Too much to know Blair, Ann
2010, 20101130, 2010-11-02, 20100101
eBook, Book
The flood of information brought to us by advancing technology is often accompanied by a distressing sense of "information overload," yet this experience is not unique to modern times. In fact, says ...Ann M. Blair in this intriguing book, the invention of the printing press and the ensuing abundance of books provoked sixteenth- and seventeenth-century European scholars to register complaints very similar to our own. Blair examines methods of information management in ancient and medieval Europe as well as the Islamic world and China, then focuses particular attention on the organization, composition, and reception of Latin reference books in print in early modern Europe. She explores in detail the sophisticated and sometimes idiosyncratic techniques that scholars and readers developed in an era of new technology and exploding information.
Abstract Motivation Complex diseases are often caused and characterized by misregulation of multiple biological pathways. Differential network analysis aims to detect significant rewiring of ...biological network structures under different conditions and has become an important tool for understanding the molecular etiology of disease progression and therapeutic response. With few exceptions, most existing differential network analysis tools perform differential tests on separately learned network structures that are computationally expensive and prone to collapse when grouped samples are limited or less consistent. Results We previously developed an accurate differential network analysis method—Differential Dependency Networks (DDN), that enables joint learning of common and rewired network structures under different conditions. We now introduce the DDN3.0 tool that improves this framework with three new and highly efficient algorithms, namely, unbiased model estimation with a weighted error measure applicable to imbalance sample groups, multiple acceleration strategies to improve learning efficiency, and data-driven determination of proper hyperparameters. The comparative experimental results obtained from both realistic simulations and case studies show that DDN3.0 can help biologists more accurately identify, in a study-specific and often unknown conserved regulatory circuitry, a network of significantly rewired molecular players potentially responsible for phenotypic transitions. Availability The Python package of DDN3.0 is freely available at https://github.com/cbil-vt/DDN3. A user’s guide and a vignette are provided at https://ddn-30.readthedocs.io/. Supplementary information Supplementary data are available at Bioinformatics online.
Abstract Motivation Single nucleotide polymorphism (SNP) markers are increasingly popular for population genomics and inferring ancestry for individuals of unknown origin. Because large SNP datasets ...are impractical for rapid and routine analysis, diagnostics rely on panels of highly informative markers. Strategies exist for selecting these markers, however, resources for efficiently evaluating their performance are limited for non-model systems. Results snpAIMeR is a user-friendly R package that evaluates the efficacy of genomic markers for the cluster assignment of unknown individuals. It is intended to help minimize panel size and genotyping effort by determining the informativeness of candidate diagnostic markers. Provided genotype data from individuals of known origin, it uses leave-one-out cross-validation to determine population assignment rates for individual markers and marker combinations. Availability snpAIMeR is available on CRAN (https://CRAN.R-project.org/package=snpAIMeR). Supplementary information Supplementary data are available at Bioinformatics online.
Abstract Motivation Understanding the molecular evolutionary history of organisms usually requires visual comparison of genomic regions from related species or strains. Although several applications ...already exist to achieve this task, they are either too old, too limited or too complex for most user’s needs. Results GenoFig is a graphical application for the visualisation of prokaryotic genomic regions, intended to be as easy to use as possible and flexible enough to adapt to a variety of needs. GenoFig allows the personalized representation of annotations extracted from GenBank files in a consistent way across sequences, using regular expressions. It also provides several unique options to optimize the display of homologous regions between sequences, as well as other more classical features such as sequence GC percent or GC-skew representations. In summary, GenoFig is a simple, free, and highly configurable tool to explore the evolution of specific genomic regions in prokaryotes and to produce publication-ready figures. Availability Genofig is fully available at https://forgemia.inra.fr/public-pgba/genofig under a GPL 3.0 licence. Supplementary information Supplementary data are available at Bioinformatics online.
MolDy: molecular dynamics simulation made easy Khan, Mohd Imran; Pathania, Sheetal; Al-Rabia, Mohammed W ...
Bioinformatics (Oxford, England),
06/2024, Letnik:
40, Številka:
6
Journal Article
Recenzirano
Odprti dostop
Abstract Motivation Molecular dynamics (MD) is a computational experiment that is crucial for understanding the structure of biological macro and micro molecules, their folding, and the ...inter-molecular interactions. Accurate knowledge of these structural features is the cornerstone in drug development and elucidating macromolecules functions. The open-source GROMACS biomolecular MD simulation program is recognized as a reliable and frequently used simulation program for its precision. However, the user requires expertise, and scripting skills to carrying out MD simulations. Results We have developed an end-to-end interactive MD simulation application, MolDy for Gromacs. This front-end application provides a customizable user interface integrated with the Python and Perl-based logical backend connecting the Linux shell and Gromacs software. The tool performs analysis and provides the user with simulation trajectories and graphical representations of relevant biophysical parameters. The advantages of MolDy are (i) user-friendly, does not requiring the researcher to have prior knowledge of Linux; (ii) easy installation by a single command; (iii) freely available for academic research; (iv) can run with minimum configuration of operating systems; (v) has valid default prefilled parameters for beginners, and at the same time provides scope for modifications for expert users. Availability and implementation MolDy is available freely as compressed source code files with user manual for installation and operation on GitHub: https://github.com/AIBResearchMolDy/Moldyv01.git and on https://aibresearch.com/innovations.
Abstract Motivation Next-generation sequencing libraries are constructed with numerous synthetic constructs such as sequencing adapters, barcodes, and unique molecular identifiers. Such sequences can ...be essential for interpreting results of sequencing assays, and when they contain information pertinent to an experiment, they must be processed and analyzed. Results We present a tool called splitcode, that enables flexible and efficient parsing, interpreting, and editing of sequencing reads. This versatile tool facilitates simple, reproducible preprocessing of reads from libraries constructed for a large array of single-cell and bulk sequencing assays. Availability and implementation The splitcode program is available at http://github.com/pachterlab/splitcode.
The increasing development of sequence-based machine learning models has raised the demand for manipulating sequences for this application. However, existing approaches to edit and evaluate genome ...sequences using models have limitations, such as incompatibility with structural variants, challenges in identifying responsible sequence perturbations, and the need for vcf file inputs and phased data. To address these bottlenecks, we present Sequence Mutator for Predictive Models (SuPreMo), a scalable and comprehensive tool for performing and supporting in silico mutagenesis experiments. We then demonstrate how pairs of reference and perturbed sequences can be used with machine learning models to prioritize pathogenic variants or discover new functional sequences.
SuPreMo was written in Python, and can be run using only one line of code to generate both sequences and 3D genome disruption scores. The codebase, instructions for installation and use, and tutorials are on the Github page: https://github.com/ketringjoni/SuPreMo/tree/main.
Supplementary data are available at Bioinformatics online.
Abstract Summary The vast amount of publicly available genomic data requires analysis and visualization tools. Here, we present figeno, an application for generating publication-quality FIgures for ...GENOmics. Figeno particularly focuses on multi-region views across genomic breakpoints and on long reads with base modifications. In addition, we support epigenomic data including ATAC-seq, ChIP-seq or HiC, as well as whole genome sequencing data with copy numbers and structural variants. Availability and implementation Figeno is available as a python package with both a command line and graphical user interface. It can be installed via PyPI and the source code is available at https://github.com/CompEpigen/figeno.
Today, the prediction of structures of large protein complexes solely from their sequence information requires prior knowledge of the stoichiometry of the complex. To address this challenge, we have ...enhanced the Monte Carlo Tree Search algorithms in MoLPC to enable the assembly of protein complexes while simultaneously predicting their stoichiometry.
In MoLPC2, we have improved the predictions by allowing sampling alternative AlphaFold predictions. Using MoLPC2, we accurately predicted the structures of 50 out of 175 non-redundant protein complexes (TM-score > = 0.8) without knowing the stoichiometry. MoLPC2 provides new opportunities for predicting protein complex structures without stoichiometry information.
MoLPC2 is freely available at https://github.com/hychim/molpc2. A notebook is also available from the repository for easy use.
Supplementary data are available at Bioinformatics online.
