Genome Biology, 2019-12, Vol.20 (1), p.266-266, Article 266
2019
Details
- Autor(en) / Beteiligte
- Titel
- Afann: bias adjustment for alignment-free sequence comparison based on sequencing data using neural network regression
- Ist Teil von
- Genome Biology, 2019-12, Vol.20 (1), p.266-266, Article 266
- Ort / Verlag
- England: BioMed Central
- Erscheinungsjahr
- 2019
- Link zum Volltext
- Quelle
- Elektronische Zeitschriftenbibliothek - Frei zugängliche E-Journals
- Beschreibungen/Notizen
- Alignment-free methods, more time and memory efficient than alignment-based methods, have been widely used for comparing genome sequences or raw sequencing samples without assembly. However, in this study, we show that alignment-free dissimilarity calculated based on sequencing samples can be overestimated compared with the dissimilarity calculated based on their genomes, and this bias can significantly decrease the performance of the alignment-free analysis. Here, we introduce a new alignment-free tool, Alignment-Free methods Adjusted by Neural Network (Afann) that successfully adjusts this bias and achieves excellent performance on various independent datasets. Afann is freely available at https://github.com/GeniusTang/Afann.
- Sprache
- Englisch
- Identifikatoren
- ISSN: 1474-760X, 1474-7596eISSN: 1474-760XDOI: 10.1186/s13059-019-1872-3Titel-ID: cdi_doaj_primary_oai_doaj_org_article_c2b78009b83249cf82db3d2ff5275dda
- Format
- –
- Schlagworte
- Alignment-free, Animals, Bias, Bias adjustment, d 2 ∗ , d 2 s $d_{2}^{}, \protect d_{2}^{s}, Genomes, Genomics - methods, High-Throughput Nucleotide Sequencing, kmer, Method, Methods, Neural network regression, Neural networks, Neural Networks, Computer, NGS, Primates - genetics, Quercus - genetics, Regression Analysis, Software