Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Evolutionary Relationships through Genome Comparisons02:54

Evolutionary Relationships through Genome Comparisons

6.0K
Genome comparison is one of the excellent ways to interpret the evolutionary relationships between organisms. The basic principle of genome comparison is that if two species share a common feature, it is likely encoded by the DNA sequence conserved between both species. The advent of genome sequencing technologies in the late 20th century enabled scientists to understand the concept of conservation of domains between species and helped them to deduce evolutionary relationships across diverse...
6.0K
Phylogenetic Trees03:21

Phylogenetic Trees

45.8K
Phylogenetic trees come in many forms. It matters in which sequence the organisms are arranged from the bottom to the top of the tree, but the branches can rotate at their nodes without altering the information. The lines connecting individual nodes can be straight, angled, or even curved.
45.8K
Gene Evolution - Fast or Slow?02:05

Gene Evolution - Fast or Slow?

7.2K
The genomes of eukaryotes are punctuated by long stretches of sequence which do not code for proteins or RNAs. Although some of these regions do contain crucial regulatory sequences, the vast majority of this DNA serves no known function. Typically, these regions of the genome are the ones in which the fastest change, in evolutionary terms, is observed, because there is typically little to no selection pressure acting on these regions to preserve their sequences.
In contrast, regions which code...
7.2K
Phylogeny01:23

Phylogeny

45.0K
Phylogeny is concerned with the evolutionary diversification of organisms or groups of organisms. A group of organisms with a name is called a taxon (singular). Taxa (plural) can span different levels of the evolutionary hierarchy. For instance, the group containing all birds is a taxon (comprising the class Aves), and the group of all species of daisies (the genus Bellis) is a taxon. Phylogenies can likewise include just one genus (i.e., depict species relationships) or span an entire kingdom.
45.0K
Survival Tree01:19

Survival Tree

129
Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
 Building a Survival Tree
Constructing a...
129
Applications of Molecular Taxonomy01:20

Applications of Molecular Taxonomy

51
Molecular taxonomy has revolutionized the understanding and classification of bacteria, providing precise insights into their diversity, evolutionary relationships, and ecological roles. By utilizing molecular techniques such as DNA sequencing and fingerprinting, researchers have made significant strides in various fields related to bacterial studies.Resolving Taxonomic AmbiguitiesMolecular taxonomy has been instrumental in distinguishing closely related bacterial species initially thought to...
51

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Min-frame transformation enables more sensitive viral genome alignment.

bioRxiv : the preprint server for biology·2026
Same author

TIPP-SD: A new method for species detection in microbiomes.

PLoS computational biology·2026
Same author

LAML-Pro: Joint Maximum Likelihood Inference of Cell Genotypes and Cell Lineage Trees.

bioRxiv : the preprint server for biology·2026
Same author

TIPP3 and TIPP3-fast: Improved abundance profiling in metagenomics.

PLoS computational biology·2025
Same author

Biological databases in the age of generative artificial intelligence.

Bioinformatics advances·2025
Same author

Advances in Estimating Level-1 Phylogenetic Networks from Unrooted SNPs.

Journal of computational biology : a journal of computational molecular cell biology·2024
Same journal

Region-aware bridge modeling enables interpretable mesoscale representation of spatial transcriptomic tissue sections.

Bioinformatics advances·2026
Same journal

Microbiome differential abundance methodologies to detect relevant taxa associated with chemotherapy toxicity rate in colorectal cancer.

Bioinformatics advances·2026
Same journal

maldipickr dereplicates microbial MALDI-TOF spectra to facilitate multiplexed isolation.

Bioinformatics advances·2026
Same journal

RAM-MSA: an anytime memory-bounded method for exact multiple sequence alignment using path finding.

Bioinformatics advances·2026
Same journal

Interpretable machine learning for low-sample multi-omics: a case study of ferret vaccine response.

Bioinformatics advances·2026
Same journal

DeepTaxa: a hybrid CNN-BERT framework for 16S rRNA taxonomic classification.

Bioinformatics advances·2026
See all related articles

Related Experiment Video

Updated: Aug 9, 2025

Using Phylogenetic Analysis to Investigate Eukaryotic Gene Origin
08:57

Using Phylogenetic Analysis to Investigate Eukaryotic Gene Origin

Published on: August 14, 2018

16.0K

SCAMPP+FastTree: improving scalability for likelihood-based phylogenetic placement.

Gillian Chu1, Tandy Warnow1

  • 1Computer Science, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA.

Bioinformatics Advances
|February 23, 2023
PubMed
Summary
This summary is machine-generated.

We improved phylogenetic placement by combining SCAMPP and FastTree with pplacer. This new method, pplacer-SCAMPP-FastTree, scales to larger datasets and offers better accuracy than previous approaches.

More Related Videos

A Practical Guide to Phylogenetics for Nonexperts
12:00

A Practical Guide to Phylogenetics for Nonexperts

Published on: February 5, 2014

35.4K
A Concoction Pipeline for Generating Molecular Operational Taxonomic Units (MOTUs) Among Riparian and Aquatic Beetles
10:23

A Concoction Pipeline for Generating Molecular Operational Taxonomic Units (MOTUs) Among Riparian and Aquatic Beetles

Published on: July 11, 2025

161

Related Experiment Videos

Last Updated: Aug 9, 2025

Using Phylogenetic Analysis to Investigate Eukaryotic Gene Origin
08:57

Using Phylogenetic Analysis to Investigate Eukaryotic Gene Origin

Published on: August 14, 2018

16.0K
A Practical Guide to Phylogenetics for Nonexperts
12:00

A Practical Guide to Phylogenetics for Nonexperts

Published on: February 5, 2014

35.4K
A Concoction Pipeline for Generating Molecular Operational Taxonomic Units (MOTUs) Among Riparian and Aquatic Beetles
10:23

A Concoction Pipeline for Generating Molecular Operational Taxonomic Units (MOTUs) Among Riparian and Aquatic Beetles

Published on: July 11, 2025

161

Area of Science:

  • Computational Biology
  • Bioinformatics
  • Phylogenetics

Background:

  • Phylogenetic placement is crucial for understanding evolutionary relationships.
  • Current methods like pplacer, using RAxML, face scalability limitations with large datasets.
  • Existing scalable methods include APPLES-2 and pplacer-SCAMPP.

Purpose of the Study:

  • To enhance the scalability and accuracy of phylogenetic placement methods.
  • To investigate the impact of different parameter estimation techniques on pplacer's performance.
  • To evaluate a novel combination of SCAMPP and FastTree for phylogenetic placement.

Main Methods:

  • Utilized pplacer, a maximum likelihood-based phylogenetic placement tool.
  • Investigated the use of FastTree as an alternative to RAxML for parameter estimation.
  • Developed and evaluated the pplacer-SCAMPP-FastTree approach, combining SCAMPP with FastTree.

Main Results:

  • Using FastTree with pplacer significantly improves scalability for large backbone trees.
  • The combined pplacer-SCAMPP-FastTree method achieves scalability comparable to APPLES-2.
  • pplacer-SCAMPP-FastTree demonstrates improved accuracy over similarly scalable methods.

Conclusions:

  • The combination of SCAMPP and FastTree offers a highly scalable and accurate solution for phylogenetic placement.
  • This approach overcomes the limitations of traditional pplacer implementations on large datasets.
  • pplacer-SCAMPP-FastTree represents a significant advancement in phylogenetic analysis tools.