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At least 1,045 records · Page 58Linked to original sources

PubMed2004

Role of the director of pharmacy: the first six months.

PURPOSE: Interviewing for a director of pharmacy position is discussed, and recommended activities for a pharmacy director's first six months on the job are described. SUMMARY: A prospective pharmacy director should first target the specific positions available that best fit his or her skills. The individual should tour the hospital and the pharmacy for each position of interest. Interviews should include the pharmacy staff, the nursing staff, the direct-report manager, and the medical staff. The hospital's financial condition should be assessed and a postinterview analysis conducted. Once hired, the new director should devote the first three months to an assessment of pharmacy services, staff, and customers; a regulatory and accreditation review; and assessments of organizational leadership, information systems, and clinical services. In addition, key documents, such as hospital policies and procedures, should be reviewed. At the end of the first three months, two documents, a summary of the new director's initial assessments and a chart showing current workflow, should be completed. Tasks for the first six months on the job include devising a work plan to address issues, developing reporting systems and metrics, setting priorities, creating staffing and clinical plans, promoting staff development creating and revising procedures, and evaluating outsourcing needs. The director should take a moment from time to time to celebrate successes with the staff. CONCLUSION: The director of pharmacy has one of the most complex jobs in the hospital. Planning, communicating, networking, writing, listening, leading, and motivating are skills necessary for success.

Humans↗

PubMed2006

Developments in mass spectrometry for the analysis of complex protein mixtures.

State-of-the-art proteomics workflows involve multiple interdependent steps: sample preparation, protein-peptide separation, mass spectrometry and data analysis. While improvements in any of these steps can increase the depth and breadth of analysis, advances in mass spectrometry have catalysed many of the most important developments. We discuss common classes of mass analysers and how these analysers are put together to produce some of the most popular mass spectrometry platforms. The capabilities of these platforms determine how they can be used in a variety of common proteomic strategies and, in turn, what types of biological questions can be addressed. Moving forward, powerful new hybrid mass spectrometers and application of emerging types of tandem mass spectrometry promise that our ability to analyse complex mixtures of proteins will continue to advance.

Complex Mixtures↗

PubMed2005

Evolution of web services in bioinformatics.

Bioinformaticians have developed large collections of tools to make sense of the rapidly growing pool of molecular biological data. Biological systems tend to be complex and in order to understand them, it is often necessary to link many data sets and use more than one tool. Therefore, bioinformaticians have experimented with several strategies to try to integrate data sets and tools. Owing to the lack of standards for data sets and the interfaces of the tools this is not a trivial task. Over the past few years building services with web-based interfaces has become a popular way of sharing the data and tools that have resulted from many bioinformatics projects. This paper discusses the interoperability problem and how web services are being used to try to solve it, resulting in the evolution of tools with web interfaces from HTML/web form-based tools not suited for automatic workflow generation to a dynamic network of XML-based web services that can easily be used to create pipelines.

Computational Biology↗

PubMed2026

Machine learning approaches for cancer prognosis and diagnosis via non-coding RNA: a comprehensive review.

Non-coding RNAs (ncRNAs), once considered genomic dark matter, are now established as key regulators of gene expression with widespread roles in cellular homeostasis and disease. In cancer, ncRNA expression is frequently and systematically dysregulated, and many of these molecules circulate in stable, protected form within biofluids, offering a compelling basis for non-invasive or minimally invasive diagnostic strategies. However, their clinical translation remains substantially hindered to date due to biological complexity, technical noise, and high dimensionality inherent to ncRNA expression datasets. In this context, machine learning (ML) has emerged as a powerful analytical tool to address these challenges, enabling the identification of subtle, reproducible ncRNA signatures predictive of diverse malignancies. This review critically evaluates ML-driven frameworks for cancer diagnosis and prognosis across four ncRNA subclasses, namely miRNAs, lncRNAs, circRNAs, and piRNAs, while also acknowledging the biophysical and thermodynamic models that reinforce ncRNA bioinformatics. Despite substantial methodological progress in ML-based cancer diagnosis and prognosis, key challenges persist, including tumor biological heterogeneity, limited multicenter validation, and the lack of widely adopted standardized protocols for preprocessing, normalization, and reporting workflows. Furthermore, many current ML models lack interpretability in biological or clinical context, constraining their translational utility. By synthesizing recent advances and identifying unresolved barriers, this review charts a roadmap for developing a robust, clinically actionable ncRNA biomarker platform for cancer detection. With global cancer incidence projected to exceed 35 million annual cases by 2050, validated ncRNA-ML-driven frameworks hold potential to revolutionize early-stage detection and personalized therapeutic strategies, thereby reducing the escalating socio-economic burden of cancer worldwide.

Humans↗

PubMed2026

Phage bioinformatics tools: a review of computational approaches for bacteriophage research.

Rising clinical interest in phage therapy and the exponential growth of metagenomic sequence catalogues have driven a rapid expansion of bacteriophage bioinformatics. More than 80 dedicated tools, mostly published since 2020, now span identification, assembly, annotation, taxonomy, lifestyle prediction, defence-system detection, and host prediction. Aimed at experienced practitioners and developers, this review synthesizes the field through the lens of three successive computational paradigms: sequence homology, bounded by database completeness; machine learning, constrained by labelled training data; and foundation models, which now achieve Matthews correlation coefficients above 0.95 in identification tasks and, through structure-informed prediction, raise functional annotation to over half of phage genes. Furthermore, we map the upstream components, namely, gene callers, homology engines, protein language models, and structural search tools, that underpin most downstream pipelines, exposing shared infrastructure and ecosystem-level fragility when dependencies change. To translate this into practice, we propose web-based and command-line reference workflows calibrated to user expertise and sample types. Finally, we set an agenda for the next wave of tool development. Roughly half of phage genes still resist functional annotation despite structural methods; no broadly generalizable strain-level host predictor exists for phage therapy; varying true-positive rates (0%-97%) underscore the absence of standardized community benchmarks analogous to Critical Assessment of Structure Prediction or Critical Assessment of Metagenome Interpretation. As generative genome models begin designing synthetic phages, progress will depend less on producing standalone tools than on rigorous evaluation, interoperable infrastructure, and clinically meaningful prediction targets.

Computational Biology↗

PubMed2006

Automated protein function prediction--the genomic challenge.

Overwhelmed with genomic data, biologists are facing the first big post-genomic question--what do all genes do? First, not only is the volume of pure sequence and structure data growing, but its diversity is growing as well, leading to a disproportionate growth in the number of uncharacterized gene products. Consequently, established methods of gene and protein annotation, such as homology-based transfer, are annotating less data and in many cases are amplifying existing erroneous annotation. Second, there is a need for a functional annotation which is standardized and machine readable so that function prediction programs could be incorporated into larger workflows. This is problematic due to the subjective and contextual definition of protein function. Third, there is a need to assess the quality of function predictors. Again, the subjectivity of the term 'function' and the various aspects of biological function make this a challenging effort. This article briefly outlines the history of automated protein function prediction and surveys the latest innovations in all three topics.

Algorithms↗

PubMed2006

Metabolomics Standards Workshop and the development of international standards for reporting metabolomics experimental results.

Informatics standards and controlled vocabularies are essential for allowing information technology to help exchange, manage, interpret and compare large data collections. In a rapidly evolving field, the challenge is to work out how best to describe, but not prescribe, the use of these technologies and methods. A Metabolomics Standards Workshop was held by the US National Institutes of Health (NIH) to bring together multiple ongoing standards efforts in metabolomics with the NIH research community. The goals were to discuss metabolomics workflows (methods, technologies and data treatments) and the needs, challenges and potential approaches to developing a Metabolomics Standards Initiative that will help facilitate this rapidly growing field which has been a focus of the NIH roadmap effort. This report highlights specific aspects of what was presented and discussed at the 1st and 2nd August 2005 Metabolomics Standards Workshop.

Cell Physiological Phenomena↗

PubMed2006

Open source tools and toolkits for bioinformatics: significance, and where are we?

This review summarizes important work in open-source bioinformatics software that has occurred over the past couple of years. The survey is intended to illustrate how programs and toolkits whose source code has been developed or released under an Open Source license have changed informatics-heavy areas of life science research. Rather than creating a comprehensive list of all tools developed over the last 2-3 years, we use a few selected projects encompassing toolkit libraries, analysis tools, data analysis environments and interoperability standards to show how freely available and modifiable open-source software can serve as the foundation for building important applications, analysis workflows and resources.

Algorithms↗

PubMed1997

Hopper: software for automating data tracking and flow in DNA sequencing.

MOTIVATION: Genome-scale DNA sequencing is a multistep process in which large numbers of small template clones are propagated, purified, sequenced and analyzed on acrylamide gels. A significant challenge to these projects is the scale at which the data handling must be done. Hence, large-scale sequencing facilities will benefit from tracking template DNA information (purification methods, reaction and electrophoresis conditions) in a systematic fashion. A lack of software tools that support automated sample entry, and automatic data storage, retrieval and analysis are a major hindrance to recording and using laboratory workflow information to monitor the overall quality of data production. RESULTS: The UNIX file system has been used to prototype automation of the flow of data from the ABI sequencer to a data repository. Data are automatically processed by a central Perl program, Hopper, which runs a series of programs that analyze data quality (read length estimate, fraction of indeterminate bases, and number of contaminating and repetitive sequences), assemble shotgun sequence data, and generates simple reports describing the results.

Database Management Systems↗

PubMed1998

The LabBase system for data management in large scale biology research laboratories.

MOTIVATION: The development of laboratory information management systems (LIMSs) for large scale biology research projects can be a challenging problem. Many such projects generate complex datasets via complex procedures that undergo continuous refinement. A key software challenge is to simplify the database-development task so that databases can be built and modified quickly enough to keep pace with changing project-requirements. RESULTS: LabBase extends the facilities offered by relational database systems to simplify the task of creating databases for large scale biology research projects. LabBase provides a structural object data model, similar to ACEDB, and adds to this the concepts of Materials, Steps, and States: Materials are objects representing the identifiable things that participate in a laboratory protocol; Steps are objects reporting the results of a laboratory or analytical procedure; and States are objects denoting places in a laboratory protocol. The system provides a data definition language for succinctly defining laboratory databases, and operations for conveniently storing and retrieving data in such databases. The system also provides support for workflow management. LabBase is implemented in Perl5 and provides a natural interface for laboratory application programs written in Perl. AVAILABILITY: The software is freely available. Contact the authors. CONTACT: nat@jax.org

Computational Biology↗

PubMed2003

MADGE: scalable distributed data management software for cDNA microarrays.

MOTIVATION: The human genome project and the development of new high-throughput technologies have created unparalleled opportunities to study the mechanism of diseases, monitor the disease progression and evaluate effective therapies. Gene expression profiling is a critical tool to accomplish these goals. The use of nucleic acid microarrays to assess the gene expression of thousands of genes simultaneously has seen phenomenal growth over the past five years. Although commercial sources of microarrays exist, investigators wanting more flexibility in the genes represented on the array will turn to in-house production. The creation and use of cDNA microarrays is a complicated process that generates an enormous amount of information. Effective data management of this information is essential to efficiently access, analyze, troubleshoot and evaluate the microarray experiments. RESULTS: We have developed a distributable software package designed to track and store the various pieces of data generated by a cDNA microarray facility. This includes the clone collection storage data, annotation data, workflow queues, microarray data, data repositories, sample submission information, and project/investigator information. This application was designed using a 3-tier client server model. The data access layer (1st tier) contains the relational database system tuned to support a large number of transactions. The data services layer (2nd tier) is a distributed COM server with full database transaction support. The application layer (3rd tier) is an internet based user interface that contains both client and server side code for dynamic interactions with the user. AVAILABILITY: This software is freely available to academic institutions and non-profit organizations at http://www.genomics.mcg.edu/niddkbtc.

Database Management Systems↗

PubMed2025

VirDetector: a bioinformatic pipeline for virus surveillance using nanopore sequencing.

SUMMARY: Virus surveillance programmes are designed to counter the growing threat of viral outbreaks to human health. Nanopore sequencing, in particular, has proven to be suitable for this purpose, as it is readily available and provides rapid results. However, as special bioinformatic programs are required to extract the relevant information from the sequencing data, applications are needed that allow users without extensive bioinformatics knowledge to carry out the relevant analysis steps. We present VirDetector, a bioinformatic pipeline for virus surveillance using nanopore sequencing. The pipeline automatically installs all required programs and databases and allows all its steps to be executed with a single console command. After preprocessing the samples, including the possibility for basecalling, the pipeline classifies each sample taxonomically and reconstructs the viral consensus genomes, which are then used in phylogenetic analyses. This streamlined workflow provides a user-friendly and efficient solution for monitoring viral pathogens. AVAILABILITY AND IMPLEMENTATION: VirDetector is freely available at https://github.com/NLKaiser/VirDetector and https://zenodo.org/records/14637302 (10.5281/zenodo.14637302).

Nanopore Sequencing↗

PubMed2025

Bayesian inference of fitness landscapes via tree-structured branching processes.

MOTIVATION: The complex dynamics of cancer evolution, driven by mutation and selection, underlies the molecular heterogeneity observed in tumors. The evolutionary histories of tumors of different patients can be encoded as mutation trees and reconstructed in high resolution from single-cell sequencing data, offering crucial insights for studying fitness effects of and epistasis among mutations. Existing models, however, either fail to separate mutation and selection or neglect the evolutionary histories encoded by the tumor phylogenetic trees. RESULTS: We introduce FiTree, a tree-structured multi-type branching process model with epistatic fitness parameterization and a Bayesian inference scheme to learn fitness landscapes from single-cell tumor mutation trees. Through simulations, we demonstrate that FiTree outperforms state-of-the-art methods in inferring the fitness landscape underlying tumor evolution. Applying FiTree to a single-cell acute myeloid leukemia dataset, we identify epistatic fitness effects consistent with known biological findings and quantify uncertainty in predicting future mutational events. The new model unifies probabilistic graphical models of cancer progression with population genetics, offering a principled framework for understanding tumor evolution and informing therapeutic strategies. AVAILABILITY AND IMPLEMENTATION: The Python package FiTree and the analysis workflows are available at https://github.com/cbg-ethz/FiTree.

Bayes Theorem↗

PubMed2025

CREMSA: compressed indexing of (ultra) large multiple sequence alignments.

MOTIVATION: Recent viral outbreaks motivate the systematic collection of pathogenic genomes in order to accelerate their study and monitor the apparition/spread of variants. Due to their limited length and temporal proximity of their sequencing, viral genomes are usually organized, and analyzed as oversized Multiple Sequence Alignments (MSAs). Such MSAs are largely ungapped, and mostly homogeneous on a column-wise level but not at a sequential level due to local variations, hindering the performances of sequential compression algorithms. RESULTS: In order to enable an efficient handling of MSAs, including subsequent statistical analyses, we introduce CREMSA (Column-wise Run-length Encoding for MSAs), a new index that builds on sparse bitvector representations to compress an existing or streamed MSA, all the while allowing for an expressive set of accelerated requests to query the alignment without prior decompression. Using CREMSA, a 65 GB MSA consisting of 1.9M SARS-CoV 2 genomes could be compressed into 22 MB using less than half a gigabyte of main memory, while executing access requests in the order of 100 ns. Such a speed up enables a comprehensive analysis of covariation over this very large MSA. We further assess the impact of the sequence ordering on the compressibility of MSAs and propose a resorting strategy that, despite the proven NP-hardness of an optimal sort, induces greatly increased compression ratios at a marginal computational cost. AVAILABILITY AND IMPLEMENTATION: CREMSA is freely accessible at https://gitlab.univ-lille.fr/cremsa/cremsa. The Snakemake workflow for the benchmarks is available at: https://gitlab.univ-lille.fr/cremsa/bench. The data used in the paper is on Zenodo at https://zenodo.org/records/14698859 and https://zenodo.org/records/15100011.

SARS-CoV-2↗

PubMed2025

Exploiting uniqueness: seed-chain-extend alignment on elastic founder graphs.

SUMMARY: Sequence-to-graph alignment is a central challenge of computational pangenomics. To overcome the theoretical hardness of the problem, state-of-the-art tools use seed-and-extend or seed-chain-extend heuristics to alignment. We implement a complete seed-chain-extend alignment workflow based on indexable elastic founder graphs (iEFGs) that support linear-time exact searches unlike general graphs. We show how to construct iEFGs, find high-quality seeds, chain, and extend them at the scale of a telomere-to-telomere assembled human chromosome. AVAILABILITY AND IMPLEMENTATION: Our sequence-to-graph alignment tool and the scripts to replicate our experiments are available in https://github.com/algbio/SRFAligner.

Software↗

PubMed2025

HSDSnake: a user-friendly SnakeMake pipeline for analysis of duplicate genes in eukaryotic genomes.

SUMMARY: Gene duplication is a well-known driver of molecular evolution-it acts as a source of genetic novelty, thereby providing the raw substrate for organismal adaption. However, detecting different types of gene duplicates and comparing them in sequence datasets can be difficult. Existing tools can identify and classify gene duplicates that have arisen by various processes, but have limitations; for example, some do not have a user-friendly workflow and can include many intermediate steps requiring manual adjustments of parameters and/or are not maintained for the benefit of research community members. Here, we have developed HSDSnake, a user-friendly SnakeMake pipeline that can detect and classify gene duplications into five categories: dispersed, proximal, tandem, transposed, and whole genome. It also curates and evaluates the highly similar gene duplicates (HSDs) in each gene duplication category with reliance on both sequence similarity and conserved domains. Lastly, the detected gene duplicates can be visualized within a KEGG functional pathway framework and the substitution rates (Ka, Ks, and their Ka/Ks ratio) can be analyzed for all the duplicate gene pairs. We demonstrate HSDSnake's capabilities by analyzing two reference genomes directly downloaded from NCBI and provide detailed instructions for each step. AVAILABILITY AND IMPLEMENTATION: The HSDSnake pipeline uses SnakeMake and Conda to run and install dependencies. The distribution version is available online at GitHub: https://github.com/zx0223winner/HSDSnake and the archived version at Zenodo is https://doi.org/10.5281/zenodo.15521945.

Software↗

PubMed2025

CAGEcleaner: reducing genomic redundancy in gene cluster mining.

SUMMARY: Mining homologous biosynthetic gene clusters (BGCs) typically involves searching colocalised genes against large genomic databases. However, the high degree of genomic redundancy in these databases often propagates into the resulting hit sets, complicating downstream analyses and visualization. To address this challenge, we present CAGEcleaner, a Python-based pipeline with auxiliary bash scripts designed to reduce redundancy in gene cluster hit sets by dereplicating the genomes that host these hits. CAGEcleaner integrates seamlessly with widely used gene cluster mining tools, such as cblaster and CAGECAT, enabling efficient filtering and streamlining BGC discovery workflows. AVAILABILITY AND IMPLEMENTATION: Source code and documentation is hosted at GitHub (https://github.com/LucoDevro/CAGEcleaner) and Zenodo (https://doi.org/10.5281/zenodo.14726119) under an MIT license. For accessibility, CAGEcleaner is installable from Bioconda (https://anaconda.org/bioconda/cagecleaner) and PyPi (https://pypi.org/project/cagecleaner/), and is also available as a Docker image from DockerHub (https://hub.docker.com/r/lucodevro/cagecleaner).

Software↗

PubMed2025

LegionProfiler: a computational tool for the identification of virulence factors and classification of Legionella pneumophila serogroup 1 isolates.

SUMMARY: Legionella pneumophila has significantly contributed to multiple cases of pneumonia with a high rate of mortality globally. Its ability to exploit host mechanisms through several expressed virulence factors poses challenges for diagnosis, treatment, and outbreak control. To address this, we developed LegionProfiler, a computational tool that swiftly identifies virulence factor protein domains within genome assemblies of Legionella pneumophila serogroup 1 isolates and classifies them into high- or low-virulence groups. LegionProfiler automates the probing of genome assemblies for virulence-associated protein domains and determines the isolate's potential to cause severe pneumonia infection. The LegionProfiler workflow is made available through a user-friendly interface to enhance technical control of infectious sources and adds important insights to the general epidemiology of clinical isolates. It could also support the development of targeted therapeutic strategies that will improve patient treatment. AVAILABILITY AND IMPLEMENTATION: LegionProfiler is freely accessible as a web service at https://legionprofiler.uni-muenster.de, and can also be run locally in a Docker container. The source code can be found at https://imigitlab.uni-muenster.de/heiderlab/legionprofiler or at Zenodo (DOI: 10.5281/zenodo.15592325).

Legionella pneumophila↗