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The GNUMAP algorithm: unbiased probabilistic mapping of oligonucleotides from next-generation sequencing.

ABSTRACT: MOTIVATION:The advent of next-generation sequencing technologies has increased the accuracy and quantity of sequence data, opening the door to greater opportunities in genomic research. RESULTS:In this article, we present GNUMAP (Genomic Next-generation Universal MAPper), a program capable of overcoming two major obstacles in the mapping of reads from next-generation sequencing runs. First, we have created an algorithm that probabilistically maps reads to repeat regions in the genome on a quantitative basis. Second, we have developed a probabilistic Needleman-Wunsch algorithm which utilizes _prb.txt and _int.txt files produced in the Solexa/Illumina pipeline to improve the mapping accuracy for lower quality reads and increase the amount of usable data produced in a given experiment. AVAILABILITY:The source code for the software can be downloaded from http://dna.cs.byu.edu/gnumap.

SUBMITTER: Clement NL

PROVIDER: S-EPMC6276904 | biostudies-other | 2010 Jan

REPOSITORIES: biostudies-other

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The GNUMAP algorithm: unbiased probabilistic mapping of oligonucleotides from next-generation sequencing.

Clement Nathan L NL Snell Quinn Q Clement Mark J MJ Hollenhorst Peter C PC Purwar Jahnvi J Graves Barbara J BJ Cairns Bradley R BR Johnson W Evan WE

Bioinformatics (Oxford, England) 20091027 1

<h4>Motivation</h4>The advent of next-generation sequencing technologies has increased the accuracy and quantity of sequence data, opening the door to greater opportunities in genomic research.<h4>Results</h4>In this article, we present GNUMAP (Genomic Next-generation Universal MAPper), a program capable of overcoming two major obstacles in the mapping of reads from next-generation sequencing runs. First, we have created an algorithm that probabilistically maps reads to repeat regions in the gen ...[more]

PMID: 19861355

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Project description:IntroductionMetagenomic next-generation sequencing (mNGS) assay for detecting infectious agents is now in the stage of being translated into clinical practice. With no approved approaches or guidelines available, laboratories adopt customized mNGS assays to detect clinical samples. However, the accuracy, reliability, and problems of these routinely implemented assays are not clear.ObjectivesTo evaluate the performance of 90 mNGS laboratories under routine testing conditions through analyzing identical samples.MethodsEleven microbial communities were generated using 15 quantitative microbial suspensions. They were used as reference materials to evaluate the false negatives and false positives of participating mNGS protocols, as well as the ability to distinguish genetically similar organisms and to identify true pathogens from other microbes based on fictitious case reports.ResultsHigh interlaboratory variability was found in the identification and the quantitative reads per million reads (RPM) values of each microbe in the samples, especially when testing microbes present at low concentrations (1 × 103 cell/ml or less). 42.2% (38/90) of the laboratories reported unexpected microbes (i.e. false positive problem). Only 56.7% (51/90) to 83.3% (75/90) of the laboratories showed a sufficient ability to obtain clear etiological diagnoses for three simulated cases combined with patient information. The analysis of the performance of mNGS in distinguishing genetically similar organisms in three samples revealed that only 56.6% to 63.0% of the laboratories recovered RPM ratios (RPM S. aureus /RPM S. epidermidis ) within the range of a 2-fold change of the initial input ratios (indicating a relatively low level of bias).ConclusionThe high interlaboratory variability found in both identifying microbes and distinguishing true pathogens emphasizes the urgent need for improving the accuracy and comparability of the results generated across different mNGS laboratories, especially in the detection of low-microbial-biomass samples.

Dataset Information

The GNUMAP algorithm: unbiased probabilistic mapping of oligonucleotides from next-generation sequencing.

Publications

The GNUMAP algorithm: unbiased probabilistic mapping of oligonucleotides from next-generation sequencing.

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