Biomedical subjects

Michael M Hoffman

Publications and source records attributed to Michael M Hoffman.

4 recordsLinked to original sources

PubMed2026

Detection and classification of lymphoma from cell-free methylome data.

Diagnosing lymphoma traditionally relies on invasive tissue biopsies, which can yield insufficient material for histopathological evaluation and carry a risk of complications. Minimally invasive assessment of cell-free DNA (cfDNA) in plasma offers a promising alternative for lymphoma detection that could aid the rapid evaluation of malignant vs. benign lymphadenopathy. Here, we examine the methylome of plasma samples from 165 lymphoma patients and 47 controls using cell-free methylated DNA immunoprecipitation and high-throughput sequencing (cfMeDIP-seq). Differential methylation analysis of a discovery cohort (142 out of 212 samples) revealed 13,897 hypermethylated genomic regions in lymphoma cases, which were subsequently used for classification using regularized binomial generalized linear models. In a validation cohort (70 samples), we identified lymphomas with an accuracy of 0.89, positive predictive value (PPV) of 0.90 and negative predictive value (NPV) of 0.87. cfDNA methylation scores were significantly associated with orthogonal measures of cfDNA tumor burden, stage, and clinical outcomes. Our results highlight the feasibility of cfDNA methylation profiling as a sensitive and minimally invasive method for detecting lymphoma.

Journal Article↗

PubMed2026

Segzoo: a turnkey system that summarizes genome annotations.

MOTIVATION: Segmentation and automated genome annotation (SAGA) techniques, such as Segway and ChromHMM, assign labels to every part of the genome, identifying similar patterns across multiple genomic input signals. Inferring biological meaning in these patterns remains challenging. Doing so requires a time-consuming process of manually downloading reference data, running multiple analysis methods, and interpreting many individual results. RESULTS: To simplify these tasks, we developed the turnkey system Segzoo. As input, Segzoo only requires a genome annotation file in browser extensible data (BED) format. It automatically downloads the rest of the data required for comparisons. Segzoo performs analyses using these data and summarizes results in a single visualization. AVAILABILITY AND IMPLEMENTATION: The source code for Python ≥ 3.7 on Linux is freely available for download at https://github.com/hoffmangroup/segzoo under the GNU General Public License (GPL) version 2. Segzoo is also available in the Bioconda package segzoo: https://anaconda.org/bioconda/segzoo. We have deposited in Zenodo the version of the Segzoo source which produced the results in this article (https://doi.org/10.5281/zenodo.10988775), other code and data used to produce the results (https://doi.org/10.5281/zenodo.10477083), and the results (https://doi.org/10.5281/zenodo.10477106).

Software↗

PubMed2006

Estimating the neutral rate of nucleotide substitution using introns.

Evolutionary biologists frequently rely on estimates of the neutral rate of evolution when characterizing the selective pressure on protein-coding genes. We introduce a new method to estimate this value based on intron nucleotide substitutions. The new method uses a metascript model that considers alternative splicing forms and an algorithm to pair orthologous introns, which we call Introndeuce. We compare the intron method with a widely used method that uses observed substitutions in synonymous coding nucleotides, by using both methods to estimate the neutral rate for human-dog and mouse-rat comparisons. The estimates of the 2 methods correlate strongly (r(S) = 0.75), but cannot be considered directly equivalent. We also investigate the effect of alignment error and G + C content on the variance in the intron method: in both cases there is an effect, and it is species-pair specific. Although the intron method may be more useful for shorter evolutionary distances, it is less useful at longer distances due to the poor alignment of less-conserved positions.

Algorithms↗

PubMed2004

AANT: the Amino Acid-Nucleotide Interaction Database.

We have created an Amino Acid-Nucleotide Interaction Database (AANT; http://aant.icmb.utexas. edu/) that categorizes all amino acid-nucleotide interactions from experimentally determined protein-nucleic acid structures, and provides users with a graphic interface for visualizing these interactions in aggregate. AANT accomplishes this by extracting individual amino acid-nucleotide interactions from structures in the Protein Data Bank, combining and superimposing these interactions into multiple structure files (e.g. 20 amino acids x 5 nucleotides) and grouping structurally similar interactions into more readily identifiable clusters. Using the Chime web browser plug-in, users can view 3D representations of the superimpositions and clusters. The unique collection and representation of data on amino acid-nucleotide interactions facilitates understanding the specificity of protein-nucleic acid interactions at a more fundamental level, and allows comparison of otherwise extremely disparate sets of structures. Moreover, by modularly representing the fundamental interactions that govern binding specificity it may prove possible to better engineer nucleic acid binding proteins.

Amino Acids↗