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

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.

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mtDNAcombine: tools to combine sequences from multiple studies

BMC Bioinformatics ◽

10.1186/s12859-021-04048-0 ◽

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.

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A Bayesian Implementation of the Multispecies Coalescent Model with Introgression for Phylogenomic Analysis

Molecular Biology and Evolution ◽

10.1093/molbev/msz296 ◽

2019 ◽

Vol 37 (4) ◽

pp. 1211-1223 ◽

Cited By ~ 8

Author(s):

Tomáš Flouri ◽

Xiyun Jiao ◽

Bruce Rannala ◽

Ziheng Yang

Keyword(s):

Gene Flow ◽

Genomic Sequence ◽

Sequence Data ◽

Incomplete Lineage Sorting ◽

Mosquito Species ◽

Phylogenomic Analysis ◽

Data Sets ◽

Lineage Sorting ◽

Coalescent Model ◽

Multispecies Coalescent

Abstract Recent analyses suggest that cross-species gene flow or introgression is common in nature, especially during species divergences. Genomic sequence data can be used to infer introgression events and to estimate the timing and intensity of introgression, providing an important means to advance our understanding of the role of gene flow in speciation. Here, we implement the multispecies-coalescent-with-introgression model, an extension of the multispecies-coalescent model to incorporate introgression, in our Bayesian Markov chain Monte Carlo program Bpp. The multispecies-coalescent-with-introgression model accommodates deep coalescence (or incomplete lineage sorting) and introgression and provides a natural framework for inference using genomic sequence data. Computer simulation confirms the good statistical properties of the method, although hundreds or thousands of loci are typically needed to estimate introgression probabilities reliably. Reanalysis of data sets from the purple cone spruce confirms the hypothesis of homoploid hybrid speciation. We estimated the introgression probability using the genomic sequence data from six mosquito species in the Anopheles gambiae species complex, which varies considerably across the genome, likely driven by differential selection against introgressed alleles.

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Eutherian comparative genomic analysis protocol

10.21203/rs.2.1502/v3 ◽

2020 ◽

Author(s):

Marko Premzl

Keyword(s):

Genomic Sequence ◽

Sequence Data ◽

Genomic Analysis ◽

Comparative Genomic Analysis ◽

Third Party ◽

Comparative Genomic ◽

Data Sets ◽

Gene Data ◽

Sequence Errors ◽

Analysis Protocol

Abstract The eutherian genomics momentum greatly advanced biology and medicine. Nevertheless, future revisions and updates of eutherian genomic sequence data sets were expected, due to potential genomic sequence errors and incompleteness of genomic sequences. The eutherian comparative genomic analysis protocol was established as guidance in protection against potential genomic sequence errors in public eutherian genomic sequence assemblies. The protocol revised, updated and published 12 major eutherian gene data sets, including 1853 complete coding sequences deposited in European Nucleotide Archive as curated third party data gene data sets under accession numbers: FR734011-FR734074, HF564658-HF564785, HF564786-HF564815, HG328835-HG329089, HG426065-HG426183, HG931734-HG931849, LM644135-LM644234, LN874312-LN874522, LT548096-LT548244, LT631550-LT631670, LT962964-LT963174 and LT990249-LT990597.

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Eutherian comparative genomic analysis protocol

10.21203/rs.2.1502/v2 ◽

2019 ◽

Author(s):

Marko Premzl

Keyword(s):

Genomic Sequence ◽

Sequence Data ◽

Genomic Analysis ◽

Comparative Genomic Analysis ◽

Third Party ◽

Comparative Genomic ◽

Data Sets ◽

Gene Data ◽

Sequence Errors ◽

Analysis Protocol

Abstract The eutherian genomics momentum greatly advanced biology and medicine. Nevertheless, future revisions and updates of eutherian genomic sequence data sets were expected, due to potential genomic sequence errors and incompleteness of genomic sequences. The eutherian comparative genomic analysis protocol was established as guidance in protection against potential genomic sequence errors in public eutherian genomic sequence assemblies. The protocol revised, updated and published 11 major eutherian gene data sets, including 1504 complete coding sequences deposited in European Nucleotide Archive as curated third party data gene data sets under accession numbers: FR734011-FR734074, HF564658-HF564785, HF564786-HF564815, HG328835-HG329089, HG426065-HG426183, HG931734-HG931849, LM644135-LM644234, LN874312-LN874522, LT548096-LT548244, LT631550-LT631670 and LT962964-LT963174.

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Genomic variants among threatened Acropora corals

10.1101/349910 ◽

2018 ◽

Cited By ~ 4

Author(s):

S. A. Kitchen ◽

A. Ratan ◽

O. C. Bedoya-Reina ◽

R. Burhans ◽

N. D. Fogarty ◽

...

Keyword(s):

Large Scale ◽

Genomic Sequence ◽

Sequence Data ◽

Cellular Stress Response ◽

Model Organisms ◽

Additional Species ◽

Genomic Resources ◽

Coral Genus ◽

The Galaxy ◽

Galaxy Server

ABSTRACTGenomic sequence data for non-model organisms are increasingly available requiring the development of efficient and reproducible workflows. Here, we develop the first genomic resources and reproducible workflows for two threatened members of the reef-building coral genus Acropora. We generated genomic sequence data from multiple samples of the Caribbean A. cervicornis (staghorn coral) and A. palmata (elkhorn coral), and predicted millions of nucleotide variants among these two species and the Pacific A. digitifera. A subset of predicted nucleotide variants were verified using restriction length polymorphism assays and proved useful in distinguishing the two Caribbean Acroporids and the hybrid they form (“A. prolifera”). Nucleotide variants are freely available from the Galaxy server (usegalaxy.org), and can be analyzed there with computational tools and stored workflows that require only an internet browser. We describe these data and some of the analysis tools, concentrating on fixed differences between A. cervicornis and A. palmata. In particular, we found that fixed amino acid differences between these two species were enriched in proteins associated with development, cellular stress response and the host’s interactions with associated microbes, for instance in the Wnt pathway, ABC transporters and superoxide dismutase. Identified candidate genes may underlie functional differences in the way these threatened species respond to changing environments. Users can expand the presented analyses easily by adding genomic data from additional species as they become available.Article SummaryWe provide the first comprehensive genomic resources for two threatened Caribbean reef-building corals in the genus Acropora. We identified genetic differences in key pathways and genes known to be important in the animals’ response to the environmental disturbances and larval development. We further provide a list of candidate loci for large scale genotyping of these species to gather intra- and interspecies differences between A. cervicornis and A. palmata across their geographic range. All analyses and workflows are made available and can be used as a resource to not only analyze these corals but other non-model organisms.

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Eutherian comparative genomic analysis protocol

10.21203/rs.2.1502/v4 ◽

2021 ◽

Author(s):

Marko Premzl

Keyword(s):

Genomic Sequence ◽

Sequence Data ◽

Genomic Analysis ◽

Comparative Genomic Analysis ◽

Third Party ◽

Comparative Genomic ◽

Data Sets ◽

Gene Data ◽

Sequence Errors ◽

Analysis Protocol

Abstract The eutherian genomics momentum greatly advanced biological and medical sciences. Yet, future revisions and updates of eutherian genomic sequence data sets were expected, due to potential genomic sequence errors and incompleteness of genomic sequences. The eutherian comparative genomic analysis protocol was established as guidance in protection against potential genomic sequence errors in public eutherian genomic sequence assemblies. The protocol revised, updated and published 14 major eutherian gene data sets, including 2615 complete coding sequences deposited in European Nucleotide Archive as curated third party data gene data sets under accession numbers: FR734011-FR734074, HF564658-HF564785, HF564786-HF564815, HG328835-HG329089, HG426065-HG426183, HG931734-HG931849, LM644135-LM644234, LN874312-LN874522, LT548096-LT548244, LT631550-LT631670, LT962964-LT963174, LT990249-LT990597, LR130242-LR130508 and LR760818-LR761312.

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

International Journal of Genomics ◽

10.1155/2016/2374610 ◽

2016 ◽

Vol 2016 ◽

pp. 1-12 ◽

Cited By ~ 3

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.

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Surveying the Manifold Divergence of an Entire Protein Class for Statistical Clues to Underlying Biochemical Mechanisms

Statistical Applications in Genetics and Molecular Biology ◽

10.2202/1544-6115.1666 ◽

2011 ◽

Vol 10 (1) ◽

Cited By ~ 11

Author(s):

Andrew F. Neuwald

Keyword(s):

Genomic Sequence ◽

Sequence Data ◽

Sampling Strategy ◽

Major Protein ◽

Manual Curation ◽

Protein Class ◽

Biological Interpretation ◽

Biochemical Mechanisms ◽

Design Analyses ◽

Aaa Atpases

Certain residues have no known function yet are co-conserved across distantly related protein families and diverse organisms, suggesting that they perform critical roles associated with as-yet-unidentified molecular properties and mechanisms. This raises the question of how to obtain additional clues regarding these mysterious biochemical phenomena with a view to formulating experimentally testable hypotheses. One approach is to access the implicit biochemical information encoded within the vast amount of genomic sequence data now becoming available. Here, a new Gibbs sampling strategy is formulated and implemented that can partition hundreds of thousands of sequences within a major protein class into multiple, functionally-divergent categories based on those pattern residues that best discriminate between categories. The sampler precisely defines the partition and pattern for each category by explicitly modeling unrelated, non-functional and related-yet-divergent proteins that would otherwise obscure the analysis. To aid biological interpretation, auxiliary routines can characterize pattern residues within available crystal structures and identify those structures most likely to shed light on the roles of pattern residues. This approach can be used to define and annotate automatically subgroup-specific conserved domain profiles based on statistically-rigorous empirical criteria rather than on the subjective and labor-intensive process of manual curation. Incorporating such profiles into domain database search sites (such as the NCBI BLAST site) will provide biologists with previously inaccessible molecular information useful for hypothesis generation and experimental design. Analyses of P-loop GTPases and of AAA+ ATPases illustrate the sampler’s ability to obtain such information.

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An Effective Approach for Analyzing “Prefinished” Genomic Sequence Data

Genome Research ◽

10.1101/gr.9.2.189 ◽

1999 ◽

Vol 9 (2) ◽

pp. 189-194

Author(s):

Peter M. Kuehl ◽

Jane M. Weisemann ◽

Jeffrey W. Touchman ◽

Eric D. Green ◽

Mark S. Boguski

Keyword(s):

Large Scale ◽

Genomic Sequence ◽

Sequence Data ◽

Biological Information ◽

Analytical Strategy ◽

Sequence Contigs ◽

Additional Information ◽

Daunting Task ◽

Sequencing Strategy ◽

Finished Sequence

Ongoing efforts to sequence the human genome are already generating large amounts of data, with substantial increases anticipated over the next few years. In most cases, a shotgun sequencing strategy is being used, which rapidly yields most of the primary sequence in incompletely assembled sequence contigs (“prefinished” sequence) and more slowly produces the final, completely assembled sequence (“finished” sequence). Thus, in general, prefinished sequence is produced in excess of finished sequence, and this trend is certain to continue and even accelerate over the next few years. Even at a prefinished stage, genomic sequence represents a rich source of important biological information that is of great interest to many investigators. However, analyzing such data is a challenging and daunting task, both because of its sheer volume and because it can change on a day-by-day basis. To facilitate the discovery and characterization of genes and other important elements within prefinished sequence, we have developed an analytical strategy and system that uses readily available software tools in new combinations. Implementation of this strategy for the analysis of prefinished sequence data from human chromosome 7 has demonstrated that this is a convenient, inexpensive, and extensible solution to the problem of analyzing the large amounts of preliminary data being produced by large-scale sequencing efforts. Our approach is accessible to any investigator who wishes to assimilate additional information about particular sequence data en route to developing richer annotations of a finished sequence.[Our software system is available via an extensive web supplement to this article at http://www.ncbi.nlm.nih.gov/Kuehl/prefinished.]

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Novel Comparison of Evaluation Metrics for Gene Ontology Classifiers Reveals Drastic Performance Differences

10.1101/427096 ◽

2018 ◽

Author(s):

Ilya Plyusnin ◽

Liisa Holm ◽

Petri Törönen

Keyword(s):

Gene Ontology ◽

False Positive ◽

Sequence Data ◽

Evaluation Metrics ◽

Data Sets ◽

Go Annotation ◽

Dilution Series ◽

Manual Curation ◽

Novel Approach ◽

Evaluation Metric

AbstractAutomated protein annotation using the Gene Ontology (GO) plays an important role in the biosciences. Evaluation has always been considered central to developing novel annotation methods, but little attention has been paid to the evaluation metrics themselves. Evaluation metrics define how well an annotation method performs and allows for them to be ranked against one another. Unfortunately, most of these metrics were adopted from the machine learning literature without establishing whether they were appropriate for GO annotations.We propose a novel approach for comparing GO evaluation metrics called Artificial Dilution Series (ADS). Our approach uses existing annotation data to generate a series of annotation sets with different levels of correctness (referred to as their signal level). We calculate the evaluation metric being tested for each annotation set in the series, allowing us to identify whether it can separate different signal levels. Finally, we contrast these results with several false positive annotation sets, which are designed to expose systematic weaknesses in GO assessment.We compared 37 evaluation metrics for GO annotation using ADS and identified drastic differences between metrics. We show that some metrics struggle to differentiate between different signal levels, while others give erroneously high scores to the false positive data sets. Based on our findings, we provide guidelines on which evaluation metrics perform well with the Gene Ontology and propose improvements to several well-known evaluation metrics. In general, we argue that evaluation metrics should be tested for their performance and we provide software for this purpose (https://bitbucket.org/plyusnin/ads/). ADS is applicable to other areas of science where the evaluation of prediction results is non-trivial.Author SummaryIn the biosciences, predictive methods are becoming increasingly necessary as novel sequences are generated at an ever-increasing rate. The volume of sequence data necessitates Automated Function Prediction (AFP) as manual curation is often impossible. Unfortunately, selecting the best AFP method is complicated by researchers using different evaluation metrics. Furthermore, many commonly-used metrics can give misleading results. We argue that the use of poor metrics in AFP evaluation is a result of the lack of methods to benchmark the metrics themselves. We propose an approach called Artificial Dilution Series (ADS). ADS uses existing data sets to generate multiple artificial AFP results, where each result has a controlled error rate. We use ADS to understand whether different metrics can distinguish between results with known quantities of error. Our results highlight dramatic differences in performance between evaluation metrics.

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