All samples were obtained under institutional IRB approval and with documented informed consent. A complete list of samples is given in Table S2 . Whole-exome capture libraries were constructed and sequenced on Illumina HiSeq flowcells to average coverage of 118x. Whole-genome sequencing was done with the Illumina GA-II or Illumina HiSeq sequencer, achieving an average of ~30X coverage depth. Reads were aligned to the reference human genome build hg19 using an implementation of the Burrows-Wheeler Aligner, and a BAM file was produced for each tumor and normal sample using the Picard pipeline6 (link). The Firehose pipeline was used to manage input and output files and submit analyses for execution. The MuTect30 and Indelocator (Sivachenko, A. et al., manuscript in preparation) algorithms were used to identify somatic single-nucleotide variants (SSNVs) and short somatic insertions and deletions, respectively. Mutation spectra were analyzed using non-negative matrix factorization (NMF). Significantly mutated genes were identified using MutSigCV, which estimates the background mutation rate (BMR) for each gene-patient-category combination based on the observed silent mutations in the gene and noncoding mutations in the surrounding regions. Because in most cases these data are too sparse to obtain accurate estimates, we increased accuracy by pooling data from other genes with similar properties (e.g. replication time, expression level). Significance levels (p-values) were determined by testing whether the observed mutations in a gene significantly exceed the expected counts based on the background model. False Discovery Rates (q-values) were then calculated, and genes with q≤0.1 were reported as significantly mutated. Full methods details are listed in Supplementary Information .
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Silent Mutation
Silent Mutation
Silent mutation refers to a genetic change that does not result in a change in the amino acid sequence of the encoded protein.
These mutations often occur in the third position of a codon, a phenomenon known as the 'wobble' position, where the base substitution does not alter the final amino acid.
While silent mutations were once thought to be biologically insignificant, recent research has shown that they can have subtle effects on protein folding, stability, and expression levels.
Silent Mutation Analysis using AI-driven platforms like PubCompare.ai can help researchers locate the best protocols from literature, preprints, and patents, enhancing reproducibility and driving scientific discovery forward.
By unveiling hidden insights and facilitating informed decisions, these tools enable researchers to experince the fuuture of scientific discovery today.
These mutations often occur in the third position of a codon, a phenomenon known as the 'wobble' position, where the base substitution does not alter the final amino acid.
While silent mutations were once thought to be biologically insignificant, recent research has shown that they can have subtle effects on protein folding, stability, and expression levels.
Silent Mutation Analysis using AI-driven platforms like PubCompare.ai can help researchers locate the best protocols from literature, preprints, and patents, enhancing reproducibility and driving scientific discovery forward.
By unveiling hidden insights and facilitating informed decisions, these tools enable researchers to experince the fuuture of scientific discovery today.
Most cited protocols related to «Silent Mutation»
Diploid Cell
DNA Replication
Exome
Gene Deletion
Genes
Genes, vif
Genetic Background
Genome, Human
Insertion Mutation
Multiple Acyl Coenzyme A Dehydrogenase Deficiency
Mutation
Neoplasms
Nucleotides
Patients
Silent Mutation
TMB was defined as the number of somatic, coding, base substitution, and indel mutations per megabase of genome examined. All base substitutions and indels in the coding region of targeted genes, including synonymous alterations, are initially counted before filtering as described below. Synonymous mutations are counted in order to reduce sampling noise. While synonymous mutations are not likely to be directly involved in creating immunogenicity, their presence is a signal of mutational processes that will also have resulted in nonsynonymous mutations and neoantigens elsewhere in the genome. Non-coding alterations were not counted. Alterations listed as known somatic alterations in COSMIC and truncations in tumor suppressor genes were not counted, since our assay genes are biased toward genes with functional mutations in cancer [63 (link)]. Alterations predicted to be germline by the somatic-germline-zygosity algorithm were not counted [64 (link)]. Alterations that were recurrently predicted to be germline in our cohort of clinical specimens were not counted. Known germline alterations in dbSNP were not counted. Germline alterations occurring with two or more counts in the ExAC database were not counted [65 (link)]. To calculate the TMB per megabase, the total number of mutations counted is divided by the size of the coding region of the targeted territory. The nonparametric Mann–Whitney U-test was subsequently used to test for significance in difference of means between two populations.
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Antigens
Biological Assay
Cosmic composite resin
Diploid Cell
Genes
Genome
Germ Line
INDEL Mutation
Malignant Neoplasms
Mutation
Population Group
Silent Mutation
Tumor Suppressor Genes
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Anabolism
Cells
Clone Cells
Cloning Vectors
Cytokinesis
DNA, Complementary
DNA-Directed DNA Polymerase
DNA Restriction Enzymes
Escherichia coli
Genes
Infection
Nucleotides
Oligonucleotide Primers
Parent
Plasmids
Platinum
Poly A
Reverse Transcription
RNA, Viral
SARS-CoV-2
Silent Mutation
Untranslated Regions
Viral Genome
Virus
Base Pairing
Base Sequence
Exons
Genes
Genes, vif
Mutation
Mutation, Nonsense
Primates
Silent Mutation
Vision
The first part of the IntOGen-mutations pipeline assesses the potential functional impact of somatic mutations detected across the cohort of tumor samples. The Ensembl variant effect predictor10 (link) (VEP, v.70) script and precomputed cache files, downloaded from the Ensembl FTP site (ftp://ftp.ensembl.org/pub/ ), are used to determine the consequences of somatic mutations in annotated functional elements. The pipeline obtains SIFT11 (link) and PolyPhen2 (ref. 12 (link)) functional impact from VEP. Precomputed MutationAssessor13 (link) functional impacts are obtained from the MutationAssessor Web server (http://www.mutationassessor.org/ ) during the installation of the pipeline and are queried locally during execution. The transformation of functional impact scores to account for the baseline tolerance of genes to germline mutation (transFIC), described elsewhere14 (link), has been reimplemented in Python as a module of the IntOGen-mutations pipeline.
The pipeline implements an expression filter to disregard genes that are not expressed across the tumor samples in the cohort. This list of expressed genes is an optional input to the pipeline, which excludes all genes outside the list from the foreground of both OncodriveFM and OncodriveCLUST (see below) while keeping their mutations in the background. In the current release of the IntOGen-mutations Web discovery tool, we have employed as a filter the list of genes expressed across any of the 12 pan-cancer data sets (ref. syn1734155).
The OncodriveFM and OncodriveCLUST approaches, also described elsewhere7 (link),9 (link), have been reimplemented as IntOGen-mutations pipeline modules and are available as independent programs from two Git-controlled repositories athttps://bitbucket.org/bbglab/ . Briefly, OncodriveFM receives as input the list of synonymous, nonsynonymous and frameshift-indel mutations and their corresponding SIFT, PolyPhen2 and MutationAssessor scores. Then it assesses whether any gene shows a trend toward the accumulation of mutations with high functional impact as compared to the background distribution of these functional impact scores in all mutations detected across the cohort of tumor samples (FM bias). For each functional impact score included in the pipeline, the method produces an empirical P value that evaluates this FM bias. These three P values are subsequently combined using Fisher's approach to produce one integrated P value for each gene. To account for possible nondependence between the three P values included in the combination, the IntOGen-mutations Web discovery tool considers as significant those with a false discovery rate (FDR) below 0.05.
OncodriveFM also computes an FM bias for pathways. Three z scores are computed in this case to assess the trend of pathways to accumulate mutations with high functional impact. The z scores are combined using Stouffer's approach, and the combined z score is transformed into an integrated P value.
OncodriveCLUST, on the other hand, receives as input two separate lists of mutations: potentially protein-affecting mutations (nonsynonymous, stop and splice site) and silent mutations (synonymous), with their corresponding locations across the proteins' sequences. It then assesses the significance of the trend of potentially protein-affecting mutations to be clustered with respect to a background represented by the homologous trend for silent mutations.
Genes mutated in less than 1% of the samples in projects whose median of mutations per sample was below 100 were not analyzed by OncodriveFM. In projects with higher median of mutations per samples, this threshold was set to 5 samples with mutations. For OncodriveCLUST, the thresholds were 3 and 5 mutated samples, respectively. These and many other parameters of the pipeline are configurable by the user, as explained in its documentation.
In addition to third-party (and in-house) software and data, IntOGen-mutations pipeline installation requires some Python libraries. The most important of these are the numpy and scipy scientific computing libraries and the statsmodels Python statistical library.
The pipeline also relies on other external data files. During pipeline installation, all of the needed external and third-party data files are downloaded and correctly placed, and external libraries are downloaded and compiled, thereby creating a Python environment where the pipeline executes.
The analysis of the 4,623 tumor samples currently included in the IntOGen-mutations Web discovery tool takes approximately 5 h on an eight-core, 12 GB RAM computer.
The pipeline implements an expression filter to disregard genes that are not expressed across the tumor samples in the cohort. This list of expressed genes is an optional input to the pipeline, which excludes all genes outside the list from the foreground of both OncodriveFM and OncodriveCLUST (see below) while keeping their mutations in the background. In the current release of the IntOGen-mutations Web discovery tool, we have employed as a filter the list of genes expressed across any of the 12 pan-cancer data sets (ref. syn1734155).
The OncodriveFM and OncodriveCLUST approaches, also described elsewhere7 (link),9 (link), have been reimplemented as IntOGen-mutations pipeline modules and are available as independent programs from two Git-controlled repositories at
OncodriveFM also computes an FM bias for pathways. Three z scores are computed in this case to assess the trend of pathways to accumulate mutations with high functional impact. The z scores are combined using Stouffer's approach, and the combined z score is transformed into an integrated P value.
OncodriveCLUST, on the other hand, receives as input two separate lists of mutations: potentially protein-affecting mutations (nonsynonymous, stop and splice site) and silent mutations (synonymous), with their corresponding locations across the proteins' sequences. It then assesses the significance of the trend of potentially protein-affecting mutations to be clustered with respect to a background represented by the homologous trend for silent mutations.
Genes mutated in less than 1% of the samples in projects whose median of mutations per sample was below 100 were not analyzed by OncodriveFM. In projects with higher median of mutations per samples, this threshold was set to 5 samples with mutations. For OncodriveCLUST, the thresholds were 3 and 5 mutated samples, respectively. These and many other parameters of the pipeline are configurable by the user, as explained in its documentation.
In addition to third-party (and in-house) software and data, IntOGen-mutations pipeline installation requires some Python libraries. The most important of these are the numpy and scipy scientific computing libraries and the statsmodels Python statistical library.
The pipeline also relies on other external data files. During pipeline installation, all of the needed external and third-party data files are downloaded and correctly placed, and external libraries are downloaded and compiled, thereby creating a Python environment where the pipeline executes.
The analysis of the 4,623 tumor samples currently included in the IntOGen-mutations Web discovery tool takes approximately 5 h on an eight-core, 12 GB RAM computer.
Amino Acid Sequence
cDNA Library
Diploid Cell
Frameshift Mutation
Gene Expression
Genes
Germ-Line Mutation
Immune Tolerance
INDEL Mutation
Malignant Neoplasms
Mutation
Mutation Accumulation
Neoplasms
Proteins
Python
Silent Mutation
Most recents protocols related to «Silent Mutation»
We constructed a total of 34 mutants across the three genes consisting of 12 CAT-I mutants, 13 NDM-1 mutants, and 9 aadB mutants. We used inverse PCR to introduce the mutations. We also used inverse PCR to construct a control plasmid, pSKunk1-ΔGene, which had the coding region of the studied antibiotic resistance genes deleted.
For the C26D and C26S mutants in NDM-1, we found that an IS4-like element ISVsa5 family transposase insertion would occur within the NDM-1 gene during the six hours of induced monoculture growth (supplementary Text, Supplementary Material online). We made two synonymous mutations within the 5′-GCTGAGC-3′ insertion site that fully overlapped codons 23 and 24 to reduce transposase insertion and get an accurate measure of the collateral fitness effects for the C26D and C26S mutations. The new sequence was 5′-GTTATCA-3′. Inverse PCR was used to introduce these synonymous mutations. All mutant plasmids were transformed into NEB 5-alpha LacIq electrocompetent cells.
For the C26D and C26S mutants in NDM-1, we found that an IS4-like element ISVsa5 family transposase insertion would occur within the NDM-1 gene during the six hours of induced monoculture growth (
Antibiotic Resistance, Microbial
Chloramphenicol O-Acetyltransferase
Codon
Genes
Inverse PCR
Mutation
Pancreatic alpha Cells
Plasmids
Silent Mutation
Transposase
Burrows-Wheeler Aligner (BWA version 0.7.11) alignment algorithm was used to align the human reference genome (UCSC hg19). Next, Genome Analysis Toolkit (GATK, version 3.6) (31 (link)) module IndelRealigner and VarScan software were used to call somatic mutations (31 (link), 32 (link)), and ANNOVAR annotated all variants. The following filtering criteria were applied to the mutation candidates to identify SNVs and Indels: (a) variants within intron were deleted; (b) mutations reported in more than 1% of the population in the 1000 Genomes Project (1000gAUG_2015ALL); (c) Mutations were then filtered against common single nucleotide polymorphisms (SNPs) found in dbSNP (http://www.ncbi.nlm.nih.gov/SNP ); (d) synonymous variants were excluded; (e) variants with less than 50 supporting reads were removed.
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Diploid Cell
Genome
Genome, Human
INDEL Mutation
Introns
Mutation
Silent Mutation
Strains
The training cohort included 344 patients with NSCLC treated with ICIs (Immunotherapy, MSKCC, Nat Genet 2019) (9 (link)). Validation cohort 1 was integrated from three public cohorts sequenced by the WES, including 56 patients with NSCLC treated with anti-PD-(L)1 therapy (15 (link)), 75 patients with NSCLC treated with PD-1 plus CTLA-4 blockade (LUAD only) (16 (link)), and 69 patients with NSCLC treated with anti-PD-(L)1 monotherapy at Sun Yat-sen University Cancer Center (SYSUCC) (17 (link)). Validation cohort 2 was a pan-cancer cohort including 1181 patients treated with anti-PD-(L)1 therapy (From Samstein cohort, 350 patients with NSCLC, and 130 patients with unknown cancer were excluded) (9 (link)). Validation cohort 3 was also a pan-cancer cohort including 193 patients treated with anti-PD-(L)1 therapy (15 (link)) (From Miao cohort, 56 patients with NSCLC were excluded). Both training and validation cohorts were selected based on the following criteria: (i) patients with no mutation information were excluded; (ii) synonymous mutation, copy number variation, and fusion genes were excluded; (iii) genes were mutated in at least three samples. In addition, data from non-ICI treatment TCGA NSCLC cohorts were used for further exploration, including RNA-seq data downloaded from UCSC Xena (University of California Santa Cruz) (https://xenabrowser.net/datapages/ ), immune subtype data along with survival data acquired from Thorsson et al. (18 (link)), and mutation data obtained from Ellrott et al. (29 (link)). In addition, six single-cell RNA sequencing data of LUAD patients from Bischoff, P., et al. (30 (link)) were included to reveal the gene expression features in different cell types (30 (link)). In addition, a retrospective southwest hospital clinical (SHC) cohort, with 82 lung cancer patients, was utilized to analyze the correlation between the predictive model and TMB. Of these, 77 were NSCLC, and the remaining were primary lung cancer. Survival data could not be acquired because of the loss of follow-up after surgery. All the samples were collected in the Southwest Hospital, and multiple gene panel target sequencing was conducted. The detailed clinical characteristics of patients in the training cohort, validation cohort 1-3, TCGA cohort, and SHC cohort are summarized in Supplementary Tables 2–7 Supplementary Table 8
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Cells
Copy Number Polymorphism
Cytotoxic T-Lymphocyte Antigen 4
Gene Expression
Genes
Genets
Immunotherapy
Lung Cancer
Malignant Neoplasms
Multiple Birth Offspring
Mutation
Neoplasms, Unknown Primary
Non-Small Cell Lung Carcinoma
Operative Surgical Procedures
Patients
RNA-Seq
Silent Mutation
Cells were maintained in 5% CO2 at 37°C in media conditions described by the vendor or the source laboratory and were tested monthly for mycoplasma by PCR (Young et al., 2010 (link)). KO cells were generated by infection with lentiviruses constitutively expressing Cas9 and appropriate sgRNAs. All experiments were performed with early passage lines within 3 mo of defrosting. MOLM13, MV4-11, BV173, K562, H1975, LU65, HCC827 cells were from lab stocks. KYSE520 was obtained from the Deutsche Sammlung von Mikroorganismen und Zellkulturen (German Collection of Microorganisms and Cell Cultures GmbH). KG1a was obtained from Dr. Christopher Park (NYU Grossman School of Medicine, New York, NY, USA) in June 2020; MOLM14 was obtained from Dr. Iannis Aifantis (NYU Grossman School of Medicine, New York, NY, USA) in July 2020; KU812, KYO1, EOL1, OCI-M1 were provided from Dr. Ross L. Levine (Memorial Sloan Kettering Cancer Center, New York, NY, USA) in March 2021. MOLM13, MV4-11, BV173, K562, H1975, LU65, HCC827, KYSE520, KG1a, KU812, KYO1, and EOL1 were cultured in RPMI supplemented with 10% FBS and 1% penicillin/streptomycin. OCI-M1 was maintained in IMDM supplemented with 10% FBS and 1% penicillin/streptomycin.
sgRNAs for non-targeting control and the target genes INPPL1, MAP4K5, LZTR1, and RIT1 were as follows (target sequence): non-targeting: 5′-AACCGGCTGCGCGTTTGCAA-3′; INPPL1-sg1: 5′-GCAGGGCGCACACAAGGCCC-3′; INPPL1-sg2: 5′-CCTGGATATCCATGTCCAGG-3′; MAP4K5-sg1: 5′-AGGACTACGAACTCGTCCAG-3′; MAP4K5-sg2: 5′-TAGGCCAGAAATGTACACAC-3′; LZTR1-sg1: 5′-TATGGTCGAAGTCCACGCTC-3′; LZTR1-sg2: 5′-CGGCCGAGTGGTGGTAACGG-3′; RIT1-sg1: 5′-ACGTACTGACGATACACCTG-3′; RIT1-sg2: 5′-TCGGTGGCTGATGAACTGCA-3′.
Parental and KO MV4-11 cells for mouse experiments were generated with non-targeting, INPPL1-sg1, MAP4K5-sg1, and LZTR1-sg1. sgRNA resistant constructs for INPPL1 and MAP4K5 for re-expression were generated through making silent mutations in PAM sequence for INPPL1-sg1 and MAP4K5-sg1.
sgRNAs for non-targeting control and the target genes INPPL1, MAP4K5, LZTR1, and RIT1 were as follows (target sequence): non-targeting: 5′-AACCGGCTGCGCGTTTGCAA-3′; INPPL1-sg1: 5′-GCAGGGCGCACACAAGGCCC-3′; INPPL1-sg2: 5′-CCTGGATATCCATGTCCAGG-3′; MAP4K5-sg1: 5′-AGGACTACGAACTCGTCCAG-3′; MAP4K5-sg2: 5′-TAGGCCAGAAATGTACACAC-3′; LZTR1-sg1: 5′-TATGGTCGAAGTCCACGCTC-3′; LZTR1-sg2: 5′-CGGCCGAGTGGTGGTAACGG-3′; RIT1-sg1: 5′-ACGTACTGACGATACACCTG-3′; RIT1-sg2: 5′-TCGGTGGCTGATGAACTGCA-3′.
Parental and KO MV4-11 cells for mouse experiments were generated with non-targeting, INPPL1-sg1, MAP4K5-sg1, and LZTR1-sg1. sgRNA resistant constructs for INPPL1 and MAP4K5 for re-expression were generated through making silent mutations in PAM sequence for INPPL1-sg1 and MAP4K5-sg1.
Cell Culture Techniques
Cells
Genes
Infection
Lentivirus
Malignant Neoplasms
Mus
Mycoplasma
Parent
Penicillins
Silent Mutation
Streptomycin
Genomic DNA was extracted from whole blood from all patients except P17, for whom DNA was obtained from SV40-transformed fibroblasts. The whole exome was sequenced at the Genomics Core Facility of the Imagine Institute (Paris, France), the Yale Center for Genome Analysis the New York Genome Center, and The American Genome Center (Uniformed Services University of the Health Sciences, Bethesda, MD, USA), and the Genomics Division–Institute of Technology and Renewable Energies of the Canarian Health System sequencing hub (Canary Islands, Spain), as previously reported (Asano et al., 2021 (link)). The whole-exome sequences of the patients were filtered against the complete International Union of Immunological Societies list of genes (Tangye et al., 2022 (link)), with the retention of variants with an allele frequency below 0.001. We excluded synonymous mutations, downstream, upstream, intron and non-coding transcript variants and intergenic variants. We also excluded variants predicted to be benign and we checked the quality of the exome sequences. The mutation significance cutoff (http://pec630.rockefeller.edu:8080/MSC/ ) was used to determine whether variants were likely to be damaging.
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BLOOD
Exome
Fibroblasts
Genes
Genome
Introns
Mutation
Patients
Retention (Psychology)
Silent Mutation
Simian virus 40
Strains
Top products related to «Silent Mutation»
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The Q5 Site-Directed Mutagenesis Kit is a laboratory tool designed for introducing precise mutations into DNA sequences. It provides a streamlined workflow for generating site-specific changes in plasmid or linear DNA templates.
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More about "Silent Mutation"
synonymous mutations, codon, wobble position, protein folding, protein stability, protein expression, Q5 Site-Directed Mutagenesis, QuikChange II XL Site-Directed Mutagenesis, QIAamp DNA Mini Kit, HiSeq 2500, HiSeq 2000, Lipofectamine 2000, Lipofectamine 3000, Lipofectamine RNAiMAX, GeneArt