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NO-BP

NO-BP is a term used to describe a class of compounds known as nitric oxide-binding proteins.
These proteins play a crucial role in the regulation of nitric oxide (NO) signaling pathways within biological systems.
NO-BPs are involved in diverse physiological processes, such as vasodilation, neurotransmission, and immune function.
By binding to and modulating the activity of NO, these proteins help maintain homeostasis and ensure the proper functioning of various biological systems.
Research into NO-BPs is essential for understanding the complexities of NO signaling and developing potential therapeutic interventions for conditions associated with NO dysregulation.
The study of NO-BPs continues to be an active area of investigation in the field of biomedical sciences.

Most cited protocols related to «NO-BP»

1
Differential Binding Analysis of Histone Marks and TF Data
Cited 63 times
Analyses used csaw v1.2.1. Reads were extended to 100 bp and counted into windows for each library. The window size was set to 150 bp for simulated histone mark data or 10 bp for simulated TF data. Start positions of adjacent windows were separated by 50 bp along the genome. For filtering, reads were also counted into 2 kbp bins, and the median average abundance of all bins was used as a global estimate of the background abundance. This estimate was downscaled for comparison to the window abundances, based on the difference in the size of the bins and windows. Windows were filtered to retain only those with a two-fold or greater increase in the average abundance above the scaled background estimate. This corresponds to genomic regions where there is substantial enrichment over the non-specific background.
Counts from the remaining windows were tested for significant DB using edgeR v3.10.0. Briefly, an abundance-dependent trend in the NB dispersions was fitted to all windows, using the estimateDisp function. A GLM was fitted to the counts for each window using the trended NB dispersion. The quasi-likelihood (QL) dispersion was estimated from the GLM deviance. An abundance-dependent trend was robustly fitted to the QL dispersions across all windows, and the QL dispersion estimate for each window was shrunk to this trend. Finally, a P-value for DB in each window was computed using the QL F-test.
Windows were clustered into genomic regions using a nearest-neighbor approach, where adjacent windows no more than 100 bp apart were placed into the same cluster. A maximum cluster width of 5 kbp was set to avoid chaining. The P-values for all windows in each cluster were combined using Simes’ method, and the Benjamini–Hochberg (BH) method was applied on the combined P-values from all clusters.
Analyses were conducted using R 3.2.0 and Bioconductor 3.1 for UNIX.
Lun A.T, & Smyth G.K. (2015). csaw: a Bioconductor package for differential binding analysis of ChIP-seq data using sliding windows. Nucleic Acids Research, 44(5), e45.
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Publication 2015
BP 100 DNA Library Genome Histone Code NO-BP
2
ChIP-Seq Analysis of Histone Modifications
Cited 51 times
Libraries were obtained from the NCBI Gene Expression Omnibus using the accession number GSE53490 (19 (link)). Two replicate libraries were present to examine H3K4me3 marking in each of four biological conditions—treated wild-type, treated knockout, untreated wild-type and untreated knockout. This corresponds to Sequence Read Accession files SRR1055323 to SRR1055330, which were converted to FASTQ with the fastq-dump utility from the SRA toolkit. Reads were aligned to the mm10 build of the mouse genome, using Subread v1.4.6 (20 (link)) in paired-end mode. Unique mapping was turned on, and any ties were broken with the Hamming distance. BAM files were sorted and indexed using SAMtools v0.1.19 (http://samtools.sourceforge.net). DB detection methods were then applied to identify changes in marking between conditions.
To detect DB events with DiffBind, peaks were called in each library using MACS or HOMER in histone mode. In both cases, the same parameters were used as described for the histone mark simulations, though the fragment length was set to 200 bp based on the insert sizes of proper read pairs in each library (see Supplementary Figure S1). A consensus peak set was constructed using DiffBind, as previously described. Counting was performed for read pairs through the summarizeOverlaps function, without any removal of duplicates. Parallelization was also turned off to simplify processing. Contrasts between groups were set up with minMembers of 2 and the statistical analysis was performed with edgeR.
For csaw, properly paired reads were identified as inward-facing intra-chromosomal pairs that were no more than 600 bp apart. The interval spanned by each proper pair represents the fragment from which the reads were sequenced. The number of fragments overlapping each 150 bp window was counted for each library. Again, the starts of adjacent windows were separated by 50 bp. Background-based filtering of windows was performed as described above. Windows in unassigned contigs or the mitochrondrial genome were also discarded. To remove composition biases, fragments were counted into 10 kbp bins. Normalization factors were computed from these counts, using the trimmed mean of M-values (TMM) method without precision weighting. These factors were used to scale the library sizes when testing for DB windows in edgeR's QL framework. Clustering of windows into genomic regions was performed, P-values were combined for each region and the BH method was applied as previously described. This analysis was repeated with 1500 bp windows for a low-resolution analysis, where the starts of adjacent windows were separated by 500 bp.
Lun A.T, & Smyth G.K. (2015). csaw: a Bioconductor package for differential binding analysis of ChIP-seq data using sliding windows. Nucleic Acids Research, 44(5), e45.
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Publication 2015
2'-deoxyuridylic acid Biopharmaceuticals Chromosomes Contrast Media DNA Library DNA Replication Gene Expression Genome Histone Code histone H3 trimethyl Lys4 Histones Mice, House NO-BP
3
Versatile CRISPR/Cas9 Vector Construction
Cited 22 times
Vector expressing both gRNA and mCherry (pCAGmCherry-gRNA) was generated as previously described30 (link). To construct gRNA expression vectors, each 20 bp target sequence was sub-cloned into pCAGmCherry-gRNA or gRNA_Cloning Vector (Addgene 41824). The CRISPR/Cas9 target sequences (20 bp target and 3 bp PAM sequence (underlined)) used in this study include: scramble, GCTTAGTTACGCGTGGACGAAGG; mutant GFP, CAGG GTAATCTCGAGAGCTTAGG; MH1, GCCGCTTTACTTAGGTCCCCGGG; and MH2, GGAGATCCACTCTCGAGCCCGGG; for PITCh donor: mouse Tubb3, AGCTGCGAGCAACTTCACTTGGG; human TUBB3, AGCTGCGAGCAGCTT CACTTGGG; human KCNQ1, AGTACGTGGGCCTCTGGGGGCGG; the downstream of CAG promoter in Ai14 mouse, TAGGAACTTCTTAGGGCCCGCGG; rat Mertk for HITI, GAGGACCACTGCAACGGGGCTGG; rat Mertk for HDR, TCAGGTGCTTAGGCATTTCGTGG. The Scramble-gRNA target sequence we designed is an artificial sequence that does not exist in human, mouse and rat genomes. We used the off-target finder software Cas-OFFinder (http://www.rgenome.net/cas-offinder/) to confirm that there were no genomic target sites within 2-bp mismatches. We have confirmed that the Scramble-gRNA can cut its target site in the donor vector (Extended Data Fig. 1b). pMDLg/pRRE, pRSV-Rev and pMD2.G (Addgene 12251, 12253 and 12259) were used for packaging lentiviruses. pEGIP*35 and tGFP (Addgene 26776 and 26864) were used for examining HDR and HITI efficiencies. To construct IRESmCherry-0c, IRESmCherry-1c, IRESmCherry-2c, IRESmCherry-MH, IRESmCherry-HDR-0c and IRESmCherry-HDR-2c, IRES and mCherry sequences were amplified with Cas9 target sequence by PCR from pEGIP*35 and pCAGmCherry-gRNA, respectively and co-integrated into pCR-bluntII vector (Invitrogen) with zero, one or two CAS9/gRNA target sequences. Cas9 expression plasmid (hCas9) was purchased from Addgene (41815). To generate different NLS-dCas9 constructs, pMSCV-LTR-dCas9-VP64-BFP (Addgene 46912) was used to amplify dCas9, which was subsequently subcloned into pCAG-containing plasmid with different NLS and 3 × Flag tag. To construct pCAG-Cas9 (no NLS), pCAG-1NLS-Cas9-1NLS and pCAG-1BPNLS-Cas9-1BPNLS, D10A and H840A mutations of dCas9 plasmids were exchanged to wild-type sequence by In-Fusion HD Cloning kit (Clontech). Then, pCAG-Cas9-2AGFP (no NLS), pCAG-1NLS-Cas9-1NLS-2AGFP and pCAG-1BPNLS-Cas9-1BPNLS-2AGFP were constructed by adding 2AGFP downstream of Cas9. To construct pCAG-floxSTOP-1BPNLS-Cas9-1BPNLS, 1BPNLS-Cas9-1BPNLS was amplified by PCR and exchanged with GFP of pCAG-floxSTOP-EGFP-N1 vector31 (link). To construct HITI donor plasmids for mouse and human Tubb3 gene (Tubb3-1c, Tubb3-2c, Tubb3-2cd, hTUBB3-1c and hTUBB3-2c) and PITCh donor (Tubb3-MH), GFP was subcloned into pCAG-floxSTOP plasmid with one or two CAS9/gRNA target sequences. To construct HDR donor for mouse Tubb3 gene (Tubb3-HR), GFP, 5′ and 3′ homology arms were amplified from pCAG-GFP-N1 or mouse genome, then subcloned into pCAG-floxSTOP plasmid. pCAG-ERT2-Cre-ERT2 was purchased from Addgene (13777). PX551 and PX552 were purchased from Addgene (60957 and 60958). To construct AAV-Cas9, nEF (hybrid EF1 α/HTLV) promoter (Invivogen) was exchanged with Mecp2 promoter of PX551. To construct donor/gRNA AAVs for HITI, donor DNA sandwiched by Cas9/gRNA target sequence, gRNA expression cassette and GFPKASH (or mCherryKASH) expression cassettes were subcloned between ITRs of PX552, and generated pAAV-mTubb3, pAAV-Ai14-HITI, pAAV-Ai14-luc, pAAV-Ai14-scramble and pAAV-rMertk-HITI. For pAAV-rMertk-HITI, exon 2 of rat Mertk gene including the surrounding intron is sandwiched by Cas9/gRNA target sequence, which is expected to integrate within intron 1 of Mertk by HITI. For HDR AAV (pAAV-Ai14-HDR and pAAV-rMertk-HDR), the homology arms were amplified by PCR from mouse and rat genome DNA, and subcloned into AAV backbone plasmid. The plasmids described in this manuscript will be available to academic researchers through Addgene.
Suzuki K., Tsunekawa Y., Hernandez-Benitez R., Wu J., Zhu J., Kim E.J., Hatanaka F., Yamamoto M., Araoka T., Li Z., Kurita M., Hishida T., Li M., Aizawa E., Guo S., Chen S., Goebl A., Soligalla R.D., Qu J., Jiang T., Fu X., Jafari M., Esteban C.R., Berggren W.T., Lajara J., Nuñez-Delicado E., Guillen P., Campistol J.M., Matsuzaki F., Liu G.H., Magistretti P., Zhang K., Callaway E.M., Zhang K, & Belmonte J.C. (2016). In vivo genome editing via CRISPR/Cas9 mediated homology-independent targeted integration. Nature, 540(7631), 144-149.
Publication 2016
Arm, Upper c-Mer Tyrosine Kinase Cloning Vectors Clustered Regularly Interspaced Short Palindromic Repeats Elongation Factor 1alpha Exons Genes Genome Homo sapiens Hybrids Internal Ribosome Entry Sites Introns Lentivirus MECP2 protein, human Mice, Laboratory mitogen-activated protein kinase 3, human Mutation NO-BP Plasmids T-Cell Leukemia Viruses, Human Tissue Donors TUBB3 protein, human Vertebral Column
4
Normalizing Mitochondrial DNA Copy Number
Cited 18 times
It is known that DNA content can vary in mitochondria from different cells or tissues, depending, for instance, on energy requirements. Thus, in samples from distinct areas of a specific organ, one could expect discrepancies in the ability to amplify the mtDNA based not on different levels of lesions within the sample but simply from fluctuation in the number of copies of the mitochondrial genome present. Therefore, to normalize for mitochondrial copy number, we routinely amplify an additional short fragment (no longer than 300 bp) of the mitochondrial gene under study. The idea is that the amplification of the short fragment reflects only undamaged DNA due to the low probability of introducing lesions in small segments. The results obtained with the short sequence are used to monitor the copy number of the mitochondrial genome and, more importantly, to normalize the data obtained with the large (7–15 kb) fragment. As noted above if DNA is extracted using an automated system employing the QIAcube (QIAGEN, catalog number 9001292) with the QIAamp DNA mini kit for human samples (QIAGEN, catalog number 51304), linearization of the mtDNA is necessary to get an accurate level of mtDNA. We have found that HaeII is compatible with our primer sets for mouse mtDNA (seeFig. 3) and PvuII or ClaI are compatible with our primer sets for human mtDNA.
Furda A., Santos J.H., Meyer J.N, & Van Houten B. (2014). Quantitative PCR-Based Measurement of Nuclear and Mitochondrial DNA Damage and Repair in Mammalian Cells. Methods in molecular biology (Clifton, N.J.), 1105, 419-437.
Publication 2014
DNA, Mitochondrial Genes, Mitochondrial Genome, Mitochondrial Homo sapiens Mitochondria Mitochondrial Inheritance Mus NO-BP Oligonucleotide Primers Somatostatin-Secreting Cells Tissues
5
Bipolar Disorder Family Study Protocol
Cited 15 times
Parents with BP were recruited through advertisement (53.0%), adult BP studies (31.0%), and outpatient clinics (16.0%). There were no differences in BP subtype, age at BP onset, or rates of non-BP disorders on the basis of recruitment source. Parents were required to fulfill DSM-IV17 criteria for BP-I or BP-II. Exclusion criteria included current or lifetime diagnoses of schizophrenia, mental retardation, mood disorders secondary to substance abuse, medical conditions, or medication use, and living more than 200 miles away from Pittsburgh, Pennsylvania.
Control parents consisted of healthy parents or parents with non-BP psychiatric disorders from the community and were group matched by age, sex, and neighborhood using the area code and the first 3 digits of the telephone number and the zip code of the parents with BP. The exclusion criteria for the control parents were the same as those for the parents with BP, with the additional requirements that neither of the biological parents could have BP and they could not have a first-degree relative with BP. Control parents were recruited by the University Center for Social and Urban Research, University of Pittsburgh in an approximate ratio of 1 control parent for every 2 parents with BP.
With the exception of children who were unable to participate (eg, those diagnosed with mental retardation), all offspring aged 6 to 18 years from each family were included in the study.
Birmaher B., Axelson D., Monk K., Kalas C., Goldstein B., Hickey M.B., Obreja M., Ehmann M., Iyengar S., Shamseddeen W., Kupfer D, & Brent D. (2009). Lifetime Psychiatric Disorders in School-aged Offspring of Parents With Bipolar Disorder: The Pittsburgh Bipolar Offspring Study. Archives of general psychiatry, 66(3), 287-296.
Publication 2009
Adult Biopharmaceuticals Child, Exceptional Intellectual Disability Mental Disorders Mood Disorders NO-BP Parent Pharmaceutical Preparations Schizophrenia Substance Abuse Thumb

Most recents protocols related to «NO-BP»

1
Cloning and Expression of Giardia Giardin Proteins
RNA was extracted from 1 × 107 trophozoites in 1 ml of TRIzol reagent (Vazyme, Nanjing, China), genomic DNA was extracted from G. duodenalis total RNA using MonScript dsDNase (Monad, Wuhan, China) and complementary DNA (cDNA) was synthesized using the MonScript RTIII Super Mix (Monad) according to the manufacturer’s instructions.
The CDS sequence information of the target genes of G. duodenalis was obtained from the NCBI GenBank. Specific seamless cloning primers were designed separately for each target gene using Primer 5.0. The forward primers (5ʹ− 3ʹ) were composed of three parts, namely the overlap sequences with EcoRV-linearized pcDNA3.1(+) vector (TGGTGGAATTCTGCAGAT) and the initiation codon ATG and GNN (if the first base of the target gene was not G). This was done to enhance the expression efficiency. Additionally, no fewer than 16-bp combinative bases (40–60% GC contents/Tm value around 55 °C) were included. The reverse primers (5ʹ− 3ʹ) were composed of two parts, namely the overlap sequences with EcoRV-linearized pcDNA3.1(+) vector (GCCGCCACTGTGCTGGAT) and no fewer than 16-bp combinative bases (excluding the last two bases of the stop codon, such as AA or GA to make the recombinant plasmids express their tag protein). The primer sequences are listed in Table 1 and were synthesized by Comate Bioscience Company Limited (Changchun, China).

Primer sequences of recombinant pcDNA3.1(+)-alpha-2 and alpha-7.3 giardins

Primer nameGenbank numberSequence (5ʹ-3ʹ)Product size (bp)
Alpha-2 giardinXM_001706906F:TGGTGGAATTCTGCAGATATGGTTCCGAAGCTATCCCAGA892
R:GCCGCCACTGTGCTGGATACTCCCTTAGGCGCCAGA
Alpha-7.3 giardinXM_001708403F:TGGTGGAATTCTGCAGATATGGCTGCAGCGAAGGCTA886
R:GCCGCCACTGTGCTGGATACATGACGTGCCAGAGGAC

The underlined parts were introduced bases to enhance the expression efficiency of recombinant pcDNA3.1(+)-alpha-2 and alpha-7.3 giardins

F Forward primer, R reverse primer

The targets were amplified using Pfu (Tiangen, Beijing, China) or Ex-taq (Takara Biomedical Technology [Beijing] Co., Ltd., Beijing, China) DNA Polymerase, with the prepared G. duodenalis cDNA as the template. The eukaryotic expression vector plasmid pcDNA3.1(+) was linearized with the restriction enzyme EcoRV and dephosphorylated using Fast AP (Thermo Fisher Scientific). Both linearized pcDNA3.1(+) fragments and amplified target gene fragments were purified using a DNA Gel Purification Kit (Tiangen) and quantified using a Nanodrop ND-2000 (Thermo Fisher Scientific). The pcDNA3.1(+) fragments and each target gene fragment were recombined using the MonClone Single Assembly Cloning Mix (Monad Biotech Co., Ltd., Suzhou, China) and confirmed using DNA sequencing by Comate Bioscience Company Limited (Changchun, China).
Zhao P., Li J., Li X., Dong J., Wang X., Zhang N., Li S., Sun M., Zhang X., Wang Z., Liang M., Li Y., Cao L, & Gong P. (2023). The NLRP3 inflammasome recognizes alpha-2 and alpha-7.3 giardins and decreases the pathogenicity of Giardia duodenalis in mice. Parasites & Vectors, 16, 85.
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Publication 2023
Biomedical Technology Cloning Vectors Codon, Initiator Codon, Terminator DNA, A-Form DNA, Complementary DNA-Directed DNA Polymerase DNA Restriction Enzymes Eukaryota Genes Genome NO-BP Oligonucleotide Primers Plasmids Proteins trizol Trophozoite
2
Multiplex PCR and LAMP Assay for Insect Identification
The PCR primers were validated and designed to amplify DNA fragments no longer than 700 bp to ensure that all multiplex PCR systems are well suited for tested insects with samples collected from only legs/antenna/whole body. After testing individual primer pairs in singleplex reactions to ensure proper amplification, optimal annealing temperatures were determined using PCRs, and primer concentrations were adjusted to balance the successful amplification of all fragments using standardized DNA templates, as described by Yashiro et al. [2 (link)]
TOYOBO KOD-FX neo (Toyobo Life Science) was used for general PCR to confirm the species through sequencing and primer checking. Appropriate primers were used with the PCR amplification protocol that included 2 min denaturation at 94 °C, followed by 40 cycles of denaturation at 94 °C for 15 s, annealing at 60 °C for 15 s, and extension at 68 °C for 15 s. SYBR green was used to visualize the amplified DNA fragments after they were separated on 1.5% agarose gel electrophoresis (Life Technologies, Grand Island, NY, USA). All experiments were conducted with at least three biological replicates.
The multiplex primer mixture comprised BPH_lamp3_F33/B33, SBPH_lamp1_F311/B34, and WBPH_lamp1_F31/B312 in a ratio of 1:1:1. A multiplex reaction (20 μL volume) was conducted with 3 μL of the respective primer mixture (10 pmol/μL), 1 μL gDNA (10 ng/μL), 10 μL 2X buffer with 15 mM MgCl2, 1.6 μL dNTP (each dNTP), including 0.15 KOD-FX neo (1 unit/μL), and PCR grade water 5.25 μL. The multiplex PCR reaction was conducted for 2 min at 94 °C, followed by denaturation at 94 °C for 20 s, annealing at 53 °C for 20 s, extension at 68 °C for 20 s, and final extension at 68 °C for 2 min, and holding at 8 °C for an unlimited period. Amplification was carried out using an Applied Biosystems ProFlex PCR system (Thermo Fisher Scientific, Waltham, MA, USA).
The LAMP assay was carried out according to the manufacturer’s instructions using a WarmStart® LAMP Kit (New England Biolabs, Ipswich, MA, USA). The LAMP conditions were selected based on our previous studies [30 (link),40 (link)]. The LAMP assay was run for 60 min at 61 °C, 63 °C, and 65 °C with four primers (F3, B3, FIP, and BIP) to optimize the reaction temperature using an Applied Biosystems ProFlex PCR system (Thermo Fisher Scientific). The efficiency of loop primer(s) was tested/evaluated in the presence of additional loop primer(s) at 61 °C for 30 min. The detection limit of gDNA was also tested using six primers at 61 °C for 30 min.
Rahman M.M., Nam H., Choi N, & Kim J. (2023). Development of Molecular-Based Species Identification and Optimization of Reaction Conditions for Molecular Diagnosis of Three Major Asian Planthoppers (Hemiptera: Delphacidae). Insects, 14(2), 124.
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Publication 2023
Biopharmaceuticals Buffers Electrophoresis, Agar Gel Human Body Insecta LAMP3 protein, human LAMP assay Leg lysosomal-associated membrane protein 1, human Magnesium Chloride Multiplex Polymerase Chain Reaction NO-BP Oligonucleotide Primers SYBR Green I
3
Predicting Tissue-Specific Enhancer Activity
We built a deep convolutional neural network to predict tissue-specific enhancer activity directly from the enhancer DNA sequence. The DLM comprises five convolution layers with 320, 320, 240, 240, and 480 kernels, respectively (table S14). Higher-level convolution layers receive input from larger genomic ranges and are able to represent more complex patterns than the lower layers. The convolutional layers are followed by a fully connected layer with 180 neurons, integrating the information from the full length of 1000-bp sequence. In total, the DLM has 3,631,401 trainable parameters. We used the Python library Keras version 2.4.0 (https://github.com/keras-team/keras) to implement our model.
The model was trained for each of the four temporal-spatial groups of enhancers (CS16, CS23, F2F, and F2O). The positive sets contain the human embryonic enhancers of each group. The DHS profiles of non–CNS-related and nonembryonic tissues from Roadmap Epigenomics projects (55 (link)), which do not overlap the positive sets, were collected as the negative training set of the DL model. The reason we used DHS sites not overlapping embryonic neocortex H3K27ac peaks as negative control regions is that we aim to identify tissue-specific enhancers of embryonic neocortex, and DHS is a good representation of active chromatin. The fact that DHS in general overlaps H3K27ac makes it a stringent control, and in fact, our choice of DHS as the control is analogous to DeepSEA, which uses the genomic regions not overlapping the positive set and with at least one TF binding as the negative set, which broadly overlap with DHS regions.
Training and testing sets were split by chromosomes. Chromosomes 8 and 9 were excluded from training to test prediction performances. Chromosome 6 was used as the validation set, and the rest of the autosomes were used for training. Each training sample consists of a 1000-bp sequence (and their reverse complement) from the human GRCh37 (hg19) reference genome. Larger DL score of the genomic sequence corresponds to a higher propensity to be an active enhancer. The genomic sequence with DLM score ≥ 0.197 (FPR ≤ 0.1) is predicted to be active enhancers. We used the difference of the DLM score induced by a human-macaque single-nucleotide mutation to estimate its impact on enhancer activity.
Given a human (hg19) or macaque (rheMac2) enhancer, we used liftOver (56 (link)) to identify their orthologs. Only the reciprocal counterparts with their length difference no more than 50 bp were considered to be ortholog pairs. For a human sequence with n mutations relative to its macaque ortholog, to score the impact of combinations of m (m < n) mutations on enhancer activity, all possible combinations of m (n choose m) human alleles at the human-macaque mutation sites were introduced to the macaque orthologs if the total number of combinations (n choose m) is no more than 10,000; otherwise, we randomly sample 10,000 combinations of m human alleles from the human-macaque mutation sites and introduce them to the macaque ortholog. The change of DL score caused by the set of introduced human mutations was used to estimate their impact on enhancer activity.
We applied the same convolutional neural network architecture to build a HepG2 enhancer (H3K27ac peaks centered by DNase peaks) classifier. Next, we further used the HepG2 DLM to evaluate the allele-specific effects on enhancer activity using raQTLs (52 (link)).
Li S., Hannenhalli S, & Ovcharenko I. (2023). De novo human brain enhancers created by single-nucleotide mutations. Science Advances, 9(7), eadd2911.
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Publication 2023
Alleles Chromatin Chromosomes Chromosomes, Human, Pair 6 Chromosomes, Human, Pair 8 Deoxyribonuclease I DNA Library Embryo Enhancer Elements, Genetic Genome Homo sapiens Macaca Mutation Neocortex Neurons NO-BP Nucleotides Python Tissues Tissue Specificity
4
Constructing Genetic Maps via OneMap
Genetic maps were constructed in R (version 3.6.1) [58 ] with package OneMap (Version 2.1.1) [59 (link)]. Prior to importing the datasets into R/Onemap, variants were tested for Mendelian segregation (χ2 goodness of fit) and those where p < 0.00001 were removed with a custom Perl script (removeDistorted.pl) The resulting files, femalePT.vcf.gz and malePT.vcf.gz, are available at https://github.com/nicotralab/chen-et-al-sex-determination [49 ]. Each dataset was then thinned with vcftools [60 (link)] to ensure that the distance between adjacent variants was no less than 5000 bp and converted from vcf format into OneMap’s “.raw” format with a bash shell script (thin-for-onemap.v2.sh). After this step, we were left with 23,462 variants (20,058 SNPs and 3404 indels) for the maternal genome and 22,359 variants (19,771 SNPs and 2863 indels) for the paternal genome (Additional Files 10 and 11).
We constructed linkage maps in R (version 3.6.1; [58 ] with the package OneMap (Version 2.1.1) [59 (link)]. Variants with identical genotypes in the F1 animals were binned to create single markers for linkage mapping with the function find_bins(). After binning, we recalculated segregation distortion using a Bonferroni-corrected p-value of 0.05 and removed any remaining distorted markers. This resulted in a set of 977 markers for the maternal genome and 487 for the paternal genome. Two-point tests were used to calculate recombination fractions and LOD scores for each pair of markers, and linkage groups between non-distorted markers identified with a maximum recombination fraction (rf) of 0.4 and minimum LOD score determined by the OneMap function suggest_lod() (6.14 for the female dataset and 5.56 for the male dataset). In both cases, 15 linkage groups were obtained.
To order markers within each linkage group, the function order_seq() was used. This function selects an initial set of five markers and applies an exhaustive search to determine the order with the lowest LOD score. To this framework map, the remaining markers are added one-by-one to optimize the total LOD score of the growing map. Recombination fractions were converted to gastrozooid (cM) units using the Kosambi map function.
Most of the initial maps contained pairs of markers that were placed within 0.0001 cM of each other by the OneMap software. Upon closer inspection, we discovered that these markers were simply markers that were located on the same contig in the reference genome but had their alternative alleles in opposite phase of one another. Since these markers were essentially redundant to one another we decided to remove them from the maps. To do this we identified them in the initial maps with a custom perl script (identify_redundant_markers.pl), then removed them using the drop_marker function in OneMap.
A recombination fraction plot was then generated with the function rf_graph_table and visually inspected to identify misplaced markers. These were removed from the map and re-inserted with the try_seq() function. Markers that could not be confidently placed were removed entirely from the final maps. Summary statistics for each map were calculated using the Genetic Map Comparator [61 (link)]. The “unbinned” maps were created by using the custom perl script unbin_markers_in_map.pl.
Chen R., Sanders S.M., Ma Z., Paschall J., Chang E.S., Riscoe B.M., Schnitzler C.E., Baxevanis A.D, & Nicotra M.L. (2023). XY sex determination in a cnidarian. BMC Biology, 21, 32.
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Publication 2023
Alleles Animals Chromosome Mapping Females Genome Genotype INDEL Mutation Males Microtubule-Associated Proteins NO-BP Recombination, Genetic Sex Determination Analysis Single Nucleotide Polymorphism
5
Characterization of 2-Bromopropane Purity
2‐BP of reagent grade (Wako 1st grade, greater than 99.7% pure) was obtained from Fujifilm Wako Pure Chemical Industries, LTD. Each lot of 2‐BP used in the present study was analyzed for identity by mass spectrometer and for stability by gas chromatography prior to and after use. The mass spectrometry peak was consistent with the literature values for 2‐BP, confirming the identity of 2‐BP. No aberrant gas chromatography peaks were detected before or after use of each lot substances, confirming the stability of the 2‐BP used in the present study. Additionally, 2‐BP in the chambers was monitored using gas chromatography, and no aberrant gas chromatographic peaks of 2‐BP were detected in the inhalation exposure chambers.
Senoh H., Kasai T., Hirai S., Furukawa Y., Misumi K., Goto Y., Takanobu K., Matsumoto M., Fukushima S, & Aiso S. (2023). Multi‐organ carcinogenicity by inhalation exposure to 2‐Bromopropane in rats. Journal of Occupational Health, 65(1), e12388.
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Publication 2023
Gas Chromatography Inhalation Exposure Mass Spectrometry NO-BP Substance Use

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Brassica 60k infinium snp array by Illumina
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Miseq300 platform by Illumina
Sourced in United States
The MiSeq300 platform is a benchtop next-generation sequencing (NGS) system designed for a wide range of applications, including targeted gene sequencing, metagenomics, and small-scale whole-genome sequencing. The platform features a compact and user-friendly design, and is capable of generating high-quality sequencing data with industry-leading accuracy and speed.
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Genomestudio methylation module software by Illumina
Sourced in United States
The GenomeStudio Methylation module software is a component of the GenomeStudio software suite developed by Illumina. Its core function is to analyze and visualize DNA methylation data generated from Illumina's DNA methylation microarray platforms.
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Green taq mix by Vazyme
Sourced in China
Green Taq Mix is a ready-to-use PCR reaction mixture containing Taq DNA polymerase, dNTPs, MgCl2, and a proprietary buffer system. It is designed for routine DNA amplification applications.
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Easypure genomic dna kit by Transgene
Sourced in China
The EasyPure Genomic DNA Kit is a laboratory equipment designed for the rapid and efficient extraction of high-quality genomic DNA from a variety of biological samples. The kit utilizes a simple and streamlined protocol to isolate DNA, making it suitable for a range of applications in molecular biology and genetics research.
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Hiseq 3000 by Illumina
Sourced in United States, China, France, Germany, Switzerland, Belgium, United Kingdom, Finland
The HiSeq 3000 is a high-throughput sequencing system designed for large-scale genomic projects. It offers a combination of speed, throughput, and accuracy to enable researchers to generate large amounts of sequencing data efficiently. The HiSeq 3000 is capable of sequencing multiple samples simultaneously, making it well-suited for applications such as whole-genome sequencing, targeted gene sequencing, and transcriptome analysis.
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Qiacube by Qiagen
Sourced in Germany, United States, United Kingdom, Spain, Canada, Netherlands, France, Italy, Japan, Denmark, Australia, Switzerland
The QIAcube is an automated sample preparation instrument designed for the purification of nucleic acids from a variety of sample types. It is capable of performing a range of isolation and purification protocols, including DNA, RNA, and viral nucleic acid extraction. The QIAcube is intended to provide a standardized and efficient method for sample preparation, reducing manual handling and improving reproducibility.
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Primestar gxl dna polymerase by Takara Bio
Sourced in Japan, China, United States, France
PrimeSTAR GXL DNA Polymerase is a high-fidelity DNA polymerase with proofreading activity, designed for accurate and efficient DNA amplification. It has a low error rate and can amplify long DNA fragments with high efficiency.

More about "NO-BP"

Nitric Oxide-Binding Proteins (NO-BPs) are a class of crucial regulatory proteins that play a vital role in the intricate signaling pathways of nitric oxide (NO) within biological systems.
These proteins are involved in a wide range of physiological processes, including vasodilation, neurotransmission, and immune function, by binding to and modulating the activity of NO.
Research into NO-BPs is essential for understanding the complex mechanisms underlying NO signaling and developing potential therapeutic interventions for conditions associated with NO dysregulation.
The study of these proteins continues to be an active area of investigation in the biomedical sciences.
NO-BPs are closely linked to various cutting-edge genomic and molecular biology techniques, such as the Brassica 60K Illumina Infinium SNP array, MiSeq platform, MiSeq system, Miseq300 platform, GenomeStudio Methylation module software, Green Taq Mix, EasyPure Genomic DNA Kit, HiSeq 3000, and QIAcube.
These technologies enable researchers to delve deeper into the genetic and epigenetic aspects of NO-BP regulation and function.
Additionally, the PrimeSTAR GXL DNA Polymerase is a versatile tool that can be utilized in the amplification and analysis of genetic sequences related to NO-BPs, facilitating a more comprehensive understanding of their structure, expression, and interaction with other biomolecules.
By leveraging these cutting-edge techniques and tools, scientists can gain invaluable insights into the role of NO-BPs in maintaining homeostasis and ensuring the proper functioning of various biological systems.
This knowledge is crucial for the development of targeted therapies and the advancement of our understanding of nitric oxide signaling in health and disease.