Ribosome binding site recognition using neural networks

Oliveira, Márcio Ferreira da Silva; Mendes, Daniele Quintella; Ferrari, Luciana Itida; Vasconcelos, Ana Tereza Ribeiro de

doi:10.1590/s1415-47572004000400028

Cited by 3 publications

(3 citation statements)

References 5 publications

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“…(33) 22 values were used to train the ANN and in Ref. (34) only 10 ribosome binding site sequences were used during the training phase of the ANN. These training set sizes are limited due to the lack of specific examples in the literature, but make possible further studies in a more detailed and complex way (32)(33)(34).…”

Section: Resultsmentioning

confidence: 99%

“…Although these experimental data can be considered not enough for testing the ANN, other work showed that even with a small set of data one can produce reliable results and contribute to further studies as described in Refs. (32)(33)(34). As will be discussed, even though only a few experimental data were used, the results showed qualitative prediction of free magnesium ion concentration.…”

Section: Methodsmentioning

confidence: 91%

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Analysis of the applicability of artificial neural networks for studying blood plasma: determination of magnesium ion concentration as a case study

Liparini

Carvalho²,

Belchior

2005

Clinical Chemistry and Laboratory Medicine (CCLM)

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Artificial neural networks are suggested for use in predicting metal ion concentration in human blood plasma. Simulated and available experimental data are used to train the artificial neural network. Particularly, using 850 simulated samples, the network predicted the magnesium-free ion concentration with an average error smaller than 1%. Clinical data recently reported for 20 patients were considered and the artificial neural network predicted the concentration of free magnesium ion with an average error of about 6%. Overall, the approach of using artificial neural networks as an alternative or complementary strategy to deal with the analysis of human blood plasma can be useful for clinical diagnostics, if there is sufficient data to train the artificial neural network.

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Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 91%

Analysis of the applicability of artificial neural networks for studying blood plasma: determination of magnesium ion concentration as a case study

Liparini

Carvalho²,

Belchior

2005

Clinical Chemistry and Laboratory Medicine (CCLM)

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“…In addition, MGA offers an approach for the analysis of ribosomal binding sites (RBSs) to detect specific patterns of ribosomal sequences in species. However, due to their tendency to undergo highly degenerative changes, RBSs are particularly difficult to identify [10]. In the line of pipelines used to facilitate the comparative analysis of high throughput sequencing, MOCAT [11] is a modular tool for processing raw sequence reads produced by the Illumina technology [12].…”

Section: Introductionmentioning

confidence: 99%

Computational workflow for the fine-grained analysis of metagenomic samples

et al. 2016

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BackgroundThe field of metagenomics, defined as the direct genetic analysis of uncultured samples of genomes contained within an environmental sample, is gaining increasing popularity. The aim of studies of metagenomics is to determine the species present in an environmental community and identify changes in the abundance of species under different conditions. Current metagenomic analysis software faces bottlenecks due to the high computational load required to analyze complex samples.ResultsA computational open-source workflow has been developed for the detailed analysis of metagenomes. This workflow provides new tools and datafile specifications that facilitate the identification of differences in abundance of reads assigned to taxa (mapping), enables the detection of reads of low-abundance bacteria (producing evidence of their presence), provides new concepts for filtering spurious matches, etc. Innovative visualization ideas for improved display of metagenomic diversity are also proposed to better understand how reads are mapped to taxa. Illustrative examples are provided based on the study of two collections of metagenomes from faecal microbial communities of adult female monozygotic and dizygotic twin pairs concordant for leanness or obesity and their mothers.ConclusionsThe proposed workflow provides an open environment that offers the opportunity to perform the mapping process using different reference databases. Additionally, this workflow shows the specifications of the mapping process and datafile formats to facilitate the development of new plugins for further post-processing. This open and extensible platform has been designed with the aim of enabling in-depth analysis of metagenomic samples and better understanding of the underlying biological processes.Electronic supplementary materialThe online version of this article (doi:10.1186/s12864-016-3063-x) contains supplementary material, which is available to authorized users.

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Temperate Streptococcus thermophilus phages expressing superinfection exclusion proteins of the Ltp type

et al. 2014

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Lipoprotein Ltp encoded by temperate Streptococcus thermophilus phage TP-J34 is the prototype of the wide-spread family of host cell surface-exposed lipoproteins involved in superinfection exclusion (sie). When screening for other S. thermophilus phages expressing this type of lipoprotein, three temperate phages—TP-EW, TP-DSM20617, and TP-778—were isolated. In this communication we present the total nucleotide sequences of TP-J34 and TP-778L. For TP-EW, a phage almost identical to TP-J34, besides the ltp gene only the two regions of deviation from TP-J34 DNA were analyzed: the gene encoding the tail protein causing an assembly defect in TP-J34 and the gene encoding the lysin, which in TP-EW contains an intron. For TP-DSM20617 only the sequence of the lysogeny module containing the ltp gene was determined. The region showed high homology to the same region of TP-778. For TP-778 we could show that absence of the attR region resulted in aberrant excision of phage DNA. The amino acid sequence of mature LtpTP-EW was shown to be identical to that of mature LtpTP-J34, whereas the amino acid sequence of mature LtpTP-778 was shown to differ from mature LtpTP-J34 in eight amino acid positions. LtpTP-DSM20617 was shown to differ from LtpTP-778 in just one amino acid position. In contrast to LtpTP-J34, LtpTP-778 did not affect infection of lactococcal phage P008 instead increased activity against phage P001 was noticed.

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Ribosome binding site recognition using neural networks

Cited by 3 publications

References 5 publications

Analysis of the applicability of artificial neural networks for studying blood plasma: determination of magnesium ion concentration as a case study

Analysis of the applicability of artificial neural networks for studying blood plasma: determination of magnesium ion concentration as a case study

Computational workflow for the fine-grained analysis of metagenomic samples

Temperate Streptococcus thermophilus phages expressing superinfection exclusion proteins of the Ltp type

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