Jahn / Vinar / Vinar Comparative Genomics

20th International Conference, RECOMB-CG 2023, Istanbul, Turkey, April 14–15, 2023, Proceedings
1. Auflage 2023
ISBN: 978-3-031-36911-7
Verlag: Springer International Publishing
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, 249 Seiten, eBook

Reihe: Lecture Notes in Bioinformatics

This book constitutes the refereed proceedings of the 20th Annual RECOMB Satellite Workshop on Comparative Genomics, RECOMB-CG 2023 which took place in Istanbul, Turkey, in April 2023.  The 15 full papers included in this book were carefully reviewed and selected from 25 submissions. The papers present cutting edge research in comparative genomics, with an emphasis on computational approaches and novel experimental results. Chapters "Inferring Clusters of Orthologous and Paralogous Transcripts" and "Gene Order Phylogeny via Ancestral Genome Reconstruction under Dollo" are published Open Access under Creative Commons Attribution license (CC BY 4.0).

Zielgruppe

Research

Autoren/Hrsg.

Jahn, Katharina

Vinar, Tomáš

Vinar, TomáS

Inhaltsverzeichnis

Classifying the Post-Duplication Fate of Paralogous Genes.- Inferring clusters of orthologous and paralogous transcripts.- On the class of double distance problems.- The Floor is Lava - Halving Genomes with Viaducts, Piers and Pontoons.- Two strikes against the phage recombination problem.- Physical mapping of two nested fixed inversions in the X chromosome of the malaria mosquito Anopheles messeae.- Gene order phylogeny via ancestral genome reconstruction under Dollo.- Prior Density Learning in Variational Bayesian Phylogenetic Parameters Inference.- The Asymmetric Cluster Affinity Cost.- The k-RF Measures for Labeled Trees.- Bounding the Number of Reticulations in a Tree-Child Network That Displays a Set of Trees.- Finding agreement cherry-reduced subnetworks in level-1 networks.- CONSULT-II: Taxonomic identification using Locality Sensitive Hashing.- MAGE: Strain Level Profiling of Metagenome Samples.- MoTERNN: Classifying the Mode of Cancer Evolution Using Recursive Neural Networks.