MIMI: Molecular Isotope Mass Identifier for stable isotope-labeled Fourier transform ultra-high mass resolution data analysis - BMC Bioinformatics BMC Bioinformatics - Ultra High Resolution (UHR) mass spectrometry systems with Fourier Transform Ion Cyclotron Resonance (FT-ICR) are often used to analyze the composition of complex mixtures of...

MIMI: Molecular Isotope Mass Identifier for stable isotope-labeled Fourier transform ultra-high mass resolution data analysis #BMCBioinformatics link.springer.com/article/10.1...

mspms: an R package and GUI for multiplex substrate profiling by mass spectrometry - BMC Bioinformatics Background Multiplex Substrate Profiling by Mass Spectrometry (MSP-MS) is a powerful method for determining the substrate specificity of proteolytic enzymes, which is essential for developing protease...

mspms: an R package and GUI for multiplex substrate profiling by mass spectrometry #BMCBioinformatics link.springer.com/article/10.1...

Frag’n’Flow: automated workflow for large-scale quantitative proteomics in high performance computing environments - BMC Bioinformatics BMC Bioinformatics - Analysing large-scale mass spectrometry-based complex proteomics datasets often overwhelm desktop computational resources and require manual configuration for analysis. While...

Frag’n’Flow: automated workflow for large-scale quantitative proteomics in high performance computing environments #BMCBioinformatics link.springer.com/article/10.1...

SeqForge: a scalable platform for alignment-based searches, motif detection, and sequence curation across meta/genomic datasets. #Metagenomics #AlignmentBasedSearches #Genomics #Bioinformatics #BMCbioinformatics
link.springer.com/article/10.1...

MOV&RSim: computational modelling of cancer-specific variants and sequencing reads characteristics for realistic tumoral sample simulation. #DataSimulation #CancerVariants #SequencingReads #ComputationalModelling #BMCBioinformatics 🧪🧬 🖥️
link.springer.com/article/10.1...

TaxaPLN: a taxonomy-aware augmentation strategy for microbiome-trait classification including metadata. #Microbiome #DataAugmentation #GenerativeModel #BMCbioinformatics 🧪🧬 🖥️
link.springer.com/article/10.1...

H3NGST: a fully automated, web-based platform for end-to-end ChIP-seq analysis. #ChIPseq #WebTool #Bioinformatics #Genomics #BMCbioinformatics 🧪🧬 🖥️
bmcbioinformatics.biomedcentral.com/articles/10....

Learning-based parallel acceleration for HaplotypeCaller. #VariantCalling #HaplotypeCaller #GATK #Genomics #Bioinformatics #BMCbioinformatics 🧬 🖥️
bmcbioinformatics.biomedcentral.com/articles/10....

NetStart 2.0: prediction of eukaryotic translation initiation sites using a protein language model. #TranslationInitiationStart #TranslationalControl #DeepLearning #ProteinLanguageModel #BMCbioinformatics #Genomics #Bioinformatics 🧬 🖥️
bmcbioinformatics.biomedcentral.com/articles/10....

GenMasterTable: a user-friendly desktop application for filtering, summarising, and visualising large-scale annotated genetic variants. #GeneticVariants #VariantExploration #DataVisualization #GUI #BMCbioinformatics 🧬 🖥️
bmcbioinformatics.biomedcentral.com/articles/10....

Dotplotic: a lightweight visualization tool for BLAST + alignments and genomic annotations. #BLAST #Visualization #Genomics #Bioinformatics #BMCbioinformatics
bmcbioinformatics.biomedcentral.com/articles/10....

Accurate human genome analysis with element avidity sequencing. #Sequencing #Avidity #BMCBioinformatics #Bioinformatics #Genomics @elembio.bsky.social 🧬 🖥️
bmcbioinformatics.biomedcentral.com/articles/10....

Soft graph clustering for single-cell RNA sequencing data. #SingleCell #scRNAseq #Clustering #Bioinformatics #Genomics #BMCbioinformatics 🧬 🖥️
bmcbioinformatics.biomedcentral.com/articles/10....

PhyloScape: interactive and scalable visualization platform for phylogenetic trees. r#PhylogeneticTees #Visualization #Bioinformatics #BMCbioinformatics
bmcbioinformatics.biomedcentral.com/articles/10....

clonevdjseq: A workflow and bioinformatics management system for sequencing, archiving, and analysis of VDJ sequences from clonal libraries. #VDJ #Sequencing #Bioinformatics #BMCbioinformatics 🧬 🖥️
bmcbioinformatics.biomedcentral.com/articles/10....

An evaluation methodology for machine learning-based tandem mass spectra similarity prediction - BMC Bioinformatics Background Untargeted tandem mass spectrometry serves as a scalable solution for the organization of small molecules. One of the most prevalent techniques for analyzing the acquired tandem mass spectr...

An evaluation methodology for machine learning-based tandem mass spectra similarity prediction #BMCBioinformatics bmcbioinformatics.biomedcentral.com/articles/10....

MAFcounter: an efficient tool for counting the occurrences of k-mers in MAF files.#Kmers #MAFfiles #MultipleAlignments #Bioinformatics #Genomics #BMCbioinformatics 🧬 🖥️
bmcbioinformatics.biomedcentral.com/articles/10....

Pytrf: a python package for finding tandem repeats from genomic sequences. #TandemRepeats #Python #Bioinformatics #Genomics #BMCbioinformatics 🧬 🖥️
bmcbioinformatics.biomedcentral.com/articles/10....

‘gitana’ (phyloGenetic Imaging Tool for Adjusting Nodes and other Arrangements), a tool for plotting phylogenetic trees into ready-to-publish figures - BMC Bioinformatics Background Phylogenetic trees are essential diagrams used in different sciences, such as evolutionary biology or taxonomy, and they depict the relationships between a given set of taxa sharing a…

‘gitana’ (phyloGenetic Imaging Tool for Adjusting Nodes and other Arrangements), a tool for plotting phylogenetic trees into ready-to-publish figures. #Phylogenetics #ReadyToPublishFigures #Bioinformatics #Genomics #BMCbioinformatics 🧬 🖥️
bmcbioinformatics.biomedcentral.com/articles/10....

SEQSIM: A novel bioinformatics tool for comparisons of promoter regions—a case study of calcium binding protein spermatid associated 1 (CABS1). #PromoterRegions #GenomeFeatures #DNAbindingProteins #Bioinformatics #Genomics #BMCbioinformatics
bmcbioinformatics.biomedcentral.com/articles/10....

Differential expression analysis with inmoose, the integrated multi-omic open-source environment in Python - BMC Bioinformatics Background Differential gene expression analysis is a prominent technique for the analysis of biomolecular data to identify genetic features associated with phenotypes. Limma—for microarray data –,…

Differential expression analysis with inmoose, the integrated multi-omic open-source environment in Python. #DifferentialExpression #Python #Bioinformatics #Genomics #BMCbioinformatics 🧬 🖥️
bmcbioinformatics.biomedcentral.com/articles/10....

GNNMutation: a heterogeneous graph-based framework for cancer detection - BMC Bioinformatics Background When genes are translated into proteins, mutations in the gene sequence can lead to changes in protein structure and function as well as in the interactions between proteins. These changes…

GNNMutation: a heterogeneous graph-based framework for cancer detection. #GraphNeuronalNetworks #GeneticMutations #ProteinInteractions #CancerPrediction #WES # Bioinformatics #BMCbioinformatics
bmcbioinformatics.biomedcentral.com/articles/10....

Pytrf: a python package for finding tandem repeats from genomic sequences - BMC Bioinformatics Background Tandem repeats (TRs) are major sources of genetic variation and important genetic markers. Their expansions are not only involved in gene expression regulation but also associated with…

Pytrf: a python package for finding tandem repeats from genomic sequences. #TandemRepeats #Bioinformatics #Genomics #Python #BMCbioinformatics 🧬 🖥️
bmcbioinformatics.biomedcentral.com/articles/10....

Blastn2dotplots: multiple dot-plot visualizer for genome comparisons - BMC Bioinformatics Background Dot-plots, along with linear comparisons, are fundamental visualization methods in genome comparisons, widely used for analyzing structural variations, repeat regions, and sequence…

Blastn2dotplots: multiple dot-plot visualizer for genome comparisons. #DotPlot #Visualization #GenomeComparison #Genomics #Bioinformatics #BMCbioinformatics 🧬 🖥️
bmcbioinformatics.biomedcentral.com/articles/10....

BiomiX, a user-friendly bioinformatic tool for democratized analysis and integration of multiomics data - BMC Bioinformatics Background Interpreting biological system changes requires interpreting vast amounts of multi-omics data. While user-friendly tools exist for single-omics analysis, integrating multiple omics still re...

BiomiX, a user-friendly bioinformatic tool for democratized analysis and integration of multiomics data #BMCBioinformatics bmcbioinformatics.biomedcentral.com/articles/10....

Pheno-Ranker: a toolkit for comparison of phenotypic data stored in GA4GH standards and beyond - BMC Bioinformatics Background Phenotypic data comparison is essential for disease association studies, patient stratification, and genotype–phenotype correlation analysis. To support these efforts, the Global Alliance f...

Pheno-Ranker: a toolkit for comparison of phenotypic data stored in GA4GH standards and beyond. #PhenotypicData #GA4GH #Genomics #BMCbioinformatics 🧬 🖥️
bmcbioinformatics.biomedcentral.com/articles/10....

PIVOT: platform for interactive analysis and visualization of transcriptomics data - BMC Bioinformatics Background Many R packages have been developed for transcriptome analysis but their use often requires familiarity with R and integrating results of different packages requires scripts to wrangle the datatypes. Furthermore, exploratory data analyses often generate multiple derived datasets such as data subsets or data transformations, which can be difficult to track. Results Here we present PIVOT, an R-based platform that wraps open source transcriptome analysis packages with a uniform user interface and graphical data management that allows non-programmers to interactively explore transcriptomics data. PIVOT supports more than 40 popular open source packages for transcriptome analysis and provides an extensive set of tools for statistical data manipulations. A graph-based visual interface is used to represent the links between derived datasets, allowing easy tracking of data versions. PIVOT further supports automatic report generation, publication-quality plots, and program/data state saving, such that all analysis can be saved, shared and reproduced. Conclusions PIVOT will allow researchers with broad background to easily access sophisticated transcriptome analysis tools and interactively explore transcriptome datasets.

PIVOT: platform for interactive analysis and visualization of transcriptomics data bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-... #bmcbioinformatics

ImageJ2: ImageJ for the next generation of scientific image data - BMC Bioinformatics Background ImageJ is an image analysis program extensively used in the biological sciences and beyond. Due to its ease of use, recordable macro language, and extensible plug-in architecture, ImageJ enjoys contributions from non-programmers, amateur programmers, and professional developers alike. Enabling such a diversity of contributors has resulted in a large community that spans the biological and physical sciences. However, a rapidly growing user base, diverging plugin suites, and technical limitations have revealed a clear need for a concerted software engineering effort to support emerging imaging paradigms, to ensure the software’s ability to handle the requirements of modern science. Results We rewrote the entire ImageJ codebase, engineering a redesigned plugin mechanism intended to facilitate extensibility at every level, with the goal of creating a more powerful tool that continues to serve the existing community while addressing a wider range of scientific requirements. This next-generation ImageJ, called “ImageJ2” in places where the distinction matters, provides a host of new functionality. It separates concerns, fully decoupling the data model from the user interface. It emphasizes integration with external applications to maximize interoperability. Its robust new plugin framework allows everything from image formats, to scripting languages, to visualization to be extended by the community. The redesigned data model supports arbitrarily large, N-dimensional datasets, which are increasingly common in modern image acquisition. Despite the scope of these changes, backwards compatibility is maintained such that this new functionality can be seamlessly integrated with the classic ImageJ interface, allowing users and developers to migrate to these new methods at their own pace. Conclusions Scientific imaging benefits from open-source programs that advance new method development and deployment to a diverse audience. ImageJ has continuously evolved with this idea in mind; however, new and emerging scientific requirements have posed corresponding challenges for ImageJ’s development. The described improvements provide a framework engineered for flexibility, intended to support these requirements as well as accommodate future needs. Future efforts will focus on implementing new algorithms in this framework and expanding collaborations with other popular scientific software suites.

A great milestone, and a practical & useful summary of ImageJ2 system.

ImageJ2: ImageJ for the next generation of scientific image data bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-... #bmcbioinformatics

Thanks to @ctrueden et al for their tireless commits!

A new network representation of the metabolism to detect chemical transformation modules - BMC Bioinformatics Background Metabolism is generally modeled by directed networks where nodes represent reactions and/or metabolites. In order to explore metabolic pathway conservation and divergence among organisms, previous studies were based on graph alignment to find similar pathways. Few years ago, the concept of chemical transformation modules, also called reaction modules, was introduced and correspond to sequences of chemical transformations which are conserved in metabolism. We propose here a novel graph representation of the metabolic network where reactions sharing a same chemical transformation type are grouped in Reaction Molecular Signatures (RMS). Results RMS were automatically computed for all reactions and encode changes in atoms and bonds. A reaction network containing all available metabolic knowledge was then reduced by an aggregation of reaction nodes and edges to obtain a RMS network. Paths in this network were explored and a substantial number of conserved chemical transformation modules was detected. Furthermore, this graph-based formalism allows us to define several path scores reflecting different biological conservation meanings. These scores are significantly higher for paths corresponding to known metabolic pathways and were used conjointly to build association rules that should predict metabolic pathway types like biosynthesis or degradation. Conclusions This representation of metabolism in a RMS network offers new insights to capture relevant metabolic contexts. Furthermore, along with genomic context methods, it should improve the detection of gene clusters corresponding to new metabolic pathways.

A new network representation of the metabolism to detect chemical transformation modules http://www.biomedcentral.com/1471-2105/16/385 #bmcbioinformatics

ANGSD: Analysis of Next Generation Sequencing Data - BMC ... Background High-throughput DNA sequencing technologies ar...

"ANGSD: Analysis of Next Generation Sequencing Data” http://www.biomedcentral.com/1471-2105/15/356/abstract #bmcbioinformatics