Dataset Information

GAM-NGS: genomic assemblies merger for next generation sequencing.

ABSTRACT:

Background

In recent years more than 20 assemblers have been proposed to tackle the hard task of assembling NGS data. A common heuristic when assembling a genome is to use several assemblers and then select the best assembly according to some criteria. However, recent results clearly show that some assemblers lead to better statistics than others on specific regions but are outperformed on other regions or on different evaluation measures. To limit these problems we developed GAM-NGS (Genomic Assemblies Merger for Next Generation Sequencing), whose primary goal is to merge two or more assemblies in order to enhance contiguity and correctness of both. GAM-NGS does not rely on global alignment: regions of the two assemblies representing the same genomic locus (called blocks) are identified through reads' alignments and stored in a weighted graph. The merging phase is carried out with the help of this weighted graph that allows an optimal resolution of local problematic regions.

Results

GAM-NGS has been tested on six different datasets and compared to other assembly reconciliation tools. The availability of a reference sequence for three of them allowed us to show how GAM-NGS is a tool able to output an improved reliable set of sequences. GAM-NGS is also a very efficient tool able to merge assemblies using substantially less computational resources than comparable tools. In order to achieve such goals, GAM-NGS avoids global alignment between contigs, making its strategy unique among other assembly reconciliation tools.

Conclusions

The difficulty to obtain correct and reliable assemblies using a single assembler is forcing the introduction of new algorithms able to enhance de novo assemblies. GAM-NGS is a tool able to merge two or more assemblies in order to improve contiguity and correctness. It can be used on all NGS-based assembly projects and it shows its full potential with multi-library Illumina-based projects. With more than 20 available assemblers it is hard to select the best tool. In this context we propose a tool that improves assemblies (and, as a by-product, perhaps even assemblers) by merging them and selecting the generating that is most likely to be correct.

SUBMITTER: Vicedomini R

PROVIDER: S-EPMC3633056 | biostudies-literature | 2013

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

GAM-NGS: genomic assemblies merger for next generation sequencing.

Vicedomini Riccardo R Vezzi Francesco F Scalabrin Simone S Arvestad Lars L Policriti Alberto A

BMC bioinformatics 20130422

<h4>Background</h4>In recent years more than 20 assemblers have been proposed to tackle the hard task of assembling NGS data. A common heuristic when assembling a genome is to use several assemblers and then select the best assembly according to some criteria. However, recent results clearly show that some assemblers lead to better statistics than others on specific regions but are outperformed on other regions or on different evaluation measures. To limit these problems we developed GAM-NGS (Ge ...[more]

PMID: 23815503

Similar Datasets

Project description:BACKGROUND:Next-Generation Sequencing (NGS) technology has exceptionally increased the ability to sequence DNA in a massively parallel and cost-effective manner. Nevertheless, NGS data analysis requires bioinformatics skills and computational resources well beyond the possibilities of many "wet biology" laboratories. Moreover, most of projects only require few sequencing cycles and standard tools or workflows to carry out suitable analyses for the identification and annotation of genes, transcripts and splice variants found in the biological samples under investigation. These projects can take benefits from the availability of easy to use systems to automatically analyse sequences and to mine data without the preventive need of strong bioinformatics background and hardware infrastructure. RESULTS:To address this issue we developed an automatic system targeted to the analysis of NGS data obtained from large-scale transcriptome studies. This system, we named NGS-Trex (NGS Transcriptome profile explorer) is available through a simple web interface http://www.ngs-trex.org and allows the user to upload raw sequences and easily obtain an accurate characterization of the transcriptome profile after the setting of few parameters required to tune the analysis procedure. The system is also able to assess differential expression at both gene and transcript level (i.e. splicing isoforms) by comparing the expression profile of different samples.By using simple query forms the user can obtain list of genes, transcripts, splice sites ranked and filtered according to several criteria. Data can be viewed as tables, text files or through a simple genome browser which helps the visual inspection of the data. CONCLUSIONS:NGS-Trex is a simple tool for RNA-Seq data analysis mainly targeted to "wet biology" researchers with limited bioinformatics skills. It offers simple data mining tools to explore transcriptome profiles of samples investigated taking advantage of NGS technologies.

Project description:BACKGROUND:Next generation sequencing (NGS) has become a universal practice in modern molecular biology. As the throughput of sequencing experiments increases, the preparation of conventional multiplexed libraries becomes more labor intensive. Conventional library preparation typically requires quality control (QC) testing for individual libraries such as amplification success evaluation and quantification, none of which occur until the end of the library preparation process. RESULTS:In this study, we address the need for a more streamlined high-throughput NGS workflow by tethering real-time quantitative PCR (qPCR) to conventional workflows to save time and implement single tube and single reagent QC. We modified two distinct library preparation workflows by replacing PCR and quantification with qPCR using SYBR Green I. qPCR enabled individual library quantification for pooling in a single tube without the need for additional reagents. Additionally, a melting curve analysis was implemented as an intermediate QC test to confirm successful amplification. Sequencing analysis showed comparable percent reads for each indexed library, demonstrating that pooling calculations based on qPCR allow for an even representation of sequencing reads. To aid the modified workflow, a software toolkit was developed and used to generate pooling instructions and analyze qPCR and melting curve data. CONCLUSIONS:We successfully applied fluorescent amplification for next generation sequencing (FA-NGS) library preparation to both plasmids and bacterial genomes. As a result of using qPCR for quantification and proceeding directly to library pooling, the modified library preparation workflow has fewer overall steps. Therefore, we speculate that the FA-NGS workflow has less risk of user error. The melting curve analysis provides the necessary QC test to identify and troubleshoot library failures prior to sequencing. While this study demonstrates the value of FA-NGS for plasmid or gDNA libraries, we speculate that its versatility could lead to successful application across other library types.

Project description:The livestock industry in Mongolia accounts for 24% of national revenue, with one third of the population maintaining a pastoral lifestyle. This close connection between Mongolian population and livestock is a major concern for pathogen transfer, especially given the increase in vector-borne diseases globally. This study examines blood samples from livestock to assess the prevalence of tick-borne bacterial infections across three provinces in Mongolia (Dornogovi, Selenge, Töv). Whole blood samples from 243 livestock (cattle=38, camel=11, goat=85, horse=22, sheep=87) were analyzed with 16S metagenomics next-generation sequencing (NGS) to screen for bacterial pathogens. Positive-NGS samples for Anaplasma, Bartonella, Ehrlichia, Francisella, Leptospira, and Rickettsia were confirmed by conventional PCR and DNA sequencing. Prevalence rates of Anaplasma, Bartonella, and Ehrlichia were 57.6%, 12.8%, and 0.4%, respectively. A significant difference in the prevalence of Anaplasma spp. in livestock by province was observed, with a higher prevalence in Selenge (74.2%, p<0.001) and Töv (64.2% p = 0.006) compared to the semi-arid region of Dornogovi (39.8%). In contrast, no association was observed in Bartonella prevalence by provinces. All Anaplasma sequences (N = 139) were characterized as A. ovis. For Bartonella species characterization, phylogenetic analyses of gltA and rpoB genes identified three Bartonella species including B. bovis, B. melophagi and Candidatus B. ovis. Bartonella bovis was detected in all 22-positive cattle, while B. melophagi and Candidatus B. ovis were found in four and three sheep, respectively. This study identifies a high prevalence of tick-borne pathogens within the livestock population and to our knowledge, is the first time NGS methods have been used to explore tick-borne diseases in Mongolia. Further research is needed in Mongolia to better understand the clinical and economic burdens associated with tick-borne diseases in both livestock and pastoral herder populations.

Dataset Information

GAM-NGS: genomic assemblies merger for next generation sequencing.

Background

Results

Conclusions

Publications

GAM-NGS: genomic assemblies merger for next generation sequencing.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets