IUPAC-IUB Commission on Biochemical Nomenclature (CBN). Abbreviations and symbols for nucleic acids, polynucleotides and their constituents. Recommendations 1970

doi:10.1042/bj1200449

Cited by 25 publications

(7 citation statements)

References 11 publications

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“…The input file for BMGE is a multiple sequence alignment in FASTA (or PHYLIP sequential) format. The user must indicate whether the sequences are amino acids or DNA (with standard one-letter coding [ 26 , 32 ]; see Table 1 ). It is also possible to consider DNA sequences as codons, which allows the multiple sequence alignment to be handled with amino acid substitution matrices.…”

Section: Methodsmentioning

confidence: 99%

“…These two recoding strategies may prove useful to minimize some biases when dealing with datasets with known heterogeneous composition across sequences. As these two recoding approaches use only the standard one-letter nucleotide alphabet [ 26 ] (see Table 1 ), the resulting datasets can be given to all phylogeny inference programs, in contrast to alternative recoding techniques based on non-standard alphabet cardinality such as the "Dayhoff classes" 6-residue alphabet [ 27 , 28 ] (see also [ 29 ] for discussion on other recoding schemes). Moreover, the use of degenerated codons allow fast inference of trees, in particular with Maximum Likelihood (ML) methods which are faster with nucleotide sequences than with amino acid ones.…”

Section: Introductionmentioning

confidence: 99%

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BMGE (Block Mapping and Gathering with Entropy): a new software for selection of phylogenetic informative regions from multiple sequence alignments

2010

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BackgroundThe quality of multiple sequence alignments plays an important role in the accuracy of phylogenetic inference. It has been shown that removing ambiguously aligned regions, but also other sources of bias such as highly variable (saturated) characters, can improve the overall performance of many phylogenetic reconstruction methods. A current scientific trend is to build phylogenetic trees from a large number of sequence datasets (semi-)automatically extracted from numerous complete genomes. Because these approaches do not allow a precise manual curation of each dataset, there exists a real need for efficient bioinformatic tools dedicated to this alignment character trimming step.ResultsHere is presented a new software, named BMGE (Block Mapping and Gathering with Entropy), that is designed to select regions in a multiple sequence alignment that are suited for phylogenetic inference. For each character, BMGE computes a score closely related to an entropy value. Calculation of these entropy-like scores is weighted with BLOSUM or PAM similarity matrices in order to distinguish among biologically expected and unexpected variability for each aligned character. Sets of contiguous characters with a score above a given threshold are considered as not suited for phylogenetic inference and then removed. Simulation analyses show that the character trimming performed by BMGE produces datasets leading to accurate trees, especially with alignments including distantly-related sequences. BMGE also implements trimming and recoding methods aimed at minimizing phylogeny reconstruction artefacts due to compositional heterogeneity.ConclusionsBMGE is able to perform biologically relevant trimming on a multiple alignment of DNA, codon or amino acid sequences. Java source code and executable are freely available at ftp://ftp.pasteur.fr/pub/GenSoft/projects/BMGE/.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

BMGE (Block Mapping and Gathering with Entropy): a new software for selection of phylogenetic informative regions from multiple sequence alignments

2010

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“…Besides the four concrete bases A, C, G and T/U, IUPAC notation for nucleic acids, used in the FASTA sequence file format, contains 11 ambiguity codes, covering all possible combinations of more than one base [26]. For example, code S can represent a combination of C and G. Any method which calls SNVs can encode these variants within a FASTA file.…”

Section: Resultsmentioning

confidence: 99%

Interpreting Viral Deep Sequencing Data with GLUE

Singer

Thomson

Hughes

et al. 2019

Viruses

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Using deep sequencing technologies such as Illumina’s platform, it is possible to obtain reads from the viral RNA population revealing the viral genome diversity within a single host. A range of software tools and pipelines can transform raw deep sequencing reads into Sequence Alignment Mapping (SAM) files. We propose that interpretation tools should process these SAM files, directly translating individual reads to amino acids in order to extract statistics of interest such as the proportion of different amino acid residues at specific sites. This preserves per-read linkage between nucleotide variants at different positions within a codon location. The samReporter is a subsystem of the GLUE software toolkit which follows this direct read translation approach in its processing of SAM files. We test samReporter on a deep sequencing dataset obtained from a cohort of 241 UK HCV patients for whom prior treatment with direct-acting antivirals has failed; deep sequencing and resistance testing have been suggested to be of clinical use in this context. We compared the polymorphism interpretation results of the samReporter against an approach that does not preserve per-read linkage. We found that the samReporter was able to properly interpret the sequence data at resistance-associated locations in nine patients where the alternative approach was equivocal. In three cases, the samReporter confirmed that resistance or an atypical substitution was present at NS5A position 30. In three further cases, it confirmed that the sofosbuvir-resistant NS5B substitution S282T was absent. This suggests the direct read translation approach implemented is of value for interpreting viral deep sequencing data.

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“…The IUPAC signal letter code for the wild-type amino acid, the amino acid position, and the identity of the mutated amino acid are shown (e.g. I47A) [12] .…”

Section: Resultsmentioning

confidence: 99%

The Geogenomic Mutational Atlas of Pathogens (GoMAP) Web System

et al. 2014

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We present a new approach for pathogen surveillance we call Geogenomics. Geogenomics examines the geographic distribution of the genomes of pathogens, with a particular emphasis on those mutations that give rise to drug resistance. We engineered a new web system called Geogenomic Mutational Atlas of Pathogens (GoMAP) that enables investigation of the global distribution of individual drug resistance mutations. As a test case we examined mutations associated with HIV resistance to FDA-approved antiretroviral drugs. GoMAP-HIV makes use of existing public drug resistance and HIV protein sequence data to examine the distribution of 872 drug resistance mutations in ∼502,000 sequences for many countries in the world. We also implemented a broadened classification scheme for HIV drug resistance mutations. Several patterns for geographic distributions of resistance mutations were identified by visual mining using this web tool. GoMAP-HIV is an open access web application available at http://www.bio-toolkit.com/GoMap/project/

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IUPAC-IUB Commission on Biochemical Nomenclature (CBN). Abbreviations and symbols for nucleic acids, polynucleotides and their constituents. Recommendations 1970

Cited by 25 publications

References 11 publications

BMGE (Block Mapping and Gathering with Entropy): a new software for selection of phylogenetic informative regions from multiple sequence alignments

BMGE (Block Mapping and Gathering with Entropy): a new software for selection of phylogenetic informative regions from multiple sequence alignments

Interpreting Viral Deep Sequencing Data with GLUE

The Geogenomic Mutational Atlas of Pathogens (GoMAP) Web System

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