geneRFinder: gene finding in distinct metagenomic data complexities
Abstract Background Microbes perform a fundamental economic, social, and environmental role in our society. Metagenomics makes it possible to investigate microbes in their natural environments (the complex communities) and their interactions. The way they act is usually estimated by looking at the f...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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BMC
2021-02-01
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| Series: | BMC Bioinformatics |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s12859-021-03997-w |
| _version_ | 1818676388121542656 |
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| author | Raíssa Silva Kleber Padovani Fabiana Góes Ronnie Alves |
| author_facet | Raíssa Silva Kleber Padovani Fabiana Góes Ronnie Alves |
| author_sort | Raíssa Silva |
| collection | DOAJ |
| description | Abstract Background Microbes perform a fundamental economic, social, and environmental role in our society. Metagenomics makes it possible to investigate microbes in their natural environments (the complex communities) and their interactions. The way they act is usually estimated by looking at the functions they play in those environments and their responsibility is measured by their genes. The advances of next-generation sequencing technology have facilitated metagenomics research however it also creates a heavy computational burden. Large and complex biological datasets are available as never before. There are many gene predictors available that can aid the gene annotation process though they lack handling appropriately metagenomic data complexities. There is no standard metagenomic benchmark data for gene prediction. Thus, gene predictors may inflate their results by obfuscating low false discovery rates. Results We introduce geneRFinder, an ML-based gene predictor able to outperform state-of-the-art gene prediction tools across this benchmark by using only one pre-trained Random Forest model. Average prediction rates of geneRFinder differed in percentage terms by 54% and 64%, respectively, against Prodigal and FragGeneScan while handling high complexity metagenomes. The specificity rate of geneRFinder had the largest distance against FragGeneScan, 79 percentage points, and 66 more than Prodigal. According to McNemar’s test, all percentual differences between predictors performances are statistically significant for all datasets with a 99% confidence interval. Conclusions We provide geneRFinder, an approach for gene prediction in distinct metagenomic complexities, available at gitlab.com/r.lorenna/generfinder and https://osf.io/w2yd6/ , and also we provide a novel, comprehensive benchmark data for gene prediction—which is based on The Critical Assessment of Metagenome Interpretation (CAMI) challenge, and contains labeled data from gene regions—available at https://sourceforge.net/p/generfinder-benchmark . |
| first_indexed | 2024-12-17T08:42:41Z |
| format | Article |
| id | doaj.art-f8fdb0cb92414d48b53e2bf28b324477 |
| institution | Directory Open Access Journal |
| issn | 1471-2105 |
| language | English |
| last_indexed | 2024-12-17T08:42:41Z |
| publishDate | 2021-02-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Bioinformatics |
| spelling | doaj.art-f8fdb0cb92414d48b53e2bf28b3244772022-12-21T21:56:17ZengBMCBMC Bioinformatics1471-21052021-02-0122111710.1186/s12859-021-03997-wgeneRFinder: gene finding in distinct metagenomic data complexitiesRaíssa Silva0Kleber Padovani1Fabiana Góes2Ronnie Alves3Vale Institute of TechnologyPPGCC, Federal University of ParáICMC, University of São PauloVale Institute of TechnologyAbstract Background Microbes perform a fundamental economic, social, and environmental role in our society. Metagenomics makes it possible to investigate microbes in their natural environments (the complex communities) and their interactions. The way they act is usually estimated by looking at the functions they play in those environments and their responsibility is measured by their genes. The advances of next-generation sequencing technology have facilitated metagenomics research however it also creates a heavy computational burden. Large and complex biological datasets are available as never before. There are many gene predictors available that can aid the gene annotation process though they lack handling appropriately metagenomic data complexities. There is no standard metagenomic benchmark data for gene prediction. Thus, gene predictors may inflate their results by obfuscating low false discovery rates. Results We introduce geneRFinder, an ML-based gene predictor able to outperform state-of-the-art gene prediction tools across this benchmark by using only one pre-trained Random Forest model. Average prediction rates of geneRFinder differed in percentage terms by 54% and 64%, respectively, against Prodigal and FragGeneScan while handling high complexity metagenomes. The specificity rate of geneRFinder had the largest distance against FragGeneScan, 79 percentage points, and 66 more than Prodigal. According to McNemar’s test, all percentual differences between predictors performances are statistically significant for all datasets with a 99% confidence interval. Conclusions We provide geneRFinder, an approach for gene prediction in distinct metagenomic complexities, available at gitlab.com/r.lorenna/generfinder and https://osf.io/w2yd6/ , and also we provide a novel, comprehensive benchmark data for gene prediction—which is based on The Critical Assessment of Metagenome Interpretation (CAMI) challenge, and contains labeled data from gene regions—available at https://sourceforge.net/p/generfinder-benchmark .https://doi.org/10.1186/s12859-021-03997-wGene predictionMachine learningMetagenomics |
| spellingShingle | Raíssa Silva Kleber Padovani Fabiana Góes Ronnie Alves geneRFinder: gene finding in distinct metagenomic data complexities BMC Bioinformatics Gene prediction Machine learning Metagenomics |
| title | geneRFinder: gene finding in distinct metagenomic data complexities |
| title_full | geneRFinder: gene finding in distinct metagenomic data complexities |
| title_fullStr | geneRFinder: gene finding in distinct metagenomic data complexities |
| title_full_unstemmed | geneRFinder: gene finding in distinct metagenomic data complexities |
| title_short | geneRFinder: gene finding in distinct metagenomic data complexities |
| title_sort | generfinder gene finding in distinct metagenomic data complexities |
| topic | Gene prediction Machine learning Metagenomics |
| url | https://doi.org/10.1186/s12859-021-03997-w |
| work_keys_str_mv | AT raissasilva generfindergenefindingindistinctmetagenomicdatacomplexities AT kleberpadovani generfindergenefindingindistinctmetagenomicdatacomplexities AT fabianagoes generfindergenefindingindistinctmetagenomicdatacomplexities AT ronniealves generfindergenefindingindistinctmetagenomicdatacomplexities |