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NextGenMap: Fast and accurate read mapping in highly polymorphic genomes

TLDR
NextGenMap is reported, a fast and accurate read mapper, which aligns reads reliably to a reference genome even when the sequence difference between target and reference genome is large, i.e. highly polymorphic genome.
Abstract
Summary: When choosing a read mapper, one faces the trade off between speed and the ability to map reads in highly polymorphic regions. Here, we report NextGenMap, a fast and accurate read mapper, which reduces this dilemma. NextGenMap aligns reads reliably to a reference genome even when the sequence difference between target and reference genome is large, i.e. highly polymorphic genome. At the same time, NextGenMap outperforms current mapping methods with respect to runtime and to the number of correctly mapped reads. NextGenMap efficiently uses the available hardware by exploiting multi-core CPUs as well as graphic cards (GPUs), if available. In addition, NextGenMap handles automatically any read data independent of read length and sequencing technology. Availability: NextGenMap source code and documentation are available at: http://cibiv.github.io/NextGenMap/ Contact: fritz.sedlazeck@univie.ac.at Supplementary information: Supplementary data are available at Bioinformatics online.

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- 01 Dec 2022 - 
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Advancing Mitochondrial Metagenomics: A New Assembly Strategy and Validating the Power of Seed-Based Approach

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References
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Journal ArticleDOI

Fast and accurate short read alignment with Burrows–Wheeler transform

TL;DR: Burrows-Wheeler Alignment tool (BWA) is implemented, a new read alignment package that is based on backward search with Burrows–Wheeler Transform (BWT), to efficiently align short sequencing reads against a large reference sequence such as the human genome, allowing mismatches and gaps.
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Fast gapped-read alignment with Bowtie 2

TL;DR: Bowtie 2 combines the strengths of the full-text minute index with the flexibility and speed of hardware-accelerated dynamic programming algorithms to achieve a combination of high speed, sensitivity and accuracy.
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Genotype and SNP calling from next-generation sequencing data

TL;DR: Recently developed statistical methods both improve and quantify the considerable uncertainty associated with genotype calling, and will especially benefit the growing number of studies using low- to medium-coverage data.
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Stampy: a statistical algorithm for sensitive and fast mapping of Illumina sequence reads.

TL;DR: A read mapper, Stampy, which uses a hybrid mapping algorithm and a detailed statistical model to achieve both speed and sensitivity, particularly when reads include sequence variation, which results in a higher useable sequence yield and improved accuracy compared to that of existing software.
Journal ArticleDOI

Tools for mapping high-throughput sequencing data

TL;DR: This survey focuses on classifying mappers through a wide number of characteristics to allow practitioners to compare the mappers more easily and find those that are most suitable for their specific problem.
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