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Picea

Picea is a genus of evergreen coniferous trees, also known as spruce, that are widely distributed across the Northern Hemisphere.
These majestic trees are charactierized by their distinctive needle-like leaves, pendulous cones, and pyramidal growth habit.
Spruce trees play a vital role in forest ecosystems, providing shelter and food for a diverse array of wildlife.
With their adaptability to a range of climates and soil types, Picea species are valued for their commercial and ornamental applications in forestry, horticulture, and landscaping.
Researchers can explore a comprehensive database of Picea-related protocols, products, and research findings through the innovative PubCompare.ai platform, empowering evidence-based decisions and advancing scientific discovery.

Most cited protocols related to «Picea»

Two-Stage Sulfuric Acid Hydrolysis for Lignin Analysis

The use of a two-stage sulfuric acid hydrolysis for the analysis of lignin dates to the turn of the 20th century, although the use of concentrated acid to release sugars from wood dates to the early 19th century (7 ). Klason, in 1906, is often credited as the first to use sulfuric acid to isolate lignin from wood (7 −9 ). The method became named after Klason, and the insoluble residue from the test is known as “Klason lignin.” An English translation of a Klason paper, from this period (10 ), describes his attempt to determine the structure of spruce wood lignin. According to Brauns (7 ), Klason’s method originally used 72 wt % sulfuric acid; he later reduced this to 66 wt % to gelatinize the wood. He filtered the solids and subjected them to a second hydrolysis in 0.5 wt % hydrochloric acid.
Although Klason is generally credited as being the first to use sulfuric acid for lignin analysis, Sherrard and Harris (11 ) credit the use of sulfuric acid to Fleschsig in 1883, Ost and Wilkening in 1912, and König and Rump in 1913. According to Harris (12 ), Fleschsig, in 1883, dissolved cotton cellulose and converted it nearly quantitatively into sugars using strong sulfuric acid followed by dilution and heating. According to Browning (13 ), Ost and Wilkening introduced the use of 72 wt % sulfuric acid for lignin determinations in 1910. A translated paper by Heuser (14 ) credited König and Ost and Wilkening for the sulfuric acid lignin method. Dore (15 ) described several improved analytical methods (cellulose, lignin, soluble pentosans, mannan, and galactan) for the summative analysis of coniferous woods. The discrepancies in attribution may be due to differing definitions for the method cited (e.g., first to use acid to determine lignin, first to use sulfuric acid, first to use 72 wt % sulfuric acid, etc.) and to missed citations across continental distances in the early 20th century.

Sluiter J.B., Ruiz R.O., Scarlata C.J., Sluiter A.D, & Templeton D.W. (2010). Compositional Analysis of Lignocellulosic Feedstocks. 1. Review and Description of Methods. Journal of Agricultural and Food Chemistry, 58(16), 9043-9053.

Publication 2010

Acids Cellulose Galactans Gossypium Hydrochloric acid Hydrolysis Lignin Mannans Pentosan Sulfuric Polyester Picea Sugars sulfuric acid Technique, Dilution Tracheophyta Xylose

Automated vs. Manual DNA Library Preparation

All prepared samples started with 3 µg of DNA and were fragmented
identically using the Covaris system. Following fragmentation the manual library
preparations were performed as specified in the standard protocol, excluding the
second agarose gel separation. Three libraries of spruce genomic DNA were
prepared manually with insert sizes of about 190 bp, 320 bp and 700 bp
respectively, for comparison with the automatically generated libraries.
The automatic library preparation protocol was used to prepare two spruce samples
as well as three human cancer cell line samples (A-431 [16] (link) and U-2 OS[17] (link)). Two of
the samples, one of each kind, were prepared with NEBNext DNA Sample Prep Master
Mix Set 1 (New England Biolabs) reagents instead of the paired end sample
preparation kit (Illumina) specified in the standard protocol. To be able to
assess the effect of the automatic size selection, one of the automatically
prepared cancer cell line samples was manually size selected by agarose gel
separation as specified in the standard protocol. Fragmented samples and
generated libraries were all evaluated using either the High Sensitivity or DNA
7500 kit for the Bioanalyzer.
Cluster generation of the prepared samples was performed using a HiSeq Paired-End
Cluster Generation kit according to manufacturers instructions. Flow cells were
clustered with one library and 1% phiX control library spike inper lane.
The 320 bp manual library was prepared with final concentrations of 6, 7 and 8
pM and loaded in lane 1–3. The concentration of all automatically
generated libraries loaded in lane 4–8 was 7 pM. The 190 bp, 320 bp and
700 bp manual libraries were also used in later instrument runs, with
concentrations varying between 6–11 pM.
Sequencing of the clustered flow cell was preformed according to
manufacturer’s instructions with settings for generation of 2×76
paired end reads.

Borgström E., Lundin S, & Lundeberg J. (2011). Large Scale Library Generation for High Throughput Sequencing. PLoS ONE, 6(4), e19119.

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Publication 2011

Cell Lines Cells DNA Library Genomic Library Homo sapiens Hypersensitivity Malignant Neoplasms Picea Sepharose

Illumina Chloroplast Genome Assembly

After the run, image analysis, base calling and error estimation were performed using Illumina/Solexa Pipeline (version 0.2.2.6). Perl scripts were used to sort and bin all sequences using the three (5′) nucleotide tags; these tags were removed prior to evaluation with Reference Guided Assembler (RGA; R. Shen and T. Mockler, in preparation) or de novo assembly. Examination of Illumina Q-values revealed a decrease after cycle 33 (data not shown), thus the three 3′ bases were trimmed, and 30-mers were used in all subsequent analyses (31 (link)). Binned 30-mers were evaluated relative to the appropriate Pinus reference (P. thunbergii, NC_001631; P. koraiensis, NC_004677) using the program RGA in order to estimate the genome coverage.
To assemble chloroplast genomes using Illumina/Solexa microreads, we used a three-step process. First, de novo assemblies were attempted using Velvet Assembler 0.4 (32 (link)) using a hash length of 19, minimum average coverage of 5×, and minimum contig length of 100 bp. Second, contigs were aligned to a reference genome sequence using CodonCode version 2.0.4 (CodonCode Corporation, Dedham, MA, USA; http://www.codoncode.com/) and standard settings for global alignments. Picea sitchensis was aligned to the previously published chloroplast genome of P. thunbergii (NC001631) and the species of Pinus subgenus Strobus were aligned to P. koraiensis (NC004677). The assembly of P. contorta used a draft plastome of P. ponderosa as its reference (A. Liston and R. Cronn, unpublished results). Prior to alignment, an ‘N’ was added to the ends of each contig, in order to differentiate assembly gaps (dashes flanked by the added ‘N's) from deletions (dashes) relative to the reference. Contigs that failed to align to the reference genome were scanned for chloroplast sequence homology using BLASTN (http://www.ncbi.nlm.nih.gov/). Successful matches typically contained >100 bp insertions relative to the reference genome; these contigs were manually inserted into the alignment. Between 67% and 98% of the contigs aligned to the reference genome. Unaligned contigs apparently represent nontarget PCR amplicons (data not shown). The final de novo assemblies covered 78.1–94.6% of the reference genome (excluding deletions and including insertions relative to the reference). Third, gaps between the de novo contigs were replaced with the reference sequence, and this chimeric assembly was used as a ‘pseudo-reference’ for reference-guided assembly with the program RGA. RGA aligns microreads to their best match in a reference sequence, and then creates a guided consensus sequence from the aligned overlapping reads. RGA outputs the resulting contigs, singletons, the real coverage of each base in the assembly, and identifies SNPs based on microread density in the assembled sequence compared to the reference and Q-values at specific position on each microread. RGA settings used were ≤2 mismatches per microread, Q-values ≥20, read depth ≥3 and SNP acceptance requiring ≥70% of reads in agreement. The pseudo-reference created from de novo assemblies and the reference sequences were corrected using RGA.
Final sequences were annotated using standard settings in the program DOGMA [(33 (link)), http://dogma.ccbb.utexas.edu/]. Multiple alignments were made using MAFFT v. 5 (34 (link)), and full alignments with annotations were visualized using the VISTA viewer (34 (link),35 (link)). See Supplementary Figure 1 for full annotation summaries. In addition, nucleotide positions corresponding to primer locations were changed to ‘N’, as the use of complementary forward and reverse primers at a single site precluded us from obtaining genomic sequence for these positions.
Figure 1.

Relative frequencies of barcode error by barcode tag (CCT, GGT), experiment (S1, S6) and nucleotide position (1 (link),2 (link), 3 ). Observed frequencies of erroneous, nontag nucleotides are indicated by position 1 (salmon), 2 (blue) and 3 (green); first and second position errors were far more common than third position errors. Slices within a position are scaled proportionately to the number of base calls for that nucleotide; if errors were present at equal frequencies within a base position, each slice would be of equal size and would not extend beyond the perimeter of the circle. In all experiments, errors involving substitutions to ‘A’ were more frequent than expected for position 1 and 3, where errors involving substitutions to ‘T’ were more frequent than expected for position 2.

Cronn R., Liston A., Parks M., Gernandt D.S., Shen R, & Mockler T. (2008). Multiplex sequencing of plant chloroplast genomes using Solexa sequencing-by-synthesis technology. Nucleic Acids Research, 36(19), e122.

Publication 2008

BP 100 Chimera Chloroplasts Consensus Sequence Gene Deletion Genome Genome, Chloroplast GPER protein, human Insertion Mutation Marijuana Abuse Nucleotides Oligonucleotide Primers Perimetry Picea Pinus Salmo salar

Whole Genome Shotgun Assembly of Loblolly Pine

A detailed description of the whole genome shotgun sequencing, assembly, and validation of the V1.0 and V1.01 loblolly pine genomes is contained in [9 (link)].
To compare the contiguity of our V1.01 whole genome shotgun assembly to contemporary conifer genome assemblies the scaffold sequences for white spruce genome [7 (link)] and Norway spruce [8 (link)] were obtained from Genbank.
CEGMA analysis of the core gene set [18 (link)] performed on the V1.0 and V1.01 loblolly pine genomes was obtained as described in [9 (link)]. Similarly, a Norway spruce analysis was performed with results consistent with those reported in [8 (link)]. The results for the white spruce assembly were taken directly from [7 (link)].
To assemble the mitochondrial genome, a subset of the WGS sequence consisting of 255 bp paired end MiSeq reads from four Illumina paired end libraries (median insert sizes: 325, 441, 565, and 637) were selected for an independent organelle assembly. The 28.5 Mbp of sequence, representing less than 0.3× nuclear genomic coverage, was assembled using SOAPdenovo2 (K = 127). The resulting contigs were aligned using nucmer to a database containing the loblolly pine chloroplast, sequencing vector, 102 BACs, and 50 complete plant mitochondria. Contigs were identified and labeled as mitochondrial if they aligned exclusively to existing mitochondrial sequence and had high coverage (> = 8×) and G + C% (> = 44%). The contigs were then combined with additional linking libraries, the LPMP_23 mate pair library and all DiTag libraries, and assembled a second time with SOAPdenovo2. Subsequently intra-scaffold gaps were closed using and GapCloser (v1.12). The assembled sequences were iteratively scaffolded and gaps were closed until no assembly improvements could be made.

Neale D.B., Wegrzyn J.L., Stevens K.A., Zimin A.V., Puiu D., Crepeau M.W., Cardeno C., Koriabine M., Holtz-Morris A.E., Liechty J.D., Martínez-García P.J., Vasquez-Gross H.A., Lin B.Y., Zieve J.J., Dougherty W.M., Fuentes-Soriano S., Wu L.S., Gilbert D., Marçais G., Roberts M., Holt C., Yandell M., Davis J.M., Smith K.E., Dean J.F., Lorenz W.W., Whetten R.W., Sederoff R., Wheeler N., McGuire P.E., Main D., Loopstra C.A., Mockaitis K., deJong P.J., Yorke J.A., Salzberg S.L, & Langley C.H. (2014). Decoding the massive genome of loblolly pine using haploid DNA and novel assembly strategies. Genome Biology, 15(3), R59.

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Publication 2014

Chloroplasts Cloning Vectors DNA Library Genes Genome Genome, Mitochondrial Mitochondria Mitochondrial Inheritance Organelles Picea Pinus taeda Plants Tracheophyta

Chromosomal Mapping of Anopheline Genomes

Methods for chromosomal mapping of scaffolds [92 , 93 (link)] are detailed for A. albimanus [27 ], A. atroparvus [25 , 26 , 58 (link)], A. stephensi (SDA-500) [25 ], A. stephensi (Indian) [21 ], and A. sinensis (Chinese) [23 ]. A. funestus mapping built on previous results [59 (link)–61 (link)] with additional FISH mapping (Additional file 1: Figure S11) used to further develop the physical map by considering several different types of mapping results. A. stephensi mapping also extended previous efforts [94 (link)] by aligning FISH probes to the AsteI2 scaffolds with BLAST, and designing and hybridising new probes targeting specific scaffolds to increase the coverage. The complete ‘frozen’ input datasets of the physically mapped scaffolds for each of the six assemblies are presented in Additional file 4, with the usable scaffold pair adjacencies in Additional file 1: Table S6, the definitive mapped A. funestus scaffolds in Additional file 1: Table S7, and the definitive chromosome-mapped scaffolds for each of the six assemblies as well as for A. arabiensis in Additional file 5. These adjacencies were compared with the Camsa-generated two-way consensus assemblies, as well as the predictions from each method and the conservative and liberal consensus assemblies (Fig. 4a; Additional file 1: Table S8). RNAseq-based scaffolding has been employed for very large genomes such as the Norway spruce [95 (link)] and the Loblolly pine [96 (link)], but is also applicable to smaller genomes where more compact gene structures would make it less likely to erroneously skip intervening intronic scaffolds/contigs. The RNAseq-based adjacency predictions used genome-mapped paired-end sequencing data for 13 of the anophelines available from VectorBase [53 , 54 (link)] (Release VB-2017-02), including those from the Anopheles 16 Genomes Project [25 ] and an A. stephensi (Indian) male/female study [97 (link)]. Agouti [62 (link)] analyses were performed (requiring unique read mapping and a minimum coverage of 5 reads) to identify transcript-supported scaffold adjacencies for these 13 anophelines, complemented with Rascaf [98 (link)] predictions (Additional file 1: Table S9). These adjacencies were compared with the Camsa-generated two-way consensus assemblies, as well as the predictions from each method and the conservative and liberal consensus assemblies (Fig. 4b; Additional file 1: Table S10). See Additional file 1 for further details for physical mapping and Agouti adjacencies and their comparisons.

Waterhouse R.M., Aganezov S., Anselmetti Y., Lee J., Ruzzante L., Reijnders M.J., Feron R., Bérard S., George P., Hahn M.W., Howell P.I., Kamali M., Koren S., Lawson D., Maslen G., Peery A., Phillippy A.M., Sharakhova M.V., Tannier E., Unger M.F., Zhang S.V., Alekseyev M.A., Besansky N.J., Chauve C., Emrich S.J, & Sharakhov I.V. (2020). Evolutionary superscaffolding and chromosome anchoring to improve Anopheles genome assemblies. BMC Biology, 18, 1.

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Publication 2020

Anopheles Chinese Chromosomes Cuniculus Females Fishes Freezing Genetic Structures Genome Introns Males Physical Examination Picea Pinus taeda

Most recents protocols related to «Picea»

Tree Ring Width Protocol for Spruce Forests

We randomly selected and sampled 15 dominant spruce trees at each forest site. Considering the lack of direction-specific effects on variability in tree radial growth [22 ], one core per tree was extracted using a Pressler borer (Haglof Company Group) at breast height (1.3 m). To avoid wood compression, the cores were sampled in a direction parallel to the slope. All samples were measured using a VIAS TimeTable device with a measuring length of 78 cm and resolution <1/100 mm (©SCIEM, Vienna, Austria). The TRW series were measured (accuracy of 0.01 mm) and cross-dated using PAST4 [23 ]. Missing and false rings were corrected using both PAST4 [23 ] and COFECHA [24 ].
To remove non-climatic, age-related growth trends from the raw tree ring width (TRW) series as well as other non-climatic factors (e.g., competition), we applied cubic smoothing splines with a 50% frequency cutoff at 100 years [25 ] using ARSTAN software [26 ]. We used this method to preserve interannual to multi-decadal growth variations [27 ]. TRW indices were calculated as residuals after applying an adaptive power transformation to the raw measurement series [28 ]. The site chronologies were calculated using bi-weight robust means. Similarities among the site TRW chronologies were assessed using statistical criteria (correlation coefficient; Gleichläufigkeit [29 ]) and visual comparison. Negative extremes were defined by years in which the standardised TRW chronology (period replicated >20 TRWi series) exceeded the -1.0 multiple of a standard deviation (SD).

Hruška J., Oulehle F., Chuman T., Kolář T., Rybníček M., Trnka M, & McDowell W.H. (2023). Forest growth responds more to air pollution than soil acidification. PLOS ONE, 18(3), e0256976.

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Publication 2023

Acclimatization Breast Climate Cuboid Bone Forests Medical Devices Picea Trees

Atmospheric Deposition Monitoring in Načetín

Atmospherice deposition (bulk precipitation and spruce throughfall) was measured at Načetín experimental forest since 1994 [20 ]. The historical record of atmospheric deposition was estimated during MAGIC model calibration (S1 Annex) using the record of coal mining in the region [2 (link)]. Atmospheric chemistry (annual SO₂ concentrations) was measured at Zinwald station, Germany [35 ]. Dust emissions were derived from the database of the Czech Hydrometeorological Institute [2 (link)].

Hruška J., Oulehle F., Chuman T., Kolář T., Rybníček M., Trnka M, & McDowell W.H. (2023). Forest growth responds more to air pollution than soil acidification. PLOS ONE, 18(3), e0256976.

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Publication 2023

Dietary Fiber Forests Picea

Plant Growth Conditions Across Species

Seeds of tomato (Solanum lycopersicum, cv Micro Tom), Arabidopsis thaliana and corn (Zea mays) were germinated and grown in soil pots. Plants (height 20–25 cm) of white spruce (Picea glauca) were purchased from Nature Hills (USA). These plants were grown in a growth room at 25°C under 8 h of darkness and 16 h of cool white light of 250 μmol photons m^-2 s^-1. Tubers of waterlily (Nymphaea colorata), an aquatic angiosperm, were purchased from Greenpro (USA) and grown in the field near the university campus in south Florida.

Zhang X.H., Swait D., Jin X.L., Vichyavichien P., Nifakos N., Kaplan N., Raymond L, & Harlin J.M. (2023). Evolutionary analysis of KED-rich proteins in plants. PLOS ONE, 18(3), e0279772.

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Publication 2023

Arabidopsis thalianas Darkness Light Lycopersicon esculentum Magnoliopsida Marijuana Abuse Nymphaea Picea Plant Embryos Plants Plant Tubers Zea mays

Quantifying Wound-Induced Gene Expression

Unwounded leaves were collected immediately before wounding treatment and stored at -80°C. For wounding treatment, leaves from the same plants were pinched with a pair of tweezers as described by [7 (link)]. After 0.5 h and 1 h, the wounded leaves were collected sequentially. Leaves (unwounded, and wounded for 0.5 h and 1 h, all from the same plants) were ground in liquid N₂ to fine powders with mortars and pestles. RNA was extracted using Total RNA Mini kit (plant) (Geneaid/FroggaBio, USA) with DNase treatment. First-strand cDNA was made using SuperScript IV reverse transcriptase (Invitrogen, USA) and a mixture of oligo dT and random primers according to the manufacturer’s instruction.
Quantitative real-time PCR (qPCR) using SYBR Green (Life Technologies, USA) was carried out to measure the relative KED transcription levels in leaves of tomato, Arabidopsis, corn, spruce and waterlily. Reference genes for ΔCt normalization were tomato phosphoglycerate kinase [8 (link)], Arabidopsis thaliana TIP41-like protein [9 (link)], corn Elongation factor 1-α [10 (link)], white spruce Elongation factor 1-α [11 (link)] and waterlily actin [12 (link)]. At least three plants from each species were tested. The primer sequences are listed in S1 Table in S1 File.

Zhang X.H., Swait D., Jin X.L., Vichyavichien P., Nifakos N., Kaplan N., Raymond L, & Harlin J.M. (2023). Evolutionary analysis of KED-rich proteins in plants. PLOS ONE, 18(3), e0279772.

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Publication 2023

Actins Arabidopsis Arabidopsis thalianas Deoxyribonuclease I DNA, Complementary Elongation Factor 1alpha Genes Lycopersicon esculentum Maize Nymphaea oligo (dT) Oligonucleotide Primers Phosphoglycerate Kinase Picea Plants Powder Proteins Real-Time Polymerase Chain Reaction RNA-Directed DNA Polymerase SYBR Green I Transcription, Genetic

Pinaceae Transcriptome Data Collection

From open-access databases, raw transcriptome data for 15 species were downloaded. Among the species, 12 species belong to 10 genera of Pinaceae, including Abies firma, Cathaya argyrophylla, Cedrus deodara, Keteleeria evelyniana, Larix gmelinii, Picea abies, Picea smithiana, Pinus armandii, Pinus elliottii, Pinus massoniana, Pinus taeda, Pseudolarix amabilis, Pseudotsuga menziesii, Tsuga dumosa and Tsuga longibracteata, and the three species Cycas panzhihuaensis, Araucaria cunninghamii, and Platycladus orientalis were used as outgroups (Supplementary Table S1).

Jiang K., Du C., Huang L., Luo J., Liu T, & Huang S. (2023). Phylotranscriptomics and evolution of key genes for terpene biosynthesis in Pinaceae. Frontiers in Plant Science, 14, 1114579.

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Publication 2023

Abies Araucaria Cedrus Cycas Fir, Douglas Larix Picea Pinaceae Pinus Pinus abies Pinus taeda Thuja orientalis Transcriptome Tsuga

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More about "Picea"

Discover the Wonders of Picea: Evergreen Conifers, Spruce Trees, and Their Diverse Applications

Picea, the genus of evergreen coniferous trees commonly known as spruce, is a majestic and widely distributed group found across the Northern Hemisphere.
These stately trees are characterized by their distinctive needle-like foliage, pendulous cones, and pyramidal growth habit, making them a beloved sight in forest ecosystems.
Spruce trees play a vital role in supporting diverse wildlife, providing shelter and sustenance for a wide array of species.
With their adaptability to a range of climates and soil types, Picea species are highly valued for their commercial and ornamental applications in forestry, horticulture, and landscaping.
Researchers can explore a comprehensive database of Picea-related protocols, products, and research findings through the innovative PubCompare.ai platform.
This AI-driven tool empowers evidence-based decisions and advances scientific discovery by enabling the comparison of various protocols and the identification of the most effective approaches.
Whether you're studying the growth and development of spruce trees, investigating their chemical composition, or exploring their ecological significance, PubCompare.ai offers a wealth of information to support your research.
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Embrace the power of PubCompare.ai and unlock new insights into the fascinating world of Picea, the evergreen coniferous trees that continue to captivate researchers and nature enthusiasts alike.