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William H Majoros

Publications and source records attributed to William H Majoros.

9 recordsLinked to original sources
PubMed2025

Bayesian estimation of allele-specific expression in the presence of phasing uncertainty.

MOTIVATION: Allele-specific expression (ASE) analyses aim to detect imbalanced expression of maternal versus paternal copies of an autosomal gene. Such allelic imbalance can result from a variety of cis-acting causes, including disruptive mutations within one copy of a gene that impact the stability of transcripts, as well as regulatory variants outside the gene that impact transcription initiation. Current methods for ASE estimation suffer from a number of shortcomings, such as relying on only one variant within a gene, assuming perfect phasing information across multiple variants within a gene, or failing to account for alignment biases and possible genotyping errors. RESULTS: We developed BEASTIE, a Bayesian hierarchical model designed for precise ASE quantification at the gene level, based on given genotypes and RNA-Seq data. BEASTIE addresses the complexities of allelic mapping bias, genotyping error, and phasing errors by incorporating empirical phasing error rates derived from Genome-in-a-Bottle individual NA12878. BEASTIE surpasses existing methods in accuracy, especially in scenarios with high phasing errors. This improvement is critical for identifying rare genetic variants often obscured by such errors. Through rigorous validation on simulated data and application to real data from the 1000 Genomes Project, we establish the robustness of BEASTIE. These findings underscore the value of BEASTIE in revealing patterns of ASE across gene sets and pathways. AVAILABILITY AND IMPLEMENTATION: The software is freely available from Github (https://github.com/x811zou/BEASTIE); and Zendo (DOI: 10.5281/zenodo.15062124).

Bayes Theorem↗
PubMed2005

Genomic sequence of the pathogenic and allergenic filamentous fungus Aspergillus fumigatus.

Aspergillus fumigatus is exceptional among microorganisms in being both a primary and opportunistic pathogen as well as a major allergen. Its conidia production is prolific, and so human respiratory tract exposure is almost constant. A. fumigatus is isolated from human habitats and vegetable compost heaps. In immunocompromised individuals, the incidence of invasive infection can be as high as 50% and the mortality rate is often about 50% (ref. 2). The interaction of A. fumigatus and other airborne fungi with the immune system is increasingly linked to severe asthma and sinusitis. Although the burden of invasive disease caused by A. fumigatus is substantial, the basic biology of the organism is mostly obscure. Here we show the complete 29.4-megabase genome sequence of the clinical isolate Af293, which consists of eight chromosomes containing 9,926 predicted genes. Microarray analysis revealed temperature-dependent expression of distinct sets of genes, as well as 700 A. fumigatus genes not present or significantly diverged in the closely related sexual species Neosartorya fischeri, many of which may have roles in the pathogenicity phenotype. The Af293 genome sequence provides an unparalleled resource for the future understanding of this remarkable fungus.

Allergens↗
PubMed2005

Efficient decoding algorithms for generalized hidden Markov model gene finders.

BACKGROUND: The Generalized Hidden Markov Model (GHMM) has proven a useful framework for the task of computational gene prediction in eukaryotic genomes, due to its flexibility and probabilistic underpinnings. As the focus of the gene finding community shifts toward the use of homology information to improve prediction accuracy, extensions to the basic GHMM model are being explored as possible ways to integrate this homology information into the prediction process. Particularly prominent among these extensions are those techniques which call for the simultaneous prediction of genes in two or more genomes at once, thereby increasing significantly the computational cost of prediction and highlighting the importance of speed and memory efficiency in the implementation of the underlying GHMM algorithms. Unfortunately, the task of implementing an efficient GHMM-based gene finder is already a nontrivial one, and it can be expected that this task will only grow more onerous as our models increase in complexity. RESULTS: As a first step toward addressing the implementation challenges of these next-generation systems, we describe in detail two software architectures for GHMM-based gene finders, one comprising the common array-based approach, and the other a highly optimized algorithm which requires significantly less memory while achieving virtually identical speed. We then show how both of these architectures can be accelerated by a factor of two by optimizing their content sensors. We finish with a brief illustration of the impact these optimizations have had on the feasibility of our new homology-based gene finder, TWAIN. CONCLUSIONS: In describing a number of optimizations for GHMM-based gene finders and making available two complete open-source software systems embodying these methods, it is our hope that others will be more enabled to explore promising extensions to the GHMM framework, thereby improving the state-of-the-art in gene prediction techniques.

Algorithms↗
PubMed2005

Gene discovery in the Acanthamoeba castellanii genome.

Acanthamoeba castellanii is a free-living amoeba found in soil, freshwater, and marine environments and an important predator of bacteria. Acanthamoeba castellanii is also an opportunistic pathogen of clinical interest, responsible for several distinct diseases in humans. In order to provide a genomic platform for the study of this ubiquitous and important protist, we generated a sequence survey of approximately 0.5 x coverage of the genome. The data predict that A. castellanii exhibits a greater biosynthetic capacity than the free-living Dictyostelium discoideum and the parasite Entamoeba histolytica, providing an explanation for the ability of A. castellanii to inhabit a diversity of environments. Alginate lyase may provide access to bacteria within biofilms by breaking down the biofilm matrix, and polyhydroxybutyrate depolymerase may facilitate utilization of the bacterial storage compound polyhydroxybutyrate as a food source. Enzymes for the synthesis and breakdown of cellulose were identified, and they likely participate in encystation and excystation as in D. discoideum. Trehalose-6-phosphate synthase is present, suggesting that trehalose plays a role in stress adaptation. Detection and response to a number of stress conditions is likely accomplished with a large set of signal transduction histidine kinases and a set of putative receptor serine/threonine kinases similar to those found in E. histolytica. Serine, cysteine and metalloproteases were identified, some of which are likely involved in pathogenicity.

Acanthamoeba castellanii↗
PubMed2004

An empirical analysis of training protocols for probabilistic gene finders.

BACKGROUND: Generalized hidden Markov models (GHMMs) appear to be approaching acceptance as a de facto standard for state-of-the-art ab initio gene finding, as evidenced by the recent proliferation of GHMM implementations. While prevailing methods for modeling and parsing genes using GHMMs have been described in the literature, little attention has been paid as of yet to their proper training. The few hints available in the literature together with anecdotal observations suggest that most practitioners perform maximum likelihood parameter estimation only at the local submodel level, and then attend to the optimization of global parameter structure using some form of ad hoc manual tuning of individual parameters. RESULTS: We decided to investigate the utility of applying a more systematic optimization approach to the tuning of global parameter structure by implementing a global discriminative training procedure for our GHMM-based gene finder. Our results show that significant improvement in prediction accuracy can be achieved by this method. CONCLUSIONS: We conclude that training of GHMM-based gene finders is best performed using some form of discriminative training rather than simple maximum likelihood estimation at the submodel level, and that generalized gradient ascent methods are suitable for this task. We also conclude that partitioning of training data for the twin purposes of maximum likelihood initialization and gradient ascent optimization appears to be unnecessary, but that strict segregation of test data must be enforced during final gene finder evaluation to avoid artificially inflated accuracy measurements.

Algorithms↗
PubMed2003

GlimmerM, Exonomy and Unveil: three ab initio eukaryotic genefinders.

We present three programs for ab initio gene prediction in eukaryotes: Exonomy, Unveil and GlimmerM. Exonomy is a 23-state Generalized Hidden Markov Model (GHMM), Unveil is a 283-state standard Hidden Markov Model (HMM) and GlimmerM is a previously-described genefinder which utilizes decision trees and Interpolated Markov Models (IMMs). All three are readily re-trainable for new organisms and have been found to perform well compared to other genefinders. Results are presented for Arabidopsis thaliana. Cases have been found where each of the genefinders outperforms each of the others, demonstrating the collective value of this ensemble of genefinders. These programs are all accessible through webservers at http://www.tigr.org/software.

Arabidopsis↗
PubMed2002

The genome sequence of the malaria mosquito Anopheles gambiae.

Anopheles gambiae is the principal vector of malaria, a disease that afflicts more than 500 million people and causes more than 1 million deaths each year. Tenfold shotgun sequence coverage was obtained from the PEST strain of A. gambiae and assembled into scaffolds that span 278 million base pairs. A total of 91% of the genome was organized in 303 scaffolds; the largest scaffold was 23.1 million base pairs. There was substantial genetic variation within this strain, and the apparent existence of two haplotypes of approximately equal frequency ("dual haplotypes") in a substantial fraction of the genome likely reflects the outbred nature of the PEST strain. The sequence produced a conservative inference of more than 400,000 single-nucleotide polymorphisms that showed a markedly bimodal density distribution. Analysis of the genome sequence revealed strong evidence for about 14,000 protein-encoding transcripts. Prominent expansions in specific families of proteins likely involved in cell adhesion and immunity were noted. An expressed sequence tag analysis of genes regulated by blood feeding provided insights into the physiological adaptations of a hematophagous insect.

Animals↗
PubMed2002

A comparison of whole-genome shotgun-derived mouse chromosome 16 and the human genome.

The high degree of similarity between the mouse and human genomes is demonstrated through analysis of the sequence of mouse chromosome 16 (Mmu 16), which was obtained as part of a whole-genome shotgun assembly of the mouse genome. The mouse genome is about 10% smaller than the human genome, owing to a lower repetitive DNA content. Comparison of the structure and protein-coding potential of Mmu 16 with that of the homologous segments of the human genome identifies regions of conserved synteny with human chromosomes (Hsa) 3, 8, 12, 16, 21, and 22. Gene content and order are highly conserved between Mmu 16 and the syntenic blocks of the human genome. Of the 731 predicted genes on Mmu 16, 509 align with orthologs on the corresponding portions of the human genome, 44 are likely paralogous to these genes, and 164 genes have homologs elsewhere in the human genome; there are 14 genes for which we could find no human counterpart.

Animals↗