Keyword search (4,163 papers available)

"Bioinformatics" Keyword-tagged Publications:

Title Authors PubMed ID
1 Age estimation via electrocardiogram from smartwatches Adib A; Zhu WP; Ahmad MO; 41142465
ENCS
2 Algorithmic reconstruction of glioblastoma network complexity Uthamacumaran A; Craig M; 35479408
PHYSICS
3 Evaluating Programs for Predicting Genes and Transcripts with RNA-Seq Support in Fungal Genomes. Reid I 29876820
CSFG

 

Title:Evaluating Programs for Predicting Genes and Transcripts with RNA-Seq Support in Fungal Genomes.
Authors:Reid I
Link:https://www.ncbi.nlm.nih.gov/pubmed/29876820?dopt=Abstract
DOI:10.1007/978-1-4939-7804-5_17
Publication:Methods in molecular biology (Clifton, N.J.)
Keywords:BioinformaticsCleaning short sequence readsGene predictionRNA-SeqTranscript prediction
PMID:29876820 Category:Methods Mol Biol Date Added:2019-06-07
Dept Affiliation: CSFG
1 Centre for Structural and Functional Genomics, Concordia University, Montreal, QC, Canada. ian.reid@concordia.ca.

Description:

Evaluating Programs for Predicting Genes and Transcripts with RNA-Seq Support in Fungal Genomes.

Methods Mol Biol. 2018;1775:209-227

Authors: Reid I

Abstract

The steps needed to computationally predict genes and transcripts in fungal genomes with support from RNA-Seq data are described in detail for three prediction programs: CodingQuarry, BRAKER1, and Harfang. These programs predicted from 86% to 92% (Harfang) of the genes in a manually curated reference set for Aspergillus niger strain NRRL3. Genes with little or no RNA-Seq read coverage were predicted less successfully than genes with adequate coverage.

PMID: 29876820 [PubMed - indexed for MEDLINE]





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