FINDER: an automated software package to annotate eukaryotic genes from RNA-Seq data and associated protein sequences

Abstract

Background 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 often uncertain, making it difficult 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. Results 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. Conclusions 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—ideal for bench researchers with limited experience in handling computational tools.

Welcome to the big leaves: best practices for improving genome annotation in non-model plant genomes

“…When extrinsic evidence from RNA-seq and protein alignments are available, workflow packages like MAKER (Cantarel et al, 2008) and BRAKER (Hoff et al, 2019) can assist in training ab initio prediction tools. While these workflows can simplify the integration across external evidence, downstream packages are still required to select or modify the resulting predictions (Haas et al, 2008;Banerjee et al, 2021;Gabriel et al, 2021).…”

Pangenomics in crop improvement—from coding structural variations to finding regulatory variants with pangenome graphs

“…Gene annotation MAKER2 (Holt & Yandell, 2011), BRAKER2 (Brůna et al, 2021), FINDER (Banerjee et al, 2021), Funannotate (Palmer & Stajich, 2017) Not available Performing independent annotation of individual genomes may lead to artefacts (Bayer et al, 2017;König et al, 2016) Annotations of linear genomes can be projected onto graph using VG annotate/rna.…”

“…Without change, the world is on track to reach 840 million undernourished people by 2030 (FAO et al., 2020). The reasons for an increasingly undernourished population are manifold including unequal resource distribution, food waste, and crop loss arising from climate change (Barrera & Hertel, 2021; Hasegawa et al., 2016; Janssens et al., 2020). While an integrated approach is vital to successfully curb this alarming trend, climate‐change‐resilient crops are needed to counter more frequent and extreme weather events that affect mostly populations with already high rates of undernourishment (FAO et al., 2020).…”

SEAseq: a portable and cloud-based chromatin occupancy analysis suite

“…Multiple pieces of software exist for each analysis step, and attempts have been made to link these tools together in cohesive pipelines [ 4 – 10 ], as has been done for other analysis types [ 11 – 15 ]. However, these pipelines; summarized in Fig.…”