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A novel approach to the detection of unusual mitochondrial protein change suggests hypometabolism of ancestral simians: Supplemental Files
<p><strong>Supplementary Fig. S1</strong>: θ<sub>evo</sub> calculated for each analyzed edge for specific OXPHOS complexes. Analyses were performed as in fig. 1F, except that SPCSs calculated from mtDNA-encoded protein positions in Complex I, Complex III, Complex IV, or Complex V were used to generate θevo values.</p> <p><strong>Supplementary Fig. S2</strong>: Mammalian orders differ in their propensity for potentially efficacious mitochondrial protein substitutions within specific OXPHOS complexes (median calculations). Analysis was performed as in fig. 2A, except that θ<sub>evo</sub> values were obtained by analysis of mtDNA-encoded Complex I, Complex III, Complex IV, or Complex V polypeptides.</p> <p><strong>Supplementary Fig. S3</strong>: Mammalian orders differ in their propensity for potentially efficacious mitochondrial protein substitutions within specific OXPHOS complexes (median confidence intervals). Analysis was performed as in (<em>A</em>) fig. 2B or (<em>B</em>) fig. 2C, except that θ<sub>evo</sub> values were obtained by analysis of mtDNA-encoded Complex I, Complex III, Complex IV, or Complex V proteins.</p> <p><strong>Supplementary Fig. S4</strong>: Mammalian families differ in their propensity for potentially efficacious mitochondrial protein substitutions at specific OXPHOS complexes (median calculations). Analysis was performed as in fig. 3A, except that θ<sub>evo</sub> values were obtained by analysis of mtDNA-encoded Complex I, Complex III, Complex IV, or Complex V subunits.</p> <p><strong>Supplementary Fig. S5</strong>: Mammalian families differ in their propensity for potentially efficacious mitochondrial protein substitutions at specific OXPHOS complexes (median confidence intervals ordered by lower 90% median confidence limit). Analysis was performed as in fig. 3B, except that θ<sub>evo</sub> values were obtained by analysis of mtDNA-encoded Complex I, Complex III, Complex IV, or Complex V proteins.</p> <p><strong>Supplementary Fig. S6</strong>: Mammalian families differ in their propensity for potentially efficacious mitochondrial protein substitutions at specific OXPHOS complexes (median confidence intervals ordered by upper 90% median confidence limit). Analysis was performed as in fig. 3C, except that θ<sub>evo</sub> values were obtained by analysis of mtDNA-encoded Complex I, Complex III, Complex IV, or Complex V polypeptides.</p> <p>---</p> <p><strong>Supplementary File 1</strong>: All predicted protein substitutions along all edges at positions containing less than 2% gaps across input and ancestral sequences are listed, along with associated taxonomy information, TSS, and branch length. All alignment positions refer to Bos taurus reference sequences.</p> <p><strong>Supplementary File 2</strong>: The TSS calculated for each mitochondrial protein alignment position. All alignment positions refer to Bos taurus reference sequences.</p> <p><strong>Supplementary File 3</strong>: SPCS and θevo outputs are provided for analyses across all mitochondria-encoded positions, as well as for focused analyses of specific OXPHOS complexes and individual proteins.</p> <p><strong>Supplementary File 4</strong>: A GenBank flat file containing RefSeq entries for mammalian mtDNAs, as well as the entry for the reptile Anolis punctatus.</p> <p><strong>Supplementary File 5</strong>: A maximum likelihood inferred tree generated by a RAxML-NG analysis of concatenated and aligned protein coding sequences from mammalian and Anolis punctatusmtDNAs.</p> <p><strong>Supplementary File 6</strong>: Bootstrap replicates were generated from the alignment of concatenated protein coding sequences. Felsenstein’s Bootstrap Proportions (Felsenstein 1985) were calculated and used to label the maximum likelihood inferred tree of mammalian mtDNAs.</p> <p><strong>Supplementary File 7</strong>: Bootstrap replicates were generated using concatenated mammalian mtDNA coding sequences. Transfer Bootstrap Expectations (Lemoine 2018) were calculated and used to label the maximum likelihood inferred tree of mammalian mtDNAs.</p> <p><strong>Supplementary File 8</strong>: PAGAN tree output produced using aligned amino acid sequences and the rooted maximum likelihood inferred tree as input.</p>
Bayesian Methods for Ancestral State Reconstruction in Morphosyntax
<p>Supplementary files to accompany journal submission.</p> <p>Files are:</p> <p> </p> <p>tree.pdf - pdf consensus tree, for illustration</p> <p>data.txt - coding file</p> <p>TREE_Set.t - nexus format sample of trees.</p> <p>sources.pdf - source materials used for languages</p>
Reconstruction of full-length LINE-1 progenitors from ancestral genomes (Supplementary Data)
<p><strong>Web Supplementary Files</strong></p> <ul> <li>Web Supplementary File 1 - FASTA files containing full-length reconstruction input sequences<strong>: full_length_reconstruction_input_sequence_fastas.zip</strong></li> <li>Web Supplementary File 2 - FASTA files containing Muscle alignments of the full-length reconstruction input sequences.<strong> full_length_reconstruction_input_sequence_alns.zip</strong></li> <li>Web Supplementary File 3 - FASTA file of full-length reconstructed sequences:<strong> full_length_reconstructions.fa</strong></li> <li>Web Supplementary File 4 - Table of full-length reconstruction statistics: <strong>full_length_reconstruction_stats.csv</strong></li> <li>Web Supplementary File 5 - FASTA files containing ORF reconstruction input sequences:<strong> orf_fastas.zip</strong></li> <li>Web Supplementary File 6 - FASTA files containing Macse alignments of the ORF reconstruction input sequences:<strong> ORF_reconstruction_input_sequence_alns.zip</strong></li> <li>Web Supplementary File 7 - Table of ORF reconstruction statistics: <strong>ORF_reconstructions.fa</strong></li> <li>Web Supplementary File 8 - Table of ORF reconstruction statistics: <strong>ORF_reconstruction_stats.csv</strong></li> <li>Web Supplementary File 9 - Table of Composite Sequences: <strong>bestfl_selection_fixed_CS_seqs.csv</strong></li> <li>Web Supplementary File 10 - Database of gold standards: <strong>L1_goldstandards.csv</strong></li> </ul> <p><strong>Data Underlying Figures</strong></p> <ul> <li>RepeatMasker scans of hg38 and ancestral genomes:<strong> </strong><strong>anc_gen_RM_out_files.zip</strong></li> <li><strong>Figure 4</strong> <ul> <li>4A <ul> <li>Source alignment of 54 composite sequences: <strong>220121_dropped12+L1ME3A_muscle.nt.afa</strong></li> <li>Tree produced using the alignment and FastTree: <strong>220121_dropped12+L1ME3A.tree</strong></li> </ul> </li> <li>4B <ul> <li>Source alignment of 67 Dfam L1 subfamily 3’ end models: <strong>200123_dfam_3ends.fa.muscle.aln</strong></li> <li>Tree produced using the alignment: <strong>200123_dfam_3ends.fa.muscle.aln.tree</strong></li> </ul> </li> </ul> </li> <li><strong>Figure 5</strong> <ul> <li>KZFP-TE enrichment p-values (from Barazandeh <em>et al</em> 2018):<strong> TE_KZFP_enrichment_pvals.xlsx</strong></li> <li>KZFP-TE top 500 peak overlap (from Barazandeh <em>et al</em> 2018): <strong>top500_peak_overlap.xlsx</strong></li> </ul> </li> <li><strong>Figure 6</strong> <ul> <li>RepeatMasker .out file for the Composite Sequence custom library queried against hg38: <strong>CS_RM_hg38.fa.out.gz</strong></li> </ul> </li> <li><strong>Figure S2</strong> <ul> <li>RepeatMasker scan .out file of hg38 (CG corrected Kimura Divergence values are in last column): <strong>hg38+KimDiv_RM.out</strong></li> <li>RepeatMasker scan .out file of the Progressive Cactus eutherian ancestral genome (CG corrected Kimura Divergence values are in last column): <strong>Progressive_Cactus_Euth+KimDiv_RM.out</strong></li> <li>RepeatMasker scan .out file of the Ancestors 1.1 eutherian ancestral genome (CG corrected Kimura Divergence values are in last column): <strong>Ancestors_Euth+KimDiv_RM.out</strong></li> </ul> </li> <li><strong>Figure S5</strong> <ul> <li>RepeatMasker scan .out files for Progressive Cactus simian and primate reconstructed ancestral genomes: <strong>progCactus_RM_outfiles.zip</strong></li> <li>S5A <ul> <li>FASTA files containing Cactus genome-derived reconstructed sequences equivalent to the L1MA2, L1MA4, and L1MD1-3 best full-length sequences: <strong>progCactus_reconstruction_bestFL_equivalents.zip</strong></li> </ul> </li> <li>S5B <ul> <li>FASTA files containing Muscle alignments of Cactus genome-derived full-length reconstruction input sequences: <strong>progCactus_reconstruction_input_sequence_alns.zip</strong></li> </ul> </li> </ul> </li> <li><strong>Figure S6</strong> <ul> <li>S6A <ul> <li>Results of Conserved Domain scans of Cactus genome-derived full-length reconstructed sequences: <strong>CD_search_results_short_nms.txt</strong></li> </ul> </li> <li>S6B-D <ul> <li>Character posterior probabilities of “best” full-length reconstructed sequences: <strong>best_fl_post_probs.zip</strong></li> </ul> </li> </ul> </li> <li><strong>Figure S7</strong> <ul> <li>S7B-C <ul> <li>Results of Conserved Domain scans of translated initial full-length reconstructed sequences: <strong>initial_recons_all_3frametrans_CD-search.txt</strong></li> <li>Results of Conserved Domain scans of translated reconstructed ORFs: <strong>recons_ORF1-2_all_3frametrans_CD-search.csv</strong></li> </ul> </li> </ul> </li> <li><strong>Figure S15</strong> <ul> <li>S15A <ul> <li>Source alignment of 67 composite sequences: <strong>bestfl_selection_fixed_CS_seqs_muscle.nt.afa</strong></li> <li>Tree produced using the alignment: <strong>bestfl_selection_fixed_CS_seqs_muscle.nt.afa.tree</strong></li> </ul> </li> <li>S15B-E <ul> <li>Source Muscle alignments for phylogenetic trees of reconstructed sequence components: <ul> <li>ORF2: <strong>ORF2_keep54_muscle.nt.afa</strong></li> <li>5’ UTR: <strong>5utr_keep54_muscle.nt.afa</strong></li> <li>ORF1: <strong>ORF1_keep54_muscle.nt.afa</strong></li> <li>3’ UTR: <strong>3utr_keep54_muscle.nt.afa</strong></li> </ul> </li> <li>Trees produced using above alignments: <ul> <li>ORF2: <strong>ORF2_keep54_muscle.nt.afa.tree</strong></li> <li>5’ UTR: <strong>5utr_keep54_muscle.nt.afa.tree</strong></li> <li>ORF1: <strong>ORF1_keep54_muscle.nt.afa.tree</strong></li> <li>3’ UTR: <strong>3utr_keep54_muscle.nt.afa.tree</strong></li> </ul> </li> </ul> </li> </ul> </li> <li><strong>Figure S17</strong> <ul> <li>Unfiltered BLAST results of Composite Sequences queried against hg38: <strong>CS_hg38_blastn.csv.zip</strong></li> <li>BED file of L1 instances annotated using BLAST pipeline: <strong>BLAST_L1_hits.bed</strong></li> </ul> </li> </ul>
Qualitative dataset based on ancestral knowledge about coffee crops
<p> </p> <p>The qualitative dataset is about coffee pests based on the ancestral knowledge of coffee farmers in the Department of Cauca, Colombia. The dataset has been obtained from a survey applied to coffee growers with 432 records and 41 variables collected weekly from September 2020 to August 2021. The qualitative dataset includes climatic conditions, productive activities, external conditions, and coffee bio-aggressors. This dataset allows researchers to find patterns for coffee crop protection by means of ancestral knowledge not detected by real-time agricultural sensors. As far as we are concerned, there are no datasets like the one presented in this paper with similar characteristics of qualitative value that express the empirical knowledge of coffee farmers used to detect triggers of causal behaviors of pests and diseases in coffee crops.</p>
Human ancestral structure data from cobraa
<p>This is an updated version for the data from our paper on human ancestral population structure. The previous upload has truncated marginal decoding files. Here, I upload the full decoding files from cobraa-path (each state is a tuple of time and path), from which the marginal path probabilities can be easily obtained (see below). I also upload the final inference files from cobraa, for panmictic (PSMC) inference and the best fitting structured inference. These files exist for all of the 26 populations in the 1000 Genomes Project (one sample per population).</p> <p>To get the marginal path probabilities, the script marginalise_fulldecoding.py can be used. Example usage (the file paths will have to be changed):<br>Usage<br>python human_ancestral_structure_v2/marginalise_fulldecoding.py -chrom 20 -popsam GBR_HG00118 -outprefix /home/trevor/testingdelete250531 -decode_file human_ancestral_structure/decoding/GBR_HG00118/chr20.txt.gz</p> <p>Write all in a bash loop with<br>for chrom in {1..22}; do for popsam in GBR_HG00118 TSI_NA20752 IBS_HG01783 FIN_HG00266 CEU_NA12718 CHS_HG00443 KHV_HG02113 CHB_NA18530 CDX_HG02373 JPT_NA18939 BEB_HG03006 PJL_HG03234 GIH_NA20845 STU_HG03753 ITU_HG03977 PUR_HG01171 CLM_HG01250 PEL_HG02285 MXL_NA19648 ESN_HG03515 YRI_NA18488 MSL_HG03212 GWD_HG02568 ACB_HG01882 ASW_NA19625 LWK_NA19017; do echo popsam=${popsam}, chrom=${chrom}; python human_ancestral_structure_v2/marginalise_fulldecoding.py -chrom ${chrom} -popsam ${popsam} -outprefix /home/trevor/testingdelete250531 -decode_file human_ancestral_structure/decoding/${popsam}/chr${chrom}.txt.gz ; echo; done; done</p> <p>Please post questions on the GitHub https://github.com/trevorcousins/cobraa</p>
Identifying climatic drivers of hybridization with a new ancestral niche reconstruction method
<p>Applications of molecular phylogenetic approaches have uncovered evidence of hybridization across numerous clades of life, yet the environmental factors responsible for driving opportunities for hybridization remain obscure. Verbal models implicating geographic range shifts that brought species together during the Pleistocene have often been invoked, but quantitative tests using paleoclimatic data are needed to validate these models. Here, we produce a phylogeny for Heuchereae, a clade of 15 genera and 83 species in Saxifragaceae, with complete sampling of recognized species, using 277 nuclear loci and nearly complete chloroplast genomes. We then employ an improved framework with a coalescent simulation approach to test and confirm previous hybridization hypotheses and identify one new intergeneric hybridization event. Focusing on the North American distribution of Heuchereae, we introduce and implement a newly developed approach to reconstruct potential past distributions for ancestral lineages across all species in the clade and across a paleoclimatic record extending from the late Pliocene. Time calibration based on both nuclear and chloroplast trees recovers a mid- to late-Pleistocene date for most inferred hybridization events, a timeframe concomitant with repeated geographic range restriction into overlapping refugia. Our results indicate an important role for past episodes of climate change, and the contrasting responses of species with differing ecological strategies, in generating novel patterns of range contact among plant communities and therefore new opportunities for hybridization. The new ancestral niche method flexibly models the shape of niche while incorporating diverse sources of uncertainty and will be an important addition to the current comparative methods toolkit.</p>
Paleolithic Divergence and Multiple Neolithic Expansions of Ancestral Nomadic Emperor-related Paternal Lineages
Open the record for dataset details and reuse information.
Predicted gene expression in ancestrally diverse populations leads to discovery of susceptibility loci for lifestyle and cardiometabolic traits
<p>Full summary statistics for the publication "Predicted gene expression in ancestrally diverse populations leads to discovery of susceptibility loci for lifestyle and cardiometabolic traits". </p> <p>The files, bmi.UKBBsummary.txt and height.UKBBsummary.txt, contain tissue specific associations with body mass index (BMI) and height respectively. The suffix UKBB450k indicates results from all ~450,000 European ancestry individuals in UK Biobank. The suffix UKBB50k corresponds to results from a subset of 50,000 Europeans in the UK Biobank. The suffix PAGE corresponds to results from ~50,000 individuals in the Population Architecture using Genomics and Epidemiology (PAGE) study. </p> <p>The file PAGE_PrediXcan_associations.txt includes trait~tissue specific GReX associations for 25 traits. The first field specifies the tissue.trait.gene of the association results.</p>
Chronogram or phylogram for ancestral state estimation? Model-fit statistics indicate the branch lengths underlying a binary character's evolution: R scripts and simulated trees
<p>All R scripts used in this study, and the set of simulated phylogenetic trees used in the study.</p> <p>1. Modern methods of ancestral state estimation (ASE) incorporate branch length information, and it has been demonstrated that ASEs are more accurate when conducted on the branch lengths most correlated with a character's evolution; however, a reliable method for choosing between alternate branch length sets for discrete characters has not yet been proposed.<br><br>2. In this study, we simulate paired chronograms and phylograms, and generate binary characters that evolve in correlation with one of these. We then investigate (1) the effect of alternate branch lengths on ASE error, and (2) whether phylogenetic signal statistics and/or model-fit statistic can be used to select the branch lengths most correlated with a binary character.<br><br>3. In agreement with previous studies, we find that ASEs are more accurate when conducted on the branch lengths most correlated with the character. Phylogenetic signal statistics show limited utility for selecting the correct branch lengths, but model-fit statistics are found to be more accurate, with the correct branch lengths generally returning greater model-fit (lower AICc and BIC values). Using this method to choose between alternate branch length sets is more accurate when tree and character properties are more favorable for model optimization, and when shape differences between alternate phylogenies are greater.<br><br>4. Our results indicate that researchers conducting ASEs on discrete characters should carefully consider which branch lengths are appropriate, and, in the absence of other evidence, we suggest estimating model-fit values over alternate branch length sets and evolutionary models and choosing the branch length/model combination that returns better model fit.</p>
The Huanan Market Origin of SARS-CoV-2 is unlikely: The ancestral lineage containing specimen appears to have arisen from laboratory contamination
<p>• There is universal agreement that the lineage B/L is not the ancestral SARS-CoV-2; lineage A/S is the most ancestral lineage<br> • Until the Gao paper, no lineage A/S virus was identified at the Huanan Market, making the market an unlikely origin for the pandemic<br> • The Gao paper found one specimen, A20, with both lineage A/S and B/L<br> • The lineage B/L reads in A20 and the specimen Ct matched the expected findings<br> • The lineage A/S reads were anomalously high compared to the Ct and meet the definition of a statistical outlier<br> • The SARS-CoV-2 reads from the A20 sample also had two SNVs not seen in GISAID sequences until at least 60-90 days after the specimen was collected from the market<br> • This analysis supports a finding that the ancestral lineage A/S virus sequences in sample A20 were not present on the glove on January 1, 2020 when the sample was collected but instead arose by inadvertent laboratory contamination later, probably during metagenomic sequencing</p> <p>The absence of an unimpeachable ancestral lineage specimen at the Hunan Market makes it unlikely the market was the origin of the pandemic.</p>
Data and Source codes: Ancestral sex-role plasticity facilitates the evolution of same-sex sexual behavior
<p>This repository provides access to the tracking data and analysis code used for the manuscript:</p> <p>Ancestral sex-role plasticity facilitates the evolution of same-sex sexual behavior</p> <p>by Nobuaki Mizumoto<sup>1</sup>, Thomas Bourguignon<sup>1</sup>, and Nathan W. Bailey<sup>2</sup></p> <p><sup>1</sup> Okinawa Institute of Science & Technology Graduate University, Onna-son, Okinawa, Japan <br /><br> <sup>2</sup> School of Biology, University of St Andrews, St Andrews, U.K. <br /></p> <p>published in the Proceedings of the National Academy of Sciences of the United States of America.</p>
Fig. 16. Xeruca formosensis, zoea I. A in Fig. 3 in Historical Biogeography of the Group (Anura, Leptodactylidae): Identification of Ancestral Areas and Events that Modeled their Distribution.
Fig. 16. Xeruca formosensis, zoea I. A, carapace; B, antennule; C, antenna; D, maxillule; E, maxilla; F, first maxilliped; G, second maxilliped; H, pleon and telson.
Fig. 15. Tubuca paradussumieri, zoea I. A in Fig. 3 in Historical Biogeography of the Group (Anura, Leptodactylidae): Identification of Ancestral Areas and Events that Modeled their Distribution.
Fig. 15. Tubuca paradussumieri, zoea I. A, carapace; B, antennule; C, antenna; D, maxillule; E, maxilla; F, first maxilliped; G, second maxilliped; H, pleon and telson.
Fig. 13. Tubuca coarctata, zoea I. A in Fig. 3 in Historical Biogeography of the Group (Anura, Leptodactylidae): Identification of Ancestral Areas and Events that Modeled their Distribution.
Fig. 13. Tubuca coarctata, zoea I. A, carapace; B, antennule; C, antenna; D, maxillule; E, maxilla; F, first maxilliped; G, second maxilliped; H, pleon and telson.
Fig. 12. Tubuca arcuata, zoea I. A in Fig. 3 in Historical Biogeography of the Group (Anura, Leptodactylidae): Identification of Ancestral Areas and Events that Modeled their Distribution.
Fig. 12. Tubuca arcuata, zoea I. A, carapace; B, antennule; C, antenna; D, maxillule; E, maxilla; F, first maxilliped; G, second maxilliped; H, pleon and telson.
Fig. 11. Tubuca acuta, zoea I. A in Fig. 3 in Historical Biogeography of the Group (Anura, Leptodactylidae): Identification of Ancestral Areas and Events that Modeled their Distribution.
Fig. 11. Tubuca acuta, zoea I. A, carapace; B, antennule; C, antenna; D, maxillule; E, maxilla; F, first maxilliped; G, second maxilliped; H, pleon and telson.
Fig. 9. Paraleptuca crassipes, zoea I. A in Fig. 3 in Historical Biogeography of the Group (Anura, Leptodactylidae): Identification of Ancestral Areas and Events that Modeled their Distribution.
Fig. 9. Paraleptuca crassipes, zoea I. A, carapace; B, antennule; C, antenna; D, maxillule; E, maxilla; F, first maxilliped; G, second maxilliped; H, pleon and telson.
Fig. 8. Gelasimus vocans, zoea I. A in Fig. 3 in Historical Biogeography of the Group (Anura, Leptodactylidae): Identification of Ancestral Areas and Events that Modeled their Distribution.
Fig. 8. Gelasimus vocans, zoea I. A, carapace; B, antennule; C, antenna; D, maxillule; E, maxilla; F, first maxilliped; G, second maxilliped; H, pleon and telson.
Fig. 10. Paraleptuca splendida, zoea I. A in Fig. 3 in Historical Biogeography of the Group (Anura, Leptodactylidae): Identification of Ancestral Areas and Events that Modeled their Distribution.
Fig. 10. Paraleptuca splendida, zoea I. A, carapace; B, antennule; C, antenna; D, maxillule; E, maxilla; F, first maxilliped; G, second maxilliped; H, pleon and telson.
Fig. 7. Gelasimus tetragonon, zoea I. A in Fig. 3 in Historical Biogeography of the Group (Anura, Leptodactylidae): Identification of Ancestral Areas and Events that Modeled their Distribution.
Fig. 7. Gelasimus tetragonon, zoea I. A, carapace; B, antennule; C, antenna; D, maxillule; E, maxilla; F, first maxilliped; G, second maxilliped; H, pleon and telson.
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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.