Evidence map›Paper›PMID 33731361›Full record

ArticleGenome research2021

Ultrafast functional profiling of RNA-seq data for nonmodel organisms.

Peng Liu, Jessica Ewald, Jose Hector Galvez, Jessica Head, Doug Crump, Guillaume Bourque, Niladri Basu, Jianguo Xia

Open access · bronzeAbstract read
In one paragraph

Article in Genome research, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing 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

9 citing papers in PubMed, 25 citations in OpenAlex.

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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 at 2 institutions in 1 country.

Peng LiuFaculty of Agricultural and Environmental Sciences, McGill University, Montreal, Quebec H9X 3V9, Canada.
Jessica EwaldFaculty of Agricultural and Environmental Sciences, McGill University, Montreal, Quebec H9X 3V9, Canada.
Jose Hector GalvezDepartment of Human Genetics, McGill University, Montreal, Quebec H3A 0C7, Canada.
Jessica HeadFaculty of Agricultural and Environmental Sciences, McGill University, Montreal, Quebec H9X 3V9, Canada.
Doug CrumpEnvironment and Climate Change Canada, National Wildlife Research Centre, Ottawa, Ontario K1A 0H3, Canada.
Guillaume BourqueDepartment of Human Genetics, McGill University, Montreal, Quebec H3A 0C7, Canada.ORCID 0000-0002-3933-9656
Niladri BasuFaculty of Agricultural and Environmental Sciences, McGill University, Montreal, Quebec H9X 3V9, Canada.
Jianguo XiaFaculty of Agricultural and Environmental Sciences, McGill University, Montreal, Quebec H9X 3V9, Canada.ORCID 0000-0003-2040-2624
McGill University · CAEnvironment and Climate Change Canada · CA

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Computational time and cost remain a major bottleneck for RNA-seq data analysis of nonmodel organisms without reference genomes. To address this challenge, we have developed Seq2Fun, a novel, all-in-one, ultrafast tool to directly perform functional quantification of RNA-seq reads without transcriptome de novo assembly. The pipeline starts with raw read quality control: sequencing error correction, removing poly(A) tails, and joining overlapped paired-end reads. It then conducts a DNA-to-protein search by translating each read into all possible amino acid fragments and subsequently identifies possible homologous sequences in a well-curated protein database. Finally, the pipeline generates several informative outputs including gene abundance tables, pathway and species hit tables, an HTML report to visualize the results, and an output of clean reads annotated with mapped genes ready for downstream analysis. Seq2Fun does not have any intermediate steps of file writing and loading, making I/O very efficient. Seq2Fun is written in C++ and can run on a personal computer with a limited number of CPUs and memory. It can process >2,000,000 reads/min and is >120 times faster than conventional workflows based on de novo assembly, while maintaining high accuracy in our various test data sets.

Indexed as

Gene Expression ProfilingRNA-SeqTranscriptomeWorkflow

Identifiers

PMID33731361
PMCPMC8015844
OpenAlexW3138924738

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
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.