Search arXiv⌕ Search
Home/Archive/arXiv

arXiv · 1311.1910

The hemagglutinin mutation E391K of pandemic 2009 influenza revisited

Jan P. Radomski·Piotr Płoński·Włodzimierz Zagórski-Ostoja
View original source ↗Read source document ↗Export metadata ↓

Abstract

Phylogenetic analyses based on small to moderately sized sets of sequential data lead to overestimating mutation rates in influenza hemagglutinin (HA) by at least an order of magnitude. Two major underlying reasons are: the incomplete lineage sorting, and a possible absence in the analyzed sequences set some of key missing ancestors. Additionally, during neighbor joining tree reconstruction each mutation is considered equally important, regardless of its nature. Here we have implemented a heuristic method optimizing site dependent factors weighting differently 1st, 2nd, and 3rd codon position mutations, allowing to extricate incorrectly attributed sub-clades. The least squares regression analysis of distribution of frequencies for all mutations observed on a partially disentangled tree for a large set of unique 3243 HA sequences, along all nucleotide positions, was performed for all mutations as well as for non-equivalent amino acid mutations: in both cases demonstrating almost flat gradients, with a very slight downward slope towards the 3'-end positions. The mean mutation rates per sequence per year were 3.83*10^-4 for the all mutations, and 9.64*10^-5 for the non-equivalent ones.

Explore related subjects

q-bio.GNq-bio.PE

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Jan P. Radomski, Piotr Płoński, Włodzimierz Zagórski-Ostoja. 2013-11-08. The hemagglutinin mutation E391K of pandemic 2009 influenza revisited. https://doi.org/10.1016/j.ympev.2013.08.020

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

arXiv2026

Revolutionizing Genomics with Reinforcement Learning Techniques

In recent years, Reinforcement Learning (RL) has emerged as a powerful tool for solving a wide range of problems, including decision-making and genomics. The exponential growth of raw genomic data over the past two decades has exceeded the capacity of manual analysis, leading to a growing interest in automatic data analysis and processing. RL algorithms are capable of learning from experience with minimal human supervision, making them well-suited for genomic data analysis and interpretation. One of the key benefits of using RL is the reduced cost associated with collecting labeled training data, which is required for supervised learning. While there have been numerous studies examining the applications of Machine Learning (ML) in genomics, this survey focuses exclusively on the use of RL in various genomics research fields, including gene regulatory networks (GRNs), genome assembly, and sequence alignment. We present a comprehensive technical overview of existing studies on the application of RL in genomics, highlighting the strengths and limitations of these approaches. We then discuss potential research directions that are worthy of future exploration, including the development of more sophisticated reward functions as RL heavily depends on the accuracy of the reward function, the integration of RL with other machine learning techniques, and the application of RL to new and emerging areas in genomics research. Finally, we present our findings and conclude by summarizing the current state of the field and the future outlook for RL in genomics.

q-bio.GN↗
arXiv2026

Transcriptomic Models for Immunotherapy Response Prediction Show Limited Cross-cohort Generalisability

Immune checkpoint inhibitors (ICIs) have transformed cancer therapy; yet substantial proportion of patients exhibit intrinsic or acquired resistance, making accurate pre-treatment response prediction a critical unmet need. Transcriptomics-based biomarkers derived from bulk and single-cell RNA sequencing (scRNA-seq) offer a promising avenue for capturing tumour-immune interactions, yet the cross-cohort generalisability of existing prediction models remains unclear.We systematically benchmark nine state-of-the-art transcriptomic ICI response predictors, five bulk RNA-seq-based models (COMPASS, IRNet, NetBio, IKCScore, and TNBC-ICI) and four scRNA-seq-based models (PRECISE, DeepGeneX, Tres and scCURE), using publicly available independent datasets unseen during model development. Overall, predictive performance was modest: bulk RNA-seq models performed at or near chance level across most cohorts, while scRNA-seq models showed only marginal improvements. Pathway-level analyses revealed sparse and inconsistent biomarker signals across models. Although scRNA-seq-based predictors converged on immune-related programs such as allograft rejection, bulk RNA-seq-based models exhibited little reproducible overlap. PRECISE and NetBio identified the most coherent immune-related themes, whereas IRNet predominantly captured metabolic pathways weakly aligned with ICI biology. Together, these findings demonstrate the limited cross-cohort robustness and biological consistency of current transcriptomic ICI prediction models, underscoring the need for improved domain adaptation, standardised preprocessing, and biologically grounded model design.

q-bio.GN↗
arXiv2026

Large Language Model Agents for Evidence Based Genetic Disease Severity Classification

Disease severity classification for genetic conditions is subjective and labor-intensive, creating bottlenecks in genomic screening, where commercial panels vary widely in size and overlap. We developed an autonomous AI agent integrating Reasoning and Acting (ReAct) with Retrieval-Augmented Generation (RAG) to classify 10,211 Human Phenotype Ontology terms. It uses American College of Medical Genetics (ACMG)-endorsed severity guidelines and American College of Obstetricians and Gynecologists (ACOG) quality-of-life criteria to retrieve PubMed literature, generate interpretable reasoning chains, and independently verify claims. At the phenotype level, using expert-curated cohorts, the agent achieved 93.55% accuracy (MCC 0.9237) with 82.6% to 91.4% of claims supported by direct evidence or valid inferences. Gene-level severity was aggregated across 8,738 pairs, identifying 3,283 autosomal recessive pairs with severe or profound presentations. External validation showed 95.2% concordance with Mackenzie's Mission gene list. This system enables standardized panel design by providing reliable, automated classification supported by direct evidence.

q-bio.GN↗