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At least 307 records · Page 17Linked to original sources
PubMed2005

HideNseek, a post-genome approach to locate transgenes exemplified in Arabidopsis thaliana.

SUMMARY: Determination of transgene location is essential for investigating the effects of position on transgene expression levels and facilitates cloning of the resident gene affected by insertion. Currently used PCR-based approaches for determination of transgene location are relatively complicated and often fail when the transgene is duplicated, rearranged or fragmented. HideNseek is a new bioinformatics tool that allows computation of transgene locations, provided that a suitable genomic restriction enzyme digestion profile is available. Since the new approach is not based on the terminal sequences of the transgene insert, it is less sensitive to transgene duplication, rearrangement or fragmentation. HideNseek has been tested experimentally and by in silico simulation. The experimental example provided here shows that this simple approach is feasible, permitting rapid location of transgenes with little bench work. AVAILABILITY: available on request from the authors. SUPPLEMENTARY DATA: HideNseek input and output examples, experimental procedures and figures showing experimental results are provided as supplementary files: Supplementary material 1, 2, 3 and Supplementary figures (Figs 1 and 2), respectively. Supplementary data is available at Bioinformatics online.

Algorithms↗
PubMed2003

A library of efficient bioinformatics algorithms.

In this paper we review some of the existing projects available in the bioinformatics field for facilitating the development of programs, but for which minimising the running time is not of primary importance. We point out the advantages of open source libraries for such tasks and we discuss some of the open source licenses available. Finally, we present the project ALiBio, which is aimed at facilitating the development of efficient programs in bioinformatics.

Algorithms↗
PubMed2026

scSNViz: visualization and analysis of cell-specific expressed SNVs.

MOTIVATION: Accurately characterizing expressed genetic variation at the single-cell level is essential for understanding transcriptional heterogeneity, allelic regulation, and mutational dynamics within complex tissues. However, few tools enable comprehensive visualization and quantitative analysis of expressed variants across individual cells. RESULTS: scSNViz is an R package for the exploration, quantification, and visualization of expressed single-nucleotide variants (SNVs) from cell-barcoded single-cell RNA sequencing (scRNA-seq) data. The software supports estimation of variant allele fractions, clustering of SNV expression profiles, and 2D and 3D visualization of individual SNVs or user-defined SNV groups. Beyond visualization, scSNViz facilitates investigation of cell-, cluster-, or lineage-specific variant expression patterns, as well as allelic dynamics including imprinting, random allele inactivation, and transcriptional bursting. It interoperates seamlessly with established single-cell frameworks-Seurat for clustering, Slingshot for trajectory inference, scType for cell-type annotation, and CopyKat for copy-number profiling-enabling integrative multi-omic analyses of expressed variation. AVAILABILITY AND IMPLEMENTATION: scSNViz is implemented in R and freely available at https://github.com/HorvathLab/scSNViz (DOI: 10.5281/zenodo.17307516). The package includes comprehensive documentation and example workflows designed for users with limited bioinformatics experience.

Software↗
PubMed1998

CINEMA--a novel colour INteractive editor for multiple alignments.

CINEMA is a new editor for manipulating and generating multiple sequence alignments. The program provides both an interface to existing databases of alignments on the Internet and a tool for constructing and modifying alignments locally. It is written in Java, so executable code will run on most major desktop platforms without modification. The implementation is highly flexible, so the applet can be easily customised with additional functions; and the object classes are reusable, promoting rapid development of program extensions. Formerly, such extended functionality might have been provided via browser plug-ins, which have to be downloaded and installed on every client before loading data. Now, for the first time, an applet is available that allows interactive client-side processing of an alignment, which can then be stored or processed automatically on the server. The program is embedded in a comprehensive help file and is accessible both as a stand-alone tool on UCL's Bioinformatics Server; http:/(/)www.biochem.ucl.ac.uk/bsm/dbbrowser+ ++/CINEMA2.02/, and as an integral part of the PRINTS protein fingerprint database. Exploitation of such novel technologies revolutionises the way users may interact with public databases in the future: bioinformatics centres need not simply provide data, but are now able to offer the means by which information is visualised and manipulated, without the requirement for users to install software.

Color Perception↗
PubMed2004

A comparative phylogenetic approach for dating whole genome duplication events.

MOTIVATION: Whole genome duplications have played a major role in determining the structure of eukaryotic genomes. Current evidence revealing large blocks of duplicated chromatin yields new insights into the evolutionary history of species, but also presents a major challenge for researchers attempting to utilize comparative genomics techniques. Understanding the timing of duplication events relative to divergence among taxa is critical to accurate and comprehensive cross-species comparisons. RESULTS: We describe a large-scale approach to estimate the timing of duplication events in a phylogenetic context. The methodology has been previously utilized for analysis of Arabidopsis and Saccharomyces duplication events. This new implementation provides a more flexible and reusable framework for these analyses. Scripts written in the Python programming language drive a number of freely available bioinformatics programs, creating a no-cost tool for researchers. The usefulness of the approach is demonstrated through genome-scale analysis of Arabidopsis and Oryza (rice) duplications. AVAILABILITY: Software and documentation are freely available from http://plantgenome.agtec.uga.edu/bioinformatics/dating/

Algorithms↗
PubMed2004

PCOGR: phylogenetic COG ranking as an online tool to judge the specificity of COGs with respect to freely definable groups of organisms.

BACKGROUND: The rapidly increasing number of completely sequenced genomes led to the establishment of the COG-database which, based on sequence homologies, assigns similar proteins from different organisms to clusters of orthologous groups (COGs). There are several bioinformatic studies that made use of this database to determine (hyper)thermophile-specific proteins by searching for COGs containing (almost) exclusively proteins from (hyper)thermophilic genomes. However, public software to perform individually definable group-specific searches is not available. RESULTS: The tool described here exactly fills this gap. The software is accessible at http://www.uni-wh.de/pcogr and is linked to the COG-database. The user can freely define two groups of organisms by selecting for each of the (current) 66 organisms to belong either to groupA, to the reference groupB or to be ignored by the algorithm. Then, for all COGs a specificity index is calculated with respect to the specificity to groupA, i. e. high scoring COGs contain proteins from the most of groupA organisms while proteins from the most organisms assigned to groupB are absent. In addition to ranking all COGs according to the user defined specificity criteria, a graphical visualization shows the distribution of all COGs by displaying their abundance as a function of their specificity indexes. CONCLUSIONS: This software allows detecting COGs specific to a predefined group of organisms. All COGs are ranked in the order of their specificity and a graphical visualization allows recognizing (i) the presence and abundance of such COGs and (ii) the phylogenetic relationship between groupA- and groupB-organisms. The software also allows detecting putative protein-protein interactions, novel enzymes involved in only partially known biochemical pathways, and alternate enzymes originated by convergent evolution.

Escherichia coli↗
PubMed2006

High performance GRID based implementation for genomics and protein analysis.

Starting from the genomic and proteomic sequence data, a complex computational infrastructure as been established with the objective to develop a GRID based system to to automate the analysis, prediction and annotation processes of genomic DNA. To support of this type of analysis, several algorithms as been used to recognize biological signals involved in the identification of genes and proteins. The system implemented can be use to analyse the content of the large number of genomic sequences. For this reason, the system realized is capable of using a computational architecture specifically designed for intensive computing based on GRID technologies developed throughout the BIOINFOGRID European project. We developed a GRID based workflow to correlate different kind of Bioinformatics data, going from the Genomics Nucleotide to the Protein Sequence. The first step in the workflow consists of submitting a nucleotide sequence that is elaborated by a specific software for gene prediction. In particular this tool performs a search in the nucleotide sequence to find out the key components of gene. The predicted gene is then translated in the corresponding protein sequence. Based on protein sequence is then possible to identify the domains that characterize the protein functionality using specific tools of domain prediction. Protein domains classification are very important in the analysis of the macromolecular functionality. To analyze a whole protein family from large genome of various organism means to elaborate a large amount of data that requires huge computational resources. To analyze all this data we suggest the use of a high performance platform based on grid technology. We have implemented our applications on a wide area grid platform for scientific applications [http://www.grid.it and http://grid-it.cnaf.infn.it] composed of about 1000 CPU's. The grid infrastructure consists in a collection of computing elements and storage elements that jointly concur to define a platform for high performance elaboration. In this study a grid based application is presented to compute the protein domain analysis in a distributed way. This approach has high performance because the protein domains are checked with different software in parallel in different grid sites.

Computational Biology↗
PubMed2026

Practicing Data Science in Interactive Notebooks.

The Jupyter Notebook is a platform for interactive computing that displays code and results in the same browser, making it valuable for teaching, prototyping, data analysis, and collaboration. Its explicit and transparent structure greatly reproducibility while its backend server supports flexible deployment. In the past few years, Jupyter notebooks and similar tools have become increasingly popular. In this chapter, we will review key aspects of data analysis in a cloud environment and demonstrate common tasks for analyzing metabolomics data using template notebooks. This is an accompaniment to the basic bioinformatics tools and essential data science toolkit introduced in the first edition.

Software↗
PubMed2024

ONCOLINER: A new solution for monitoring, improving, and harmonizing somatic variant calling across genomic oncology centers.

The characterization of somatic genomic variation associated with the biology of tumors is fundamental for cancer research and personalized medicine, as it guides the reliability and impact of cancer studies and genomic-based decisions in clinical oncology. However, the quality and scope of tumor genome analysis across cancer research centers and hospitals are currently highly heterogeneous, limiting the consistency of tumor diagnoses across hospitals and the possibilities of data sharing and data integration across studies. With the aim of providing users with actionable and personalized recommendations for the overall enhancement and harmonization of somatic variant identification across research and clinical environments, we have developed ONCOLINER. Using specifically designed mosaic and tumorized genomes for the analysis of recall and precision across somatic SNVs, insertions or deletions (indels), and structural variants (SVs), we demonstrate that ONCOLINER is capable of improving and harmonizing genome analysis across three state-of-the-art variant discovery pipelines in genomic oncology.

Humans↗