Biology subjects

Mumphrey, M. B.

Publications and source records attributed to Mumphrey, M. B..

4 recordsLinked to original sources

bioRxiv2023

MHCnvex: Likelihood-based model for calling the copy number variations and loss of heterozygosity in MHC class I and II locus

MotivationAccurate detection of copy number variation (CNV) and loss of heterozygosity (LOH) in the major histocompatibility complex (MHC) locus is of great significance to both clinicians and researchers since it has the potential to inform treatment decisions, particularly in the context of immunotherapy. However, due to the high level of polymorphism in this region, calling copy number variations is a challenging task and requires special methodology. To address this challenge, we have developed a tool with a wide range of applicability to call CNV and LOH in the MHC region. ResultsTo address the challenge mentioned above, we have developed MHCnvex, an algorithm that accurately calls haplotype level CNVs for the genes in the MHC class I and II locus. MHCnvex presents a novel approach based on likelihood models for detecting CNVs at haplotype level. Additionally, this method integrates the MHC locus with other adjacent loci from the short arm of chromosome 6 to enhance the accuracy of the calls. The incorporation of a statistical approach and the examination of the broader chromosome 6 region, rather than just the MHC locus alone, make MHCnvex less vulnerable to local coverage biases (commonly associated with MHC locus). The performance of MHCnvex has been evaluated according to different measures including concordance with MHC flanking regions and changes in the allelic expression of MHC genes due to alteration. MHCnvex has also shown to significantly reduce the variability of calculated coverage for CNV analysis. AvailabilityImplementation of MHCnvex algorithm is available as an R package at: https://github.com/NoshadHo/MHCnvex

bioinformatics↗

bioRxiv2023

HLAProphet: Personalized allele-level quantification of the HLA proteins

Loss of HLA expression in tumor cells is a commonly observed phenotype that is known to be associated with T-cell evasion. Proteogenomic characterizations of the molecular mechanisms underpinning this loss of HLA expression are hindered by the polymorphic nature of the HLA proteins, with most individuals having germline HLA sequences that are highly divergent from the sequences found in standard reference databases. To address this issue, we have developed HLAProphet, an algorithm that utilizes HLA types from paired DNA sequencing data to provide personalized allele-level quantification of the HLA proteins from TMT mass spectrometry data. We show that HLAProphet triples the number of tryptic peptide identifications made by standard reference based approaches, and produces protein expression values that have high concordance with RNA expression and known loss of heterozygosity events.

bioinformatics↗

bioRxiv2023

Distinct mutational processes shape selection of MHC class I and class II mutations across primary and metastatic tumors

Disruption of antigen presentation via loss of MHC expression is a strategy whereby cancer cells escape immune surveillance and develop resistance to immunotherapy. We developed the personalized genomics algorithm Hapster and accurately called somatic mutations within the MHC genes of 10,001 primary and 2,199 metastatic tumors, creating a catalog of 1663 nonsynonymous mutations that provide key insights into MHC mutagenesis. We found that MHC-I genes are among the most frequently mutated genes in both primary and metastatic tumors, while MHC-II mutations are more restricted. Recurrent deleterious mutations are found within haplotype and cancer-type specific hotspots associated with distinct mutational processes. Functional classification of MHC residues revealed significant positive selection for mutations disruptive to the B2M, peptide, and T-cell binding interfaces, as well as MHC chaperones. At the cohort level, all cancers with positive selection for MHC mutations are responsive to immune checkpoint inhibitors, underscoring the translational relevance of our findings.

cancer biology↗

bioRxiv2021

OptiFit: an improved method for fitting amplicon sequences to existing OTUs

Assigning amplicon sequences to operational taxonomic units (OTUs) is often an important step in characterizing the composition of microbial communities across large datasets. OptiClust, a de novo OTU clustering method, has been shown to produce higher quality OTU assignments than other methods and at comparable or faster speeds. A notable difference between de novo clustering and database-dependent reference clustering methods is that OTU assignments from de novo methods may change when new sequences are added to a dataset. However, in some cases one may wish to incorporate new samples into a previously clustered dataset without performing clustering again on all sequences, such as when comparing across datasets or deploying machine learning models where OTUs are features. Existing reference-based clustering methods produce consistent OTUs, but they only consider the similarity of each query sequence to a single reference sequence in an OTU, thus resulting in OTU assignments that are significantly worse than those generated by de novo methods. To provide an efficient and robust method to fit amplicon sequence data to existing OTUs, we developed the OptiFit algorithm. Inspired by OptiClust, OptiFit considers the similarity of all pairs of reference and query sequences in an OTU to produce OTUs of the best possible quality. We tested OptiFit using four microbiome datasets with two different strategies: by clustering to an external reference database or by splitting the dataset into a reference and query set and clustering the query sequences to the reference set after clustering it using OptiClust. The result is an improved implementation of closed and open-reference clustering. OptiFit produces OTUs of similar quality as OptiClust and at faster speeds when using the split dataset strategy, although the OTU quality and processing speed depends on the database chosen when using the external database strategy. OptiFit provides a suitable option for users who require consistent OTU assignments at the same quality afforded by de novo clustering methods. ImportanceAdvancements in DNA sequencing technology have allowed researchers to affordably generate millions of sequence reads from microorganisms in diverse environments. Efficient and robust software tools are needed to assign microbial sequences into taxonomic groups for characterization and comparison of communities. The OptiClust algorithm produces high quality groups by comparing sequences to each other, but the assignments can change when new sequences are added to a dataset, making it difficult to compare different studies. Other approaches assign sequences to groups by comparing them to sequences in a reference database to produce consistent assignments, but the quality of the groups produced is reduced compared to OptiClust. We developed OptiFit, a new reference-based algorithm that produces consistent yet high quality assignments like OptiClust. OptiFit allows researchers to compare microbial communities across different studies or add new data to existing studies without sacrificing the quality of the group assignments.

microbiology↗