AIMC Topic: Genome, Mitochondrial
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DeLUCS: Deep learning for unsupervised clustering of DNA sequences.
PloS one
Jan 21, 2022
We present a novel Deep Learning method for the Unsupervised Clustering of DNA Sequences (DeLUCS) that does not require sequence alignment, sequence homology, or (taxonomic) identifiers. DeLUCS uses Frequency Chaos Game Representations (FCGR) of prim...
Effective machine-learning assembly for next-generation amplicon sequencing with very low coverage.
BMC bioinformatics
Dec 11, 2019
BACKGROUND: In short-read DNA sequencing experiments, the read coverage is a key parameter to successfully assemble the reads and reconstruct the sequence of the input DNA. When coverage is very low, the original sequence reconstruction from the read...
Human mitochondrial genome compression using machine learning techniques.
Human genomics
Oct 22, 2019
BACKGROUND: In recent years, with the development of high-throughput genome sequencing technologies, a large amount of genome data has been generated, which has caused widespread concern about data storage and transmission costs. However, how to effe...
ML-DSP: Machine Learning with Digital Signal Processing for ultrafast, accurate, and scalable genome classification at all taxonomic levels.
BMC genomics
Apr 3, 2019
BACKGROUND: Although software tools abound for the comparison, analysis, identification, and classification of genomic sequences, taxonomic classification remains challenging due to the magnitude of the datasets and the intrinsic problems associated ...
The first two mitochondrial genomes from Taeniopterygidae (Insecta: Plecoptera): Structural features and phylogenetic implications.
International journal of biological macromolecules
Dec 29, 2017
The complete mitochondrial genomes (mitogenomes) of Taeniopteryx ugola and Doddsia occidentalis (Plecoptera: Taeniopterygidae) were firstly sequenced from the family Taeniopterygidae. The 15,353-bp long mitogenome of T. ugola and the 16,020-bp long m...
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