Dataset Information

Towards Plant Species Identification in Complex Samples: A Bioinformatics Pipeline for the Identification of Novel Nuclear Barcode Candidates.

ABSTRACT: Monitoring of the food chain to fight fraud and protect consumer health relies on the availability of methods to correctly identify the species present in samples, for which DNA barcoding is a promising candidate. The nuclear genome is a rich potential source of barcode targets, but has been relatively unexploited until now. Here, we show the development and use of a bioinformatics pipeline that processes available genome sequences to automatically screen large numbers of input candidates, identifies novel nuclear barcode targets and designs associated primer pairs, according to a specific set of requirements. We applied this pipeline to identify novel barcodes for plant species, a kingdom for which the currently available solutions are known to be insufficient. We tested one of the identified primer pairs and show its capability to correctly identify the plant species in simple and complex samples, validating the output of our approach.

SUBMITTER: Angers-Loustau A

PROVIDER: S-EPMC4725681 | biostudies-literature | 2016

REPOSITORIES: biostudies-literature

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Publications

Towards Plant Species Identification in Complex Samples: A Bioinformatics Pipeline for the Identification of Novel Nuclear Barcode Candidates.

Angers-Loustau Alexandre A Petrillo Mauro M Paracchini Valentina V Kagkli Dafni M DM Rischitor Patricia E PE Puertas Gallardo Antonio A Patak Alex A Querci Maddalena M Kreysa Joachim J

PloS one 20160125 1

Monitoring of the food chain to fight fraud and protect consumer health relies on the availability of methods to correctly identify the species present in samples, for which DNA barcoding is a promising candidate. The nuclear genome is a rich potential source of barcode targets, but has been relatively unexploited until now. Here, we show the development and use of a bioinformatics pipeline that processes available genome sequences to automatically screen large numbers of input candidates, ident ...[more]

PMID: 26807711

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Project description:Next-Generation Sequencing (NGS) is widely used to investigate genomic variation. In several studies, the genetic variation of Mycobacterium tuberculosis has been analyzed in sputum samples without previous culture, using target enrichment methodologies for NGS. Alignments obtained by different programs generally map the sequences under default parameters, and from these results, it is assumed that only Mycobacterium reads will be obtained. However, variants of interest microorganism in clinical samples can be confused with a vast collection of reads from other bacteria, viruses, and human DNA. Currently, there are no standardized pipelines, and the cleaning success is never verified since there is a lack of rigorous controls to identify and remove reads from other sputum-microorganisms genetically similar to M. tuberculosis. Therefore, we designed a bioinformatic pipeline to process NGS data from sputum samples, including several filters and quality control points to identify and eliminate non-M. tuberculosis reads to obtain a reliable genetic variant report. Our proposal uses the SURPI software as a taxonomic classifier to filter input sequences and perform a mapping that provides the highest percentage of Mycobacterium reads, minimizing the reads from other microorganisms. We then use the filtered sequences to perform variant calling with the GATK software, ensuring the mapping quality, realignment, recalibration, hard-filtering, and post-filter to increase the reliability of the reported variants. Using default mapping parameters, we identified reads of contaminant bacteria, such as Streptococcus, Rhotia, Actinomyces, and Veillonella. Our final mapping strategy allowed a sequence identity of 97.8% between the input reads and the whole M. tuberculosis reference genome H37Rv using a genomic edit distance of three, thus removing 98.8% of the off-target sequences with a Mycobacterium reads loss of 1.7%. Finally, more than 200 unreliable genetic variants were removed during the variant calling, increasing the report's reliability.

Dataset Information

Towards Plant Species Identification in Complex Samples: A Bioinformatics Pipeline for the Identification of Novel Nuclear Barcode Candidates.

Publications

Towards Plant Species Identification in Complex Samples: A Bioinformatics Pipeline for the Identification of Novel Nuclear Barcode Candidates.

Similar Datasets

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