Dataset Information

OPTIMA: sensitive and accurate whole-genome alignment of error-prone genomic maps by combinatorial indexing and technology-agnostic statistical analysis.

ABSTRACT: BACKGROUND:Resolution of complex repeat structures and rearrangements in the assembly and analysis of large eukaryotic genomes is often aided by a combination of high-throughput sequencing and genome-mapping technologies (for example, optical restriction mapping). In particular, mapping technologies can generate sparse maps of large DNA fragments (150 kilo base pairs (kbp) to 2 Mbp) and thus provide a unique source of information for disambiguating complex rearrangements in cancer genomes. Despite their utility, combining high-throughput sequencing and mapping technologies has been challenging because of the lack of efficient and sensitive map-alignment algorithms for robustly aligning error-prone maps to sequences. RESULTS:We introduce a novel seed-and-extend glocal (short for global-local) alignment method, OPTIMA (and a sliding-window extension for overlap alignment, OPTIMA-Overlap), which is the first to create indexes for continuous-valued mapping data while accounting for mapping errors. We also present a novel statistical model, agnostic with respect to technology-dependent error rates, for conservatively evaluating the significance of alignments without relying on expensive permutation-based tests. CONCLUSIONS:We show that OPTIMA and OPTIMA-Overlap outperform other state-of-the-art approaches (1.6-2 times more sensitive) and are more efficient (170-200 %) and precise in their alignments (nearly 99 % precision). These advantages are independent of the quality of the data, suggesting that our indexing approach and statistical evaluation are robust, provide improved sensitivity and guarantee high precision.

SUBMITTER: Verzotto D

PROVIDER: S-EPMC4719737 | biostudies-literature | 2016

REPOSITORIES: biostudies-literature

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OPTIMA: sensitive and accurate whole-genome alignment of error-prone genomic maps by combinatorial indexing and technology-agnostic statistical analysis.

Verzotto Davide D M Teo Audrey S AS Hillmer Axel M AM Nagarajan Niranjan N

GigaScience 20160119

<h4>Background</h4>Resolution of complex repeat structures and rearrangements in the assembly and analysis of large eukaryotic genomes is often aided by a combination of high-throughput sequencing and genome-mapping technologies (for example, optical restriction mapping). In particular, mapping technologies can generate sparse maps of large DNA fragments (150 kilo base pairs (kbp) to 2 Mbp) and thus provide a unique source of information for disambiguating complex rearrangements in cancer genome ...[more]

PMID: 26793302

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Project description:BackgroundSystems biology increasingly relies on deep sequencing with combinatorial index tags to associate biological sequences with their sample, cell, or molecule of origin. Accurate data interpretation depends on the ability to classify sequences based on correct decoding of these combinatorial barcodes. The probability of correct decoding is influenced by both sequence quality and the number and arrangement of barcodes. The rising complexity of experimental designs calls for a probability model that accounts for both sequencing errors and random noise, generalizes to multiple combinatorial tags, and can handle any barcoding scheme. The needs for reproducibility and community benchmark standards demand a peer-reviewed tool that preserves decoding quality scores and provides tunable control over classification confidence that balances precision and recall. Moreover, continuous improvements in sequencing throughput require a fast, parallelized and scalable implementation.Results and discussionWe developed a flexible, robustly engineered software that performs probabilistic decoding and supports arbitrarily complex barcoding designs. Pheniqs computes the full posterior decoding error probability of observed barcodes by consulting basecalling quality scores and prior distributions, and reports sequences and confidence scores in Sequence Alignment/Map (SAM) fields. The product of posteriors for multiple independent barcodes provides an overall confidence score for each read. Pheniqs achieves greater accuracy than minimum edit distance or simple maximum likelihood estimation, and it scales linearly with core count to enable the classification of > 11 billion reads in 1 h 15 m using < 50 megabytes of memory. Pheniqs has been in production use for seven years in our genomics core facility.ConclusionWe introduce a computationally efficient software that implements both probabilistic and minimum distance decoders and show that decoding barcodes using posterior probabilities is more accurate than available methods. Pheniqs allows fine-tuning of decoding sensitivity using intuitive confidence thresholds and is extensible with alternative decoders and new error models. Any arbitrary arrangement of barcodes is easily configured, enabling computation of combinatorial confidence scores for any barcoding strategy. An optimized multithreaded implementation assures that Pheniqs is faster and scales better with complex barcode sets than existing tools. Support for POSIX streams and multiple sequencing formats enables easy integration with automated analysis pipelines.

Project description:BackgroundError processing and inhibitory control enable the adjustment of behaviors to meet task demands. Functional magnetic resonance imaging studies report brain activation abnormalities in patients with obsessive-compulsive disorder (OCD) during both processes. However, conclusions are limited by inconsistencies in the literature and small sample sizes. Therefore, the aim here was to perform a meta-analysis of the existing literature using unthresholded statistical maps from previous studies.MethodsA voxelwise seed-based d mapping meta-analysis was performed using t-maps from studies comparing patients with OCD and healthy control subjects (HCs) during error processing and inhibitory control. For the error processing analysis, 239 patients with OCD (120 male; 79 medicated) and 229 HCs (129 male) were included, while the inhibitory control analysis included 245 patients with OCD (120 male; 91 medicated) and 239 HCs (135 male).ResultsPatients with OCD, relative to HCs, showed longer inhibitory control reaction time (standardized mean difference = 0.20, p = .03, 95% confidence interval = 0.016, 0.393) and more inhibitory control errors (standardized mean difference = 0.22, p = .02, 95% confidence interval = 0.039, 0.399). In the brain, patients showed hyperactivation in the bilateral dorsal anterior cingulate cortex, supplementary motor area, and pre-supplementary motor area as well as right anterior insula/frontal operculum and anterior lateral prefrontal cortex during error processing but showed hypoactivation during inhibitory control in the rostral and ventral anterior cingulate cortices and bilateral thalamus/caudate, as well as the right anterior insula/frontal operculum, supramarginal gyrus, and medial orbitofrontal cortex (all seed-based d mapping z value >2, p < .001).ConclusionsA hyperactive error processing mechanism in conjunction with impairments in implementing inhibitory control may underlie deficits in stopping unwanted compulsive behaviors in the disorder.

Dataset Information

OPTIMA: sensitive and accurate whole-genome alignment of error-prone genomic maps by combinatorial indexing and technology-agnostic statistical analysis.

Publications

OPTIMA: sensitive and accurate whole-genome alignment of error-prone genomic maps by combinatorial indexing and technology-agnostic statistical analysis.

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