Orphelia: predicting genes in metagenomic sequencing reads

By Katharina Jasmin Hoff, Thomas Lingner, Peter Meinicke, M. Tech. 2009

143 citations recorded by the source corpus.

Metagenomic sequencing projects yield numerous sequencing reads of a diverse range of uncultivated and mostly yet unknown microorganisms. In many cases, these sequencing reads cannot be assembled into longer contigs. Thus, gene prediction tools that were originally developed for whole-genome analysis are not suitable for processing metagenomes. Orphelia is a program for predicting genes in short DNA sequences that is available through a web server application (http://orphelia.gobics.de). Orphelia utilizes prediction models that were created with machine learning techniques on the basis of a wide range of annotated genomes. In contrast to other methods for metagenomic gene prediction, Orphelia has fragment length-specific prediction models for the two most popular sequencing techniques in metagenomics, chain termination sequencing and pyrosequencing. These models ensure highly specific gene predictions.

Subfields: Molecular Biology

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Citations in the corpus

Combining gene prediction methods to improve metagenomic gene annotation (2011, methodology): “In metagenomics, single genomes cannot be assembled and therefore, it is a challenge to predict genes within these short sequences [3].”
Machine learning for metagenomics: methods and tools (2015, background): “Orphelia (Hoff et al., 2008, 2009) is a two-stage machine learning approach.”
Metagenomics: Tools and Insights for Analyzing Next-Generation Sequencing Data Derived from Biodiversity Studies (2015, methodology): “CDSs are identified using a number of tools including MetaGeneMark, 84 Metagene, 85 Prodigal, 86 Orphelia, 87 and FragGeneScan, 88 all of which utilize ab initio gene prediction algorithms.”
FragGeneScan: predicting genes in short and error-prone reads (2010, methodology): “The nine genomes are the same as those in Table 2, and were used for testing gene prediction in short reads (16,18).”
Ab initio gene identification in metagenomic sequences (2010, result): “The authors of new tools have shown that their performance is comparable to performance of the original ‘heuristic model’ method (Supplementary Table S3 in (14)) (16).”
Metagenomics - a guide from sampling to data analysis (2012, methodology): “A number of tools were specifically designed to handle metagenomic prediction of CDS, including FragGeneScan [24], MetaGeneMark [62], MetaGeneAnnotator (MGA)/ Metagene [63] and Orphelia [64,65].”
Chapter 12: Human Microbiome Analysis (2012, background): “Conversely, de novo methods have been developed to directly bin [67,68,69] and call genes within [61,62] metagenomic sequences using DNA features alone (GC content, codon usage, etc.”