> Genes & Molecular Sequences > Gene or Genome > Genetic Loci

Genetic Loci

Genetic loci refer to the specific physical locations of genes or other DNA sequences within the genome.
These loci play a crucial role in understanding genetic variation, inheritance patterns, and the identification of disease-associated regions.
Researchers can leverage PubCompare.ai's AI-driven platform to enhance reproducibility and accuracy in genetic loci optimization.
The platform effortlessly locates protocols from literature, pre-prints, and patents, and provides AI-driven comparisons to identify the best protocols and products, streamlining the research process for genetcis loci optimization.

Most cited protocols related to «Genetic Loci»

Robust Knock-in Mouse Line Generation

Targeting constructs were generated using a combined gene synthesis (GenScript Corp.) and molecular cloning approach. Briefly, to target the Rosa26 locus, a cassette containing the following components was constructed: FRT – LoxP – Stop codons – 3x SV40 polyA – LoxP – EYFP – WPRE – bGH polyA – AttB – PGK promoter – FRT – Neo – PGK polyA – AttP. For most targeting vectors, this cassette was cloned into a Rosa-CAG targeting vector3 (link), downstream of the CAG promoter and upstream of the 3′ arm, to generate the final EYFP targeting vector. Unique restriction sites flanking the EYFP gene were used to replace EYFP with alternative reporter genes. For the Ai2 vector, which lacks the WPRE, the CAG promoter was inserted between the first FRT and LoxP sites, and the cassette was cloned immediately downstream of the 5′ homology arm. The final targeting vectors contained 5′ and 3′ homology arms of 1.1 kb and 4.3 kb, as well as a PGK-DTA cassette for negative selection. Targeting constructs for knock-in Cre lines inserted into other gene loci were constructed in similar ways.
The targeting vectors were linearized and transfected into the 129/B6 F1 hybrid ES cell line G442 (link) using an Amaxa electroporator. G418-resistant ES clones were screened by Southern blot analysis of HindIII digested DNA, which was probed with a 1.1 kb genomic fragment from immediately upstream of the 5′ arm. We observed a recombination rate of about 25% for the four constructs. Positive ES clones were injected into C57BL/6J blastocysts to obtain chimeric mice following standard procedures. Both ES cell transfections and blastocyst injections were performed by the University of Washington Transgenic Resources Program. Due to the robustness of the G4 cells, high-percentage chimeras and high rates of germline transmission were routinely obtained. Chimeric mice were bred with either C57BL/6J mice to obtain germline transmission or various Cre-driver lines for direct characterization.
An Ai9 ES cell clone with strong germline transmission potency was used in subsequent transfections for the Flp-mediated exchange strategy outlined in Supplementary Figure 4 online. Ai9 ES cells were co-transfected using a Bio-Rad electroporator with 100 μg of pCAGGS-FLPe (Open Biosystems) and 40 μg of an incoming replacement vector. After 8 to 10 days of Hygromycin B selection, surviving colonies that also appeared green by fluorescence microscopy were picked and screened by PCR using primer sets designed to confirm a correct insertion of the incoming vector at the 5′ and 3′ FRT recombinase sites.

Madisen L., Zwingman T.A., Sunkin S.M., Oh S.W., Zariwala H.A., Gu H., Ng L.L., Palmiter R.D., Hawrylycz M.J., Jones A.R., Lein E.S, & Zeng H. (2009). A robust and high-throughput Cre reporting and characterization system for the whole mouse brain. Nature neuroscience, 13(1), 133-140.

Publication 2009

Anabolism Animals, Transgenic antibiotic G 418 Blastocyst Cells Chimera Clone Cells Cloning Vectors Codon, Terminator Embryonic Stem Cells Genes Genes, Reporter Genetic Loci Genome Germ Cells Germ Line Hybrid Cells Hygromycin B Mice, Inbred C57BL Microscopy, Fluorescence Mus N-fluoresceinylphosphatidylethanolamine Oligonucleotide Primers Poly A Recombinase Recombination, Genetic Rosa Simian virus 40 Southern Blotting Transfection Transmission, Communicable Disease

Robust Knock-in Mouse Line Generation

Publication 2009

Panda Genome Assembly and Annotation

We constructed the paired-end DNA libraries with insert sizes larger than 2 kb by self-ligation of the DNA fragments and merging the two ends of the DNA fragment. We randomly fragmented the circularized DNA and enriched the fragments crossing the merged boundaries using magnetic beads with biotin and streptavidin. The sequencing process followed the manufacturer’s instructions (Illumina), and the fluorescent images were processed to sequences using the Illumina data processing pipeline (v1.1).
The genome sequence was assembled with short reads using SOAPdenovo software⁶ (http://soap.genomics.org.cn), which adopts the de Bruijn graph data structure to construct contigs^{7 (link)}. The reads were then realigned to the contig sequence, and the paired-end relationship between the reads was transferred to linkage between contigs. We constructed scaffolds starting with short paired-ends and then iterated the scaffolding process, step by step, using longer insert size paired-ends. To fill the intra-scaffold gaps, we used the paired-end information to retrieve read pairs that had one read well-aligned on the contigs and another read located in the gap region, then did a local assembly for the collected reads.
Known transposable elements were identified using RepeatMasker (version 3.2.6)¹⁴ against the Repbase^{31 (link)} transposable element library (version 2008-08-01), and highly diverged transposable elements were identified with RepeatProteinMask¹⁴ by aligning the genome sequence to the curated transposable-element-related proteins. A de novo panda repeat library was constructed using RepeatModeller¹⁴. Using evidence-based gene prediction, the human and dog genes (Ensembl release 52) were projected onto the panda genome, and the gene loci were defined by using both sequence similarity and whole-genome synteny information. De novo gene prediction was performed using Genscan^{16 (link)} and Augustus^{17 (link)}. A reference gene set was created by merging all of the gene sets. The sequencing reads were mapped on the panda genome sequence using SOAPaligner^{8 (link)}, and heterozygous SNPs were identified by SOAPsnp^{9 (link)}.

Li R., Fan W., Tian G., Zhu H., He L., Cai J., Huang Q., Cai Q., Li B., Bai Y., Zhang Z., Zhang Y., Wang W., Li J., Wei F., Li H., Jian M., Li J., Zhang Z., Nielsen R., Li D., Gu W., Yang Z., Xuan Z., Ryder O.A., Leung F.C., Zhou Y., Cao J., Sun X., Fu Y., Fang X., Guo X., Wang B., Hou R., Shen F., Mu B., Ni P., Lin R., Qian W., Wang G., Yu C., Nie W., Wang J., Wu Z., Liang H., Min J., Wu Q., Cheng S., Ruan J., Wang M., Shi Z., Wen M., Liu B., Ren X., Zheng H., Dong D., Cook K., Shan G., Zhang H., Kosiol C., Xie X., Lu Z., Zheng H., Li Y., Steiner C.C., Lam T.T., Lin S., Zhang Q., Li G., Tian J., Gong T., Liu H., Zhang D., Fang L., Ye C., Zhang J., Hu W., Xu A., Ren Y., Zhang G., Bruford M.W., Li Q., Ma L., Guo Y., An N., Hu Y., Zheng Y., Shi Y., Li Z., Liu Q., Chen Y., Zhao J., Qu N., Zhao S., Tian F., Wang X., Wang H., Xu L., Liu X., Vinar T., Wang Y., Lam T.W., Yiu S.M., Liu S., Zhang H., Li D., Huang Y., Wang X., Yang G., Jiang Z., Wang J., Qin N., Li L., Li J., Bolund L., Kristiansen K., Wong G.K., Olson M., Zhang X., Li S., Yang H., Wang J, & Wang J. (2009). The sequence and de novo assembly of the giant panda genome. Nature, 463(7279), 311-317.

Publication 2009

Amino Acid Sequence Biotin DNA Library DNA Transposable Elements Genes Genetic Loci Genome Heterozygote Homo sapiens Ligation Selfish DNA Single Nucleotide Polymorphism Streptavidin Synteny

Simulation of Plant sRNA-seq Libraries

sRNA-seq libraries from Arabidopsis thaliana, Oryza sativa, and Zea mays were obtained from the NCBI Sequence Read Archive (SRA) (Supplemental Material, Table S1). Libraries were selected that had > 5 million raw reads, were available in an unprocessed format, and were derived from an Illumina instrument. 3′-Adapter sequences were discovered using find_3p_adapter.pl (available at http://sites.psu.edu/axtell/), and removed using ShortStack’s internal adapter trimming protocol. Simulated sRNA-seq libraries were produced to closely emulate real sRNA-seq data. This process was accomplished through a custom python script and wrapper run under default settings: sRNA-simulator.py (File S1). This script uses a real sRNA-seq library as the basis for each simulated library. Real sRNA-seq libraries were aligned using bowtie (Langmead et al. 2009 (link)) reporting all alignments. Regions of the genome that had no alignments were removed from consideration as simulated loci, while genomic regions prone to alignments with certain length classes of sRNAs became candidate regions for simulated heterochromatic siRNA (hc-siRNA; 23–24 nt) and trans-acting siRNA (21 nt) loci. miRNA candidate regions were picked based on prior annotated loci, available through miRBase (Kozomara and Griffiths-Jones 2014 (link)). Simulated loci were chosen from these candidate regions at random. Five million reads were then generated from these simulated loci, generating roughly 3.25 M hc-siRNA, 1.5 M miRNA, and 250 k tasiRNA reads. Loci were made to approximate real loci in size and pattern: hc-siRNA as primarily 24 nt RNAs from 200- to 1000-nt loci, from both genomic strands; miRNA as 21-nt RNAs from 125-nt loci with a miRNA and miRNA* pattern; tasiRNA as 21-nt RNAs from 140-nt loci producing a number of phased reads, from both genomic strands. All three loci types produced a realistic distribution of differently sized or shifted reads to simulate misprocessing. Sequencing errors are simulated at a rate of one mis-sequenced base per 10,000 reads. Unlike real data, simulated reads are traceable to their loci of origin, and thus are suitable to discern correct placements from incorrect ones. PolyA+ mRNA-seq data were obtained from SRA (Table S1). Reference genome versions were TAIR10 (A. thaliana), IRGSP7 (O. sativa), and B73v3 (Z. mays).

Johnson N.R., Yeoh J.M., Coruh C, & Axtell M.J. (2016). Improved Placement of Multi-mapping Small RNAs. G3: Genes|Genomes|Genetics, 6(7), 2103-2111.

Full text: Click here

Publication 2016

Arabidopsis thalianas DNA Library Genetic Loci Genome MicroRNAs mRNA, Polyadenylated Oryza sativa Python RNA RNA, Small Interfering Trans-Acting siRNA Zea mays

Accuracy Assessment of Gene Prediction Tools

For assessment of GeneMark-EP as well as ProtHint accuracy, we selected annotated genomes from diverse clades: fungi, worms, plants, insects and vertebrae (Table 1). The genome length varied from <100 Mb (Neurospora crassa) to >1.3 Gb (Danio rerio). With exception of Solanum lycopersicum, a species representing large genome plants important for economy, all selected species are model organisms whose genomes presumably have high-quality annotation. To assess accuracy of gene prediction made for model species, we compared genes predicted and annotated on a whole genome scale. In case of S. lycopersicum, we used a limited set of genes, validated by available RNA-Seq data. In all genomic datasets, contigs not assigned to any chromosome were excluded from the analysis as well as genomes of organelles.
We used OrthoDB v10 protein database (23 (link)) as an all-inclusive source of protein sequences. Still, for generating protein hints for particular species we used subsets of OrthoDB: plant proteins for gene prediction in Arabidopsis thaliana, arthropod proteins for gene prediction in Drosophila melanogaster, etc. (Table 2).
As an additional test set, we used annotation of major protein isoforms available in the APPRIS database (24 (link)); this assessment was done for C. elegans, D. melanogaster and D. rerio (Supplementary Table S1). Arguably, accuracy of prediction of major isoforms is of significant interest, since in a gene locus the major isoform was observed to be expressed in higher volume than other (minor) isoforms (24 (link)).

Brůna T., Lomsadze A, & Borodovsky M. (2020). GeneMark-EP+: eukaryotic gene prediction with self-training in the space of genes and proteins. NAR Genomics and Bioinformatics, 2(2), lqaa026.

Publication 2020

Amino Acid Sequence Arabidopsis thalianas Arthropod Proteins Arthropods Chromosomes Drosophila melanogaster Fungi Gene Products, Protein Genes Genes, Plant Genes, vif Genetic Loci Genome Helminths Inclusion Bodies Insecta Lycopersicon esculentum Neurospora crassa Organelles Plant Proteins Plants Protein Annotation Protein Isoforms Proteins RNA-Seq Vertebra Zebrafish

Most recents protocols related to «Genetic Loci»

Probabilistic Model for Genotype Inference from Sequencing Data

At a given genetic locus for a tested person, we assume the data consists of a vector

c = (c_{1}, c_{2}, c_{3}, c_{4})

of counts of reads corresponding to nucleotides A, C, G, T, respectively. Assume the true genotype at this locus is

g = (g_{1}, g_{2})

, coded as two indices between 1 and 4. We describe the relationship between c and g as a result of two separate stochastic events: First, the proportion q of DNA segments after PCR that are based on

g_{1}

among those based on either

g_{1}

g_{2}

is modelled as

q = k / m

where

k \sim Binomial (m, 1 / 2)

. Here m is an integer parameter connected to the sample, representing the approximate number of DNA templates from the sample that end up founding PCR amplicons for this locus. For high m, we have

q \approx 1 / 2

, while for lower m, q can be close or even equal to 0 or 1, meaning that one of the alleles failed to be picked up in the PCR process.
Writing

C = c_{1} + c_{2} + c_{3} + c_{4}

and

β = (β_{1}, β_{2}, β_{3}, β_{4})

, we model

c ∣ q, g \sim Multinomial (C, β)

where for

i = 1, 2, 3, 4

\begin{matrix} β_{i} = (1 - e) (q I (g_{1} = i) + (1 - q) I (g_{2} = i)) + \frac{e}{4} \end{matrix}

where e is a small positive model parameter1 relating to e.g. sequencing or mapping errors. In other words, for each observation of a read, there is a small probability e that it is in fact unrelated to the underlying genotypes, and the probability is then 1/4 for reporting each genotype. With probability

1 - e

, the read is based on

g_{1}

with probability q and on

g_{2}

with probability

1 - q

. Putting the two stochastic events together we get that

\begin{matrix} Pr (c ∣ g) = \sum_{k = 0}^{m} Pr (c ∣ q = k / m, g) (\begin{matrix} m \\ k \end{matrix}) 2^{- m} . \end{matrix}

In Sect. 3.1 we compare calculated probabilities from the model above with real data to argue that the two parameters m and e in our model can capture the most important features of variability in observational data. We note that low quality and quantity DNA samples can be modelled with a low m (sometimes in the range 5–10), since few and damaged DNA molecules is directly correlated to a low m. On the other hand, e, usually attributed to sequencing or mapping errors, is generally low (quite close to zero) with modern sequencing and bioinformatic tools [21 (link)].
To make likelihood computations for case sample data with the model above, a user has to provide information about the parameters m and e. One possibility is to use the results of Sect. 3.1 to select values. Alternatively a user may provide priors: If M possible values

m_{1}, m_{2}, \dots, m_{M}

with probabilities

p_{1}, p_{2}, \dots, p_{M}

approximately describes prior knowledge about the parameter m, and similarly E possible values

e_{1}, \dots, e_{E}

with probabilities

q_{1}, \dots, q_{E}

describes a prior for the parameter e, then we may compute

\begin{matrix} Pr (data ∣ pedigree) = \sum_{i = 1}^{M} \sum_{j = 1}^{E} Pr (data ∣ pedigree, m_{i}, e_{j}) p_{i} q_{j} . \end{matrix}

Mostad P., Tillmar A, & Kling D. (2023). Improved computations for relationship inference using low-coverage sequencing data. BMC Bioinformatics, 24, 90.

Full text: Click here

Publication 2023

Alleles Cloning Vectors DNA Genetic Loci Genotype GZMB protein, human Nucleotides

Genetic Risk Factors of Carotid Atherosclerosis

In the study, we used the student’s t and the Pearson’s chi-square tests to compare whether there were significant differences in the anthropometric and biochemical measurements between CP-positive and -negative subjects. All anthropometric and clinical factors, which showed significant associations with carotid atherosclerosis in uni-variable analyses, were subject to multi-variable analyses. We used logistic regression model with stepwise selection method to obtain the best-fit model which includes conventional cardio-metabolic risk factors only. Then, each DM SNPs was separately added to the best-fit model to assess their independent effect on carotid atherosclerosis. The strength of association between each SNP and carotid atherosclerosis was manifested by multivariable-adjusted odds ratio (OR). To reduce the influence of false negativity, we used 0.10 as the pre-set inclusion criteria of promising SNPs. We further generated multi-locus genetic risk scores (GRSs) by summing the number of risk alleles or genotypes for each individual and then assessed the associations between GRSs and carotid atherosclerosis. All statistical analyses were performed using SAS 9.4 (SAS Institute Inc., Cary, NC, USA).

Wu T.W., Chou C.L., Cheng C.F., Lu S.X., Wu Y.J, & Wang L.Y. (2023). Associations of genetic markers of diabetes mellitus with carotid atherosclerosis: a community-based case–control study. Cardiovascular Diabetology, 22, 51.

Full text: Click here

Publication 2023

Alleles Carotid Atherosclerosis Genetic Loci Genotype Single Nucleotide Polymorphism Student

Wheat RCC1 Gene Collinearity Analysis

The RCC1 gene loci of wheat and its related genome donors were extracted from the corresponding annotated gff3 file (downloaded from Ensembl Plants, http://plants.ensembl.org/index.html) using a perl script. The Multiple Collinearity Scan toolkit (MCScanX) was used to analyze the gene collinearity among wheat, emmer wheat, and Aegilops tauschii with the default parameters (Kozlov et al., 2019 (link)
).
Homolog analysis of RCC1 genes among the A, B, and D genomes of wheat was performed based on the aligned result. The chromosomal distribution and collinearity of RCC1 genes among the wheat and its donors and of the homoeologous RCC1 genes among A, B, and D genomes were visualized by the circle package in R (Gu et al., 2014 (link)
).

An X., Zhao S., Luo X., Chen C., Liu T., Li W., Zou L, & Sun C. (2023). Genome-wide identification and expression analysis of the regulator of chromosome condensation 1 gene family in wheat (Triticum aestivum L.). Frontiers in Plant Science, 14, 1124905.

Full text: Click here

Publication 2023

Aegilops Chromosomes Donors Genes Genetic Loci Genome Plants Radionuclide Imaging Triticum aestivum

Molecular Analysis for Malaria Recrudescence

To distinguish between recrudescence and re-infection, 4 drops of blood from malaria-positive patients were collected on filter paper on day zero before treatment, and on any day of recurrent P. falciparum malaria. Molecular analysis was conducted following the previously described method [19 (link)], with slight modifications. Briefly, blood spotted filter papers were soaked for 24 h in 1 mL of 0.5% saponin-1 phosphate buffered saline. The mixture was washed twice in 1-mL PBS and boiled for 8 min in 100 mL PCR-grade water to release DNA from the cells. To elute the extracted DNA, 150 µL Buffer AE was added to each well using a multichannel pipette and incubated for 1 min at room temperature. This setup was then centrifuged at 2608 RCF for 8 min. DNA was recovered and stored at -80 °C. Nested PCR was performed on the extracted DNA for subsequent genotyping of P. falciparum polymorphic gene loci encoding Merozoite surface protein 2 (MSP-2) using the method described by [20 (link)]. A master mix was prepared according to manufacturer instructions (New England Bio Labs, Massachusetts, USA). 24 µL of the Master Mix was added to the PCR 96 well plate and 25 µL of the master mix was also added to the negative PCR control. The plates were sealed using a thermo-seal plate sealer and placed in the PCR thermo-cycler. Amplification was then performed under the following conditions; denaturation (94 °C), annealing (55 °C), and extension (72 °C). Amplification was confirmed by running the nested PCR product together with a DNA ladder on the QIAxcel capillary electrophoresis. The result was classified as recrudescence if at least one identical MSP2 allele was detected in both ACT pre-treatment and ACT post-treatment blood samples. Blood samples where MSP2 alleles did not match ACT pre- and ACT post-treatment were classified as new infections. Any sample, which failed to amplify was classified as undetermined. Blood samples, which showed recrudescence of parasites during any follow up day were further genotyped for P. falciparum k13 resistance markers. The primers used in this protocol are shown in Table 1.Table 1

Showing Merozoite Surface Proteins-2 (MSP-2) Amplification primers

Primer name	Sequence (5′ → 3′)	Purpose
MSP-2(1)	ATGAAGGTAATTAAAACATTGTCTATTATA	External forward primer
MSP-2(4)	ATATGGCAAAAGATAAAACAAGTGTTGCTG	External reverse primer
MSP-2(A1)	CAGAAAGTAAGCCTTCTACTGG	Internal forward primer (IC3D7)
MSP-2(A2)	GATTTGTTTCGGCATTATTATGA	Internal reverse primer (IC3D7)
MSP-2(B1)	CAAATGAAGGTTCTAATACTA	External forward primer (FC27)
MSP-2(B2)	GCTTTGGGTCCTTCTTCAGTTGATTC	Internal reverse primer (FC27)

Maniga J.N., Samuel M., John O., Rael M., Muchiri J.N., Bwogo P., Martin O., Sankarapandian V., Wilberforce M., Albert O., Onkoba S.K., Adebayo I.A., Adeyemo R.O, & Akinola S.A. (2023). Novel Plasmodium falciparum k13 gene polymorphisms from Kisii County, Kenya during an era of artemisinin-based combination therapy deployment. Malaria Journal, 22, 87.

Full text: Click here

Publication 2023

Alleles BLOOD Buffers Cells Electrophoresis, Capillary Genetic Loci Infection Malaria Malaria, Falciparum Membrane Proteins Merozoites Nested Polymerase Chain Reaction Neutrophil Oligonucleotide Primers Parasites Patients Phocidae Phosphates Recrudescence Reinfection Saline Solution Saponin

Real-time PCR Assay for Tuberculosis Drug Resistance

High-resolution melting curve real-time PCR experiments were performed with the SLAN-96S Real-time fluorescence quantitative PCR detection system (Zeesan Biotech, Xiamen, China), using an MTB drug resistance mutation detection kit (Fluorescent PCR melting curve method, Zeesan Biotech, Xiamen, China). Nine PCR reaction systems were performed simultaneously, in a final volume of 25 μL containing 2 μL template DNA. The reaction program was: 50°C, 60s; 95°C, 600 s; 95°C, 15 s, 70°C, 20s (reduced 1°C for each cycle), 76°C, 25 s for 13 cycles; 95°C, 15 s, 57°C, 20s, 76°C, 25 s for 42 cycles; 95°C, 120 s; 40°C, 120 s; 45°C–85°C, fluorescence signals were collected every 1°C for this period. The anti-tuberculosis drugs and their corresponding gene resistance loci are shown in Table 1. The software analyzes the differences in the shape of the melting curve between a sample and the wild-type control strain (M. tuberculosis H37Rv) by generating a difference plot curve. This plot helps with clustering samples into groups having similar melting curves; hence, sequence polymorphisms can be detected.

Yang H., Li A., Dang L., Kang T., Ren F., Ma J., Zhou Y., Yang Y., Lei J, & Zhang T. (2023). A rapid, accurate, and low-cost method for detecting Mycobacterium tuberculosis and its drug-resistant genes in pulmonary tuberculosis: Applications of MassARRAY DNA mass spectrometry. Frontiers in Microbiology, 14, 1093745.

Full text: Click here

Publication 2023

Antitubercular Agents Fluorescence Genetic Loci Genetic Polymorphism Mutation Mycobacterium tuberculosis H37Rv Real-Time Polymerase Chain Reaction Strains Substance Abuse Detection

Top products related to «Genetic Loci»

Lipofectamine 2000 by Thermo Fisher Scientific

Sourced in United States, China, Germany, United Kingdom, Canada, Japan, France, Italy, Switzerland, Australia, Spain, Belgium, Denmark, Singapore, India, Netherlands, Sweden, New Zealand, Portugal, Poland, Israel, Lithuania, Hong Kong, Argentina, Ireland, Austria, Czechia, Cameroon, Taiwan, Province of China, Morocco

Lipofectamine 2000 is a cationic lipid-based transfection reagent designed for efficient and reliable delivery of nucleic acids, such as plasmid DNA and small interfering RNA (siRNA), into a wide range of eukaryotic cell types. It facilitates the formation of complexes between the nucleic acid and the lipid components, which can then be introduced into cells to enable gene expression or gene silencing studies.

Trizol reagent by Thermo Fisher Scientific

Sourced in United States, China, Japan, Germany, United Kingdom, Canada, France, Italy, Australia, Spain, Switzerland, Netherlands, Belgium, Lithuania, Denmark, Singapore, New Zealand, India, Brazil, Argentina, Sweden, Norway, Austria, Poland, Finland, Israel, Hong Kong, Cameroon, Sao Tome and Principe, Macao, Taiwan, Province of China, Thailand

TRIzol reagent is a monophasic solution of phenol, guanidine isothiocyanate, and other proprietary components designed for the isolation of total RNA, DNA, and proteins from a variety of biological samples. The reagent maintains the integrity of the RNA while disrupting cells and dissolving cell components.

Qiaamp dna mini kit by Qiagen

Sourced in Germany, United States, France, United Kingdom, Netherlands, Spain, Japan, China, Italy, Canada, Switzerland, Australia, Sweden, India, Belgium, Brazil, Denmark

The QIAamp DNA Mini Kit is a laboratory equipment product designed for the purification of genomic DNA from a variety of sample types. It utilizes a silica-membrane-based technology to efficiently capture and purify DNA, which can then be used for various downstream applications.

Rneasy mini kit by Qiagen

Sourced in Germany, United States, United Kingdom, Netherlands, Spain, Japan, Canada, France, China, Australia, Italy, Switzerland, Sweden, Belgium, Denmark, India, Jamaica, Singapore, Poland, Lithuania, Brazil, New Zealand, Austria, Hong Kong, Portugal, Romania, Cameroon, Norway

The RNeasy Mini Kit is a laboratory equipment designed for the purification of total RNA from a variety of sample types, including animal cells, tissues, and other biological materials. The kit utilizes a silica-based membrane technology to selectively bind and isolate RNA molecules, allowing for efficient extraction and recovery of high-quality RNA.

Hiseq 2500 by Illumina

Sourced in United States, China, Germany, United Kingdom, Canada, Switzerland, Sweden, Japan, Australia, France, India, Hong Kong, Spain, Cameroon, Austria, Denmark, Italy, Singapore, Brazil, Finland, Norway, Netherlands, Belgium, Israel

The HiSeq 2500 is a high-throughput DNA sequencing system designed for a wide range of applications, including whole-genome sequencing, targeted sequencing, and transcriptome analysis. The system utilizes Illumina's proprietary sequencing-by-synthesis technology to generate high-quality sequencing data with speed and accuracy.

Sas 9 by SAS Institute

Sourced in United States, Austria, Japan, Belgium, United Kingdom, Cameroon, China, Denmark, Canada, Israel, New Caledonia, Germany, Poland, India, France, Ireland, Australia

SAS 9.4 is an integrated software suite for advanced analytics, data management, and business intelligence. It provides a comprehensive platform for data analysis, modeling, and reporting. SAS 9.4 offers a wide range of capabilities, including data manipulation, statistical analysis, predictive modeling, and visual data exploration.

Nanodrop 2000 by Thermo Fisher Scientific

Sourced in United States, China, Germany, Japan, United Kingdom, Spain, Canada, France, Australia, Italy, Switzerland, Sweden, Denmark, Lithuania, Belgium, Netherlands, Uruguay, Morocco, India, Czechia, Portugal, Poland, Ireland, Gabon, Finland, Panama

The NanoDrop 2000 is a spectrophotometer designed for the analysis of small volume samples. It enables rapid and accurate quantification of proteins, DNA, and RNA by measuring the absorbance of the sample. The NanoDrop 2000 utilizes a patented sample retention system that requires only 1-2 microliters of sample for each measurement.

Dneasy blood and tissue kit by Qiagen

Sourced in Germany, United States, United Kingdom, Spain, Canada, Netherlands, Japan, China, France, Australia, Denmark, Switzerland, Italy, Sweden, Belgium, Austria, Hungary

The DNeasy Blood and Tissue Kit is a DNA extraction and purification product designed for the isolation of genomic DNA from a variety of sample types, including blood, tissues, and cultured cells. The kit utilizes a silica-based membrane technology to efficiently capture and purify DNA, providing high-quality samples suitable for use in various downstream applications.

Dneasy blood tissue kit by Qiagen

Sourced in Germany, United States, United Kingdom, Netherlands, Spain, Japan, China, Canada, France, Australia, Switzerland, Italy, Belgium, Denmark, Sweden

The DNeasy Blood & Tissue Kit is a DNA extraction and purification kit designed for the efficient isolation of high-quality genomic DNA from a variety of sample types, including whole blood, tissue, and cultured cells. The kit utilizes a silica-based membrane technology to capture and purify DNA, providing a reliable and consistent method for DNA extraction.

Spss 19.0 software for windows by IBM

Sourced in United States

SPSS 19.0 software for Windows is a powerful statistical analysis tool designed for data management, analysis, and reporting. It provides a comprehensive set of features to help users analyze and interpret complex data, including advanced statistical techniques, data visualization, and reporting capabilities.

What are Genetic Loci?

How can PubCompare.ai assist with Genetic Loci research?

PubCompare.ai allows researchers to screen protocol literature more efficiently and leverage AI to pinpoint critical insights. The platform can help researchers identify the most effective protocols related to Genetic Loci for their specific research goals. PubCompare.ai's AI-driven analysis can highlight key differences in protocol effectiveness, enabling researchers to choose the best option for reproducibility and accuracy.

What are the different types or variations of Genetic Loci?

Genetic loci can vary in terms of the specific DNA sequences they encompass, the genes they contain, and the traits or characteristics they are associated with. Some common variations include disease-associated loci, trait-linked loci, and regulatory loci that control gene expression. Researchers can use PubCompare.ai to explore the nuances of different genetic loci and find the most relevant protocols for their area of study.

How can Genetic Loci be applied in research and clinical settings?

Genetic loci play a critical role in a wide range of applications, including genetic mapping, genome-wide association studies, and the identification of disease-causing mutations. By understanding the specific locations and characteristics of genetic loci, researchers and clinicians can gain insights into the genetic basis of diseases, develop targeted therapies, and improve diagnostic accuracy. PubCompare.ai's platform can assist in finding the best protocols to optimize these applications.

What are some common challenges in working with Genetic Loci?

One of the key challenges in working with genetic loci is ensuring the reproducibility and accuracy of research findings. Researchers may face difficulties in locating the most relevant protocols, comparing the effectiveness of different approaches, and identifying the best practices for genetic loci optimization. PubCompare.ai's AI-driven platform can help overcome these challenges by providing a seamless and efficient way to search for, compare, and choose the most effective protocols for your specific research needs.

More about "Genetic Loci"

Genetic loci refer to the specific physical locations of genes or other DNA sequences within the genome.
These genomic regions, also known as gene loci or genetic locations, play a crucial role in understanding genetic variation, inheritance patterns, and the identification of disease-associated areas.
Researchers can leverage PubCompare.ai's AI-driven platform to enhance reproducibility and accuracy in genetic loci optimization.
The platform effortlessly locates protocols from literature, pre-prints, and patents, providing AI-driven comparisons to help identify the best protocols and products.
This streamlines the research process for genetcis loci optimization.
Discover how PubCompare.ai can assist you in your genetic loci research by locating and comparing relevant protocols, such as those utilizing Lipofectamine 2000 for transfection, TRIzol reagent for RNA extraction, QIAamp DNA Mini Kit or DNeasy Blood and Tissue Kit for DNA isolation, RNeasy Mini Kit for RNA purification, and HiSeq 2500 for high-throughput sequencing.
Leverage statistical analysis tools like SAS 9.4 or SPSS 19.0 to analyze your genetic loci data, and use NanoDrop 2000 for nucleic acid quantification.
PubCompare.ai's AI-driven platform can enhance your research workflow, ensuring greater reproducibility and accuracy in your genetic loci optimization studies.