ARYANA: Aligning Reads by Yet Another Approach

Abstract Motivation Although there are many different algorithms and software tools for aligning sequencing reads, fast gapped sequence search is far from solved. Strong interest in fast alignment is best reflected in the $106 prize for the Innocentive competition on aligning a collection of reads to a given database of reference genomes. In addition, de novo assembly of next-generation sequencing long reads requires fast overlap-layout-concensus algorithms which depend on fast and accurate alignment. Contribution We introduce ARYANA, a fast gapped read aligner, developed on the base of BWA indexing infrastructure with a completely new alignment engine that makes it significantly faster than three other aligners: Bowtie2, BWA and SeqAlto, with comparable generality and accuracy. Instead of the time-consuming backtracking procedures for handling mismatches, ARYANA comes with the seed-and-extend algorithmic framework and a significantly improved efficiency by integrating novel algorithmic techniques including dynamic seed selection, bidirectional seed extension, reset-free hash tables, and gap-filling dynamic programming. As the read length increases ARYANA's superiority in terms of speed and alignment rate becomes more evident. This is in perfect harmony with the read length trend as the sequencing technologies evolve. The algorithmic platform of ARYANA makes it easy to develop mission-specific aligners for other applications using ARYANA engine. Availability ARYANA with complete source code can be obtained from http://github.com/aryana-aligne

Findings of DTI-p maps in comparison with T 2 /T 2 -FLAIR to assess postoperative hyper-signal abnormal regions in patients with glioblastoma

Abstract Purpose The aim of this study was to compare diffusion tensor imaging (DTI) isotropic map (p-map) with current radiographically (T 2/T 2 -FLAIR) methods based on abnormal hyper-signal size and location of glioblastoma tumor using a semi-automatic approach. Materials and methods Twenty-five patients with biopsy-proved diagnosis of glioblastoma participated in this study. T 2, T 2 -FLAIR images and diffusion tensor imaging (DTI) were acquired 1 week before radiotherapy. Hyper-signal regions on T 2, T 2 -FLAIR and DTI p-map were segmented by means of semi-automated segmentation. Manual segmentation was used as ground truth. Dice Scores (DS) were calculated for validation of semiautomatic method. Discordance Index (DI) and area difference percentage between the three above regions from the three modalities were calculated for each patient. Results Area of abnormality in the p-map was smaller than the corresponding areas in the T 2 and T 2 -FLAIR images in 17 patients; with mean difference percentage of 30 ± 0.15 and 35 ± 0.15, respectively. Abnormal region in the p-map was larger than the corresponding areas in the T 2 -FLAIR and T 2 images in 4 patients; with mean difference percentage of 26 ± 0.17 and 29 ± 0.28, respectively. This region in the p-map was larger than the one in the T 2 image and smaller than the one in the T 2 -FLAIR image in 3 patients; with mean difference percentage of 34 ± 0.08 and 27 ± 0.06, respectively. Lack of concordance was observed ranged from 0.214–0.772 for T 2 -FLAIR/p-map (average: 0.462 ± 0.18), 0.266–0.794 for T 2 /p-map (average: 0.468 ± 0.13) and 0.123–0.776 for T 2 / T 2 -FLAIR (average: 0.423 ± 0.2). These regions on three modalities were segmented using a semi-automatic segmentation method with over 86% sensitivity, 90% specificity and 89% dice score for three modalities. Conclusion It is noted that T 2 , T 2 -FLAIR and DTI p-maps represent different but complementary information for delineation of glioblastoma tumor margins. Therefore, this study suggests DTI p-map modality as a candidate to improve target volume delineation based on conventional modalities, which needs further investigations with follow-up data to be confirmed