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MarFERReT: an open-source, version-controlled reference library of marine microbial eukaryote functional genes
<p>Metatranscriptomics generates large volumes of sequence data about transcribed genes in natural environments. Taxonomic annotation of these datasets depends on availability of curated reference sequences. For marine microbial eukaryotes, current reference libraries are limited by gaps in sequenced organism diversity and barriers to updating libraries with new sequence data, resulting in taxonomic annotation of only about half of eukaryotic environmental transcripts. Here, we introduce version 1.0 of the Marine Functional EukaRyotic Reference Taxa (MarFERReT), an updated marine microbial eukaryotic sequence library with a version-controlled framework designed for taxonomic annotation of eukaryotic metatranscriptomes. We gathered 902 marine eukaryote genomes and transcriptomes from multiple sources and assessed these candidate entries for sequence quality and cross-contamination issues, selecting 800 validated entries for inclusion in the library. MarFERReT v1 contains reference sequences from 800 marine eukaryotic genomes and transcriptomes, covering 453 species- and strain-level taxa, totaling nearly 28 million protein sequences with associated NCBI and PR2 Taxonomy identifiers and Pfam functional annotations. An accompanying MarFERReT project repository hosts containerized build scripts, documentation on installation and use case examples, and information on new versions of MarFERReT.<br><br>MarFERReT is linked to a code repository hosting containerized build scripts, documentation on installation and use case examples, and information on new versions of MarFERReT here: <a href="https://github.com/armbrustlab/marferret">https://github.com/armbrustlab/marferret</a></p> <p>The raw source data for the 902 candidate entries considered for MarFERReT v1.1.1, including the 800 accepted entries, are available for download from their respective online locations. The source URL for each of the entries is listed here in MarFERReT.v1.1.1.entry_curation.csv, and detailed instructions and code for downloading the raw sequence data from source are available in the MarFERReT code repository (<a href="https://github.com/armbrustlab/marferret/blob/main/docs/process_clean_marmicrodb.log.sh">link</a>). </p> <p>This repository release contains MarFERReT database files from the v1.1.1 MarFERReT release using the following MarFERReT library build scripts: <strong>assemble_marferret.sh</strong>, <strong>pfam_annotate.sh</strong>, and <strong>build_diamond_db.sh</strong><br><br>The following MarFERReT data products are available in this repository:</p> <p><strong>MarFERReT.v1.1.1.metadata.csv</strong><br>This CSV file contains descriptors of each of the 902 database entries, including data source, taxonomy, and sequence descriptors. Data fields are as follows:</p> <ol> <li><strong>entry_id</strong>: Unique MarFERReT sequence entry identifier.</li> <li><strong>accepted: </strong>Acceptance into the final MarFERReT build (Y/N). The Y/N values can be adjusted to customize the final build output according to user-specific needs.</li> <li><strong>marferret_name</strong>: A human and machine friendly string derived from the NCBI Taxonomy organism name; maintaining strain-level designation wherever possible.</li> <li><strong>tax_id</strong>: The NCBI Taxonomy ID (taxID).</li> <li><strong>pr2_accession</strong>: Best-matching PR2 accession ID associated with entry</li> <li><strong>pr2_rank</strong>: The lowest shared rank between the entry and the pr2_accession</li> <li><strong>pr2_taxonomy</strong>: PR2 Taxonomy classification scheme of the pr2_accession</li> <li><strong>data_type</strong>: Type of sequence data; transcriptome shotgun assemblies (TSA), gene models from assembled genomes (genome), and single-cell amplified genomes (SAG) or transcriptomes (SAT).</li> <li><strong>data_source</strong>: Online location of sequence data; the Zenodo data repository (<a href="../">Zenodo</a>), the datadryad.org repository (<a href="http://datadryad.org/">datadryad.org</a>), MMETSP re-assemblies on Zenodo (MMETSP)17, NCBI GenBank (<a href="https://www.ncbi.nlm.nih.gov/genbank/">NCBI</a>), JGI Phycocosm (<a href="https://phycocosm.jgi.doe.gov/phycocosm/home">JGI-Phycocosm</a>), the TARA Oceans portal on Genoscope (<a href="http://www.genoscope.cns.fr/tara/">TARA</a>), or entries from the Roscoff Culture Collection through the METdb database repository (<a href="https://metdb.sb-roscoff.fr/metdb/">METdb</a>).</li> <li><strong>source_link</strong>: URL where the original sequence data and/or metadata was collected.</li> <li><strong>pub_year</strong>: Year of data release or publication of linked reference.</li> <li><strong>ref_link</strong>: Pubmed URL directs to the published reference for entry, if available.</li> <li><strong>ref_doi</strong>: DOI of entry data from source, if available.</li> <li><strong>source_filename</strong>: Name of the original sequence file name from the data source.</li> <li><strong>seq_type</strong>: Entry sequence data retrieved in nucleotide (nt) or amino acid (aa) alphabets.</li> <li><strong>n_seqs_raw</strong>: Number of sequences in the original sequence file.</li> <li><strong>source_name:</strong> Full organism name from entry source</li> <li><strong>original_taxID</strong>: Original NCBI taxID from entry data source metadata, if available</li> <li><strong>alias:</strong> Additional identifiers for the entry, if available</li> </ol> <p><br><strong>MarFERReT.v1.1.1.curation.csv</strong><br>This CSV file contains curation and quality-control information on the 902 candidate entries considered for incorporation into MarFERReT v1, including curated NCBI Taxonomy IDs and entry validation statistics. Data fields are as follows:</p> <ol> <li><strong>entry_id:</strong> Unique MarFERReT sequence entry identifier</li> <li><strong>marferret_name: </strong>Organism name in human and machine friendly format, including additional NCBI taxonomy strain identifiers if available.</li> <li><strong>tax_id</strong>: Verified NCBI taxID used in MarFERReT</li> <li><strong>taxID_status</strong>: Status of the final NCBI taxID (Assigned, Updated, or Unchanged)</li> <li><strong>taxID_notes</strong>: Notes on the original_taxID</li> <li><strong>n_seqs_raw</strong>: Number of sequences in the original sequence file</li> <li><strong>n_pfams</strong>: Number of Pfam domains identified in protein sequences</li> <li><strong>qc_flag</strong>: Early validation quality control flags for the following: LOW_SEQS; less than 1,200 raw sequences; LOW_PFAMS; less than 500 Pfam domain annotations.</li> <li><strong>flag_Lasek</strong>: Flag notes from Lasek-Nesselquist and Johnson (2019); contains the flag 'FLAG_LASEK' indicating ciliate samples reported as contaminated in this study.</li> <li><strong>VV_contam_pct</strong>: Estimated contamination reported for MMETSP entries in Van Vlierberghe et al., (2021).</li> <li><strong>flag_VanVlierberghe: </strong>Flag for a high level of estimated contamination, from 'flag_VanVlierberghe' values over 50%: FLAG_VV.</li> <li><strong>rp63_npfams</strong>: Number of ribosomal protein Pfam domains out of 63 total.</li> <li><strong>rp63_contam_pct</strong>: Percent of total ribosomal protein sequences with an inferred taxonomic identity in any lineage other than the recorded identity, as described in the Technical Validation section from analysis of 63 Pfam ribosomal protein domains.</li> <li><strong>flag_rp63</strong>: Flag for a high level of estimated contamination, from 'rp63_contam_pct' values over 50%: FLAG_RP63.</li> <li><strong>flag_sum: </strong>Count of the number of flag columns (`qc_flag`, `flag_Lasek`, `flag_VanVlierberghe`, and `flag_rp63`). All entries with one or more flag are nominally rejected ('accepted' = N); entries without any flags are validated and accepted ('accepted' = Y).</li> <li><strong>accepted: </strong>Acceptance into the final MarFERReT build (Y or N).</li> </ol> <p> </p> <p><strong>MarFERReT.v1.1.1.proteins.faa.gz</strong><br>This Gzip-compressed FASTA file contains the 27,951,013 final translated and clustered protein sequences for all 800 accepted MarFERReT entries. The sequence defline contains the unique identifier for the sequence and its reference (mftX, where 'X' is a ten-digit integer value). </p> <p> </p> <p><strong>MarFERReT.v1.1.1.taxonomies.tab.gz</strong><br>This Gzip-compressed tab-separated file is formatted for interoperability with the DIAMOND protein alignment tool commonly used for downstream analyses and contains some columns without any data. Each row contains an entry for one of the MarFERReT protein sequences in MarFERReT.v1.proteins.faa.gz. Note that 'accession.version' and 'taxid' are populated columns while 'accession' and 'gi' have NA values; the latter columns are required for back-compatibility as input for the DIAMOND alignment software and LCA analysis. </p> <p>The columns in this file contain the following information:</p> <ol> <li><strong>accession</strong>: (NA)</li> <li><strong>accession.version</strong>: The unique MarFERReT sequence identifier ('mftX').</li> <li><strong>taxid</strong>: The NCBI Taxonomy ID associated with this reference sequence.</li> <li><strong>gi</strong>: (NA).</li> </ol> <p> </p> <p><strong>MarFERReT.v1.1.1.proteins_info.tab.gz</strong><br>This Gzip-compressed tab-separated file contains a row for each final MarFERReT protein sequence with the following columns:</p> <ol> <li><strong>aa_id</strong>: the unique identifier for each MarFERReT protein sequence.</li> <li><strong>entry_id</strong>: The unique numeric identifier for each MarFERReT entry.</li> <li><strong>source_defline</strong>: The original, unformatted sequence identifier</li> </ol> <p> </p> <p><strong>MarFERReT.v1.1.1.best_pfam_annotations.csv.gz<br></strong>This Gzip-compressed CSV file contains the best-scoring Pfam annotation for intra-species clustered protein sequences from the 800 validated MarFERReT entries; derived from the hmmsearch annotations against Pfam 34.0 functional domains. This file contains the following fields:</p> <ol> <li><strong>aa_id</strong>: The unique MarFERReT protein sequence ID ('mftX').</li> <li><strong>pfam_name</strong>: The shorthand Pfam protein family name.</li> <li><strong>pfam_id</strong>: The Pfam identifier.</li> <li><strong>pfam_eval</strong>: hmm profile match e-value score</li> <li><strong>pfam_score:</strong> hmm profile match bitscore</li> </ol> <p><br><strong>MarFERReT.v1.1.1.dmnd</strong><br>This binary file is the indexed database of the MarFERReT protein library with embedded NCBI taxonomic information generated by the DIAMOND makedb tool using the build_diamond_db.sh script from the MarFERReT /scripts/ library. This can be used as the reference DIAMOND database for annotating environment sequences from eukaryotic metatranscriptomes. <br><br></p>
Supplemental data files: Beyond the reference: gene expression variation and transcriptional response to RNAi in C. elegans
<p>This dataset holds all non-GEO-hosted supplemental data files for manuscript "Beyond the reference: gene expression variation and transcriptional response to RNAi in <em>C. elegans</em>". Please see the linked preprint/publication for full details.</p> <p>The PDF _guide_to_datafiles.pdf gives details on the format and content of each of the included files.</p>
A comprehensive evaluation of binning methods to recover human gut microbial species from a non-redundant reference gene catalog - Supporting Data
<p><strong>Description </strong></p> <p>The following files are available : </p> <ul> <li>Simulated non-redundant Gene Catalog (SGC) composed of 128267 genes;</li> <li>Gene abundance profiles across 40 samples: raw read counts, gene length normalized base counts, depth file computed by the jgi_summarize_bam_contig_depth script provided by MetaBAT;</li> <li>Gold Standard (GS) and Gold Standard Single Assignment (GS_SA) binning results;</li> <li>Binning results obtained on the SGC with nine binning methods: MSPminer, MGS-canopy, DAS Tool, MaxBin2, MetaBAT2, SolidBin, CONCOCT, COCACOLA and MyCC.</li> </ul> <p><strong>License</strong></p> <p>These files are licensed under a <a href="http://creativecommons.org/licenses/by/4.0/">Creative Commons Attribution 4.0 International License</a>.</p>
Figure 3 in Validation of reference genes for quantitative expression analysis by qPCR in various tissues of date mussel (Lithophaga lithophaga)
Figure 3. Pairwise variation (V-value) of candidate reference genes in date mussel (L. lithophaga) using geNorm.
Neurogenomic divergence during speciation by reinforcement of mating behaviors in chorus frogs (Pseudacris) – De novo reference transcriptome: Assemblerd contigs and gene annotations
<p>Assembled contigs (Trinity) and gene annotations (Trinotate) of a reference transcriptome for the Upland Chorus Frog, <em>Pseudacris feriarum</em>. Data to assemble the contigs were obtained by sequencing four tissue types: Brain, eyes, testis, and somatic (liver/heart/lung/skin/muscle). Raw reads are stored in the NCBI-SRA database (BioProject PRJNA723357).</p>
Power of Bayesian and heuristic tests to detect cross-species introgression with reference to gene flow in the Tamias quadrivittatus group of North American chipmunks
<p>In the past two decades genomic data have been widely used to detect historical gene flow between species in a variety of plants and animals. The Tamias quadrivittatus group of North America chipmunks, which originated through a series of rapid speciation events, are known to undergo massive amounts of mitochondrial introgression. Yet in a recent analysis of targeted nuclear loci from the group, no evidence for cross-species introgression was detected, indicating widespread cytonuclear discordance. The study used the heuristic method HyDe to detect gene flow, which may suffer from low power. Here we use the Bayesian method implemented in the program bpp to reanalyze these data. We develop a Bayesian test of introgression, calculating the Bayes factor via the Savage-Dickey density ratio using the Markov chain Monte Carlo (MCMC) sample under the model of introgression. We take a stepwise approach to constructing an introgression model by adding introgression events onto a well-supported binary species tree. The analysis detected robust evidence for multiple ancient introgression events affecting the nuclear genome, with introgression probabilities reaching 63%. We estimate population parameters and highlight the fact that species divergence times may be seriously underestimated if ancient cross-species gene flow is ignored in the analysis. We examine the assumptions and performance of HyDe, and demonstrate that it lacks power if gene flow occurs between sister lineages or if the mode of gene flow does not match the assumed hybrid speciation model with symmetrical population sizes. Our analyses highlight the power of likelihood-based inference of cross-species gene flow using genomic sequence data.</p>
mTAGs: taxonomic profiling using degenerate consensus reference sequences of ribosomal RNA gene
<p>mTAGs is a tool for the taxonomic profiling of metagenomes. It detects sequencing reads belonging to the small subunit of the ribosomal RNA (SSU-rRNA) gene and annotates them through the alignment to full-length degenerate consensus SSU-rRNA reference sequences. The tool is capable of processing single-end and pair-end metagenomic reads, takes advantage of the information contained in any region of the SSU-rRNA gene and provides relative abundance profiles at multiple taxonomic ranks (Domain, Phylum, Class, Order, Family, Genus and OTUs defined at a 97% sequence identity cutoff).</p>
Power of Bayesian and heuristic tests to detect cross-species introgression with reference to gene flow in the Tamias quadrivittatus group of North American chipmunks
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Neurogenomic divergence during speciation by reinforcement of mating behaviors in chorus frogs (Pseudacris) – De novo reference transcriptome raw data, contigs and gene annotations
<p>RNA-Seq raw data used in the assembly and annotation of a reference transcriptome for the Upland Chorus Frog, <em>Pseudacris feriarum</em>. Raw data were obtained by sequencing of four tissue types: Brain, eyes, testis, and somatic. Assembled contigs (Trinity) and gene annotations (Trinotate) are also provided.</p>
Reference genes for quantitative Arabidopsis single molecule RNA fluorescence in situ hybridization
Abstract Subcellular mRNA quantities and spatial distributions are fundamental for driving gene regulatory programmes. Single molecule RNA fluorescence in situ hybridization (smFISH) uses fluorescent probes to label individual mRNA molecules, thereby facilitating both localization and quantitative studies. Validated reference mRNAs function as positive controls and are required for calibration. Here we present selection criteria for the first set of Arabidopsis smFISH reference genes. Following sequence and transcript data assessments, four mRNA probe sets were selected for imaging. Transcript counts per cell, correlations with cell size, and corrected fluorescence intensities were all calculated for comparison. In addition to validating reference probe sets, we present sample preparation steps that can retain green fluorescent protein fluorescence, thereby providing a method for simultaneous RNA and protein detection. In summary, our reference gene analyses, modified protocol, and simplified quantification method together provide a firm foundation for future quantitative single molecule RNA studies in Arabidopsis root apical meristem cells.
Figure 1 in Validation of reference genes for quantitative expression analysis by qPCR in various tissues of date mussel (Lithophaga lithophaga)
Figure 1. Distribution of Cq values of candidate reference genes in date mussel (L. lithophaga).
Figure 2 in Validation of reference genes for quantitative expression analysis by qPCR in various tissues of date mussel (Lithophaga lithophaga)
Figure 2. Average expression stability (M-value) of reference genes evaluated by geNorm.
Gene Ontology Data Reference
<p>Referential manifest of GO data releases.</p>
Massive Chinese domestic pigs provide missing sequences in reference genome and reveal non-coding sequence variations regulating gene expression across Eurasian boars
<p>This dataset contains novel sequences in Chinese domestic pigs but is absent in Sscrofa 11.1 reference genome. The detailed information for each file is recorded in the README file.</p>
Gene prediction for: A reference genome for ecological restoration of the sunflower sea star, Pycnopodia helianthoides
<div> <div> <div> <div>Wildlife diseases, such as the sea star wasting (SSW) epizootic that outbroke in the mid-2010s, appear to be associated with acute and/or chronic abiotic environmental change; dissociating the effects of different drivers can be difficult. The sunflower sea star,<em> Pycnopodia helianthoides</em>, was the species most severely impacted during the SSW outbreak, which overlapped with periods of anomalous atmospheric and oceanographic conditions, and there is not yet a consensus on the cause(s). Genomic data may reveal underlying molecular signatures that implicate a subset of factors and, thus, clarify past events while also setting the scene for effective restoration efforts. To advance this goal, we used Pacific Biosciences HiFi long sequencing reads and Dovetail Omni-C proximity reads to generate a highly contiguous genome assembly that was then annotated using RNA-seq-informed gene prediction. The genome assembly is 484 Mb long, with contig N50 of 1.9 Mb, scaffold N50 of 21.8 Mb, BUSCO completeness score 96.1%, and 22 major scaffolds consistent with prior evidence that sea star genomes comprise 22 autosomes. These statistics generally fall between those of other recently assembled chromosome-scale assemblies for two species in the distantly related asteroid genus <em>Pisaster</em>. These novel genomic resources for <em>Pycnopodia helianthoides</em> will underwrite population genomic, comparative genomic, and phylogenomic analyses — as well as their integration across scales — of SSW and environmental stressors. This data resource contains the files associated with gene prediction.</div> </div> </div> </div>
Gene prediction for: A reference genome for ecological restoration of the sunflower sea star, Pycnopodia helianthoides
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Reference genes for quantitative Arabidopsis single molecule RNA fluorescence in situ hybridization
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Identification and selection of optimal reference genes for qPCR-based gene expression analysis in Fucus distichus under various abiotic stresses
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Data from: Estimating the molecular evolutionary rates of mitochondrial genes referring to Quaternary Ice Age events with inferred population expansions and dispersals in Japanese Apodemus
Background: Determining reliable evolutionary rates of molecular markers is essential in illustrating historical episodes with phylogenetic inferences. Although emerging evidence has suggested a high evolutionary rate for intraspecific genetic variation, it is unclear how long such high evolutionary rates persist because a recent calibration point is rarely available. Other than using fossil evidence, it is possible to estimate evolutionary rates by relying on the well-established temporal framework of the Quaternary glacial cycles that would likely have promoted both rapid expansion events and interisland dispersal events. Results: We examined mitochondrial cytochrome b (Cytb) and control region (CR) gene sequences in two Japanese wood mouse species, Apodemus argenteus and A. speciosus, of temperate origin and found signs of rapid expansion in the population from Hokkaido, the northern island of Japan. Assuming that global warming after the last glacial period 7–10 thousand years before present (kyr BP) was associated with the expansion, the evolutionary rates (sites per million years, myr) of Cytb and CR were estimated as 11–16% and 22–32%, respectively, for A. argenteus, and 12–17% and 17–24%, respectively, for A. speciosus. Additionally, the significant signature of rapid expansion detected in the mtDNA sequences of A. speciosus from the remaining southern main islands, Honshu, Shikoku, and Kyushu, provided an estimated Cytb evolutionary rate of 3.1%/site/myr under the assumption of a postglacial population expansion event long ago, most probably at 130 kyr BP. Bayesian analyses using the higher evolutionary rate of 11–17%/site/myr for Cytb supported the recent demographic or divergence events associated with the Last Glacial Maximum. However, the slower evolutionary rate of 3.1%/site/myr would be reasonable for several divergence events that were associated with glacial periods older than 130 kyr BP. Conclusions: The faster and slower evolutionary rates of Cytb can account for divergences associated with the last and earlier glacial maxima, respectively, in the phylogenetic inference of murine rodents. The elevated evolutionary rate seemed to decline within 100,000 years.
Data from: Chromosome-level reference genome assembly and gene editing of the dead-leaf butterfly Kallima inachus
<p class="p">The leaf resemblance of <i><span>Kallima</span></i> (Nymphalidae) butterflies<i> </i>is an important ecological adaptive mechanism that increases survival. However, the genetic mechanism underlying ecological adaptation remains unclear owing to a dearth of genomic information. Herein, we revealed the karyotype (n = 31) of the dead-leaf butterfly <i><span>Kallima inach</span></i><i><span>us</span></i>, assembled its high-quality chromosome-level reference genome (568.92 Mb; contig N50: 19.20 Mb), and identified its Z and candidate W chromosomes. To our knowledge, this is the first study to report on these aspects of this species. In the assembled genome, 15,309 protein-coding genes and 49.86% repeat elements were annotated. Phylogenetic analysis showed that <i><span>K. inachus</span></i> diverged from <i><span>Melitaea cinxia </span></i>(no leaf resemblance), both of which are in Nymphalinae, around 40 million years ago. Demographic analysis indicated that the effective population size of <i><span>K. inach</span></i><i><span>us</span></i> decreased during the last interglacial period in the Pleistocene. The wings of adults with the pigmentary gene <i><span>ebony</span></i> knocked out using CRISPR/Cas9 showed phenotypes in which the orange dorsal region and entire ventral surface darkened, suggesting its vital role in the ecological adaption of dead-leaf butterflies. Our results provide important genome resources for investigating the genetic mechanism underlying protective resemblance in dead-leaf butterflies and insights into the molecular basis of protective coloration.</p>
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