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      HaploMerger2: rebuilding both haploid sub-assemblies from high-heterozygosity diploid genome assembly

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      Bioinformatics
      Oxford University Press

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          Abstract

          Summary

          De novo assembly is a difficult issue for heterozygous diploid genomes. The advent of high-throughput short-read and long-read sequencing technologies provides both new challenges and potential solutions to the issue. Here, we present HaploMerger2 (HM2), an automated pipeline for rebuilding both haploid sub-assemblies from the polymorphic diploid genome assembly. It is designed to work on pre-existing diploid assemblies, which are typically created by using de novo assemblers. HM2 can process any diploid assemblies, but it is especially suitable for diploid assemblies with high heterozygosity (≥3%), which can be difficult for other tools. This pipeline also implements flexible and sensitive assembly error detection, a hierarchical scaffolding procedure and a reliable gap-closing method for haploid sub-assemblies. Using HM2, we demonstrate that two haploid sub-assemblies reconstructed from a real, highly-polymorphic diploid assembly show greatly improved continuity.

          Availability and Implementation

          Source code, executables and the testing dataset are freely available at https://github.com/mapleforest/HaploMerger2/releases/.

          Supplementary information

          Supplementary data are available at Bioinformatics online.

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          Most cited references9

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          Aggressive assembly of pyrosequencing reads with mates

          Motivation: DNA sequence reads from Sanger and pyrosequencing platforms differ in cost, accuracy, typical coverage, average read length and the variety of available paired-end protocols. Both read types can complement one another in a ‘hybrid’ approach to whole-genome shotgun sequencing projects, but assembly software must be modified to accommodate their different characteristics. This is true even of pyrosequencing mated and unmated read combinations. Without special modifications, assemblers tuned for homogeneous sequence data may perform poorly on hybrid data. Results: Celera Assembler was modified for combinations of ABI 3730 and 454 FLX reads. The revised pipeline called CABOG (Celera Assembler with the Best Overlap Graph) is robust to homopolymer run length uncertainty, high read coverage and heterogeneous read lengths. In tests on four genomes, it generated the longest contigs among all assemblers tested. It exploited the mate constraints provided by paired-end reads from either platform to build larger contigs and scaffolds, which were validated by comparison to a finished reference sequence. A low rate of contig mis-assembly was detected in some CABOG assemblies, but this was reduced in the presence of sufficient mate pair data. Availability: The software is freely available as open-source from http://wgs-assembler.sf.net under the GNU Public License. Contact: jmiller@jcvi.org Supplementary information: Supplementary data are available at Bioinformatics online.
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            Redundans: an assembly pipeline for highly heterozygous genomes

            Many genomes display high levels of heterozygosity (i.e. presence of different alleles at the same loci in homologous chromosomes), being those of hybrid organisms an extreme such case. The assembly of highly heterozygous genomes from short sequencing reads is a challenging task because it is difficult to accurately recover the different haplotypes. When confronted with highly heterozygous genomes, the standard assembly process tends to collapse homozygous regions and reports heterozygous regions in alternative contigs. The boundaries between homozygous and heterozygous regions result in multiple assembly paths that are hard to resolve, which leads to highly fragmented assemblies with a total size larger than expected. This, in turn, causes numerous problems in downstream analyses such as fragmented gene models, wrong gene copy number, or broken synteny. To circumvent these caveats we have developed a pipeline that specifically deals with the assembly of heterozygous genomes by introducing a step to recognise and selectively remove alternative heterozygous contigs. We tested our pipeline on simulated and naturally-occurring heterozygous genomes and compared its accuracy to other existing tools. Our method is freely available at https://github.com/Gabaldonlab/redundans.
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              WindowMasker: window-based masker for sequenced genomes.

              Matches to repetitive sequences are usually undesirable in the output of DNA database searches. Repetitive sequences need not be matched to a query, if they can be masked in the database. RepeatMasker/Maskeraid (RM), currently the most widely used software for DNA sequence masking, is slow and requires a library of repetitive template sequences, such as a manually curated RepBase library, that may not exist for newly sequenced genomes. We have developed a software tool called WindowMasker (WM) that identifies and masks highly repetitive DNA sequences in a genome, using only the sequence of the genome itself. WM is orders of magnitude faster than RM because WM uses a few linear-time scans of the genome sequence, rather than local alignment methods that compare each library sequence with each piece of the genome. We validate WM by comparing BLAST outputs from large sets of queries applied to two versions of the same genome, one masked by WM, and the other masked by RM. Even for genomes such as the human genome, where a good RepBase library is available, searching the database as masked with WM yields more matches that are apparently non-repetitive and fewer matches to repetitive sequences. We show that these results hold for transcribed regions as well. WM also performs well on genomes for which much of the sequence was in draft form at the time of the analysis. WM is included in the NCBI C++ toolkit. The source code for the entire toolkit is available at ftp://ftp.ncbi.nih.gov/toolbox/ncbi_tools++/CURRENT/. Once the toolkit source is unpacked, the instructions for building WindowMasker application in the UNIX environment can be found in file src/app/winmasker/README.build. Supplementary data are available at ftp://ftp.ncbi.nlm.nih.gov/pub/agarwala/windowmasker/windowmasker_suppl.pdf
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                Author and article information

                Journal
                Bioinformatics
                Bioinformatics
                bioinformatics
                Bioinformatics
                Oxford University Press
                1367-4803
                1367-4811
                15 August 2017
                12 April 2017
                12 April 2017
                : 33
                : 16
                : 2577-2579
                Affiliations
                State Key Laboratory of Biocontrol, Guangdong Key Laboratory of Pharmaceutical Functional Genes, School of Life Sciences, Sun Yat-Sen University, Guangzhou 510275, People’s Republic of China
                Author notes
                [* ]To whom correspondence should be addressed.

                Associate Editor: Bonnie Berger

                Article
                btx220
                10.1093/bioinformatics/btx220
                5870766
                28407147
                05fd72be-90b9-464a-806e-0735f3f98b0b
                © The Author 2017. Published by Oxford University Press.

                This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License ( http://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com

                History
                : 12 December 2016
                : 31 March 2017
                : 11 April 2017
                Page count
                Pages: 3
                Categories
                Applications Notes
                Genome Analysis

                Bioinformatics & Computational biology
                Bioinformatics & Computational biology

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