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      Giant viruses with an expanded complement of translation system components

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          GeneMarkS: a self-training method for prediction of gene starts in microbial genomes. Implications for finding sequence motifs in regulatory regions.

          J Besemer (2001)
          Improving the accuracy of prediction of gene starts is one of a few remaining open problems in computer prediction of prokaryotic genes. Its difficulty is caused by the absence of relatively strong sequence patterns identifying true translation initiation sites. In the current paper we show that the accuracy of gene start prediction can be improved by combining models of protein-coding and non-coding regions and models of regulatory sites near gene start within an iterative Hidden Markov model based algorithm. The new gene prediction method, called GeneMarkS, utilizes a non-supervised training procedure and can be used for a newly sequenced prokaryotic genome with no prior knowledge of any protein or rRNA genes. The GeneMarkS implementation uses an improved version of the gene finding program GeneMark.hmm, heuristic Markov models of coding and non-coding regions and the Gibbs sampling multiple alignment program. GeneMarkS predicted precisely 83.2% of the translation starts of GenBank annotated Bacillus subtilis genes and 94.4% of translation starts in an experimentally validated set of Escherichia coli genes. We have also observed that GeneMarkS detects prokaryotic genes, in terms of identifying open reading frames containing real genes, with an accuracy matching the level of the best currently used gene detection methods. Accurate translation start prediction, in addition to the refinement of protein sequence N-terminal data, provides the benefit of precise positioning of the sequence region situated upstream to a gene start. Therefore, sequence motifs related to transcription and translation regulatory sites can be revealed and analyzed with higher precision. These motifs were shown to possess a significant variability, the functional and evolutionary connections of which are discussed.
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            tRNAscan-SE: A Program for Improved Detection of Transfer RNA Genes in Genomic Sequence

            T. M. Lowe, S. Eddy (1997)
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              Toward almost closed genomes with GapFiller

              Marten Boetzer, Walter Pirovano (2012)
              De novo assembly is a commonly used application of next-generation sequencing experiments. The ultimate goal is to puzzle millions of reads into one complete genome, although draft assemblies usually result in a number of gapped scaffold sequences. In this paper we propose an automated strategy, called GapFiller, to reliably close gaps within scaffolds using paired reads. The method shows good results on both bacterial and eukaryotic datasets, allowing only few errors. As a consequence, the amount of additional wetlab work needed to close a genome is drastically reduced. The software is available at http://www.baseclear.com/bioinformatics-tools/.
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                Author and article information

                Journal
                Title: Science
                Abbreviated Title: Science
                Publisher: American Association for the Advancement of Science (AAAS)
                ISSN (Print): 0036-8075
                ISSN (Electronic): 1095-9203
                Publication date Created: April 06 2017
                Publication date (Print): April 06 2017
                Volume: 356
                Issue: 6333
                Pages: 82-85
                Article
                DOI: 10.1126/science.aal4657
                PubMed ID: 28386012
                SO-VID: 34a7e9fa-bc0d-4fef-b76a-5075933e0eb9
                Copyright © © 2017
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