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Mendelowitz L.M.,Center for Bioinformatics and Computational Biology | Schwartz D.C.,Laboratory for Molecular and Computational Genomics | Schwartz D.C.,University of Wisconsin - Madison | Pop M.,Center for Bioinformatics and Computational Biology | Pop M.,University of Maryland University College
Bioinformatics | Year: 2016

Motivation: The Optical Mapping System discovers structural variants and potentiates sequence assembly of genomes via scaffolding and comparisons that globally validate or correct sequence assemblies. Despite its utility, there are few publicly available tools for aligning optical mapping datasets. Results: Here we present software, named 'Maligner', for the alignment of both single molecule restriction maps (Rmaps) and in silico restriction maps of sequence contigs to a reference. Maligner provides two modes of alignment: an efficient, sensitive dynamic programming implementation that scales to large eukaryotic genomes, and a faster indexed based implementation for finding alignments with unmatched sites in the reference but not the query. We compare our software to other publicly available tools on Rmap datasets and show that Maligner finds more correct alignments in comparable runtime. Lastly, we introduce the M-Score statistic for normalizing alignment scores across restriction maps and demonstrate its utility for selecting high quality alignments. © 2015 The Author 2015. Published by Oxford University Press. All rights reserved.

Ye C.,Center for Bioinformatics and Computational Biology | Hsiao C.,Center for Bioinformatics and Computational Biology | Hsiao C.,University of Maryland University College | Bravo H.C.,Center for Bioinformatics and Computational Biology | Bravo H.C.,University of Maryland University College
Bioinformatics | Year: 2014

Motivation: Base-calling of sequencing data produced by highthroughput sequencing platforms is a fundamental process in current bioinformatics analysis. However, existing third-party probabilistic or machine-learning methods that significantly improve the accuracy of base-calls on these platforms are impractical for production use due to their computational inefficiency. Results: We directly formulate base-calling as a blind deconvolution problem and implemented BlindCall as an efficient solver to this inverse problem. BlindCall produced base-calls at accuracy comparable to state-of-the-art probabilistic methods while processing data at rates 10 times faster in most cases. The computational complexity of BlindCall scales linearly with read length making it better suited for new long-read sequencing technologies. © The Author 2013. Published by Oxford University Press.

Crowgey E.L.,Center for Bioinformatics and Computational Biology | . Stabley D.L.,DuPont Company | Chen C.,Center for Bioinformatics and Computational Biology | Huang H.,Center for Bioinformatics and Computational Biology | And 5 more authors.
Journal of Biomolecular Techniques | Year: 2015

Next-generation sequencing (NGS) technologies provide the potential for developing high-throughput and low-cost platforms for clinical diagnostics. A limiting factor to clinical applications of genomic NGS is downstream bioinformatics analysis for data interpretation. We have developed an integrated approach for end-to-end clinical NGS data analysis from variant detection to functional profiling. Robust bioinformatics pipelines were implemented for genome alignment, single nucleotide polymorphism (SNP), small insertion/deletion (InDel), and copy number variation (CNV) detection of whole exome sequencing (WES) data from the Illumina platform. Quality-control metrics were analyzed at each step of the pipeline by use of a validated training dataset to ensure data integrity for clinical applications. We annotate the variants with data regarding the disease population and variant impact. Custom algorithms were developed to filter variants based on criteria, such as quality of variant, inheritance pattern, and impact of variant on protein function. The developed clinical variant pipeline links the identified rare variants to Integrated Genome Viewer for visualization in a genomic context and to the Protein Information Resource’s iProXpress for rich protein and disease information. With the application of our system of annotations, prioritizations, inheritance filters, and functional profiling and analysis, we have created a unique methodology for downstream variant filtering that empowers clinicians and researchers to interpret more effectively the relevance of genomic alterations within a rare genetic disease. © 2015 ABRF.

Liu B.,Center for Bioinformatics and Computational Biology | Liu B.,University of Maryland University College | Gibbons T.,Center for Bioinformatics and Computational Biology | Gibbons T.,University of Maryland University College | And 4 more authors.
Proceedings - 2010 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2010 | Year: 2010

A major goal of metagenomics is to characterize the microbial diversity of an environment. The most popular approach relies on 16S rRNA sequencing, however this approach can generate biased estimates due to differences in the copy number of the 16S rRNA gene between even closely related organisms, and due to PCR artifacts. The taxonomic composition can also be determined from whole-metagenome sequencing data by matching individual sequences against a database of reference genes. One major limitation of prior methods used for this purpose is the use of a universal classification threshold for all genes at all taxonomic levels. We propose that better classification results can be obtained by tuning the taxonomic classifier to each matching length, reference gene, and taxonomic level. We present a novel taxonomic profiler MetaPhyler, which uses marker genes as a taxonomic reference. Results on simulated datasets demonstrate that MetaPhyler outperforms other tools commonly used in this context (CARMA, Megan and PhymmBL). We also present interesting results obtained by applying MetaPhyler to a real metagenomic dataset. ©2010 IEEE.

Correa-Agudelo E.,Center for Bioinformatics and Computational Biology | Hernandez A.M.,Center for Bioinformatics and Computational Biology | Ferrin C.,Center for Bioinformatics and Computational Biology | Gomez J.D.,Center for Bioinformatics and Computational Biology
Conference on Human Factors in Computing Systems - Proceedings | Year: 2015

Each year, anti-personnel mines contribute to a vast number of amputated victims due to the current conict in Colombia. The recovery of such victims, includes psycho-motor therapies to reduce neuromuscular aftermaths, such as phantom limb pain (PLP). Therefore, improving rehabilitation strategies can potentially have a large positive impact in the recovery outcomes for long-term treatments. ViLimbs is a Virtual-Reality-based (VR) system aimed at enhancing classical mirror therapy for amputees using an immersive video-wall that renders the patient himself with a superimposed virtual limb. The patient-virtual-limb interaction is achieved by means of brain computer interfaces (BCI) and myoelectric signals read from the remaining part of the limb. Thus, voluntary movements get naturally transferred from patients to virtual limbs. Last but not least, patients sense of ownership over an alien virtual limb is enhanced through multisensory feedback (cardio-visual), which tends to lessen rehabilitation times. Copyright is held by the author/owner(s).

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