Accurate DNA Sequencing Research Articles

ULTRA-Effective labeling of tandem repeats in genomic sequence

Abstract In the age of long read sequencing, genomics researchers now have access to accurate repetitive DNA sequence (including satellites) that, due to the limitations of short read sequencing, could previously be observed only as unmappable fragments. Tools that annotate repetitive sequence are now more important than ever, so that we can better understand newly uncovered repetitive sequences, and also so that we can mitigate errors in bioinformatic software caused by those repetitive sequences. To that end, we introduce the 1.0 release of our tool for identifying and annotating locally-repetitive sequence, ULTRA (ULTRA Locates Tandemly Repetitive Areas). ULTRA is fast enough to use as part of an efficient annotation pipeline, produces state-of-the-art reliable coverage of repetitive regions containing many mutations, and provides interpretable statistics and labels for repetitive regions. It is released under an open license, and is available for download at https://github.com/TravisWheelerLab/ULTRA

ULTRA-Effective Labeling of Repetitive Genomic Sequence.

In the age of long read sequencing, genomics researchers now have access to accurate repetitive DNA sequence (including satellites) that, due to the limitations of short read sequencing, could previously be observed only as unmappable fragments. Tools that annotate repetitive sequence are now more important than ever, so that we can better understand newly uncovered repetitive sequences, and also so that we can mitigate errors in bioinformatic software caused by those repetitive sequences. To that end, we introduce the 1.0 release of our tool for identifying and annotating locally-repetitive sequence, ULTRA (ULTRA Locates Tandemly Repetitive Areas). ULTRA is fast enough to use as part of an efficient annotation pipeline, produces state-of-the-art reliable coverage of repetitive regions containing many mutations, and provides interpretable statistics and labels for repetitive regions. It released under an open license, and available for download at https://github.com/TravisWheelerLab/ULTRA.

Machine learning empowered next generation DNA sequencing: perspective and prospectus.

The pursuit of ultra-rapid, cost-effective, and accurate DNA sequencing is a highly sought after aspect of personalized medicine development. With recent advancements, mainstream machine learning (ML) algorithms hold immense promise for high throughput DNA sequencing at the single nucleotide level. While ML has revolutionized multiple domains of nanoscience and nanotechnology, its implementation in DNA sequencing is still in its preliminary stages. ML-aided DNA sequencing is especially appealing, as ML has the potential to decipher complex patterns and extract knowledge from complex datasets. Herein, we present a holistic framework of ML-aided next-generation DNA sequencing with domain knowledge to set directions toward the development of artificially intelligent DNA sequencers. This perspective focuses on the current state-of-the-art ML-aided DNA sequencing, exploring the opportunities as well as the future challenges in this field. In addition, we provide our personal viewpoints on the critical issues that require attention in the context of ML-aided DNA sequencing.

Slip-Coupled Electroosmosis and Electrophoresis Dictate DNA Translocation Speed in Solid-State Nanopores.

Controlling the DNA translocation speed is critical in nanopore sequencing, but remains rather challenging in practice, as attributable to a complex coupling between nanoscale fluidics and electrically mediated migration of DNA in a dynamically evolving manner. One important factor influencing the translocation speed is the DNA-liquid slippage stemming from the hydrophobic nature of the oligonucleotide, an aspect that has been widely ignored in the reported literature. In an effort to circumvent this conceptual deficit, here we first develop an analytical model to bring out the slip-mediated coupling between the electroosmosis and DNA-electrophoresis in a solid-state nanopore at low surface charge limits, ignoring the end effects. Subsequently, we compare these results with the numerical simulation data on electrokinetically modulated DNA translocation in such a nanopore, albeit of finite length with due accommodation of the end effects, connecting two end reservoirs by deploying a fully coupled Poisson-Nernst-Plank-Stokes flow model. Both the numerical and analytical results indicate that the DNA translocation speed is a linearly increasing function of the slip length, with more than four-fold increase being observed for a slip length as minimal as 0.5 nm as compared to the no-slip scenario. Considering specific strategies on demand for arresting high translocation speeds for accurate DNA sequencing, the above results establish a theoretical proposition for the same, premised on an analytical expression of the DNA-hydrophobicity modulated enhancement in the translocation speed for designing a nanopore-based sequencing platform─a paradigm that remained to be underemphasized thus far.

Analysis of mutational genotyping using correctable decoding sequencing with superior specificity.

The ability to accurately identify SNPs or low-abundance mutations is important for early clinical diagnosis of diseases, but the existing high-throughput sequencing platforms are limited in terms of their accuracy. Here, we propose a correctable decoding sequencing strategy that may be used for high-throughput sequencing platforms. This strategy is based on adding a mixture of two types of mononucleotides, natural nucleotide and cyclic reversible termination (CRT), for cyclic sequencing. Using the synthetic characteristic of CRTs, about 75% of the calls are unambiguous for a single sequencing run, and the remaining ambiguous sequence can be accurately deduced by two parallel sequencing runs. We demonstrate the feasibility of this strategy, and its cycle efficiency can reach approximately 99.3%. This strategy is proved to be effective for correcting errors and identifying whether the sequencing information is correct or not. And its conservative theoretical error rate was determined to be 0.0009%, which is lower than that of Sanger sequencing. In addition, we establish that the information of only a single sequencing run can be used to detect samples with known mutation sites. We apply this strategy to accurately identify a mutation site in mitochondrial DNA from human cells.

Machine Learning Aided Interpretable Approach for Single Nucleotide-Based DNA Sequencing using a Model Nanopore

Solid-state nanopore-based electrical detection of DNA nucleotides with the quantum tunneling technique has emerged as a powerful strategy to be the next-generation sequencing technology. However, experimental complexity has been a foremost obstacle in achieving a more accurate high-throughput analysis with industrial scalability. Here, with one of the nucleotide training data sets of a model monolayer gold nanopore, we have predicted the transmission function for all other nucleotides with root-mean-square error scores as low as 0.12 using the optimized eXtreme Gradient Boosting Regression (XGBR) model. Further, the SHapley Additive exPlanations (SHAP) analysis helped in exploring the interpretability of the XGBR model prediction and revealed the complex relationship between the molecular properties of nucleotides and their transmission functions by both global and local interpretable explanations. Hence, experimental integration of our proposed machine-learning-assisted transmission function prediction method can offer a new direction for the realization of cheap, accurate, and ultrafast DNA sequencing.

TP53 mutations in Pap test DNA are a potential biomarker of ovarian cancer risk in high-risk women (226)

<b>Objectives:</b> The use of Pap tests as a molecular diagnostic for high-grade serous ovarian carcinoma (HGSOC) is a promising concept, but studies to detect tumor DNA report limited sensitivity. <i>TP53</i> somatic mutations from clonal expansions in non-neoplastic tissue can be detected in Pap tests and blood and could potentially be utilized for cancer risk assessment. We aimed to determine whether <i>TP53</i> somatic mutations in blood and Pap test DNA are associated with HGSOC in women with and without germline <i>BRCA1</i> or <i>BRCA2 (BRCA)</i> mutations. <b>Methods:</b> Peripheral blood and Pap samples were collected from 30 women with HGSOC (10 with a germline <i>BRCA</i> mutation) and 40 women without cancer (20 with a germline <i>BRCA</i> mutation) who underwent gynecologic surgery at the University of Washington. Genomic DNA from buffy coats and Pap cell pellets was used for ultra-deep sequencing of the <i>TP53</i> coding region (mean depth ~3300x) using duplex sequencing for error correction. Mutations were characterized based on pathogenicity and presence in the UMD <i>TP53</i> Cancer Database. Mutation frequencies (total coding mutations divided by nucleotides sequenced) were compared between groups using t-tests. Fitted linear regression models were used to explore age-related trends, and logistic regression models were used to predict cancer with adjustments for age. <b>Results:</b> Total 436 coding <i>TP53</i> mutations were identified in DNA from Pap tests, and 262 mutations were identified in the DNA from blood. Pap tests demonstrated an overall higher <i>TP53</i> mutation frequency in patients with HGSOC than in those without cancer (2.0x10^-6 vs 1.4x10^-6, p=0.03), but this effect was driven by and limited to patients with <i>BRCA</i> mutations (2.4x10^-6 vs 1.4x10^-6, p=0.01). Women with <i>BRCA</i> mutations and HGSOC also demonstrated a greater frequency of mutations affecting the DNA-binding domain, predicted to be pathogenic, and frequently found in cancer (referred to as ‘cancerlike') (all p<0.05). Cancer-like mutations increased with age in Pap test samples from women without cancer (p<0.01, R²=0.23) but not in patients with HGSOC. Age-adjusted logistic regression confirmed that cancer-like <i>TP53</i> mutation frequency was associated with HGSOC in patients with germline <i>BRCA</i> mutations (OR: 4.2, 95% CI: 1.2-14.3). There were no significant differences between HGSOC and non-cancer cases when examining <i>TP53</i> mutations in blood samples. Among 22 cases with known <i>TP53</i> tumor mutations, the tumor mutation was identified in six Pap samples (27%) and zero blood samples. <b>Conclusions:</b> The identification of HGSOC-specific mutations in Pap tests, even with highly accurate DNA sequencing methods, has limited sensitivity for cancer detection. However, Pap tests can detect abundant low-frequency cancer-like <i>TP53</i> mutations at a higher rate in women with germline <i>BRCA</i> mutations and HGSOC compared to women without cancer. Somatic <i>TP53</i> mutation frequency using age-related thresholds should be further explored as a biomarker for ovarian cancer risk in high-risk women.

DeLUCS: Deep learning for unsupervised clustering of DNA sequences.

We present a novel Deep Learning method for the Unsupervised Clustering of DNA Sequences (DeLUCS) that does not require sequence alignment, sequence homology, or (taxonomic) identifiers. DeLUCS uses Frequency Chaos Game Representations (FCGR) of primary DNA sequences, and generates “mimic” sequence FCGRs to self-learn data patterns (genomic signatures) through the optimization of multiple neural networks. A majority voting scheme is then used to determine the final cluster assignment for each sequence. The clusters learned by DeLUCS match true taxonomic groups for large and diverse datasets, with accuracies ranging from 77% to 100%: 2,500 complete vertebrate mitochondrial genomes, at taxonomic levels from sub-phylum to genera; 3,200 randomly selected 400 kbp-long bacterial genome segments, into clusters corresponding to bacterial families; three viral genome and gene datasets, averaging 1,300 sequences each, into clusters corresponding to virus subtypes. DeLUCS significantly outperforms two classic clustering methods (K-means++ and Gaussian Mixture Models) for unlabelled data, by as much as 47%. DeLUCS is highly effective, it is able to cluster datasets of unlabelled primary DNA sequences totalling over 1 billion bp of data, and it bypasses common limitations to classification resulting from the lack of sequence homology, variation in sequence length, and the absence or instability of sequence annotations and taxonomic identifiers. Thus, DeLUCS offers fast and accurate DNA sequence clustering for previously intractable datasets.

Evaluation of TP53 mutations in Pap test and blood DNA as novel biomarkers of ovarian cancer risk

<h3>Objectives:</h3> The use of Pap tests as a molecular diagnostic for high-grade serous ovarian carcinoma (HGSC) is a promising concept, but studies to detect tumor DNA report limited sensitivity. ‘Cancer-like' somatic mutations from clonal expansions in non-neoplastic tissue can be detected in Pap tests and blood, and could potentially be utilized for cancer risk-assessment. We aimed to determine whether <i>TP53</i> somatic mutations in blood and Pap test DNA are associated with HGSC or with inherited ovarian cancer susceptibility. <h3>Methods:</h3> Peripheral blood and Pap samples were collected from 26 women with HGSC (10 with germline <i>BRCA1</i> or <i>BRCA2</i> [<i>BRCA</i>] mutations) and 35 women without cancer (17 with germline <i>BRCA</i> mutations), all of whom underwent gynecologic surgery at a single academic institution. Blood samples alone were available for an additional four women. DNA was extracted from the blood buffy coat layer and Pap samples. The coding region of <i>TP53</i> was deeply sequenced (mean depth ~3800x) using duplex sequencing, an ultra-accurate error-correction sequencing approach. Mutations frequently found in the UMD <i>TP53</i> Cancer Database were termed ‘cancer-like'. Mutation frequencies (total coding mutations divided by nucleotides sequenced, MF) were compared between groups using t-tests, restricted to those between the ages of 40 and 60 for improved age-matching. Age-related trends were explored with fitted linear regression models. <h3>Results:</h3> 572 coding-region <i>TP53</i> mutations were identified in DNA from Pap tests from 26 women with HGSC (300 mutations) and 35 women without cancer (272 mutations). 287 mutations were identified in the DNA from blood buffy coat from 29 women with HGSC (161 mutations) and 36 without cancer (226 mutations). DNA from Pap tests demonstrated a higher MF in patients with HGSC than those without cancer (2.5x10^-6 vs. 1.6x10^-6, p=0.03). Cancer-like MF was also significantly higher in patients with HGSC (8.8x10^-7 vs. 5.0x10^-7, p=0.01). There were no significant differences in MF amongst cases without cancer with or without germline <i>BRCA</i> mutations, or amongst cases without cancer in those with <i>BRCA1</i> vs <i>BRCA2</i> germline mutations. Cancer-like mutations increased with age in Pap test samples from women without cancer (p<0.01, R2 0.34) but not in patients with HGSC (p=0.58, R2 0.01). In DNA from blood, there were no significant differences in average MF between HGSC and non-cancer cases nor between <i>BRCA</i> wildtype and mutant cases. No age-related correlations were found. Among twelve cases where the tumor mutation was known, duplex sequencing identified the mutation in five Pap samples (42%) and zero blood samples. <h3>Conclusions:</h3> Identifying the tumor-specific mutation in Pap tests, even with highly accurate DNA sequencing methods, does not reliably detect ovarian cancer. However, Pap tests carry abundant low frequency cancer-like <i>TP53</i> mutations, which are found in higher rates in women with HGSC than without. In patients without cancer, these <i>TP53</i> mutations increase linearly with age. Somatic <i>TP53</i> mutation frequency using age-related thresholds should be further explored as a biomarker for ovarian cancer risk.

A machine learning approach for accurate and real-time DNA sequence identification

BackgroundThe all-electronic Single Molecule Break Junction (SMBJ) method is an emerging alternative to traditional polymerase chain reaction (PCR) techniques for genetic sequencing and identification. Existing work indicates that the current spectra recorded from SMBJ experimentations contain unique signatures to identify known sequences from a dataset. However, the spectra are typically extremely noisy due to the stochastic and complex interactions between the substrate, sample, environment, and the measuring system, necessitating hundreds or thousands of experimentations to obtain reliable and accurate results.ResultsThis article presents a DNA sequence identification system based on the current spectra of ten short strand sequences, including a pair that differs by a single mismatch. By employing a gradient boosted tree classifier model trained on conductance histograms, we demonstrate that extremely high accuracy, ranging from approximately 96 % for molecules differing by a single mismatch to 99.5 % otherwise, is possible. Further, such accuracy metrics are achievable in near real-time with just twenty or thirty SMBJ measurements instead of hundreds or thousands. We also demonstrate that a tandem classifier architecture, where the first stage is a multiclass classifier and the second stage is a binary classifier, can be employed to boost the single mismatched pair’s identification accuracy to 99.5 %.ConclusionsA monolithic classifier, or more generally, a multistage classifier with model specific parameters that depend on experimental current spectra can be used to successfully identify DNA strands.

Improved reference genome of the arboviral vector Aedes albopictus

BackgroundThe Asian tiger mosquito Aedes albopictus is globally expanding and has become the main vector for human arboviruses in Europe. With limited antiviral drugs and vaccines available, vector control is the primary approach to prevent mosquito-borne diseases. A reliable and accurate DNA sequence of the Ae. albopictus genome is essential to develop new approaches that involve genetic manipulation of mosquitoes.ResultsWe use long-read sequencing methods and modern scaffolding techniques (PacBio, 10X, and Hi-C) to produce AalbF2, a dramatically improved assembly of the Ae. albopictus genome. AalbF2 reveals widespread viral insertions, novel microRNAs and piRNA clusters, the sex-determining locus, and new immunity genes, and enables genome-wide studies of geographically diverse Ae. albopictus populations and analyses of the developmental and stage-dependent network of expression data. Additionally, we build the first physical map for this species with 75% of the assembled genome anchored to the chromosomes.ConclusionThe AalbF2 genome assembly represents the most up-to-date collective knowledge of the Ae. albopictus genome. These resources represent a foundation to improve understanding of the adaptation potential and the epidemiological relevance of this species and foster the development of innovative control measures.

Systematic Approach toward Accurate and Efficient DNA Sequencing via Nanoconfinement.

Coarse-grained modeling tools are employed to simulate the mechanics of DNA loading within a nanoscale confinement and predict semiflexible polymer conformations within the confinement, providing design recommendations for DNA-sequencing devices. A workflow is developed to quantify competing requirements of efficiency and accuracy and extract metrics that guide design optimization. The mean first-passage time for DNA loading is calculated as a function of the nanochannel geometry and the applied electric field. We analyze the interplay between the free energy of confinement and the electric potential energy in achieving high-throughput, base-pair detection. The single-read probability is investigated as informative metrics for sequencing accuracy and for sensing-strategy design. High cost, low throughput, and low accuracy have so far limited the adoption of nanochannel analysis and other long-read technologies. Our work directly addresses these limitations with a systematic approach that is scalable to long molecules and complex geometries.

MSH1 is required for maintenance of the low mutation rates in plant mitochondrial and plastid genomes

Mitochondrial and plastid genomes in land plants exhibit some of the slowest rates of sequence evolution observed in any eukaryotic genome, suggesting an exceptional ability to prevent or correct mutations. However, the mechanisms responsible for this extreme fidelity remain unclear. We tested seven candidate genes involved in cytoplasmic DNA replication, recombination, and repair (POLIA, POLIB, MSH1, RECA3, UNG, FPG, and OGG1) for effects on mutation rates in the model angiosperm Arabidopsis thaliana by applying a highly accurate DNA sequencing technique (duplex sequencing) that can detect newly arisen mitochondrial and plastid mutations even at low heteroplasmic frequencies. We find that disrupting MSH1 (but not the other candidate genes) leads to massive increases in the frequency of point mutations and small indels and changes to the mutation spectrum in mitochondrial and plastid DNA. We also used droplet digital PCR to show transmission of de novo heteroplasmies across generations in msh1 mutants, confirming a contribution to heritable mutation rates. This dual-targeted gene is part of an enigmatic lineage within the mutS mismatch repair family that we find is also present outside of green plants in multiple eukaryotic groups (stramenopiles, alveolates, haptophytes, and cryptomonads), as well as certain bacteria and viruses. MSH1 has previously been shown to limit ectopic recombination in plant cytoplasmic genomes. Our results point to a broader role in recognition and correction of errors in plant mitochondrial and plastid DNA sequence, leading to greatly suppressed mutation rates perhaps via initiation of double-stranded breaks and repair pathways based on faithful homologous recombination.

Sequence-Based Characterization of Intratumoral Bacteria-A Guide to Best Practice.

Tumors are hospitable environments to bacteria and several recent studies on cancer patient samples have introduced the concept of an endogenous tumor microbiome. For a variety of reasons, this putative tumor microbiome is particularly challenging to investigate, and a failure to account for the various potential pitfalls will result in erroneous results and claims. Before this potentially extremely medically-significant habitat can be accurately characterized, a clear understanding of all potential confounding factors is required, and a best-practice approach should be developed and adopted. This review summarizes all of the potential issues confounding accurate bacterial DNA sequence analysis of the putative tumor microbiome, and offers solutions based on related research with the hope of assisting in the progression of research in this field.

Novel approach for accurate detection of contaminating human mitochondrial DNA in next-generation sequencing data

DNA molecules are degraded after the death of an organism. However, the degree and rate of DNA degradation enormously vary depending on environmental conditions, such as temperature or humidity, which greatly affect DNA preservation. Most samples excavated in warm, humid, or dry areas are often poorly preserved samples with from <0.1%-1% endogenous DNA. In these degraded samples, the contamination by exogenous DNA remains a potential challenge, no matter how much effort is made to prevent it. For an accurate DNA sequence analysis, quality control must be thoroughly performed, using the mitochondrial DNA as an indicator of exogenous DNA contamination. Here, we propose a practical approach for detecting exogenous human mitochondrial macro haplogroups, and discuss the effectiveness of this approach using simulated data. Our approach is based on the Bayes classification, which is a supervised machine learning algorithm, and it can detect a contaminating macro haplogroup in high-throughput sequencing data. This approach can help validate the quality of high-throughput sequencing data from possibly contaminated or degraded human samples.

The Asian‐Melanesian bambusicolous genus Myriodiscus is related to the genus Tympanis, the North American‐European tree pathogen

AbstractSpecies of Tympanis are well‐known pathogens in Holarctic forests, but we know little about their relationship to other genera in Tympanidaceae. The genus Myriodiscus, remarkable macroscopically and a possible pathogen on bamboo, has a complicated taxonomic history and has not been conclusively placed phylogenetically. Species of Myriodiscus have been described under two other generic names, Ascotremellopsis and Gelatinomyces, the latter being related to the pathogenic genus Collophorina. There has been no formal synonymy of these three bambusicolous genera or agreement on their systematic positions. We combine accurate micromorphology and DNA sequence data to show the link between Tympanis and Myriodiscus and reveal previously unrecognized features of the latter. These two genera show a type of ascus development unique in the Leotiomycetes. With this new data, we have resolved past confusions over the identity of these fungi, determined their systematic position and have proposed the proper synonymies for Myriodiscus sparassoides and one new combination (M. conus).

Challenges in fabricating graphene nanodevices for electronic DNA sequencing

Abstract

Highly accurate fluorogenic DNA sequencing with information theory-based error correction.

Eliminating errors in next-generation DNA sequencing has proved challenging. Here we present error-correction code (ECC) sequencing, a method to greatly improve sequencing accuracy by combining fluorogenic sequencing-by-synthesis (SBS) with an information theory-based error-correction algorithm. ECC embeds redundancy in sequencing reads by creating three orthogonal degenerate sequences, generated by alternate dual-base reactions. This is similar to encoding and decoding strategies that have proved effective in detecting and correcting errors in information communication and storage. We show that, when combined with a fluorogenic SBS chemistry with raw accuracy of 98.1%, ECC sequencing provides single-end, error-free sequences up to 200 bp. ECC approaches should enable accurate identification of extremely rare genomic variations in various applications in biology and medicine.

Novel Method for High-Throughput Full-Length IGHV-D-J Sequencing of the Immune Repertoire from Bulk B-Cells with Single-Cell Resolution.

Efficient and accurate high-throughput DNA sequencing of the adaptive immune receptor repertoire (AIRR) is necessary to study immune diversity in healthy subjects and disease-related conditions. The high complexity and diversity of the AIRR coupled with the limited amount of starting material, which can compromise identification of the full biological diversity makes such sequencing particularly challenging. AIRR sequencing protocols often fail to fully capture the sampled AIRR diversity, especially for samples containing restricted numbers of B lymphocytes. Here, we describe a library preparation method for immunoglobulin sequencing that results in an exhaustive full-length repertoire where virtually every sampled B-cell is sequenced. This maximizes the likelihood of identifying and quantifying the entire IGHV-D-J repertoire of a sample, including the detection of rearrangements present in only one cell in the starting population. The methodology establishes the importance of circumventing genetic material dilution in the preamplification phases and incorporates the use of certain described concepts: (1) balancing the starting material amount and depth of sequencing, (2) avoiding IGHV gene-specific amplification, and (3) using Unique Molecular Identifier. Together, this methodology is highly efficient, in particular for detecting rare rearrangements in the sampled population and when only a limited amount of starting material is available.

Telomere and centromere&amp;mdash;DNA tandem arrays on the chromosome

The eukaryotic genome contains a large amount of tandem repeated sequences. Among these repeats, telomere and the centromere are two unique structures on a chromosome, which play an important role in maintaining the stability and function of the genome. A telomere is repetitive nucleotide sequences that located at the ends of chromosomes, which protects the chromosome from deterioration or fusion with another chromosome. Although the repetitive sequences of telomeres in different species are different, telomeres share the same structure. The typical structure of telomere consists of the D-loop and T-loop, which are protected by Shelterin complex. The telomere length is preserved by the enzyme telomerase which elongates telomeres through its reverse transcription activity. It has been widely accepted that telomere attrition is related to aging and cancer. The centromere is a region of the chromosome, which ensures correct separation of daughter chromosomes during cell division. According to the position on the chromosome, the centromere is classified into metacentric, submetacentric, acrocentric, telocentric and holocentric. It can also be divided into point centromere, regional centromere and holocentromere due to the characteristics of the repetitive sequences, in line with the characteristics of the centromeric repetitive sequences. The damaged centromere can cause a large number of chromosomal anomalies such as chromosome rearrangements and breaks, multi-branched chromosomes, and whole-arm deletions, etc. These abnormal chromosomes will lead to serious diseases, such as cancer, ICF syndrome, even cell death. Both the telomere and centromere are composed of highly repetitive sequences and combined with a variety of proteins to form a complex structure. Because of their numerous repetitive DNA sequences, accurate DNA sequencing of telomere and centromere is technically difficult. So there are still many unanswered questions regarding their functions and their working mechanisms. This paper briefly reviews the history of discovering telomere and centromere, the structural characteristics of the telomere and centromere, including the difference of telomere and centromere DNA sequences among different species, the functions of specific proteins binding in these regions. This review also emphasizes the relationships between the repetitive sequences and aging, ICF syndrome or the occurrence of diseases, especially cancer. At last, the paper highlights and prospects the hot research topics in telomere and centromere studies, such as the methods of telomere length measurement, studies on the characteristics of telomerase, and research about centromere repetitive sequence. Hopefully, the fast development of research technology including sequencing technics will help to further understand the biological roles of telomere and centromere, thus facilitate tumor treatment and aging research.