Browse
Submit Data
Databases
API
Help

Dataset Information

23 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Phylogeny-driven target selection for large-scale genome-sequencing (and other) projects.

ABSTRACT: Despite the steadily decreasing costs of genome sequencing, prioritizing organisms for sequencing remains important in large-scale projects. Phylogeny-based selection is of interest to identify those organisms whose genomes can be expected to differ most from those that have already been sequenced. Here, we describe a method that infers a phylogenetic scoring independent of which set of organisms has previously been targeted, which is computationally simple and easy to apply in practice. The scoring itself, as well as pre- and post-processing of the data, is illustrated using two real-world examples in which the method has already been applied for selecting targets for genome sequencing. These projects are the JGI CSP Genomic Encyclopedia of Bacteria and Archaea phase I, targeting 1,000 type strains, and, on a smaller-scale, the phylogenomics of the Roseobacter clade. Potential artifacts of the method are discussed and compared to a selection approach based on the taxonomic classification.

SUBMITTER: Goker M

PROVIDER: S-EPMC3746418 | biostudies-literature | 2013

REPOSITORIES: biostudies-literature

ACCESS DATA

Json Xml

Publications

Phylogeny-driven target selection for large-scale genome-sequencing (and other) projects.

Göker Markus M Klenk Hans-Peter HP

Standards in genomic sciences 20130520 2

Despite the steadily decreasing costs of genome sequencing, prioritizing organisms for sequencing remains important in large-scale projects. Phylogeny-based selection is of interest to identify those organisms whose genomes can be expected to differ most from those that have already been sequenced. Here, we describe a method that infers a phylogenetic scoring independent of which set of organisms has previously been targeted, which is computationally simple and easy to apply in practice. The sco ...[more]

PMID: 23991265

Similar Datasets

Estimating exome genotyping accuracy by comparing to data from large scale sequencing projects.

Project description:With exome sequencing becoming a tool for mutation detection in routine diagnostics there is an increasing need for platform-independent methods of quality control. We present a genotype-weighted metric that allows comparison of all the variant calls of an exome to a high-quality reference dataset of an ethnically matched population. The exome-wide genotyping accuracy is estimated from the distance to this reference set, and does not require any further knowledge about data generation or the bioinformatics involved. The distances of our metric are visualized by non-metric multidimensional scaling and serve as an intuitive, standardizable score for the quality assessment of exome data.

| S-EPMC3978951 | biostudies-literature

Quantifying unobserved protein-coding variants in human populations provides a roadmap for large-scale sequencing projects.

Project description:As new proposals aim to sequence ever larger collection of humans, it is critical to have a quantitative framework to evaluate the statistical power of these projects. We developed a new algorithm, UnseenEst, and applied it to the exomes of 60,706 individuals to estimate the frequency distribution of all protein-coding variants, including rare variants that have not been observed yet in the current cohorts. Our results quantified the number of new variants that we expect to identify as sequencing cohorts reach hundreds of thousands of individuals. With 500K individuals, we find that we expect to capture 7.5% of all possible loss-of-function variants and 12% of all possible missense variants. We also estimate that 2,900 genes have loss-of-function frequency of <0.00001 in healthy humans, consistent with very strong intolerance to gene inactivation.

| S-EPMC5095512 | biostudies-literature

Parallel sequencing lives, or what makes large sequencing projects successful.

Project description:T47D_rep2 and b1913e6c1_51720e9cf were 2 Hi-C samples. They were born and processed at the same time, yet their fates were very different. The life of b1913e6c1_51720e9cf was simple and fruitful, while that of T47D_rep2 was full of accidents and sorrow. At the heart of these differences lies the fact that b1913e6c1_51720e9cf was born under a lab culture of Documentation, Automation, Traceability, and Autonomy and compliance with the FAIR Principles. Their lives are a lesson for those who wish to embark on the journey of managing high-throughput sequencing data.

| S-EPMC5714127 | biostudies-literature

Unexpected cross-species contamination in genome sequencing projects.

Project description:The raw data from a genome sequencing project sometimes contains DNA from contaminating organisms, which may be introduced during sample collection or sequence preparation. In some instances, these contaminants remain in the sequence even after assembly and deposition of the genome into public databases. As a result, searches of these databases may yield erroneous and confusing results. We used efficient microbiome analysis software to scan the draft assembly of domestic cow, Bos taurus, and identify 173 small contigs that appeared to derive from microbial contaminants. In the course of verifying these findings, we discovered that one genome, Neisseria gonorrhoeae TCDC-NG08107, although putatively a complete genome, contained multiple sequences that actually derived from the cow and sheep genomes. Our findings illustrate the need to carefully validate findings of anomalous DNA that rely on comparisons to either draft or finished genomes.

| S-EPMC4243333 | biostudies-literature

Primer design for large scale sequencing.

Project description:We have developed PRIDE, a primer design program that automatically designs primers in single contigs or whole sequencing projects to extend the already known sequence and to double strand single-stranded regions. The program is fully integrated into the Staden package (GAP4) and accessible with a graphical user interface. PRIDE uses a fuzzy logic-based system to calculate primer qualities. The computational performance of PRIDE is enhanced by using suffix trees to store the huge amount of data being produced. A test set of 110 sequencing primers and 11 PCR primer pairs has been designed on genomic templates, cDNAs and sequences containing repetitive elements to analyze PRIDE's success rate. The high performance of PRIDE, combined with its minimal requirement of user interaction and its fast algorithm, make this program useful for the large scale design of primers, especially in large sequencing projects.

| S-EPMC147651 | biostudies-other

A novel methodology for large-scale phylogeny partition.

Project description:Understanding the determinants of virus transmission is a fundamental step for effective design of screening and intervention strategies to control viral epidemics. Phylogenetic analysis can be a valid approach for the identification of transmission chains, and very-large data sets can be analysed through parallel computation. Here we propose and validate a new methodology for the partition of large-scale phylogenies and the inference of transmission clusters. This approach, on the basis of a depth-first search algorithm, conjugates the evaluation of node reliability, tree topology and patristic distance analysis. The method has been applied to identify transmission clusters of a phylogeny of 11,541 human immunodeficiency virus-1 subtype B pol gene sequences from a large Italian cohort. Molecular transmission chains were characterized by means of different clinical/demographic factors, such as the interaction between male homosexuals and male heterosexuals. Our method takes an advantage of a flexible notion of transmission cluster and can become a general framework to analyse other epidemics.

| S-EPMC6045912 | biostudies-literature

Simplified large-scale Sanger genome sequencing for influenza A/H3N2 virus.

Project description:BACKGROUND: The advent of next-generation sequencing technologies and the resultant lower costs of sequencing have enabled production of massive amounts of data, including the generation of full genome sequences of pathogens. However, the small genome size of the influenza virus arguably justifies the use of the more conventional Sanger sequencing technology which is still currently more readily available in most diagnostic laboratories. RESULTS: We present a simplified Sanger-based genome sequencing method for sequencing the influenza A/H3N2 virus in a large-scale format. The entire genome sequencing was completed with 19 reverse transcription-polymerase chain reactions (RT-PCRs) and 39 sequencing reactions. This method was tested on 15 native clinical samples and 15 culture isolates, respectively, collected between 2009 and 2011. The 15 native clinical samples registered quantification cycle values ranging from 21.0 to 30.56, which were equivalent to 2.4×10(3)-1.4×10(6) viral copies/µL of RNA extract. All the PCR-amplified products were sequenced directly without PCR product purification. Notably, high quality sequencing data up to 700 bp were generated for all the samples tested. The completed sequence covered 408,810 nucleotides in total, with 13,627 nucleotides per genome, attaining 100% coding completeness. Of all the bases produced, an average of 89.49% were Phred quality value 40 (QV40) bases (representing an accuracy of circa one miscall for every 10,000 bases) or higher, and an average of 93.46% were QV30 bases (one miscall every 1000 bases) or higher. CONCLUSIONS: This sequencing protocol has been shown to be cost-effective and less labor-intensive in obtaining full influenza genomes. The constant high quality of sequences generated imparts confidence in extending the application of this non-purified amplicon sequencing approach to other gene sequencing assays, with appropriate use of suitably designed primers.

| S-EPMC3669369 | biostudies-literature

Generation and Analysis of Large-Scale Data-Driven Mycobacterium tuberculosis Functional Networks for Drug Target Identification.

Project description:Technological developments in large-scale biological experiments, coupled with bioinformatics tools, have opened the doors to computational approaches for the global analysis of whole genomes. This has provided the opportunity to look at genes within their context in the cell. The integration of vast amounts of data generated by these technologies provides a strategy for identifying potential drug targets within microbial pathogens, the causative agents of infectious diseases. As proteins are druggable targets, functional interaction networks between proteins are used to identify proteins essential to the survival, growth, and virulence of these microbial pathogens. Here we have integrated functional genomics data to generate functional interaction networks between Mycobacterium tuberculosis proteins and carried out computational analyses to dissect the functional interaction network produced for identifying drug targets using network topological properties. This study has provided the opportunity to expand the range of potential drug targets and to move towards optimal target-based strategies.

| S-EPMC3235424 | biostudies-other

Identification of selection signals by large-scale whole-genome resequencing of cashmere goats.

Project description:Inner Mongolia and Liaoning cashmere goats are two outstanding Chinese multipurpose breeds that adapt well to the semi-arid temperate grassland. These two breeds are characterized by their soft cashmere fibers, thus making them great models to identify genomic regions that are associated with cashmere fiber traits. Whole-genome sequencing of 70 cashmere goats produced more than 5.52 million single-nucleotide polymorphisms and 710,600 short insertions and deletions. Further analysis of these genetic variants showed some population-specific molecular markers for the two cashmere goat breeds that are otherwise phenotypically similar. By analyzing F ST and ?? outlier values, we identified 135 genomic regions that were associated with cashmere fiber traits within the cashmere goat populations. These selected genomic regions contained genes, which are potential involved in the production of cashmere fiber, such as FGF5, SGK3, IGFBP7, OXTR, and ROCK1. Gene ontology enrichment analysis of identified short insertions and deletions also showed enrichment in keratinocyte differentiation and epidermal cell differentiation. These findings demonstrate that this genomic resource will facilitate the breeding of cashmere goat and other Capra species in future.

| S-EPMC5680388 | biostudies-literature

ProteinInferencer: Confident protein identification and multiple experiment comparison for large scale proteomics projects.

Project description:Shotgun proteomics generates valuable information from large-scale and target protein characterizations, including protein expression, protein quantification, protein post-translational modifications (PTMs), protein localization, and protein-protein interactions. Typically, peptides derived from proteolytic digestion, rather than intact proteins, are analyzed by mass spectrometers because peptides are more readily separated, ionized and fragmented. The amino acid sequences of peptides can be interpreted by matching the observed tandem mass spectra to theoretical spectra derived from a protein sequence database. Identified peptides serve as surrogates for their proteins and are often used to establish what proteins were present in the original mixture and to quantify protein abundance. Two major issues exist for assigning peptides to their originating protein. The first issue is maintaining a desired false discovery rate (FDR) when comparing or combining multiple large datasets generated by shotgun analysis and the second issue is properly assigning peptides to proteins when homologous proteins are present in the database. Herein we demonstrate a new computational tool, ProteinInferencer, which can be used for protein inference with both small- or large-scale data sets to produce a well-controlled protein FDR. In addition, ProteinInferencer introduces confidence scoring for individual proteins, which makes protein identifications evaluable. This article is part of a Special Issue entitled: Computational Proteomics.

| S-EPMC4630118 | biostudies-literature

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data