Enhancing eukaryotic gene structure annotation via changepoint analysis of short-read coverage data
Creators: name: Sagnik Banerjee
name: Bhandary, Priyanka
name: Woodhouse, Margaret
name: Sen, Taner Z.
name: Wise, Roger P.
name: Andorf, Carson
Publisher: name: F1000 Research Limited
Year: 2020

description: An important yet challenging step of genome annotation is to locate genes and transcripts within chromosomes and infer their structures. Complexities in eukaryotic genomes, like the presence of transposable elements, make the job of annotation difficult. To complicate matters even more, there are genes that produce numerous transcripts and also overlap with other genes. Different transcripts of the same gene originate from the same genome loci, produce isomeric proteins, but differ from each other by their intron signature. Such subtle differences among transcripts can be best captured by generating full-length sequences. Reconstructing each full-length transcript accurately from short-read data collected from multiple tissue/condition is major challenge despite the existence of several assemblers. We present, FINDER, which overcomes these challenges by employing the meta-assembler PsiCLASS to generate transcript models. FINDER, annotates both coding and non-coding genes and also assign confidence classes to each model depending on the variety of data used for its construction. FINDER is equipped to detect transcripts with micro-exons (&lt;15 bp). Finally, FINDER, combines predicted gene models from BRAKER2 with assembled gene models to attain the goal of exhaustive gene discovery and obtaining.<br> <br> Reductions in sequencing costs have increased the accessibility of whole genome expression studies which in turn has led to better understanding of their respective biosystems. These studies have been extended to non-model organisms for which a comprehensive gene annotation is often lacking. Inaccurate and/or missing gene annotations hinder the holistic understanding of a biosystem by failing to detect genes involved in bio-chemical pathways owing to the absence of essential structural components (like missing exons). Correct identification of transcription start sites and proper construction of gene structures is crucial to downstream applications (e.g. promoter mining, protein interaction study, etc.), thus making accurate genome annotation extremely relevant.<br> <br> A number of different techniques have been researched to generate accurate gene models. Gene Annotation tools like BRAKER2 and MAKER2 predict protein-coding genes from mapped RNA-Seq data using gene predictors like SNAP, Augustus and GeneMark. While these predictors can determine coding regions with acceptable accuracy, there is no current support available to annotate untranslated regions (UTRs). The approach of annotating only protein-coding regions lead to partial gene structures that interfere with epigenetic analysis which directly impacts about 13,000 transcripts in the well-annotated Arabidopsis thaliana genome since those transcripts have 5’ UTR of length greater than 250. Most BRAKER2 gene models have only one transcript thereby causing a plunge in transcript sensitivity. Also, BRAKER2 does not predict any non-coding genes. To overcome the shortcomings of previous genome annotators, we have designed a pipeline called FINDER which integrates predicted gene models with assembled transcript models. As compared to BRAKER and MAKER, FINDER was designed to be easier to install, operate, and interrupt results. FINDER downloads short reads from NCBI, conducts optimized read mapping with STAR and uses PsiCLASS to conduct meta-assembly. The pipeline is designed to assess each gene model based on expression coverage and modify transcript boundaries to better comply with the expression pattern. Finally, FINDER incorporates predicted gene models from BRAKER2 if those do not overlap with assembled models but have some representation in the provided proteome.<br> <br> Arabidopsis thaliana is a model organism with one of the best gene annotations making it ideal to validate and evaluate gene annotation pipelines. Gene models generated by FINDER was found to be far superior to those constructed by BRAKER2 or other assemblers. Most approaches use Stringtie and/or Scallop to generate assemblies from multiple samples and then create a meta-assembly using Stringtie merge. FINDER used changepoint detection method to identify abrupt changes in the pattern of expression and uses it to split gene models. FINDER uses OLEGO – a special aligning algorithm to detect transcripts with micro-exons. These enhancements led to the recognition of more genes and improved the transcript F1 score from 42.37 to 49.28. Finally, gene models predicted by BRAKER2 that have high similarity with proteins and do not overlap with the assembled transcripts are included in the final annotations boosting the transcript F1 score to 50.03.<br> <br> Our results demonstrate that overall FINDER generates better gene models that are more consistent with the ground truth. Unlike BRAKER2, FINDER can annotate the entire structure of transcripts including UTRs and non-coding genes. FINDER enhances transcript modeling by constructing multiple transcripts from expression pattern thereby registering a higher sensitivity compared to BRAKER2. Analysis was carried out with gene models grouped into different categories depending on their location, proximity to other genes, presence of micro-exons, etc. In all the categories FINDER was able to construct the best transcript models. Several genes in eukaryotes lie in close proximity to one another. In the event of simultaneous expression of such closely spaced transcripts, bleeding of signal occurs which makes it difficult to distinguish between the two separate expression profiles. Most assemblers do not account for signal bleeding thereby leading to merged transcript models. FINDER uses changepoint detection to enhance transcript structures through statistical analysis of expression profile culminating in the discovery of more genes. We are confident that FINDER will pave the way for better gene structure annotation in the future.