Abstract
Summary
Most tools for normalizing NanoString gene expression data, apart from the default NanoString nCounter software, are R packages that focus on technical normalization and lack ...configurable parameters. However, content normalization is the most sensitive, experiment-specific, and relevant step to preprocess NanoString data. Currently this step requires the use of multiple tools and a deep understanding of data management by the researcher. We present GUANIN, a comprehensive normalization tool that integrates both new and well-established methods, offering a wide variety of options to introduce, filter, choose, and evaluate reference genes for content normalization. GUANIN allows the introduction of genes from an endogenous subset as reference genes, addressing housekeeping-related selection problems. It performs a specific and straightforward normalization approach for each experiment, using a wide variety of parameters with suggested default values. GUANIN provides a large number of informative output files that enable the iterative refinement of the normalization process. In terms of normalization, GUANIN matches or outperforms other available methods. Importantly, it allows researchers to interact comprehensively with the data preprocessing step without programming knowledge, thanks to its easy-to-use Graphical User Interface (GUI).
Availability and implementation
GUANIN can be installed with pip install GUANIN and it is available at https://pypi.org/project/guanin/. Source code, documentation, and case studies are available at https://github.com/julimontoto/guanin under the GPLv3 license.
Abstract Summary Snipit is an analysis and visualization tool designed for summarizing single nucleotide polymorphisms in sequences in comparison to a reference sequence. This tool efficiently ...catalogues nucleotide and amino acid differences, enabling clear comparisons through customizable, publication-ready figures. With features such as configurable colour palettes, customizable record sorting, and the ability to output figures in multiple formats, snipit offers a user-friendly interface for researchers across diverse disciplines. In addition, snipit includes a specialized recombi-mode for illustrating recombination patterns, which can highlight otherwise often difficult-to-detect relationships between sequences. Availability and implementation Snipit is an open-source python-based tool that is hosted on GitHub under a GNU-GPL 3.0 licence (https://github.com/aineniamh/snipit). It can be installed from PyPi using pip. Source code and additional documentation can be found on the GitHub repository.
Abstract Summary Measuring cellular energetics is essential to understanding a matrix’s (e.g. cell, tissue, or biofluid) metabolic state. The Agilent Seahorse machine is a common method to measure ...real-time cellular energetics, but existing analysis tools are highly manual or lack functionality. The Cellular Energetics Analysis Software (ceas) R package fills this analytical gap by providing modular and automated Seahorse data analysis and visualization. Availability and implementation ceas is available on CRAN (https://cran.r-project.org/package=ceas). Source code and installable tarballs are freely available for download at https://github.com/jamespeapen/ceas/releases/ under the MIT license. Package documentation may be found at https://jamespeapen.github.io/ceas/. ceas is implemented in R and is supported on macOS, Windows and Linux.
Abstract
Summary
Raw nanopore signals can be analyzed while they are being generated, a process known as real-time analysis. Real-time analysis of raw signals is essential to utilize the unique ...features that nanopore sequencing provides, enabling the early stopping of the sequencing of a read or the entire sequencing run based on the analysis. The state-of-the-art mechanism, RawHash, offers the first hash-based efficient and accurate similarity identification between raw signals and a reference genome by quickly matching their hash values. In this work, we introduce RawHash2, which provides major improvements over RawHash, including more sensitive quantization and chaining algorithms, weighted mapping decisions, frequency filters to reduce ambiguous seed hits, minimizers for hash-based sketching, and support for the R10.4 flow cell version and POD5 and SLOW5 file formats. Compared to RawHash, RawHash2 provides better F1 accuracy (on average by 10.57% and up to 20.25%) and better throughput (on average by 4.0× and up to 9.9×) than RawHash.
Availability and implementation
RawHash2 is available at https://github.com/CMU-SAFARI/RawHash. We also provide the scripts to fully reproduce our results on our GitHub page.
Abstract Motivation The Galaxy application is a popular open-source framework for data intensive sciences, counting thousands of monthly users across more than 100 public servers. To support a ...growing number of users and a greater variety of use cases, the complexity of a production-grade Galaxy installation has also grown, requiring more administration effort. There is a need for a rapid and reproducible Galaxy deployment method that can be maintained at high-availability with minimal maintenance. Results We describe the Galaxy Helm chart that codifies all elements of a production-grade Galaxy installation into a single package. Deployable on Kubernetes clusters, the chart encapsulates supporting software services and implements the best-practices model for running Galaxy. It is also the most rapid method available for deploying a scalable, production-grade Galaxy instance on one’s own infrastructure. The chart is highly configurable, allowing systems administrators to swap dependent services if desired. Notable uses of the chart include on-demand, fully-automated deployments on AnVIL, providing training infrastructure for the Bioconductor project, and as the AWS-recommended solution for running Galaxy on the Amazon cloud. Availability and implementation The source code for Galaxy Helm is available at https://github.com/galaxyproject/galaxy-helm, the corresponding Helm package at https://github.com/CloudVE/helm-charts, and the required Galaxy container image https://github.com/galaxyproject/galaxy-docker-k8s.
Abstract Motivation Mendelian randomization (MR) is a widely used approach to estimate causal effect of variation in gene expression on complex traits. Among several MR-based algorithms, ...transcriptome-wide summary statistics-based Mendelian Randomization approach (TWMR) enables the uses of multiple SNPs as instruments and multiple gene expression traits as exposures to facilitate causal inference in observational studies. Results Here we present a Python-based implementation of TWMR and revTWMR. Our implementation offers GPU computational support for faster computations and robust computation mode resilient to highly correlated gene expressions and genetic variants Availability and implementation pyTWMR is available at github.com/soreshkov/pyTWMR.
Abstract Summary Identification and quantification of phosphorylation sites are essential for biological interpretation of a phosphoproteomics experiment. For data independent acquisition mass ...spectrometry-based (DIA-MS) phosphoproteomics, extracting a site-level report from the output of current processing software is not straightforward as multiple peptides might contribute to a single site, multiple phosphorylation sites can occur on the same peptides, and protein isoforms complicate site specification. Currently only limited support is available from a commercial software package via a platform-specific solution with a rather simple site quantification method. Here, we present sitereport, a software tool implemented in an extendable Python package called msproteomics to report phosphosites and phosphopeptides from a DIA-MS phosphoproteomics experiment with a proven quantification method called MaxLFQ. We demonstrate the use of sitereport for downstream data analysis at site level, allowing benchmarking different DIA-MS processing software tools. Availability and implementation sitereport is available as a command line tool in the Python package msproteomics, released under the Apache License 2.0 and available from the Python Package Index (PyPI) at https://pypi.org/project/msproteomics and GitHub at https://github.com/tvpham/msproteomics.
