An introduction to reconstructing ancestral genomes
Lior Pachter
Abstract
Recent advances in high-throughput genomics technologies have resulted in the sequencing of large numbers of (near) complete genomes. These genome sequences are being mined for important functional elements, such as genes. They are also being compared and contrasted in order to identify other functional sequences, such as those involved in the regulation of genes. In cases where DNA sequences from different organisms can be determined to have originated from a common ancestor, it is natural to try to infer the an- cestral sequences. The reconstruction of ancestral genomes can lead to insights about genome evolution, and the origins and diversity of function. There are a number of interesting foundational questions associated with reconstructing ancestral genomes: Which statistical models for evolution should be used for making inferences about ancestral sequences? How should extant genomes be compared in order to facilitate ancestral reconstruction? Which portions of ancestral genomes can be reconstructed reliably, and what are the limits of ancestral reconstruction? We discuss recent progress on some of these questions, offer some of our own opinions, and highlight interesting mathematics, statistics, and computer science problems.
Create a lesson
Related papers
Learning Interpretable Tumor Microenvironment Representations by Fitting Pan-Cancer Cell State-Niche Correlation
Xiao Xiao, Jiashu He, Shiyang Zhang et al.
Optimizing RNA yield using deep neural networks coupled to massively parallel screening
Dinghai Zheng, Justin Hong, Jun Wang et al.
A Conditional Structure-Aware Generative Transformer for Multi-Objective Design of m1Ψ-Modified RNA 5' UTRs
Narges Zarnaghinaghsh, Ahmadreza Mofayezi, Byung-Jun Yoon
mLS-GKM: Efficient Multi-class Regulatory Sequence Classification with Gapped k-mer SVMs
Kieran Howard, Nathan Harmston
DNA Methylation Profiling in Melanoma: From Lesion Classification to Therapeutic Stratification
Jana T. Winterstein, Lukas Heinlein, Günter Raddatz et al.
Hepatitis C Virus Genotyping with a Transformer Neural Network
Ariella Aro, Taimá Furuyama, Marcelo R. S. Briones et al.