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Posts from the ‘Weekly Discussion’ Category

Weekly reading: RADICAL – Random Addition Concatenation Analysis

An interesting paper and powerful tool that cuts empirically through the longstanding and somewhat dispiriting total evidence ‘versus’ partitions discussion thread in our field.

Narechania, A., R.H. Baker, R. Sit, S.O. Kolokotronis, R. DeSalle & P.J. Planet. 2012. Random Addition Concatenation Analysis: A novel approach to the exploration of phylogenomic signal reveals strong agreement between core and shell genomic partitions in the Cyanobacteria. Genome Biology and Evolution 4(1): 30-43. Available on-line here.

Weekly reading: SISRS – Site Identification from Short Read Sequences

This week’s discussion will focus on a novel method to identify and separate signal from noise for Next-Generation sequencing datasets – SISRS.

Schwartz, R.S., K. Harkins, A.C. Stone & R.A. Cartwright. 2014. A composite genome approach to identify phylogenetically informative data from Next-Generation Sequencing.

Weekly reading: Intermittent evolution and robustness of phylogenetic models

Models of evolution used in phylogenetic reconstruction make specific assumptions which (in their entirety, and globally applied) are ultimately wrong. They are also approximately right. What does this even mean? This week’s reading gets us into the notion of robustness of phylogenetic models to violations of their inherent assumptions. An important piece of the “which method should I use?” puzzle. Let’s see if we can identify other pieces too.

Nguyen, M.A.T., T. Gesell & A. von Haeseler. 2012. ImOSM: Intermittent evolution and robustness of phylogenetic methods. Molecular Biology and Evolution 29: 663-673. Available on-line here.

Weekly reading: Bayesian analysis outperforms parsimony for morphological data

We had a lively Weekly Discussion of Kumar et al. 2012, and are staying with the general theme (hereby undemocratically coined) of “new insights in statistical phylogenetics/phylogenomics”. Models, biases, assumptions, data. Thus, for next week:

Wright, A.M. & D.M. Hillis. 2014. Bayesian analysis using a simple likelihood model outperforms parsimony for estimation of phylogeny from discrete morphological data. PLoS ONE 9(10): e109210. Available on-line here.

Weekly reading: Kumar et al. on Statistics and Truth in Phylogenomics

It seems as though we are moving on in the Weekly Discussion series from NextGen (a.k.a. massively parallel) sequencing methods to (possibly more entertaining) “what it all means” themes. Last week Lemmon et al. 2012 was appreciated as one of the most promising NextGen approaches for systematics; however we concluded that while the presentation was very impressive, the crustacean use case chosen to illustrate the method’s unique resolution strengths was not challenging enough.

Hence, up this week: statistics and truth. It is not statistics or truth, though if it were then I would prefer the latter.

Kumar, S., A.J. Filipski, F.U. Battistuzzi, S.L. Kosakovsky Pond & K. Tamura. 2012. Statistics and truth in phylogenomics. Molecular Biology and Evolution 29: 457-472. Available on-line here.

Weekly reading: Anchored Hybrid Enrichment

Another, ground laying paper for us to read and discuss this week on one of the most potent new NGS approaches: Anchored Hybrid Enrichment.

Lemmon, A.R., S.A. Emme & E.M. Lemmon. 2012. Anchored Hybrid Enrichment for massively high-throughput phylogenomics. Systematic Biology 61: 727–744. Available on-line here.

Weekly reading: Bybee et al. on Targeted Amplicon Sequencing

Next up in the weekly Massive, Parallel Sequencing discussion series is:

Bybee et al. 2011. Targeted Amplicon Sequencing (TAS): a scalable Next-Gen approach to multilocus, multitaxa phylogenetics. Genome Biology and Evolution 2011, 3: 1312–1323. Available on-line here.

Weekly reading: Lemmon & Lemmon on high-throughput genomic data for phylogenetics

Time to direct our Weekly Discussion series towards Next-Generation Sequencing (NGS) – theory and methods. First up is:

Lemmon, E.M. & A.R. Lemmon. 2013. High-throughput genomic data in systematics and phylogenetics. Annual Review of Ecology, Evolution, and Systematics 44: 99–121. Available on-line here.