Evidence map›Paper›PMID 39715684›Full record

ArticleGenome research2025

Evaluation of strategies for evidence-driven genome annotation using long-read RNA-seq.

Alejandro Paniagua, Cristina Agustín-García, Francisco J Pardo-Palacios, Thomas Brown, Maite De Maria, Nancy D Denslow, Camila J Mazzoni, Ana Conesa

Abstract read
In one paragraph

Article in Genome research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing 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

5 citing papers in PubMed.

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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

8 authors.

Alejandro Paniagua *Institute for Integrative Systems Biology, Spanish National Research Council, Paterna 46980, Spain.ORCID 0000-0002-0566-8830
Cristina Agustín-García *Institute for Integrative Systems Biology, Spanish National Research Council, Paterna 46980, Spain.
Francisco J Pardo-PalaciosInstitute for Integrative Systems Biology, Spanish National Research Council, Paterna 46980, Spain.ORCID 0000-0002-3067-0166
Thomas BrownDepartment of Evolutionary Genetics, Leibniz Institute for Zoo and Wildlife Research, 10315 Berlin, Germany.ORCID 0000-0001-8293-4816
Maite De MariaDepartment of Physiological Sciences, Center for Environmental and Human Toxicology, University of Florida, Gainesville, Florida 32611, USA.ORCID 0000-0002-5251-4869
Nancy D DenslowDepartment of Physiological Sciences, Center for Environmental and Human Toxicology, University of Florida, Gainesville, Florida 32611, USA.ORCID 0000-0002-3946-3112
Camila J MazzoniDepartment of Evolutionary Genetics, Leibniz Institute for Zoo and Wildlife Research, 10315 Berlin, Germany.ORCID 0000-0001-6758-5427
Ana ConesaInstitute for Integrative Systems Biology, Spanish National Research Council, Paterna 46980, Spain; ana.conesa@csic.es.ORCID 0000-0001-9597-311X

Funding

Development of methods for transcript quantification anddifferential expression analysis using long-read sequencing technologiesR21HG011280 · NHGRI · UNIVERSITY OF FLORIDA · PI CONESA CEGARRA, ANA VICTORIA · 2020 to 2020
$307k
NHGRI NIH HHS R21 HG011280
6 · The paper itself

Abstract

While the production of a draft genome has become more accessible due to long-read sequencing, the annotation of these new genomes has not been developed at the same pace. Long-read RNA sequencing offers a promising solution for enhancing gene annotation. In this study, we explore how sequencing platforms, Oxford Nanopore R9.4.1 chemistry or Pacific Biosciences (PacBio) Sequel II CCS, and data processing methods influence evidence-driven genome annotation using long reads. Incorporating PacBio transcripts into our annotation pipeline significantly outperformed traditional methods, such as ab initio predictions and short-read-based annotations. We applied this strategy to a nonmodel species, the Florida manatee, and compared our results to existing short-read-based annotation. At the loci level, both annotations were highly concordant, with 90% agreement. However, at the transcript level, the agreement was only 35%. We identified 4906 novel loci, represented by 5707 isoforms, with 64% of these isoforms matching known sequences in other mammalian species. Overall, our findings underscore the importance of using high-quality curated transcript models in combination with ab initio methods for effective genome annotation.

Indexed as

GenomeMolecular Sequence AnnotationRNA-SeqSequence Analysis, RNAAnimals

Identifiers

PMID39715684
PMCPMC12047274

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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.