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Tandem Repeat Sequences

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Most cited protocols related to «Tandem Repeat Sequences»

1
Pilon: Automated Genome Improvement and Variant Detection
Cited 1335 times
Pilon generates a modified genome as a FASTA file, including all single-base, small indel, gap filling, mis-assembly and large-event corrections from the input genome. In the assembly improvement case, this is the improved assembly consensus. In variant detection mode, this is the reference sequence which has been edited to represent the consensus of the given sample more closely.
Pilon can optionally generate a Variant Call Format (VCF) [http://vcftools.sourceforge.net/specs.html] file, which lists copious detailed information about the base and indel evidence at every base position in the genome, including two scores regarding variant quality: the QUAL column, and a depth-normalized call quality (QD) field in the INFO column. For additional details on the VCF format, we refer to the VCF specification referred above. Changes generated by local reassembly, often triggered by larger polymorphisms in variant calling applications, are included as structural variant records (SVTYPE  =  INS and SVTYPE  =  DEL). Pilon can also, optionally, generate a “changes” file which lists the edits applied from input to output genome in tabular form, including source and destination coordinates and source and destination sequence. Finally, Pilon will optionally (with the —tracks option) output a series of visualization tracks (“bed” and “wig” files) suitable for viewing in genome browsers such as IGV [35] (link) and GenomeView [36] (link). Tracks include basic metrics across the genome, such as sequence coverage and physical coverage, as well as some of the calculated metrics Pilon uses in its heuristics for finding potential areas of mis-assembly, such as percentage of valid read pairs covering every location.
Pilon's standard output also contains useful information, including coverage levels, percentage of the input genome confirmed, a summary of the changes made, as well as some specifically flagged issues which were not corrected, such as potentially large collapsed repeat regions, potential regions of mis-assembly which were not able to be corrected, and detected tandem repeats that were not resolved.
Walker B.J., Abeel T., Shea T., Priest M., Abouelliel A., Sakthikumar S., Cuomo C.A., Zeng Q., Wortman J., Young S.K, & Earl A.M. (2014). Pilon: An Integrated Tool for Comprehensive Microbial Variant Detection and Genome Assembly Improvement. PLoS ONE, 9(11), e112963.
Publication 2014
Genetic Polymorphism Genome INDEL Mutation Physical Examination Tandem Repeat Sequences
2
Automated CRISPR Identification Pipeline
Cited 520 times
CRISPRFinder core routines were developed in Perl under Debian Linux. The input of the web tool is a genomic query sequence of length up to 67 Mb in ‘FASTA’ format. Possible locations of CRISPRs (consisting of at least one motif) are detected by finding maximal repeats. A maximal repeat (26 ) is a repeat that cannot be extended in either direction without incurring a mismatch. The total number of maximal repeats in a sequence of size n is linear (less than n) which is interesting since the computation may be done in linear time using a suffix-tree-based algorithm. A CRISPR pattern of two DRs and a spacer may be considered as a maximal repeat where the repeated sequences are separated by a sequence of approximately the same length.
The operation of the program can be divided into four main steps summarized in Figure 1: (Step 1) browsing the maximal repeats of length 23–55 bp interspaced by sequences of 25–60 bp, (Step 2) selecting the DR consensus according to a defined score taking into account the number of occurrences of the candidate DR in the whole genome and privileging internal mismatches between the DRs rather than mismatches in the first or the last nucleotides, (Step 3) defining candidate CRISPRs after checking if they fit CRISPR definition, (Step 4) eliminating residual tandem repeats.

CRISPR Finder flow chart. (Step 1) Browsing the maximal repeats to get possible CRISPR localizations using the Vmatch program. (Step 2) Consensus DR selection according to candidate occurrences and a score computation: the score privileges internal mismatches between direct repeats of a cluster rather than boundary mismatches. (Step 3) DR and spacers size check. (Step 4) Tandem repeats elimination using ClustalW for aligning spacers.

In the first step, maximal repeats are found by the software Vmatch (http://www.vmatch.de/), the upgrade of REPuter (22–24 ). Vmatch is based on a comprehensive implementation of enhanced suffix arrays (27 ) which provides the power of suffix trees with lower space requirements. A one nucleotide mismatch is allowed permitting minimal CRISPRs with a single nucleotide mutation between DRs to be found. Hereafter, the obtained maximal repeats are grouped to define regions of possible CRISPRs with a display of consensus DR candidates related to each cluster.
The second step is aimed at retrieving the DR consensus of each cluster. The difficulty resides especially in the identification of boundaries, which is very important to extract the correct spacers and compare DRs. In fact, the consensus DR is selected as the maximal repeat which occurs the most in the whole underlying genome sequence with respect to the forward and the reverse complement directions (since two CRISPRs having the same DR consensus may be in opposite directions). Thus, ambiguity in the choice of a DR will be eliminated in the case of presence of similar DRs in other CRISPRs of the related genomic sequence. However, if occurrence numbers are equal, more than a single DR consensus candidate are kept and later compared. Given a candidate consensus DR, the pattern search program fuzznuc of the EMBOSS package (28 (link)) is applied to get DRs’ positions in the related cluster. As the first or the last DR in a CRISPR may be diverged/truncated, a mismatch of one-third of the DR length is allowed between the flanking DRs and the candidate consensus DR, whereas smaller nucleotide differences are allowed between the other DRs to take into account possible single mutations. In case of multiple DR candidates, a score is computed and the best one (minimum) is picked. This score favours candidates which are encountered more frequently, rather than consensus DR showing less internal mismatches.
Once the DR consensus is determined, the corresponding spacers (Step 3) are extracted according to the DR boundaries determined previously. The spacer length is not allowed to be shorter than 0.6 or longer than 2.5 times the DR length. These sizes are in the range of CRISPRs described in the literature.
The last step consists in discarding false CRISPRs. Therefore, tandem repeats are eliminated by comparing the consensus DR with the spacer if there is only one spacer, or by comparing spacers between each other. The comparison is done with the CLUSTALW program (29 (link)) and the percentage of identity between spacers is not allowed to exceed 60%. Finally, candidates having at least three motifs and at least two exactly identical DRs are considered as confirmed CRISPRs. The remaining candidates are considered as questionable. These should be critically investigated by, for example, checking for intraspecies size variation of the locus.
Grissa I., Vergnaud G, & Pourcel C. (2007). CRISPRFinder: a web tool to identify clustered regularly interspaced short palindromic repeats. Nucleic Acids Research, 35(Web Server issue), W52-W57.
Publication 2007
Clustered Regularly Interspaced Short Palindromic Repeats Direct Repeat Genome Mutation Nucleotides Repetitive Region Tandem Repeat Sequences Trees
3
Illumina HiSeq 2500 Sequencing and Variant Calling
Cited 286 times
Sequences were generated on the Illumina HiSeq 2500 and base-calling was carried out using Ibis44 (link). Reads were merged and adapter trimmed as described1 (link) and mapped to the human reference genome using BWA (version: 0.5.10). Genotyping was carried out using GATK (version 1.3). We restrict analyses to regions of the genome that are non-repetitive (excluding tandem repeats), unique (requiring at least 50%, or all, overlapping 35-mers covering a position to map uniquely, allowing for one mismatch), and fall within the central 95% of the coverage distribution corrected for GC bias (SI 5b). The supplementary information describes the details of data processing and other analyses.
Prüfer K., Racimo F., Patterson N., Jay F., Sankararaman S., Sawyer S., Heinze A., Renaud G., Sudmant P.H., de Filippo C., Li H., Mallick S., Dannemann M., Fu Q., Kircher M., Kuhlwilm M., Lachmann M., Meyer M., Ongyerth M., Siebauer M., Theunert C., Tandon A., Moorjani P., Pickrell J., Mullikin J.C., Vohr S.H., Green R.E., Hellmann I., Johnson P.L., Blanche H., Cann H., Kitzman J.O., Shendure J., Eichler E.E., Lein E.S., Bakken T.E., Golovanova L.V., Doronichev V.B., Shunkov M.V., Derevianko A.P., Viola B., Slatkin M., Reich D., Kelso J, & Pääbo S. (2013). The complete genome sequence of a Neandertal from the Altai Mountains. Nature, 505(7481), 43-49.
Publication 2013
Genome Genome, Human GPER protein, human Homo sapiens Tandem Repeat Sequences
4
Great Ape Genomic Variation Analysis
Cited 158 times
We sequenced to a mean coverage of 25X (Illumina HiSeq 2000) a total of 79 great ape individuals, representing 10 subspecies and four genera of great apes from a variety of populations across the African continent and Southeast Asia. SNPs were called using GATK12 (link) after BWA28 (link) mapping to the human genome (NCBI Build 36) using relaxed mapping parameters. Samples combined by species were realigned around putative indels. SNP calling was then performed on the combined individuals for each species. For indels, we used the GATK Unified Genotyper to produce an initial set of indel candidates applying several quality filters and removing variants overlapping segmental duplications and tandem repeats. We also removed groups of indels clustering within 10 bp to eliminate possible artifacts in problematic regions. Conservative allelic imbalance filters were used to eliminate false heterozygotes that may affect demographic analyses, some of which are sensitive to low levels of contamination. We estimate that the application of this filter resulted in a 14% false negative rate for heterozygotes. Our multispecies study design facilitated this assessment of contamination, which may remain undetected in studies focused on assessing diversity within a single species. The amount of cross-species contamination was estimated from the amount of non-endogenous mitochondrial sequence present in an individual. Because we wished to compare patterns of variation between and within species, we report all variants with respect to coordinates of the human genome reference. For FRAPPE analyses, we used MAF0.06 (human, orangutan, and bonobo) and 0.05 (chimpanzee and gorilla) to remove singletons. For most of the analyses, we only used autosomal markers, except in the X/A analysis. To determine the amount of inbreeding, we calculated the heterozygosity genome-wide in windows of 1 Mbp with 200 kbp sliding windows. We then clustered together the neighboring regions to account for runs of homozygosity. For the PSMC analyses, we called the consensus bases using SAMtools29 (link). Underlying raw sequence data is available through the SRA (PRJNA189439/SRP018689). Data generated in this work are available from http://biologiaevolutiva.org/greatape/. A complete description of the material and methods is provided in the Supplementary Note.
Prado-Martinez J., Sudmant P.H., Kidd J.M., Li H., Kelley J.L., Lorente-Galdos B., Veeramah K.R., Woerner A.E., O’Connor T.D., Santpere G., Cagan A., Theunert C., Casals F., Laayouni H., Munch K., Hobolth A., Halager A.E., Malig M., Hernandez-Rodriguez J., Hernando-Herraez I., Prüfer K., Pybus M., Johnstone L., Lachmann M., Alkan C., Twigg D., Petit N., Baker C., Hormozdiari F., Fernandez-Callejo M., Dabad M., Wilson M.L., Stevison L., Camprubí C., Carvalho T., Ruiz-Herrera A., Vives L., Mele M., Abello T., Kondova I., Bontrop R.E., Pusey A., Lankester F., Kiyang J.A., Bergl R.A., Lonsdorf E., Myers S., Ventura M., Gagneux P., Comas D., Siegismund H., Blanc J., Agueda-Calpena L., Gut M., Fulton L., Tishkoff S.A., Mullikin J.C., Wilson R.K., Gut I.G., Gonder M.K., Ryder O.A., Hahn B.H., Navarro A., Akey J.M., Bertranpetit J., Reich D., Mailund T., Schierup M.H., Hvilsom C., Andrés A.M., Wall J.D., Bustamante C.D., Hammer M.F., Eichler E.E, & Marques-Bonet T. (2013). Great ape genetic diversity and population history. Nature, 499(7459), 471-475.
Publication 2013
Allelic Imbalance Genome Genome, Human Gorilla gorilla Heterozygote HIVEP1 protein, human Homo sapiens Homozygote INDEL Mutation Mitochondria Negroid Races Pan paniscus Pan troglodytes Pongidae Pongo pygmaeus Segmental Duplications, Genomic Tandem Repeat Sequences
5
Illumina HiSeq 2000 Sequencing and Variant Calling
Cited 122 times
All sequencing was performed on the Illumina HiSeq 2000 and base-calling was carried out using Ibis 1.1.6 33 (link). Reads were merged and remaining adaptor sequences trimmed before being aligned to the Human reference genome (GRCh37/1000 Genomes) using BWA (version 0.5.10) 34 (link). GATK version 1.3 (v1.3–14-g348f2b) was used to produce genotype calls for each site. We excluded from analysis tandem repeats and regions of the genome that are not unique. We considered only genomic regions that fall within the 95% coverage distribution (SI 7) and where at least 99 % of overlapping 35mers covering a position map uniquely, allowing 1 mismatch.
Fu Q., Li H., Moorjani P., Jay F., Slepchenko S.M., Bondarev A.A., Johnson P.L., Petri A.A., Prüfer K., de Filippo C., Meyer M., Zwyns N., Salazar-Garcia D.C., Kuzmin Y.V., Keates S.G., Kosintsev P.A., Razhev D.I., Richards M.P., Peristov N.V., Lachmann M., Douka K., Higham T.F., Slatkin M., Hublin J.J., Reich D., Kelso J., Viola T.B, & Pääbo S. (2014). The genome sequence of a 45,000-year-old modern human from western Siberia. Nature, 514(7523), 445-449.
Publication 2014
Genome Genome, Human Genotype Tandem Repeat Sequences

Most recents protocols related to «Tandem Repeat Sequences»

1
Genome Composition Analysis of Ranodon sibiricus
Overlapping paired-end shotgun reads were merged using PEAR v.0.9.11 (Zhang et al., 2014 (link)) with the following parameter values based on our library insert size and trimming parameters: min-assemble-length 36, max-assemble-length 490, min-overlap size 10. After merging, 7,385,166 reads remained (including both merged and singletons), with an N50 of 388 bp and total length of 1,997,175,501 bp. To calculate the percentage of the R. sibiricus genome composed of different TEs, these shotgun reads were masked with RepeatMasker v-4.1.0 using two versions of our Ranodon-derived repeat library: one that included the unknown repeats and the other that excluded them. In both cases, simple repeats were identified using the Tandem Repeat Finder module implemented in RepeatMasker. The overall results were summarized at the levels of TE class, order, and superfamily.
Wang J., Yuan L., Tang J., Liu J., Sun C., Itgen M.W., Chen G., Sessions S.K., Zhang G, & Mueller R.L. (2023). Transposable element and host silencing activity in gigantic genomes. Frontiers in Cell and Developmental Biology, 11, 1124374.
Publication 2023
DNA Library Genome Pears Tandem Repeat Sequences
2
Comprehensive Characterization of Genomic Insertions
Insertion events are known to be caused by various mechanisms and have various consequences [26 (link)]. To characterize and investigate the origins of the detected insertions, we decomposed them into TRs, TEs, tandem duplications (TDs), satellite sequences, dispersed duplications, processed pseudogenes, alternative sequences, “deletions” in GRCh38, and nuclear mitochondrial DNA sequences (NUMTs).
We first applied Tandem Repeats Finder (TRF) [27 ] to all inserted sequences and defined TRs as having (1) element lengths < 50 bp and (2) covering more than 50% of an inserted sequence. After filtering TRs, we identified TEs using RepeatMasker [28 ] if (1) an inserted sequence covered a TE > 50%, (2) the inserted sequence was covered by the TE > 50% (reciprocal overlap), and (3) the total substitutions and indels were < 50% (matching condition).
Previous studies have reported that TDs are understudied but widespread [26 (link), 29 (link)]. After detecting TRs and TEs, we manually reviewed the remaining insertions and found that they contained TDs derived from non-repetitive regions in the reference. We considered these insertions as TDs. To identify this class of insertions, we aligned all insertions except TRs to GRCh38 using BLAT [30 (link)]. We then collected insertions mapped to original breakpoints within 5 bp with > 90% in BLAT identity and defined them as TDs. In this process, missing TRs with long repeat elements were found. Therefore, they were added to the TR callset if (1) an inserted sequence aligned within 500 bp from the insertion breakpoint and (2) the ratio of the total number of matching bases to the insertion length was > 0.5.
To understand the remaining insertions, we manually checked their features by aligning them to the reference using BLAT [30 (link)]. We identified insertions that were aligned from end to end to different chromosomal regions with high identity (> 90%). We defined these insertions as dispersed duplications. Next, we detected insertions aligned to a series of exons and untranslated regions (UTRs) of coding genes with high identity (> 90%) and classified them as processed pseudogenes. We also found other insertions aligned to the alternative sequences (e.g., “alt” or “fix” sequences) on BLAT with high identity (> 90%). We classified them as alternative sequences. Some of the insertions left at this point were thought to have arisen by deletion events in GRCh38 because they were securely aligned to the chimpanzee reference genome (panTro6), although they were classified as insertions when compared with GRCh38 [3 (link)]. We aligned the remaining insertions to the panTro6 assembly and categorized the insertions that lifted over panTro6 with high accuracy (> 90%) within 100 bp of the inserted position on GRCh38 as "deletions” in GRCh38. After this, the remaining insertions were manually reviewed, and features of the genomic regions (segmental duplications or self-chain) were examined.
Ikemoto K., Fujimoto H, & Fujimoto A. (2023). Localized assembly for long reads enables genome-wide analysis of repetitive regions at single-base resolution in human genomes. Human Genomics, 17, 21.
Publication 2023
BP 100 Chromosomes Deletion Mutation DNA, Mitochondrial Exons Gene Deletion Gene Insertion Genes Genome INDEL Mutation Insertion Mutation Mitochondria Pan troglodytes Pseudogenes Repetitive Region Segmental Duplications, Genomic Tandem Repeat Sequences Untranslated Regions
3
Genomic Analysis of PfGARP in Malaria Parasites
Sequence analysis included 80 nucleotide sequences of PfGARP from Thai isolates, one clinical isolate from Guinea (isolate MDCU32) and 18 publicly available complete gene sequences whose isolate names, country of origins and their GenBank accession numbers are as follows: 3D7 (Netherlands from West Africa, AL844501), CD01 (Congo, LR129686), Dd2 (Indochina, LR131290), FC27 (Papua New Guinea, J03998), FCC1/HN (Hainan in China, AF251290), GA01 (Gambia, LR131386), GB4 (Ghana, LR131402), KH1 (Cambodia, LR131418), KH2 (Cambodia, LR131306), HB3 (Honduras, LR131338), IGH-CR14 (India, GG6656811), IT (Brazil, LR131322), KE01 (Kenya, LR131354), ML01 (Mali, LR131481), SD01 (Sudan, LR131466), SN01 (Senegal, LR131434), TG01 (Togo, LR131450), and UGT5.1 (Vietnam, KE124372). Of these, the 3D7, FC27and FCC1/HN sequences were determined by Sanger dideoxy-chain termination method whereas the remaining isolates were assembled sequences from next-generation sequencing platforms (Supplemental Table S1). Sequence alignment was performed by using the CLUSTAL_X program, taken into account appropriate codon match in the coding region by manual adjustment to maintain the reading frame. The sequence from the FC27 strain was used as a reference6 (link). Searching for nucleotide repeats was performed by using the Tandem Repeats Finder version 4.0 program with the default option. Nucleotide diversity (π), the rate of synonymous substitutions per synonymous site (dS) and the rate of nonsynonymous substitutions per nonsynonymous site (dN) were determined from the average values of sequence differences in all pairwise comparison of each taxon and the standard error was computed from 1000 bootstrap pseudoreplicates implemented in the MEGA 6.0 program41 (link). Haplotype diversity and its sampling variance were computed by taking into account the presence of gaps in the aligned sequences using the DnaSP version 5.10 program42 (link). Natural selection on codon substitution was determined by using fast unconstrained Bayesian approximation (FUBAR) method in the Datamonkey Web-Server43 (link),44 (link). Neighbor-joining phylogenetic tree based on nucleotide sequences was constructed by using maximum composite likelihood parameter whereas maximum likelihood tree was built using Tamura-Nei model with the rate variation model allowed for some sites to be evolutionarily invariable. The Arlequin 3.5.2.2 software was deployed to determine genetic differentiation between populations, the fixation index (FST), using analysis of molecular variance approach (AMOVA) akin to the Weir and Cockerham’s method but taken into account the number of mutations between haplotypes45 (link). One hundred permutations were deployed to determine the significance levels of the fixation indices. Prediction of linear B cell epitopes in PfGARP was performed by using a sequence similarity to known experimentally verified epitopes from the Immune Epitope DataBase (IEDB) implemented in the BepiBlast Web Server11 (link). Furthermore, linear B cell epitopes were also predicted based on protein language models implemented in BepiPred-3.012 (link). Potential HLA-class II-binding peptides were analyzed by using the IEDB recommended 2.22 algorithm with a default 12–18 amino acid residues option. The predicted HLA-class II-binding peptides were predicted based on the percentile rank < 10 and the IC50 threshold for HLA binding affinity ≤ 1000 nM14 (link). The analysis mainly concerned the common HLA class II haplotypes among Thai populations with allele frequency > 0.113 (link).
Rojrung R., Kuamsab N., Putaporntip C, & Jongwutiwes S. (2023). Analysis of sequence diversity in Plasmodium falciparum glutamic acid-rich protein (PfGARP), an asexual blood stage vaccine candidate. Scientific Reports, 13, 3951.
Publication 2023
Amino Acids Codon Epitopes Epitopes, B-Lymphocyte Genes Genetic Drift Haplotypes Hereditary Nonpolyposis Colorectal Cancer Type 1 Mutation Natural Selection Nucleotides Peptides Population Group Proteins Reading Frames Sequence Alignment Sequence Analysis Strains Tandem Repeat Sequences Thai Trees
4
Comprehensive Genome Repeat Analysis
Tandem Repeats Finder v 4.0.7 program was applied to detect tandem repeats in the genome [101 (link)]. Homolog-based and de novo prediction methods were integrated to identify transposable elements (TEs). For the de novo search, LTR_Finder v1.0.6 [102 (link)] and RepeatModeler v1.0.8 [103 ] were employed to find repetitive elements with specific consensus models. For the homology-based search, RepeatMasker v4.0.6 and RepeatProteinMask v4.0.6 against the Repbase v21.01 database were used at the nucleotide and protein levels respectively [104 , 105 (link)]. Secondly, RepeatMasker was employed again to detect species-specific TEs against the database concatenated by the results of LTR_Finder and RepeatModeler together. All other species used in this work were annotated repetitive elements following the same pipeline for comparative analysis.
Guo Y., Meng L., Wang M., Zhong Z., Li D., Zhang Y., Li H., Zhang H., Seim I., Li Y., Jiang A., Ji Q., Su X., Chen J., Fan G., Li C, & Liu S. (2023). Hologenome analysis reveals independent evolution to chemosymbiosis by deep-sea bivalves. BMC Biology, 21, 51.
Publication 2023
DNA Transposable Elements Genome Nucleotides Proteins Repetitive Region Tandem Repeat Sequences
5
Comprehensive Genome Repeat Annotation
Considering that the repetitive elements of many species investigated in this study are either not well annotated and/or not publicly available, we re-annotated the repetitive elements of all the sampled species except human using the same strategy. Repetitive elements of the human genome (GRCh38/hg38) have been well annotated and thus were downloaded from UCSC directly. Repetitive elements in the genomes of the rest species were identified by homology searches against known repeat databases and de novo predictions as previously described.110 (link) Briefly, we carried out homology searches for known repetitive elements in each genome assembly by screening the Repbase-derived RepeatMasker libraries with RepeatMasker (setting -nolow -no_is -norna -engine ncbi) and the transposable element protein database with RepeatProteinMask (an application within the RepeatMasker package; setting -noLowSimple -pvalue 0.0001 -engine ncbi). For de novo prediction, RepeatModeler was executed on the genome assembly to build a de novo repeat library for each species, respectively. Then RepeatMasker was employed to align the genome sequences to the de novo library for identifying repetitive elements. We also searched each genome assembly for tandem repeats using Tandem Repeats Finder100 (link) with parameters Match = 2 Mismatch = 7 Delta = 7 PM = 80 PI = 10 Minscore = 50 MaxPeriod = 2000. To confirm the reliability of our annotations, we compared our repeat annotation results of the fruit fly Drosophila melanogaster and the zebrafish Danio rerio with those downloaded from UCSC and observed good consistency (Figures S3A and S3B).
Zhang P., Zhu Y., Guo Q., Li J., Zhan X., Yu H., Xie N., Tan H., Lundholm N., Garcia-Cuetos L., Martin M.D., Subirats M.A., Su Y.H., Ruiz-Trillo I., Martindale M.Q., Yu J.K., Gilbert M.T., Zhang G, & Li Q. (2023). On the origin and evolution of RNA editing in metazoans. Cell Reports, 42(2), 112112.
Publication 2023
DNA Library DNA Transposable Elements Drosophila Genome Genome, Human Genomic Library Homo sapiens Repetitive Region Tandem Repeat Sequences Zebrafish

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More about "Tandem Repeat Sequences"

Tandem repeat sequences are a fascinating area of genomic research, with applications ranging from genetic diagnostics to bioinformatics.
These repetitive DNA elements, where a short nucleotide sequence is repeated consecutively, can provide valuable insights into an organism's evolutionary history and genetic diversity.
Analyzing tandem repeat sequences often involves a range of experimental techniques, such as the Dual-Luciferase Reporter Assay System, which is widely used to measure gene expression levels.
Lipofectamine 2000, a transfection reagent, can be employed to introduce genetic material into cells, while DMEM and FBS are common cell culture media components.
The Dual luciferase assay kit is a powerful tool for quantifying the activities of two different luciferase reporter enzymes, enabling researchers to normalize and compare gene expression levels.
Penicillin and streptomycin are antibiotics often used in cell culture to prevent bacterial contamination.
The Luciferase Assay System is another valuable tool for measuring luciferase activity, which can be used as a proxy for gene expression.
GeneArt is a technology that allows for the efficient and precise engineering of synthetic DNA sequences, which can be important in tandem repeat sequence research.
By leveraging these various techniques and technologies, researchers can gain a deeper understanding of tandem repeat sequences, their evolutionary origins, and their potential applications in fields such as diagnostics, forensics, and population genetics.
With the help of AI-driven platforms like PubCompare.ai, researchers can identify the most reproducible and accurate protocols for their tandem repeat sequence research, saving time and effort on trial-and-error.