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Using Phylogenetic Analysis to Investigate Eukaryotic Gene Origin

Using Phylogenetic Analysis to Investigate Eukaryotic Gene Origin

Published on: August 14, 2018

TMRS: an algorithm for computing the time to the most recent substitution event from a multiple alignment column.

Hisanori Kiryu¹, Yuto Ichikawa², Yasuhiro Kojima¹

¹1Department of Computational Biology and Medical Sciences, GSFS, University of Tokyo, 5-1-5 Kashiwanoha, Kashiwa, Chiba Japan.

Algorithms for Molecular Biology : AMB

|December 14, 2019

Summary

This summary is machine-generated.

New algorithms estimate evolutionary time of DNA substitutions. This helps identify genomic sites crucial for unique species traits, particularly in human evolution and brain development.

Keywords:

Comparative genomics Phylogenetic trees Probabilistic models

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An Integrated Approach for Microprotein Identification and Sequence Analysis

An Integrated Approach for Microprotein Identification and Sequence Analysis

Published on: July 12, 2022

Area of Science:

Computational Biology
Evolutionary Genomics
Bioinformatics

Background:

Growing genomic data offers evolutionary insights.
Current computational tools for inferring evolutionary events are limited.

Purpose of the Study:

Develop algorithms to estimate evolutionary time of substitution events.
Quantify confidence in these time estimates.
Investigate the role of specific substitution timings in human evolution.

Main Methods:

Probabilistic models of sequence evolution.
Dynamic programming for standard deviation calculation.
Analysis of multiple sequence alignments.

Main Results:

Algorithms estimate evolutionary time () to the most recent substitution.
Standard deviation () computed for confidence assessment.
Identified human genomic sites with substitutions between key speciation events.
Found enrichment of substitutions in brain-specific enhancers.

Conclusions:

Novel algorithms estimate evolutionary time of sequence substitutions.
Useful for Evolutionary Developmental Biology (Evo-Devo) studies.
Facilitates identification of genomic sites important for unique biological features.