Molecular phylogeny of the spiny lobster genus Panulirus (Decapoda: Palinuridae)

2001 ◽  
Vol 52 (8) ◽  
pp. 1037 ◽  
Author(s):  
Margaret B. Ptacek ◽  
Shane K. Sarver ◽  
Michael J. Childress ◽  
William F. Herrnkind
Keyword(s):  
Molecular Phylogeny ◽  
Sequence Data ◽  
Large Subunit ◽  
Spiny Lobster ◽  
Sequence Divergence ◽  
Sequence Alignments ◽  
Multiple Sequence ◽  
Nucleotide Sequence Data ◽  
Group Iii ◽  
Group I

Phylogenetic relationships among all described species and four subspecies (total of 21 taxa) of the spiny lobster genus Panulirus White, 1847, were examined with nucleotide sequence data from portions of two mitochondrial genes, large-subunit ribosomal RNA (16S) and cytochrome oxidase subunit I (COI). Multiple sequence alignments were subjected to maximum-parsimony, neighbour-joining, and maximum-likelihood analysis with Jasus edwardsii as the outgroup. Two major lineages within Panulirus were recovered by all three methods for both the 16S and COI alignments analysed separately and for the combined alignment. The first lineage included all species of Panulirus classified as Groups I and II by previous morphologically based definitions. The second included all species classified as Groups III and IV. Relationships within major lineages were not well resolved; the molecular phylogeny did not support separation of Group I from Group II or of Group III from Group IV. The degree of sequence divergence between different pairs of species was higher in pairwise comparisons between species in Group I/II (16S: 2.8–19.4%; COI: 12.4–31.8%) than in those between species in Group III/IV (16S: 5.3–13.2%; COI: 12.6–19.6%). This pattern suggests that the Group I/II lineage may represent an earlier radiation of species within Panulirus.

VisFeature: a stand-alone program for visualizing and analyzing statistical features of biological sequences

2019 ◽  
Author(s):  
Jun Wang ◽  
Pu-Feng Du ◽  
Xin-Yu Xue ◽  
Guang-Ping Li ◽  
Yuan-Ke Zhou ◽  
...  
Keyword(s):  
Sequence Data ◽  
Software Tool ◽  
Data Retrieval ◽  
Supplementary Information ◽  
Statistical Features ◽  
Biological Sequence ◽  
Sequence Alignments ◽  
Multiple Sequence ◽  
Source Codes ◽  
Multiple Sequence Alignments

Abstract Summary Many efforts have been made in developing bioinformatics algorithms to predict functional attributes of genes and proteins from their primary sequences. One challenge in this process is to intuitively analyze and to understand the statistical features that have been selected by heuristic or iterative methods. In this paper, we developed VisFeature, which aims to be a helpful software tool that allows the users to intuitively visualize and analyze statistical features of all types of biological sequence, including DNA, RNA and proteins. VisFeature also integrates sequence data retrieval, multiple sequence alignments and statistical feature generation functions. Availability and implementation VisFeature is a desktop application that is implemented using JavaScript/Electron and R. The source codes of VisFeature are freely accessible from the GitHub repository (https://github.com/wangjun1996/VisFeature). The binary release, which includes an example dataset, can be freely downloaded from the same GitHub repository (https://github.com/wangjun1996/VisFeature/releases). Contact [email protected] or [email protected] Supplementary information Supplementary data are available at Bioinformatics online.

Prediction of mutation effects using a deep temporal convolutional network

2019 ◽  
Vol 36 (7) ◽  
pp. 2047-2052 ◽  
Author(s):  
Ha Young Kim ◽  
Dongsup Kim
Keyword(s):  
Latent Variable ◽  
Sequence Data ◽  
Generative Model ◽  
Supplementary Information ◽  
Biological Research ◽  
Sequence Alignments ◽  
Variable Model ◽  
Convolutional Network ◽  
Direct Optimization ◽  
Multiple Sequence

Abstract Motivation Accurate prediction of the effects of genetic variation is a major goal in biological research. Towards this goal, numerous machine learning models have been developed to learn information from evolutionary sequence data. The most effective method so far is a deep generative model based on the variational autoencoder (VAE) that models the distributions using a latent variable. In this study, we propose a deep autoregressive generative model named mutationTCN, which employs dilated causal convolutions and attention mechanism for the modeling of inter-residue correlations in a biological sequence. Results We show that this model is competitive with the VAE model when tested against a set of 42 high-throughput mutation scan experiments, with the mean improvement in Spearman rank correlation ∼0.023. In particular, our model can more efficiently capture information from multiple sequence alignments with lower effective number of sequences, such as in viral sequence families, compared with the latent variable model. Also, we extend this architecture to a semi-supervised learning framework, which shows high prediction accuracy. We show that our model enables a direct optimization of the data likelihood and allows for a simple and stable training process. Availability and implementation Source code is available at https://github.com/ha01994/mutationTCN. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Organization of the multigene families of African Swine Fever Virus

Fine Focus ◽  
2017 ◽  
Vol 3 (2) ◽  
pp. 155-170 ◽  
Author(s):  
Jacob Imbery ◽  
Chris Upton
Keyword(s):  
African Swine Fever Virus ◽  
Sequence Divergence ◽  
African Swine Fever ◽  
Fever Virus ◽  
Multigene Families ◽  
Sequence Alignments ◽  
Multiple Sequence ◽  
Multiple Sequence Alignments ◽  
Viral Genomes ◽  
Gene Maps

African swine fever virus is a complex DNA virus that infects swine and is spread by ticks. Mortality rates in domestic pigs are very high and the virus is a significant threat to pork farming. The genomes of 16 viruses have been sequenced completely, but these represent only a few of the 23 genotypes. The viral genome is unusual in that it contains 5 multigene families, each of which contain 3-19 duplicated copies (paralogs). There is significant sequence divergence between the paralogs in a single virus and between the orthologs in the different viral genomes. This, together with the fact that in most of the multigene families there are numerous gene indels that create truncations and fusions, makes annotation of these regions very difficult; it has led to inconsistent annotation of the 16 viral genomes. In this project, we have created multiple sequence alignments for each of the multigene families and have produced gene maps to help researchers more easily understand the organization of the multigene families among the different viruses. These gene maps will help researchers ascertain which members of the multigene families are present in each of the viruses. This is critical because some of the multigene families are known to be associated with virus virulence.

scholarly journals SECAPR - A bioinformatics pipeline for the rapid and user-friendly alignment of hybrid enrichment sequences, from raw reads to alignments

2018 ◽  
Author(s):  
Tobias Andermann ◽  
Angela Cano ◽  
Alexander Zizka ◽  
Christine Bacon ◽  
Alexandre Antonelli
Keyword(s):  
Evolutionary Biology ◽  
Sequence Data ◽  
Model Organisms ◽  
Sequencing Data ◽  
Bioinformatics Pipeline ◽  
Sequence Alignments ◽  
Multiple Sequence ◽  
Sequence Capture ◽  
Sequencing Platforms ◽  
User Friendly

Evolutionary biology has entered an era of unprecedented amounts of DNA sequence data, as new sequencing platforms such as Massive Parallel Sequencing (MPS) can generate billions of nucleotides within less than a day. The current bottleneck is how to efficiently handle, process, and analyze such large amounts of data in an automated and reproducible way. To tackle these challenges we introduce the Sequence Capture Processor (SECAPR) pipeline for processing raw sequencing data into multiple sequence alignments for downstream phylogenetic and phylogeographic analyses. SECAPR is user-friendly and we provide an exhaustive tutorial intended for users with no prior experience with analyzing MPS output. SECAPR is particularly useful for the processing of sequence capture (= hybrid enrichment) datasets for non-model organisms, as we demonstrate using an empirical dataset of the palm genus Geonoma (Arecaceae). Various quality control and plotting functions help the user to decide on the most suitable settings for even challenging datasets. SECAPR is an easy-to-use, free, and versatile pipeline, aimed to enable efficient and reproducible processing of MPS data for many samples in parallel.

scholarly journals A minimum reporting standard for multiple sequence alignments

2020 ◽  
Author(s):  
Thomas KF Wong ◽  
Subha Kalyaanamoorthy ◽  
Karen Meusemann ◽  
David K Yeates ◽  
Bernhard Misof ◽  
...  
Keyword(s):  
Amino Acids ◽  
Sequence Data ◽  
Pivotal Role ◽  
Sequence Alignments ◽  
Reporting Standard ◽  
Multiple Sequence ◽  
Molecular Sequence Data ◽  
Molecular Sequence ◽  
Multiple Sequence Alignments

ABSTRACTMultiple sequence alignments (MSAs) play a pivotal role in studies of molecular sequence data, but nobody has developed a minimum reporting standard (MRS) to quantify the completeness of MSAs in terms of completely-specified nucleotides or amino acids. We present an MRS that relies on four simple completeness metrics. The metrics are implemented in AliStat, a program developed to support the MRS. A survey of published MSAs illustrates the benefits and unprecedented transparency offered by the MRS.

scholarly journals A minimum reporting standard for multiple sequence alignments

2020 ◽  
Vol 2 (2) ◽  
Author(s):  
Thomas K F Wong ◽  
Subha Kalyaanamoorthy ◽  
Karen Meusemann ◽  
David K Yeates ◽  
Bernhard Misof ◽  
...  
Keyword(s):  
Amino Acids ◽  
Sequence Data ◽  
Pivotal Role ◽  
Sequence Alignments ◽  
Reporting Standard ◽  
Multiple Sequence ◽  
Molecular Sequence Data ◽  
Molecular Sequence ◽  
Multiple Sequence Alignments

Abstract Multiple sequence alignments (MSAs) play a pivotal role in studies of molecular sequence data, but nobody has developed a minimum reporting standard (MRS) to quantify the completeness of MSAs in terms of completely specified nucleotides or amino acids. We present an MRS that relies on four simple completeness metrics. The metrics are implemented in AliStat, a program developed to support the MRS. A survey of published MSAs illustrates the benefits and unprecedented transparency offered by the MRS.

Ectopic expression of a Eucalyptus grandis SVP orthologue alters the flowering time of Arabidopsis thaliana

2004 ◽  
Vol 31 (3) ◽  
pp. 217 ◽  
Author(s):  
Elizabeth M. Brill ◽  
John M. Watson
Keyword(s):  
Arabidopsis Thaliana ◽  
Sequence Data ◽  
Expression Patterns ◽  
Ectopic Expression ◽  
Eucalyptus Grandis ◽  
Floral Organ ◽  
Sequence Alignments ◽  
Nucleotide Sequence Data ◽  
Mads Box Gene ◽  
Nucleotide Database

A new MADS-box gene, EgrSVP was isolated from vegetative tips of Eucalyptus grandis Hill ex Maiden saplings. This gene was expressed in vegetative tissues such as shoots, leaves and roots, as well as in unopened floral buds. DNA sequence alignments indicate that EgrSVP shares the highest level of sequence identity with PkMADS1, JOINTLESS, IbMADS3 and SVP. Phylogenetically, it is grouped in the JOINTLESS clade, the members of which share similar expression patterns. Transgenic Arabidopsis thaliana (L.) Heynh. plants overexpressing EgrSVP, exhibited a variety of altered phenotypes, including homeotic floral organ transformation, indeterminate floral development, multiple inflorescences and coflorescences, and some degree of late flowering.The nucleotide sequence data reported will appear in the GenBank Nucleotide Database under the accession number AY263809.

scholarly journals Identification and analysis of proton-translocating pyrophosphatases in the methanogenic archaeonMethanosarcina mazei

Archaea ◽  
2002 ◽  
Vol 1 (1) ◽  
pp. 1-7 ◽  
Author(s):  
Sebastian Bäumer ◽  
Sabine Lentes ◽  
Gerhard Gottschalk ◽  
Uwe Deppenmeier
Keyword(s):  
Specific Activity ◽  
Sequence Data ◽  
Sequence Similarity ◽  
Open Reading Frames ◽  
Inorganic Pyrophosphatase ◽  
Amino Acid Sequence Similarity ◽  
Sequence Alignments ◽  
Multiple Sequence ◽  
Inorganic Pyrophosphate ◽  
Reading Frames

Analysis of genome sequence data from the methanogenic archaeonMethanosarcina mazeiGö1 revealed the existence of two open reading frames encoding proton-translocating pyrophosphatases (PPases). These open reading frames are linked by a 750-bp intergenic region containing TC-rich stretches and are transcribed in opposite directions. The corresponding polypeptides are referred to as Mvp1 and Mvp2 and consist of 671 and 676 amino acids, respectively. Both enzymes represent extremely hydrophobic, integral membrane proteins with 15 predicted transmembrane segments and an overall amino acid sequence similarity of 50.1%. Multiple sequence alignments revealed that Mvp1 is closely related to eukaryotic PPases, whereas Mvp2 shows highest homologies to bacterial PPases. Northern blot experiments with RNA from methanol-grown cells harvested in the mid-log growth phase indicated that only Mvp2 was produced under these conditions. Analysis of washed membranes showed that Mvp2 had a specific activity of 0.34 U mg (protein)–1. Proton translocation experiments with inverted membrane vesicles prepared from methanol-grown cells showed that hydrolysis of 1 mol of pyrophosphate was coupled to the translocation of about 1 mol of protons across the cytoplasmic membrane. Appropriate conditions formvp1 expression could not be determined yet. The pyrophosphatases ofM. mazeiGö1 represent the first examples of this enzyme class in methanogenic archaea and may be part of their energy-conserving system. Abbreviations: DCCD,N,N′-dicyclohexylcarbodiimide; PPase, inorganic pyrophosphatase; PPi, inorganic pyrophosphate; Δp, proton motive force.

A Re-Examination of Generic Concepts of Baeomycetoid Lichens Based on Phylogenetic Analyses of Nuclear ssu and Lsu Ribosomal Dna

1999 ◽  
Vol 31 (5) ◽  
pp. 409-418 ◽  
Author(s):  
Jamie L. Platt ◽  
Joseph W. Spatafora
Keyword(s):  
Ribosomal Dna ◽  
Sequence Data ◽  
Phylogenetic Analyses ◽  
Large Subunit ◽  
Small Subunit ◽  
Lsu Rdna ◽  
Character State ◽  
Nucleotide Sequence Data ◽  
Lichen Symbiosis ◽  
Data Support

AbstractThe lichen symbiosis has evolved several times within the fungal kingdom, although the total number of lichenization events leading to extant taxa is still unclear. Two lichenized families, the Icmadophilaceae and Baeomycetaceae have been classified in the Helotiales. Because the Helotiales are predominantly nonlichenized, this suggests that these families represent independent evolutionary episodes of lichenization from the Lecanorales. As a first step towards understanding the evolution of the lichen symbiosis within this order, we tested recent hypotheses concerning the segregation of lichen genera between the two lichen families. Specifically, we used phylogenetic analyses of nucleotide sequence data from nuclear small-subunit and large-subunit ribosomal DNA to test the morphology-based hypotheses that Dibaeis is a distinct genus from Baeomyces and that Dibaeis is a member of the Icmadophilaceae rather than the Baeomycetaceae. Phylogenetic analyses of nuclear SSU rDNA and combined SSU and LSU rDNA data support the hypothesis that Dibaeis is more closely related to IcmadophUa than it is to Baeomyces. Therefore, these data support the resurrection of Dibaeis from its previous synonymy with Baeomyces based on the characters of ascocarp colour and ascus morphology. The recognition of two distinct genera is also consistent with character state distribution of unique lichen acids.

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