Cost Of Genome Sequencing Research Articles

Development of a haplotype reference panel and analysis of genetic structure in Golden Pompano (Trachinotus ovatus)

Trachinotus ovatus, a valuable marine fish, is primarily farmed in the South China Sea. Intensive aquaculture can lead to genetic uniformity and disease risks. Additionally, the high cost of genome sequencing restricts knowledge of genetic variation, hindering genetic analysis and breeding advancements. This study investigated the genetic diversity and structure of Trachinotus ovatus populations within the South China Sea aquaculture context, with the goal of strengthening sustainability. Additionally, the study also enhanced breeding strategies by developing a high-quality haplotype reference panel and an efficient imputation process. Through high-throughput sequencing (depth＞20X), 173 samples from three key breeding sites (SanYa, Hainan; Zhanjiang, Guangdong; and Beihai, Guangxi) were analyzed, revealing substantial genetic diversity and distinct population structures. Notably, the Beihai (BH) population exhibited significant stratification, suggesting regional genetic uniqueness. Low levels of genomic inbreeding coefficient and the identification of unique SNP loci across populations underscored the genetic richness within these Trachinotus ovatus communities. A major constructed of this work is the creation of the first high-quality haplotype reference panel for the species, demonstrating genotype imputation's viability as a cost-effective alternative for large-scale genetic studies in aquaculture. In particular, a Shapeit reference panel with 5476,550 SNPs was created, excelling in accuracy across various depths, notably achieving up to 95.8 % imputation accuracy at 1X sequencing depth. This high accuracy across populations shows the importance of genetic diversity in the panel. This research not only advances the understanding of Trachinotus ovatus genetic foundations but also provides crucial insights for conservation efforts and genetic analysis. By establishing a robust haplotype reference panel for Trachinotus ovatus helps lower the costs of future genetic research and variation studies, this study lays the foundation for sustainable aquaculture practices and breeding programs.

From Sanger to genome sequencing - an overview of DNA sequencing technologies

There is no technique that would make a greater contribution to the development of genetics, molecular biology and medicine than DNA sequencing. For many years, the method based on enzymatic DNA synthesis developed by Frederic Sanger was the gold standard in this area. At the end of the 20th century, there was a dynamic development of next-generation sequencing (NGS) technologies, which ended the era of single gene analysis and initiated the era of genome sequencing. Despite fierce competition, one NGS technology has practically completely dominated the global market. In the article, we present our own review of DNA sequencing methods, starting from the Sanger method to high-throughput second- and third-generation sequencing technologies, with particular emphasis on those that have achieved commercial success. We present their short history, principles of operation, technical possibilities, applications and limitations. In the summary, we reveal how much human genome sequencing costs at the current stage of the genomic revolution and outline the prospects for further development of genomics.

Exome copy number variant detection, analysis, and classification in a large cohort of families with undiagnosed rare genetic disease

Copy number variants (CNVs) are significant contributors to the pathogenicity of rare genetic diseases and, with new innovative methods, can now reliably be identified from exome sequencing. Challenges still remain in accurate classification of CNV pathogenicity. CNV calling using GATK-gCNV was performed on exomes from a cohort of 6,633 families (15,759 individuals) with heterogeneous phenotypes and variable prior genetic testing collected at the Broad Institute Center for Mendelian Genomics of the Genomics Research to Elucidate the Genetics of Rare Diseases consortium and analyzed using the seqr platform. The addition of CNV detection to exome analysis identified causal CNVs for 171 families (2.6%). The estimated sizes of CNVs ranged from 293bp to 80 Mb. The causal CNVs consisted of 140 deletions, 15 duplications, 3 suspected complex structural variants (SVs), 3 insertions, and 10 complex SVs, the latter two groups being identified by orthogonal confirmation methods. To classify CNV variant pathogenicity, we used the 2020 American College of Medical Genetics and Genomics/ClinGen CNV interpretation standards and developed additional criteria to evaluate allelic and functional data as well as variants on the X chromosome to further advance the framework. We interpreted 151 CNVs as likely pathogenic/pathogenic and 20 CNVs as high-interest variants of uncertain significance. Calling CNVs from existing exome data increases the diagnostic yield for individuals undiagnosed after standard testing approaches, providing a higher-resolution alternative to arrays at a fraction of the cost of genome sequencing. Our improvements to the classification approach advances the systematic framework to assess the pathogenicity of CNVs.

Recovering high-quality bacterial genomes from cross-contaminated cultures: a case study of marine Vibrio campbellii

BackgroundEnvironmental monitoring of bacterial pathogens is critical for disease control in coastal marine ecosystems to maintain animal welfare and ecosystem function and to prevent significant economic losses. This requires accurate taxonomic identification of environmental bacterial pathogens, which often cannot be achieved by commonly used genetic markers (e.g., 16S rRNA gene), and an understanding of their pathogenic potential based on the information encoded in their genomes. The decreasing costs of whole genome sequencing (WGS), combined with newly developed bioinformatics tools, now make it possible to unravel the full potential of environmental pathogens, beyond traditional microbiological approaches. However, obtaining a high-quality bacterial genome, requires initial cultivation in an axenic culture, which is a bottleneck in environmental microbiology due to cross-contamination in the laboratory or isolation of non-axenic strains.ResultsWe applied WGS to determine the pathogenic potential of two Vibrio isolates from coastal seawater. During the analysis, we identified cross-contamination of one of the isolates and decided to use this dataset to evaluate the possibility of bioinformatic contaminant removal and recovery of bacterial genomes from a contaminated culture. Despite the contamination, using an appropriate bioinformatics workflow, we were able to obtain high quality and highly identical genomes (Average Nucleotide Identity value 99.98%) of one of the Vibrio isolates from both the axenic and the contaminated culture. Using the assembled genome, we were able to determine that this isolate belongs to a sub-lineage of Vibrio campbellii associated with several diseases in marine organisms. We also found that the genome of the isolate contains a novel Vibrio plasmid associated with bacterial defense mechanisms and horizontal gene transfer, which may offer a competitive advantage to this putative pathogen.ConclusionsOur study shows that, using state-of-the-art bioinformatics tools and a sufficient sequencing effort, it is possible to obtain high quality genomes of the bacteria of interest and perform in-depth genomic analyses even in the case of a contaminated culture. With the new isolate and its complete genome, we are providing new insights into the genomic characteristics and functional potential of this sub-lineage of V. campbellii. The approach described here also highlights the possibility of recovering complete bacterial genomes in the case of non-axenic cultures or obligatory co-cultures.

Knowledge, Attitude and Ethical Perception Towards Precision Medicine Among Healthcare Professionals- a Questionnaire Survey

Precision medicine, also known as personalized medicine, is an emerging approach in healthcare that tailors medical treatment and prevention strategies to individual patients based on their unique genetic makeup, lifestyle, and environmental factors. It represents a paradigm shift from the traditional “one size fits all” approach to a more targeted and precise form of medical care. In precision medicine, extensive genomic analysis and other high-throughput technologies are employed to identify genetic variations and molecular markers associated with specific diseases or treatment responses. This wealth of genetic information allows healthcare professionals to make more accurate diagnoses, predict disease risks, and design personalized treatment plans. The application of precision medicine spans various medical fields, including oncology, cardiovascular diseases, neurology, and infectious diseases. In oncology, for example, molecular profiling of tumors enables the identification of specific genetic alterations that guide the selection of targeted therapies, leading to improved patient outcomes and reduced adverse effects. Precision medicine also holds great promise in preventive medicine. By identifying an individual’s genetic predispositions to certain diseases, clinicians can recommend tailored lifestyle modifications or interventions to mitigate the risk. Additionally, the integration of electronic health records, wearable devices, and mobile health applications facilitates real-time monitoring and personalized interventions, enhancing patient engagement and overall healthcare outcomes. Despite its potential, several challenges need to be addressed for the widespread implementation of precision medicine. These include the high cost of genomic sequencing and data analysis, ensuring data privacy and security, and the need for interdisciplinary collaboration among researchers, clinicians, and policymakers. In conclusion, precision medicine represents a transformative approach to healthcare, enabling personalized and targeted interventions that have the potential to revolutionize patient care, improve treatment outcomes, and reduce healthcare costs. Efforts to overcome the challenges associated with precision medicine are crucial for its successful integration into routine clinical practice.

CDBProm: the Comprehensive Directory of Bacterial Promoters.

The decreasing cost of whole genome sequencing has produced high volumes of genomic information that require annotation. The experimental identification of promoter sequences, pivotal for regulating gene expression, is a laborious and cost-prohibitive task. To expedite this, we introduce the Comprehensive Directory of Bacterial Promoters (CDBProm), a directory of in-silico predicted bacterial promoter sequences. We first identified that an Extreme Gradient Boosting (XGBoost) algorithm would distinguish promoters from random downstream regions with an accuracy of 87%. To capture distinctive promoter signals, we generated a second XGBoost classifier trained on the instances misclassified in our first classifier. The predictor of CDBProm is then fed with over 55 million upstream regions from more than 6000 bacterial genomes. Upon finding potential promoter sequences in upstream regions, each promoter is mapped to the genomic data of the organism, linking the predicted promoter with its coding DNA sequence, and identifying the function of the gene regulated by the promoter. The collection of bacterial promoters available in CDBProm enables the quantitative analysis of a plethora of bacterial promoters. Our collection with over 24 million promoters is publicly available at https://aw.iimas.unam.mx/cdbprom/.

The cost of proband and trio exome and genome analysis in rare disease: A micro-costing study

PurposeRare disease genomic testing is a complex process involving various resources. Accurate resource estimation is required for informed prioritization and reimbursement decisions. This study aims to analyze the costs and cost drivers of clinical genomic testing. MethodsBased on genomic sequencing workflows we microcosted limited virtual panel analysis on exome sequencing backbone, proband and trio exome, and genome testing for proband and trio analysis in 2023 Australian Dollars ($). Deterministic and probabilistic sensitivity analyses were undertaken. ResultsPanel testing costs AUD $2373 ($733-$6166), and exome sequencing costs $2823 ($802-$7206) and $5670 ($2006-$11,539) for proband and trio analysis, respectively. Genome sequencing costs $4840 ($2153-$9890) and $11,589 ($5842-$16,562) for proband and trio analysis. The most expensive cost component of genomic testing was sequencing (36.9%-69.4% of total cost), with labor accounting for 27.1%-63.2% of total cost. ConclusionWe provide a comprehensive analysis of rare disease genomic testing costs, for a range of clinical testing types and contexts. This information will accurately inform economic evaluations of rare disease genomic testing and decision making on policy settings that assist with implementation, such as genomic testing reimbursement.

Development and evaluation of a haplotype reference panel of Zhikong scallop (Chlamys farreri) for genotype imputation

Single nucleotide polymorphisms (SNPs) across the genome are remarkably useful resources for investigating the genome architecture, studying the genetic basis of complex traits and implementation of genomic selection. However, information on high-density SNPs is unavailable for most aquaculture species owing to the high costs of whole genome sequencing. Genotype imputation is an attractive approach for obtaining large number of genomic variants at a low cost. In this study, the first haplotype reference panel was constructed for Chlamys farreri, an economically important mollusk species and a major contributor to aquaculture in China. A total of 174 scallops from six populations in the main scallop culture area were selected for whole-genome resequencing. More than 87.47 million SNPs were identified and 3,968,417 SNPs genome-wide were selected for constructing the haplotype reference panel for C. farreri. The number of SNPs per chromosome ranged from 125,000 to 279,981 with an average spacing of 242 bases. Several factors were evaluated based on the imputation performance of the reference panel. The imputation accuracies of 19 chromosomes ranged from 0.91 to 0.95, and there were no significant differences among them (p > 0.05). The imputation accuracies of populations could reach 0.95, 0.93, and 0.90 for ZY, PR, and CS respectively, which confirmed a positive relationship between the kinship within the population and the imputation accuracy. The imputation accuracies increased with sequencing depth and plateaued at 0.5×, reaching accuracies of 0.91–0.93 for artificially breeding populations. Taken together, we constructed a haplotype reference panel for C. farreri, which can provide a cost-effective way to extend the limited number of genotypes for boosting the power of downstream analyses such as genome-wide association studies and genomic selection.

Identification and high-throughput genotyping of single nucleotide polymorphism markers in a non-model conifer (Abies nordmanniana (Steven) Spach)

Single nucleotide polymorphism (SNP) markers are powerful tools for investigating population structures, linkage analysis, and genome-wide association studies, as well as for breeding and population management. The availability of SNP markers has been limited to the most commercially important timber species, primarily due to the cost of genome sequencing required for SNP discovery. In this study, a combination of reference-based and reference-free approaches were used to identify SNPs in Nordmann fir (Abies nordmanniana), a species previously lacking genomic sequence information. Using a combination of a genome assembly of the closely related Silver fir (Abies alba) species and a de novo assembly of low-copy regions of the Nordmann fir genome, we identified a high density of reliable SNPs. Reference-based approaches identified two million SNPs in common between the Silver fir genome and low-copy regions of Nordmann fir. A combination of one reference-free and two reference-based approaches identified 250 shared SNPs. A subset of 200 SNPs were used to genotype 342 individuals and thereby tested and validated in the context of identity analysis and/or clone identification. The tested SNPs successfully identified all ramets per clone and five mislabeled individuals via identity and genomic relatedness analysis. The identified SNPs will be used in ad hoc breeding of Nordmann fir in Denmark.

Implementation of different relationship estimate methodologies in breeding value prediction in kiwiberry (Actinidia arguta)

In dioecious crops such as Actinidia arguta (kiwiberries), some of the main challenges when breeding for fruit characteristics are the selection of potential male parents and the long juvenile period. Currently, breeding values of male parents are estimated through progeny tests, which makes the breeding of new kiwiberry cultivars time-consuming and costly. The application of best linear unbiased prediction (BLUP) would allow direct estimation of sex-related traits and speed up kiwiberry breeding. In this study, we used a linear mixed model approach to estimate narrow sense heritability for one vine-related trait and five fruit-related traits for two incomplete factorial crossing designs. We obtained BLUPs for all genotypes, taking into consideration whether the relationship was pedigree-based or marker-based. Owing to the high cost of genome sequencing, it is important to understand the effects of different sources of relationship matrices on estimating breeding values across a breeding population. Because of the increasing implementation of genomic selection in crop breeding, we compared the effects of incorporating different sources of information in building relationship matrices and ploidy levels on the accuracy of BLUPs’ heritability and predictive ability. As kiwiberries are autotetraploids, multivalent chromosome formation and occasionally double reduction can occur during meiosis, and this can affect the accuracy of prediction. This study innovates the breeding programme of autotetraploid kiwiberries. We demonstrate that the accuracy of BLUPs of male siblings, without phenotypic observations, strongly improved when a tetraploid marker-based relationship matrix was used rather than parental BLUPs and female siblings with phenotypic observations.

Reference-based genome compression using the longest matched substrings with parallelization consideration

BackgroundA large number of researchers have devoted to accelerating the speed of genome sequencing and reducing the cost of genome sequencing for decades, and they have made great strides in both areas, making it easier for researchers to study and analyze genome data. However, how to efficiently store and transmit the vast amount of genome data generated by high-throughput sequencing technologies has become a challenge for data compression researchers. Therefore, the research of genome data compression algorithms to facilitate the efficient representation of genome data has gradually attracted the attention of these researchers. Meanwhile, considering that the current computing devices have multiple cores, how to make full use of the advantages of the computing devices and improve the efficiency of parallel processing is also an important direction for designing genome compression algorithms.ResultsWe proposed an algorithm (LMSRGC) based on reference genome sequences, which uses the suffix array (SA) and the longest common prefix (LCP) array to find the longest matched substrings (LMS) for the compression of genome data in FASTA format. The proposed algorithm utilizes the characteristics of SA and the LCP array to select all appropriate LMSs between the genome sequence to be compressed and the reference genome sequence and then utilizes LMSs to compress the target genome sequence. To speed up the operation of the algorithm, we use GPUs to parallelize the construction of SA, while using multiple threads to parallelize the creation of the LCP array and the filtering of LMSs.ConclusionsExperiment results demonstrate that our algorithm is competitive with the current state-of-the-art algorithms in compression ratio and compression time.

Whole genome sequencing for more accurate TMB and mutational signatures in common cancers.

e15173 Background: Due to the dramatic decrease in cost of genome sequencing, we are entering the era of whole genome sequencing (WGS) at $100. Tumor mutational burden (TMB) and mutational signatures have been introduced as potential prognostic/predictive cancer biomarkers and mostly assessed by targeted cancer gene sequencing panels. However, they are truly whole genome-wide events and thus more accurately estimated by whole genome sequencing data. Methods: We analyzed WGS data produced by Genome Insight Inc. that encompass > 1,300 breast, lung, and hepatocellular carcinoma samples, collectively. Our genome-wide TMBs (wgsTMB) were systematically compared with the TMB estimates by targeted sequencing methods, such as conventional whole-exome sequencing (WES) and targeted panel sequencing from FoundationOne CDx (F1CDx). In parallel, we leveraged genome-wide somatic mutations for validating mutational signatures projected from the targeted sequencing methods. Results: Pembrolizumab is FDA approved for solid tumors with high TMB (≥ 10 mutations/Mb) assessed by F1CDx. This criterion classified that 546 of the tumors (41%) in our cohort as high F1CDx TMB (f1TMB), who may benefit from immunotherapy. However, the high f1TMB classification was inconsistent to the genome-wide TMB classification ( p < 2.2e-16). For example, 132 high f1TMB tumors (24.1%; 132/546) showed lower than the median wgsTMB estimate. On the flip side, 32% of the low f1TMB tumors (251/784) demonstrate higher than median wgsTMB estimate (see table). Although there is no approved criterion in WGS that defines hypermutators, our data illustrates that WGS will be helpful for redefining the criteria for selecting patients for immune checkpoint inhibitors. The wgsTMB was also an essential factor for accurate estimation of mutational signatures from targeted sequencing. For example, in high TMB tumors, mutational signature estimated from WES showed on average 0.87 cosine similarity with signatures from WGS. However, the correlation was substantially reduced in low TMB tumors, showing only ~0.64 on average. Particularly, ~12% of breast tumors with considerable levels of homologous recombination deficiency (HRD) signatures by WGS were not captured by whole-exome sequencing based approaches. Further exploration should be performed with WGS to select proper patients for targeted therapies, including PARP inhibitors. Conclusions: Despite the current guidelines depending on targeted sequencing in precision oncology, we showed that whole-genome sequencing can deliver more accurate cancer genome profiles, such as, but not limited to, TMB and mutational signatures. Given its more comprehensive capabilities and affordable cost, we believe that WGS-based precision oncology is medically necessary. [Table: see text]

A comparison of various feature extraction and machine learning methods for antimicrobial resistance prediction in streptococcus pneumoniae

Streptococcus pneumoniae is one of the major concerns of clinicians and one of the global public health problems. This pathogen is associated with high morbidity and mortality rates and antimicrobial resistance (AMR). In the last few years, reduced genome sequencing costs have made it possible to explore more of the drug resistance of S. pneumoniae, and machine learning (ML) has become a popular tool for understanding, diagnosing, treating, and predicting these phenotypes. Nucleotide k-mers, amino acid k-mers, single nucleotide polymorphisms (SNPs), and combinations of these features have rich genetic information in whole-genome sequencing. This study compares different ML models for predicting AMR phenotype for S. pneumoniae. We compared nucleotide k-mers, amino acid k-mers, SNPs, and their combinations to predict AMR in S. pneumoniae for three antibiotics: Penicillin, Erythromycin, and Tetracycline. 980 pneumococcal strains were downloaded from the European Nucleotide Archive (ENA). Furthermore, we used and compared several machine learning methods to train the models, including random forests, support vector machines, stochastic gradient boosting, and extreme gradient boosting. In this study, we found that key features of the AMR prediction model setup and the choice of machine learning method affected the results. The approach can be applied here to further studies to improve AMR prediction accuracy and efficiency.

The TeleKidSeq pilot study: incorporating telehealth into clinical care of children from diverse backgrounds undergoing whole genome sequencing

BackgroundThe COVID-19 pandemic forced healthcare institutions and many clinical research programs to adopt telehealth modalities in order to mitigate viral spread. With the expanded use of telehealth, there is the potential to increase access to genomic medicine to medically underserved populations, yet little is known about how best to communicate genomic results via telehealth while also ensuring equitable access. NYCKidSeq, a multi-institutional clinical genomics research program in New York City, launched the TeleKidSeq pilot study to assess alternative forms of genomic communication and telehealth service delivery models with families from medically underserved populations.MethodsWe aim to enroll 496 participants between 0 and 21 years old to receive clinical genome sequencing. These individuals have a neurologic, cardiovascular, and/or immunologic disease. Participants will be English- or Spanish-speaking and predominantly from underrepresented groups who receive care in the New York metropolitan area. Prior to enrollment, participants will be randomized to either genetic counseling via videoconferencing with screen-sharing or genetic counseling via videoconferencing without screen-sharing. Using surveys administered at baseline, results disclosure, and 6-months post-results disclosure, we will evaluate the impact of the use of screen-sharing on participant understanding, satisfaction, and uptake of medical recommendations, as well as the psychological and socioeconomic implications of obtaining genome sequencing. Clinical utility, cost, and diagnostic yield of genome sequencing will also be assessed.DiscussionThe TeleKidSeq pilot study will contribute to innovations in communicating genomic test results to diverse populations through telehealth technology. In conjunction with NYCKidSeq, this work will inform best practices for the implementation of genomic medicine in diverse, English- and Spanish-speaking populations.

Identification of genetic regions associated with sex determination in date palm: A computational approach

Sex determination of date palm seedlings is the challengeable effort for breeders. Different studies based on molecular markers and genome sequencing have provided some insight in to the genetic regions related to sex determination in date palms in general. But due to differences in cultivar population structure and also cost of whole genome sequencing, we may need a more suitable approach in developing countries for this task. Therefore, we suggest using a combination of different available molecular markers and a computational approach to identify the genetic regions involved in sex differentiation in date palm cultivars. In this study we used twenty-three cultivars including 7 male and 16 female cultivars that were examined by 30 different dominant and co-dominant molecular markers which deal with different genomic regions. Grouping of the tree samples based on 178 loci resulted in genetic differentiation of the studied male and female palm trees. Multiple correspondence analysis (MCA) bi-plot also showed genetic difference within male and female trees. Heatmap plot specified those markers which differentiate date palm trees. SSR (simple sequence repeats) and IRAP (inter retrotransposon amplified polymorphism) markers provided sex linked markers for male cultivars. In present study, we introduced sex specific alleles for Iranian male date palm cultivars as a fast track in seedlings. Different association studies performed identified the candidate genetic regions which are significantly associated with sex differentiation in date palm cultivars.

Health inequity in genomic personalized medicine in underrepresented populations: a look at the current evidence

Improvements in sequencing technology coupled with dramatic declines in the cost of genome sequencing have led to a proportional growth in the size and number of genetic datasets since the release of the human genetic sequence by The Human Genome Project (HGP) international consortium. The HGP was undeniably a significant scientific success, a turning point in human genetics and the beginning of human genomics. This burst of genetic information has led to a greater understanding of disease pathology and the potential of employing this data to deliver more precise patient care. Hence, the recognition of high-penetrance disease-causing mutations which encode drivers of disease has made the management of most diseases more specific. Nonetheless, while genetic scores are becoming more extensively used, their application in the real world is expected to be limited due to the lack of diversity in the data used to construct them. Underrepresented populations, such as racial and ethnic minorities, low-income individuals, and those living in rural areas, often experience greater health disparities and worse health outcomes compared to the general population. These disparities are often the result of systemic barriers, such as poverty, discrimination, and limited access to healthcare. Addressing health inequity in underrepresented populations requires addressing the underlying social determinants of health and implementing policies and programs which promoted health equity and reduce disparities. This can include expanding access to affordable healthcare, addressing poverty and unemployment, and promoting policies that combat discrimination and racism.

Close-kin mark-recapture methods to estimate demographic parameters of mosquitoes.

Close-kin mark-recapture (CKMR) methods have recently been used to infer demographic parameters such as census population size and survival for fish of interest to fisheries and conservation. These methods have advantages over traditional mark-recapture methods as the mark is genetic, removing the need for physical marking and recapturing that may interfere with parameter estimation. For mosquitoes, the spatial distribution of close-kin pairs has been used to estimate mean dispersal distance, of relevance to vector-borne disease transmission and novel biocontrol strategies. Here, we extend CKMR methods to the life history of mosquitoes and comparable insects. We derive kinship probabilities for mother-offspring, father-offspring, full-sibling and half-sibling pairs, where an individual in each pair may be a larva, pupa or adult. A pseudo-likelihood approach is used to combine the marginal probabilities of all kinship pairs. To test the effectiveness of this approach at estimating mosquito demographic parameters, we develop an individual-based model of mosquito life history incorporating egg, larva, pupa and adult life stages. The simulation labels each individual with a unique identification number, enabling close-kin relationships to be inferred for sampled individuals. Using the dengue vector Aedes aegypti as a case study, we find the CKMR approach provides unbiased estimates of adult census population size, adult and larval mortality rates, and larval life stage duration for logistically feasible sampling schemes. Considering a simulated population of 3,000 adult mosquitoes, estimation of adult parameters is accurate when ca. 40 adult females are sampled biweekly over a three month period. Estimation of larval parameters is accurate when adult sampling is supplemented with ca. 120 larvae sampled biweekly over the same period. The methods are also effective at detecting intervention-induced increases in adult mortality and decreases in population size. As the cost of genome sequencing declines, CKMR holds great promise for characterizing the demography of mosquitoes and comparable insects of epidemiological and agricultural significance.

Genome sequencing with gene panel-based analysis for rare inherited conditions in a publicly funded healthcare system: implications for future testing

NHS genetics centres in Scotland sought to investigate the Genomics England 100,000 Genomes Project diagnostic utility to evaluate genome sequencing for in rare, inherited conditions. Four regional services recruited 999 individuals from 394 families in 200 rare phenotype categories, with negative historic genetic testing. Genome sequencing was performed at Edinburgh Genomics, and phenotype and sequence data were transferred to Genomics England for variant calling, gene-based filtering and variant prioritisation. NHS Scotland genetics laboratories performed interpretation, validation and reporting. New diagnoses were made in 23% cases – 19% in genes implicated in disease at the time of variant prioritisation, and 4% from later review of additional genes. Diagnostic yield varied considerably between phenotype categories and was minimal in cases with prior exome testing. Genome sequencing with gene panel filtering and reporting achieved improved diagnostic yield over previous historic testing but not over now routine trio-exome sequence tests. Re-interpretation of genomic data with updated gene panels modestly improved diagnostic yield at minimal cost. However, to justify the additional costs of genome vs exome sequencing, efficient methods for analysis of structural variation will be required and / or cost of genome analysis and storage will need to decrease.

Multimodal biomedical AI.

The increasing availability of biomedical data from large biobanks, electronic health records, medical imaging, wearable and ambient biosensors, and the lower cost of genome and microbiome sequencing have set the stage for the development of multimodal artificial intelligence solutions that capture the complexity of human health and disease. In this Review, we outline the key applications enabled, along with the technical and analytical challenges. We explore opportunities in personalized medicine, digital clinical trials, remote monitoring and care, pandemic surveillance, digital twin technology and virtual health assistants. Further, we survey the data, modeling and privacy challenges that must be overcome to realize the full potential of multimodal artificial intelligence in health.

CMIC: an efficient quality score compressor with random access functionality

BackgroundOver the past few decades, the emergence and maturation of new technologies have substantially reduced the cost of genome sequencing. As a result, the amount of genomic data that needs to be stored and transmitted has grown exponentially. For the standard sequencing data format, FASTQ, compression of the quality score is a key and difficult aspect of FASTQ file compression. Throughout the literature, we found that the majority of the current quality score compression methods do not support random access. Based on the above consideration, it is reasonable to investigate a lossless quality score compressor with a high compression rate, a fast compression and decompression speed, and support for random access.ResultsIn this paper, we propose CMIC, an adaptive and random access supported compressor for lossless compression of quality score sequences. CMIC is an acronym of the four steps (classification, mapping, indexing and compression) in the paper. Its framework consists of the following four parts: classification, mapping, indexing, and compression. The experimental results show that our compressor has good performance in terms of compression rates on all the tested datasets. The file sizes are reduced by up to 21.91% when compared with LCQS. In terms of compression speed, CMIC is better than all other compressors on most of the tested cases. In terms of random access speed, the CMIC is faster than the LCQS, which provides a random access function for compressed quality scores.ConclusionsCMIC is a compressor that is especially designed for quality score sequences, which has good performance in terms of compression rate, compression speed, decompression speed, and random access speed. The CMIC can be obtained in the following way: https://github.com/Humonex/Cmic.