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Characterizing Alternative Polyadenylation in Male Germ Cells Using Poly(A)-seq

2016· dissertation· en· W2343173335 on OpenAlexfundno aff
Holly L. Tran

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicRNA Research and Splicing
Canadian institutionsnot available
FundersQueen's UniversityNational Institutes of HealthBrown University
KeywordsPolyadenylationCleavage and polyadenylation specificity factorThree prime untranslated regionUntranslated regionGene isoformBiologyMessenger RNAPost-transcriptional modificationGeneGeneticsCell biologyRNA-binding protein

Abstract

fetched live from OpenAlex

Post-transcriptional processing of mRNA subsequently determines its longevity, transport, and gene expression. Alternative polyadenylation (APA), one form of post-transcriptional processing, is the use of a variant polyadenylation signal and polyA-site for transcript cleavage and addition of adenine residues. The occurrence of this phenomenon in the 3’-untranslated region (3’UTR) can lead to transcript isoforms differing in 3’UTR length and as a result, alter the downstream cis-regulatory elements in use. Transcript modification at the 3’-end in alternative polyadenylation has been shown to foster a number of diseases in altering what would otherwise be normal protein expression and is furthermore emerging as a driver of spermatogenesis. To date, the global mechanisms that control the post-transcriptional processing in male germ cells remain unknown. PolyA-seq is a strand-specific, quantitative method for the high-throughput sequencing of 3’-ends of transcripts post-transcriptionally modified in polyadenylation. It has the ability to accurately and globally map polyA-sites. To study the molecular regulation of sperm production, an in-depth bioinformatics analysis was performed on available PolyA-seq data to identify male germ cell transcripts that uniquely use alternative polyadenylation, a novel method was developed to isolate and purify male germ cells from testicular tissue, and libraries were prepared for PolyA-seq from isolated male germ cells. Findings show no significant global difference in polyadenylation signal use between testicular and liver tissues when the same polyadenylation site is compared for human PolyA-seq data. Further annotation suggests a conservation in polyadenylation signal and polyA-site use across tissue types in the same species. Transcripts in the liver were more likely to use the canonical polyadenylation signal in comparison to those in the testis, lending further evidence of increased variant polyadenylation signal use in male germ cells attributed to alternative polyadenylation. Moreover, manual identification of known alternatively polyadenylated transcripts in testis from mice suggests that PolyA-seq is a reliable method for transcriptome characterization. Isolation and purification of male germ cells was successful using DRAQ5 nuclear stain. Using the verified isolated male germ cells, PolyA-seq libraries were generated. Comparison of different polyadenylation sites for the same transcripts between testis and liver PolyA-Seq still needs to be conducted. Also, increasing yields of polyA+ RNA for PolyA-Seq library prep will facilitate successful sequencing of 3’-ends. Further investigation of male germ cell-specific transcripts associated with alternative polyadenylation will lead to an improved understanding of molecular regulation involved in spermatogenesis and factors that cause male infertility.

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)0.001
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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.016
GPT teacher head0.307
Teacher spread0.290 · 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
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

Citations0
Published2016
Admission routes1
Has abstractyes

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