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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. [ABSTRACT FROM AUTHOR]

Interactions between barley and the powdery mildew pathogen, Blumeria graminis f. sp. hordei, ( Bgh) are determined by unique combinations of host resistance genes, designated Mildew-resistance locus ( Ml), and cognate pathogen avirulence genes. These interactions occur both dependent and independent of Rar1 (required for Mla12 resistance) and Sgt1 (Suppressor of G-two allele of skp1), which are differentially required for diverse plant disease-resistance pathways. We have isolated two new functional Mla alleles, Rar1-independent Mla7 and Rar1-dependent Mla10, as well as the Mla paralogs, Mla6-2 and Mla13-2. Utilizing the inherent diversity amongst Mla-encoded proteins, we identified the only two amino acids exclusively conserved in RAR1-dependent MLA6, MLA10, MLA12, and MLA13 that differ at the corresponding position in RAR1-independent MLA1 and MLA7. Two- and three-dimensional modeling places these residues on a predicted surface of the sixth leucine-rich repeat (LRR) domain at positions distinct from those within the β-sheets hypothesized to determine resistance specificity. Site-directed mutagenesis of these residues indicates that RAR1 independence requires the presence of an aspartate at position 721, as mutation of this residue to a structurally similar, but uncharged, asparagine did not alter RAR1 dependence. These results demonstrate that a single-amino acid substitution in the sixth MLA LRR can alter host signaling but not resistance specificity to B. graminis. [ABSTRACT FROM AUTHOR]