Gene Identification in Prokaryotic Genomes, Phages, Metagenomes, and EST Sequences with GeneMarkS Suite

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Abstract
Table of Contents
Figures
Literature Cited
Supplementary Material

Abstract

This unit describes how to use several gene?finding programs from the GeneMark line developed for finding protein?coding ORFs in genomic DNA of prokaryotic species, in genomic DNA of eukaryotic species with intronless genes, in genomes of viruses and phages, and in prokaryotic metagenomic sequences, as well as in EST sequences with spliced?out introns. These bioinformatics tools were demonstrated to have state?of?the?art accuracy and have been frequently used for gene annotation in novel nucleotide sequences. An additional advantage of these sequence?analysis tools is that the problem of algorithm parameterization is solved automatically, with parameters estimated by iterative self?training (unsupervised training). Curr. Protoc. Bioinform. 35:4.5.1?4.5.17. © 2011 by John Wiley & Sons, Inc.

Keywords: gene finding; hidden Markov model; unsupervised parameter estimation

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Introduction
Basic Protocol 1: Using GeneMarkS
Basic Protocol 2: Using GeneMark.hmm for Prokaryotic Gene Prediction
Basic Protocol 3: Using GeneMark for Prokaryotic Gene Prediction
Basic Protocol 4: Using the Heuristic Approach for Prokaryotic Model Building
Basic Protocol 5: Using MetaGeneMark for Finding Genes in Metagenomes
Guidelines for Understanding Results
Commentary
Literature Cited
Figures

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Materials

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Figures

Figure 4.5.1 User interface for the GeneMarkS gene‐finding program. Required input includes a DNA sequence in FASTA format. The algorithm (1) estimates the HMM model parameters from the input sequence via unsupervised iterative training, and (2) makes a final run of GeneMark.hmm to get gene predictions.

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Figure 4.5.2 User interface for the GeneMark.hmm program. Required input includes a DNA sequence in FASTA format and the set of species‐specific parameters for the model.

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Figure 4.5.3 The text output of GeneMark.hmm using both the Atypical and Typical model. The predicted gene coordinates, the strand, and the model type are listed for each gene.

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Figure 4.5.4 The graphical output from GeneMark combined with the output of GeneMark.hmm. The genes predicted by the Typical model are shown in black (solid line); the genes predicted by the Atypical model are shown in red (dashed line). The black horizontal bars indicate protein coding regions predicted by GeneMark.hmm.

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Figure 4.5.5 The user interface of GeneMark. Required input includes a DNA sequence in FASTA format and the set of species‐specific parameters of the model.

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Figure 4.5.6 The text output of the GeneMark program. Open reading frames predicted as genes are listed, along with the average coding potential of an ORF and the probability for alternative ATG triplets to be a translation start .

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Figure 4.5.7 The graphical output of GeneMark. The six different panels represent the six possible reading frames, three of each in the direct and reverse strands.

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Figure 4.5.8 The user interface for the Heuristic approach. Required input includes a DNA sequence in FASTA format. This method finds model parameters for the gene‐prediction algorithm from the given (short) sequence. GeneMark.hmm runs with these parameters to predict genes.

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Figure 4.5.9 The user interface for the MetaGeneMark program. Required input includes a set of DNA sequences in multi‐ FASTA format.

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Literature Cited

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