GeneMark: web software for gene finding in prokaryotes, eukaryotes and viruses

Besemer, John; Borodovsky, Mark

doi:10.1093/nar/gki487

Cited by 961 publications

(675 citation statements)

References 16 publications

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“…The remaining reads were then assembled using Forge assembler (using '-metagenomic' for metagenome assembly; (Diguistini et al, 2009)). Genes were predicted using GeneMark v2.6c with a heuristic model, based on the GC content (Besemer and Borodovsky, 2005). Functional annotation was performed as in Kunin et al (2008) and Gianoulis et al (2009).…”

Section: Computational and Statistical Analysesmentioning

confidence: 99%

The human small intestinal microbiota is driven by rapid uptake and conversion of simple carbohydrates

et al. 2012

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The human gastrointestinal tract (GI tract) harbors a complex community of microbes. The microbiota composition varies between different locations in the GI tract, but most studies focus on the fecal microbiota, and that inhabiting the colonic mucosa. Consequently, little is known about the microbiota at other parts of the GI tract, which is especially true for the small intestine because of its limited accessibility. Here we deduce an ecological model of the microbiota composition and function in the small intestine, using complementing culture-independent approaches. Phylogenetic microarray analyses demonstrated that microbiota compositions that are typically found in effluent samples from ileostomists (subjects without a colon) can also be encountered in the small intestine of healthy individuals. Phylogenetic mapping of small intestinal metagenome of three different ileostomy effluent samples from a single individual indicated that Streptococcus sp., Escherichia coli, Clostridium sp. and high G þ C organisms are most abundant in the small intestine. The compositions of these populations fluctuated in time and correlated to the short-chain fatty acids profiles that were determined in parallel. Comparative functional analysis with fecal metagenomes identified functions that are overrepresented in the small intestine, including simple carbohydrate transport phosphotransferase systems (PTS), central metabolism and biotin production. Moreover, metatranscriptome analysis supported high level in-situ expression of PTS and carbohydrate metabolic genes, especially those belonging to Streptococcus sp. Overall, our findings suggest that rapid uptake and fermentation of available carbohydrates contribute to maintaining the microbiota in the human small intestine.

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Section: Computational and Statistical Analysesmentioning

confidence: 99%

The human small intestinal microbiota is driven by rapid uptake and conversion of simple carbohydrates

et al. 2012

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“…A variety of tools for ab initio gene prediction are available. We used Glimmer (Delcher et al 2007) and GeneMark (Borodovsky et al 2003;Besemer and Borodovsky 2005), the tools first used to annotate the previous P. syringae genomes. Glimmer and GeneMark were trained and parameterized on the published Pto DC3000 genome before use on our draft Pto DC3000 and Por 1_6 assemblies.…”

Section: Genome Annotationmentioning

confidence: 99%

De novo assembly using low-coverage short read sequence data from the rice pathogen Pseudomonas syringae pv. oryzae

Reinhardt¹,

Baltrus²,

Nishimura³

et al. 2008

Genome Res.

133

109

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We developed a novel approach for de novo genome assembly using only sequence data from high-throughput short read sequencing technologies. By combining data generated from 454 Life Sciences (Roche) and Illumina (formerly known as Solexa sequencing) sequencing platforms, we reliably assembled genomes into large scaffolds at a fraction of the traditional cost and without use of a reference sequence. We applied this method to two isolates of the phytopathogenic bacteria Pseudomonas syringae. Sequencing and reassembly of the well-studied tomato and Arabidopsis pathogen, Pto DC3000 , facilitated development and testing of our method. Sequencing of a distantly related rice pathogen, Por 1_6 , demonstrated our method's efficacy for de novo assembly of novel genomes. Our assembly of Por 1_6 yielded an N50 scaffold size of 531,821 bp with >75% of the predicted genome covered by scaffolds over 100,000 bp. One of the critical phenotypic differences between strains of P. syringae is the range of plant hosts they infect. This is largely determined by their complement of type III effector proteins. The genome of Por 1_6 is the first sequenced for a P. syringae isolate that is a pathogen of monocots, and, as might be predicted, its complement of type III effectors differs substantially from the previously sequenced isolates of this species. The genome of Por 1_6 helps to define an expansion of the P. syringae pangenome, a corresponding contraction of the core genome, and a further diversification of the type III effector complement for this important plant pathogen species.

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“…From the success of GeneMark as an accurate tool to recognize and annotate genes in genome projects, a family of GeneMark programs have been developed including GeneMark.hmm [6], GeneMarkS [7], GeneMarkE [8], GenScan [9], EuGene [10], and GeneTack [11]. The…”

Section: Literature Reviewmentioning

confidence: 99%

Constraint-Based System for Genomic Analysis

Kerdprasop¹,

Kerdprasop²

2015

IJIET

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Abstract-Recent advent of the new high-throughput biological technologies has brought more challenges to the computer science community in terms of the amount and variety of biological data awaiting for analysis. Computationally intensive techniques such as pattern recognition and machine learning algorithms have been applied to extract knowledge from several biological domains ranging from genomics, proteomics to system biology and evolution process. Learning techniques applied to the computational biology are mostly in the category of classification. Therefore, the sequence analysis problem has to be formulated as classification task, which is quite difficult due to the unobvious one-to-one mapping of the problem. In this paper, we propose a different setting of sequence analysis formulation based on the nucleotide patterns using a constraint logic programming paradigm, in which the sequence alignment can be performed through pattern matching techniques. With available knowledge from the field of pattern mining, we can apply the well-established techniques within the new framework of constraint programming. However, to make the system efficiently work, we need a new set of constraint solver algorithms specifically designed for the sequence analysis problem. The design and implementation of such algorithms are thus the main focus of our research project. We propose in this paper the design of a constraint-based system for genomic sequence analysis including the algorithm for the constraint solver, a major part of the proposed system. Index Terms-Genomic sequence analysis, constraint-based system, constraint solver algorithm, constraint programming. I. INTRODUCTIONLiving organisms contain multiples cells to perform different functions. There are two basic types of cells: prokaryote cells (found in bacteria) and eukaryote cells (appeared in plants and animals). Contained within the cell membrane are several organelles and thousands of different types of molecules, the important one is DNA (deoxyribonucleic acid) that carries the entire genetic inheritance, or genes, of the cell. DNA is a long polymer molecule that contains sugar, phosphate group, and a mixture of four different nucleotide bases: adenine (A), cytosine (C), guanine (G), and thymine (T).In genetic information in biological systems (Fig. 1) that firstly DNA is copied to more DNA in the replication process, then DNA is transcribed into mRNA (or messenger-RNA) in a transcription process and finally mRNA is translated (by ribosome) into protein in a translation process.This overall process of biological protein synthesis is known as gene expression. Understanding the process of gene expression in different types of cells and under different conditions is one of the fundamental research aspects of genomics, which is all the studies related to genes.In prokaryotes, genetic information is encoded continuously on a DNA strand. But in eukaryotes, regions that code for protein (called exons) are interrupted by the non-coding regions (called introns). During t...

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GeneMark: web software for gene finding in prokaryotes, eukaryotes and viruses

Cited by 961 publications

References 16 publications

The human small intestinal microbiota is driven by rapid uptake and conversion of simple carbohydrates

The human small intestinal microbiota is driven by rapid uptake and conversion of simple carbohydrates

De novo assembly using low-coverage short read sequence data from the rice pathogen Pseudomonas syringae pv. oryzae

Constraint-Based System for Genomic Analysis

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