets about diseased states. However, RNA-Seq libraries prepared from such highly fragmented FFPE RNA cancer samples yield limited information since these libraries contain a majority of rRNA reads, which decreases sequencing depth and coverage. Current commercially available rRNA-removal kits are not designed to remove fragments of rRNA, which poses a significant limitation for preparing highly informative RNA-Seq libraries from FFPE RNA samples. Here, we present RNA-Seq results obtained using a novel rRNA removal technology (“Ribo-ZERO™”) and a novel, ligation-free process for preparing directional di-tagged DNA fragment libraries (“ScriptSeq™ Technology”) for RNA-Seq. Using these methods, directional di-tagged DNA fragment libraries can be prepared in about 6 hours from either intact or fragmented (e.g., FFPE) total RNA samples (as little as 100 ng FFPE total RNA sample required). Less than 2 % of the sequence reads from libraries generated from total RNA from either intact or FFPE samples map to rRNA sequences (28S, 18S, 5.8S and 5S). This reduction in rRNA sequence reads from FFPE RNA samples improves sequence depth and coverage, and increases the percentage of uniquely mapped reads, increasing the information obtained from these diseased samples. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 102nd Annual Meeting of the American Association for Cancer Research; 2011 Apr 2-6; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2011;71(8 Suppl):Abstract nr 4857. doi:10.1158/1538-7445.AM2011-4857

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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: Methods · Consensus signal: Methods
Teacher disagreement score0.007
Threshold uncertainty score0.022

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0070.008

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 head0.057
GPT teacher head0.374
Teacher spread0.317 · 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 source (direct Gemma or distilled Codex), not a consensus.

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

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

Citations0
Published2011
Admission routes1
Has abstractyes

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