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.

Abstract Summary: The affordability of high-throughput sequencing has created an unprecedented surge in the use of genomic data in basic, translational and clinical research. The rapid evolution of sequencing technology, coupled with its broad adoption across biology and medicine, necessitates fast, collaborative interdisciplinary discussion. SEQanswers provides a real-time knowledge-sharing resource to address this need, covering experimental and computational aspects of sequencing and sequence analysis. Developers of popular analysis tools are among the >4000 active members, and ~40 peer-reviewed publications have referenced SEQanswers. Availability: The SEQanswers community is freely accessible at http://SEQanswers.com/ Contact: [email protected]; [email protected] Supplementary information: Supplementary data are available at Bioinformatics online.

SUMMARY: The Evolutionary Trace Viewer (ETV) provides a one-stop environment in which to run, visualize and interpret Evolutionary Trace (ET) predictions of functional sites in protein structures. ETV is implemented using Java to run across different operating systems using Java Web Start technology. AVAILABILITY: The ETV is available for download from our website at http://mammoth.bcm.tmc.edu/traceview/index.html. This webpage also links to sample trace results and a user manual that describes ET Viewer functions in detail.

MOTIVATION: The development of powerful automatic methods for the comparison of protein sequences has become increasingly important. Profile-to-profile comparisons allow for the use of broader information about protein families, resulting in more sensitive and accurate comparisons of distantly related sequences. A key part in the comparison of two profiles is the method for the calculation of scores for the position matches. A number of methods based on various theoretical considerations have been proposed. We implemented several previously reported scoring functions as well as our own functions, and compared them on the basis of their ability to produce accurate short ungapped alignments of a given length. RESULTS: Our results suggest that the family of the probabilistic methods (log-odds based methods and prof_sim) may be the more appropriate choice for the generation of initial 'seeds' as the first step to produce local profile-profile alignments. The most effective scoring systems were the closely related modifications of functions previously implemented in the COMPASS and Picasso methods.

ARROGANT (ARRay OrGANizing Tool) is a software tool developed to facilitate the identification, annotation and comparison of large collections of genes or clones. The objective is to enable users to compile gene/clone collections from different databases, allowing them to design experiments and analyze the collections as well as associated experimental data efficiently. ARROGANT can relate different sequence identifiers to their common reference sequence using the UniGene database, allowing for the comparison of data from two different microarray experiments. ARROGANT has been successfully used to analyze microarray expression data for colon cancer, to compile genes potentially related to cardiac diseases for subsequent resequencing (to identify single nucleotide polymorphisms, SNPs), to design a new comprehensive human cDNA microarray for cancer, to combine and compare expression data generated by different microarrays and to provide annotation for genes on custom and Affymetrix chips.