相关论文: SnakeLines: integrated set of computational pipeli…

Reproducibility and sustainability present significant challenges in bioinformatics software development, where rapidly evolving tools and complex workflows often result in short-lived or difficult-to-adapt pipelines. This paper introduces…

Processing high-throughput DNA sequencing data of individuals or populations requires stringing together independent software tools with many parameters, often leading to non-reproducible pipelines and datasets. We developed grenepipe to…

The rise of big data systems has created a need for benchmarks to measure and compare the capabilities of these systems. Big data benchmarks present unique scalability challenges. The supercomputing community has wrestled with these…

Systematic reviews, which entail the extraction of data from large numbers of scientific documents, are an ideal avenue for the application of machine learning. They are vital to many fields of science and philanthropy, but are very…

Biosciences have been revolutionized by next generation sequencing (NGS) technologies in last years, leading to new perspectives in medical, industrial and environmental applications. And although our motivation comes from biosciences, the…

Compendium Manager is a command-line tool written in Python to automate the provisioning, launch, and evaluation of bioinformatics pipelines. Although workflow management tools such as Snakemake and Nextflow enable users to automate the…

The aim of this paper is to develop an approach to visualizations that benefits from distributed computing. Three schemes of process distribution are considered: parallel, pipeline, and expanding pipeline computations. Expanding pipeline…

Applying machine learning to biological sequences - DNA, RNA and protein - has enormous potential to advance human health, environmental sustainability, and fundamental biological understanding. However, many existing machine learning…

The marine environment is one of the most important sources for microbial biodiversity on the planet. These microbes are drivers for many biogeochemical processes, and their enormous genetic potential is still not fully explored or…

Machine learning (ML) offers powerful methods for detecting and modeling associations often in data with large feature spaces and complex associations. Many useful tools/packages (e.g. scikit-learn) have been developed to make the various…

DNA is a promising medium for digital information storage for its exceptional density and durability. While prior studies advanced coding theory, workflow design, and simulation tools, challenges such as synthesis costs, sequencing errors,…

In the rapidly evolving domain of next generation sequencing and bioinformatics analysis, data generation is one aspect that is increasing at a concomitant rate. The burden associated with processing large amounts of sequencing data has…

Massively parallel sequencing techniques have revolutionized biological and medical sciences by providing unprecedented insight into the genomes of humans, animals, and microbes. Modern sequencing platforms generate enormous amounts of…

High throughput genome sequencing technologies such as RNA-Seq and Microarray have the potential to transform clinical decision making and biomedical research by enabling high-throughput measurements of the genome at a granular level.…

Analyses of targeted genomic sequencing data from next-generation-sequencing (NGS) technologies typically involves mapping reads to a reference sequence or clustering reads. For a number of species a reference genome is not available so the…

Summary: Mapping ancient DNA to a reference genome is challenging as it involves numerous steps, is time-consuming and has to be repeated within a study to assess the quality of extracts and libraries; as a result, the mapping needs to be…

Recent advances in computational methods for designing biological sequences have sparked the development of metrics to evaluate these methods performance in terms of the fidelity of the designed sequences to a target distribution and their…

Genomic sequence analysis plays a crucial role in various scientific and medical domains. Traditional machine-learning approaches often struggle to capture the complex relationships and hierarchical structures of sequence data when working…

Evaluating the computational reproducibility of data analysis pipelines has become a critical issue. It is, however, a cumbersome process for analyses that involve data from large populations of subjects, due to their computational and…

Bioinformatics research is characterized by voluminous and incremental datasets and complex data analytics methods. The machine learning methods used in bioinformatics are iterative and parallel. These methods can be scaled to handle big…