Streamlining Quantitative Analysis of Long RNA Sequencing Reads.

Sebastian Oeck, Alicia I Tüns, Sebastian Hurst, Alexander Schramm

Abstract read

In one paragraph

Article in International journal of molecular sciences, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it

0cells of the map it votes in

0citing papers in PubMed

–field-weighted citation impact

1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Sebastian OeckDepartment of Medical Oncology, West German Cancer Center, University Hospital Essen, University of Duisburg-Essen, 45147 Essen, Germany.ORCID 0000-0001-9695-8771

Alicia I TünsDepartment of Medical Oncology, West German Cancer Center, University Hospital Essen, University of Duisburg-Essen, 45147 Essen, Germany.ORCID 0000-0003-0025-1304

Sebastian HurstInstitute of Cell Biology, University of Münster, 48149 Münster, Germany.

Alexander SchrammDepartment of Medical Oncology, West German Cancer Center, University Hospital Essen, University of Duisburg-Essen, 45147 Essen, Germany.ORCID 0000-0001-7670-7529

Funding

Deutsche Forschungsgemeinschaft SFB 876Deutsche Forschungsgemeinschaft TP C1Deutsche Krebshilfe T-LOCK

6 · The paper itself

Abstract

Transcriptome analyses allow for linking RNA expression profiles to cellular pathways and phenotypes. Despite improvements in sequencing methodology, whole transcriptome analyses are still tedious, especially for methodologies producing long reads. Currently, available data analysis software often lacks cost- and time-efficient workflows. Although kit-based workflows and benchtop platforms for RNA sequencing provide software options, e.g., cloud-based tools to analyze basecalled reads, quantitative, and easy-to-use solutions for transcriptome analysis, especially for non-human data, are missing. We therefore developed a user-friendly tool, termed Alignator, for rapid analysis of long RNA reads requiring only FASTQ files and an Ensembl cDNA database reference. After successful mapping, Alignator generates quantitative information for each transcript and provides a table in which sequenced and aligned RNA are stored for further comparative analyses.

Indexed as

SoftwareAnimalsBase SequenceCarcinoma, Lewis LungCell Line, TumorGene Expression ProfilingHigh-Throughput Nucleotide SequencingInformation Storage and RetrievalMiceRNASequence AlignmentSequence Analysis, RNARNAAlignatoralignmentFASTQindexlong readsOxford Nanopore TechnologyPacBioRNAsequencing tool

Identifiers

PMID33019615

DOI10.3390/ijms21197259

PMCPMC7584020

What OpenQuestion holds

Textmetadata

LicenceCC BY

Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.