Justin van der Hooft The central theme of my research is the integration of metabolome and genome mining tools to accelerate and improve functional annotations of biosynthesis genes and specialized molecules.
The presently available gene annotation approaches are based on features that are unavailable in short reading sequences generated from next generation sequencing, which results in substandard performance for metagenomic samples.
Innovative programs have been developed that enhance performance in undersized reading sequences. Here the plan is to benchmark ten metagenomic gene prediction programs and combine their predictions to improve metagenomic read gene annotation. Performance optimization and benchmarking of presently available metagenomic gene prediction programs for enhancing both prediction and annotation accuracies.
To do so, a simulation would be performed on an artificial dataset composed of some coding part, noncoding, and incompletely coding metagenomic reads. Then several metrics would be introduced in order to compare the predictors. And then different combinations of the prediction programs would be used in order to combine their predictions to improve accuracy.
Metagenomic analysis can be defined as the classification of microbial genomes through the direct isolation of genomic sequences from the environment without former cultivation .
Samples from an environment are sequenced using next-generation sequencing NGSalso known as high-throughput sequencing technologies which yields short read length sequences .
Accurate gene annotation for environmental samples is necessary so that correct functional classification of genes could be done, and it overlays a path for efficient functional studies in metagenomics.
Presently available gene predictors can be characterized in two different groups. The first group consists of the ab initio predictors, which train model parameters on already known annotations which then predict the unknown annotations, are widely used in gene prediction .
Currently there are many ab-initio gene-finding programs, e. The second group of gene prediction programs, homology-based programs, which predict genes by aligning input sequences to the closest homologous sequence in the database.
It is hardly possible to use these mentioned traditional gene prediction methods in metagenomics. These are very conventional approaches and are restricted by the recognition of Open Reading Frames, which begin with a start codon and end with an in-frame stop codon .
Similarly, homology-based approaches for gene predictions are heavily databases dependent which contain known sequences only, and thus a limited set, of genes.
Therefore, some modern tools have emerged to address these problems for metagenomic reads. Summary of these commonly used Metagenomic Gene Prediction Programs: It is a metagenomic gene prediction tool for short, environmental DNA sequences with unknown phylogenetic origin .
Orphelia is constructed on a two-stage machine learning approach.
An artificial neural network combines the features and calculates the probability for each ORF in a fragment. A greedy strategy computes a probable combination of high scoring ORFs with an overlap restraint.metagenomics." PhD (Doctor of Philosophy) thesis, University of Iowa, Ellen Marie Black A thesis submitted in partial fulfillment of the requirements for the Doctor of Philosophy degree in Civil and Environmental Engineering in the Graduate College of The University of Iowa May Background.
In shotgun metagenomics, microbial communities are studied through direct sequencing of DNA without any prior cultivation.
By comparing gene abundances estimated from the generated sequencing reads, functional differences between the communities can be identified. In a second postdoc at the National Animal Disease Center, he used multi ’omics (metagenomics, metabolomics, metatranscriptomics, and 16S community profiling) approaches to studying the swine and turkey microbiome in efforts to find alternatives to antibiotics in animal agriculture.
This free Science essay on Essay: Metagenomics is perfect for Science students to use as an example. in Metagenomics Johannes Alneberg Doctoral Thesis, KTH Royal Institute of Technology Engineering Sciences in Chemistry, Biotechnology and Health this thesis, aims at reconstruction of the complete DNA sequence of an organism, i.e.
its genome, directly from short metagenomic sequences. III Acknowledgements I would like to thank my mentor, Professor Dr. Hamza El Dorry for advising me and supervising my thesis, Dr. Mohammed Ghazy for co-advising and for his continuous help and support.