,CottonGen (https://www.cottongen.org) is a curated and integrated web-based relational database providing access to publicly available genomic, genetic and breeding data to enable basic, translational and applied research in cotton. Built using the open-source Tripal database infrastructure, CottonGen supersedes CottonDB and the Cotton Marker Database, which includes sequences, genetic and physical maps, genotypic and phenotypic markers and polymorphisms, quantitative trait loci (QTLs), pathogens, germplasm collections and trait evaluations, pedigrees, and relevant bibliographic citations, with enhanced tools for easier data sharing, mining, visualization, and data retrieval of cotton research data. CottonGen contains annotated whole genome sequences, unigenes from expressed sequence tags (ESTs), markers, trait loci, genetic maps, genes, taxonomy, germplasm, publications and communication resources for the cotton community. Annotated whole genome sequences of Gossypium raimondii are available with aligned genetic markers and transcripts. These whole genome data can be accessed through genome pages, search tools and GBrowse, a popular genome browser. Most of the published cotton genetic maps can be viewed and compared using CMap, a comparative map viewer, and are searchable via map search tools. Search tools also exist for markers, quantitative trait loci (QTLs), germplasm, publications and trait evaluation data. CottonGen also provides online analysis tools such as NCBI BLAST and Batch BLAST.,This project is funded/supported by Cotton Incorporated, the USDA-ARS Crop Germplasm Research Unit at College Station, TX, the Southern Association of Agricultural Experiment Station Directors, Bayer CropScience, Corteva/Agriscience, Dow/Phytogen, Monsanto, Washington State University, and NRSP10.,,

,The CottonGen CottonCyc Pathways Database, part of CottonGen, supports searching and browsing the following CottonCyc databases:,This Cyc database was constructed using PathwayTools version 20.0 using the gene models from the JGI v2.0 D5 genome assembly of Gossypium raimondii. There has been no manual curation of this Cyc database. Pathway predictions were made using PathwayTools and in-silico v2.1 annotations as provided by JGI.,This Cyc database was constructed using PathwayTools version 20.0 using the gene models from the CGP-BGI v1.0 AD1 genome assembly of Gossypium hirsutum. There has been no manual curation of this Cyc database. Pathway predictions were made using PathwayTools and in-silico v1.0 annotations as provided by CGP-BGI.,Search parameters include genes, proteins, RNAs, compounds, reactions, pathways, growth media, and BLAST search.,,

,Conserved syntenic regions among publicly available cotton genomes were analyzed by CottonGen and made available using the Tripal Synteny Viewer developed by the Fei Bioinformatics Lab from the Boyce Thomson Institute at Cornell University. Analysis was done using MCScanX (Wang et al. 2012) with default settings and blast files were made using blastp with an expectation value cutoff < 1e-10, maximum alignment of 5, and maximum scores of 5.,The synteny viewer displays all the conserved syntenic blocks between a selected chromosome of a genome and another genome in a circular and tabular layout. Once a block is chosen in the circular or tabular layout, all the genes in the block are shown in a graphic and tabular format. The gene names have hyperlinks to gene pages where detailed information of the gene can be accessed. The ‘synteny’ section of the gene page displays all the orthologs and the paralogs with link to the corresponding syntenic blocks or gene pages.,,

,Sequence Retrieval allows users to download nucleotide and protein sequences including chromosomes, scaffolds, genes, mRNAs, transcript coding sequences, protein, reftrans contigs and unigene contigs. For the sequences aligned to larger sequences, such as genes, mRNAs and transcript coding sequences, a numeric value specifying the number of upstream bases and downstream bases can be entered. A video and text tutorial are provided for additional help information.,,

,CottonGen offers BLAST with genome, transcriptome, peptide and marker sequence databases from Gossypium species. This can be done using nucleotide sequences or peptide sequences. BLAST functionality is similar to that on NCBI.,BLAST Programs:,

,Gramene is a curated, open-source, integrated data resource for comparative functional genomics in crops and model plant species.,

,These supplementary data tables represent gene annotations, gene ontology terms (GO), and gene coordinates from the boll weevil (Anthonomus grandis grandis, Agg) reference genome that appear in selectively swept regions and structural variations between the subspecies Agg and Agt (Anthonomus grandis thurberia). These genes were determined from two computer programs for selective analysis: RAiSD and PCAdapt. The gene tables are organized by shared/unique genes per sub-species and shared/unique genes residing on SVs, structural variations, per sub-species, which is based on >80% sequence identity. These data provide supplementary details for the enrichment tests that were carried out to associate gene function with subspecies biology, described in more detail in the manuscript: Pangenomics Links Boll Weevil Divergence with Ancient Mesoamerican Cotton Cultivation. This work is relevant to cotton managers throughout United States cotton growing regions, our international collaborators, as well as genome biologists investigating structural changes in genome architecture and their association with adaptation and trait plasticity.,

,This is a public resource highlighting efforts at ARS in developing small RNA genome information for the potato genome. Updates and progress are reported here.,,

,BIMS, the Breeding Information Management System, is a secure and comprehensive online breeding management system developed for the generic Tripal Database Platform which allows breeders to store, manage, archive and analyze their private breeding program. Breeders can load data in templates provided as well as output from the Field Book App, an android app for collecting phenotype data. In addition to the private breeders BIMS, users without accounts can also view the publicly available breeding data. The fully developed version will allow users to:,,

,On 19 June 2014, we conducted a two-tiered search (through that date) on the Web of Science Core Collection, CAB International, MEDLINE, Biological Abstracts, FSTA (Food Science and Technology Abstracts), and Zoological Record databases, using the ISI Web of Science search tool. We located 239,571 unique publications with the search terms: cotton OR Gossypium. A search of these records using the term “cover crop” resulted in 424 publications, composed of refereed articles, conference proceedings, research reports, and bulletins. With examination of these 424 eligible publications, 320 were excluded because they met our exclusion criteria: means for cover crop or no-cover crop treatments were not included, cotton yield or weed growth were not reported, article was a duplicate, article did not contain primary data (review or book), or they were not obtainable using interlibrary loan services (five articles). We did not include intercropping (cover crops grown simultaneously with cotton) studies, nor did we include studies that used weed count as the response variable. For the weed biomass effect size (ES), if an experiment included both weed and weed-free fallow no-cover-crop controls, we used the weed fallow no-cover-crop control in our analysis. If an experiment included herbicides applied over all treatments in season, we excluded the weed biomass ES but included the cotton biomass ES. We identified 104 articles that met our screening criteria (a full citation list and details of primary studies are provided in the supplemental material). Papers spanned 48 yr and were in English and Portuguese languages.,Treatment means and number of replications (sample sizes) were collected for each study. For publications reporting means for more than one no-cover-crop (control) treatment in a nonfactorial experiment, we used the no-cover-crop control that most closely approximated the cover crop treatment. If replications were given as a range, we used the smallest value. For studies that did not report number of replications, we used n = 1 unless LSD or SEs were provided, in which case we used n = 2. If data were provided in graphical form, means were extracted using WebPlotDigitizer (Rogatgi, 2011).,Multiple treatment combinations from one article were treated as independent studies (also referred to as trials or paired observations in the meta-analysis literature) and represented individual units in the meta-analysis. For example, Ashworth et al. (2018) and Li et al. (2013) examined the effects of two cover crop species over 3 yr, resulting in six studies from that article for lint yield ES. Vasilakoglou et al. (2011) studied control of three weed genera by four varieties of one cover crop species, resulting in 12 studies for the weed control ES. Although, the use of multiple studies from one publication has the disadvantage of increasing the dependence among studies that are assumed to be independent (Gurevitch and Hedges, 1999), the greater number of studies maximizes the meta-analysis’ statistical power (Lajeuness and Forbes, 2003). This approach has been used often in agricultural and plant biology meta-analyses (Mayerhofer et al., 2013; McGrath and Lobell, 2013; Ferraretto and Shaver, 2015). Therefore, we derived 1117 studies from 104 articles. As in prior meta-analyses (Ashworth et al., 2018; Mayerhofer et al., 2013), we used the final time point in the meta-analysis for studies that included data for multiple time points in one season. One exception was weed control, as an article used in this meta-analysis reported means that were recorded at three time points during the season (Norsworthy et al., 2010). Considering that each year of an experiment provides varying growing conditions only weakly correlated with other years (repeated measures across years is not needed in our experience), we considered each year as an independent study in the meta-analysis.,