scholarly journals mtDNAcombine: tools to combine sequences from multiple studies

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Eleanor F. Miller ◽  
Andrea Manica
Keyword(s):  
Sequence Data ◽  
Data Extraction ◽  
Bayesian Skyline Plot ◽  
Model Organisms ◽  
Data Sets ◽  
Data Handling ◽  
Online Database ◽  
Genetic Studies ◽  
Wide Range ◽  
Existing Data

Abstract Background Today an unprecedented amount of genetic sequence data is stored in publicly available repositories. For decades now, mitochondrial DNA (mtDNA) has been the workhorse of genetic studies, and as a result, there is a large volume of mtDNA data available in these repositories for a wide range of species. Indeed, whilst whole genome sequencing is an exciting prospect for the future, for most non-model organisms’ classical markers such as mtDNA remain widely used. By compiling existing data from multiple original studies, it is possible to build powerful new datasets capable of exploring many questions in ecology, evolution and conservation biology. One key question that these data can help inform is what happened in a species’ demographic past. However, compiling data in this manner is not trivial, there are many complexities associated with data extraction, data quality and data handling. Results Here we present the mtDNAcombine package, a collection of tools developed to manage some of the major decisions associated with handling multi-study sequence data with a particular focus on preparing sequence data for Bayesian skyline plot demographic reconstructions. Conclusions There is now more genetic information available than ever before and large meta-data sets offer great opportunities to explore new and exciting avenues of research. However, compiling multi-study datasets still remains a technically challenging prospect. The mtDNAcombine package provides a pipeline to streamline the process of downloading, curating, and analysing sequence data, guiding the process of compiling data sets from the online database GenBank.

scholarly journals mtDNAcombine: tools to combine sequences from multiple studies

2020 ◽  
Author(s):  
Eleanor F. Miller ◽  
Andrea Manica
Keyword(s):  
Conservation Biology ◽  
Sequence Data ◽  
Data Extraction ◽  
Bayesian Skyline Plot ◽  
Model Organisms ◽  
Data Handling ◽  
Genetic Studies ◽  
Genetic Sequence ◽  
Wide Range ◽  
Existing Data

AbstractToday an unprecedented amount of genetic sequence data is stored in publicly available repositories. For decades now, mitochondrial DNA (mtDNA) has been the workhorse of genetic studies, and as a result, there is a large volume of mtDNA data available in these repositories for a wide range of species. Indeed, whilst whole genome sequencing is an exciting prospect for the future, for most non-model organisms’ classical markers such as mtDNA remain widely used. By compiling existing data from multiple original studies, it is possible to build powerful new datasets capable of exploring many questions in ecology, evolution and conservation biology. One key question that these data can help inform is what happened in a species’ demographic past. However, compiling data in this manner is not trivial, there are many complexities associated with data extraction, data quality and data handling. Here we present the mtDNAcombine package, a collection of tools developed to manage some of the major decisions associated with handling multi-study sequence data with a particular focus on preparing mtDNA data for Bayesian Skyline Plot demographic reconstructions.

scholarly journals SNP-CRISPR: a web tool for SNP-specific genome editing

2019 ◽  
Author(s):  
Chiao-Lin Chen ◽  
Jonathan Rodiger ◽  
Verena Chung ◽  
Raghuvir Viswanatha ◽  
Stephanie E. Mohr ◽  
...  
Keyword(s):  
Genome Editing ◽  
Model Systems ◽  
Model Organisms ◽  
Data Sets ◽  
Nucleotide Polymorphisms ◽  
Web Tool ◽  
Guide Rna ◽  
Wide Range ◽  
Sgrna Design ◽  
Reference Genomes

ABSTRACTCRISPR-Cas9 is a powerful genome editing technology in which a single guide RNA (sgRNA) confers target site specificity to achieve Cas9-mediated genome editing. Numerous sgRNA design tools have been developed based on reference genomes for humans and model organisms. However, existing resources are not optimal as genetic mutations or single nucleotide polymorphisms (SNPs) within the targeting region affect the efficiency of CRISPR-based approaches by interfering with guide-target complementarity. To facilitate identification of sgRNAs (1) in non-reference genomes, (2) across varying genetic backgrounds, or (3) for specific targeting of SNP-containing alleles, for example, disease relevant mutations, we developed a web tool, SNP-CRISPR (https://www.flyrnai.org/tools/snp_crispr/). SNP-CRISPR can be used to design sgRNAs based on public variant data sets or user-identified variants. In addition, the tool computes efficiency and specificity scores for sgRNA designs targeting both the variant and the reference. Moreover, SNP-CRISPR provides the option to upload multiple SNPs and target single or multiple nearby base changes simultaneously with a single sgRNA design. Given these capabilities, SNP-CRISPR has a wide range of potential research applications in model systems and for design of sgRNAs for disease-associated variant correction.

scholarly journals SNP-CRISPR: A Web Tool for SNP-Specific Genome Editing

2019 ◽  
Vol 10 (2) ◽  
pp. 489-494 ◽  
Author(s):  
Chiao-Lin Chen ◽  
Jonathan Rodiger ◽  
Verena Chung ◽  
Raghuvir Viswanatha ◽  
Stephanie E. Mohr ◽  
...  
Keyword(s):  
Genome Editing ◽  
Model Systems ◽  
Model Organisms ◽  
Data Sets ◽  
Nucleotide Polymorphisms ◽  
Web Tool ◽  
Guide Rna ◽  
Wide Range ◽  
Sgrna Design ◽  
Reference Genomes

CRISPR-Cas9 is a powerful genome editing technology in which a single guide RNA (sgRNA) confers target site specificity to achieve Cas9-mediated genome editing. Numerous sgRNA design tools have been developed based on reference genomes for humans and model organisms. However, existing resources are not optimal as genetic mutations or single nucleotide polymorphisms (SNPs) within the targeting region affect the efficiency of CRISPR-based approaches by interfering with guide-target complementarity. To facilitate identification of sgRNAs (1) in non-reference genomes, (2) across varying genetic backgrounds, or (3) for specific targeting of SNP-containing alleles, for example, disease relevant mutations, we developed a web tool, SNP-CRISPR (https://www.flyrnai.org/tools/snp_crispr/). SNP-CRISPR can be used to design sgRNAs based on public variant data sets or user-identified variants. In addition, the tool computes efficiency and specificity scores for sgRNA designs targeting both the variant and the reference. Moreover, SNP-CRISPR provides the option to upload multiple SNPs and target single or multiple nearby base changes simultaneously with a single sgRNA design. Given these capabilities, SNP-CRISPR has a wide range of potential research applications in model systems and for design of sgRNAs for disease-associated variant correction.

Glutton: large-scale integration of non-model organism transcriptome data for comparative analysis

2016 ◽  
Author(s):  
Alan Medlar ◽  
Laura Laakso ◽  
Andreia Miraldo ◽  
Ari Löytynoja
Keyword(s):  
Comparative Analysis ◽  
Large Scale ◽  
De Novo ◽  
Sequence Data ◽  
Model Organism ◽  
Model Organisms ◽  
Rna Seq ◽  
Reference Species ◽  
Wide Range ◽  
The Impact

AbstractHigh-throughput RNA-seq data has become ubiquitous in the study of non-model organisms, but its use in comparative analysis remains a challenge. Without a reference genome for mapping, sequence data has to be de novo assembled, producing large numbers of short, highly redundant contigs. Preparing these assemblies for comparative analyses requires the removal of redundant isoforms, assignment of orthologs and converting fragmented transcripts into gene alignments. In this article we present Glutton, a novel tool to process transcriptome assemblies for downstream evolutionary analyses. Glutton takes as input a set of fragmented, possibly erroneous transcriptome assemblies. Utilising phylogeny-aware alignment and reference data from a closely related species, it reconstructs one transcript per gene, finds orthologous sequences and produces accurate multiple alignments of coding sequences. We present a comprehensive analysis of Glutton’s performance across a wide range of divergence times between study and reference species. We demonstrate the impact choice of assembler has on both the number of alignments and the correctness of ortholog assignment and show substantial improvements over heuristic methods, without sacrificing correctness. Finally, using inference of Darwinian selection as an example of downstream analysis, we show that Glutton-processed RNA-seq data give results comparable to those obtained from full length gene sequences even with distantly related reference species. Glutton is available from http://wasabiapp.org/software/glutton/ and is licensed under the GPLv3.

Editorial: the changing nature of data

2016 ◽  
Vol 30 (7) ◽  
pp. 673-675 ◽  
Author(s):  
Steve Baron ◽  
Rebekah Russell-Bennett
Keyword(s):  
Design Methodology ◽  
Services Marketing ◽  
Data Sources ◽  
Service Research ◽  
Data Sets ◽  
Data Handling ◽  
Large Problem ◽  
Content Type ◽  
Innovative Methods ◽  
Existing Data

Purpose The purpose of this paper it to highlight the challenges of managing and handling data for services marketers that have been brought about by the contemporary environment and emerging schools of thought. Design/methodology/approach A comparison is made between conventional data collection and statistical analysis, and the need to glean information from large, pre-existing data sets for future contributions to service research. Findings For service marketers to tackle real world, large problem areas, there will be a need to develop methods of dealing with data which pre-exist in many forms, as well as data that are collected via well-established procedures. Originality/value The study should be an encouragement for services marketing researchers to develop innovative methods of data handling which recognize a world of burgeoning data sources and types.

scholarly journals A TALE of shrimps: Genome-wide survey of homeobox genes in 120 species from diverse crustacean taxa

F1000Research ◽  
2018 ◽  
Vol 7 ◽  
pp. 71
Author(s):  
Wai Hoong Chang ◽  
Alvina G. Lai
Keyword(s):  
Homeobox Genes ◽  
Whole Body ◽  
Model Organisms ◽  
Data Sets ◽  
Crop Species ◽  
Physiological Processes ◽  
Genome Wide ◽  
Wide Range ◽  
Identification And Characterization

The homeodomain-containing proteins are an important group of transcription factors found in most eukaryotes including animals, plants and fungi. Homeobox genes are responsible for a wide range of critical developmental and physiological processes, ranging from embryonic development, innate immune homeostasis to whole-body regeneration. With continued fascination on this key class of proteins by developmental and evolutionary biologists, multiple efforts have thus far focused on the identification and characterization of homeobox orthologs from key model organisms in attempts to infer their evolutionary origin and how this underpins the evolution of complex body plans. Despite their importance, the genetic complement of homeobox genes has yet been described in one of the most valuable groups of animals representing economically important food crops. With crustacean aquaculture being a growing industry worldwide, it is clear that systematic and cross-species identification of crustacean homeobox orthologs is necessary in order to harness this genetic circuitry for the improvement of aquaculture sustainability. Using publicly available transcriptome data sets, we identified a total of 4183 putative homeobox genes from 120 crustacean species that include food crop species, such as lobsters, shrimps, crayfish and crabs. Additionally, we identified 717 homeobox orthologs from 6 other non-crustacean arthropods, which include the scorpion, deer tick, mosquitoes and centipede. This high confidence set of homeobox genes will now serve as a key resource to the broader community for future functional and comparative genomics studies.

scholarly journals Inference of recombination maps from a single pair of genomes and its application to archaic samples

2018 ◽  
Author(s):  
Gustavo V. Barroso ◽  
Natasa Puzovic ◽  
Julien Y. Dutheil
Keyword(s):  
Fungal Pathogen ◽  
Sequence Data ◽  
Modern Human ◽  
Model Organisms ◽  
Human Populations ◽  
Single Pair ◽  
Genomic Distribution ◽  
Large Samples ◽  
Fundamental Goal ◽  
Wide Range

ABSTRACTUnderstanding the causes and consequences of recombination rate evolution is a fundamental goal in genetics that requires recombination maps from across the tree of life. Since statistical inference of recombination maps typically depends on large samples, reaching out studies to non-model organisms requires alternative tools. Here we extend the sequentially Markovian coalescent model to jointly infer demography and the variation in recombination along a pair of genomes. Using extensive simulations and sequence data from humans, fruit-flies and a fungal pathogen, we demonstrate that iSMC accurately infers recombination maps under a wide range of scenarios – remarkably, even from a single pair of unphased genomes. We exploit this possibility and reconstruct the recombination maps of archaic hominids. We report that the evolution of the recombination landscape follows the established phylogeny of Neandertals, Denisovans and modern human populations, as expected if the genomic distribution of crossovers in hominids is largely neutral.

scholarly journals Bayesian Evaluation of Temporal Signal in Measurably Evolving Populations

2020 ◽  
Vol 37 (11) ◽  
pp. 3363-3379 ◽  
Author(s):  
Sebastian Duchene ◽  
Philippe Lemey ◽  
Tanja Stadler ◽  
Simon Y W Ho ◽  
David A Duchene ◽  
...  
Keyword(s):  
Molecular Clock ◽  
Ancient Dna ◽  
Time Window ◽  
Sequence Data ◽  
H1n1 Influenza ◽  
Rate Variation ◽  
Data Sets ◽  
Molecular Sequence Data ◽  
Wide Range ◽  
Sampling Times

Abstract Phylogenetic methods can use the sampling times of molecular sequence data to calibrate the molecular clock, enabling the estimation of evolutionary rates and timescales for rapidly evolving pathogens and data sets containing ancient DNA samples. A key aspect of such calibrations is whether a sufficient amount of molecular evolution has occurred over the sampling time window, that is, whether the data can be treated as having come from a measurably evolving population. Here, we investigate the performance of a fully Bayesian evaluation of temporal signal (BETS) in sequence data. The method involves comparing the fit to the data of two models: a model in which the data are accompanied by the actual (heterochronous) sampling times, and a model in which the samples are constrained to be contemporaneous (isochronous). We conducted simulations under a wide range of conditions to demonstrate that BETS accurately classifies data sets according to whether they contain temporal signal or not, even when there is substantial among-lineage rate variation. We explore the behavior of this classification in analyses of five empirical data sets: modern samples of A/H1N1 influenza virus, the bacterium Bordetella pertussis, coronaviruses from mammalian hosts, ancient DNA from Hepatitis B virus, and mitochondrial genomes of dog species. Our results indicate that BETS is an effective alternative to other tests of temporal signal. In particular, this method has the key advantage of allowing a coherent assessment of the entire model, including the molecular clock and tree prior which are essential aspects of Bayesian phylodynamic analyses.

scholarly journals Integrating Genomic Data Sets for Knowledge Discovery: An Informed Approach to Management of Captive Endangered Species

2016 ◽  
Vol 2016 ◽  
pp. 1-12 ◽  
Author(s):  
Kristopher J. L. Irizarry ◽  
Doug Bryant ◽  
Jordan Kalish ◽  
Curtis Eng ◽  
Peggy L. Schmidt ◽  
...  
Keyword(s):  
Genetic Diversity ◽  
Endangered Species ◽  
Sequence Data ◽  
Genomic Data ◽  
Cost Effective ◽  
Model Organisms ◽  
Data Sets ◽  
Captive Populations ◽  
Species Survival ◽  
Population Genetic Variation

Many endangered captive populations exhibit reduced genetic diversity resulting in health issues that impact reproductive fitness and quality of life. Numerous cost effective genomic sequencing and genotyping technologies provide unparalleled opportunity for incorporating genomics knowledge in management of endangered species. Genomic data, such as sequence data, transcriptome data, and genotyping data, provide critical information about a captive population that, when leveraged correctly, can be utilized to maximize population genetic variation while simultaneously reducing unintended introduction or propagation of undesirable phenotypes. Current approaches aimed at managing endangered captive populations utilize species survival plans (SSPs) that rely upon mean kinship estimates to maximize genetic diversity while simultaneously avoiding artificial selection in the breeding program. However, as genomic resources increase for each endangered species, the potential knowledge available for management also increases. Unlike model organisms in which considerable scientific resources are used to experimentally validate genotype-phenotype relationships, endangered species typically lack the necessary sample sizes and economic resources required for such studies. Even so, in the absence of experimentally verified genetic discoveries, genomics data still provides value. In fact, bioinformatics and comparative genomics approaches offer mechanisms for translating these raw genomics data sets into integrated knowledge that enable an informed approach to endangered species management.

scholarly journals Gramene: Development and Integration of Trait and Gene Ontologies for Rice

2002 ◽  
Vol 3 (2) ◽  
pp. 132-136 ◽  
Author(s):  
Pankaj Jaiswal ◽  
Doreen Ware ◽  
Junjian Ni ◽  
Kuan Chang ◽  
Wei Zhao ◽  
...  
Keyword(s):  
Genomic Sequence ◽  
Sequence Data ◽  
Biological Information ◽  
Model Organisms ◽  
Cereal Crops ◽  
Data Sets ◽  
Genome Database ◽  
Plant Parts ◽  
Manual Curation ◽  
Plant Ontology

Gramene (http://www.gramene.org/) is a comparative genome database for cereal crops and a community resource for rice. We are populating and curating Gramene with annotated rice (Oryza sativa) genomic sequence data and associated biological information including molecular markers, mutants, phenotypes, polymorphisms and Quantitative Trait Loci (QTL). In order to support queries across various data sets as well as across external databases, Gramene will employ three related controlled vocabularies. The specific goal of Gramene is, first to provide a Trait Ontology (TO) that can be used across the cereal crops to facilitate phenotypic comparisons both within and between the genera. Second, a vocabulary for plant anatomy terms, the Plant Ontology (PO) will facilitate the curation of morphological and anatomical feature information with respect to expression, localization of genes and gene products and the affected plant parts in a phenotype. The TO and PO are both in the early stages of development in collaboration with the International Rice Research Institute, TAIR and MaizeDB as part of the Plant Ontology Consortium. Finally, as part of another consortium comprising macromolecular databases from other model organisms, the Gene Ontology Consortium, we are annotating the confirmed and predicted protein entries from rice using both electronic and manual curation.

Sign in / Sign up

Export Citation Format

Share Document