MOTIVATION: Forensic genetic genealogy (FGG) requires building and interpreting complex family trees that integrate genetic, genealogical, and contextual metadata to associate unknown donors of forensic evidence with reference samples of potential relatives. Existing genealogy platforms are not optimized for forensic workflows, lack scalable infrastructure for large pedigree trees, and pose usability challenges in investigative settings. Othram Maps is a graph-powered platform for generating, ranking, and visualizing genealogical hypotheses. It was developed to support forensic investigations but also has applications in adjacent domains such as biomedical research and genetic epidemiology. RESULTS: Othram Maps is a graph-powered web platform designed for forensic intelligence applications. Unlike traditional tree tools, it uses a flexible graph architecture that enables dynamic visualization and exploration of family trees of any size or structure encountered in forensic genetic genealogy. A custom load-on-demand engine supports smooth navigation of trees with thousands of individuals, while global and detailed views provide scalable, real-time interactivity for collaborative casework. Othram Maps is actively used by genetic genealogists and forensic practitioners to identify human remains and investigate unsolved violent crimes. AVAILABILITY AND IMPLEMENTATION: Othram Maps is freely available at https://maps.othram.com. The platform is fully web-based and runs on all modern desktop and mobile browsers without the need for installation. It is compatible with most current mobile devices, enabling secure, on-the-go access for field or collaborative work. Additional information on the platform's features, functionality, and algorithms is provided in the Methods, available as supplementary data at Bioinformatics online.

MOTIVATION: Read-merging algorithms that look solely at the reads can misalign and mis-merge the reads (especially near repetitive sequences). RESULTS: The C++ program ProSynAR has been written to take the reads' position in the reference into account when performing (and deciding whether to perform) a merge. AVAILABILITY: *Nix users can retrieve the source from GitHub (https://github.com/Benjamin-Crysup/prosynar). Windows binary available at https://github.com/Benjamin-Crysup/prosynar/releases/download/1.0/prosynar.zip. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

BACKGROUND: Tandem repeats (TR), highly variable genomic variants, are widely used in individual identification, disease diagnostics, and evolutionary studies. The recent advances in sequencing technologies and bioinformatic tools facilitate calling TR haplotypes genome widely. Both length-based and sequence-based TR alleles are used in different applications. However, sequence-based TR alleles could provide the highest precision in characterizing TR haplotypes. The need to identify the differences at the single nucleotide level between or among TR haplotypes with an easy-use bioinformatic tool is essential. RESULTS: In this study, we developed a Universal STR Allele Toolkit (USAT) for TR haplotype analysis, which takes TR haplotype output from existing tools to perform allele size conversion, sequence comparison of haplotypes, figure plotting, comparison for allele distribution, and interactive visualization. An exemplary application of USAT for analysis of the CODIS core STR loci for DNA forensics with benchmarking human individuals demonstrated the capabilities of USAT. USAT has user-friendly graphic interfaces and runs fast in major computing operating systems with parallel computing enabled. CONCLUSION: USAT is a user-friendly bioinformatics software for interpretation, visualization, and comparisons of TRs.

MOTIVATION: Current read-mapping software uses a singular specification of alignment parameters with respect to the reference. In the presence of varying reference structures (such as the repetitive regions of the human genome), alignments can be improved if those parameters are allowed vary. RESULTS: To that end, the C++ program ProDerAl was written to refine previously generated alignments using varying parameters for these problematic regions. Synthetic benchmarks show that this realignment can result in an order of magnitude fewer misaligned bases. AVAILABILITY AND IMPLEMENTATION: *Nix users can retrieve the source from GitHub (https://github.com/Benjamin-Crysup/proderal.git). Windows binary available at https://github.com/Benjamin-Crysup/proderal/releases/download/v1.1/proderal.zip. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

MOTIVATION: Assays in mitochondrial genomics rely on accurate read mapping and variant calling. However, there are known and unknown nuclear paralogs that have fundamentally different genetic properties than that of the mitochondrial genome. Such paralogs complicate the interpretation of mitochondrial genome data and confound variant calling. RESULTS: Remove the Numts! (RtN!) was developed to categorize reads from massively parallel sequencing data not based on the expected properties and sequence identities of paralogous nuclear encoded mitochondrial sequences, but instead using sequence similarity to a large database of publicly available mitochondrial genomes. RtN! removes low-level sequencing noise and mitochondrial paralogs while not impacting variant calling, while competing methods were shown to remove true variants from mitochondrial mixtures. AVAILABILITY AND IMPLEMENTATION: https://github.com/Ahhgust/RtN. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.