Atropos: specific, sensitive, and speedy trimming of sequencing reads.
Atropos: specific, sensitive, and speedy trimming of sequencing reads.
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作者：
Didion JP;Martin M;Collins FS
将原始测序读数转化为生物学见解的关键步骤是修剪衔接子序列和低质量碱基。读取微调已被证明可以提高质量和可靠性，同时降低下游分析的计算要求。许多读段修剪软件工具是可用的;然而，没有工具同时提供处理现代基于测序的实验中生成的数据的类型和体积所需的准确性、计算效率和特征集。在这里，我们介绍了Atropos，并表明它在保持领先速度的同时，以高灵敏度和特异性修剪读取。与其他最先进的读取修整工具相比，Atropos在执行时间上保持竞争力的同时，显著提高了修整精度。此外，Atropos即使在修剪测序错误率升高的数据时也能保持高准确性。Atropos提供的准确性，高性能和广泛的功能集使其成为Illumina，ABI SOLiD和其他当前一代短读测序数据集预处理的合适选择。Atropos是用Python（3.3+）编写的开源和免费软件，可在
A key step in the transformation of raw sequencing reads into biological insights is the trimming of adapter sequences and low-quality bases. Read trimming has been shown to increase the quality and reliability while decreasing the computational requirements of downstream analyses. Many read trimming software tools are available; however, no tool simultaneously provides the accuracy, computational efficiency, and feature set required to handle the types and volumes of data generated in modern sequencing-based experiments. Here we introduce Atropos and show that it trims reads with high sensitivity and specificity while maintaining leading-edge speed. Compared to other state-of-the-art read trimming tools, Atropos achieves significant increases in trimming accuracy while remaining competitive in execution times. Furthermore, Atropos maintains high accuracy even when trimming data with elevated rates of sequencing errors. The accuracy, high performance, and broad feature set offered by Atropos makes it an appropriate choice for the pre-processing of Illumina, ABI SOLiD, and other current-generation short-read sequencing datasets. Atropos is open source and free software written in Python (3.3+) and available at .
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影响因子：
7
作者：
Harrow J;Frankish A;Gonzalez JM;Tapanari E;Diekhans M;Kokocinski F;Aken BL;Barrell D;Zadissa A;Searle S;Barnes I;Bignell A;Boychenko V;Hunt T;Kay M;Mukherjee G;Rajan J;Despacio-Reyes G;Saunders G;Steward C;Harte R;Lin M;Howald C;Tanzer A;Derrien T;Chrast J;Walters N;Balasubramanian S;Pei B;Tress M;Rodriguez JM;Ezkurdia I;van Baren J;Brent M;Haussler D;Kellis M;Valencia A;Reymond A;Gerstein M;Guigó R;Hubbard TJ
通讯作者：
Hubbard TJ
影响因子：
3
作者：
Sturm M;Schroeder C;Bauer P
通讯作者：
Bauer P
使用下一代 DNA 测序数据进行变异发现和基因分型的框架。
影响因子：
30.8
作者：
通讯作者：
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影响因子：
2.8
作者：
Stirling C;Andrews S;Croft T;Vickers J;Turner P;Robinson A
通讯作者：
Robinson A
影响因子：
--
作者：
UKKONEN, E
通讯作者：
UKKONEN, E