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Java tool for PCR, in silico PCR and genotyping
<p>We performed in silico PCR analysis of several complete plant genomes using a list of primers corresponding to an inverted repeat sequence of Hordeum-Triticum Athos miniature inverted-repeat transposable element (MITE) sequences. MITE nonautonomous members of Class II element families are derived by internal deletion of autonomous elements, and they are short (70-300 bp in length) and have conserved terminal repeats.</p> <p>For example, Athos, one of the MITE families described in grasses. Athos element sequences were collected from the genome of Hordeum vulgare, of which there are about 205 per complete genome. The Athos element sequences are highly truncated, including partial loss of terminal inverted repeats in the barley genome. Sequences of terminal inverted repeats contain multiple point mutations, insertions or deletions, which creates a difficulty for the selection of universal primers that would cover all whole copies of this element. Therefore, we selected all unique sequence variants for terminal inverted repeats and used them as primers to identify and obtain complete MITE elements for genomes of other cereals and as a negative control, we used the genome of human and long-horned nomad bee (Nomada hirtipes). Since the sequences of terminal inverted repeats for Athos element were different and quite degenerate, we used all 46 unique variants simultaneously as Forward primer in the analysis. The same primer will act as Forward and also Reverse. The length of the primers was 15 nucleotides, which localise to the furthest region of the terminal inverted repeat at the Athos element. The size for the amplicon in this case could be 30 to 200 nucleotides, including truncated elements with a central part. We used search conditions with control options: type=primer number3errors=0; minlen=30; maxlen=200. The results of this analysis are represented in Table 2. In the genome of Hordeum vulgare we identified 768 Athos and related elements, which is much more than was detected by blast analysis (205 copies for GCF_904849725.1, Blast: RefSeq Genome Database). This is because we detected not only Athos elements but also related MITE elements with overlapping end repeats. </p> <p>In our analysis, we could only detect whole Athos and related elements that contained both repeats, whereas the central part could vary. For the Hordeum bulbosum genome, we detected a 1620 record number of complete Athos and related elements compared to other species of the Hordeum family. This corresponds to the doubled genome size of this species compared to other species of the Hordeum family. For wheat genomes (Aegilops tauschii, Triticum dicoccoides), being the most similar to species of the Hordeum family, numerous copies of the related Athos and related elements were detected, with this MITE occurring much more frequently in the wheat genome than in the genome of Hordeum vulgare. It is well observed that the copy number of Athos and related elements directly depends on the genome size; the larger the genome, the greater the copy number of this element detected.</p>
Text-fig. 8. Middle-ear ossicles of Metacheiromys marshi, USNM-P 452349. a – left malleus (partial), incus, and stapes in ventral view; b – right malleus in oblique anterior view (left) and oblique posterior view (right). Abbreviations: acr – anterior crus, cb – crus breve, cl – crus longum, fp – footplate, iaf – inferior articular facet, ib – incudal body, lp – lateral process, mh – mallear head, mn – manubrium, mp – muscular process, n – neck, ol – osseous lamina (broken), pcr – posterior crus, sh – stapedial head, stf – stapedial foramen, suaf – superior articular facet. in Skeletal Anatomy Of The Basicranium And Auditory Region In The Metacheiromyid Palaeanodont Metacheiromys (Mammalia, Pholidotamorpha) Based On High-Resolution Ct Scans
Text-fig. 8. Middle-ear ossicles of Metacheiromys marshi, USNM-P 452349. a – left malleus (partial), incus, and stapes in ventral view; b – right malleus in oblique anterior view (left) and oblique posterior view (right). Abbreviations: acr – anterior crus, cb – crus breve, cl – crus longum, fp – footplate, iaf – inferior articular facet, ib – incudal body, lp – lateral process, mh – mallear head, mn – manubrium, mp – muscular process, n – neck, ol – osseous lamina (broken), pcr – posterior crus, sh – stapedial head, stf – stapedial foramen, suaf – superior articular facet.
Data corresponding to: Evaluation of sequencing and PCR-based methods for the quantification of the viral genome formula
<p>Viruses show great diversity in their genome organisation. Multipartite viruses package their genome segments into separate particles, most or all of which are required to initiate infection in the host cell. The benefits of such seemingly inefficient genome organization are not well understood. One hypothesised benefit of multipartition is that it allows for flexible changes in gene expression by altering the frequency of each genome segment in different environments, such as encountering different host species. The ratio of the frequency of segments is termed the genome formula (GF). Thus far, formal studies quantifying the GF have been performed for well-characterised virus-host systems in experimental settings using RT-qPCR. However, to understand GF variation in natural populations or novel virus-host systems, a comparison of several methods for GF estimation including high-throughput sequencing (HTS) based methods is needed. Currently, it is unclear how HTS-methods compare a golden standard, such as RT-qPCR. Here we show a comparison of multiple GF quantification methods (RT-qPCR, RT-digital PCR, Illumina RNAseq and Nanopore direct RNA sequencing) using three host plants (<em>Nicotiana tabacum</em>, <em>Nicotiana benthamiana</em>, and <em>Chenopodium quinoa</em>) infected with cucumber mosaic virus (CMV), a tripartite RNA virus. Our results show that all methods give roughly similar results, though there is a significant method effect on genome formula estimates. While the RT-qPCR and RT-dPCR GF estimates are congruent, the GF estimates from HTS methods deviate from those found with PCR. Our findings emphasise the need to tailor the GF quantification method to the experimental aim, and highlight that it may not be possible to compare HTS and PCR-based methods directly. The difference in results between PCR-based methods and HTS highlights that the choice of quantification technique is not trivial.</p>
Cryptosporidium Nested-PCR on 18S rDNA Tutorial
<p>Video tutorial for<em> Cryptosporidium</em> spp. identification by nested-PCR on 18S rDNA locus.</p>
APPENDIX 9 in The Amidella clade in Europe (Basidiomycota: Amanitaceae): clarification of the contentious Amanita valens (E.-J.Gilbert) Bertault and the importance of taxon-specific PCR primers for identification
APPENDIX 9. — Phylogenetic reconstruction of ITS sequences from Amanita curtipes related specimens collected in worldwide locations (Appendix 10), using A. lepiotoides Barla as outgroup. The known epithets are next to the brackets, sp designating unknown taxa. Using a 75% coverage cutoff, a total of 584 aligned positions were analysed with the Maximum Likelihood method and the General Time Reversible substitution model (Nei & Kumar 2000), with a discrete Gamma distribution to model evolutionary rate differences among sites (5 categories [gamma parameter 0.4648]). Bootstrap support values above 0.66 (200 replicates) are shown next to the relevant nodes.
APPENDIX 10. — Additional ITS sequences used for Appendix 9 in The Amidella clade in Europe (Basidiomycota: Amanitaceae): clarification of the contentious Amanita valens (E.-J.Gilbert) Bertault and the importance of taxon-specific PCR primers for identification
APPENDIX 10. — Additional ITS sequences used for Appendix 9, approximately in the same order. Abbreviations: MO, Mushroom Observer (https://mushroomobserver.org); iNAT, iNaturalist (https://www.inaturalist.org/observations).
APPENDIX 8 in The Amidella clade in Europe (Basidiomycota: Amanitaceae): clarification of the contentious Amanita valens (E.-J.Gilbert) Bertault and the importance of taxon-specific PCR primers for identification
APPENDIX 8. — Examples of basidiospore outlines from the Spring 2015 Luzianes collections. Scale bar: 10 μm.
APPENDIX 4 in The Amidella clade in Europe (Basidiomycota: Amanitaceae): clarification of the contentious Amanita valens (E.-J.Gilbert) Bertault and the importance of taxon-specific PCR primers for identification
APPENDIX 4. — Descriptive (mean ± SE) plots for basidiospore length (L), width (l) and L/l ratio (Q). Sample identifiers refer to the Ode samples (Spring 2015) and P01 (Spring 2010).
APPENDIX 2 in The Amidella clade in Europe (Basidiomycota: Amanitaceae): clarification of the contentious Amanita valens (E.-J.Gilbert) Bertault and the importance of taxon-specific PCR primers for identification
APPENDIX 2. — Example of a matched comparison of Amanita ponderosa Malençon & R.Heim (ApoIf2-ITS4) and A. pseudovalens comb. nov., stat. nov. (ITS3- ApsIr3) for the same set of samples representing these two species, along with others of A. curtipes E.-J.Gilbert and A. lepiotoides Barla, and an unknown sample (SM-DB 61). The whole contents of each 20 µL PCR reactions were loaded, to help detect faint signals. Negative controls (neg.) with water instead of DNA. Amanita ponderosa is represented by the valens type (G73) and three samples from Spring 2010 (B07, P08, B10). Amanita pseudovalens is represented by the pseudovalens type (G74), the two MPU samples, Ode05 and Ode07. The Amanita curtipes samples (M03, M04 and M05) may produce a weak co-migrating band with the ITS3-ApsIR3 primer pair, and the A. lepiotoides samples (G08 and Eusk) were negative in both cases. Positive control reactions were not attempted because herbaria materials may be contaminated with other fungi.
FIG. 6 in The Amidella clade in Europe (Basidiomycota: Amanitaceae): clarification of the contentious Amanita valens (E.-J.Gilbert) Bertault and the importance of taxon-specific PCR primers for identification
FIG. 6. — Results of PCR amplifications using discriminant probes for Amanita pseudovalens comb. nov., stat. nov.: A, ITS region: ITS3-ApsIr3 (all samples positive with concurring control with primers ITS3-ITS4, not shown); B, LSU region: NLC2R-ApsLr2 (all positive with control NLC2R-LR5); Ode15 and Ode16 were negative and positive, respectively, in another amplification (not shown); M, molecular marker; neg. designates the negative control (water); pos. designates the positive control (the P01 extract).
FIG. 1 in The Amidella clade in Europe (Basidiomycota: Amanitaceae): clarification of the contentious Amanita valens (E.-J.Gilbert) Bertault and the importance of taxon-specific PCR primers for identification
FIG. 1. — Locations of the collections used in this study (see Table 1 for details). The letters designate Landscape Units: P, Alto Alentejo; R, Alentejo Central; S, Baixo Alentejo; U, Serras do Algarve e do Litoral Alentejano. The area is outlined on the inset with a yellow rectangle. Source: DGT, Carta de Unidades de Paisagem (CUP), https://www.dgterritorio.gov.pt/dados-abertos
FIG. 2 in The Amidella clade in Europe (Basidiomycota: Amanitaceae): clarification of the contentious Amanita valens (E.-J.Gilbert) Bertault and the importance of taxon-specific PCR primers for identification
FIG. 2. — Phylogenetic placement of the type nrDNA sequences, in relation to ITS + LSU sequences from european Amanita Pers. taxa belonging to the Amidella clade (only the terminal epithets are shown). Using a 70% coverage cutoff, a total of 1121 aligned positions were analysed with the Maximum Likelihood method and the Tamura-Nei (Tamura & Nei 1993) substitution model, with a discrete Gamma distribution to model evolutionary rate differences among sites (five categories [gamma parameter 0.7526]), allowing for some sites to be evolutionarily invariable (40.90% sites). The percentage bootstrap support for each node (1000 replicates) is shown next to the branches. The support for each species clade is 99% (Amanita pseudovalens comb. nov., stat. nov.), 100% (Amanita curtipes E.-J.Gilbert), 100% (Amanita lepiotoides Barla) and 100% (Amanita ponderosa Malençon & R.Heim). The scale indicates 0.02 substitutions per site.
FIG. 3 in The Amidella clade in Europe (Basidiomycota: Amanitaceae): clarification of the contentious Amanita valens (E.-J.Gilbert) Bertault and the importance of taxon-specific PCR primers for identification
FIG. 3. — Photographs of Amanita pseudovalens var. tartessiana var. nov. specimens from Odemira. Some images taken in the field are matched with corresponding ones taken in the laboratory: A, grouped basidiomes (Ode12) and their appearance upon arrival at the laboratory; B, outcropping away from the vegetation, showing the coarse soil texture in this case (Ode02); inset shows squamulose inner cuticle remains; C, another specimen with cuticle squamules (Ode11); D, specimen with cracked cuticle (Ode05); E, example with stipe squamules (Ode06); F, side view of an emerging basidiome (Ode03) and the same specimen showing the darkened squamules on the stipe; G, view of the hymenophore and the appendiculate pileus margin (Ode02, specimen different from B); H, details of an immature basidiome (Ode06, specimen different from E) showing the annulus and insertion of the lamellae (left), and longitudinal section (right).
FIG. 5 in The Amidella clade in Europe (Basidiomycota: Amanitaceae): clarification of the contentious Amanita valens (E.-J.Gilbert) Bertault and the importance of taxon-specific PCR primers for identification
FIG. 5. — Ground view of the Luzianes locations, showing the Cistus ladanifer L. dominance: A, Luzianes A; B, Luzianes B. Both photos were taken in spring 2015 by Ana C. Silva.
FIG. 4 in The Amidella clade in Europe (Basidiomycota: Amanitaceae): clarification of the contentious Amanita valens (E.-J.Gilbert) Bertault and the importance of taxon-specific PCR primers for identification
FIG. 4. — Plotting of the measurements summarised in Table 5 (dots), comparing with the limits of the sporographs, based on the descriptions in Neville & Poumarat (2004) for Amanita curtipes f. pseudovalens Neville & Poumarat (continuous line) and A. ponderosa f. ponderosa Malençon & R.Heim (dashed line).
Dataset for Multiplex-PCR detection and Nanopore-based genotyping of fish pathogens
<p>This is a revised zip file contains scripts, initial fastq files, assembled amplicon (public and from this study) as well as bioinformatics intermediate files used for this study.</p> <p>Changelog:</p> <p>1. Fixed a bug in the 02_consensus.sh to enable proper removal of amplicons with zero depth</p> <p>2. Added a script (06_unclassified_read.sh) to extract and annotate reads that previously could not align to the 4 reference gene segment. Now the previously unclassified reads will be re-align (raw fastq) back to the gene segments as well as an additional tilapia genome assembly to gauge amount of reads mapping to the host genome. Furthermore, any read that still fail to align with minimap2 was subsequently aligned using blastn (-word_size 15 -evalue 0.01) against the same sequences.</p> <p>File Structure and Descriptions</p> <p>├── 01_process.sh : primer trimming, length-based filtering, read alignment, alignment filtering (unique hit) and extraction of uniquely hit reads for consensus generation<br> ├── 02_consensus.sh : [need artic conda env] Generation of consensus based on uniquely-mapped reads and minimal read depth of 20x required to call a variant (or it will be masked)<br> ├── 03_cleanup.sh: General folder and intermediate file re-organization<br> ├── 04_filter.sh: [need quast conda env] statistic of consensus generated and filtering of consensus with one or more ambiguous base (N), not suitable for haplotype<br> ├── 05_cluster.sh: clustering of consensus based on 100% identity threshold to generate putative haplotype<br> ├── 06_unclassified_read.sh: Extraction and annotation of unclassified reads using lenient criteria and with host reference genome as added reference<br> ├── Amplicon_FastQ folder: uniquely mapped fastq files for consensus generation<br> ├── BAM: alignment files generated from minimap2 used as input for the artic pipeline to identify variants<br> ├── Cluster_Rep.txt: Consensus sequences that were chosen to represent each haplotype<br> ├── Consensus folder: consensus fasta files generated for each sample containing sequences for each specific pathogen<br> ├── Coverage folder: coverage and base-level read depth for each sample and each pathogen reference genes<br> ├── Filter: individual fasta sequences (only 1 sequence per file) for each pathogen and each sample without any ambiguous base for subsequent clustering analysis<br> ├── Full_Haplotype.fasta: all possible haplotype sequences generated for each pathogen<br> ├── Gap_Analysis.tsv: Table with percentage of gap (0-100%) for each consensus sequence generated (used for filtering)<br> ├── Haplotype folder: Intermediate file and sample-level haplotype used to infer final haplotype and generate haplotype summary<br> ├── Haplotype_summary.tsv: Table with sample ID and their respectively pathogen haplotype<br> ├── Minimap2_PAF: Intermediate alignment generated from minimap2 used to generate the count table<br> ├── FailMinimap2 folder: FastQ files that didn't align using minimap2. Will be subsequently aligned using blastN (more sensitive) against the same reference sequences as minimap2<br> ├── Host_4Pathogen.fasta: Fasta file containing the tilapia genome and 4 pathogen (primer binding site included)<br> ├── Original: fastq with original naming prior to renaming based on sampleID. a script (rename.sh) was included to show renaming scheme<br> ├── primer.fasta: Primer sequences used for identifying and trimming reads with flanking primer sequence<br> ├── primer.fasta.fai: the index file for primer.fasta<br> ├── PrimerTrim folder: Primer-trimmed reads<br> ├── quast_results: consensus statistics generated by quast<br> ├── RawCount.tsv: Count table generated that can used as a input to generate figure<br> ├── RawFastq folder: Raw reads that have been renamed to reflect sample information<br> ├── readme.md: The current readme file<br> ├── ref_full_latest.fasta: Reference sequence of (gene segments) 4 pathogens e.g. TilV, ISKNV, SAG (Streptococcus agalactiae), FNO (Francisella noatunensis subsp. orientalis)<br> ├── ref_full_latest.primer.fasta: Same as above but with their primer binding sequence trimmed similar to the processed reads<br> ├── ref_full_latest.primer.fasta.fai<br> ├── RenameHaplotype: Script to perform reorganization of cdhit output<br> ├── Seq.stat.tsv: Sequencing statistics<br> ├── Uniq_PAF: Minimap2 alignment file for raw reads that initially failed quality check (no primer present and/or less than 80% query coverage / not unique alignment)<br> ├── Unmap: Raw reads that initial failed quality check (no primer on both ends / less than 80% query coverage / not unique alignment) <br> └── VCF: VCF files from medaka variant calling used to generate the final consensus</p>
Prediction of pCR by Preoperative Biopsy in Breast Cancer With cCR After Neoadjuvant Chemotherapy.
ClinicalTrials.gov study NCT03273426. IPD Sharing: YES. Countries: 1. Publications: 1.
Supplemental files to: Probe-based quantitative PCR and RPA-Cas12a molecular diagnostics for detection of the tomato pest Phthorimaea absoluta (Lepidoptera: Gelechiidae)
Open the record for dataset details and reuse information.
Data corresponding to: Evaluation of sequencing and PCR-based methods for the quantification of the viral genome formula
Open the record for dataset details and reuse information.
Methods for Extracting and Characterizing RNA from Urine: for downstream PCR and RNAseq Analysis
<p>Readily accessible samples such as urine or blood are seemingly ideal for differentiating and stratifying patients, however, it has proven a daunting task to identify reliable biomarkers in such samples. Noncoding RNA holds great promise as a source of biomarkers distinguishing physiologic wellbeing or illness.</p>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.