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Gene Conversion

Gene conversion is a type of genetic recombination where a DNA sequence is copied and replaced with a highly similar or identical sequence from another genomic location.
This process can generate new genetic variations and is important for DNA repair and genome evolution.
PubCompare.ai's AI-driven Gene Conversion Analysis can help researchers easily locate relevant protocols from the literature, pre-prints, and patents, and use cutting-edge comparisons to identify the best protocols and products for their needs.
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Most cited protocols related to «Gene Conversion»

Datasets were obtained mainly from GEO (http://www.ncbi.nlm.nih.gov/geo/) and TCGA (https://tcga-data.nci.nih.gov) after searching for keywords related to cancer, survival, and gene expression technologies. Additionally, a few were obtained from author’s websites and from ArrayExpress (http://www.ebi.ac.uk/arrayexpress/). The data source used is shown in the web interface. We favored cancer types above two different cohorts and datasets containing survival data over 30 samples in which censoring indicator and time to death, recurrence, relapse, or metastasis were provided. Clinical data was provided by dataset authors via personal email when not available online in corresponding repositories. Datasets were annotated from provider files as found up to September 2012, and were quantile-normalized and log2 transformed when needed. From TCGA, all datasets were obtained at the gene level (level 3). RNA-Seq counts data were log2 transformed. In some cancer types where many datasets were found for the same gene expression platform, we also provide a merged meta-base. In meta-bases, datasets were quantile normalized; probesets means were equalized conserving the standard deviation by each cohort; and datasets were merged by probeset id. At the moment we provide meta-bases for breast, lung, and ovarian cancer. To facilitate gene searches and conversions between gene identifiers, human gene information was used and obtained from the NCBI FTP site (ftp://ftp.ncbi.nih.gov/gene/DATA/GENE_INFO/Mammalia/Homo_sapiens.gene_info.gz). To simplify the user interface, datasets were grouped by related organ or tissue using disease ontologies [10] (link).
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Publication 2013
Breast Gene Conversion Gene Expression Genes Homo sapiens Lung Malignant Neoplasms Mammals Neoplasm Metastasis Ovarian Cancer Recurrence Relapse RNA-Seq Tissues
Table 1 shows the comparisons between NetworkAnalyst 3.0 and several other well-known web-based tools dedicated to functional profiling of transcriptomics data, including WebGestalt (29 (link)), g:Profiler (30 (link)) and Enrichr (31 (link)). The WebGestalt web application, first released in 2005, provides comprehensive enrichment analysis for 12 selected organisms and also supports user-supplied functional enrichment categories. The g:Profiler tool suite, first released in 2007, provides the broadest species coverage by supporting >200 species and corresponding gene ID conversions. Additional features include mapping human single nucleotide polymorphism (SNP) to gene name as well as ortholog search. The Enrichr web server, first released in 2013, provides the broadest functional coverage by supporting enrichment analysis against >100 gene set libraries. A key contribution of Enrichr is its curation effort, and allowing users to download their curated gene sets. Another unique feature of Enrichr is its support for BED file as input for enrichment analysis. These three tools are powerful web-based platforms that offer rich annotations for a given gene list. In contrast, NetworkAnalyst distinguishes itself from other web-based tool by providing cutting-edge network visualization, versatile visual analytics, comprehensive support for gene expression profiling, meta-analysis and multi-list comparisons. NetworkAnalyst 3.0 offers an end-to-end solution for RNAseq analysis - from raw reads mapping to differential expression analysis and identification of important pathways and functions.
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Publication 2019
Gene Annotation Gene Conversion Gene Expression Profiling Gene Library Genes Homo sapiens Single Nucleotide Polymorphism
We used the program Bayesian Serial SimCoal (BSSC [22] (link)) to simulate DNA sequence data under different structural and demographic models. We simulated a 600bp fragment of the mitochondrial D-loop, commonly used in BSP studies due to its high nucleotide diversity. The sequences were set to evolve according to a HKY model with kappa = 50, gamma distributed rate heterogeneity (shape parameter 0.5) and a rate of 32% per million years per bp [6] (link) (note that this rate is subject to estimation uncertainty, but here it serves to provide a conversion between genetic distance and real time), equivalent to 0.001344 mutations per sequence per generation (using the estimated buffalo generation time of 7 years [20] (link)). We emulated the actual marker used in the buffalo case study in the simulations to facilitate comparisons, and because BSPs are almost always used in the context of dated genealogies with time measured in years. For all scenarios, we carried out 100 replicate simulations to incorporate coalescent stochasticity [23] and identify general patterns across stochastic replicates of the same demographic history. Essentially, this corresponds to simulating 100 non-linked genetic markers with the high information content of the D-loop. We were thus able to assess the performance of multi-locus inference and ensure that our conclusions were not limited by the use of a single locus. This makes our results more comparable to multi-locus data that are likely to become common in the genomic era. Two example input files for BSSC are supplied to show the details of our simulations (File S1 and File S2).
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Publication 2013
Bromsulphalein Buffaloes DNA Replication Gamma Rays Gene Conversion Genetic Heterogeneity Genetic Markers Genome Mitochondria Mutation Nucleotides
Custom R and python code was used to demultiplex the sequencing data and trim ends. Sequencing reads were assigned to segregants based on the 5 base-pair (bp) barcode at the beginning of each read. The internal 19 bp transposon sequence and 10 bp on the right end of each read were removed. Reads were aligned to the S288C reference genome using the Burrows-Wheeler Aligner (BWA)33 with the '-q 30' parameter. SAMTools34 was run with the ‘view’ command and ‘-bHsq 1' parameters to retain uniquely mapping reads. Sequence variants were identified using SAMTools34 with the ‘mpileup' command and parameters '-d 10000 -D -u'. 42,689 high-confidence sequence variants between BY and RM were determined from sequencing the parental strains at greater than 50-fold coverage. Variants in the segregants were restricted to these 42,689 expected sites using 'bcftools view'34 with the parameters '-N -c -g -v -P flat'. Genotype likelihoods for the BY and RM alleles for each genotypic variant were extracted from the VCF file using custom R code.
For each segregant and chromosome, a hidden Markov model (HMM) was used to calculate the posterior likelihood that the read data was coming from the BY allele, the RM allele, a BY gene-conversion event or an RM gene-conversion event. Genotypes were called as the BY variant if the log10 ratio of the BY posterior likelihood and the RM posterior likelihood was greater than 2, the RM variant if this ratio was less than −2, and missing data if between −2 and 2. 1,008 out of 1,056 segregants had between 25 and 120 recombination breakpoints and at least 35,000 markers with genotype calls, and these were retained for downstream analysis. Genotypic markers were excluded if their allele frequency was greater than 56% or less than 45%, or if they were not called in at least 99% of the segregants. This resulted in a final set of 30,594 genotypic markers. Markers with missing data were imputed using the Viterbi algorithm as implemented in the R/qtl package35 . Adjacent markers with the same genotypes in all segregants were collapsed to one unique marker, resulting in a final set of 11,623 unique genotypic markers.
Publication 2012
Alleles Base Pairing Base Sequence Chromosomes Gene Conversion Genetic Diversity Genome Genotype Jumping Genes Parent Python Recombination, Genetic Strains
DT40Cre1, which displays increased Ig gene conversion due to a v-myb transgene and contains a tamoxifen-inducible Cre recombinase, has been described previously (Arakawa et al. 2001 (link)). DT40Cre1AID–/– was generated by the targeted disruption of both AID alleles of DT40Cre1 (Arakawa et al. 2002 (link)). AIDR was derived from DT40Cre1AID–/– after stable integration of a floxed AID-IRES-GFP bicistronic cassette, in which both AID and GFP are expressed from the same β-actin promoter. AIDRψV was derived from AIDR by transfection of pψVDel1-25 (see Figure 1A). Stable transfectants that had integrated the construct into the rearranged light chain locus were then identified by locus-specific PCR. Targeted integration of pψVDel1-25 results in the deletion of the entire ψV gene loci starting 0.4 kb upstream of ψV25 and ending 1 bp downstream of ψV1. AIDRψVpartial was produced in a similar way as was AIDRψV, by transfection of pψVDel3-25, which, upon targeted integration, leads to a partial deletion of the ψV loci starting 0.4 kb upstream of ψV25 and ending 1 bp downstream of ψV3. Cell culture and electroporation were performed as previously described (Arakawa et al. 2002 (link)). XRCC3–/– was derived from DT40Cre1 by deleting amino acids 72–170 of the XRCC3 gene following transfection of XRCC3 knockout constructs. Clones that underwent targeted integration were initially identified by long-range PCR, and the XRCC3 deletion was then confirmed by Southern blot analysis.
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Publication 2004
Actins Alleles Amino Acids Cell Culture Techniques Clone Cells Cre recombinase Deletion Mutation Electroporation Gene Conversion Gene Deletion Genes Internal Ribosome Entry Sites Light Oncogenes, myb Southern Blotting Tamoxifen Transfection Transgenes XRCC3 protein, human

Most recents protocols related to «Gene Conversion»

Different gene duplication patterns were identified using DupGen_finder (Qiao et al., 2019 (link)). First, an all-vs.-all local BLASTP search was performed using protein sequences to screen for potential homologous gene pairs in each genome (E < 1e-10, top 5 matches, and m8 format output). Then, the A. thaliana protein sequence was selected as the outgroup and the DupGen_finder-unique program was used to identify five gene duplication events (i.e., TD, PD, DSD, TRD, and WGD). The detect_gene_conversion pipeline (Qiao et al., 2019 (link)) was used to identify homologous gene quartets and analyze gene conversions.
The synonymous substitution rate (Ks) and the non-synonymous substitution rate (Ka) for the duplicated gene pairs were calculated using KaKs_calculator 2.0 and the NG model (Wang et al., 2010 (link)) (Supplementary Table S3). The Ks values were converted to divergence times using the formula T = Ks·(2r)−1, where T is the divergence time and r is the neutral substitution rate (6.50 × 10−9) (Gaut et al., 1996 (link)).
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Publication 2023
Amino Acid Sequence Arabidopsis thalianas Gene Conversion Gene Duplication Genes Genes, Duplicate Genome Screenings, Genetic
The gene expression (RNA-Seq) was download from TARGET-OS dataset (https://ocg.cancer.gov/programs/target). We created an mRNA matrix using R and processed the transcriptome data to perform gene ID conversion with the corresponding script. We then compared the high- and low-expression survival curves by R software.
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Publication 2023
Gene Conversion Gene Expression Malignant Neoplasms RNA, Messenger RNA-Seq Transcriptome
The GSE73680 and GSE117518 expression matrices were log2 transformed and normalized by the R function “normalizeBetweenArrays.” Furthermore, the GPL17077 annotation table was downloaded to convert gene probes to symbols in GSE73680, whereas the validation data set GSE117518 gene conversion was completed by the R package “idmap3” due to lack of a probe-ID corresponding relationship in GPL21827. On the basis that the p-value < 0.05 and the log2 fold change (logFC) cut-off absolute value > 1, GSE73680 successfully adopted the LIMMA package to select DEGs. Then, the DEG volcano picture produced by R packages “ggplot2” and “ggrepel” was imported to GO/KEGG/DO for ranking gene functions and pathways by applying the “clusterProfiler” (Yu et al., 2012 (link)), “GOplot” (Walter et al., 2015 (link)), and “DOSE” R packages.
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Publication 2023
Gene Conversion Genes Operator, Genetic
To investigate if accelerated evolution in TFBSs was driven by positive selection, we used the INSIGHT model to infer positive selection on the seven accelerated, non-overlapping TFBS groups in the human lineage50 (link)–52 (link). We obtained INSIGHT2, a highly efficient implementation of the INSIGHT model, from https://github.com/CshlSiepelLab/FitCons2. Then, we applied INSIGHT2 to each TFBS group under accelerated evolution and the collection of all TFBSs from ENCODE. INSIGHT2 provided Dp and SE[Dp], that is, the expected number of adaptive substitutions per kilobase and its standard error, as well as ρ and SE[ρ] which quantified the fraction of sites under selection within functional elements and its standard error. We performed the Wald test to examine if Dp was significantly different from 0 for each TFBS group. Under the null hypothesis of Dp = 0, we assumed that the z-statistic, DpSE[Dp] , asymptotically followed a 50:50 mixture of a point probability mass at 0 and a half standard normal distribution88 . We conducted comparisons of ρ among the seven accelerated TFBS groups and the collection of all TFBSs from ENCODE (Supplementary Table 2). To identify the role of GC-biased gene conversion in the accelerated evolution of the seven TFBS groups, we used the phastBias model to infer gene conversion disparity B in the lineage where accelerated evolution occurred, identical to the best-fit lineage found in model comparison (Fig. 7).
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Publication 2023
Acclimatization Biological Evolution Gene Conversion Homo sapiens
The following fly lines were used: hypomorphic hhbar3 (FlyBase ID FBal0031487), hh null (hhAC/TM3, Bloomington #1749), nubHackFLP (described below), nubbin AC-62 (nubbin-Gal4, Bloomington #25754), hh[KO;FRT]/TM3 (ref. 34 (link), kindly provided by J.P Vincent), w1118 and FRT82B kindly provided by C. Klämbt, FRT82B Rps3 (bearing Gal80, Gal80 not being used in this study as a tool) kindly provided by S. Luschnig. The nubHackFLP flies used also beared additional markers without influence on the results and of no relevance to the present study. Injection of pRIVwhite carrying the wild-type hh sequence or hh variants coding for R/A and R/E variant yielded hh[KO;hh]/TM3, hh[KO;hhR/A]/TM3, and hh[KO;hhR/E]/TM3 (this lab). For clonal analysis of homozygous alleles of all forms, a wing-blade-specific FLP source on chromosome II was generated. To this end, nubbin-Gal4 was chosen as the target for CRISPR-mediated gene conversion by using the pHackGal4FLP (described earlier) derivative of the Hack-Gal4 Vector pBPGUw-Hack-QF2 (Addgene #80276), following the method of ref. 80 (link). pHackGal4FLP was injected in a Cas9; nubbin AC-6-bearing background. Subsequent screening of the F1 offspring for positive convertants, as revealed by the presence of 3P3-DSRED, yielded 12 integration events from 500 injected embryos. 3P3-DSRED was then floxed out and isogenic lines were established. We refer to this chromosome as nubFLP. We used the following genotype for clonal analysis of the hh rescue constructs in the eye and wing disks (Fig. 3): yw eyFLP3.5/yw; nubFLP/+; FRT82B (Gal80) Rps3/FRT82B, hh[KO;hhtransgene or no hh]. This enabled us to study clones in eyes and wings in the same animals. The resulting fly eye phenotypes were captured with a Nikon SMZ25 microscope and quantified by using Nikon F-package software version 4.5.01. w1118 flies or heterozygous flies of the indicated genotypes served as positive controls. Wings of the adult F1 were collected and mounted in Hoyer’s medium, and total intervein areas and total wing areas were quantified by MoticImage software version 2.0. All fly crosses and maintenance were conducted at 25 °C. For wing quantifications, ten male and ten female wings were analyzed for each data set and ratios between L3-L4 intervein areas and L2-L3 intervein areas were determined.
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Publication 2023
Adult Alleles Chromosomes Clone Cells Cloning Vectors Clustered Regularly Interspaced Short Palindromic Repeats Embryo Females Gene Conversion Genotype Heterozygote Homozygote Males Microscopy Phenotype RPS3 protein, human Wings, Animal

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More about "Gene Conversion"

Gene conversion is a type of genetic recombination where a DNA sequence is copied and replaced with a highly similar or identical sequence from another genomic location.
This process can generate new genetic variations and is important for DNA repair and genome evolution.
PubCompare.ai's AI-driven Gene Conversion Analysis can help researchers easily locate relevant protocols from the literature, pre-prints, and patents, and use cutting-edge comparisons to identify the best protocols and products for their needs.
Genetic recombination, also known as genetic crossing-over, is a crucial process in biology that involves the exchange of genetic material between homologous chromosomes during meiosis.
Gene conversion is a specific type of genetic recombination where a DNA sequence is copied and replaced with a highly similar or identical sequence from another genomic location.
This process can generate new genetic variations and is important for DNA repair and genome evolution.
Researchers can utilize tools like DMEM (Dulbecco's Modified Eagle Medium), GeneSpring GX software, SapphireAmp Fast PCR Master Mix, Opti-MEM, FACSCalibur flow cytometer, DPBS (Dulbecco's Phosphate-Buffered Saline), SE buffer, FACScan, Ex Taq DNA polymerase, and FuGENE HD to study and manipulate gene conversion processes in their experiments.
These tools can help streamline the research process and improve the accuracy and reproducibility of the results.
PubCompare.ai's AI-driven Gene Conversion Analysis can further enhance the research process by helping researchers easily locate relevant protocols from the literature, pre-prints, and patents, and use cutting-edge comparisons to identify the best protocols and products for their needs.
This can help researchers save time, improve the quality of their work, and take their research to the next level.