ORFhunteR: An accurate approach to the automatic identification and annotation of open reading frames in human mRNA molecules[Formula presented]

Vasily V. Grinev; Mikalai M. Yatskou; Victor V. Skakun; Maryna K. Chepeleva; Petr V. Nazarov

doi:10.1016/j.simpa.2022.100268

ORFhunteR: An accurate approach to the automatic identification and annotation of open reading frames in human mRNA molecules[Formula presented]

Vasily V. Grinev^*, Mikalai M. Yatskou, Victor V. Skakun, Maryna K. Chepeleva, Petr V. Nazarov

^*Corresponding author for this work

Research output: Contribution to journal › Article › Research › peer-review

2 Citations (Scopus)

Abstract

The coding potential of RNA molecules can be estimated using algorithms that find open reading frames (ORFs). However, previously developed algorithms show limited performance. We developed a computational approach dedicated to the automatic identification of ORFs in a large set of human mRNA molecules. It is based on the vectorization of nucleotide sequences followed by classification using a random forest. The predictive model was validated on human mRNA molecules from the NCBI RefSeq and Ensembl databases and demonstrated almost 95% accuracy in detecting true ORFs. Our method is implemented into a powerful R/Bioconductor package ORFhunteR.

Original language	English
Article number	100268
Journal	Software Impacts
Volume	12
DOIs	https://doi.org/10.1016/j.simpa.2022.100268
Publication status	Published - May 2022

Keywords

Classification
Gene prediction
Open reading frame
Transcriptome

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10.1016/j.simpa.2022.100268Licence: CC BY

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title = "ORFhunteR: An accurate approach to the automatic identification and annotation of open reading frames in human mRNA molecules[Formula presented]",

abstract = "The coding potential of RNA molecules can be estimated using algorithms that find open reading frames (ORFs). However, previously developed algorithms show limited performance. We developed a computational approach dedicated to the automatic identification of ORFs in a large set of human mRNA molecules. It is based on the vectorization of nucleotide sequences followed by classification using a random forest. The predictive model was validated on human mRNA molecules from the NCBI RefSeq and Ensembl databases and demonstrated almost 95% accuracy in detecting true ORFs. Our method is implemented into a powerful R/Bioconductor package ORFhunteR.",

keywords = "Classification, Gene prediction, Open reading frame, Transcriptome",

author = "Grinev, {Vasily V.} and Yatskou, {Mikalai M.} and Skakun, {Victor V.} and Chepeleva, {Maryna K.} and Nazarov, {Petr V.}",

note = "Funding Information: VVG, MMY, VVS, and MKC were supported by the Ministry of Education of the Republic of Belarus , grant GPSR “Convergenciya–2020” N3.08.3 (registration number 20190531 ). PVN and MKC were supported by the Luxembourg National Research Fund ( C17/BM/11664971/DEMICS ). Publisher Copyright: {\textcopyright} 2022 The Author(s)",

year = "2022",

month = may,

doi = "10.1016/j.simpa.2022.100268",

language = "English",

volume = "12",

journal = "Software Impacts",

issn = "2665-9638",

publisher = "Elsevier BV",

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T2 - An accurate approach to the automatic identification and annotation of open reading frames in human mRNA molecules[Formula presented]

AU - Grinev, Vasily V.

AU - Yatskou, Mikalai M.

AU - Skakun, Victor V.

AU - Chepeleva, Maryna K.

AU - Nazarov, Petr V.

N1 - Funding Information: VVG, MMY, VVS, and MKC were supported by the Ministry of Education of the Republic of Belarus , grant GPSR “Convergenciya–2020” N3.08.3 (registration number 20190531 ). PVN and MKC were supported by the Luxembourg National Research Fund ( C17/BM/11664971/DEMICS ). Publisher Copyright: © 2022 The Author(s)

PY - 2022/5

Y1 - 2022/5

N2 - The coding potential of RNA molecules can be estimated using algorithms that find open reading frames (ORFs). However, previously developed algorithms show limited performance. We developed a computational approach dedicated to the automatic identification of ORFs in a large set of human mRNA molecules. It is based on the vectorization of nucleotide sequences followed by classification using a random forest. The predictive model was validated on human mRNA molecules from the NCBI RefSeq and Ensembl databases and demonstrated almost 95% accuracy in detecting true ORFs. Our method is implemented into a powerful R/Bioconductor package ORFhunteR.

AB - The coding potential of RNA molecules can be estimated using algorithms that find open reading frames (ORFs). However, previously developed algorithms show limited performance. We developed a computational approach dedicated to the automatic identification of ORFs in a large set of human mRNA molecules. It is based on the vectorization of nucleotide sequences followed by classification using a random forest. The predictive model was validated on human mRNA molecules from the NCBI RefSeq and Ensembl databases and demonstrated almost 95% accuracy in detecting true ORFs. Our method is implemented into a powerful R/Bioconductor package ORFhunteR.

KW - Classification

KW - Gene prediction

KW - Open reading frame

KW - Transcriptome

UR - http://www.scopus.com/inward/record.url?scp=85126923296&partnerID=8YFLogxK

U2 - 10.1016/j.simpa.2022.100268

DO - 10.1016/j.simpa.2022.100268

M3 - Article

AN - SCOPUS:85126923296

SN - 2665-9638

VL - 12

JO - Software Impacts

JF - Software Impacts

M1 - 100268

ER -

ORFhunteR: An accurate approach to the automatic identification and annotation of open reading frames in human mRNA molecules[Formula presented]

Abstract

Keywords

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