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Oryziinae

Oryziinae is a subfamily of small, monophyletic rice-like grasses within the family Poaceae.
This group encompasses several genera, including the economically important rice genus Oryza.
Oryziinae species are primarily distributed across Asia, and play a crucial role in global food production.
Understanding the biology, cultivation, and genetic diversity of Oryziinae taxa is essential for improving rice yields and developing more resilient crop varieties.
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Most cited protocols related to «Oryziinae»

The six bacterial isolates used in the real-read tests each belong to a different species: Acinetobacter baumannii, Citrobacter koseri, Enterobacter kobei, an unnamed Haemophilus species (given the placeholder name Haemophilus sp002998595 in GTDB R202 [28 , 29 ]), Klebsiella oxytoca and Klebsiella variicola. Sequencing was previously described in Wick et al. (2021) [19 ]. Briefly, isolates were cultured overnight at 37°C in Luria-Bertani broth and DNA was extracted using GenFind v3 according to the manufacturer’s instructions (Beckman Coulter). The same DNA extract was used to sequence each isolate using three different approaches: ONT ligation, ONT rapid and Illumina (S11(A) Fig). For ONT ligation, we followed the protocol for the SQK-LSK109 ligation sequencing kit and EXP-NBD104 native barcoding expansion (Oxford Nanopore Technologies). For ONT rapid, we followed the protocol for the SQK-RBK004 rapid barcoding kit (Oxford Nanopore Technologies). All ONT libraries were sequenced on MinION R9.4.1 flow cells. ONT read sets were basecalled and demultiplexed with Guppy v5.0.7, using the super-accuracy model. For Illumina, we followed a modified Illumina DNA Prep protocol (catalogue number 20018705), whereby the reaction volumes were quartered to conserve reagents. Illumina libraries were sequenced on the NovaSeq 6000 using SP reagent kits v1.0 (300 cycles, Illumina Inc.), producing 150 bp paired-end reads with a mean insert size of 331 bp. The resulting Illumina read pairs were shuffled and evenly split into two separate read sets, which were combined with the ONT read sets to produce two independent hybrid read sets (S11(B) Fig). We repeated this process (from culture to sequencing) to generate another two hybrid read sets for a total of four hybrid read sets per isolate. All reads are available in the manuscript’s data repository (bridges.monash.edu/articles/dataset/Polypolish_paper_dataset/16727680).
For each hybrid read set, we performed a long-read-only assembly using Trycycler v0.5.0 [3 ] and Medaka v1.4.3 [8 ], following the instructions in Trycycler’s documentation (S11(C) Fig and S6 Table). One of the ONT read sets for K. oxytoca MSB1_2C had very low depth (10×) and was therefore not able to yield a high-quality long-read-only assembly, leaving only three assemblies for this genome. We were able to produce four complete (circularised) long-read-only assemblies for the other five genomes, giving a total of 23 assemblies which served as the ‘unpolished’ assemblies in our real-read tests.
Polished genome sequences were generated by running the short-read polishers as described above (S11(D) Fig). For the single-tool tests, each polisher was run consecutively three times on each assembly. For the greedy combination tests, each polisher (excluding the hybrid polishers and wtpoa which performed poorly in the single-tool tests) was run once on each genome, and the best-performing polisher was defined as the one with the smallest total pairwise distance in its output assemblies. The best-performing polisher’s output was then used as input for another round of polishing until there were no more improvements. The greedy combination tests were then performed again with Polypolish excluded.
To assess the quality of real-read assemblies, we used the edlib [26 ] library to perform a global alignment of the chromosome sequences for all pairwise combinations within each genome (S11(E) Fig). The total distance was used as a metric of assembly quality, with lower values being better and a value of zero indicating that all assemblies for the genome were identical. We also ran ALE [18 ] on each real-read assembly using short-read alignments from BWA-MEM [16 ].
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Publication 2022
Acinetobacter calcoaceticus Bacteria Cells Chromosomes Citrobacter koseri DNA Library Enterobacter kobei Genome Hemophilus Hybrids Klebsiella oxytoca Klebsiella variicola Lebistes Ligation Oryziinae
UCSC released a new Conservation (13 (link)) annotation track on the March 2006 (Build 36, hg18) human genome in June 2007. This track displays multiz (14 (link)) multiple alignments of 27 vertebrate species to the human genome, along with measurements of evolutionary conservation across all 28 species and a separate measurement of conservation across the placental mammal subset of species (18 organisms). Included in the track are 5 new high-quality assemblies—horse, platypus, lizard, stickleback and medaka; 6 new low-coverage mammalian genomes—bushbaby, tree shrew, guinea pig, hedgehog, common shrew and cat; 6 updated assemblies—chimp, cow, chicken, frog, fugu and zebrafish; and 10 assemblies included in the previous version of the Conservation track—rhesus, mouse, rat, rabbit, dog, armadillo, elephant, tenrec, opossum and tetraodon. In addition to the expanded species list, the new Conservation track has been enhanced to include additional filtering of pairwise alignments for each species to reduce paralogous alignments and information about the quality of aligning species sequence included in the multiple alignments downloads. A similar Conservation annotation of at least 30 species is scheduled for release on the July 2007 (Build 37, mm9) mouse assembly in the last quarter of 2007.
Publication 2007
Armadillos Biological Evolution Bush Babies Cavia Chickens Didelphidae Elephants Equus caballus Erinaceidae Eutheria Genome Genome, Human Lizards Macaca mulatta Mammals Mice, House Oryziinae Pan troglodytes Platypus, Duckbilled Rabbits Rana Shrews Sticklebacks Strains Takifugu Tenrec Tupaiidae Vertebrates Zebrafish
As a starting point for comparative genome analyses, we integrated predicted trout genes in vertebrate gene families based on Ensembl version 66 (February 2012)50 (link). The 46,585 predicted trout proteins were compared against 13,264 gene families from 14 representative vertebrate species comprising mammals, birds and fish (Supplementary Fig. 6). Trout genes were included in 8,739 vertebrate gene trees (Supplementary Table 7). By comparison, other genes from other vertebrate genomes are included in 7,131 (takifugu) to 9,453 (Human) gene families, suggesting that annotated trout genes cover the vast majority of vertebrate gene families. A dedicated Genomicus server ( http://www.genomicus.biologie.ens.fr/genomicus-trout-01.01/) provides access to trout genes and their phylogenetic trees, as well as syntenic relationships with other genomes (Supplementary Fig. 7).
DCS blocks are defined as runs of genes in a non-salmonid (that is, non-duplicated by the Ss4R event) genome that are distributed on two different chromosomes (or non-anchored scaffolds) in the rainbow trout genome; the exact gene order does not need to be conserved. We systematically compared the gene locations in rainbow trout with those of medaka, stickleback, tetraodon and takifugu using ad-hoc scripts to identify pairs of regions in the rainbow trout genome that are syntenic with single regions in non-salmonid species, and that correspond to DCS blocks. Pairs of paralogous trout genes on two different chromosomes (or non-anchored scaffolds) that belong to a DCS block are most likely duplicates originating from the Ss4R WGD event and are called ohnologues; there were 6,733 pairs of ohnologues. Genes that are inserted in a DCS block based on synteny with a non-salmonid species, but have no paralogous gene on the other chromosome or scaffold, are most likely former Ss4R duplicates in which one of the duplicated genes was lost, and are called singletons. Each pair of duplicated regions within a DCS block is descended from a single ancestral region in the pre-duplication genome. The organization of these ancestral regions into an ancestral chromosome was deduced from the synteny relationships with non-salmonid genomes using a clustering method implemented in Walktrap51 . The Ts3R-duplicated regions in the ancestral karyotype were obtained by orthology with the Ts3R-duplicated regions in the medaka genome, which were themselves deduced from the DCS blocks between the medaka and chicken genomes obtained as described above. DCS blocks can be very short, as they are dependent on assembly continuity and scaffold anchoring. Fine-scale analysis of duplicated regions and genes was restricted to 915 scaffolds that could be paired into 569 DCS blocks for at least part of their lengths, and that share at least 4 ohnologous genes. The longest scaffold in these DCS blocks is 5,466,130 bp long and the shortest is 25,207 bp long. These 915 scaffolds contain a total of 171 miRNAs and 13,352 genes (29% of the trout genome), of which 8,624 are ohnologues and 4,728 are singletons. These scaffolds were aligned using LastZ52 , resulting in 85,050 local alignments with a mean identity of 86.7%.
To better understand the fate of inactivated gene copies, protein sequences predicted from a given gene model were also aligned to their paralogous region using exonerate53 (link) with the ‘—model protein2genome’ option (Supplementary Methods). Rates of gene loss since the Ts3R WGD were calculated by linear extrapolation.
Publication 2014
Amino Acid Sequence Aves Chickens Chromosomes Comparative Genomic Hybridization Fishes Gene Order Genes Genes, Duplicate Genome Karyotype Mammals MicroRNAs Oncorhynchus mykiss Oryziinae Proteins Salmonidae SSTR4 protein, human Sticklebacks Synteny Takifugu Trees Trout Vertebrates
The following genome sequence files were curated from the Genome Bioinformatics Group of University of California, Santa Cruz [25 ]: Human, March 2006 (hg18); Chimpanzee, March 2006 (panTro2); Rhesus, January 2006 (rheMac2); Rat, November 2004 (rn4); Mouse, February 2006 (mm8); Cat, March 2006 (felCat3); Dog, May 2005 (canFam2); Horse, January 2007 (equCab1); Cow, March 2005 (bosTau2); Opossum, January 2006 (monDom4); Chicken, May 2006 (galGal3); Xenopus tropicalis, August 2005 (xenTro2); Zebrafish, March 2006 (danRer4); Tetraodon, February 2004 (tetNig1); Fugu, October 2004 (fr2); Stickleback, February 2006 (gasAcu1); Medaka, April 2006 (oryLat1); D. melanogaster, April 2006 (dm3); D. simulans, April 2005 (droSim1); D. sechellia, October 2005 (droSec1); D. yakuba, November 2005 (droYak2); D. erecta, August 2005 (droEre1); D. ananassae, August 2005 (droAna2); D. pseudoobscura, November 2005 (dp3); D. persimilis, October 2005 (droPer1); D. virilis, August 2005 (droVir2); D. mojavensis, August 2005 (droMoj2); D. grimshawi, August 2005 (droGri1); C. elegans, January 2007 (ce4); C. brenneri, January 2007 (caePb1); C. briggsae, January 2007 (cb3); C. remanei, March 2006 (caeRem2); and P. pacificus, February 2007 (priPac1); The genome sequence files for the Elephant, June 2005; Hedgehog, June 2006 and Armadillo, June 2005 were downloaded from the Broad Institute [26 ].
The following bacteria genome sequence files were curated from the BacMap database of University of Alberta [27 ]: Staphylococcus aureus COL; Staphylococcus aureus MRSA252; Staphylococcus aureus MSSA476, Staphylococcus aureus Mu50; Staphylococcus aureus MW2; Staphylococcus aureus N315; Staphylococcus aureus subsp. aureus NCTC 8325; Staphylococcus aureus RF122; Staphylococcus aureus subsp. aureus USA300; Staphylococcus epidermidis ATCC 12228; Staphylococcus epidermidis RP62; Staphylococcus haemolyticus JCSC1435; Escherichia coli 536; Escherichia coli APEC O1; Escherichia coli CFT073; Escherichia coli O157:H7 EDL933; Escherichia coli K12 MG1655; Escherichia coli W3110; Escherichia coli O157:H7 Sakai; Klebsiella pneumoniae MGH 78578; Salmonella enterica Choleraesuis SC-B67; Salmonella enterica Paratypi A ATCC 9150; Salmonella typhimurium LT2; Salmonella enterica CT18; Salmonella enterica Ty2; Shigella boydii Sb227; Shigella dysenteriae Sd197; Shigella flexneri 2a 2457T; and Shigella flexneri 301. The genome sequence files for Staphylococcus aureus subsp. aureus JH1, Staphylococcus aureus subsp. aureus JH9, Staphylococcus aureus Mu3, and Staphylococcus aureus subsp. aureus str. Newman were curated from the European Bioinformatics Institute of the European Molecular Biology Laboratory [28 ]. The genome sequence file for Escherichia coli UT189 was taken from Enteropathogen Resource Integration Center [29 ], and genome sequence data for Salmonella bongori was downloaded from the Sanger Institute Sequencing Centre [30 (link)].
The mosquito genome sequence files for Aedes aegypti, Anopheles gambiae and Culex pipiens were curated from the VectorBase database [31].
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Publication 2008
Aedes Anopheles gambiae Armadillos Caenorhabditis elegans Chickens Culex Culicidae Didelphidae Drosophila melanogaster Drosophila simulans Elephants Equus caballus Erinaceidae Escherichia coli Escherichia coli K12 Escherichia coli O157 Europeans Genome Genome, Bacterial Homo sapiens Klebsiella pneumoniae Macaca mulatta Mice, House Oryziinae Pan troglodytes Salmonella bongori Salmonella enterica Salmonella typhimurium LT2 Shigella boydii Shigella dysenteriae Shigella flexneri Staphylococcus aureus Staphylococcus aureus subsp. aureus Staphylococcus epidermidis Staphylococcus haemolyticus Sticklebacks Takifugu Xenopus Zebrafish
Atlantic salmon (S. salar) coding mRNA sequences (12,062 sequences)54 (link) were translated into protein sequences. Blastp reciprocal best hits between these salmon and trout proteins were aligned with MUSCLE55 (link)56 (link), and rates of silent substitution (dS values) of the corresponding coding sequences were calculated using the Yang and Nielsen method in PAML4.4 (ref. 57 (link)). Ohnologous gene sequences from fish genomes were obtained from Ensembl Treebest gene trees and DCS analyses. MUSCLE alignment of protein sequences followed by PAML4 analysis of the coding sequences (CDS) was used to compute the dS and dN values for pairs of ohnologous sequences originating from the Ts3R WGD in stickleback, tetraodon, medaka and zebrafish, and for trout Ss4R ohnologues. Trout Ts3R ohnologues represent a special case, because each copy (for example, A and B) was further duplicated by the Ss4R, and thus may be represented by one (if the other Ss4R ohnologue was lost) or two sequences (for example, A1, A2 and B1, B2). A given Ss4R ohnologue was always aligned separately to each Ss4R duplicate copy stemming from the Ts3R (for example, A1 to B1 and B2), when they existed. Alignments were then concatenated to compute dS values, which thus represents an average dS (resp. dN) value for the Ts3R duplication for a given family of ohnologues. When Ss4R ohnologues existed in more than two copies, because of subsequent local duplication (for example, A1, A2, A3), we aligned each possible combination of pairs using MUSCLE55 (link)56 (link) (for example, A1–B1, A2–B1, A3–B1, etc.) and then concatenated alignments as before (for example, A1–B1 with A2–B1), in all possible combinations of two concatenated alignments, each leading to a dS (resp. dN) value. The smallest dS value among all alignments was considered the most conservative and retained for further analysis, together with the corresponding dN. The rate of selective constraints on orthologues and ohnologues was calculated with PAML4.4 (ω=dN/dS) using the method of Yang and Nielsen58 (link). A linear extrapolation from the dS comparison was used to infer the timing of the Ss4R.
Publication 2014
Amino Acid Sequence Exons Fishes Genes Genome Muscle Tissue Oryziinae Proteins Respiratory Rate RNA, Messenger Salmo salar Sequence Alignment SSTR4 protein, human Sticklebacks Trees Trout Zebrafish

Most recents protocols related to «Oryziinae»

Isolates that carried aerobactin were considered for long-read sequencing. To capture the diversity of the putative aerobactin-encoding plasmids, 16 isolates were selected for Oxford Nanopore sequencing based on the diversity in STs and AbSTs, 4 of which were from Thailand. DNA was extracted from pure cultures using the GenFind v3 kit (Beckman Coulter Life Sciences) on a Biomek i7 instrument, using the protocol ‘DNA extraction from bacteria using GenFind v3’. Library preparation was done using the SQK-LSK109 ligation sequencing kit. The sequencing was performed on an Oxford Nanopore GridION instrument (Oxford Nanopore Technologies), using a MinION R9.4.1 flow cell. Guppy version 5.0.14 (Oxford Nanopore Technologies) was used for basecalling and demultiplexing, using the super-accuracy basecalling model.
The genomes that were both Illumina and Nanopore sequenced were subjected to hybrid assembly. First, the long reads were quality controlled with NanoPlot [31 (link)] version 1.33.1. Then, Filtlong [32 ] version 0.2.0 was used to discard the lowest 10 % of reads based on length and quality. Unicycler was used to generate hybrid assemblies based on the filtered long reads and untrimmed Illumina reads. If the hybrid assembly failed, Filtlong was run again and set to remove the lowest 20 % of reads.
Genomes that were either incomplete after hybrid assembly or failed to assemble twice were subjected to long-read assembly and consensus analysis using Trycycler [33 (link)] version 0.5.1. Briefly, Trycycler was used to generate 12 subsets of reads, where each set of four subsets were independently assembled using Minipolish [34 (link)] version 0.1.2, Flye [35 (link)] version 2.9 and Raven (https://github.com/lbcb-sci/raven) version 1.6.1. Then, the contigs were clustered, and potential outliers were removed. If a cluster was represented by less than four contigs, a new subset of 24 read sets were generated and assembled as above. The contig clusters were then reconciled and aligned, before a consensus was made. The resulting long-read assembly was polished by using Medaka (Oxford Nanopore Technologies) version 1.4.4 and two rounds of Pilon [36 (link)] version 1.23.
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Publication 2023
aerobactin Cells DNA, Bacterial DNA Library Genome Hybrids Lebistes Ligation Oryziinae Plasmids Ravens
Genomic DNA of the strains sequenced in this study was extracted and purified using Genomic-tips 100/G and a genomic DNA buffer set (Qiagen) from overnight cultures in lysogeny broth (LB) at 37 °C following the manufacturer’s protocol, with minor modifications including the addition of SDS (final concentration 1 %) after adding buffer B2 and incubation for 1 h at 50 °C. For short-read sequencing, libraries were prepared using the Nextera XT DNA sample prep kit (Illumina) or NEBNext Ultra II FS DNA library preparation kit (New England Biolabs) and sequenced using the Illumina MiSeq platform to generate paired-end sequence reads (301 bp ×2). Reads were trimmed by Platanus trim [27 (link)] with default parameters and assembled by Platanus_B_v1.3.1 [28 (link)]. Scaffolds ≥300 bp were used in this study.
To determine complete genome sequences, size selection of purified DNA was performed using magnetic beads (AMPure XP; Beckman Coulter) to obtain longer DNA fragments. Sequencing libraries were prepared using a rapid barcoding kit (SQK-RBK004), sequenced using the R9.4.1 flow cell with the Oxford Nanopore Technologies (ONT) MinION platform, and base-called using Guppy GPU ver. 3.4.5. (ONT). Reads were trimmed using NanoFilt [29 (link)] with the following parameters: minimum length=7 000 bp, minimum quality score=10 and 5'-terminal 100 bases cutting. For sequencing of three strains (93_161312, CEC13091 and F690), only≥15 kb reads were used for assembly to gain better results. For sequencing of strain F765, the minimum length was changed to 2000 bp to salvage its small plasmid sequence. ONT read assembly and polishing were performed using the microPIPE pipeline [30 (link)]. In brief, trimmed ONT reads were assembled using Flye (v2.8.3) [31 (link)] with the option ‘--plasmids’ and polished with ONT reads using four iterations of Racon (v1.4.20) [32 (link)] followed by one iteration of Medaka (v1.4.3) (GitHub – https://github.com/nanoporetech/medaka). Output contigs were further refined with Illumina short reads using NextPolish (v1.3.1) (GitHub – https://github.com/Nextomics/NextPolish). As the chromosome of strain 93_161312 and the plasmids of strain F690 were not circularized, manual curation was performed using Minimap2 [33 (link)], Integrative Genomics Viewer (igv) [34 (link)] and GenomeMatcher v3.0.2 [35 (link)] to obtain their circular chromosome and plasmid sequences.
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Publication 2023
Buffers Chromosomes DNA Library Genome Lebistes Lysogeny Oryziinae Plasmids Strains
Data quality control and assembly were performed using an established pipeline [28 (link)]. Briefly, raw reads were trimmed using Trimmomatic (version 0.39) with default parameters settings [29 (link)]. Assemblies were generated by unicycler (v0.3.0b) [30 (link)], and checked for standard quality parameters using quast (version 5.0.2) [31 (link)]. Genomes that met all the following quality criteria were included in the downstream analyses: estimated mean read coverage cut-off of ≥30×, 1.6 Mb ±160 kb genome assemblies, and contig count ≤200. The mean assembly length was 1 642 135 bp, with a mean read coverage of 98.2-fold, ranging from 30.2- to 269.4-fold. The mean G+C content was 30. 85 mol%, ranging from 30.06 to 31.13 mol%. A long-read assembly of strain N18-1277 was generated using Flye 2.8.1 [32 (link)], long-read polished with Medaka v1.5.0 (https://github.com/nanoporetech/medaka) and short-read polished with Polypolish v0.5.0 [33 (link)] and polca implemented in MaSuRCA v4.0.4 [34 (link)].
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Publication 2023
Genome Oryziinae Strains
We downloaded the Pathogenwatch pairwise distance matrix and corresponding neighbour-joining tree for the full set of assemblies. The distance matrix is available at https://figshare.com/s/6026855223031e769d8a (DOI: 10.6084 /m9.figshare.19745608), and the tree for interactive viewing at Microreact (https://microreact.org/project/sUrpBsvXi1aiKD7ssPv9pu-nanopore-only-assemblies-for-genomic-surveillance-of-klebsiella-pneumoniae). Pathogenwatch calculates pairwise SNP distances between genomes based on a concatenated alignment of 1972 genes (2 172 367 bp) that make up the core gene library for K. pneumoniae in Pathogenwatch and infers a neighbour-joining tree from the resulting pairwise distance matrix [46 (link)]. Here, we assessed the feasibility of identifying potential nosocomial transmission clusters using these distance matrices. Several studies have proposed thresholds in the range of 21–25 genome-wide SNPs for identifying nosocomial transmission clusters of K. pneumoniae [66–68 (link)]. However, as Pathogenwatch calls SNPs only in 1972 core genes and not genome-wide, we compared the SNP distances calculated by Pathogenwatch with genome-wide SNP counts obtained by mapping short reads to a reference genome to determine the equivalent cut-off for clustering analysis using Pathogenwatch distances. To do this, we used the genome-wide SNP alignment generated previously for n=270 K. pneumoniae isolated at Alfred Health, based on mapping of Illumina reads to the K. pneumoniae NTUH-K2044 reference genome using the RedDog pipeline [69 ] (see full details in [43 (link)]). Pairwise SNP counts were extracted using snp-dist [70 (link)]. Assemblies for these 270 genomes (assembled from Illumina reads de novo using SPAdes optimised with Unicycler v0.4.74, see full details in [43 (link)]) were uploaded to Pathogenwatch, and the pairwise distance matrix was downloaded and compared against that generated from RedDog. We then used R to fit a linear regression model for Pathogenwatch distances as a function of genome-wide mapping-based SNP distances (see Fig. S1). This indicated that a Pathogenwatch distance threshold of 10 SNPs would be approximately equivalent to the established genome-wide distance threshold of 25. These thresholds assume accurate basecalling from Illumina data. To ascertain a corresponding threshold distance using ONT-only data, we compared pairwise Pathogenwatch distances calculated using ONT-only (SUP+Medaka) assemblies vs Illumina assemblies, for pairs of strains linked via probable transmission clusters. Using R to fit a linear regression model indicated that an ONT-only Pathogenwatch distance of 50 SNPs would approximate the Illumina-based Pathogenwatch distance of 10 SNPs or genome-wide distance of 25 SNPs (see Fig. 2).
We compared the topologies of neighbour-joining trees generated from Pathogenwatch distance matrices calculated using SUP+Medaka, HAC+Medaka or Fast+Medaka assemblies against the reference tree (calculated from hybrid SUP+Medaka+pilon assemblies), using the tanglegram function in the R package dendextend v1.15.2 to generate comparative tree plots and calculate entanglement coefficients. We also used the phytools package v1.0–3 in R to compute the Robinson–Foulds distance [71, 72 (link)] between tree topologies, which represents a sum of the number of partitions inferred by the first tree but not the second tree and that inferred by the second tree but not the first tree.
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Publication 2023
Gene Library Genes Genome Hybrids Klebsiella pneumoniae Oryziinae Single Nucleotide Polymorphism Strains Transmission, Communicable Disease Trees
Nanopore raw data (fast5) were analyzed using Guppy Version 4.5.2 software (ONT) [4 (link)]. Basecalling of data was repeated using the parameter "--config dna_r9.4.1_450 bps_hac.cfg--num_callers 4 --cpu_threads_per_caller 4" then barcode recognition was conducted using the parameter "--barcode_kits EXPNBD104 EXP-NBD114" followed by trimming of sequences using the parameter" “--config configuration.cfg--trim_barcodes”. Thereafter, sequence data were counted using NanoPlot v1.28.1 [7 (link)] and variant calls were found using medaka v1.3.2 [8 ]. Finally, raw reads were mapped to the Mycobacterium tuberculosis H37Rv genomic reference sequence then trimmed reads were assembled onto the reference genome using Genomics software.
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Publication 2023
Genome Lebistes Mycobacterium tuberculosis H37Rv Oryziinae

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More about "Oryziinae"

Oryziinae, a subfamily of small, monophyletic rice-like grasses within the Poaceae family, encompasses several economically important genera, including the rice genus Oryza.
These Oryziinae species are primarily distributed across Asia and play a crucial role in global food production.
Understanding the biology, cultivation, and genetic diversity of Oryziinae taxa is essential for improving rice yields and developing more resilient crop varieties.
Researchers can leverage the power of AI-driven platforms like PubCompare.ai to optimize their Oryziinae research protocols.
These tools can help locate the best methods from literature, preprints, and patents, ensuring reproducibility and accuracy in their studies.
By leveraging intelligent comparisons, researchers can identify the ideal protocols and products for their Oryziinae investigations, streamlining their workflows and elevating the quality of their research.
In addition to Oryziinae, researchers may also utilize various tools and reagents to support their studies, such as the Medaka model organism, LightCycler 480 for real-time PCR, the pGEM-T Easy vector for cloning, TRIzol and TRIzol reagent for RNA extraction, the PrimeScript RT reagent kit for reverse transcription, the RNeasy Mini Kit for RNA purification, the MinION sequencer for long-read sequencing, and ISOGEN for RNA isolation.
The Medaka v 1.0.3 reference genome can also provide valuable insights for comparative genomics and genetic analyses.
By incorporating these related terms, abbreviations, and key subtopics, researchers can create comprehensive and SEO-optimized content that expands on the Oryziinae topic, enhancing the discoverability and utility of their research.