S1 Table. Sample metadata for individual mice. S2 Table. CD3+ + T cell recovery and sequence yield from 10X single-cell library. S3 Table. Expression of marker genes across T cell clusters. S4 Table. Results of find all markers reported with average Log Fold Change, percent expression of cells, Gene name and both identified and simplified cluster name. S5 Table. Results of find markers show differential expression of genes in Naïve populations of DN T cells compared to CD4 T cells. S6 Table. Results of find markers show differential expression of genes in IL7r negative DN T cells compared to IL7r- CD4 T cells. S7 Table. Results of find markers show differential expression of genes in a mixed DN and CD4 effector T cell population compared to IL7 positive CD4 Teff and effector Tregs. S8 Table. Results of find markers show differential expression of genes in a mixed DN and CD4 effector T cell population compared to IL7 positive DN effector T cells. S9 Table. Results of find markers show differential expression of genes in IL7r negative DN T cells compared to Il7r+ + DN T cells. S10 Table. Results of find markers show differential expression of genes in Gzma positive CD8 T cells compared to Gzma negative CD8 T cells. S11 Table. Table showing GO enrichment of differentially expressed genes between populations of mixed DN and CD4 effector T cells and CD4 effector Tcells and Tregs. S12 Table. Table showing GO enrichment of differentially expressed genes between populations of Gzma positive CD8 T cells and Gzma negative CD8 T cells. S13 Table. Frequency of T cell clones separated by cluster appearance. Clusters refer to simple classification. Rows listed by specific TCRab clone CDR3 sequence. S14 Table. Differential expression of genes in clones specific to each clustered group. Fold change and Pct 1 refer to the group in the group column and pct 2 represents the proportion of expressing cells in all clones from other groups. S15 Table. Infiltrating (matching) TCR clones counted by sample type. Alpha beta TCR sequence for CDR3 region is reported. S16 Table. Differential expression of genes between islets-matching cells in blood and blood-matching cells in islets. The positive fold change shows enriched blood. S17 Table. Differential expression of genes between islets-matching and non-matching cells in blood. Positive Log fold-change shows enriched in matching. S18 Table. Differential expression of genes between blood-matching and non-matching cells in islets. Positive log fold-change. S19 Table. Exact matches between TCR-seq predicted CDR3 regions and experimentally validated TCR-pMHC interactions in the VDJdb. (XLSX)

Skeletal muscle repair is driven by the coordinated self-renewal and fusion of myogenic stem and progenitor cells. Single-cell gene expression analyses of myogenesis have been hampered by the poor sampling of rare and transient cell states that are critical for muscle repair, and do not inform the spatial context that is important for myogenic differentiation. Here, we demonstrate how large-scale integration of single-cell and spatial transcriptomic data can overcome these limitations. We created a single-cell transcriptomic dataset of mouse skeletal muscle by integration, consensus annotation, and analysis of 23 newly collected scRNAseq datasets and 88 publicly available single-cell (scRNAseq) and single-nucleus (snRNAseq) RNA-sequencing datasets. The resulting dataset includes more than 365,000 cells and spans a wide range of ages, injury, and repair conditions. Together, these data enabled identification of the predominant cell types in skeletal muscle, and resolved cell subtypes, including endothelial subtypes distinguished by vessel-type of origin, fibro/adipogenic progenitors defined by functional roles, and many distinct immune populations. The representation of different experimental conditions and the depth of transcriptome coverage enabled robust profiling of sparsely expressed genes. We built a densely sampled transcriptomic model of myogenesis, from stem cell quiescence to myofiber maturation and identified rare, transitional states of progenitor commitment and fusion that are poorly represented in individual datasets. We performed spatial RNA sequencing of mouse muscle at three time points after injury and used the integrated dataset as a reference to achieve a high-resolution, local deconvolution of cell subtypes. We also used the integrated dataset to explore ligand-receptor co-expression patterns and identify dynamic cell-cell interactions in muscle injury response. We provide a public web tool to enable interactive exploration and visualization of the data. Our work supports the utility of large-scale integration of single-cell transcriptomic data as a tool for biological discovery.

Methods Mice. The Cornell University Institutional Animal Care and Use Committee (IACUC) approved all animal protocols, and experiments were performed in compliance with its institutional guidelines. Adult C57BL/6J mice (mus musculus) were obtained from Jackson Laboratories (#000664; Bar Harbor, ME) and were used at 4-7 months of age. Aged C57BL/6J mice were obtained from the National Institute of Aging (NIA) Rodent Aging Colony and were used at 20 months of age. For new scRNAseq experiments, female mice were used in each experiment.

Mouse injuries and single-cell isolation. To induce muscle injury, both tibialis anterior (TA) muscles of old (20 months) C57BL/6J mice were injected with 10 µl of notexin (10 µg/ml; Latoxan; France). At 0, 1, 2, 3.5, 5, or 7 days post-injury (dpi), mice were sacrificed and TA muscles were collected and processed independently to generate single-cell suspensions. Muscles were digested with 8 mg/ml Collagenase D (Roche; Switzerland) and 10 U/ml Dispase II (Roche; Switzerland), followed by manual dissociation to generate cell suspensions. Cell suspensions were sequentially filtered through 100 and 40 μm filters (Corning Cellgro #431752 and #431750) to remove debris. Erythrocytes were removed through incubation in erythrocyte lysis buffer (IBI Scientific #89135-030).

Single-cell RNA-sequencing library preparation. After digestion, single-cell suspensions were washed and resuspended in 0.04% BSA in PBS at a concentration of 106 cells/ml. Cells were counted manually with a hemocytometer to determine their concentration. Single-cell RNA-sequencing libraries were prepared using the Chromium Single Cell 3’ reagent kit v3 (10x Genomics, PN-1000075; Pleasanton, CA) following the manufacturer’s protocol. Cells were diluted into the Chromium Single Cell A Chip to yield a recovery of 6,000 single-cell transcriptomes. After preparation, libraries were sequenced using on a NextSeq 500 (Illumina; San Diego, CA) using 75 cycle high output kits (Index 1 = 8, Read 1 = 26, and Read 2 = 58). Details on estimated sequencing saturation and the number of reads per sample are shown in Sup. Data 1.

Spatial RNA sequencing library preparation. Tibialis anterior muscles of adult (5 mo) C57BL6/J mice were injected with 10µl notexin (10 µg/ml) at 2, 5, and 7 days prior to collection. Upon collection, tibialis anterior muscles were isolated, embedded in OCT, and frozen fresh in liquid nitrogen. Spatially tagged cDNA libraries were built using the Visium Spatial Gene Expression 3’ Library Construction v1 Kit (10x Genomics, PN-1000187; Pleasanton, CA) (Fig. S7). Optimal tissue permeabilization time for 10 µm thick sections was found to be 15 minutes using the 10x Genomics Visium Tissue Optimization Kit (PN-1000193). H&E stained tissue sections were imaged using Zeiss PALM MicroBeam laser capture microdissection system and the images were stitched and processed using Fiji ImageJ software. cDNA libraries were sequenced on an Illumina NextSeq 500 using 150 cycle high output kits (Read 1=28bp, Read 2=120bp, Index 1=10bp, and Index 2=10bp). Frames around the capture area on the Visium slide were aligned manually and spots covering the tissue were selected using Loop Browser v4.0.0 software (10x Genomics). Sequencing data was then aligned to the mouse reference genome (mm10) using the spaceranger v1.0.0 pipeline to generate a feature-by-spot-barcode expression matrix (10x Genomics).

Download and alignment of single-cell RNA sequencing data. For all samples available via SRA, parallel-fastq-dump (github.com/rvalieris/parallel-fastq-dump) was used to download raw .fastq files. Samples which were only available as .bam files were converted to .fastq format using bamtofastq from 10x Genomics (github.com/10XGenomics/bamtofastq). Raw reads were aligned to the mm10 reference using cellranger (v3.1.0).

Preprocessing and batch correction of single-cell RNA sequencing datasets. First, ambient RNA signal was removed using the default SoupX (v1.4.5) workflow (autoEstCounts and adjustCounts; github.com/constantAmateur/SoupX). Samples were then preprocessed using the standard Seurat (v3.2.1) workflow (NormalizeData, ScaleData, FindVariableFeatures, RunPCA, FindNeighbors, FindClusters, and RunUMAP; github.com/satijalab/seurat). Cells with fewer than 750 features, fewer than 1000 transcripts, or more than 30% of unique transcripts derived from mitochondrial genes were removed. After preprocessing, DoubletFinder (v2.0) was used to identify putative doublets in each dataset, individually. BCmvn optimization was used for PK parameterization. Estimated doublet rates were computed by fitting the total number of cells after quality filtering to a linear regression of the expected doublet rates published in the 10x Chromium handbook. Estimated homotypic doublet rates were also accounted for using the modelHomotypic function. The default PN value (0.25) was used. Putative doublets were then removed from each individual dataset. After preprocessing and quality filtering, we merged the datasets and performed batch-correction with three tools, independently- Harmony (github.com/immunogenomics/harmony) (v1.0), Scanorama (github.com/brianhie/scanorama) (v1.3), and BBKNN (github.com/Teichlab/bbknn) (v1.3.12). We then used Seurat to process the integrated data. After initial integration, we removed the noisy cluster and re-integrated the data using each of the three batch-correction tools.

Cell type annotation. Cell types were determined for each integration method independently. For Harmony and Scanorama, dimensions accounting for 95% of the total variance were used to generate SNN graphs (Seurat::FindNeighbors). Louvain clustering was then performed on the output graphs (including the corrected graph output by BBKNN) using Seurat::FindClusters. A clustering resolution of 1.2 was used for Harmony (25 initial clusters), BBKNN (28 initial clusters), and Scanorama (38 initial clusters). Cell types were determined based on expression of canonical genes (Fig. S3). Clusters which had similar canonical marker gene expression patterns were merged.

Pseudotime workflow. Cells were subset based on the consensus cell types between all three integration methods. Harmony embedding values from the dimensions accounting for 95% of the total variance were used for further dimensional reduction with PHATE, using phateR (v1.0.4) (github.com/KrishnaswamyLab/phateR).

Deconvolution of spatial RNA sequencing spots. Spot deconvolution was performed using the deconvolution module in BayesPrism (previously known as “Tumor microEnvironment Deconvolution”, TED, v1.0; github.com/Danko-Lab/TED). First, myogenic cells were re-labeled, according to binning along the first PHATE dimension, as “Quiescent MuSCs” (bins 4-5), “Activated MuSCs” (bins 6-7), “Committed Myoblasts” (bins 8-10), and “Fusing Myoctes” (bins 11-18). Culture-associated muscle stem cells were ignored and myonuclei labels were retained as “Myonuclei (Type IIb)” and “Myonuclei (Type IIx)”. Next, highly and differentially expressed genes across the 25 groups of cells were identified with differential gene expression analysis using Seurat (FindAllMarkers, using Wilcoxon Rank Sum Test; results in Sup. Data 2). The resulting genes were filtered based on average log2-fold change (avg_logFC > 1) and the percentage of cells within the cluster which express each gene (pct.expressed > 0.5), yielding 1,069 genes. Mitochondrial and ribosomal protein genes were also removed from this list, in line with recommendations in the BayesPrism vignette. For each of the cell types, mean raw counts were calculated across the 1,069 genes to generate a gene expression profile for BayesPrism. Raw counts for each spot were then passed to the run.Ted function, using

This sc-RNAseq dataset is composed of disease-unaffected epidermal samples from 96 skin biopsies: 18 from published datasets - GSE173706, GSE249279 – and 78 newly generated ones. Biopsy sample and protocol details, and curated cell-type signature genes, are available in the scRNASeq_source_info_FigShare spreadsheet of this dataset. Processed Seurat object are provided herein. Raw data are available in SRA (id PRJNA1054546). Biopsies originated from seven body sites (face, scalp, axilla, palmoplantar, arm, leg, and back). The skin biopsies were separated into epidermis and dermis before dissociated and enriched for various cell fractions (keratinocytes, fibroblasts, and endothelial cells) and immune cells (myeloid and lymphoid cells) to up sample rare cell types. In total, across body sites, 274,834 cells were profiled, including 96,194 keratinocytes. Seurat v3.0. was utilized to normalize, scale, and reduce the dimensionality of the data. Low quality cells containing less than 200 genes per cell as well as greater than 5,000 genes per cell were filtered out. Cells containing more mitochondrial genes than the permitted quantile of 0.05 were removed. Ambient RNA was removed using R package SoupX v1.6.2. Doublets were removed using scDblFinder v1.12.0. Principal components (PC) were obtained from the topmost 2,000 variable genes, and the Uniform Manifold Approximation and Projection (UMAP) dimensional reduction technique was applied to the 30 topmost variable PC-reduced dataset. Batch effect correction was performed utilizing harmony v1.0, using donor as batch. After batch correction, cells were clustered using shared nearest neighbor modularity optimization-based clustering. Cluster marker genes were identified with FindAllMarkers; cluster corresponding cell type was identified by comparing marker genes to curated cell-type signature genes. Differential expression by keratinocyte subtype was performed with Seurat (v4.3.0) FindMarkers function by comparing keratinocyte subtype to non-keratinocyte clusters. The log fold-change of the average expression between a keratinocyte subtype cluster compared to the rest of clusters is utilized as keratinocyte-subtype gene expression statistic.

Development of the dorsal aorta is a key step in the establishment of the adult blood-forming system since hematopoietic stem and progenitor cells (HSPCs) arise from ventral aortic endothelium in all vertebrate animals studied. Work in zebrafish has demonstrated that arterial and venous endothelial precursors arise from distinct subsets of lateral plate mesoderm. Here, we profile the transcriptome of the earliest detectable endothelial cells (ECs) during zebrafish embryogenesis to demonstrate that tissue-specific EC programs initiate much earlier than previously appreciated, by the end of gastrulation. Classic studies in the chick embryo showed that paraxial mesoderm generates a subset of somite-derived endothelial cells (SDECs) that incorporate into the dorsal aorta to replace HSPCs as they exit the aorta and enter circulation. We describe a conserved program in the zebrafish, where a rare population of endothelial precursors delaminates from the dermomyotome to incorporate exclusively into the developing dorsal aorta. Although SDECs lack hematopoietic potential, they act as a local niche to support the emergence of HSPCs from neighboring hemogenic endothelium. Thus, at least three subsets of ECs contribute to the developing dorsal aorta: vascular ECs, hemogenic ECs, and SDECs. Taken together, our findings indicate that the distinct spatial origins of endothelial precursors dictate different cellular potentials within the developing dorsal aorta. Methods Single-cell RNA sample preparation After FACS, total cell concentration and viability were ascertained using a TC20 Automated Cell Counter (Bio-Rad). Samples were then resuspended in 1XPBS with 10% BSA at a concentration between 800-3000 per ml. Samples were loaded on the 10X Chromium system and processed as per manufacturer’s instructions (10X Genomics). Single cell libraries were prepared as per the manufacturer’s instructions using the Single Cell 3’ Reagent Kit v2 (10X Genomics). Single cell RNA-seq libraries and barcode amplicons were sequenced on an Illumina HiSeq platform. Single-cell RNA sequencing analysis The Chromium 3’ sequencing libraries were generated using Chromium Single Cell 3’ Chip kit v3 and sequenced with (actually, I don’t know:( what instrument was used?). The Ilumina FASTQ files were used to generate filtered matrices using CellRanger (10X Genomics) with default parameters and imported into R for exploration and statistical analysis using a Seurat package (La Manno et al., 2018). Counts were normalized according to total expression, multiplied by a scale factor (10,000), and log-transformed. For cell cluster identification and visualization, gene expression values were also scaled according to highly variable genes after controlling for unwanted variation generated by sample identity. Cell clusters were identified based on UMAP of the first 14 principal components of PCA using Seurat’s method, Find Clusters, with an original Louvain algorithm and resolution parameter value 0.5. To find cluster marker genes, Seurat’s method, FindAllMarkers. Only genes exhibiting significant (adjusted p-value < 0.05) a minimal average absolute log2-fold change of 0.2 between each of the clusters and the rest of the dataset were considered as differentially expressed. To merge individual datasets and to remove batch effects, Seurat v3 Integration and Label Transfer standard workflow (Stuart et al., 2019)

The scRNA-seq data were collected from previously published datasets (GSE140228, GSE139555, GSE155698, GSE121636, and GSE139324), adhering to the following selection criteria: 1) presence of T cells, 2) treatment-naïve patients, 3) solid tumors, and 4) inclusion of at least tumor and blood samples.
Each scRNA-seq dataset underwent separate preprocessing in R (v4.0.2). We filtered out cells from the original count matrices that had fewer than 200 genes detected or more than 10% mitochondrial UMI counts and we only kept genes detected in at least 3 cells. Then, we applied Seurat (v4.0.5) with default parameters for count data normalization and scaling. Each cell was assigned a cell cycle score using the CellCycleScoring function and we computed the difference between the G2M and S phase scores. This approach allows for the separation of non-cycling from cycling cells while minimizing the differences in cell cycle phase among proliferating cells. The SelectIntegrationFeatures function was ran with the nfeatures parameter set to 3,000 before merging all samples from each dataset. These integration features were then used for Principal Component Analysis (PCA) and Uniform Manifold Approximation and Projection (UMAP). Clustering was performed using the Louvain algorithm with the resolution parameter set to 2.0 for all datasets. Finally, T cells were isolated based on CD3D and CD3G genes expression (CD3D or CD3G expression level > 0).

To integrate heterogeneous data from different sources, a two-step procedure was applied. We first concatenated all datasets together and ran the scaling and PCA steps based on the top 3,000 highly variable genes identified by the FindVariableFeatures function with the “vst” method. Harmony was applied for batch effect correction then UMAP and clustering using the Louvain algorithm with the resolution parameter set to 2.0 were performed on the harmony reduction. Examining the result from the first clustering run, we identified contamination clusters and clusters that arose from unwanted factors: we removed the contamination clusters including low quality cells highly expressing marker genes associated with apoptosis and tissue dissociation operation, pancreatic acinar cells (expressing PRSS1, CLPS, PNLIP and CTRB1 among others), myeloid cells (expressing CD68) and B cells (expressing CD79A). Then, we performed the second run of integration and clustering excluding immunoglobulin, ribosome-protein-coding, and T cell receptor (TCR) genes (gene symbol with string pattern "^IGK|^IGH|^IGL|^IGJ|^IGS|^IGD|IGFN1", "^RP([0–9]+-|[LS])", and "^TRA|^TRB|^TRG" respectively) from the top 3,000 highly variable genes and regressing out the cell cycle difference effect as well as the percentage of mitochondrial UMI counts. Harmony (v0.1.0) was applied again for batch effect correction and UMAP was performed on the harmony reduction.
T cell subtypes identification and annotation was performed by clustering cells using the Louvain algorithm with the resolution parameter set to 4.1 after iterative testing from 3.5 to 5.0 by 0.1 (more granular than default), computing clusters signatures based on differential gene expression using the FindAllMarkers function with the “MAST” method and interrogating known gene markers expression. A resolution value of 4.1 was notably found to be the lowest resolution value enabling the correct separation of proliferating CD4+ T cells from proliferating CD8+ T cells.

Here, we provide:Robjects pertaining to scRNA-seq (Seurat) and snATAC-seq (Signac) analysis. These contain the single-cell and single-nuclei used in downstream analyses. Tables containing information about the gene markers identified for each cluster in scRNA-seq, peak markers identified for each cluster in snATAC-seq, and motif enrichment analyses using chromVAR motif scores. Differential gene expression and motif enrichment analyses was performed using Wilcoxon rank sum test comparing the distribution of gene expression or chromVAR motif scores between cells in the cluster and all other cells. Differential peak analyses was performed using FindAllMarkers in Signac.

DNA metabarcoding is used to enumerate and identify taxa in both environmental samples and tissue mixtures, but the effectiveness of particular markers depends on their sensitivity to the taxa involved. Using multiple primer sets that amplify different genes can mitigate biases in amplification efficiency, sequence resolution, and reference data availability, but few empirical studies have evaluated markers for complementary performance. Here, we assess the individual and joint performance of 22 markers for detecting species in a DNA pool of 98 species of marine and freshwater bony fishes from geographically and phylogenetically diverse origins. We find that a portfolio of four markers targeting 12S, 16S, and two regions of COI identifies 100% of reference taxa to family and nearly 60% to species. We then use these four markers to evaluate metabarcoding of heterogeneous tissue mixtures, using experimental fishmeal to test: 1) the tissue input threshold to ensure detection; 2) how read depth scales with tissue abundance; and 3) the effect of non-target material in the mixture on recovery of target taxa. We consistently detect taxa that make up >1% of fishmeal mixtures and can detect taxa at the lowest input level of 0.01%, but rare taxa (<1%) were detected inconsistently across markers and replicates. Read counts showed only a weak correlation with tissue input, suggesting they are not a reliable quantitative proxy for relative abundance. Despite the limitations arising from primer specificity and reference data availability, our results demonstrate that a modest portfolio of markers can perform well in detecting and identifying aquatic species in complex mixtures despite heterogeneity in tissue representation, phylogenetic affinities, and from a broad geographic range. Methods Metabarcoding markers Twenty-two markers for mitochondrial (COI, 12S, 16S) and nuclear (18S, 28S) barcoding genes were selected from metabarcoding, eDNA, and Sanger sequencing barcoding studies of marine and freshwater fishes, including seafood products (Table 1). Most of these markers were designed to target bony fishes (teleosts), but we added markers targeting elasmobranchs, crustaceans, and cephalopods – taxonomic groups that are often poorly resolved by universal fish barcodes. Only markers that amplified targets <300 bp were selected because shorter fragments are more likely to amplify degraded DNA (Devloo-Delva et al., 2019, Shokralla et al., 2015; Staats et al., 2016), which is expected to be the case for highly-processed fishmeal and oil. Reference DNA pools To compare the amplification and resolution of the 22 markers before determining complementarity, we constructed two pools with equal concentrations of extracted DNA from 98 marine and freshwater teleost fishes and five elasmobranch, crustacean, and cephalopod species, in total spanning 88 genera and 60 families (full reference pool; Table 2). Samples were obtained primarily from vouchered collections, but also from fish markets to encompass commercially-important groups. We sampled muscle tissue from inside the body wall (i.e., no surface contact) for DNA extractions, in an attempt to avoid trace contamination from contact with other species. To further minimize the potential for detecting false positives from tissue contamination, we constructed a second, more restricted reference pool including only the 73 DNA extracts from vouchered museum specimens (vouchered reference pool). Experimental tissue mixture samples Metabarcoding is typically used to detect both rare and abundant constituents in mixtures, and most applications include species in unequal proportions along with varying amounts and types of non-target material. In aquaculture feeds, we will refer to the non-target material as “filler.” To evaluate detection power in actual tissue mixtures (as opposed to pools of DNA extracts), we used fishmeal mixed with different fillers. The purpose of the filler was to test whether metabarcoding data are negatively impacted by fillers, either because of a loss of on-target sequencing reads or because of potential PCR inhibition. Similarly complex and heterogeneous mixtures of tissue sources might be expected in gut content or fecal samples in more ecological applications. To create experimental fishmeal, we freeze-dried tissue from 30 of the unvouchered fish species in the full reference pool (muscle tissue from market samples; whole fish from research samples), coarsely homogenized each sample in a coffee grinder, and then finely ground using a freezer mill where each tissue sample is pulverized within a container submerged in liquid nitrogen. Each species was added one-by-one, and we cleaned all containers and tools by wiping them with a 10% bleach solution followed by 70% ethanol to decontaminate between samples. Species were assigned to one of six abundance levels: 13.33%, 3.65%, 1.91%, 1%, 0.1%, or 0.01% of the mixture (by weight), thereby spanning >3 orders of magnitude variation in representation (Table 3). Each abundance level was represented by five species, which were assigned to balance freshwater and marine habitats, major phylogenetic groups, and degree of fishery interest across levels. This experimental design allowed us to assess how dominant and rare taxa added at discrete proportions to a heterogeneous mixture relate to the proportion of sequencing reads attributed to each taxon and to compare amplification biases across multiple taxa added in the same amount to the fishmeal. To test the effect of the non-target material, the fishmeal mixtures were combined with two unique fillers for a total of seven individual experimental feeds with low (2%), medium (10%), and high (25%) proportions of fishmeal relative to filler (Table 3). Fillers included plant-derived materials – grain and grass flours – and animal byproducts – bloodmeal and feathermeal – to represent mixture constituents used in aquaculture feeds. Fishmeal proportions also mimicked potential levels of fish tissue added to aquaculture feeds, from low (0%-2%) to high (25%) proportions of fish in the feed mixture. By multiplying the proportion of fishmeal in the experimental feed by the proportion of a particular fish species in the fishmeal, we could test the detection threshold for individual taxa down to 0.0002% of total experimental mixture mass (i.e., minimum of 0.01% of a particular species in the fishmeal and 2% fishmeal in the feed). DNA extracts were quantified by a Qubit fluorometer (high-sensitivity or broad-range dsDNA assay depending on concentration range), diluted with DNAse-free water, and added in equal proportion to the full reference and vouchered reference DNA pools. DNA extracts from the 30 fishmeal species were combined in two additional mock DNA pools: one with equal concentration among all taxa (mock equal) and the other in which DNA extract concentration was proportionate to the amount of tissue included in the fishmeal (mock variable). Similar to the previous reference DNA pools, DNA pools for the mock equal and mock variable pools were prepared in triplicate (Fig. S1). Metabarcoding sequencing libraries were prepared from each pool using a two-step amplicon protocol (D’Aloia, Bogdanowicz, Harrison, & Buston, 2017) in which an initial PCR targets the gene region of interest using locus-specific primers with Nextera 5’ tails (5’-TC GTCGGCAGCGTCAGATGTGTATAAGAGACAG appended to each forward primer and 5’ -GTCTCGTGGGCTC GGAGATGTGTATAAGAGACAG to each reverse primer, details in the SI). Equal volumes of the locus-specific PCR products for each sample were pooled and a second PCR added Nextera-style sequencing adapters with unique i5 and i7 indexes that allow sequencing reads to be assigned to samples during analysis (details about reagent concentrations and PCR conditions in the SI). Rather than using combinatorial indexing, which can lead to mis-assigned reads caused by index-swapping (Carøe & Bohmann, 2020; Schnell, Bohmann, & Gilbert, 2015), we used custom-synthesized adapters with unique dual indexes (Table S1) that can unequivocally identify samples by 8-base indexes on both ends of the molecule. For each sample, PCR products for all markers were pooled into a single indexed library and sequenced using paired-end 150-bp on one lane of a HiSeq X Ten (Novogene, Inc.) with 15% PhiX to account for moderately low library complexity (following Aizpurua et al., 2018).

THIS RESOURCE IS NO LONGER IN SERVICE, documented August 22, 2016. Database of sequence tagged sites (STSs) derived from STS-based maps and other experiments. STSs are defined by PCR primer pairs and are associated with additional information such as genomic position, genes, and sequences. Chromosome maps are labeled by name of the originating organism, the map title, total markers, total UniSTSs and links to view maps as well as research documents available through PubMed, another NCBI database. The search functions within UniSTS allow the user to search by gene marker, chromosome, gene symbol and gene description terms to locate markers on specified genes. A representation of the UniSTS datasets is available by ftp. NOTE: All data from this resource have been moved to the Probe database, http://www.ncbi.nlm.nih.gov/probe. You can retrieve all UniSTS records by searching the probe database using the search term unists(properties). (use brackets insead of parenthesis). Additionally, legacy data remain on the NCBI FTP Site in the UniSTS Repository (ftp://ftp.ncbi.nih.gov/pub/ProbeDB/legacy_unists).

We introduce here the concept of Unique Pattern Informative Combinations (UPIC), a decision tool for the cost-effective design of DNA fingerprinting/genotyping experiments using simple-sequence/tandem repeat (SSR/STR) markers. After the first screening of SSR-markers tested on a subset of DNA samples, the user can apply UPIC to find marker combinations that maximize the genetic information obtained by a minimum or desirable number of markers. This allows a cost-effective planning of future experiments. We have developed Perl scripts to calculate all possible subset combinations of SSR markers, and determine based on unique patterns or alleles, which combinations can discriminate among all DNA samples included in a test. This makes UPIC an essential tool for optimizing resources when working with microsatellites. An example using real data from eight markers and 12 genotypes shows that UPIC detected groups of as few as three markers sufficient to discriminate all 12-DNA samples. Should markers for future experiments be chosen based only on polymorphism-information content (PIC), the necessary number of markers for discrimination of all samples cannot be determined. We also show that choosing markers using UPIC, an informative combination of four markers can provide similar information as using a combination of six markers (23 vs. 25 patterns, respectively), granting a more efficient planning of experiments. Perl scripts with documentation are also included to calculate the percentage of heterozygous loci on the DNA samples tested and to calculate three PIC values depending on the type of fertilization and allele frequency of the organism. The UPIC zip file contains 2 perl scripts, a README, and sample input and the resulting outputs. We would appreciate citation if you use them. As of 1 November, 2010, the zip file also contains an beta optimized script (upic_optimum_v1.1.20101101.pl) that produces a comma separated file, with all the markers that discriminate at least one line, which shows which lines have unique patterns. This allows you to select markers by score & line. Resources in this dataset:Resource Title: UPIC version 1.2. File Name: UPIC_v1.2.zip

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on January 5, 2023. Knowledge base of genetic associations and human genome epidemiology including information on population prevalence of genetic variants, gene-disease associations, gene-gene and gene- environment interactions, and evaluation of genetic tests. This tool explores HuGENet, the Human Genome Epidemiology Network, which is a global collaboration of individuals and organizations committed to the assessment of the impact of human genome variation on population health and how genetic information can be used to improve health and prevent disease. What does HuGE Navigator offer? *HuGEpedia - an encyclopedia of human genetic variation in health and disease, includes, Phenopedia and Genopedia. Phenopedia allows you to look up gene-disease association summaries by disease, and Genopedia allows you to look up gene-disease association summaries by gene. In general, HuGEpedia is a searchable database that summarizes published articles about human disease and genetic variation, including primary studies, reviews, and meta-analyses. It provides links to Pubmed abstracts, researcher contact info, trends, and more. *HuGEtools - searching and mining the literature in human genome epidemiology, includes, HuGE Literature Finder, HuGE Investigator Browser, Gene Prospector, HuGE Watch, Variant Name Mapper, and HuGE Risk Translator. *HuGE Literature Finder finds published articles in human genome epidemiology since 2001. The search query can include genes, disease, outcome, environmental factors, author, etc. Results can be filtered by these categories. It is also possible to see all articles in the database for a particular topic, such as genotype prevalence, pharmacogenomics, or clinical trial. *HuGE Investigator Browser finds investigators in a particular field of human genome epidemiology. This info is obtained using a behind-the-scenes tool that automatically parses PubMed affiliation data. *Gene Prospector is a gateway for evaluating genes in relation to disease and risk factors. This tool allows you to enter a disease or risk factor and then supplies you with a table of genes associated w/your query that are ranked based on strength of evidence from the literature. This evidence is culled from the HuGE Literature Finder and NCBI Entrez Gene - And you're given the scoring formula. The Gene Prospector results table provides access to the Genopedia entry for each gene in the list, general info including links to other resources, SNP info, and associated literature from HuGE, PubMed, GWAS, and more. It is a great place to locate a lot of info about your disease/gene of interest very quickly. *HuGE Watch tracks the evolution of published literature, HuGE investigators, genes studied, or diseases studied in human genome epidemiology. For example, if you search Trend/Pattern for Diseases Studied you'll initially get a graph and chart of the number of diseases studied per year since 1997. You can refine these results by limiting the temporal trend to a category or study type such as Gene-gene Interaction or HuGE Review. *Variant Name Mapper maps common names and rs numbers of genetic variants using information from SNP500Cancer, SNPedia, pharmGKB, ALFRED, AlzGene, PDGene, SZgene, HuGE Navigator, LSDBs, and user submissions. *HuGE Risk Translator calculates the predictive value of genetic markers for disease risk. To do so, users must enter the frequency of risk variant, the population disease risk, and the odds ratio between the gene and disease. This information is necessary in order to yield a useful predictive result. *HuGEmix - a series of HuGE related informatics utilities and projects, includes, GAPscreener, HuGE Track, Open Source. GAPscreener is a screening tool for published literature on human genetic associations; HuGE Track is a custom track built for HuGE data in the UCSC Genome Browser; and Open Source is infrastructure for managing knowledge and information from PubMed.

BackgroundSingle-cell sequencing (SCS) technologies enable analysis of gene structure and expression data at single-cell resolution. However, SCS analysis in pancreatic cancer remains largely unexplored.MethodsWe downloaded pancreatic cancer SCS data from different databases and applied appropriate dimensionality reduction algorithms. We identified 10 cell types and subsequently screened differentially expressed marker genes of these 10 cell types using FindAllMarkers analysis. Also, we evaluated the tumor immune microenvironment based on ESTIMATE and MCP-counter. Statistical enrichment was evaluated using Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway analysis. We used all candidate gene sets in KEGG database to perform gene set enrichment analysis. We used LASSO regression to reduce the number of genes in the pancreatic risk model by R package glmnet, followed by rtPCR to validate the expression of the signature genes in different pancreatic cancer cell lines.ResultsWe identified 15 cell subpopulations by dimension reduction and data clustering. We divided the 15 subpopulations into 10 distinct cell types based on marker gene expression. Then, we performed functional enrichment analysis for the 352 marker genes in pancreatic cancer cells. Based on RNA expression data and prognostic information from TCGA and GEO datasets, we identified 42 prognosis-related genes, including 5 protective genes and 37 high-risk genes, which we used to identified two molecular subtypes. C1 subtype was associated with a better prognosis, whereas C2 subtype was associated with a worse prognosis. Moreover, chemokine and chemokine receptor genes were differentially expressed between C1 and C2 subtypes. Functional and pathway enrichment uncovered functional differences between C1 and C2 subtype. We identified eight genes that could serve as potential biomarkers for prognosis prediction in pancreatic cancer patients. These genes were used to establish an 8-gene pancreatic cancer prognostic model.ConclusionsWe established an 8-gene pancreatic cancer prognostic model. This model can meaningfully predict prognosis and treatment response in pancreatic cancer patients.

Here are a few use cases for this project:

1. Educational Monitoring: The ChangAn Marker Detection model can be used in education settings to identify whether markers are present or missing in a particular room. This could help in inventory tracking and ensuring that study materials or tools are always available for students.

2. Office Supply Management: This model can be used in an office environment to track usage and availability of white and black markers. The system can notify when marker levels are low and replacements are needed, improving efficiency and productivity.

3. Event Planning: In events such as workshops or conferences, the model can be used to ensure proper arrangement of chairs, tables, and presentation materials (like markers). The system can scan the room and detect if all required items are placed correctly before the event starts.

4. Art Studios: The model can be implemented in art studios to classify and track usage of black and white markers. Artists can use this data to understand their usage patterns and timely replenish their stock.

5. Industrial Quality Control: ChangAn Marker Detection could be used in industries manufacturing markers to automatically detect and classify the different markers coming off the production line, ensuring all products meet colour specifications.

International Aid Transparency Initiative (IATI) Policy Marker Dataset

A multi-purpose dataset including all activity title and description text published to IATI with metadata for policy markers. For more information on IATI policy markers, see the element page on the IATI Standard Website. IATI is a living data source, and this dataset was last updated on 21 August, 2024. For the code to generate an updated version of this dataset, please see my Github repository here. For any… See the full description on the dataset page: https://huggingface.co/datasets/devinitorg/iati-policy-markers.

BackgroundThe diagnosis for steroid-induced osteonecrosis of the femoral head (SONFH) is hard to achieve at the early stage, which results in patients receiving ineffective treatment options and a poor prognosis for most cases. The present study aimed to find potential diagnostic markers of SONFH and analyze the effect exerted by infiltration of immune cells in this pathology.Materials and MethodsR software was adopted for identifying differentially expressed genes (DEGs) and conducting functional investigation based on the microarray dataset. Then we combined SVM-RFE, WGCNA, LASSO logistic regression, and random forest (RF) algorithms for screening the diagnostic markers of SONFH and further verification by qRT-PCR. The diagnostic values were assessed through receiver operating characteristic (ROC) curves. CIBERSORT was then adopted for assessing the infiltration of immune cells and the relationship of infiltration-related immune cells and diagnostic markers.ResultsWe identified 383 DEGs overall. This study found ARG2, MAP4K5, and TSTA3 (AUC = 0.980) to be diagnostic markers of SONFH. The results of qRT-PCR showed a statistically significant difference in all markers. Analysis of infiltration of immune cells indicated that neutrophils, activated dendritic cells and memory B cells were likely to show the relationship with SONFH occurrence and progress. Additionally, all diagnostic markers had different degrees of correlation with T cell follicular helper, neutrophils, memory B cells, and activated dendritic cells.ConclusionARG2, MAP4K5, and TSTA3 are potential diagnostic genes for SONFH, and infiltration of immune cells may critically impact SONFH occurrence and progression.

Oil Based Marker Pen Market Outlook

The global oil based marker pen market size was valued at approximately USD 1.2 billion in 2023 and is expected to reach around USD 2.4 billion by 2032, growing at a compound annual growth rate (CAGR) of 8.2% during the forecast period. This growth is driven by increasing demand across various applications such as art and design, industrial usage, and office supplies. The versatility and durability of oil-based marker pens make them a preferred choice for professionals and hobbyists alike, contributing significantly to market growth.

One of the primary growth factors for the oil based marker pen market is the rising popularity of art and design activities. With a surge in creative arts, both as a professional field and a personal hobby, there is an increased demand for high-quality, reliable art supplies. Oil-based markers are particularly favored for their vibrant colors and long-lasting ink, making them a staple in both professional artist studios and amateur art supplies. Additionally, the growing influence of social media platforms has amplified the visibility of art and craft projects, further fueling the demand for premium art materials, including oil-based marker pens.

Another significant growth driver is the industrial application of oil-based marker pens. These markers are extensively used in various industries for marking on surfaces like metal, glass, and plastic, where regular markers would fail. The durability of oil-based ink, which is resistant to water and fading, makes these markers indispensable in settings where permanence and clarity are crucial. The booming manufacturing and construction sectors are particularly notable consumers of these products, as they rely on them for labeling and coding purposes, thereby directly impacting market expansion.

The office and school supplies segment also plays a vital role in the market growth of oil-based marker pens. With a global increase in the number of educational institutions and offices, there is a consistent demand for reliable writing instruments. Oil-based markers offer an edge over water-based markers due to their longevity and smudge-proof characteristics, making them a preferred choice for official documentation and educational activities. This steady demand from the office and educational sectors is expected to sustain the market growth over the forecast period.

Regionally, Asia Pacific is anticipated to dominate the market, driven by rapid industrialization and a growing emphasis on education. Countries like China and India are witnessing significant investments in infrastructure and education, leading to heightened demand for industrial and educational supplies, including oil-based marker pens. Additionally, the presence of numerous local and international manufacturers in this region is expected to boost market growth. In contrast, North America and Europe are likely to see moderate growth, driven by stable demand from the art and design sectors and continuous advancements in marker pen technology.

Product Type Analysis

The oil based marker pen market is segmented by product type into fine tip, medium tip, and broad tip markers. Fine tip markers are widely used for detailed work, making them indispensable for artists, designers, and professionals who require precision. The market for fine tip markers is expected to grow steadily, driven by increasing use in art and design applications where intricate detailing is crucial. The versatility of fine tip markers also lends them well to use in office and educational settings, where they are used for writing and marking on a variety of surfaces.

Medium tip markers are perhaps the most versatile among the types, offering a balance between precision and coverage. These markers find applications across all major segments, including art and design, industrial use, and office supplies. The medium tip segment is expected to witness significant growth due to its adaptability and widespread acceptance in both professional and personal use. Their utility in creating bold, visible lines makes them a favorite for labeling and coding in industrial settings as well.

Broad tip markers, on the other hand, are predominantly used for applications requiring extensive coverage and visibility. Their thick, durable lines make them suitable for signage, posters, and industrial markings. The broad tip segment is projected to grow at a moderate rate, driven by consistent demand from industries that require clear and long-lasting markings on various surfaces. These markers are also popular in th

AbstractGene regulatory divergence is thought to play an important role in adaptation, yet its extent and underlying mechanisms remain largely elusive for local adaptation with gene flow. Local adaptation is widespread in marine species despite generally high connectivity and is often associated with tightly linked genomic architectures, such as chromosomal inversions. To investigate gene regulatory evolution under gene flow and the role of inversions associated with local adaptation to a steep thermal gradient, we generated RNA-seq data from Atlantic silversides (Menidia menidia) from two locally adapted populations and their F1 hybrids, reared under two temperatures. We found substantial divergence in gene expression and thermal plasticity between populations, with up to 31% of genes being differentially expressed. Reduced thermal plasticity, temperature-dependent gene misexpression and the disruption of co-expression networks in hybrids point towards a role of regulatory incompatibilities in local adaptation, particularly under colder temperatures. Chromosomal inversions show an accumulation of regulatory incompatibilities but are not consistently enriched for differentially expressed genes. Together, these results suggest that gene regulation can diverge substantially among populations despite gene flow, partly due to the accumulation of temperature-dependent regulatory incompatibilities within inversions. MethodsTo improve the contiguity of the Atlantic silverside reference genome (Tigano et al. 2021), we anchored the genome assembly to a RAD-seq based female Georgia linkage map (Akopyan et al. 2022). However, the linkage map used for this assembly differs marginally from the one in Akopyan et al. (2022), as it was constructed from genome-aligned RAD-seq data rather than de novo assembled loci. Overall, both linkage maps are highly comparable. The linkage map was constructed as described in Akopyan et al. (2022) with slight modifications outlined here: In brief, we used ddRAD sequencing (Peterson et al. 2012) to identify and genotype single nucleotide polymorphisms (SNPs) for linkage map construction from ​​568 individuals across five families, including the two founders, 138 F1 offspring, six additional F1 siblings and their 282 F2 offspring. Reads were processed in Stacks v1.48 (Catchen et al. 2013) with the module process_radtags to discard low-quality reads and reads with ambiguous barcodes or RAD cut-sites. The remaining reads were demultiplexed and aligned to the Menidia menidia reference genome v1 (Tigano et al. 2021) using Bowtie2 v2.2.9 (-very-sensitive). We only retained those reads that were uniquely mapped to the reference genome and extracted RAD loci with: i) minimum read depth of three, ii) minimum mapping quality of 10, iii) and maximum clipped proportion of 0.15. Variant calling was also performed with pstacks using the default SNP model with a genotype likelihood ratio test critical value (α) of 0.05. We built a catalog of all loci using parents (and grandparents for the F2 generation) with cstacks, and matched progeny against the catalog using sstacks. The populations module was used to filter variants to retain only the first SNP per locus and generated a VCF file for each of the two F1 families, and one for the F2 generation including the three intercross families. We constructed one female linkage map for each of our three crosses (F1 GAxNY, F1 NYxGA, F2) using Lep-MAP3 (Rastas 2017). In brief, offspring genotypes were called by accounting genotype information of parents (and grandparents in F2 family) with the ParentCall2 module, markers with high segregation distortion were removed using the distortionLod=1 option in SeparateChromosomes2, separated markers were merged into linkage groups with a logarithm of odds (LOD) score limit of 20 and minimum linkage group size of 10 markers using markers informative in females only, and we used the OrderMarkers2 module to compute genetic distances in centimorgan (i.e., recombination rates) between all adjacent markers for each linkage group using the default Haldane’s mapping function. We used maternally informative markers to construct the F1 maps, and both maternally and dually informative markers to construct the F2 map. We used the female F1 linkage map for the Georgia population to anchor and order the Atlantic silverside reference genome v1 scaffolds into chromosomes using AllMaps (Tang et al. 2015). Chromosomes were renamed based on synteny with the medaka genome. Furthermore, we converted the coordinates of RAD loci for all linkage maps from scaffold to anchored chromosome coordinates using CrossMap v.0.1.4 (Zhao et al. 2014) and identified inversions between NY and GA by comparing the F1 NY-linkage map and F2 linkage map to the GA-anchored reference genome (see (Akopyan et al. 2022) for details). Lastly, we re-annotated the anchored genome assembly (M. menidia reference genome v2) following the pipeline...

Abstract The timing of early cellular evolution, from the divergence of Archaea and Bacteria to the origin of eukaryotes, is poorly constrained. The ATP synthase complex is thought to have originated prior to the Last Universal Common Ancestor (LUCA) and analyses of ATP synthase genes, together with ribosomes, have played a key role in inferring and rooting the tree of life. We reconstruct the evolutionary history of ATP synthases using an expanded taxon sampling set and develop a phylogenetic cross-bracing approach, constraining equivalent speciation nodes to be contemporaneous, based on the phylogenetic imprint of endosymbioses and ancient gene duplications. This approach results in a highly resolved, dated species tree and establishes an absolute timeline for ATP synthase evolution. Our analyses show that the divergence of ATP synthase into F- and A/V-type lineages was a very early event in cellular evolution dating back to more than 4Ga, potentially predating the diversification of Archaea and Bacteria. Our cross-braced, dated tree of life also provides insight into more recent evolutionary transitions including eukaryogenesis, showing that the eukaryotic nuclear and mitochondrial lineages diverged from their closest archaeal (2.67-2.19Ga) and bacterial (2.58-2.12Ga) relatives at approximately the same time, with a slightly longer nuclear stem-lineage. Repository Contents 1_100Eukaryote_genomes.tar.gz: includes all protein sequence files for the 100 Eukaryotes sampled in this study. 2_Phylogenies.tar.gz: includes all files used for phylogenetic analyses. Folders are organized as follows:

1_ATPsynthase_gene_trees: this folder contains all sequence, alignment, and tree files for the ATP synthase gene trees. Files are organized as follows and are associated with the corresponding parts of the manuscript: Figure 3, Figure 5B, Supplementary Figures 5-10, Supplementary Figures 18-19

Folder '1_sequences' includes all unaligned fasta sequence files for each ATP synthase gene tree (see Methods) Folder '2_alignments' includes all alignments generated using MAFFT L-INS-i (subdirectory: 1_untrimmed) and trimmed with BMGE (subdirectory: 2_trimmed) Folder '3_treefiles' includes all IQ-TREE2 output files for all ATP synthase gene phylogenies. Any files with suffix *taxa.treefile contain the full taxonomic string for each accession. Folder '4_pdfs' includes PDF files for each ATP synthase gene tree 2_Eukaryotic_subsets: this folder contains all sequence, alignment, and tree files for ATP synthase Eukaryotic subset gene trees. Files are organized as follows and are associated with the corresponding parts of the manuscript: Supplementary Figure 11

Folder '1_sequences' includes all unaligned fasta sequence files for the eukaryotic subsets. Folder '2_alignments' includes all alignments generated using MAFFT L-INS-i (subdirectory: 1_untrimmed) and trimmed with BMGE (subdirectory: 2_trimmed). Folder '3_treefiles' includes all Bayesian trees inferred for eukaryotic subsets. Folder '4_pdfs' includes PDF files for each eukaryotic subset tree 3_21eLife_concatenated_species_tree: this folder contains all sequence, alignment, and tree files for the single gene tree and concatenated phylogeny analyses (inferred using 21 single-copy marker genes, see Methods). Files are organized as follows and are associated with the following parts of the manuscript: Figure 1, Supplementary Figure 20

Folder '1_inspection_start' corresponds to the initial manual inspection of the single gene trees and includes the following subdirectories:

Folder '1_sequences' includes all protein sequence fasta files corresponding to the 27 original single-copy marker genes Folder '2_alignments' includes all alignment files generated using MAFFT L-INS-i (subdirectory: 1_untrimmed) and trimmed with BMGE (subdirectory: 2_untrimmed) Folder '3_treefiles' includes all IQ-TREE2 output files for all phylogenies (27 single-copy marker genes) Folder '4_pdfs' includes PDF files for each single gene tree Folder '2_inspection_final' corresponds to the final manual inspection of the single gene trees and includes the following subdirectories:

Folder '1_sequences' includes all protein sequence fasta files corresponding to the final 21 single-copy marker genes Folder '2_alignments' includes all alignment files generated using MAFFT L-INS-i (subdirectory: 1_untrimmed) and trimmed with BMGE (subdirectory: 2_untrimmed) Folder '3_treefiles' includes all IQ-TREE2 output files for all phylogenies (21 single-copy marker genes) Folder '4_pdfs' includes PDF files for each single gene tree Folder '3_concatenated_phylogeny' contains concatenated alignment generated from the final 21 single-copy marker gene alignments

Folder '1_alignment' includes the concatenated alignment generated from the 21 trimmed alignments from the final inspection Folder '2_treefiles' includes all IQ-TREE2 output files for trees inferred using the two different models (subdirectories: LG+C20+R+F and LG+C60+R+F) Folder '4_Eukaryote_only_phylogeny' contains sequence, alignment, and tree files for 21 single-copy marker genes used to infer a Eukaryote-only phylogeny. Folder is organized as follows and files correspond to Supplementary Figure 3:

Folder '1_sequences' includes all protein sequence fasta files corresponding to the 21 single-copy marker genes with only Eukaryotes Folder '2_alignments' includes all alignment files generated using MAFFT L-INS-i (subdirectory: 1_untrimmed) and trimmed with BMGE (subdirectory: 2_untrimmed) Folder '3_concatenated_phylogeny' includes concatenated alignment generated from 21 single-copy markers with only Eukaryotes (subdirectory: 1_alignment) and all IQ-TREE2 output files for the concatenated phylogeny (subdirectory: 2_treefiles) Folder '4_pdfs' includes PDF files for the concatenated Eukaryote tree 4_Ribosomal_species_tree: this folder contains all sequence, alignment, and tree files for the single gene tree and concatenated phylogeny analyses (inferred using 12 ribosomal marker genes, see Methods). Files are organized as follows and are associated with the corresponding parts of the manuscript: Figure 5A, Figure 5C, Supplementary Figures 12-16, Supplementary Figure 21

Folder '1_sequences' includes all protein sequence fasta files for the original 15 ribosomal proteins. Sequence sets include the best-hit Archaea and Bacteria, and nuclear, mitochondrial, and plastid eukaryotic homologs Folder '2_alignments' includes all alignment files generated using MAFFT L-INS-i (subdirectory: 1_untrimmed) and trimmed with TRIMAL (gappy-out) (subdirectory: 2_trimmed) Folder '3_treefiles' includes all original FastTree tree files, tree files with highlighted sequences to remove (*blue-to-rem = eukaryotic nuclear homolog only; *colored-to-rem = eukaryotic nuclear, mitochondrial, and plastid homologs). PDFs of each marker gene tree are also included that depict highlighting of sequences to keep and/or remove. Folder '4_concatenated_phylogeny' contains concatenated alignment generated from the final 12 ribosomal marker genes

Folder '1_alignment' includes the concatenated alignment generated with 12 ribosomal marker proteins in MAFFT L-INS-i and trimmed with TRIMAL (gappy-out) Folder '2_phylogeny' includes all IQ-TREE2 output files for the species tree inferred using the LG+C60+R+F model 5_Dating_analysis: includes all Mcmcdate output files for the dating analyses (species tree and ATP synthase gene tree, see Methods).

Folder '0_Starting_species_phylogenies' includes the treefiles (with and without taxonomic string) for the Edited1 and Edited2 topologies that were used in the dating analyses (see Methods). Folder '1_Edited1_dating' includes all dated tree files and monitor files for braced and unbraced analyses of the Edited1 species tree topology. Data corresponds to Supplementary Figure 12, Supplementary Figure 14-15 Folder '2_Edited2_dating' includes all dated tree files and monitor files for braced and unbraced analyses of the Edited2 (focal) species tree topology. Data corresponds to Figure 5A, Figure 5C, Supplementary Figure 13, Supplementary Figure 16. Folder '3_ATP_synthase_dating' includes all dated tree files and monitor files for braced and unbraced analyses of the ATP synthase gene tree. Data corresponds to Figure 5B, Supplementary Figures 18-19. 3_Scripts.tar.gz: includes all workflows and scripts used for phylogenetic analyses.

1_workflows: includes bash workflows for phylogenetic analyses (details on software versions are included in each workflow summary):

Workflow_ATPsynthase_gene_trees.sh: generation of the ATP synthase phylogenies Workflow_21eLife_marker_phylogeny.sh: inferring the 21 marker-gene species tree Workflow_Ribosomal_species_tree.sh: inferring the 12 ribosomal marker-gene species tree Workflow_Database_annotations.sh: workflow for gene annotation for 800 sampled Archaea, Bacteria, and Eukaryota 2_R_scripts: includes R scripts used for the Eukaryote sequence contamination screening (Figure 1, Figure 2, Supplementary Figure 2, Supplementary Figures 4, 5, 8-10), presence-absence analyses (Figure 1, Figure 2, Supplementary Figure 2), and plotting tree figures (Supplementary Figures 4-10). Input mapping files and R output files are included.

Folder '1_Euk_contamination_screen' contains workflow 'Eukaryote_contamination_screen.Rmd' used to inspect Eukaryotic ATP synthase sequences for bacterial contamination Folder '2_Presence_absence' includes sub-directories:

Folder '1_Species_tree' includes the treefile(s) used for ordering the plots in Figure 1 and Supplementary Figure 2 ('1_tree'), the taxonomic and COG mapping files and the list of putative contamination to remove ('2_input_files'), the raw count table for all 800 taxa ('3_Output_files'), R output plot(s) ('4_Plotting'), and the script to generate presence-absence plots 'Presence-absence.R'. Folder '2_Eukaryotes_only' includes organelle information, protein mapping files, taxonomic mapping

Vector polygon map data of mile markers from the state of California containing 157892 features.

Mile marker GIS data consists of points along a linear feature, such as roads or railways. They serve as reference points to measure distances along these features. Mile markers are often labeled with numbers indicating their distance from a starting point, such as a highway's origin or a railway station.

These markers are invaluable for navigation, route planning, emergency response, and data collection. For example, they help drivers and emergency services identify their location precisely on a road. In transportation planning, mile markers aid in analyzing traffic patterns, determining optimal routes, and estimating travel times. Additionally, they facilitate maintenance activities by providing clear reference points for inspecting and repairing infrastructure.

This data is available for viewing and sharing as a map in a Koordinates map viewer. This data is also available for export to DWG for CAD, PDF, KML, CSV, and GIS data formats, including Shapefile, MapInfo, and Geodatabase.

Recent studies have begun to elucidate the genetic basis for phenotypic traits in salmonid species, but many questions remain before these candidate genes can be directly incorporated into conservation management. In Chinook Salmon (Oncorhynchus tshawytscha), a region of major effect for migration timing has been discovered that harbors two adjacent candidate genes (greb1L, rock1), but there has been limited work to examine the association between these genes and migratory phenotypes at the individual, compared to the population, level. To provide a more thorough test of individual phenotypic association within lineages of Chinook Salmon, 33 candidate markers were developed across a 220 Kb region on chromosome 28 previously associated with migration timing. Candidate and neutral markers were genotyped in individuals from representative collections that exhibit phenotypic variation in timing of arrival to spawning grounds from each of three lineages of Chinook Salmon. Association tests confirmed the majority of markers on chromosome 28 were significantly associated with arrival timing and the strongest association was consistently observed for markers within the rock1 gene and the intergenic region between greb1L and rock1. Candidate markers alone explained a wide range of phenotypic variation for Lower Columbia and Interior ocean-type lineages (29% and 78%, respectively), but less for the Interior stream-type lineage (5%). Individuals that were heterozygous at markers within or upstream of rock1 had phenotypes that suggested a pattern of dominant inheritance for early arrival across populations. Finally, previously published fitness estimates from the Interior stream-type lineage enabled tests of association with arrival timing and two candidate markers, which revealed that fish with homozygous mature genotypes had slightly higher fitness than fish with premature genotypes, while heterozygous fish were intermediate. Overall, these results provide additional information for individual-level genetic variation associated with arrival timing that may assist with conservation management of this species.

This dataset consists primarily of bioinformatic scripts written in python for processing comparative genomics projects along with a readme file with script usage examples. The dataset also includes excel files with the information on the genome accessions and taxonomy for sequences used for reference, 16S RNA statistics, the ratio of shared genes for bacterial comparison, Basic Local Alignment Search Tool (BLAST) results, and details of significantly shared and unshared Gene Ontology (GO) terms for bacterial genomes. This dataset supports the publication: Sevigny, J. L., Rothenheber, D., Diaz, K. S., Zhang, Y., Agustsson, K., Bergeron, R. D., & Thomas, W. K. (2019). Marker genes as predictors of shared genomic function. BMC Genomics, 20(1). doi:10.1186/s12864-019-5641-1.