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Gene Expression and Tree Growth in the CTFS-ForestGEO Plot at Harvard Forest 2017-2019
Major goals in ecosystem ecology have been to scale from leaves to canopies and to determine whether individual-level, intra-species and inter-specific variation is critical for models projecting ecosystem processes now and in the future. The project is important in that it examines these issues in detail considering genotypes and levels of gene expression all the way up to canopy level CO2 flux. Ecological genomics and transcriptomics are nascent fields that have been primarily restricted to model species in natural and (mostly) controlled environments. To date, we have very few studies of non-model organisms in nature and/or studies of functional genomics through space and time. The research is producing extraordinarily rich datasets regarding the gene expression of trees across populations, through space in each population, across the growing season and across years and linking this information to growth and gas exchange. It will, therefore, provide tremendous insights into how much variation exists in nature thereby guiding sampling designs in future ecological 'omics projects. More importantly, it will provide unusually detailed phenotypic information for important non-model species that have large impacts on the CO2 flux of eastern US forests.
Vertebrate gene family trees
<p>This is a dataset of phylogenies for 118 vertebrate gene families, used in two papers published by James Cotton and Roderic Page in 2002. The folder named "genes.zip" contains FASTA and NEXUS sequence files, and Newick-format tree files for each gene family. There is a PDF file "suppl.pdf" listing the names of each family. The file "final_dataset.gml" contains a graph of the taxonomic overlap in the gene trees. All 118 gene trees have been combined into the file "final_dataset.gtr" which is a NEXUS file with custom blocks recognised by GeneTree.</p> <table> <tbody> <tr> <th>HOVERGEN FAMILY CODE</th> <th>GENE FAMILY NAME</th> </tr> <tr> <td>FAM000030A</td> <td>wnt 5</td> </tr> <tr> <td>FAM000030B</td> <td>wnt 7</td> </tr> <tr> <td>FAM000030C</td> <td>wnt 11</td> </tr> <tr> <td>FAM000030D</td> <td>wnt/int 1</td> </tr> <tr> <td>FAM000030E</td> <td>wnt 4</td> </tr> <tr> <td>FAM000030F</td> <td>wnt 10/12</td> </tr> <tr> <td>FAM000030G</td> <td>wnt 3</td> </tr> <tr> <td>FAM000030H</td> <td>wnt 2</td> </tr> <tr> <td>FAM000030I</td> <td>wnt 8</td> </tr> <tr> <td>FAM000105</td> <td>rhodopsin</td> </tr> <tr> <td>FAM000214</td> <td>beta-B globin</td> </tr> <tr> <td>FAM000033</td> <td>protamine 1</td> </tr> <tr> <td>FAM000370</td> <td>PrP a prion-protein</td> </tr> <tr> <td>FAM000014</td> <td>growth hormone</td> </tr> <tr> <td>FAM000556</td> <td>Rag-1 recombination activation gene</td> </tr> <tr> <td>FAM001493</td> <td>c-mos proto-oncogene</td> </tr> <tr> <td>FAM001462</td> <td>tyrosine kinase / yes / fyn / src / lck</td> </tr> <tr> <td>FAM001041</td> <td>metallothionein</td> </tr> <tr> <td>FAM000364</td> <td>Ldh-2 lactate dehydrogenase-B (EC</td> </tr> <tr> <td>FAM000215</td> <td>alpha globin</td> </tr> <tr> <td>FAM000016</td> <td>placental lactogen - prolactin</td> </tr> <tr> <td>FAM000008</td> <td>insulin</td> </tr> <tr> <td>FAM000824</td> <td>phosphoglycerate kinase</td> </tr> <tr> <td>FAM000550</td> <td>neurotrophin-4 (NT-4)</td> </tr> <tr> <td>FAM001478</td> <td>tyrosine kinase receptor, c-fms oncogene</td> </tr> <tr> <td>FAM000173A</td> <td>guanine nucleotide-binding protein</td> </tr> <tr> <td>FAM000173B</td> <td>transducin alpha</td> </tr> <tr> <td>FAM000502</td> <td>cytochrome P-450 aromatase</td> </tr> <tr> <td>FAM000192</td> <td>alpha-fetoprotein / serum albumin</td> </tr> <tr> <td>FAM000627</td> <td>neurone-specific enolase</td> </tr> <tr> <td>FAM001232</td> <td>preprotrypsin (ta)</td> </tr> <tr> <td>FAM000664</td> <td>complement component 3 (C3)</td> </tr> <tr> <td>FAM000175</td> <td>Ras</td> </tr> <tr> <td>FAM000248</td> <td>alpha B-crystallin</td> </tr> <tr> <td>FAM000006</td> <td>insulin-like growth factor II</td> </tr> <tr> <td>FAM000639</td> <td>transthyretin (prealbumin)</td> </tr> <tr> <td>FAM001303</td> <td>butylcholinesterase (BCHE)</td> </tr> <tr> <td>FAM000242</td> <td>connexin / gap junction protein</td> </tr> <tr> <td>FAM000058</td> <td>dopamine D1 receptor</td> </tr> <tr> <td>FAM000055</td> <td>beta-3-adrenergic receptor .</td> </tr> <tr> <td>FAM000131</td> <td>ATPase (Na+K+, H+K+)</td> </tr> <tr> <td>FAM003983</td> <td>preprogastrin</td> </tr> <tr> <td>FAM000353</td> <td>vasopressin</td> </tr> <tr> <td>FAM000152</td> <td>acetylcholine receptor</td> </tr> <tr> <td>FAM001327</td> <td>peripherin, desmin, vimentin, GFAP</td> </tr> <tr> <td>FAM003199</td> <td>(C57BL/6J)</td> </tr> <tr> <td>FAM002881</td> <td>cytochrome P-450, 17a-hydroxylase (CYP17)</td> </tr> <tr> <td>FAM002789</td> <td>(MUAHRB-1) Ah-receptor (Ah)</td> </tr> <tr> <td>FAM001461</td> <td>tropomyosin</td> </tr> <tr> <td>FAM000385</td> <td>Y3 peptide supply factor</td> </tr> <tr> <td>FAM000378</td> <td>pancreatic polypeptide, neuropeptide Y</td> </tr> <tr> <td>FAM001329</td> <td>cytokeratin</td> </tr> <tr> <td>FAM001619</td> <td>amelogenin (enamel-specific protein)</td> </tr> <tr> <td>FAM000286</td> <td>glucagon</td> </tr> <tr> <td>FAM000330</td> <td>tissue inhibitor of</td> </tr> <tr> <td>FAM000475</td> <td>lipophilin</td> </tr> <tr> <td>FAM001060</td> <td>ornithine carbamoyltransferase</td> </tr> <tr> <td>FAM001328</td> <td>neurofilament</td> </tr> <tr> <td>FAM001370</td> <td>peroxisome proliferator</td> </tr> <tr> <td>FAM000371</td> <td>somatostatin</td> </tr> <tr> <td>FAM001607</td> <td>Wilms tumor assocated protein (WT1)</td> </tr> <tr> <td>FAM000495</td> <td>aldolase A, B, C</td> </tr> <tr> <td>FAM001365</td> <td>liver receptor homologous protein (LRH-1)</td> </tr> <tr> <td>FAM000672</td> <td>ribosomal protein S4,</td> </tr> <tr> <td>FAM000135</td> <td>Na, K-ATPase beta-1 subunit</td> </tr> <tr> <td>FAM000271</td> <td>enkephalin : 1 2.</td> </tr> <tr> <td>FAM000799</td> <td>RING10</td> </tr> <tr> <td>FAM001664</td> <td>glutamate decarboxylase</td> </tr> <tr> <td>FAM000617</td> <td>creatine kinase</td> </tr> <tr> <td>FAM001337</td> <td>amyloid beta protein precursor</td> </tr> <tr> <td>FAM000274</td> <td>basic fibroblast growth factor (bFGF)</td> </tr> <tr> <td>FAM001108</td> <td>Sl-d mutant allele kit ligand (KL)</td> </tr> <tr> <td>FAM000504</td> <td>anion exchange protein 3</td> </tr> <tr> <td>FAM001239</td> <td>prothrombin</td> </tr> <tr> <td>FAM001360</td> <td>high mobility group proteins HMG1 and HMG2</td> </tr> <tr> <td>FAM000534</td> <td>glutamine synthetase</td> </tr> <tr> <td>FAM001053</td> <td>nucleoside diphosphate kinase</td> </tr> <tr> <td>FAM001390</td> <td>low density lipoprotein receptor LDLR</td> </tr> <tr> <td>FAM001464</td> <td>Cek6 receptor tyrosine kinase</td> </tr> <tr> <td>FAM000801</td> <td>manganese-containing superoxide</td> </tr> <tr> <td>FAM003946</td> <td>Six2 / Six1 mRNA</td> </tr> <tr> <td>FAM001606</td> <td>Ikaros binding protein (Ikaros)</td> </tr> <tr> <td>FAM000350</td> <td>SPARC protein</td> </tr> <tr> <td>FAM001339</td> <td>calcium-binding protein</td> </tr> <tr> <td>FAM000553</td> <td>pyruvate kinase</td> </tr> <tr> <td>FAM000604</td> <td>t complex polypeptide 1 (Tcp-1-a)</td> </tr> <tr> <td>FAM002988</td> <td>mSlo</td> </tr> <tr> <td>FAM001266</td> <td>transcription factor / hepatocyte nuclear factor</td> </tr> <tr> <td>FAM000453</td> <td>terminal deoxynucleotidyltransferase</td> </tr> <tr> <td>FAM001642</td> <td>transformation associated protein p53</td> </tr> <tr> <td>FAM000300A</td> <td>glucose-regulated protein 78 / HSP70 PART A</td> </tr> <tr> <td>FAM000300B</td> <td>glucose-regulated protein 78 / HSP70 PART B</td> </tr> <tr> <td>FAM000492A</td> <td>alpha actin etc</td> </tr> <tr> <td>FAM000492B</td> <td>beta actin etc</td> </tr> <tr> <td>FAM000649</td> <td>Myelin Basic Protein</td> </tr> <tr> <td>FAM000843</td> <td>ribosomal protein S4</td> </tr> <tr> <td>FAM000170</td> <td>atrial natriuretic protein</td> </tr> <tr> <td>FAM000871A</td> <td>tyrosinase</td> </tr> <tr> <td>FAM000871B</td> <td>tyrosinase related protein 1</td> </tr> <tr> <td>FAM001605</td> <td>ZFX put. transcription activator</td> </tr> <tr> <td>FAM001479</td> <td>fibroblast growth factor</td> </tr> <tr> <td>FAM000160</td> <td>pro-opiomelanocortin (POMC)</td> </tr> <tr> <td>FAM001644</td> <td>fibrinogen alpha subunit</td> </tr> <tr> <td>FAM000564</td> <td>SNAP-25</td> </tr> <tr> <td>FAM000266</td> <td>nitric oxide synthase</td> </tr> <tr> <td>FAM004159</td> <td>chondroitin-6 sulfotransferase</td> </tr> <tr> <td>FAM000800</td> <td>Lmp-2 (LMPq) proteasome subunit</td> </tr> <tr> <td>FAM001595</td> <td>factor B</td> </tr> <tr> <td>FAM001134</td> <td>zona pellucida (ZP)</td> </tr> <tr> <td>FAM001632</td> <td>stromelysin-3</td> </tr> <tr> <td>FAM001366</td> <td>steroid receptor (TR2-9)</td> </tr> <tr> <td>FAM000526</td> <td>c-ski protein</td> </tr> <tr> <td>FAM002463</td> <td>thymosin beta 4 peptide</td> </tr> <tr> <td>FAM000904</td> <td>sequence-specific DNA-binding protein (AP-2)</td> </tr> <tr> <td>FAM001465</td> <td>T-cell specific tyrosine kinase (ltk)</td> </tr> <tr> <td>FAM000567</td> <td>triosephosphate isomerase</td> </tr> <tr> <td>FAM006113</td> <td>DNA-dependent RNA polymerase III, large subunit</td> </tr> <tr> <td>FAM001733</td> <td>DNA-dependent RNA polymerase II</td> </tr> </tbody> </table>
Fig. 7. Maximum-likelihood tree for the mitochondrial DNA gene Cytochrome Oxidase C subunit 1 in A new species of the catfish Neoplecostomus (Loricariidae: Neoplecostominae) from a coastal drainage in southeastern Brazil
Fig. 7. Maximum-likelihood tree for the mitochondrial DNA gene Cytochrome Oxidase C subunit 1 for specimens of Neoplecostomus microps from rio Paraíba do Sul, rio Guapi- Açu and rio Macaé, and of Neoplecostomus paraty, using TN93+G model (n=21). Neoplecostomus paranensis and Neoplecostomus ribeirensis were used as outgroups.
Time trees and Clock genes: a Systematic Review and Comparative Analysis of Contemporary Avian Migration Genetics (Dataset)
<p>Complete dataset of <em>Clock</em> and <em>Adcyap1</em> alleles, distance matrices, and migration data used in the review and meta-analysis "<strong>Time trees and Clock genes: a Systematic Review and Comparative Analysis of Contemporary Avian Migration Genetics".</strong> </p>
Functional genomics and co-occurrence in a diverse tropical tree genus: The roles of drought and defense related genes
<p>Tropical tree communities are among the most diverse in the world. A small number of genera often disproportionately contribute to this diversity. How so many species from a single genus can co-occur represents a major outstanding question in biology. Niche differences are likely to play a major role in promoting congeneric diversity, but the mechanisms of interest are often not well-characterized by the set of functional traits generally measured by ecologists. To address this knowledge gap, we used a functional genomic approach to investigate the mechanisms of co-occurrence in the hyper-diverse genus <em>Ficus</em>. Our study focused on over 800 genes related to drought and defense, providing detailed information on how these genes may contribute to the diversity of <em>Ficus</em> species. We find widespread and consistent evidence of the importance of defense gene dissimilarity in co-occurring species, providing genetic support for what would be expected under the Janzen-Connell mechanism. We also find that drought-related gene sequence similarity is related to <em>Ficus</em> co-occurrence, indicating that similar responses to drought promote co-occurrence. We provide the first detailed functional genomic evidence of how drought- and defense-related genes simultaneously contribute to the local co-occurrence in a hyper-diverse genus. Our results demonstrate the potential of community transcriptomics to identify the drivers of species co-occurrence in hyper-diverse tropical tree genera.</p>
Complex models of sequence evolution improve fit, but not gene tree discordance, for tetrapod mitogenomes
<p>Variation in gene tree estimates is widely observed in empirical phylogenomic data and is often assumed to be the result of biological processes. However, a recent study using tetrapod mitochondrial genomes to control for biological sources of variation due to their haploid, uniparentally inherited, and non-recombining nature found that levels of discordance among mitochondrial gene trees were comparable to those found in studies that assume only biological sources of variation. Additionally, they found that several of the models of sequence evolution chosen to infer gene trees were doing an inadequate job of fitting the sequence data. These results indicated that significant amounts of gene tree discordance in empirical data may be due to poor fit of sequence evolution models and that more complex and biologically realistic models may be needed. To test how the fit of sequence evolution models relates to gene tree discordance, we analyzed the same mitochondrial datasets as the previous study using two additional, more complex models of sequence evolution that each model a different biologically realistic aspect of the evolutionary process: a covarion model to incorporate heterotachy, and a model partitioned model to incorporate variable evolutionary patterns by codon position. Our results show that both additional models fit the data better than the models used in the previous study, with the covarion being consistently and strongly preferred as tree size increases. However, even these more preferred models still inferred highly discordant mitochondrial gene trees, thus deepening the mystery around what we label the "Mito-Phylo Paradox" and leading us to ask whether the observed variation could be biological after all.</p>
FI GU R E 3 Maximum likelihood phylogenetic tree of the Hyalospheniformes with a focus on Apodera, Alocodera, and Padaungiella based on COI gene sequences. Bootstrap values (bs) and Bayesian posterior probabilities (p.p.) are indicated respectively between branches. COI sequences from genera other than Apodera were retrieved from GenBank in Superficially described and ignored for 92 years, rediscovered and emended: Apodera angatakere (Amoebozoa: Arcellinida: Hyalospheniformes) is a new flagship testate amoeba taxon from Aotearoa (New Zealand)
FI GU R E 3 Maximum likelihood phylogenetic tree of the Hyalospheniformes with a focus on Apodera, Alocodera, and Padaungiella based on COI gene sequences. Bootstrap values (bs) and Bayesian posterior probabilities (p.p.) are indicated respectively between branches. COI sequences from genera other than Apodera were retrieved from GenBank
The implications of incongruence between gene tree and species tree topologies for divergence time estimation
<p>Phylogenetic analyses are increasingly being performed with datasets that incorporate hundreds of loci. Due to incomplete lineage sorting, hybridization, and horizontal gene transfer, the gene trees for these loci may often have topologies that differ from each other and from the species tree. The effect of these topological incongruences on divergence time estimation has not been fully investigated. Using a series of simulation experiments and empirical analyses, we demonstrate that when topological incongruence between gene trees and the species tree is not accounted for, the temporal duration of branches in regions of the species tree that are affected by incongruence is underestimated, whilst the duration of other branches is considerably overestimated. This effect becomes more pronounced with higher levels of topological incongruence. We show that this pattern results from erroneous estimation of the number of substitutions along branches in the species tree, although the effect is modulated by the assumptions inherent to divergence time estimation, such as those relating to the fossil record or among-branch-substitution-rate variation. By only analysing loci with gene trees that are topologically congruent with the species tree, or only taking into account the branches from each gene tree that are topologically congruent with species tree, we demonstrate that the effects of topological incongruence can be ameliorated. Nonetheless, even when topologically congruent gene trees or topologically congruent branches are selected, error in divergence time estimates remains. This stems from temporal incongruences between divergence times in species trees and divergence times in gene trees, and more importantly, the difficulty of incorporating necessary assumptions for divergence time estimation.</p>
Data from: Improving quartet graph construction for scalable and accurate species tree estimation from gene trees
<p>Summary methods are one of the dominant approaches for estimating species trees from genome-scale data. However, they can fail to produce accurate species trees when the input gene trees are highly discordant due to gene tree estimation error as well as biological processes, like incomplete lineage sorting. Here, we introduce a new summary method TREE-QMC that offers improved accuracy and scalability under these challenging scenarios. TREE-QMC builds upon the algorithmic framework of QMC (Snir and Rao 2010) and its weighted version wQMC (Avni et al. 2014). Their approach takes weighted quartets (four-leaf trees) as input and builds a species tree in a divide-and-conquer fashion, at each step constructing a graph and seeking its max cut. We improve upon this methodology in two ways. First, we address scalability by providing an algorithm to construct the graph directly from the input gene trees. By skipping the quartet weighting step, TREE-QMC has a time complexity of O(n^3 k) with some assumptions on subproblem sizes, where n is the number of species and k is the number of gene trees. Second, we address accuracy by normalizing the quartet weights to account for "artificial taxa," which are introduced during the divide phase so that solutions on subproblems can be combined during the conquer phase. Together, these contributions enable TREE-QMC to outperform the leading methods (ASTRAL-III, FASTRAL, wQFM) in an extensive simulation study. We also present the application of these methods to an avian phylogenomics data set.</p>
Phylogenomic Analyses of 2,786 Genes in 158 Lineages Support a Root of The Eukaryotic Tree of Life Between Opisthokonts and All Other Lineages
<p><strong>Abstract</strong></p> <p>Advances in phylogenetic methods and high-throughput sequencing have allowed the reconstruction of deep phylogenetic relationships in the evolutionary history of eukaryotes. Yet, the root of the eukaryotic tree of life remains elusive. The most ‘popular’ (i.e. in textbooks and reviews) hypothesis for the root is between Unikonta (Opisthokonta + Amoebozoa) and Bikonta (all other eukaryotes), which emerged from analyses of a single gene fusion and a limited sampling of eukaryotic lineages. Subsequent highly-cited studies based on concatenation of genes supported this hypothesis with some variations or proposed a root within the Excavata. However, concatenation of genes neither considers phylogenetically-informative events (i.e. gene duplications and losses), nor provides an estimate of the root. A more recent study using gene tree-species tree reconciliation methods suggested the root lies between Opisthokonta and all other eukaryotes, but only including 59 taxa and 20 genes. Here we apply a gene tree – species tree reconciliation approach to a gene-rich and taxon-rich dataset (i.e. 2,786 gene families from two sets of ~158 diverse eukaryotic lineages) to assess the root, and we iterate each analysis 100 times to quantify tree space uncertainty. Our results estimate a root between Fungi and all other eukaryotes, or between Opisthokonta and all other eukaryotes, and reject alternative popular roots from the literature. Based on further analysis of genome size we propose Opisthokonta + others as the most likely root. Finding the root of the eukaryotic tree of life is critical for the field of comparative biology as it allows us to understand the timing and mode of evolution of characters across the evolutionary history of eukaryotes.</p> <p>Methods<br> Here we provide the alignments, gene trees, inputs, and outputs from our project entitled "Phylogenomic Analyses of 2,786 Genes in 158 Lineages Support a Root of The Eukaryotic Tree of Life Between Opisthokonts and All Other Lineages". Sequences and alignments were produced using the phylogenomic pipeline PhyloToL, which contains a taxon- and gene-rich database (including eukaryotes, archaea, and bacteria). These data were then used for 1) assessing the root of the eukaryotes and 2) for comparison with other previously published hypotheses. In both cases, we used the species tree - gene tree reconciliation tool iGTP. We also did a comparison of hypotheses using the likelihood-based tool SpeciesRax</p> <p><strong>iGTP</strong></p> <p>Input data</p> <p>These data are divided into four datasets based on taxa selection. For dataset SEL+, taxa were selected based on their taxonomy; for RAN+, taxa were selected randomly among the major eukaryotic clades Opisthokonta, Amoebozoa, Archaeplastida, Excavata, SAR, and some orphan lineages. Datasets SEL- and RAN- are the same as SEL+ and RAN+, but exclude microsporidians in order to account for and avoid long branch attraction due to microsporidians fast-evolutionary rates. We chose the gene families that contain at least 25 taxa representing at least four of the five major eukaryotic clades. Additionally, at least 2 of the major clades had to contain at least 2 minor clades (e.g. Glaucophytes and Rhodophyta are minor clades in the major clade Archaeplastida). In a pilot analysis, we produced an alignment and a phylogenetic tree for each gene family using the default settings of a previous version of PhyloToL (GUIDANCE V1.3.1 sequence cutoff = 0.3 and column cutoff = 0.4; RAxML quick tree with model PROTGAMMALG and no bootstraps). Then, we kept the gene families that are exclusive of eukaryotes or the ones in which eukaryotes were monophyletic. From a total of 3,002 gene families that met our criteria, 2786 passed the initial steps of PhyloToL when including only the data from the dataset SEL+. These 2,786 gene families were used for further analyses with all datasets.</p> <p>MSAs were produced with PhyloToL (GUIDANCE V2.02 sequence cutoff = 0.3, column cutoff = 0.4, number of iterations = 5; Sela, et al. 2015). The default parameters of PhyloToL include up to five iterations of GUIDANCE V2.02 with 10 bootstraps and MAFFT V7 with algorithm E-INS-i for less than 200 sequences or “auto” option if more than 200 sequences, and maxiterate = 1000. Instead, here we run up to five iterations of GUIDANCE with 20 bootstraps and the simple MAFFT algorithm FFT-NS-2. Then, we perform an additional GUIDANCE run with 100 bootstraps and the default MAFFT parameters for PhyloToL.</p> <p>Gene trees were inferred with RAxML v.8.2.4 with 10 ML searches for best-ML tree (option "-# 10"), using the rapid hill-climbing algorithm (option "-f d") and no bootstrap replicates. The protein evolution model used was evaluated during the gene tree inference (option "-m PROTCATAUTO") by testing all models available in RAxML (e.g. JTT, LG, WAG, etc) with optimization of substitution rates and of site-specific evolutionary rates which were categorized into four distinct rate categories for greater computational efficiency.</p> <p>We ran 100 repetitions of iGTP analyses per dataset. But, given the complexity of the datasets and the heuristic nature of some key steps of the iGTP algorithm (e.g. gene tree rooting and initial starting species tree generation), in a preliminary analysis, we faced two systematic challenges with iGTP as the inferred species tree was affected by: 1) the order of the leaves in the input unrooted gene tree Newick strings (i.e. the input trees were treated as rooted even though we specified that they were not); and 2) the input gene order in the 100 replicates. Therefore, we randomly shuffled the order of the leaves in the unrooted gene trees (keeping the same topology), and randomly shuffled the order of the input gene trees in each of the 100 replicates per dataset. Here we provide the 100 input files generated for those iGTP analyses. </p> <p>Output data</p> <p>Here we also share the data generated after two analyses: 1) root assessment and 2) hypothesis testing. For the former, we allowed iGTP to calculate the more parsimonious root given our input files. For the latter, we allowed iGTP to calculate the reconciliation cost of the gene trees given the input files and constraints in the species trees to reflect previously published root hypotheses. The constraints are explained in the README file.</p> <p>SpeciesRax</p> <p>Since we removed LGT and contamination from our dataset using a series of filters, we applied the model UndatedDL instead of UndatedDTL, which implies that we only took into consideration duplications and losses and ignored the transferences. Then, the command used for SpeciesRax was...</p> <p>./generax --families forGeneRax/famFile --species-tree forGeneRax/spsTreeAn.newick --strategy SKIP --rec-model UndatedDL --per-family-rates --prefix An_DS1 --si-strategy EVAL </p> <p>input </p> <p>Here, we are sharing all the necessary files to run SpeciesRax, including the mapping file (famFile), the species trees (spsTrees; the best iGTP constrained species trees per hypothesis), and their underlying gene trees (trees_r)</p> <p>output</p> <p>Output folder from SpeciesRax, which includes log files, events (duplications, losses) counts, and statistics (i.e., reconciliation likelihood values)</p> <p>Note: <br> * As in the iGTP analyses, for the SpeciesRax files, the words Op, Fu, Di, Un, An, refer to the five root hypotheses compared: Opisthokonta-others, Fungi-others, Discoba-others, Unikonta-Bikonta, and (Ancyromonadida + Metamonada)-others, respectively. <br> * For the SpeciesRax analyses, the Un word refers to Ut (Cavalier-Smith 2003) <br> </p>
Fig. 1. The Neighbor Joining tree for 37 in Taxonomic Diversity Of The Genus Tor (Cyprinidae) From Aceh Waters In Indonesia Based On Cytochrome Oxidase Sub-Unit I (Coi) Gene
Fig. 1. The Neighbor Joining tree for 37 sequences of Tor from seven locations in Aceh Province estimated using 1000 bootstrap replications.
figure 2 The dated phylogenetic trees using the cytb gene for G. subgutturosa. Blue bars show 95 in Unraveling goitered gazelle (Gazella subgutturosa) diversification: insights from phylogeography and species distribution modeling
figure 2 The dated phylogenetic trees using the cytb gene for G. subgutturosa. Blue bars show 95% highest posterior density intervals of the estimated node ages; numbers next to the nodes are mean node ages (Mya). The red and green lines show new haplotypes from this study.
Fig. 4. Maximum likelihood tree for 43 in Molecular phylogeny of Indonesian Lymantria Tussock Moths (Lepidoptera: Erebidae) based on CO I gene sequences
Fig. 4. Maximum likelihood tree for 43 species of Lymantria based all substitutions of CO I gene (Bootstrap support are shown at the nodes; ID=specimens from Indonesia).
Fig. 5. Maximum likelihood tree for 28S in Fig. 1 in Fig. 4 in Identification of Sexually Dimorphic Genes in Pectoral Fin as Molecular Markers for Assessing the Sex of Japanese Silver Eels ().
Fig. 5. Maximum likelihood tree for 28S (A) and COX1 (B) genes. Red branches indicate the presence of tubercles on the RV margin.
Fig. 4. Maximum Likelihood phylogenetic tree generated using N in The African buffalo parasite Theileria. sp. (buffalo) can infect and immortalize cattle leukocytes and encodes divergent orthologues of Theileria parva antigen genes
Fig. 4. Maximum Likelihood phylogenetic tree generated using N-terminal sequences of T. sp. (buffalo) and T. parva PIM antigen genes. Maximum composite likelihood trees were constructed using 1000 bootstrap replicates as implemented in MEGA5; the optimal nucleotide substitution model was identified using data monkey. The tree constructed with RAxML (Stamatakis et al., 2014) using a GTR/G/I model with 100 bootstrap iterations.
Рис. 1. ФиΛогенетические Αеревья хантавируса AMRV и его прироΑного носитеΛя восточноазиатской мыши Apodemus peninsulae Thomas, 1906. А. ФиΛогенетическое Αерево восточноазиатской мыши Apodemus peninsulae, построенное метоΑом «максимаΛьного правΑопоΑобия» (ML) и поΛученное на основе анаΛиза участка гена цитохрома b мтΔНК (744 п.н.). В узΛах ветвΛения указаны бутстреп-поΑΑержки, рассчитанные ΑΛя 1000 повторов. Цветными Λиниями обозначены фиΛогенетические Λинии: Αве Китайские (зеΛеный), Корейская «Korea» (синий), Амурская «Amur» (красный). ПоΛужирным шрифтом выΑеΛены собственные образцы. Названия образцов из GenBank/NCBI быΛи сокращены; B. ФиΛогенетическое Αерево из работы Α. Н. Яшиной с ΑопоΛнениями, построенное метоΑом «бΛижайшего сосеΑа» (NJ) на основе посΛеΑоватеΛьностей фрагмента М-сегмента (2737–2980 н.п.) генома хантавирусов. В узΛах ветвΛения указаны бутстреппоΑΑержки, рассчитанные ΑΛя 1000 повторов. Жирным выΑеΛены иссΛеΑованные РНК изоΛяты (Яшина 2012; Яшина и Αр. 2019) Fig. 1. Phylogenetic trees of AMRV and its natural reservoir host — the Korean field mouse Apodemus peninsulae Thomas, 1906. A. Phylogenetic tree of the Korean field mouse Apodemus peninsulae constructed by the "maximum likelihood" method (ML). The data are obtained from the analysis of the cytochrome b mtDNA gene fragments (744 bp). Bootstrap supports calculated for 1,000 repeats are indicated in the branching nodes. Colored lines indicate phylogenetic lines: two Chinese (green), Korea (blue), and Amur (red). Own samples are highlighted in bold. The names of the samples from GenBank/NCBI have been shortened; B. Phylogenetic tree from L. N. Yashina's work with additions constructed by the neighbour joining method (NJ). It is based on the sequences of an M-segment fragment (2737–2980 bp) of the hantavirus genome. Bootstrap supports calculated for 1,000 repeats are indicated in the branching nodes. The researched RNA isolates are highlighted in bold (Yashina 2012; Yashina et al. 2019) in Variability of the gene cyt b in the Korean field mouse Apodemus peninsulae Thomas, 1906 - a reservoir host of AMRV in the Khasansky District of Primorsky Krai
Рис. 1. ФиΛогенетические Αеревья хантавируса AMRV и его прироΑного носитеΛя восточноазиатской мыши Apodemus peninsulae Thomas, 1906. А. ФиΛогенетическое Αерево восточноазиатской мыши Apodemus peninsulae, построенное метоΑом «максимаΛьного правΑопоΑобия» (ML) и поΛученное на основе анаΛиза участка гена цитохрома b мтΔНК (744 п.н.). В узΛах ветвΛения указаны бутстреп-поΑΑержки, рассчитанные ΑΛя 1000 повторов. Цветными Λиниями обозначены фиΛогенетические Λинии: Αве Китайские (зеΛеный), Корейская «Korea» (синий), Амурская «Amur» (красный). ПоΛужирным шрифтом выΑеΛены собственные образцы. Названия образцов из GenBank/NCBI быΛи сокращены; B. ФиΛогенетическое Αерево из работы Α. Н. Яшиной с ΑопоΛнениями, построенное метоΑом «бΛижайшего сосеΑа» (NJ) на основе посΛеΑоватеΛьностей фрагмента М-сегмента (2737–2980 н.п.) генома хантавирусов. В узΛах ветвΛения указаны бутстреппоΑΑержки, рассчитанные ΑΛя 1000 повторов. Жирным выΑеΛены иссΛеΑованные РНК изоΛяты (Яшина 2012; Яшина и Αр. 2019) Fig. 1. Phylogenetic trees of AMRV and its natural reservoir host — the Korean field mouse Apodemus peninsulae Thomas, 1906. A. Phylogenetic tree of the Korean field mouse Apodemus peninsulae constructed by the "maximum likelihood" method (ML). The data are obtained from the analysis of the cytochrome b mtDNA gene fragments (744 bp). Bootstrap supports calculated for 1,000 repeats are indicated in the branching nodes. Colored lines indicate phylogenetic lines: two Chinese (green), Korea (blue), and Amur (red). Own samples are highlighted in bold. The names of the samples from GenBank/NCBI have been shortened; B. Phylogenetic tree from L. N. Yashina's work with additions constructed by the neighbour joining method (NJ). It is based on the sequences of an M-segment fragment (2737–2980 bp) of the hantavirus genome. Bootstrap supports calculated for 1,000 repeats are indicated in the branching nodes. The researched RNA isolates are highlighted in bold (Yashina 2012; Yashina et al. 2019)
Fig. 2 Maximum likelihood phylogenetic tree constructed using the mitochondrial cox1 gene for 103 in Genetic diversity and population genetics of large lungworms (Dictyocaulus, Nematoda) in wild deer in Hungary
ƒFig. 2 Maximum likelihood phylogenetic tree constructed using the mitochondrial cox1 gene for 103 Dictyocaulus lungworms originating from Hungary and five lungworms from GenBank indicated by their accession numbers (one dictyocaulid worm of red deer in New Zealand and four sequences of D. viviparus). Lungworms were collected from hunted deer (fallow, red and roe deer), indicated by triangle, square and circle, respectively. Geographical collecting regions are indicated for each sample
Fig. 5. Maximum likelihood tree for 10 in Molecular phylogeny of Indonesian Zeuzera (Lepidoptera: Cossidae) wood borer moths based on CO I gene sequence
Fig. 5. Maximum likelihood tree for 10 species of Zeuzera based on all substitution of CO I gene (Bootstrap support are shown ath the nodes).
Fig. 31. Gene tree for Trigonidium Rambur, 1838 in Small crickets of New Zealand (Orthoptera: Grylloidea: Trigonidiidae and Mogoplistidae), with the description of two new genera and species
Fig. 31. Gene tree for Trigonidium Rambur, 1838 using Maximum Likelihood analysis of ~700 bp of mtDNA (COI) from 23 specimens and 1000 bootstrap replications. The tree with the highest log likelihood (-3571.04) is shown. Numbers next to the branches indicate the percentage of trees in which the associated taxa are clustered together. The tree is drawn to scale, with branch lengths measured in the number of substitutions per site. Specimens in blue were collected as part of this study; the remaining 15 sequences were obtained from the GenBank (Benson et al. 2013) and BOLD (Ratnasingham & Hebert 2007) databases. Detailed information for each specimen included in this analysis can be found in Supp. file 1: Table S11.
Code and data for molecular dating benchmark based on real and simulated Primates gene trees
<p>Benchmark of molecular clock dating applied to single gene trees separately, whose results are described in “Factors influencing the accuracy and precision in dating single gene trees” by Guillaume Louvel and Hugues Roest Crollius.</p> <ol> <li>Real Primates gene trees are analyzed to identify what characteristics of a gene tree are related to the precision of dating; </li> <li>alignments are also simulated on the tree of Primates to measure the accuracy of dating under controlled parameters such as the degree of rate variation and the length of the alignment.</li> </ol> <p><strong>Content</strong></p> <p><code>Louvel_Accuracy-dating_results_2024.tar.gz</code>:<br> - <code>notebook/</code>: statistical analyses in Python;<br> - <code>outputs/</code>: html reports with figures/tables resulting from the analysis;<br> - <code>lib/</code>: required libraries.<br> - <code>data/</code>: intermediate data needed for the final analysis (dates, gene tree features);</p> <p><code>Louvel_Accuracy-dating_dating-source_2024.tar.gz</code>:<br> - <code>dating-source/</code>: input data and config files necessary to reproduce <code>data</code>;</p> <p><code>Louvel_Accuracy-dating_raw-data-preparation_2024.tar.gz</code>:<br> - <code>raw-data-preparation/</code>: raw data and steps to produce <code>dating-source</code>.<br><br><strong>Requirements</strong><br><br>This code requires the libraries developed in the lab for this project,<br>available at <a href="https://github.com/DyogenIBENS/">github.com/DyogenIBENS/</a>, but also included here in <code>lib/</code>.<br><br>- <a href="https://github.com/DyogenIBENS/Phylorgs">Phylorgs</a><br>- <a href="https://github.com/DyogenIBENS/LibsDyogen_py3">LibsDyogen_py3</a><br>- <a href="https://github.com/DyogenIBENS/ToolsDyogen_py3">ToolsDyogen_py3</a>.</p>
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.