Location: Corn Insects and Crop Genetics ResearchTitle: FINDER: An automated software package to annotate eukaryotic genes from RNA-Seq data and associated protein sequences
|BANERJEE, SAGNIK - Iowa State University
|BHANDARY, PRIYANKA - Iowa State University
Submitted to: bioRxiv
Publication Type: Pre-print Publication
Publication Acceptance Date: 2/6/2021
Publication Date: 2/6/2021
Citation: Banerjee, S., Bhandary, P., Woodhouse, M.H., Sen, T.Z., Wise, R.P., Andorf, C.M. 2021. FINDER: An automated software package to annotate eukaryotic genes from RNA-Seq data and associated protein sequences. bioRxiv. https://doi.org/10.1101/2021.02.04.429837.
Interpretive Summary: The availability of full-genome assemblies has revolutionized genomics research. Genomes can now be quickly sequenced and assembled with high accuracy and completeness. To fully utilize these genomes, accurate gene predictions are needed. Existing gene annotation methods exist, but unfortunately they often provide incorrect or incomplete predictions. To address this need, we developed FINDER, a computational tool that optimizes the entire process of annotating genes and identifying alternative structures. We demonstrate that FINDER is capable of processing genomes of all sizes and complexities, including cereal genomes which can be large, repetitive, and complex. This tool is useful for researchers who are interested in generating accurate and comprehensive gene model annotations, and for users who have limited experience with computational programs. Impact: FINDER is a fully automated annotation workflow - ideal for BIG DATA projects for bench researchers with limited experience in handling computational tools.
Technical Abstract: Gene annotation in eukaryotes is a non-trivial task that requires meticulous analysis of accumulated transcript data. Challenges include transcriptionally active regions of the genome that contain overlapping genes, genes that produce numerous transcripts, transposable elements and numerous diverse sequence repeats. Currently available gene annotation software applications depend on pre-constructed full-length gene sequence assemblies which are not guaranteed to be error-free. The origins of these sequences are untraceable, making it impossible to identify and rectify errors in them. This hinders the creation of an accurate and holistic representation of the transcriptomic landscape across multiple tissue types and experimental conditions. Therefore, to gauge the extent of diversity in gene structures, a comprehensive analysis of genome-wide expression data is imperative. We present FINDER, a fully automated computational tool that optimizes the entire process of annotating genes and transcript structures. Unlike current state-of-the-art pipelines, FINDER automates the RNA-Seq pre-processing step by working directly with raw sequence reads and optimizes gene prediction from BRAKER2 by supplementing these reads with associated proteins. The FINDER pipeline (1) reports transcripts and recognizes genes that are expressed under specific conditions, (2) generates all possible alternatively spliced transcripts from expressed RNA-Seq data, (3) analyzes read coverage patterns to modify existing transcript models and create new ones, and (4) scores genes as high- or low-confidence based on the available evidence across multiple datasets. We demonstrate the ability of FINDER to automatically annotate a diverse pool of genomes from eight species. FINDER takes a completely automated approach to annotate genes directly from raw expression data. It is capable of processing eukaryotic genomes of all sizes and requires no manual supervision, which is ideal for bench researchers with limited experience in handling computational tools.