Automated Prediction and Annotation of Small Open Reading Frames in Microbial Genomes

Durrant, Matthew G.; Bhatt, Ami S.

doi:10.1016/j.chom.2020.11.002

Cited by 32 publications

(21 citation statements)

References 41 publications

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“…Ribosome profiling sequences mRNA transcripts that are associated with ribosomes and thus can be used to identify transcribed genes that are likely to be translated to proteins (Ingolia et al, 2009). Therefore, ribosome profiling serves as an orthogonal approach (Clauwaert et al, 2019;Ndah et al, 2017) and validation strategy (Durrant and Bhatt, 2021) to aid in small-gene prediction. We used Meta-Ribo-Seq datasets generated from four metagenomic assemblies of human fecal microbiome samples Fremin et al, , 2021 that were independent of the IMG/VR (Roux et al, 2021) and the HMP2 (Lloyd-Price et al, 2017) datasets, representing a non-overlapping validation dataset for the two resources.…”

Section: Improved Accuracy Of Small-gene Predictions In Phagesmentioning

confidence: 99%

Thousands of small, novel genes predicted in global phage genomes

2022

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Section: Improved Accuracy Of Small-gene Predictions In Phagesmentioning

confidence: 99%

Thousands of small, novel genes predicted in global phage genomes

2022

Self Cite

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“…This approach, however, has an obvious drawback, as it is not able to predict hitherto unknown sORFs. Hence, the integration of dedicated sORF prediction tools [54, 55] into this workflow might help to improve on this issue.…”

Section: Discussionmentioning

confidence: 99%

Bakta: rapid and standardized annotation of bacterial genomes via alignment-free sequence identification

et al. 2021

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Command-line annotation software tools have continuously gained popularity compared to centralized online services due to the worldwide increase of sequenced bacterial genomes. However, results of existing command-line software pipelines heavily depend on taxon-specific databases or sufficiently well annotated reference genomes. Here, we introduce Bakta, a new command-line software tool for the robust, taxon-independent, thorough and, nonetheless, fast annotation of bacterial genomes. Bakta conducts a comprehensive annotation workflow including the detection of small proteins taking into account replicon metadata. The annotation of coding sequences is accelerated via an alignment-free sequence identification approach that in addition facilitates the precise assignment of public database cross-references. Annotation results are exported in GFF3 and International Nucleotide Sequence Database Collaboration (INSDC)-compliant flat files, as well as comprehensive JSON files, facilitating automated downstream analysis. We compared Bakta to other rapid contemporary command-line annotation software tools in both targeted and taxonomically broad benchmarks including isolates and metagenomic-assembled genomes. We demonstrated that Bakta outperforms other tools in terms of functional annotations, the assignment of functional categories and database cross-references, whilst providing comparable wall-clock runtimes. Bakta is implemented in Python 3 and runs on MacOS and Linux systems. It is freely available under a GPLv3 license at https://github.com/oschwengers/bakta. An accompanying web version is available at https://bakta.computational.bio.

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“…This approach however has an obvious drawback as it is not able to predict hitherto unknown sORFs. Hence, the integration of dedicated sORF prediction tools [54,55] into this workflow might help to improve on this issue.…”

Section: Discussionmentioning

confidence: 99%

Bakta: Rapid & standardized annotation of bacterial genomes via alignment-free sequence identification

Schwengers

Jelonek

Dieckmann

et al. 2021

Preprint

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Command line annotation software tools have continuously gained popularity compared to centralized online services due to the worldwide increase of sequenced bacterial genomes. However, results of existing command line software pipelines heavily depend on taxon specific databases or sufficiently well annotated reference genomes. Here, we introduce Bakta, a new command line software tool for the robust, taxon-independent, thorough and nonetheless fast annotation of bacterial genomes. Bakta conducts a comprehensive annotation workflow including the detection of small proteins taking into account replicon metadata. The annotation of coding sequences is accelerated via an alignment-free sequence identification approach that in addition facilitates the precise assignment of public database cross references. Annotation results are exported in GFF3 and INSDC-compliant flat files as well as comprehensive JSON files facilitating automated downstream analysis. We compared Bakta to other rapid contemporary command line annotation software tools in both targeted and taxonomically broad benchmarks including isolates and metagenomic-assembled genomes. We demonstrated that Bakta outperforms other tools in terms of functional annotations, the assignment of functional categories and database cross-references whilst providing comparable wall clock runtimes. Bakta is implemented in Python 3 and runs on MacOS and Linux systems. It is freely available under a GPLv3 license at https://github.com/oschwengers/bakta. An accompanying web version is available at https://bakta.computational.bio.

show abstract

Automated Prediction and Annotation of Small Open Reading Frames in Microbial Genomes

Cited by 32 publications

References 41 publications

Thousands of small, novel genes predicted in global phage genomes

Thousands of small, novel genes predicted in global phage genomes

Bakta: rapid and standardized annotation of bacterial genomes via alignment-free sequence identification

Bakta: Rapid & standardized annotation of bacterial genomes via alignment-free sequence identification

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