MetaBinG2: a fast and accurate metagenomic sequence classification system for samples with many unknown organisms
Abstract Background Many methods have been developed for metagenomic sequence classification, and most of them depend heavily on genome sequences of the known organisms. A large portion of sequencing sequences may be classified as unknown, which greatly impairs our understanding of the whole sample....
Main Authors: | Yuyang Qiao, Ben Jia, Zhiqiang Hu, Chen Sun, Yijin Xiang, Chaochun Wei |
---|---|
Format: | Article |
Language: | English |
Published: |
BMC
2018-08-01
|
Series: | Biology Direct |
Subjects: | |
Online Access: | http://link.springer.com/article/10.1186/s13062-018-0220-y |
Similar Items
-
Massive metagenomic data analysis using abundance-based machine learning
by: Zachary N. Harris, et al.
Published: (2019-08-01) -
Communicating the promise, risks, and ethics of large-scale, open space microbiome and metagenome research
by: Daria Shamarina, et al.
Published: (2017-10-01) -
Environmental metagenome classification for constructing a microbiome fingerprint
by: Jolanta Kawulok, et al.
Published: (2019-11-01) -
Fingerprinting cities: differentiating subway microbiome functionality
by: Chengsheng Zhu, et al.
Published: (2019-10-01) -
MetaBinG: using GPUs to accelerate metagenomic sequence classification.
by: Peng Jia, et al.
Published: (2011-01-01)