scholarly journals From DNA sequences to microbial ecology: Wrangling NEON soil microbe data with the neonMicrobe R package

Ecosphere ◽  
2021 ◽  
Vol 12 (11) ◽  
Author(s):  
Clara Qin ◽  
Ryan Bartelme ◽  
Y. Anny Chung ◽  
Dawson Fairbanks ◽  
Yang Lin ◽  
...  
Keyword(s):  
Microbial Ecology ◽  
Dna Sequences ◽  
R Package ◽  
Soil Microbe

Systematics, Biogeography, and Morphological Character Evolution of the Hemiepiphytic Subfamily Monsteroideae (Araceae)

2019 ◽  
Vol 104 (1) ◽  
pp. 33-48 ◽  
Author(s):  
Alejandro Zuluaga ◽  
Martin Llano ◽  
Ken Cameron
Keyword(s):  
Dna Sequences ◽  
Pacific Islands ◽  
R Package ◽  
Morphological Characters ◽  
Ancestral State ◽  
Seed Shape ◽  
Long Distance ◽  
The Pacific ◽  
And Migration ◽  
The Tropics

The subfamily Monsteroideae (Araceae) is the third richest clade in the family, with ca. 369 described species and ca. 700 estimated. It comprises mostly hemiepiphytic or epiphytic plants restricted to the tropics, with three intercontinental disjunctions. Using a dataset representing all 12 genera in Monsteroideae (126 taxa), and five plastid and two nuclear markers, we studied the systematics and historical biogeography of the group. We found high support for the monophyly of the three major clades (Spathiphylleae sister to Heteropsis Kunth and Rhaphidophora Hassk. clades), and for six of the genera within Monsteroideae. However, we found low rates of variation in the DNA sequences used and a lack of molecular markers suitable for species-level phylogenies in the group. We also performed ancestral state reconstruction of some morphological characters traditionally used for genera delimitation. Only seed shape and size, number of seeds, number of locules, and presence of endosperm showed utility in the classification of genera in Monsteroideae. We estimated ancestral ranges using a dispersal-extinction-cladogenesis model as implemented in the R package BioGeoBEARS and found evidence for a Gondwanan origin of the clade. One tropical disjunction (Monstera Adans. sister to Amydrium Schott–Epipremnum Schott) was found to be the product of a previous Boreotropical distribution. Two other disjunctions are more recent and likely due to long-distance dispersal: Spathiphyllum Schott (with Holochlamys Engl. nested within) represents a dispersal from South America to the Pacific Islands in Southeast Asia, and Rhaphidophora represents a dispersal from Asia to Africa. Future studies based on stronger phylogenetic reconstructions and complete morphological datasets are needed to explore the details of speciation and migration within and among areas in Asia.

scholarly journals Exploring DNA structures in real-time polymerase kinetics using Pacific Biosciences sequencer data

2013 ◽  
Author(s):  
Sterling Sawaya ◽  
James Boocock ◽  
Mik Black ◽  
Neil Gemmell
Keyword(s):  
Real Time ◽  
Dna Sequences ◽  
Double Helix ◽  
Dna Structure ◽  
R Package ◽  
Dna Structures ◽  
Pacific Biosciences ◽  
Tandem Repeat Sequences ◽  
The Pacific ◽  
Polymerase Pausing

Pausing of DNA polymerase can indicate the presence of a DNA structure that differs from the canonical double-helix. Here we detail a method to investigate how polymerase pausing in the Pacific Biosciences sequencer reads can be related to DNA structure. The Pacific Biosciences sequencer uses optics to view a polymerase and its interaction with a single DNA molecule in real-time, offering a unique way to detect potential alternative DNA structures. We have developed a new way to examine polymerase kinetics and relate it to the DNA sequence by using a wavelet transform of read information from the sequencer. We use this method to examine how polymerase kinetics are related to nucleotide base composition. We then examine tandem repeat sequences known for their ability to form different DNA structures: (CGG)n and (CG)n repeats which can, respectively, form G-quadruplex DNA and Z-DNA. We find pausing around the (CGG)n repeat that may indicate the presence of G-quadruplexes in some of the sequencer reads. The (CG)n repeat does not appear to cause polymerase pausing, but its kinetics signature nevertheless suggests the possibility that alternative nucleotide conformations may sometimes be present. We discuss the implications of using our method to discover DNA sequences capable of forming alternative structures. The analyses presented here can be reproduced on any Pacific Biosciences kinetics data for any DNA pattern of interest using an R package that we have made publicly available.

scholarly journals AnnotationBustR: an R package to extract subsequences from GenBank annotations

PeerJ ◽  
2018 ◽  
Vol 6 ◽  
pp. e5179 ◽  
Author(s):  
Samuel R. Borstein ◽  
Brian C. O’Meara
Keyword(s):  
Comparative Analysis ◽  
Dna Sequences ◽  
Large Scale ◽  
Retrieval System ◽  
Single Gene ◽  
R Package ◽  
Fasta File ◽  
Search Terms ◽  
Large Numbers ◽  
Wide Range

BackgroundDNA sequences are pivotal for a wide array of research in biology. Large sequence databases, like GenBank, provide an amazing resource to utilize DNA sequences for large scale analyses. However, many sequence records on GenBank contain more than one gene or are portions of genomes. Inconsistencies in the way genes are annotated and the numerous synonyms a single gene may be listed under provide major challenges for extracting large numbers of subsequences for comparative analysis across taxa. At present, there is no easy way to extract portions from many GenBank accessions based on annotations where gene names may vary extensively.ResultsThe R packageAnnotationBustRallows users to extract sequences based on GenBank annotations through the ACNUC retrieval system given search terms of gene synonyms and accession numbers.AnnotationBustRextracts subsequences of interest and then writes them to a FASTA file for users to employ in their research endeavors.ConclusionFASTA files of extracted subsequences and accession tables generated byAnnotationBustRallow users to quickly find and extract subsequences from GenBank accessions. These sequences can then be incorporated in various analyses, like the construction of phylogenies to test a wide range of ecological and evolutionary hypotheses.

scholarly journals Inference and visualization of DNA damage patterns using a grade of membership model

2018 ◽  
Author(s):  
Hussein Al-Asadi ◽  
Kushal K Dey ◽  
John Novembre ◽  
Matthew Stephens
Keyword(s):  
Dna Damage ◽  
Quality Control ◽  
Dna Sequences ◽  
High Frequency ◽  
Topic Model ◽  
R Package ◽  
Strand Breaks ◽  
1000 Genomes ◽  
Grade Of Membership ◽  
Mismatch Rate

AbstractQuality control plays a major role in the analysis of ancient DNA (aDNA). One key step in this quality control is assessment of DNA damage: aDNA contains unique signatures of DNA damage that distinguish it from modern DNA, and so analyses of damage patterns can help confirm that DNA sequences obtained are from endogenous aDNA rather than from modern contamination. Predominant signatures of DNA damage include a high frequency of cytosine to thymine substitutions (C-to-T) at the ends of fragments, and elevated rates of purines (A & G) before the 5’ strand-breaks. Existing QC procedures help assess damage by simply plotting for each sample, the C-to-T mismatch rate along the read and the composition of bases before the 5’ strand-breaks. Here we present a more flexible and comprehensive model-based approach to infer and visualize damage patterns in aDNA, implemented in an R package aRchaic. This approach is based on a “grade of membership” model (also known as “admixture” or “topic” model) in which each sample has an estimated grade of membership in each of K damage profiles that are estimated from the data. We illustrate aRchaic on data from several aDNA studies and modern individuals from 1000 Genomes Project Consortium (2012). Here, aRchaic clearly distinguishes modern from ancient samples irrespective of DNA extraction, lab and sequencing protocols. Additionally, through an in-silico contamination experiment, we show that the aRchaic grades of membership reflect relative levels of exogenous modern contamination. Together, the outputs of aRchaic provide a concise visual summary of DNA damage patterns, as well as other processes generating mismatches in the data. Availability: aRchaic is available for download from https://www.github.com/kkdey/aRchaic.Contact: [email protected], [email protected]

scholarly journals AnnotationBustR: An R package to extract subsequences from GenBank annotations

2017 ◽  
Author(s):  
Samuel R. Borstein ◽  
Brian C. O'Meara
Keyword(s):  
Comparative Analysis ◽  
Dna Sequences ◽  
Large Scale ◽  
Retrieval System ◽  
Single Gene ◽  
R Package ◽  
Fasta File ◽  
Search Terms ◽  
Large Numbers ◽  
Wide Range

Background. DNA sequences are pivotal for a wide array of research in biology. Large sequence databases, like GenBank, provide an amazing resource to utilize DNA sequences for large scale analyses. However, many sequences on GenBank contain more than one gene or are portions of genomes, and inconsistencies in the way genes are annotated and the numerous synonyms a single gene may be listed under provide major challenges for extracting large numbers of subsequences for comparative analysis across taxa. At present, there is no easy way to extract portions from multiple GenBank accessions based on annotations where gene names may vary extensively. Results. The R package AnnotationBustR allows users to extract sequences based on GenBank annotations through the ACNUC retrieval system given search terms of gene synonyms and accession numbers. AnnotationBustR extracts portions of interest and then writes them to a FASTA file for users to employ in their research endeavors. Conclusion. FASTA files of extracted subsequences and accession tables generated by AnnotationBustR allow users to quickly find and extract subsequences from GenBank accessions. These sequences can then be incorporated in various analyses, like the construction of phylogenies to test a wide range of ecological and evolutionary hypotheses.

scholarly journals AnnotationBustR: An R package to extract subsequences from GenBank annotations

2017 ◽  
Author(s):  
Samuel R. Borstein ◽  
Brian C. O'Meara
Keyword(s):  
Comparative Analysis ◽  
Dna Sequences ◽  
Large Scale ◽  
Retrieval System ◽  
Single Gene ◽  
R Package ◽  
Fasta File ◽  
Search Terms ◽  
Large Numbers ◽  
Wide Range

Background. DNA sequences are pivotal for a wide array of research in biology. Large sequence databases, like GenBank, provide an amazing resource to utilize DNA sequences for large scale analyses. However, many sequences on GenBank contain more than one gene or are portions of genomes, and inconsistencies in the way genes are annotated and the numerous synonyms a single gene may be listed under provide major challenges for extracting large numbers of subsequences for comparative analysis across taxa. At present, there is no easy way to extract portions from multiple GenBank accessions based on annotations where gene names may vary extensively. Results. The R package AnnotationBustR allows users to extract sequences based on GenBank annotations through the ACNUC retrieval system given search terms of gene synonyms and accession numbers. AnnotationBustR extracts portions of interest and then writes them to a FASTA file for users to employ in their research endeavors. Conclusion. FASTA files of extracted subsequences and accession tables generated by AnnotationBustR allow users to quickly find and extract subsequences from GenBank accessions. These sequences can then be incorporated in various analyses, like the construction of phylogenies to test a wide range of ecological and evolutionary hypotheses.

scholarly journals Inference and visualization of DNA damage patterns using a grade of membership model

2018 ◽  
Vol 35 (8) ◽  
pp. 1292-1298 ◽  
Author(s):  
Hussein Al-Asadi ◽  
Kushal K Dey ◽  
John Novembre ◽  
Matthew Stephens
Keyword(s):  
Dna Damage ◽  
Quality Control ◽  
Dna Sequences ◽  
Topic Model ◽  
R Package ◽  
Supplementary Information ◽  
Strand Breaks ◽  
1000 Genomes ◽  
Grade Of Membership ◽  
Mismatch Rate

Abstract Motivation Quality control plays a major role in the analysis of ancient DNA (aDNA). One key step in this quality control is assessment of DNA damage: aDNA contains unique signatures of DNA damage that distinguish it from modern DNA, and so analyses of damage patterns can help confirm that DNA sequences obtained are from endogenous aDNA rather than from modern contamination. Predominant signatures of DNA damage include a high frequency of cytosine to thymine substitutions (C-to-T) at the ends of fragments, and elevated rates of purines (A & G) before the 5′ strand-breaks. Existing QC procedures help assess damage by simply plotting for each sample, the C-to-T mismatch rate along the read and the composition of bases before the 5′ strand-breaks. Here we present a more flexible and comprehensive model-based approach to infer and visualize damage patterns in aDNA, implemented in an R package aRchaic. This approach is based on a ‘grade of membership’ model (also known as ‘admixture’ or ‘topic’ model) in which each sample has an estimated grade of membership in each of K damage profiles that are estimated from the data. Results We illustrate aRchaic on data from several aDNA studies and modern individuals from 1000 Genomes Project Consortium (2012). Here, aRchaic clearly distinguishes modern from ancient samples irrespective of DNA extraction, lab and sequencing protocols. Additionally, through an in-silico contamination experiment, we show that the aRchaic grades of membership reflect relative levels of exogenous modern contamination. Together, the outputs of aRchaic provide a concise visual summary of DNA damage patterns, as well as other processes generating mismatches in the data. Availability and implementation aRchaic is available for download from https://www.github.com/kkdey/aRchaic. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Supervised Detection of Conserved Motifs in DNA Sequences with Cosmo

2007 ◽  
Vol 6 (1) ◽  
Author(s):  
Oliver Bembom ◽  
Sunduz Keles ◽  
Mark J. van der Laan
Keyword(s):  
Transcription Factor ◽  
Dna Sequences ◽  
Web Application ◽  
Ad Hoc ◽  
Profile Likelihood ◽  
R Package ◽  
Simulation Studies ◽  
Likelihood Principle ◽  
Unconstrained Model ◽  
Likelihood Approach

A number of computational methods have been proposed for identifying transcription factor binding sites from a set of unaligned sequences that are thought to share the motif in question. We here introduce an algorithm, called cosmo, that allows this search to be supervised by specifying a set of constraints that the position weight matrix of the unknown motif must satisfy. Such constraints may be formulated, for example, on the basis of prior knowledge about the structure of the transcription factor in question. The algorithm is based on the same two-component multinomial mixture model used by MEME, with stronger reliance, however, on the likelihood principle instead of more ad-hoc criteria like the E-value. The intensity parameter in the ZOOPS and TCM models, for instance, is estimated based on a profile-likelihood approach, and the width of the unknown motif is selected based on BIC. These changes allow cosmo to outperform MEME even in the absence of any constraints, as evidenced by 2- to 3-fold greater sensitivity in some simulation studies. Additional improvements in performance can be achieved by selecting the model type (OOPS, ZOOPS, or TCM) data-adaptively or by supplying correctly specified constraints, especially if the motif appears only as a weak signal in the data. The algorithm can data-adaptively choose between working in a given constrained model or in the completely unconstrained model, guarding against the risk of supplying mis-specified constraints. Simulation studies suggest that this approach can offer 3 to 3.5 times greater sensitivity than MEME. The algorithm has been implemented in the form of a stand-alone C program as well as a web application that can be accessed at http://cosmoweb.berkeley.edu. An R package is available through Bioconductor (http://bioconductor.org).

scholarly journals micRocounter: Microsatellite Characterization in Genome Assemblies

2019 ◽  
Vol 9 (10) ◽  
pp. 3101-3104 ◽  
Author(s):  
Johnathan Lo ◽  
Michelle M. Jonika ◽  
Heath Blackmon
Keyword(s):  
Dna Sequences ◽  
Evolutionary Biology ◽  
Genomic Data ◽  
Automated Analysis ◽  
R Package ◽  
Data Sets ◽  
Coding Regions ◽  
Benchmark Datasets ◽  
Computationally Intensive ◽  
Genome Assemblies

Microsatellites are repetitive DNA sequences usually found in non-coding regions of the genome. Their quantification and analysis have applications in fields from population genetics to evolutionary biology. As genome assemblies become commonplace, the need for software that can facilitate analyses has never been greater. In particular, R packages that can analyze genomic data are particularly important since this is one of the most popular software environments for biologists. We created an R package, micRocounter, to quantify microsatellites. We have optimized our package for speed, accessibility, and portability, making the automated analysis of large genomic data sets feasible. Computationally intensive algorithms were built in C++ to increase speed. Tests using benchmark datasets show a 200-fold improvement in speed over existing software. A moderately sized genome of 500 Mb can be processed in under 50 sec. Results are output as an object in R increasing accessibility and flexibility for practitioners.

scholarly journals MPRAnalyze - A statistical framework for Massively Parallel Reporter Assays

2019 ◽  
Author(s):  
Tal Ashuach ◽  
David Sebastian Fischer ◽  
Anat Kreimer ◽  
Nadav Ahituv ◽  
Fabian Theis ◽  
...  
Keyword(s):  
Dna Sequences ◽  
Linear Models ◽  
Simulated Data ◽  
R Package ◽  
Massively Parallel ◽  
Published Data ◽  
Regulatory Sequence ◽  
Regulatory Sequences ◽  
Statistical Framework ◽  
Reporter Assays

AbstractMassively parallel reporter assays (MPRAs) are a technique that enables testing thousands of regulatory DNA sequences and their variants in a single, quantitative experiment. Despite growing popularity, there is lack of statistical methods that account for the different sources of uncertainty inherent to these assays, thus effectively leveraging their promise. Development of such methods could help enhance our ability to identify regulatory sequences in the genome, understand their function under various setting, and ultimately gain a better understanding of how the regulatory code and its alteration lead to phenotypic consequence.Here we present MPRAnalyze: a statistical framework dedicated to analyzing MPRA count data. MPRAnalyze addresses the major questions that are posed in the context of MPRA experiments: estimating the magnitude of the effect of a regulatory sequence in a single condition setting, and comparing differential activity of regulatory sequences across multiple conditions. The framework uses a nested construction of generalized linear models to account for uncertainty in both DNA and RNA observations, controls for various sources of unwanted variation, and incorporates negative controls for robust hypothesis testing, thereby providing clear quantitative answers in complex experimental settings.We demonstrate the robustness, accuracy and applicability of MPR-Analyze on simulated data and published data sets and compare it against the existing analysis methodologies. MPRAnalyze is implemented as an R package and is publicly available through Bioconductor [1].

Sign in / Sign up

Export Citation Format

Share Document