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High-Throughput Nucleotide Sequencing

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DCMD: Distance-based classification using mixture distributions on microbiome data.

PLoS computational biology
Current advances in next-generation sequencing techniques have allowed researchers to conduct comprehensive research on the microbiome and human diseases, with recent studies identifying associations between the human microbiome and health outcomes f...

DeepTCR is a deep learning framework for revealing sequence concepts within T-cell repertoires.

Nature communications
Deep learning algorithms have been utilized to achieve enhanced performance in pattern-recognition tasks. The ability to learn complex patterns in data has tremendous implications in immunogenomics. T-cell receptor (TCR) sequencing assesses the diver...

seqQscorer: automated quality control of next-generation sequencing data using machine learning.

Genome biology
Controlling quality of next-generation sequencing (NGS) data files is a necessary but complex task. To address this problem, we statistically characterize common NGS quality features and develop a novel quality control procedure involving tree-based ...

Use of machine learning to identify a T cell response to SARS-CoV-2.

Cell reports. Medicine
The identification of SARS-CoV-2-specific T cell receptor (TCR) sequences is critical for understanding T cell responses to SARS-CoV-2. Accordingly, we reanalyze publicly available data from SARS-CoV-2-recovered patients who had low-severity disease ...

Machine learning reveals bilateral distribution of somatic L1 insertions in human neurons and glia.

Nature neuroscience
Retrotransposons can cause somatic genome variation in the human nervous system, which is hypothesized to have relevance to brain development and neuropsychiatric disease. However, the detection of individual somatic mobile element insertions present...

Cnngeno: A high-precision deep learning based strategy for the calling of structural variation genotype.

Computational biology and chemistry
Genotype plays a significant role in determining characteristics in an organism and genotype calling has been greatly accelerated by sequencing technologies. Furthermore, most parametric statistical models are unable to effectively call genotype, whi...

Deep learning predicts short non-coding RNA functions from only raw sequence data.

PLoS computational biology
Small non-coding RNAs (ncRNAs) are short non-coding sequences involved in gene regulation in many biological processes and diseases. The lack of a complete comprehension of their biological functionality, especially in a genome-wide scenario, has dem...

Unlocking the efficiency of genomics laboratories with robotic liquid-handling.

BMC genomics
In research and clinical genomics laboratories today, sample preparation is the bottleneck of experiments, particularly when it comes to high-throughput next generation sequencing (NGS). More genomics laboratories are now considering liquid-handling ...

Deep learning in next-generation sequencing.

Drug discovery today
Next-generation sequencing (NGS) methods lie at the heart of large parts of biological and medical research. Their fundamental importance has created a continuously increasing demand for processing and analysis methods of the data sets produced, addr...