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High-Throughput Nucleotide Sequencing

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Depression phenotype identified by using single nucleotide exact amplicon sequence variants of the human gut microbiome.

Molecular psychiatry
Single nucleotide exact amplicon sequence variants (ASV) of the human gut microbiome were used to evaluate if individuals with a depression phenotype (DEPR) could be identified from healthy reference subjects (NODEP). Microbial DNA in stool samples o...

Using transfer learning from prior reference knowledge to improve the clustering of single-cell RNA-Seq data.

Scientific reports
In many research areas scientists are interested in clustering objects within small datasets while making use of prior knowledge from large reference datasets. We propose a method to apply the machine learning concept of transfer learning to unsuperv...

DeepSV: accurate calling of genomic deletions from high-throughput sequencing data using deep convolutional neural network.

BMC bioinformatics
BACKGROUND: Calling genetic variations from sequence reads is an important problem in genomics. There are many existing methods for calling various types of variations. Recently, Google developed a method for calling single nucleotide polymorphisms (...

Effective machine-learning assembly for next-generation amplicon sequencing with very low coverage.

BMC bioinformatics
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...

Afann: bias adjustment for alignment-free sequence comparison based on sequencing data using neural network regression.

Genome biology
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 ca...

Machine Learning to Detect Alzheimer's Disease from Circulating Non-coding RNAs.

Genomics, proteomics & bioinformatics
Blood-borne small non-coding (sncRNAs) are among the prominent candidates for blood-based diagnostic tests. Often, high-throughput approaches are applied to discover biomarker signatures. These have to be validated in larger cohorts and evaluated by ...

Athena: Automated Tuning of k-mer based Genomic Error Correction Algorithms using Language Models.

Scientific reports
The performance of most error-correction (EC) algorithms that operate on genomics reads is dependent on the proper choice of its configuration parameters, such as the value of k in k-mer based techniques. In this work, we target the problem of findin...

A Deep Learning Approach for Detecting Copy Number Variation in Next-Generation Sequencing Data.

G3 (Bethesda, Md.)
Copy number variants (CNV) are associated with phenotypic variation in several species. However, properly detecting changes in copy numbers of sequences remains a difficult problem, especially in lower quality or lower coverage next-generation sequen...

Jointly Integrating VCF-Based Variants and OWL-Based Biomedical Ontologies in MongoDB.

IEEE/ACM transactions on computational biology and bioinformatics
The development of the next-generation sequencing (NGS) technologies has led to massive amounts of VCF (Variant Call Format) files, which have been the standard formats developed with 1000 Genomes Project. At the same time, with the widespread use of...

Forensic STR allele extraction using a machine learning paradigm.

Forensic science international. Genetics
We present a machine learning approach to short tandem repeat (STR) sequence detection and extraction from massively parallel sequencing data called Fragsifier. Using this approach, STRs are detected on each read by first locating the longest repeat ...