Foster thy young: Enhanced prediction of orphan genes in assembled genomes

Li, Jing; Singh, Urminder; Bhandary, Priyanka; Campbell, Jacqueline; Arendsee, Zebulun; Seetharam, Arun S.; Wurtele, Eve Syrkin

doi:10.1101/2019.12.17.880294

Cited by 11 publications

(13 citation statements)

References 144 publications

(277 reference statements)

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“…The 26 NAM genomes were annotated using a hybrid evidence and ab initio based gene prediction pipeline (81). Evidence-based predictions were directly inferred from the assembled transcripts, which were generated using five different genome-guided transcript assembly RNA-seq reads from each library were mapped to their respective NAM genomes using STAR (v2.5.3a) (88) with an iterative 2-pass mapping approach in which splice junctions generated from the first round were used to refine alignments in the subsequent round.…”

Section: Gene Model Annotationmentioning

confidence: 99%

De novo assembly, annotation, and comparative analysis of 26 diverse maize genomes

Hufford

Seetharam

Woodhouse

et al. 2021

Preprint

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We report de novo genome assemblies, transcriptomes, annotations, and methylomes for the 26 inbreds that serve as the founders for the maize nested association mapping population. The data indicate that the number of pan-genes exceeds 103,000 and that the ancient tetraploid character of maize continues to degrade by fractionation to the present day. Excellent contiguity over repeat arrays and complete annotation of centromeres further reveal the locations and internal structures of major cytological landmarks. We show that combining structural variation with SNPs can improve the power of quantitative mapping studies. Finally, we document variation at the level of DNA methylation, and demonstrate that unmethylated regions are enriched for cis-regulatory elements that overlap QTL and contribute to changes in gene expression.One sentence summaryA multi-genome analysis of maize reveals previously unknown variation in gene content, genome structure, and methylation.

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Section: Gene Model Annotationmentioning

confidence: 99%

De novo assembly, annotation, and comparative analysis of 26 diverse maize genomes

Hufford

Seetharam

Woodhouse

et al. 2021

Preprint

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“…e ., species-specific genes). As ab initio predictions of young genes can be unreliable (Seetharam et al, 2019), these were excluded. Finally, redundant copies of genes between direct evidence and ab initio predictions were identified and removed using Mikado compare (2.0rc2; (Venturini et al, 2018)) and merging was performed locus by locus, incorporating additional isoforms when necessary.…”

Section: Methodsmentioning

confidence: 99%

TheStreptochaetagenome and the evolution of the grasses

Seetharam

Bélanger

et al. 2021

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In this work, we sequenced and annotated the genome of Streptochaeta angustifolia, one of two genera in the grass subfamily Anomochlooideae, a lineage sister to all other grasses. The final assembly size is over 99% of the estimated genome size, capturing most of the gene space. Streptochaeta is similar to other grasses in the structure of its fruit (a caryopsis or grain) but has peculiar flowers and inflorescences that are distinct from those in the outgroups and in other grasses. To provide tools for investigations of floral structure, we analyzed two large families of transcription factors, AP2-like and R2R3 MYBs, that are known to control floral and spikelet development in rice and maize among other grasses. Many of these are also regulated by small RNAs. Structure of the gene trees showed that the well documented whole genome duplication at the origin of the grasses (ρ) occurred before the divergence of the Anomochlooideae lineage from the lineage leading to the rest of the grasses (the spikelet clade) and thus that the common ancestor of all grasses probably had two copies of the developmental genes. However, Streptochaeta (and by inference other members of Anomochlooideae) has lost one copy of many genes. The peculiar floral morphology of Streptochaeta may thus have derived from an ancestral plant that was morphologically similar to the spikelet-bearing grasses. We further identify 114 loci producing microRNAs and 89 loci generating phased, secondary siRNAs, classes of small RNAs known to be influential in transcriptional and post-transcriptional regulation of several plant functions.

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“…We compared the accuracy of TSEBRA with EVM, one of the most cited combiner tools to date. EVM was previously used to combine BRAKER with other predictions [39], to combine BRAKER2 predictions with RNA-seq evidence [40,41] or other RNA-seq based predictions [42], and even for combining multiple BRAKER predictions [43]. Still, it is not the most suitable task for EVM to create a BRAKER-only combination.…”

Section: Comparison With Evidencemodelermentioning

confidence: 99%

TSEBRA: Transcript Selector for BRAKER

Gabriel

Hoff

Brůna

et al. 2021

Preprint

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Background: BRAKER is a suite of automatic pipelines, BRAKER1 and BRAKER2, for the accurate annotation of protein-coding genes in eukaryotic genomes. Each pipeline trains statistical models of protein-coding genes based on provided evidence and, then predicts protein-coding genes in genomic sequences using both the extrinsic evidence and statistical models. For training and prediction, BRAKER1 and BRAKER2 incorporate complementary extrinsic evidence: BRAKER1 uses only RNA-seq data while BRAKER2 uses only a database of cross-species proteins. The BRAKER suite has so far not been able to reliably exceed the accuracy of BRAKER1 and BRAKER2 when incorporating both types of evidence simultaneously. Currently, for a novel genome project where both RNA-seq and protein data are available, the best option is to run both pipelines independently, and to pick one, likely better output. Therefore, one or another type of the extrinsic evidence would remain unexploited. Results: We present TSEBRA, a software that selects gene predictions (transcripts) from the sets generated by BRAKER1 and BRAKER2. TSEBRA uses a set of rules to compare scores of overlapping transcripts based on their support by RNA-seq and homologous protein evidence. We show in computational experiments on genomes of 11 species that TSEBRA achieves higher accuracy than either BRAKER1 or BRAKER2 running alone and that TSEBRA compares favorably with the combiner tool EVidenceModeler. Conclusion: TSEBRA is an easy-to-use and fast software tool. It can be used in concert with the BRAKER pipeline to generate a gene prediction set supported by both RNA-seq and homologous protein evidence.

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Foster thy young: Enhanced prediction of orphan genes in assembled genomes

Cited by 11 publications

References 144 publications

De novo assembly, annotation, and comparative analysis of 26 diverse maize genomes

De novo assembly, annotation, and comparative analysis of 26 diverse maize genomes

TheStreptochaetagenome and the evolution of the grasses

TSEBRA: Transcript Selector for BRAKER

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