AIMC Topic: Sequence Analysis, DNA

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repDNA: a Python package to generate various modes of feature vectors for DNA sequences by incorporating user-defined physicochemical properties and sequence-order effects.

Bioinformatics (Oxford, England)
UNLABELLED: In order to develop powerful computational predictors for identifying the biological features or attributes of DNAs, one of the most challenging problems is to find a suitable approach to effectively represent the DNA sequences. To facili...

MetaVelvet-SL: an extension of the Velvet assembler to a de novo metagenomic assembler utilizing supervised learning.

DNA research : an international journal for rapid publication of reports on genes and genomes
The assembly of multiple genomes from mixed sequence reads is a bottleneck in metagenomic analysis. A single-genome assembly program (assembler) is not capable of resolving metagenome sequences, so assemblers designed specifically for metagenomics ha...

Energy entropy vector: a novel approach for efficient microbial genomic sequence analysis and classification.

Briefings in bioinformatics
With the rapid development of genomic sequencing technologies, there is an increasing demand for efficient and accurate sequence analysis methods. However, existing methods face challenges in handling long, variable-length sequences and large-scale d...

DeepMobilome: predicting mobile genetic elements using sequencing reads of microbiomes.

Briefings in bioinformatics
MOTIVATION: Mobile genetic elements (MGEs) play an important role in facilitating the acquisition of antibiotic resistance genes (ARGs) within microbial communities, significantly impacting the evolution of antibiotic resistance. Understanding the me...

DNA forensics at forty: the way forward.

International journal of legal medicine
Forensic DNA analysis has transformed criminal investigations since its inception in 1985. Over four decades, this field has evolved through various phases-from the early stages of exploration to today's highly sophisticated methodologies. Key advanc...

Novel natural vector with asymmetric covariance for classifying biological sequences.

Gene
The genome sequences of organisms form a large and complex landscape, presenting a significant challenge in bioinformatics: how to utilize mathematical tools to describe and analyze this space effectively. The ability to compare relationships between...

ReQuant: improved base modification calling by k-mer value imputation.

Nucleic acids research
Nanopore sequencing allows identification of base modifications, such as methylation, directly from raw current data. Prevailing approaches, including deep learning (DL) methods, require training data covering all possible sequence contexts. These da...

PCVR: a pre-trained contextualized visual representation for DNA sequence classification.

BMC bioinformatics
BACKGROUND: The classification of DNA sequences is pivotal in bioinformatics, essentially for genetic information analysis. Traditional alignment-based tools tend to have slow speed and low recall. Machine learning methods learn implicit patterns fro...

Precision DNA methylation typing via hierarchical clustering of Nanopore current signals and attention-based neural network.

Briefings in bioinformatics
Decoding DNA methylation sites through nanopore sequencing has emerged as a cutting-edge technology in the field of DNA methylation research, as it enables direct sequencing of native DNA molecules without the need for prior enzymatic or chemical tre...

CSV-Filter: a deep learning-based comprehensive structural variant filtering method for both short and long reads.

Bioinformatics (Oxford, England)
MOTIVATION: Structural variants (SVs) play an important role in genetic research and precision medicine. As existing SV detection methods usually contain a substantial number of false positive calls, approaches to filter the detection results are nee...