FINDER: A fully automated pipeline to FIND accurate gene structures from proteins and RNA-Seq expression data

An important yet challenging step of genome annotation is the process of finding the location of genes and transcripts within chromosomes. 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. Perfect annotation of genes, originating from such complicated loci, is possible with full-length transcripts obtained from long-read sequencing platforms like PacBio or Nanopore. Unfortunately, these platforms require specialized library preps, produce high number of errors and are less cost-effective as sequencing on platforms like Illumina. Prevalence of complexities in the genome hinders accurate reconstructing of full-length transcripts from short Illumina reads, thereby impeding the progress of novel gene discovery and precise genome annotation. Reduction in sequencing costs have enabled numerous small labs to undertake whole genome expression studies to understand their respective biosystems better. These studies have been extended to non-model organisms for which a comprehensive gene annotation is often lacking. Absence of gene models and partially annotated genes lead to false negatives in differential expression study thereby preventing a holistic understanding of the biosystem. Correct identification of transcription start sites and proper construction of gene structures is pivotal to downstream applications (e.g. promoter mining, protein interaction study, etc.) making genome annotation extremely relevant. Annotation approaches undertaken by tools like BRAKER2 and MAKER2 involve predicting protein-coding genes from mapped RNA-Seq data. MAKER2 also allows users to enter EST sequences for annotation. These pipelines implement statistical gene predictors like SNAP and Augustus to annotate coding regions of genes. 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 bp. 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 expressed transcript models. FINDER performs 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 and have some representation in the provided proteome. Arabidopsis thaliana is a model organism with one of the best gene annotations making it ideal to validate and evaluate gene annotation pipelines. We compared our assembled gene models with predicted models from BRAKER2 and in all the categories PsiCLASS gene models were superior. Most approaches use Stringtie and/or Scallop to generate assemblies and then merge those using Stringtie merge. PsiCLASS meta-assembler was able to generate much better transcript models recoding the highest F1 score. Some transcript assemblies generated by PsiCLASS represent merged genes and exhibit exon extension for overlapping genes on the opposite strand. Signals present in expression profiles are utilized through changepoint detection to split up merged gene models. This 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 F1 score to 50.03. FINDER executes OLEGO - an aligner optimized to detect micro-exons that further improves gene predictions. Our results demonstrate that overall FINDER generates better and more consistent gene and transcript models. BRAKER2 is designed to annotate only the protein-coding regions of genes thereby generating incomplete gene models impacting epigenetics research. Annotating only a portion of a gene and reporting very few transcripts per gene reduces the sensitivity of BRAKER2. Stringtie and Scallop are popular assemblers and are widely used to generate assemblies. Stringtie merge is used to combine individual assemblies giving rise to a meta-assembly. Stringtie merge produces poor meta assemblies, because it does not take into account the expression profile of the transcripts. Changepoint detection implemented to refine PsiCLASS assemblies improves the overall gene structures showing the importance of expression profiles in redefining transcript boundaries. We are confident that FINDER will pave the way for better gene structure annotation in the future.