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Exome

Exome: The complete set of protein-coding DNA sequences within a genome.
Exome analysis has become a powerful tool for identifying genetic variants associated with human diseases and traits.
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Most cited protocols related to «Exome»

Rigorous Variant Filtering and Validation for Rare Genetic Variants

We leveraged a variety of sources of internal and external validation data to calibrate filters and evaluate the quality of filtered variants (Supplementary Information Table 7). We adjusted the standard GATK variant site filtering^{37 (link)} to increase the number of singleton variants that pass this filter, while maintaining a singleton transmission rate of 50.1%, very near the expected 50%, within sequenced trios. We then used the remaining passing variants to assess depth and genotype quality filters compared to >10,000 samples that had been directly genotyped using SNP arrays (Illumina HumanExome) and achieved 97–99% heterozygous concordance, consistent with known error rates for rare variants in chip-based genotyping^{38 (link)}. Relative to a “platinum standard” genome sequenced using five different technologies^{39 (link)}, we achieved sensitivity of 99.8% and false discovery rates (FDR) of 0.056% for single nucleotide variants (SNVs), and corresponding rates of 95.1% and 2.17% for insertions and deletions (indels). Lastly, we compared 13 representative Non-Finnish European exomes included in the call set with their corresponding 30x PCR-Free genome. The overall SNV and indel FDR was 0.14% and 4.71%, while for SNV singletons was 0.389%. The overall FDR by annotation classes missense, synonymous and protein truncating variants (including indels) were 0.076%, 0.055% and 0.471% respectively (Supplementary Information Table 5 and 6). Full details of quality assessments are described in the Supplementary Information Section 1.6.

Lek M., Karczewski K.J., Minikel E.V., Samocha K.E., Banks E., Fennell T., O'Donnell-Luria A.H., Ware J.S., Hill A.J., Cummings B.B., Tukiainen T., Birnbaum D.P., Kosmicki J.A., Duncan L.E., Estrada K., Zhao F., Zou J., Pierce-Hoffman E., Berghout J., Cooper D.N., Deflaux N., DePristo M., Do R., Flannick J., Fromer M., Gauthier L., Goldstein J., Gupta N., Howrigan D., Kiezun A., Kurki M.I., Moonshine A.L., Natarajan P., Orozco L., Peloso G.M., Poplin R., Rivas M.A., Ruano-Rubio V., Rose S.A., Ruderfer D.M., Shakir K., Stenson P.D., Stevens C., Thomas B.P., Tiao G., Tusie-Luna M.T., Weisburd B., Won H.H., Yu D., Altshuler D.M., Ardissino D., Boehnke M., Danesh J., Donnelly S., Elosua R., Florez J.C., Gabriel S.B., Getz G., Glatt S.J., Hultman C.M., Kathiresan S., Laakso M., McCarroll S., McCarthy M.I., McGovern D., McPherson R., Neale B.M., Palotie A., Purcell S.M., Saleheen D., Scharf J.M., Sklar P., Sullivan P.F., Tuomilehto J., Tsuang M.T., Watkins H.C., Wilson J.G., Daly M.J, & MacArthur D.G. (2016). Analysis of protein-coding genetic variation in 60,706 humans. Nature, 536(7616), 285-291.

Publication 2016

DNA Chips Europeans Exome Gene Deletion Genome Heterozygote Hypersensitivity INDEL Mutation Insertion Mutation Mutant Proteins Nucleotides Platinum Transmission, Communicable Disease TRIO protein, human

Comprehensive Cancer Genomic Analysis

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 pipeline^{6 (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.

Lawrence M.S., Stojanov P., Polak P., Kryukov G.V., Cibulskis K., Sivachenko A., Carter S.L., Stewart C., Mermel C.H., Roberts S.A., Kiezun A., Hammerman P.S., McKenna A., Drier Y., Zou L., Ramos A.H., Pugh T.J., Stransky N., Helman E., Kim J., Sougnez C., Ambrogio L., Nickerson E., Shefler E., Cortés M.L., Auclair D., Saksena G., Voet D., Noble M., DiCara D., Lin P., Lichtenstein L., Heiman D.I., Fennell T., Imielinski M., Hernandez B., Hodis E., Baca S., Dulak A.M., Lohr J., Landau D.A., Wu C.J., Melendez-Zajgla J., Hidalgo-Miranda A., Koren A., McCarroll S.A., Mora J., Crompton B., Onofrio R., Parkin M., Winckler W., Ardlie K., Gabriel S.B., Roberts C.W., Biegel J.A., Stegmaier K., Bass A.J., Garraway L.A., Meyerson M., Golub T.R., Gordenin D.A., Sunyaev S., Lander E.S, & Getz G. (2013). Mutational heterogeneity in cancer and the search for new cancer genes. Nature, 499(7457), 214-218.

Publication 2013

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

Automated Annotation of Genetic Variants

To annotate variants with respect to their functional consequences on genes, ANNOVAR needs to download gene annotation data sets (gene/transcript annotations and FASTA sequences) from the UCSC Genome Browser (12 (link)) and save them to local disk. Several different gene annotation systems, including RefSeq genes, UCSC Genes and the Ensembl genes, can be utilized for annotation. The ‘–downdb’ argument can be utilized for downloading necessary files automatically, if the computer is connected to the Internet. The ‘wget’ system command will be utilized for downloading, or the Net::Ftp/LWP::UserAgent modules (standard Perl modules installed in most systems by default) can be alternatively utilized. The users can specify different genome builds, such as hg18 (human), mm9 (mouse) or bosTau4 (cow), as long as they are available from the UCSC Genome Browser annotation databases. When performing gene-based annotations by Ensembl gene definitions (13 (link)), ANNOVAR will download the FASTA sequences from Ensembl as they were not available from the UCSC Genome Browser.
For region-based annotations, ANNOVAR needs to download annotation databases from the various UCSC Genome Browser tables, based on a user-specified track name. Alternatively, users can specify a custom-built annotation database conforming to Generic Feature Format 3 (GFF3), and ANNOVAR can identify variants overlapping with features annotated in the given GFF3 file. For filter-based annotations, for example, comparing mutations to those detected in the 1000 Genomes Project or dbSNP, ANNOVAR will download specific files from the corresponding websites. ANNOVAR can also download pre-computed SIFT scores for all human non-synonymous mutations, to help annotate human exomes by filter-based annotation procedure.

Wang K., Li M, & Hakonarson H. (2010). ANNOVAR: functional annotation of genetic variants from high-throughput sequencing data. Nucleic Acids Research, 38(16), e164.

Publication 2010

Exome Gene Annotation Generic Drugs Genes Genitalia Genome Homo sapiens Mice, Laboratory Missense Mutation Mutation

Identifying Causal Genes for Mendelian Diseases

To illustrate the utility of ANNOVAR in identifying causal genes for Mendelian diseases with recessive inheritance, we synthesized a whole-genome data set with ∼4.2 million SNVs and ∼0.5 million indels. These variants include all variants generated by Illumina on a male Yoruba subject (ftp://ftp.sanger.ac.uk/pub/rd/NA18507/) (14 (link)), as well as two known causal mutations for Miller syndrome (G->A mutation at chr16: 70608443 and G->C mutation at chr16: 70612611, representing G152R and G202A in the DHODH gene). We tested the variants reduction procedure on this data set using ANNOVAR, to examine whether we can identify a small subset of candidate genes that include the causal gene DHODH.
To illustrate the utility of ANNOVAR in identifying causal genes for Mendelian diseases with dominant inheritance, we synthesized whole-exome data sets. Since exome data for four Freeman–Sheldon cases were not available to us, we downloaded the exome data for eight HapMap subjects reported in (11 (link)). We then extracted the exome data for the first four subjects, including two Yoruba subjects (NA18507, NA18517) and two European Americans (NA12156 and NA12878). We next added the four known causal mutations to each of the four HapMap subjects (three C–>T mutations at chr17:10485359 and one C–>T mutation at chr17:10485360, representing R672H and R672C mutations in MYH3). We tested whether ANNOVAR can identify MYH3 as the causal gene by examining exomes from these four subjects.

Wang K., Li M, & Hakonarson H. (2010). ANNOVAR: functional annotation of genetic variants from high-throughput sequencing data. Nucleic Acids Research, 38(16), e164.

Publication 2010

Chromosome 11p Deletion Syndrome Dihydroorotate Dehydrogenase Europeans Exome Genes Genes, vif Genome HapMap INDEL Mutation Males Mutation Pattern, Inheritance

Integrative Genomics Study of EBV Lymphoblastoids

Total RNA was extracted from EBV transformed lymphoblastoid cell line pellets by the TRIzol reagent (Ambion), and mRNA and small RNA sequencing of 465 unique individuals was performed on the Illumina HiSeq2000 platform, with paired-end 75bp mRNA-seq and single-end 36bp small RNA-seq. Five samples were sequenced in replicate in each of the seven sequencing laboratories. The mRNA and small RNA reads were mapped with GEM³¹ and miraligner^{32 (link)}, respectively, with an average of 48.9M mRNA-seq reads and 1.2M miRNA reads per sample after QC. Numerous transcript features were quantified using Gencode v12^{33 (link)} and miRBase v18^{34 (link)} annotations: protein-coding and lincRNA genes (16,084 detected in >50% of samples), transcripts (67,603; with FluxCapacitor^{7 (link)}), exons (146,498), annotated splice junctions (129,805; analyzed in detail in Ferreira et al. submitted), transcribed repetitive elements (47,409), and mature miRNAs (715). Data quality was assessed by sample correlations and read and gene count distributions, and technical variation was removed by PEER normalization^{35 (link)} for the QTL and miRNA-mRNA correlation analyses^{11 (link)}. The samples clustered uniformly both before and after normalization. The genotype data was obtained from 1000 Genomes Phase 1 data set for 421 samples (80× average exome and 5× whole genome read depth), and the remaining 41 samples were imputed from Omni 2.5M SNP array data. Furthermore, we did functional reannotation for all the 1000 Genomes variants using Gencode v12. QTL mapping was done with linear regression, using genetic variants with >5% frequency in 1MB window and normalized quantifications transformed to standard normal. Permutations were used to adjust FDR to 5%. Full details are provided in Supplementary Methods.

Lappalainen T., Sammeth M., Friedländer M.R., ‘t Hoen P.A., Monlong J., Rivas M.A., Gonzàlez-Porta M., Kurbatova N., Griebel T., Ferreira P.G., Barann M., Wieland T., Greger L., van Iterson M., Almlöf J., Ribeca P., Pulyakhina I., Esser D., Giger T., Tikhonov A., Sultan M., Bertier G., MacArthur D.G., Lek M., Lizano E., Buermans H.P., Padioleau I., Schwarzmayr T., Karlberg O., Ongen H., Kilpinen H., Beltran S., Gut M., Kahlem K., Amstislavskiy V., Stegle O., Pirinen M., Montgomery S.B., Donnelly P., McCarthy M.I., Flicek P., Strom T.M., Lehrach H., Schreiber S., Sudbrak R., Carracedo Á., Antonarakis S.E., Häsler R., Syvänen A.C., van Ommen G.J., Brazma A., Meitinger T., Rosenstiel P., Guigó R., Gut I.G., Estivill X, & Dermitzakis E.T. (2013). Transcriptome and genome sequencing uncovers functional variation in humans. Nature, 501(7468), 506-511.

Publication 2013

Cell Line, Transformed DNA Replication Exome Exons Genes Genetic Diversity Genome Genotype Long Intergenic Non-Protein Coding RNA MicroRNAs Pellets, Drug Proteins Repetitive Region RNA, Messenger RNA-Seq trizol

Most recents protocols related to «Exome»

Whole Exome Sequencing for Variant Identification

Full exon sequencing (WES 1000 g) was performed by Agilent's liquid chip capture system. Genomic DNA extracted from peripheral blood for each sample was fragmented to an average size of 180–280 bp and subjected to DNA library creation using established Illumina paired-end protocols. The Agilent SureSelect Human All ExonV6 Kit (Agilent Technologies, Santa Clara, CA, USA) was used for exome capture according to the manufacturer’s instructions. The Illumina Novaseq 6000 platform (Illumina Inc., San Diego, CA, USA) was utilized for genomic DNA sequencing in Genechem Bioinformatics Technology Co., Ltd (Beijing, China) to generate 150-bp paired-end reads with a minimum coverage of 10 × for ~ 99% of the genome (mean coverage of 100 ×). After sequencing, base call files conversion and demultiplexing were performed with bcl2fastq software (Illumina). The resulting fastq data were submitted to in-house quality control software for removing low quality reads, and then were aligned to the reference human genome (hs37d5) using the Burrows-Wheeler Aligner (bwa), and duplicate reads were marked using Sambamba tools. ANNOVAR software was used to annotate the variants.
Filtering of rare variants was performed as follows: (1) variants with a MAF less than 0.01 in 1000 genomic data (1000g_all), esp6500siv2_all, gnomAD data (gnomAD_ALL and gnomAD_EAS) and in house Genechem-Zhonghua exome database from Genechem; (2) Only SNVs occurring in exons or splice sites (splicing junction 10 bp) are further analyzed since we are interested in amino acid changes. (3) Then synonymous SNVs which are not relevant to the amino acid alternation predicted by dbscSNV are discarded; The small fragment non-frameshift (< 10 bp) indel in the repeat region defined by RepeatMasker are discarded. (4) Variations are screened according to scores of SIFT, Polyphen, MutationTaster and CADD software. The potentially deleterious variations are reserved if the score of more than half of these four software support harmfulness of variations. Sites (> 2 bp) did not affect alternative splicing were removed.

Xu R., Jin Y., Tang S., Wang W., Sun Y.E., Liu Y., Zhang W., Hou B., Huang Y, & Ma Z. (2023). Association between single nucleotide variants and severe chronic pain in older adult patients after lower extremity arthroplasty. Journal of Orthopaedic Surgery and Research, 18, 184.

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Publication 2023

Amino Acids BLOOD DNA Chips DNA Library Exome Exons Frameshift Mutation Genome Genome, Human Homo sapiens INDEL Mutation Strains

Meta-analysis of DNM1L variants in Parkinson's Disease

To confirm the involvement of DNM1L variants in PD susceptibility, a meta-analysis combining public studies and our case–control study was conducted. In addition to our data, summary data from the PD variant browser were included in the meta-analysis, which comprised four studies: PD Genome Project, International Parkinson's Disease Genomic Consortium (IPDGC) Exomes, IPDGC Resequencing Project, and UK Biobank (19 (link)). Unlike our cohorts, these cohorts mainly contain European populations, which can increase the power to detect the association between DNM1L variants and PD. As no rare variants of DNM1L found in our cohort were seen in included public data, we only validated the significant association between significant common variants of DNM1L and PD in meta-analysis. We used the Hardy-Weinberg equilibrium model to estimate the SNPs in all cohorts and then excluded the variants with deviations in controls (p < 0.05). To assess the strength of the association between DNM1L variants and PD risk, a pooled OR and 95% confidence intervals (CIs) were calculated under five different models (allele, dominant, recessive, heterozygote, and homozygote model). The Cochrane Q-test and I² statistics were used to assess study heterogeneity and a significant Q-test (p < 0.1 or I² > 50%) indicated heterogeneity. Fixed- or random-effects models were selected based on the presence or absence of heterogeneity. A Z-test determined the significance of the pooled ORs. We used the FDR method to correct p-values for multiple comparisons for the variant association analysis. A p-value of < 0.05 was considered statistically significant. Moreover, the p-values of Egger's and Begg's tests were calculated to estimate the publication bias. All analyses were performed using the R package “meta.”

Liu J., Huang J., Zhao Y., Pan H., Wang Y., Liu Z., Xu Q., Sun Q., Tan J., Yan X., Li J., Tang B, & Guo J. (2023). Evaluating the association between DNM1L variants and Parkinson's disease in the Chinese population. Frontiers in Neurology, 14, 1133449.

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Publication 2023

Alleles DNM1L protein, human Europeans Exome Genetic Heterogeneity Genome Heterozygote Homozygote Parkinson Disease Population Group Single Nucleotide Polymorphism Susceptibility, Disease Vision

Whole-Exome and Whole-Genome Sequencing Pipeline

As shown in our previous study (15 (link)), the WES cohort used SureSelect Human All Exon Kit V6 (Agilent) to capture the whole-exome DNA, prepared the sample library, and then used Illumina HiSeq 10× for pair-end 2 × 150 bp sequencing. The average sequencing depth was 123×, achieving a coverage of at least 10× for 99.32% of the target region. WGS was performed using the Illumina Nova Sequencing platform in a pair-end 2 × 150 bp mode, and the average depth of coverage was about 12×. Sequencing data of both groups were processed and analyzed with the BWA-GATK-ANNOVAR pipeline (16 (link), 17 (link)). Quality control was conducted as described in our previous study (15 (link)). Samples would be removed if they had sex discrepancies, abnormal heterozygosity (>3 SD), pathogenic or likely pathogenic variants of PD-related genes, or unusual relatedness (descent > 0.15). In addition, we performed the principal component analysis (PCA) using PLINK v1.90 to assess potential population structure stratification. In subsequent analyses for the WGS cohort, gender, age, and the first five principal components of population stratification were used as covariates, whereas for the WES cohort (the control group included elderly people without neurological diseases), sex and the first five principal components for population stratification were used (18 (link)).

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Publication 2023

Aged DNA Library Exome Exons Genetic Diversity Heterozygote Homo sapiens Nervous System Disorder pathogenesis

Whole-Exome Sequencing to Identify Rare Genetic Variants

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.

García-García A., Pérez de Diego R., Flores C., Rinchai D., Solé-Violán J., Deyà-Martínez À., García-Solis B., Lorenzo-Salazar J.M., Hernández-Brito E., Lanz A.L., Moens L., Bucciol G., Almuqamam M., Domachowske J.B., Colino E., Santos-Perez J.L., Marco F.M., Pignata C., Bousfiha A., Turvey S.E., Bauer S., Haerynck F., Ocejo-Vinyals J.G., Lendinez F., Prader S., Naumann-Bartsch N., Pachlopnik Schmid J., Biggs C.M., Hildebrand K., Dreesman A., Cárdenes M.Á., Ailal F., Benhsaien I., Giardino G., Molina-Fuentes A., Fortuny C., Madhavarapu S., Conway D.H., Prando C., Schidlowski L., Martínez de Saavedra Álvarez M.T., Alfaro R., Rodríguez de Castro F., Meyts I., Hauck F., Puel A., Bastard P., Boisson B., Jouanguy E., Abel L., Cobat A., Zhang Q., Casanova J.L., Alsina L, & Rodríguez-Gallego C. (2023). Humans with inherited MyD88 and IRAK-4 deficiencies are predisposed to hypoxemic COVID-19 pneumonia. The Journal of Experimental Medicine, 220(5), e20220170.

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Publication 2023

BLOOD Exome Fibroblasts Genes Genome Introns Mutation Patients Retention (Psychology) Silent Mutation Simian virus 40 Strains

UK Biobank: A Comprehensive Cohort

The UK Biobank is a cohort comprising ∼500,000 individuals recruited through the NHS registry at age 40–69 from across the UK. Individuals were were not selected on the basis of having disease, resulting in a broad cross-section of the UK population. For all participants, a number of baseline physical measurements were taken and an extensive questionnaire completed comprising information on lifestyle, demographic and socioeconomic factors. Additionally, blood and urine samples were collected and further tests including a heel-bone ultrasound, bio-impedance, hand-grip strength, spirometry, blood pressure and several cognitive tests were performed. Each individual was further genotyped and exome sequenced. The participants also agreed to ongoing linkage of their medical records [10 (link)].

Currant H., Fitzgerald T.W., Patel P.J., Khawaja A.P., Webster A.R., Mahroo O.A, & Birney E. (2023). Sub-cellular level resolution of common genetic variation in the photoreceptor layer identifies continuum between rare disease and common variation. PLOS Genetics, 19(2), e1010587.

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Publication 2023

BLOOD Blood Pressure Calcaneus Cognitive Testing Exome Physical Examination Spirometry Ultrasonics Urine

Top products related to «Exome»

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What are the common challenges faced when conducting Exome analysis?

Exome analysis can present several challenges, including interpreting the large volume of genetic variants identified, distinguishing pathogenic from benign variants, and ensuring the reproducibility and accuracy of results. PubCompare.ai can help overcome these challenges by providing AI-driven insights to identify the most effective exome analysis protocols from the literature, preprints, and patents, enabling researchers to choose the best approach for their specific needs.

How can PubCompare.ai assist in optimizing Exome analysis workflows?

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

Are there different types or variations of Exome that researchers should be aware of?

Yes, there are several variations of Exome that researchers should be aware of. For example, Whole Exome Sequencing (WES) focuses on the protein-coding regions of the genome, while Targeted Exome Sequencing (TES) targets specific genes or genomic regions of interest. Additionally, some Exome analysis methods may involve enrichment techniques to improve coverage and depth of sequencing. PubCompare.ai can help researchers navigate these different Exome variations and identify the most suitable approach for their research objectives.

What are some specific applications of Exome analysis?

Exome analysis has become a powerful tool for identifying genetic variants associated with human diseases and traits. Researchers use Exome analysis to study the genetic basis of rare and Mendelian disorders, as well as complex diseases like cancer, neurodegenerative conditions, and metabolic disorders. Exome data can also be used for pharmacogenomic studies, personalized medicine, and population genetics research. PubCompare.ai can assist researchers in finding the most effective Exome analysis protocols to address these diverse applications.

How can PubCompare.ai help researchers optimize their Exome analysis workflows?

PubCompare.ai allows researchers to screen exome protocol literature more efficently and leverage AI to pinpoint critical insights. The platform's AI-driven analysis can highlight key differences in protocol effectiveness, enabling researchers to choose the best option for their exome analysis needs, improving reproducibility and accuaracy. PubCompare.ai helps researchers identify the most effective protocols related to Exome for their specific research goals.

More about "Exome"

Exome analysis is a powerful tool for identifying genetic variants associated with human diseases and traits.
The exome refers to the complete set of protein-coding DNA sequences within a genome, which accounts for approximately 1-2% of the entire human genome.
By focusing on the exome, researchers can efficiently and cost-effectively analyze the regions of the genome that are most likely to harbor disease-causing mutations.
Exome sequencing, often performed on platforms like the HiSeq 2000, HiSeq 2500, NovaSeq 6000, HiSeq 4000, and NextSeq 500, has become a widely adopted approach for genetic research and clinical diagnostics.
It allows researchers to identify single nucleotide variants (SNVs), small insertions and deletions (indels), and other genetic variations that may contribute to complex diseases, rare disorders, and inherited traits.
The TruSeq Exome Enrichment Kit is a popular tool used in exome sequencing workflows, allowing for the efficient capture and enrichment of the protein-coding regions of the genome.
Additionally, the DNeasy Blood & Tissue Kit is often used for high-quality DNA extraction, which is a crucial step in the exome sequencing process.
By leveraging the power of the HiSeq platform and other advanced sequencing technologies, researchers can generate high-quality exome data and utilize AI-driven platforms like PubCompare.ai to efficiently locate the best exome analysis protocols from literature, preprints, and patents.
This enables the identification of optimal solutions and products for exome research, ultimately unlocking new insights and accelerating scientific discoveries.