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AcinetoScope is an automated, comprehensive bioinformatics pipeline designed specifically for the genomic analysis of Acinetobacter baumannii, a WHO Critical Priority pathogen responsible for devastating hospital-acquired infections. It integrates seven analysis types (MLST, ABRicate, AMRFinder, Kaptive 3, APT, PlasmidFinder, and mutation detection) into a single automated workflow — from FASTA to actionable insights. The pipeline offers both gene-centric and sample-centric reporting, dynamic grouping by typing, and is optimised for HPC, cloud, and container environments.

HIDE-Deconv is a framework for characterizing cellular remodeling from bulk RNA-seq data using hierarchical cell-type deconvolution across multiple levels of cellular resolution. It provides an integrated workflow for single-cell reference preprocessing, estimation of cellular compositions, and downstream analysis of deconvolution results, including visualization, clustering, differential composition, and survival analysis.

Documentation Rectangle is an open-source Python package for single-cell-informed cell-type deconvolution of bulk and spatial transcriptomic data. Rectangle presents a novel approach to second-generation deconvolution, characterized by hierarchical signature building for fine-grained cell-type deconvolution, estimation and correction of unknown cellular content, and efficient handling of large-scale single-cell data during signature matrix computation. Rectangle was developed to overcome the current challenges in cell-type deconvolution, providing a robust and accurate methodology while ensuring a low computational profile.

Toolbox for comparative genomics of MAGs

SMBGC Annotation using Neural Networks Trained on Interpro Signatures

DeepConsensus uses gap-aware sequence transformers to correct errors in Pacific Biosciences (PacBio) Circular Consensus Sequencing (CCS) data.

Utility that performs integrated analyses of 'gene' data (a set of genes or other genomic features) with 'peak' data (a set of regions, for example ChIP peaks) to identify the genes nearest to each peak, and vice versa.

Short Python script (using Biopython library functions) to extract sequences from a FASTA, QUAL, FASTQ, or SFF file based on the list of IDs given by a column of a tabular file. The output order follows that of the tabular file, and if there are duplicates in the tabular file, there will be duplicates in the output sequence file.