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      TopHat-Recondition: a post-processor for TopHat unmapped reads

      research-article
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      BMC Bioinformatics
      BioMed Central
      RNA-seq, Deep sequencing, Sequence alignment, Sequence analysis

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          Abstract

          Background

          TopHat is a popular spliced junction mapper for RNA sequencing data, and writes files in the BAM format – the binary version of the Sequence Alignment/Map (SAM) format. BAM is the standard exchange format for aligned sequencing reads, thus correct format implementation is paramount for software interoperability and correct analysis. However, TopHat writes its unmapped reads in a way that is not compatible with other software that implements the SAM/BAM format.

          Results

          We have developed TopHat-Recondition, a post-processor for TopHat unmapped reads that restores read information in the proper format. TopHat-Recondition thus enables downstream software to process the plethora of BAM files written by TopHat.

          Conclusions

          TopHat-Recondition can repair unmapped read files written by TopHat and is freely available under a 2-clause BSD license on GitHub: https://github.com/cbrueffer/tophat-recondition.

          Electronic supplementary material

          The online version of this article (doi:10.1186/s12859-016-1058-x) contains supplementary material, which is available to authorized users.

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          Most cited references1

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          The Sweden Cancerome Analysis Network - Breast (SCAN-B) Initiative: a large-scale multicenter infrastructure towards implementation of breast cancer genomic analyses in the clinical routine

          Background Breast cancer exhibits significant molecular, pathological, and clinical heterogeneity. Current clinicopathological evaluation is imperfect for predicting outcome, which results in overtreatment for many patients, and for others, leads to death from recurrent disease. Therefore, additional criteria are needed to better personalize care and maximize treatment effectiveness and survival. Methods To address these challenges, the Sweden Cancerome Analysis Network - Breast (SCAN-B) consortium was initiated in 2010 as a multicenter prospective study with longsighted aims to analyze breast cancers with next-generation genomic technologies for translational research in a population-based manner and integrated with healthcare; decipher fundamental tumor biology from these analyses; utilize genomic data to develop and validate new clinically-actionable biomarker assays; and establish real-time clinical implementation of molecular diagnostic, prognostic, and predictive tests. In the first phase, we focus on molecular profiling by next-generation RNA-sequencing on the Illumina platform. Results In the first 3 years from 30 August 2010 through 31 August 2013, we have consented and enrolled 3,979 patients with primary breast cancer at the seven hospital sites in South Sweden, representing approximately 85% of eligible patients in the catchment area. Preoperative blood samples have been collected for 3,942 (99%) patients and primary tumor specimens collected for 2,929 (74%) patients. Herein we describe the study infrastructure and protocols and present initial proof of concept results from prospective RNA sequencing including tumor molecular subtyping and detection of driver gene mutations. Prospective patient enrollment is ongoing. Conclusions We demonstrate that large-scale population-based collection and RNA-sequencing analysis of breast cancer is feasible. The SCAN-B Initiative should significantly reduce the time to discovery, validation, and clinical implementation of novel molecular diagnostic and predictive tests. We welcome the participation of additional comprehensive cancer treatment centers. Trial registration ClinicalTrials.gov identifier NCT02306096. Electronic supplementary material The online version of this article (doi:10.1186/s13073-015-0131-9) contains supplementary material, which is available to authorized users.
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            Author and article information

            Contributors
            lao.saal@med.lu.se
            Journal
            BMC Bioinformatics
            BMC Bioinformatics
            BMC Bioinformatics
            BioMed Central (London )
            1471-2105
            4 May 2016
            4 May 2016
            2016
            : 17
            : 199
            Affiliations
            Division of Oncology and Pathology, Department of Clinical Sciences, Lund University Cancer Center, Lund University, Medicon Village Building 404-B2, Lund, 223 81 Sweden
            Article
            1058
            10.1186/s12859-016-1058-x
            4855331
            27142976
            d4cdf49b-1bae-47a9-8c01-f91735a3962b
            © Brueffer and Saal. 2016

            Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

            History
            : 13 January 2016
            : 20 April 2016
            Categories
            Software
            Custom metadata
            © The Author(s) 2016

            Bioinformatics & Computational biology
            rna-seq,deep sequencing,sequence alignment,sequence analysis

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