Completion Of Human Genome Sequence Research Articles

Beyond the Human Genome Project: The Age of Complete Human Genome Sequences and Pangenome References.

The Human Genome Project was an enormous accomplishment, providing a foundation for countless explorations into the genetics and genomics of the human species. Yet for many years, the human genome reference sequence remained incomplete and lacked representation of human genetic diversity. Recently, two major advances have emerged to address these shortcomings: complete gap-free human genome sequences, such as the one developed by the Telomere-to-Telomere Consortium, and high-quality pangenomes, such as the one developed by the Human Pangenome Reference Consortium. Facilitated by advances in long-read DNA sequencing and genome assembly algorithms, complete human genome sequences resolve regions that have been historically difficult to sequence, including centromeres, telomeres, and segmental duplications. In parallel, pangenomes capture the extensive genetic diversity across populations worldwide. Together, these advances usher in a new era of genomics research, enhancing the accuracy of genomic analysis, paving the path for precision medicine, and contributing to deeper insights into human biology.

Variant calling and benchmarking in an era of complete human genome sequences.

Genetic variant calling from DNA sequencing has enabled understanding of germline variation in hundreds of thousands of humans. Sequencing technologies and variant-calling methods have advanced rapidly, routinely providing reliable variant calls in most of the human genome. We describe how advances in long reads, deep learning, de novo assembly and pangenomes have expanded access to variant calls in increasingly challenging, repetitive genomic regions, including medically relevant regions, and how new benchmark sets and benchmarking methods illuminate their strengths and limitations. Finally, we explore the possible future of more complete characterization of human genome variation in light of the recent completion of a telomere-to-telomere human genome reference assembly and human pangenomes, and we consider the innovations needed to benchmark their newly accessible repetitive regions and complex variants.

A k-mer based metaheuristic approach for detecting COVID-19 variants

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) belongs to coronaviridae family and a change in the genetic sequence of SARS-CoV-2 is named as a mutation that causes to variants of SARS-CoV-2. In this paper, we propose a novel and efficient method to predict SARS-CoV-2 variants of concern from whole human genome sequences. In this method, we describe 16 dinucleotide and 64 trinucleotide features to differentiate SARS-CoV-2 variants of concern. The efficacy of the proposed features is proved by using four classifiers, k-nearest neighbor, support vector machines, multilayer perceptron, and random forest. The proposed method is evaluated on the dataset including 223,326 complete human genome sequences including recently designated variants of concern, Alpha, Beta, Gamma, Delta, and Omicron variants. Experimental results present that overall accuracy for detecting SARS-CoV-2 variants of concern remarkably increases when trinucleotide features rather than dinucleotide features are used. Furthermore, we use the whale optimization algorithm, which is a state-of-the-art method for reducing the number of features and choosing the most relevant features. We select 44 trinucleotide features out of 64 to differentiate SARS-CoV-2 variants with acceptable accuracy as a result of the whale optimization method. Experimental results indicate that the SVM classifier with selected features achieves about 99% accuracy, sensitivity, specificity, precision on average. The proposed method presents an admirable performance for detecting SARS-CoV-2 variants.

A Comprehensive Review: Personalized Medicine for Rare Disease Cancer Treatment.

In the United States, cancer is one of the major causes of death. In 2010 alone, over 1.5 million fresh instances were recorded and over 0.5 billion died. After the completion of human genome sequence, significant progress in characterizing human epigenomes, proteomes and metabolomes has been made; a stronger knowledge of pharmacogenomics has been established and the capacity for individual personalization of health care has grown considerably. Personalized medicine has recently been primarily used to systematically select or optimize the prevention and therapeutic care of the patient through genetic or other data about the particular patient. Molecular profiling in healthy samples and cancer patients can allow for more personalized medications than is currently available. Patient protein, genetic and metabolic information may be used for adapting medical attention to the needs of that individual. The development of complementary diagnostics is a key attribute of this medicinal model. Molecular tests measuring the level of proteins, genes or specific mutations are used to provide a specific treatment for a particular individual by stratify the status of a disease, selecting the right drugs and tailoring dosages to the particular needs of the patient. These methods are also available for assessing risk factors for a patient for a number of conditions and for tailoring individual preventive therapies. Recent advances of personalized cancer medicine, challenges and futures perspectives are discussed.

Complete human mtDNA genome sequences revealed haplotype features of the Hmong-Mien language family in Vietnam

Vietnam is the homeland of 54 different ethnicities that belong to 5 major language families of the world, including Austroasiatic, Tai-Kadai, Hmong-Mien, Sino-Tibetan and Austronesian. Hmong-Mien, an ethnolinguistic family, presumably stemmed from Southern China and later spread to the Southeast Asia region. In this study, we analyzed the mitochondrial DNA sequences taken from 120 males belonging Hmong-Mien (HM) language family in Vietnam: Dao, Hmong, and Pathen, revealing 352 unique variants. Dao has the most number of polymorphisms (230 unique SNPs occurring 1469 times), followed by Hmong (181 unique SNPs occurring 1367 times) and Pathen (159 SNPs occurring 1243 times). Genetic variations within each population and among Hmong-Mien speakers were further measured by computations of haplotype diversity (H), nucleotide diversity (π) and fixation index (FST). There are nine major haplogroups (A, B, C, D, F, G, M, N9, and R) detected, with F and B making up over half of each population (Hmong: 56.09% (23/41), Pathen: 58.33% (21/36), Dao: 62.79% (27/43)). Haplotype classification was further divided into 30 haplogroups, of which 80% of them were specific to a single minority. Dao remained the most genetically diverse (H=0.957), while Pathen was the most homogeneous population (H=0.900). In terms of genetic distance, Dao and Hmong were more distinguished from each other, while Hmong and Pathen were more related. Complete mtDNA sequences of Viet HM speakers increased the mtDNA depository, improving the understanding of the genetic structure underlying this language family.

Construction and integration of three de novo Japanese human genome assemblies toward a population-specific reference

The complete human genome sequence is used as a reference for next-generation sequencing analyses. However, some ethnic ancestries are under-represented in the reference genome (e.g., GRCh37) due to its bias toward European and African ancestries. Here, we perform de novo assembly of three Japanese male genomes using > 100× Pacific Biosciences long reads and Bionano Genomics optical maps per sample. We integrate the genomes using the major allele for consensus and anchor the scaffolds using genetic and radiation hybrid maps to reconstruct each chromosome. The resulting genome sequence, JG1, is contiguous, accurate, and carries the Japanese major allele at most loci. We adopt JG1 as the reference for confirmatory exome re-analyses of seven rare-disease Japanese families and find that re-analysis using JG1 reduces total candidate variant calls versus GRCh37 while retaining disease-causing variants. These results suggest that integrating multiple genomes from a single population can aid genome analyses of that population.

Application of Genomic Big Data in Plant Breeding:Past, Present, and Future.

Plant breeding has a long history of developing new varieties that have ensured the food security of the human population. During this long journey together with humanity, plant breeders have successfully integrated the latest innovations in science and technologies to accelerate the increase in crop production and quality. For the past two decades, since the completion of human genome sequencing, genomic tools and sequencing technologies have advanced remarkably, and adopting these innovations has enabled us to cost down and/or speed up the plant breeding process. Currently, with the growing mass of genomic data and digitalized biological data, interdisciplinary approaches using new technologies could lead to a new paradigm of plant breeding. In this review, we summarize the overall history and advances of plant breeding, which have been aided by plant genomic research. We highlight the key advances in the field of plant genomics that have impacted plant breeding over the past decades and introduce the current status of innovative approaches such as genomic selection, which could overcome limitations of conventional breeding and enhance the rate of genetic gain.

An Overview of Molecular Genetic Diagnosis Techniques.

Our understanding of genetic disease(s) has increased exponentially since the completion of human genome sequencing and the development of numerous techniques to detect genetic variants. These techniques have not only allowed us to diagnose genetic disease, but in so doing, also provide increased understanding of the pathogenesis of these diseases to aid in developing appropriate therapeutic options. Additionally, the advent of next-generation or massively parallel sequencing (NGS/MPS) is increasingly being used in the clinical setting, as it can detect a number of abnormalities from point mutations to chromosomal rearrangements as well as aberrations within the transcriptome. In this article, we will discuss the use of multiple techniques that are used in genetic diagnosis. © 2020 by John Wiley & Sons, Inc.

Overlapping protein-coding genes in human genome and their coincidental expression in tissues

The completion of human genome sequences and the advancement of next-generation sequencing technologies have engendered a clear understanding of all human genes. Overlapping genes are usually observed in compact genomes, such as those of bacteria and viruses. Notably, overlapping protein-coding genes do exist in human genome sequences. Accordingly, we used the current Ensembl gene annotations to identify overlapping human protein-coding genes. We analysed 19,200 well-annotated protein-coding genes and determined that 4,951 protein-coding genes overlapped with their adjacent genes. Approximately a quarter of all human protein-coding genes were overlapping genes. We observed different clusters of overlapping protein-coding genes, ranging from two genes (paired overlapping genes) to 22 genes. We also divided the paired overlapping protein-coding gene groups into four subtypes. We found that the divergent overlapping gene subtype had a stronger expression association than did the subtypes of 5ʹ-tandem overlapping and 3ʹ-tandem overlapping genes. The majority of paired overlapping genes exhibited comparable coincidental tissue expression profiles; however, a few overlapping gene pairs displayed distinctive tissue expression association patterns. In summary, we have carefully examined the genomic features and distributions about human overlapping protein-coding genes and found coincidental expression in tissues for most overlapping protein-coding genes.

Complete human mtDNA genome sequences from Vietnam and the phylogeography of Mainland Southeast Asia

Vietnam is an important crossroads within Mainland Southeast Asia (MSEA) and a gateway to Island Southeast Asia, and as such exhibits high levels of ethnolinguistic diversity. However, comparatively few studies have been undertaken of the genetic diversity of Vietnamese populations. In order to gain comprehensive insights into MSEA mtDNA phylogeography, we sequenced 609 complete mtDNA genomes from individuals belonging to five language families (Austroasiatic, Tai-Kadai, Hmong-Mien, Sino-Tibetan and Austronesian) and analyzed them in comparison with sequences from other MSEA countries and Taiwan. Within Vietnam, we identified 399 haplotypes belonging to 135 haplogroups; among the five language families, the sequences from Austronesian groups differ the most from the other groups. Phylogenetic analysis revealed 111 novel Vietnamese mtDNA lineages. Bayesian estimates of coalescence times and associated 95% HPD for these show a peak of mtDNA diversification around 2.5–3 kya, which coincides with the Dong Son culture, and thus may be associated with the agriculturally-driven expansion of this culture. Networks of major MSEA haplogroups emphasize the overall distinctiveness of sequences from Taiwan, in keeping with previous studies that suggested at most a minor impact of the Austronesian expansion from Taiwan on MSEA. We also see evidence for population expansions across MSEA geographic regions and language families.

Outstanding questions in the study of archaic hominin admixture.

The complete sequencing of archaic and modern human genomes has revolutionized the study of human history and evolution. The application of paleogenomics has answered questions that were beyond the scope of archaeology alone—definitively proving admixture between archaic and modern humans. Despite the remarkable progress made in the study of archaic–modern human admixture, many outstanding questions remain. Here, we review some of these questions, which include how frequent archaic–modern human admixture was in history, to what degree drift and selection are responsible for the loss and retention of introgressed sequences in modern human genomes, and how surviving archaic sequences affect human phenotypes.

Challenges and opportunities of applying P4 medicine and traditional Chinese medicine for cancer treatment and prevention in the 21st century: A medical oncologist’s perspectives

Highlights：Being one of the deadliest diseases with ever-rising incidence especially in China, cancer is a major health threat and drains the patients, their families and society more resources than all other diseases. This overview on colorectal cancer treatment and prevention intends to rally principle-based P4 medicine (predictive, preventative, personalized and participatory) practice and best of traditional Chinese medicine on pre-illness and disease prevention and treatment to lead 21st century consumer driven health care and network movement facilitated by inevitable digital revolution and artificial intelligence use in every day’s life. AbstractBeing one of the deadliest diseases, cancer needs a stronger dose of P4 medicine (Predictive, Preventive, Personalized and Participatory) first proposed by Dr. Hood and TCM intervention, as cancer treatment still largely relies on the decade-old cytotoxic chemotherapy, radiation and surgery. This overview uses colorectal cancer model to discuss pitfalls in current cancer prevention and treatment strategies, which saw many randomized phase III studies failing to meet the study primary endpoints or marginally meeting the study objectives. Complete sequencing of whole human genome provided much of the hopes as well as hypes for precision medicine, as genomic diversity, ever changing tumor mutation landscape, SNP and complex microRNA regulation from the intron region and epigenetics make genotype to phenotype correlation study increasingly challenging. As a participant of One hundred Persons Pioneers Project, I witnessed first hand how a comprehensive scientific wellness study that integrates whole genomics, microbiome, and metabolome nutrition along with comprehensive laboratory examinations can be used to diagnose pre-illness in all “healthy” participants. Pre-illness can be best intervened by none pharmaceutical means and traditional Chinese Medicine (TCM) adept in restoring internal healing mechanisms, opening up the blocked network and balancing the five-elements homeostasis. Following TCM principles, we were able to design a therapy that effectively targets colon cancer stem cells and its microenvironment leading to more doubling of overall survival with reduction in overall toxicities. Pre-illness diagnosis, cancer immunotherapy, TCM medicine is about restoring internal healing power by letting go brakes on “good” immune systems to go after the “bad” cancer cells. Time is ripe to integrate our knowledge in genomics immune systems, stem cell biology, nutrition, inflammation, metabolism, systems medicine, and modern TCM to deliver a level of care that most of major illness including cancer are now minor pre-illness and are delayed, prevented, or cured at their earliest stages along with elevation in healthy index in the individual, their families and society as a whole globally.

Proteogenomics: Integrating Next-Generation Sequencing and Mass Spectrometry to Characterize Human Proteomic Variation.

Mass spectrometry-based proteomics has emerged as the leading method for detection, quantification, and characterization of proteins. Nearly all proteomic workflows rely on proteomic databases to identify peptides and proteins, but these databases typically contain a generic set of proteins that lack variations unique to a given sample, precluding their detection. Fortunately, proteogenomics enables the detection of such proteomic variations and can be defined, broadly, as the use of nucleotide sequences to generate candidate protein sequences for mass spectrometry database searching. Proteogenomics is experiencing heightened significance due to two developments: (a) advances in DNA sequencing technologies that have made complete sequencing of human genomes and transcriptomes routine, and (b) the unveiling of the tremendous complexity of the human proteome as expressed at the levels of genes, cells, tissues, individuals, and populations. We review here the field of human proteogenomics, with an emphasis on its history, current implementations, the types of proteomic variations it reveals, and several important applications.

Translating genetic findings in hereditary nephrotic syndrome: the missing loops.

Nephrotic syndrome (NS) is a clinicopathological entity characterized by proteinuria, hypoalbuminemia, peripheral edema, and hyperlipidemia. It is the most common cause of glomerular disease in children and adults. Although the molecular pathogenesis of NS is not completely understood, data from the study of familial NS suggest that it is a "podocytopathy." Virtually all of the genes mutated in hereditary NS localize to the podocyte or its secreted products and the slit diaphragm. Since the completion of human genome sequence and the advent of next generation sequencing, at least 29 causes of single-gene NS have been identified. However, these findings have not been matched by therapeutic advances owing to suboptimal in vitro and in vivo models for the study of human glomerular disease and podocyte injury phenotypes. Multidisciplinary collaboration between clinicians, geneticists, cell biologists, and molecular physiologists has the potential to overcome this barrier and thereby speed up the translation of genetic findings into improved patient care.

Genetic diversity between two Igbo men from Owerri senatorial province as determined by autosomal short tandem repeats, Y-chromosomal short tandem repeats and mitochondrial DNA typing methods

Background: Human identification has recently been optimized following the completion of human genome sequence, by using DNA markers that exhibit the highest possible variation. However, in developing countries such as Nigeria, the application of DNA typing for identification of human subjects either for forensic or medical purposes is inadequate due to the absence of national forensic DNA laboratories and lack of the legislative framework. Materials and Methods: In this study, two male subjects of Igbo origin provided their blood, hair and buccal samples for DNA analysis. DNA was extracted, purified, quantified with human quantifiler™ polymerase chain reaction reaction mix. Hyper-variable segment 1 of the D-loop mitochondrial DNA (mtDNA HVS-1) was amplified, purified and fragments sequenced and analyzed with ABI Genetic Analyzer. A multiplex AmpFlSTR Identifiler kit amplified 15 STR plus amelogenin loci of the nuclear DNA and 16 STR of the Y-chromosome. GeneMapper ID software version 3.2 was used for the analysis of autosomal and Y-chromosome AmpFlSTR. Results: Result show that the mtDNA lineage of UMBACK subject belongs to L3e2b while ELAMBIA is assigned to L3f1b1 haplogroup. Based on the allelic frequency database, both subjects displayed uniqueness in random match probability for the autosomal allelic short-tandem repeat (STR). Based upon a mutation rate of 0.003 for Y-DNA STR markers, the two individuals most likely shared a common paternal ancestor 96 generations ago. Both subjects were assigned to E1b1a haplogroup with a 100% probability, which is consistent with the haplogroup associated with Igbo people in Nigeria. Conclusion: As expected, well-established forensic genetic tools comprising of mtDNA, autosomal and Y-chromsomomal STR typing methods were all found to distinguish two selected Nigerian Igbo individuals with a very high power of discrimination.

Integrated Model of DNA Sequence Numerical Representation and Artificial Neural Network for Human Donor and Acceptor Sites Prediction

Human Genome Project has led to a huge inflow of genomic data. After the completion of human genome sequencing, more and more effort is being put into identification of splicing sites of exons and introns (donor and acceptor sites). These invite bioinformatics to analysis the genome sequences and identify the location of exon and intron boundaries or in other words prediction of splicing sites. Prediction of splice sites in genic regions of DNA sequence is one of the most challenging aspects of gene structure recognition. Over the last two decades, artificial neural networks gradually became one of the essential tools in bioinformatics. In this paper artificial neural networks with different numerical mapping techniques have been employed for building integrated model for splice site prediction in genes. An artificial neural network is trained and then used to find splice sites in human genes. A comparison between different mapping methods using trained neural network in terms of their precision in prediction of donor and acceptor sites will be presented in this paper. Training and measuring performance of neural network are carried out using sequences of the human genome (GRch37/hg19- chr21). Simulation results indicate that using Electron-Ion Interaction Potential numerical mapping method with neural network yields to the best performance in prediction.

Inferring human population size and separation history from multiple genome sequences.

The availability of complete human genome sequences from populations across the world has given rise to new population genetic inference methods that explicitly model their ancestral relationship under recombination and mutation. So far, application of these methods to evolutionary history more recent than 20-30 thousand years ago and to population separations has been limited. Here we present a new method that overcomes these shortcomings. The Multiple Sequentially Markovian Coalescent (MSMC) analyses the observed pattern of mutations in multiple individuals, focusing on the first coalescence between any two individuals. Results from applying MSMC to genome sequences from nine populations across the world suggest that the genetic separation of non-African ancestors from African Yoruban ancestors started long before 50,000 years ago, and give information about human population history as recently as 2,000 years ago, including the bottleneck in the peopling of the Americas, and separations within Africa, East Asia and Europe.

Next Generation Sequencing – Application in Clinical Practice

Development of new sequencing methods allowed faster and more economical genomic research. With these technologies, it is now possible to determine the complete sequence of human genome in a short time period and at a relatively low cost. Introduction of next generation sequencing methods to cancer research provided a comprehensive molecular characterization of cancers and enabled deeper insights into tumor complexity, heterogeneity and evolution. Next generation technologies have been applied to identify new causal mutations in genes in hereditary cancer syndromes. More than 15 various tumor types have been already sequenced and compared to that of normal cells allowing identification of new cancer driving mutations and genome structural rearrangements. In this review, we describe technical characteristics of main next generation sequencing platforms, briefly overview their pros and cons and clinical perspective.

A high-throughput Sanger strategy for human mitochondrial genome sequencing

BackgroundA population reference database of complete human mitochondrial genome (mtGenome) sequences is needed to enable the use of mitochondrial DNA (mtDNA) coding region data in forensic casework applications. However, the development of entire mtGenome haplotypes to forensic data quality standards is difficult and laborious. A Sanger-based amplification and sequencing strategy that is designed for automated processing, yet routinely produces high quality sequences, is needed to facilitate high-volume production of these mtGenome data sets.ResultsWe developed a robust 8-amplicon Sanger sequencing strategy that regularly produces complete, forensic-quality mtGenome haplotypes in the first pass of data generation. The protocol works equally well on samples representing diverse mtDNA haplogroups and DNA input quantities ranging from 50 pg to 1 ng, and can be applied to specimens of varying DNA quality. The complete workflow was specifically designed for implementation on robotic instrumentation, which increases throughput and reduces both the opportunities for error inherent to manual processing and the cost of generating full mtGenome sequences.ConclusionsThe described strategy will assist efforts to generate complete mtGenome haplotypes which meet the highest data quality expectations for forensic genetic and other applications. Additionally, high-quality data produced using this protocol can be used to assess mtDNA data developed using newer technologies and chemistries. Further, the amplification strategy can be used to enrich for mtDNA as a first step in sample preparation for targeted next-generation sequencing.

The Nature of Nurture: Refining the Definition of the Exposome

Historical debates concerning human biology and behavior have frequently focused on contributions of nature, ie, the inherited characteristics with which we are born, and nurture, ie, life’s influences after birth. Indeed, the concept of nature vs nurture has guided our understanding of human biology for decades, if not centuries. A series of discoveries has greatly advanced the knowledge of our nature. Watson and Crick’s unraveling of the double helix revolutionized the understanding of our genetic makeup (Watson and Crick, 1953). The polymerase chain reaction allowed amplification and manipulation of genes (Saiki et al., 1988). Identified links between specific genes and disease (Saiki et al., 1985) led to new diagnostic tools and treatments. These advances spurred the Human Genome Project with success in sequencing the entire human genome (Lander et al., 2001; Venter et al., 2001). This epic undertaking of biomedical science and technology was completed with amazing speed and celebrated with great fanfare. But the limitation of genetics to predict disease rapidly became obvious; as noted by Dr Venter shortly after the completion of human genome sequence, “We simply do not have enough genes for this idea of biological determinism to be right (McKie 2001).” Genome-wide association studies (GWAS) have revealed genetic associations and networks that improve understanding of disease, but these still account for only a fraction of disease risk. With the majority of disease causation being nongenetic, the need for improved tools to quantify environmental contributions seems obvious. The simple distinction between genes and environment is blurred by knowledge that environmental exposures cause permanent genetic changes via mutagenesis and also have long-term impact on gene expression through epigenetic mechanisms. Importantly, epigenetic mechanisms are central to differentiation and development, impacting genome function before birth and throughout life. The epigenome is highly reliant on nurture, ie, the nature and timing of environmental exposures and external forces. Randy Jirtle, a pioneer in epigenomics stated “The nature vs. nurture argument is rapidly proving to be irrelevant, because we’re finding that the 2 forces interact in highly specific ways that alter gene behavior (Duke Health, 2006).” Although Dr Jirtle suggests the argument is becoming irrelevant, the reality is that biomedical research is overwhelmingly focused on the gene side of this debate. The tools and knowledge of our nature are far ahead of those for the environment. If we want to focus on the interaction between nature and nurture, we need better ways of cataloguing and integrating the complex exposures and forces that represent nurture. Such a framework is provided by the exposome.