Biology subjects

Lutfi, A.

Publications and source records attributed to Lutfi, A..

5 recordsLinked to original sources

bioRxiv2026

KlinkPPI: Single Point of Access to Protein-Protein Interactions Across Databases

Protein-protein interaction (PPI) information is distributed across resources that differ in organism coverage, identifier systems, evidence models, confidence scores and access mechanisms, so assembling and comparing evidence for a protein requires source-specific queries, identifier conversion and extensive post-processing. We present KlinkPPI, a web server that retrieves, compares and exports PPI evidence from STRING, BioGRID, IntAct, CORUM, HuRI and Predictomes from a single query. KlinkPPI accepts UniProtKB accessions, NCBI Gene and Ensembl identifiers and gene names, and performs taxonomy-aware mapping to a common identifier space. Users can query individual proteins across all resources available for an organism, or retrieve organism-wide interaction sets. Results are presented per source so that database-specific evidence, annotations and confidence values are retained, while an integrated view exposes coverage and agreement between resources. KlinkPPI deliberately does not merge heterogeneous confidence scores, nor collapse functional associations, complex co-membership, binary interactions and structural predictions into a single consensus network. Results can be exported in PSI-MI TAB 2.8-compatible or Apache Parquet format with user-selected evidence fields. KlinkPPI is freely available at https://rappsilberlab.org/KlinkPPI/ and the source code at https://github.com/Rappsilber-Laboratory/KlinkPPI. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=110 SRC="FIGDIR/small/742057v1_ufig1.gif" ALT="Figure 1"> View larger version (16K): org.highwire.dtl.DTLVardef@12dcaceorg.highwire.dtl.DTLVardef@15d9d0aorg.highwire.dtl.DTLVardef@f0fc39org.highwire.dtl.DTLVardef@13f172e_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioinformatics↗

bioRxiv2026

RapCluster: Bridging the Reproducibility Gap in Clustering Analysis

Clustering is ubiquitous across science, yet a text-mining audit of 736,399 open-access articles identified as using clustering (2000-2025) reveals common practice leaves key parameters undocumented or untuned, contributing to the reproducibility crisis in science. We developed an interactive web platform featuring 11 widely adopted clustering algorithms to enable transparent clustering analysis and reporting, aligning practical use with best practices in computational research. Code availabilityThe browser-based clustering analysis platform RapCluster is available for download at https://github.com/lutfia95/RapCluster under MIT License and accessible at https://rappsilberlab.org/rapcluster/ The web version is capacity-limited to 8 GB of memory (ca. 12,000 candidates with 130 features). All source codes used for the analysis are included with the publication.

bioinformatics↗

bioRxiv2025

MetaXtract: Extracting Metadata from Raw Files for FAIR Data Practices and Workflow Optimisation

Mass spectrometry (MS) experiments generate rich acquisition metadata that are essential for reproducibility, data sharing, and quality control (QC). Because these metadata are typically stored only in vendor-specific formats, they often remain difficult to access. MetaXtract is a lightweight tool that extracts detailed parameters directly from Thermo Fisher raw files and exposes them in structured, tabular formats. By capturing sample information, LC-MS method settings, and scan-level metrics such as retention time, total ion current, and ion injection time, MetaXtract increases transparency and ensures that essential acquisition details accompany published data and results in easy readable form. This supports FAIR data practices by improving the findability, accessibility, interoperability, and reusability of MS datasets after converting them to other formats, thereby increasing the value of deposition in public repositories. The importance of such metadata accessibility was recently highlighted by the crosslinking mass spectrometry community in efforts to advance FAIR data principles, and it extends to MS-based omics approaches more broadly. Importantly, MetaXtract enables search-free, near real-time performance monitoring by relying on acquisition-side signals, providing actionable indicators immediately after data acquisition rather than after database searching. This also caters for laboratory or depository internal streamlined QC and troubleshooting through integration into automated pipelines. By embedding acquisition parameters into routine data handling, MetaXtract strengthens reproducibility, optimises method development, and supports large-scale applications, including machine learning and secondary data analysis. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=195 HEIGHT=200 SRC="FIGDIR/small/687968v2_ufig1.gif" ALT="Figure 1"> View larger version (23K): org.highwire.dtl.DTLVardef@d835e6org.highwire.dtl.DTLVardef@babfaforg.highwire.dtl.DTLVardef@7e9d69org.highwire.dtl.DTLVardef@907993_HPS_FORMAT_FIGEXP M_FIG C_FIG HighlightsO_LIMetadata extraction from Thermo Fisher raw files C_LIO_LIEnhanced findability, accessibility, interoperability, and reusability of deposited data C_LIO_LIIntegration into workflows via GUI and command-line modes C_LIO_LITroubleshooting support by visualizing MS1/MS2 scan details C_LIO_LIIndexed MS1/MS2 peak list export enabling machine learning workflows C_LI AvailabilityMetaXtract is available for free download as open-source software at https://github.com/Rappsilber-Laboratory/MetaXtract, the software is licensed under the Apache-2.0 license.

bioinformatics↗

bioRxiv2025

MegaPX: fast and space-efficient peptide assignment method using IBF-based multi-indexing

MotivationA central problem for metaproteomic analysis is the often-unknown taxonomic composition of the analyzed microbiomes. Using a database search, the standard approach requires prior knowledge of which proteins and taxa to include in the protein reference database or to use tailored metagenome-derived databases, which are expensive and error-prone in their generation. A possible strategy to circumvent this database search issue is de novo sequencing, where peptide sequences are directly identified from mass spectra. However, these sequences must still be mapped back to potentially extensive databases. Here, alignment-based approaches enable robust and precise results, with the potential drawback of high memory usage and long run times. ResultsWe present MegaPX, a software for rapidly classifying de novo peptide sequences against large protein databases. MegaPX implemented as a C++-based tool, uses an alignment-free, k-mer-based approach as a taxonomic classification method with the possibility of generating mutated reference databases for error-tolerant searching. It uses various algorithms, including interleaved Bloom filters, to efficiently compute approximate membership queries, ensuring fast processing times while querying and indexing large databases in a multi-indexing fashion. We demonstrate the potential of MegaPX by analyzing different samples, including metaproteomics, against extensive reference databases, highlighting its use as a fast screening tool. Availability and implementationMegaPXs source code and all related documentation files are freely available in a GitHub repository (https://github.com/rki-mf2/MegaPX) under the MIT License. ContactMuthT@rki.de Supplementary informationAll supplementary tables and figures are included at the end of the preprint.

bioinformatics↗

bioRxiv2022

ReadBouncer: Precise and Scalable Adaptive Sampling for Nanopore Sequencing

Nanopore sequencers allow targeted sequencing of interesting nucleotide sequences by rejecting other sequences from individual pores. This feature facilitates the enrichment of low-abundant sequences by depleting overrepresented ones in-silico. Existing tools for adaptive sampling either apply signal alignment, which cannot handle human-sized reference sequences, or apply read mapping in sequence space relying on fast GPU base callers for real-time read rejection. Using nanopore long-read mapping tools is also not optimal when mapping shorter reads as usually analyzed in adaptive sampling applications. Here we present a new approach for nanopore adaptive sampling that combines fast CPU and GPU base calling with read classification based on Interleaved Bloom Filters (IBF). Read-Bouncer improves the potential enrichment of low abundance sequences by its high read classification sensitivity and specificity, outperforming existing tools in the field. It robustly removes even reads belonging to large reference sequences while running on commodity hardware without graphical processing units (GPUs), making adaptive sampling accessible for in-field researchers. Readbouncer also provides a user-friendly interface and installer files for end-users without a bioinformatics background. AvailabilityThe C++ source code of ReadBouncer is available at (https://gitlab.com/dacs-hpi/readbouncer).

bioinformatics↗