Large-scale Sequencing Projects Research Articles

Genetic profiling of Vietnamese population from large-scale genomic analysis of non-invasive prenatal testing data

The under-representation of several ethnic groups in existing genetic databases and studies have undermined our understanding of the genetic variations and associated traits or diseases in many populations. Cost and technology limitations remain the challenges in performing large-scale genome sequencing projects in many developing countries, including Vietnam. As one of the most rapidly adopted genetic tests, non-invasive prenatal testing (NIPT) data offers an alternative untapped resource for genetic studies. Here we performed a large-scale genomic analysis of 2683 pregnant Vietnamese women using their NIPT data and identified a comprehensive set of 8,054,515 single-nucleotide polymorphisms, among which 8.2% were new to the Vietnamese population. Our study also revealed 24,487 disease-associated genetic variants and their allele frequency distribution, especially 5 pathogenic variants for prevalent genetic disorders in Vietnam. We also observed major discrepancies in the allele frequency distribution of disease-associated genetic variants between the Vietnamese and other populations, thus highlighting a need for genome-wide association studies dedicated to the Vietnamese population. The resulted database of Vietnamese genetic variants, their allele frequency distribution, and their associated diseases presents a valuable resource for future genetic studies.

Netboxr: Automated discovery of biological process modules by network analysis in R.

Large-scale sequencing projects, such as The Cancer Genome Atlas (TCGA) and the International Cancer Genome Consortium (ICGC), have generated high throughput sequencing and molecular profiling data sets, but it is still challenging to identify potentially causal changes in cellular processes in cancer as well as in other diseases in an automated fashion. We developed the netboxr package written in the R programming language, which makes use of the NetBox algorithm to identify candidate cancer-related functional modules. The algorithm makes use of a data-driven, network-based approach that combines prior knowledge with a network clustering algorithm, obviating the need for and the limitation of independently curated functionally labeled gene sets. The method can combine multiple data types, such as mutations and copy number alterations, leading to more reliable identification of functional modules. We make the tool available in the Bioconductor R ecosystem for applications in cancer research and cell biology. The netboxr package is free and open-sourced under the GNU GPL-3 license R package available at https://www.bioconductor.org/packages/release/bioc/html/netboxr.html.

Variants in <i>PIWIL1</i> Do Not Play a Major Role in Human Male Infertility

Infertility affects one in six couples, of which half of the cases are explained by a male factor. The causes of severe spermatogenic failure remain poorly understood. Although thousands of genes are known to be involved in spermatogenesis, there is a lack of diagnostically relevant genes for this disorder. A recent report by Gou et al. suggested that variants in the D-box region of PIWIL1 are a recurrent cause of male infertility. However, we observed several inconsistencies in the sequencing data reported. In this study, we combined the genomic data from three large-scale sequencing projects adding up to a total of 2,740 male infertility cases to replicate the findings. We could not reproduce the mutational hotspot in the D-box region and did not identify variants likely to result in infertility in this gene. We conclude that variants in PIWIL1 do not play a major role in human male infertility.

LRRK2 Loss-of-Function Variants: When Less Is More.

Gain-of-function variants in leucine-rich repeat kinase 2 (LRRK2) are the most common monogenic cause of Parkinson's disease (PD), albeit with incomplete penetrance, indicating that reduction of LRRK2 kinase activity is a reasonable therapeutic strategy for LRRK2-related PD.1 However, LRRK2 inhibition in animal models was associated with abnormal phenotypes in the lung, kidney, and liver, thus raising safety concerns.2 Resources such as the Genome Aggregation Database or UK Biobank, built on large-scale sequencing projects, represent compelling tools for assessing genetic variant frequency. These were employed by Whiffin and colleagues3 to investigate the frequency of LRRK2 loss-of-function (LoF) variants in humans and their association with specific phenotypes. Among 187,518 individuals in the 2 aforementioned cohorts, they identified 352 carriers of heterozygous LRRK2 LoF variants, implying that approximately 1 in 500 humans harbors such a variant.3 Using genotyping data from the personal genetics company 23andMe, Whiffin and colleagues3 identified an additional 1,103 individuals with LRRK2 LoF variants.3 No reduction in lifespan or overrepresentation of any phenotype affecting the lung, liver, kidney, cardiovascular system, nervous system, immunity, or cancer in carriers of LRRK2 LoF variants was observed.3 In an earlier study, heterozygous LRRK2 LoF variants were identified at comparable percentages of clinically diagnosed patients with PD and controls, suggesting a lack of association between the partial loss of LRRK2 and PD.4 These results collectively suggest that an endogenous decrease of LRRK2 levels and consequently of its activity does not seem to elicit highly penetrant phenotypes, although to date LRRK2 LoF variants have never been investigated specifically in cohorts of patients with unexplained severe manifestations (including those seen in animal models upon LRRK2 inhibition). Thus, the therapeutic administration of compounds intended to decrease LRRK2 enzymatic activity (kinase inhibitors) or levels (antisense oligonucleotides) is not expected to cause harmful effects. Currently, 3 LRRK2-targeted compounds are being tested in phase 1 or 1b clinical trials: the LRRK2 kinase inhibitors DNL151 and DNL201 from Denali Therapeutics Inc., (San Francisco, CA, USA) and the antisense oligonucleotide BIIB094 from Biogen.3 As a caveat, a coinciding loss of both LRRK2 and its functional homolog LRRK1 results in earlier mortality, a loss of dopaminergic neurons, and an increase in α-synuclein in a double-knockout mouse model.5 Hence, targeting LRRK2 may not be safe in individuals with LRRK1 homozygous or heterozygous LoF variants. While it may thus be advisable to exclude the presence of such variants by genetic testing of LRRK1 in individuals prior to the administration of any LRRK2-directed treatment, LRRK2 inhibition is a promising and potentially safe new therapeutic strategy. (1) Research project: A. Conception, B. Organization, C. Execution; (2) Manuscript: A. Writing of the first draft, B. Review and Critique. T.U.: 1A, 1B,1C,2A,2B A.W.:1A,1B,1C,2A,2B T.U. reports the following: employment with University Medical Center Schleswig-Holstein, Campus Lübeck, Germany. A.W. is an advisor for medical writing at CENTOGENE GmbH and reports employment with University Medical Center Schleswig-Holstein, Campus Lübeck, Germany; honoraria from CENTOGENE GmbH; and a grant from the German Research Foundation (Deutsche Forschungsgemeinschaft) Research Unit (FOR2488).

VacPred: Sequence-based prediction of plant vacuole proteins using machine-learning techniques

Subcellular localization prediction of the proteome is one of major goals of large-scale genome or proteome sequencing projects to define the gene functions that could be possible with the help of computational modeling techniques. Previously, different methods have been developed for this purpose using multi-label classification system and achieved a high level of accuracy. However, during the validation of our blind dataset of plant vacuole proteins, we observed that they have poor performance with accuracy value range from ~1.3% to 48.5%. The results showed that the previously developed methods are not very accurate for the plant vacuole protein prediction and thus emphasize the need to develop a more accurate and reliable algorithm. In this study, we have developed various compositions as well as PSSM-based models and achieved a high accuracy than previously developed methods. We have shown that our best model achieved ~63% accuracy on blind dataset, which is far better than currently available tools. Furthermore, we have implemented our best models in the form of GUI-based free software called 'VacPred' which is compatible with both Linux and Window platform. This software is freely available for download at www.deepaklab.com/vacpred.

Abstract 1109: Integrative analysis of clinical and genomic information identifies predictive markers of metastatic risk

Abstract Despite the fact that the majority of cancer related deaths are due to metastatic disease, existing large-scale cancer sequencing projects such as The Cancer Genome Atlas have so far focused mainly on primary tumors. Moreover, most publicly available datasets in cancer genomics lack information about spatiotemporal patterns of metastatic spread. We have developed new clinical data extraction methods and performed an integrative analysis of clinical and genomic features from 34,836 patients treated at Memorial Sloan Kettering Cancer Center (MSK), stratified into 45 cancer subtypes. Tumors were sequenced with MSK-IMPACT, a targeted next-generation sequencing assay that identifies genomic alterations in 468 genes. Indications of metastatic events, including affected organs and detailed timeline annotations, were extracted from the electronic medical records. This allowed us to distinguish not only sequenced primaries (58%) from sequenced metastases (42%), but also primaries in patients with evidence of metastatic disease (33%) from patients without evidence of metastatic disease (25%) at the time of sequencing. Tumors from patients with evidence of metastatic disease exhibited increased copy-number alterations, measured as fraction of genome altered (FGA) in multiple cancer types, including prostate adenocarcinoma, endometrial endometrioid, HR+/HER2- breast invasive carcinoma and lung adenocarcinoma (all p < 0.001). This supports the existence of a positive correlation between disease burden and chromosomal instability, although this effect is weaker or non-existent in some cancer types, including hepatocellular carcinoma, ovarian serous, and cholangiocarcinoma. We investigated associations between somatic alterations and metastatic organotropisms. For example, among patients with microsatellite stable colorectal cancer, tumors that metastasized exclusively to the peritoneum were enriched in BRAF alterations when compared to patients that never metastasized to that site (21% vs. 6%; p=0.003), while tumors that metastasized exclusively to the lung were enriched in KRAS alterations (69% vs. 41%; p=0.002). By analyzing primary tumors in patients with no evidence of metastatic disease at the time of sequencing, and comparing those that would eventually metastasize with those that would not, we identified predictive markers of metastatic risk. For example, in bladder cancer, tumors with altered TP53 were twice as likely to metastasize (54% vs. 27%; p<0.001), while tumors with FGFR3 alterations exhibited the opposite trend (24% vs. 43%; p<0.001). We present a comprehensive, spatiotemporal map of metastasis that integrates clinical and molecular information and can be used to build personalized risk models of metastatic spread at target-organ resolution based on individual molecular profiles, which can enhance precision medical care and optimize clinical outcomes. Citation Format: Christopher J. Fong, Francisco Sanchez-Vega, Bastien Ngyuen, Anisha Luthra, Subhiksha Nandakumar, Henry Walch, Walid Chatila, Arjun Rajanna, Ahmet Zehir, Michael Berger, Jianjiong Gao, Nikolaus Schultz. Integrative analysis of clinical and genomic information identifies predictive markers of metastatic risk [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 1109.

Clinical interpretation of variants identified in RNU4ATAC, a non-coding spliceosomal gene.

Biallelic variants in RNU4ATAC, a non-coding gene transcribed into the minor spliceosome component U4atac snRNA, are responsible for three rare recessive developmental diseases, namely Taybi-Linder/MOPD1, Roifman and Lowry-Wood syndromes. Next-generation sequencing of clinically heterogeneous cohorts (children with either a suspected genetic disorder or a congenital microcephaly) recently identified mutations in this gene, illustrating how profoundly these technologies are modifying genetic testing and assessment. As RNU4ATAC has a single non-coding exon, the bioinformatic prediction algorithms assessing the effect of sequence variants on splicing or protein function are irrelevant, which makes variant interpretation challenging to molecular diagnostic laboratories. In order to facilitate and improve clinical diagnostic assessment and genetic counseling, we present i) an update of the previously reported RNU4ATAC mutations and an analysis of the genetic variations affecting this gene using the Genome Aggregation Database (gnomAD) resource; ii) the pathogenicity prediction performances of scores computed based on an RNA structure prediction tool and of those produced by the Combined Annotation Dependent Depletion tool for the 285 RNU4ATAC variants identified in patients or in large-scale sequencing projects; iii) a method, based on a cellular assay, that allows to measure the effect of RNU4ATAC variants on splicing efficiency of a minor (U12-type) reporter intron. Lastly, the concordance of bioinformatic predictions and cellular assay results was investigated.

Use of Chou's 5-steps rule to predict the subcellular localization of gram-negative and gram-positive bacterial proteins by multi-label learning based on gene ontology annotation and profile alignment.

To date, many proteins generated by large-scale genome sequencing projects are still uncharacterized and subject to intensive investigations by both experimental and computational means. Knowledge of protein subcellular localization (SCL) is of key importance for protein function elucidation. However, it remains a challenging task, especially for multiple sites proteins known to shuttle between cell compartments to perform their proper biological functions and proteins which do not have significant homology to proteins of known subcellular locations. Due to their low-cost and reasonable accuracy, machine learning-based methods have gained much attention in this context with the availability of a plethora of biological databases and annotated proteins for analysis and benchmarking. Various predictive models have been proposed to tackle the SCL problem, using different protein sequence features pertaining to the subcellular localization, however, the overwhelming majority of them focuses on single localization and cover very limited cellular locations. The prediction was basically established on sorting signals, amino acids compositions, and homology. To improve the prediction quality, focus is actually on knowledge information extracted from annotation databases, such as protein–protein interactions and Gene Ontology (GO) functional domains annotation which has been recently a widely adopted and essential information for learning systems. To deal with such problem, in the present study, we considered SCL prediction task as a multi-label learning problem and tried to label both single site and multiple sites unannotated bacterial protein sequences by mining proteins homology relationships using both GO terms of protein homologs and PSI-BLAST profiles. The experiments using 5-fold cross-validation tests on the benchmark datasets showed a significant improvement on the results obtained by the proposed consensus multi-label prediction model which discriminates six compartments for Gram-negative and five compartments for Gram-positive bacterial proteins.

Challenges in returning results in a genomic medicine implementation study: the Return of Actionable Variants Empirical (RAVE) study

To inform the process of returning results in genome sequencing studies, we conducted a quantitative and qualitative assessment of challenges encountered during the Return of Actionable Variants Empiric (RAVE) study conducted at Mayo Clinic. Participants (n = 2535, mean age 63 ± 7, 57% female) were sequenced for 68 clinically actionable genes and 14 single nucleotide variants. Of 122 actionable results detected, 118 were returnable; results were returned by a genetic counselor—86 in-person and 12 by phone. Challenges in returning actionable results were encountered in a significant proportion (38%) of the cohort and were related to sequencing and participant contact. Sequencing related challenges (n = 14), affecting 13 participants, included reports revised based on clinical presentation (n = 3); reports requiring corrections (n = 2); mosaicism requiring alternative DNA samples for confirmation (n = 3); and variant re-interpretation due to updated informatics pipelines (n = 6). Participant contact related challenges (n = 44), affecting 38 participants, included nonresponders (n = 20), decedents (n = 1), and previously known results (n = 23). These results should be helpful to investigators preparing for return of results in large-scale genomic sequencing projects.

Designing a Mini-Core Collection Effectively Representing 3004 Diverse Rice Accessions

Genetic diversity provides the foundation for plant breeding and genetic research. Over 3000 rice genomes were recently sequenced as part of the 3K Rice Genome (3KRG) Project. We added four additional Indian rice accessions to create a panel of 3004 accessions. However, such a large collection of germplasm is difficult to preserve and evaluate. The construction of core and mini-core collections is an efficient method for the management of genetic resources. In this study, we developed a mini-core comprising 520 accessions that captured most of the SNPs and represented all of the phenotypes and geographic regions from the original panel. The mini-core was validated using different statistical analyses and contained representatives from all major rice groups, including japonica, indica, aus/boro, and aromatic/basmati. Genome-wide association analyses of the mini-core panel efficiently reproduced the marker–trait associations identified in the original panel. Haplotype analysis validated the utility of the mini-core panel. In the current era with many ongoing large-scale sequencing projects, such a strategy for mini-core design should be useful in many crops. The rice mini-core collection developed in this study would be valuable for agronomic trait evaluation and useful for rice improvement via marker-assisted molecular breeding.

The genetics of intellectual disability: advancing technology and gene editing.

Intellectual disability (ID) is a neurodevelopmental condition affecting 1–3% of the world’s population. Genetic factors play a key role causing the congenital limitations in intellectual functioning and adaptive behavior. The heterogeneity of ID makes it more challenging for genetic and clinical diagnosis, but the advent of large-scale genome sequencing projects in a trio approach has proven very effective. However, many variants are still difficult to interpret. A combined approach of next-generation sequencing and functional, electrophysiological, and bioinformatics analysis has identified new ways to understand the causes of ID and help to interpret novel ID-causing genes. This approach offers new targets for ID therapy and increases the efficiency of ID diagnosis. The most recent functional advancements and new gene editing techniques involving the use of CRISPR–Cas9 allow for targeted editing of DNA in in vitro and more effective mammalian and human tissue-derived disease models. The expansion of genomic analysis of ID patients in diverse and ancient populations can reveal rare novel disease-causing genes.

GeVIR is a continuous gene-level metric that uses variant distribution patterns to prioritize disease candidate genes.

With large-scale population sequencing projects gathering pace, there is a need for strategies that advance disease gene prioritization1,2. Metrics that provide information about a gene and its ability to tolerate protein-altering variation can aid in clinical interpretation of human genomes and can advance disease gene discovery1-4. Previous reported methods analyzed the total variant load in a gene1-4, but did not analyze the distribution pattern of variants within a gene. Using data from 138,632 exome and genome sequences2, we developed gene variation intolerance rank (GeVIR), a continuous gene-level metric for 19,361 genes that is able to prioritize both dominant and recessive Mendelian disease genes5, that outperforms missense constraint metrics3 and that is comparable-but complementary-to loss-of-function (LOF) constraint metrics2. GeVIR is also able to prioritize short genes, for which LOF constraint cannot be estimated with confidence2. The majority of the most intolerant genes identified here have no defined phenotype and are candidates for severe dominant disorders.

Genetic insight into cytochrome P450 in Chinese from the Chinese Millionome Database.

Genetic variations of cytochrome P450 (CYP) influence the inter-individual differences in drug response. Here, we collected 8682 variants of 57 CYP genes and cytochrome P450 oxidoreductase (POR) from a large-scale sequencing project in Chinese, Chinese Millionome Database (CMDB). In addition, 52294 variants from the Genome Aggregation Database (gnomAD) had been simultaneously identified and analysed. Rare variants with a variant allele frequency (VAF)<0.01 comprised 41.4% (3594/8682) of identified variations in the CMDB, while 98.1% (51320/52294) in the gnomAD were rare. Out of 8682 variants in the CMDB, 66.9% (5808/8682) were in introns and only 4.3% (377/8682) were missense variants. In contrast, 36.2% (18929/52294) variants in the gnomAD were missense. The common alleles with a VAF over 0.1 were found in CYP1A2*1C, CYP1A2*1F, CYP2C19*2, CYP2D6*2, CYP2D6*10, CYP3A5*3 and CYP4F2*3, with a VAF of 0.161, 0.6, 0.27, 0.274, 0.678, 0.92 and 0.233, respectively. The growing number of genetic variations in CYP genes as more genomes are sequenced would increase the power to predict drug metabolism and response based on the genotype of the particular individual.

Yeasts and how they came to be.

This month’s Genome Watch highlights a large-scale sequencing project that enriches our understanding of yeast evolution and diversity.

Clinical utility of genomic sequencing.

Identifying pathogenic variation underlying pediatric developmental disease is critical for medical management, therapeutic development, and family planning. This review summarizes current genetic testing options along with their potential benefits and limitations. We also describe results from large-scale genomic sequencing projects in pediatric and neonatal populations with a focus on clinical utility. Recent advances in DNA sequencing technology have made genomic sequencing a feasible and effective testing option in a variety of clinical settings. These cutting-edge tests offer much promise to both medical providers and patients as it has been demonstrated to detect causal genetic variation in ∼25% or more of previously unresolved cases. Efforts aimed at promoting data sharing across clinical genetics laboratories and systematic reanalysis of existing genomic sequencing data have further improved diagnostic rates and reduced the number of unsolved cases. Genomic sequencing is a powerful and increasingly cost-effective alternative to current genetic tests and will continue to grow in clinical utility as more of the genome is understood and as analytical methods are improved. The evolution of genomic sequencing is changing the landscape of clinical testing and requires medical professionals who are adept at understanding and returning genomic results to patients.

Protein function prediction from dynamic protein interaction network using gene expression data.

Computational prediction of functional annotation of proteins is an uphill task. There is an ever increasing gap between functional characterization of protein sequences and deluge of protein sequences generated by large-scale sequencing projects. The dynamic nature of protein interactions is frequently observed which is mostly influenced by any new change of state or change in stimuli. Functional characterization of proteins can be inferred from their interactions with each other, which is dynamic in nature. In this work, we have used a dynamic protein-protein interaction network (PPIN), time course gene expression data and protein sequence information for prediction of functional annotation of proteins. During progression of a particular function, it has also been observed that not all the proteins are active at all time points. For unannotated active proteins, our proposed methodology explores the dynamic PPIN consisting of level-1 and level-2 neighboring proteins at different time points, filtered by Damerau-Levenshtein edit distance to estimate the similarity between two protein sequences and coefficient variation methods to assess the strength of an edge in a network. Finally, from the filtered dynamic PPIN, at each time point, functional annotations of the level-2 proteins are assigned to the unknown and unannotated active proteins through the level-1 neighbor, following a bottom-up strategy. Our proposed methodology achieves an average precision, recall and F-Score of 0.59, 0.76 and 0.61 respectively, which is significantly higher than the reported state-of-the-art methods.

Defective Tmprss3-Associated Hair Cell Degeneration in Inner Ear Organoids.

SummaryMutations in the gene encoding the type II transmembrane protease 3 (TMPRSS3) cause human hearing loss, although the underlying mechanisms that result in TMPRSS3-related hearing loss are still unclear. We combined the use of stem cell-derived inner ear organoids with single-cell RNA sequencing to investigate the role of TMPRSS3. Defective Tmprss3 leads to hair cell apoptosis without altering the development of hair cells and the formation of the mechanotransduction apparatus. Prior to degeneration, Tmprss3-KO hair cells demonstrate reduced numbers of BK channels and lower expressions of genes encoding calcium ion-binding proteins, suggesting a disruption in intracellular homeostasis. A proteolytically active TMPRSS3 was detected on cell membranes in addition to ER of cells in inner ear organoids. Our in vitro model recapitulated salient features of genetically associated inner ear abnormalities and will serve as a powerful tool for studying inner ear disorders.

LtrDetector: A tool-suite for detecting long terminal repeat retrotransposons de-novo

BackgroundLong terminal repeat retrotransposons are the most abundant transposons in plants. They play important roles in alternative splicing, recombination, gene regulation, and defense mechanisms. Large-scale sequencing projects for plant genomes are currently underway. Software tools are important for annotating long terminal repeat retrotransposons in these newly available genomes. However, the available tools are not very sensitive to known elements and perform inconsistently on different genomes. Some are hard to install or obsolete. They may struggle to process large plant genomes. None can be executed in parallel out of the box and very few have features to support visual review of new elements. To overcome these limitations, we developed LtrDetector, which uses techniques inspired by signal-processing.ResultsWe compared LtrDetector to LTR_Finder and LTRharvest, the two most successful predecessor tools, on six plant genomes. For each organism, we constructed a ground truth data set based on queries from a consensus sequence database. According to this evaluation, LtrDetector was the most sensitive tool, achieving 16–23% improvement in sensitivity over LTRharvest and 21% improvement over LTR_Finder. All three tools had low false positive rates, with LtrDetector achieving 98.2% precision, in between its two competitors. Overall, LtrDetector provides the best compromise between high sensitivity and low false positive rate while requiring moderate time and utilizing memory available on personal computers.ConclusionsLtrDetector uses a novel methodology revolving around k-mer distributions, which allows it to produce high-quality results using relatively lightweight procedures. It is easy to install and use. It is not species specific, performing well using its default parameters on genomes of varying size and repeat content. It is automatically configured for parallel execution and runs efficiently on an ordinary personal computer. It includes a k-mer scores visualization tool to facilitate manual review of the identified elements. These features make LtrDetector an attractive tool for future annotation projects involving long terminal repeat retrotransposons.

FunSPU: A versatile and adaptive multiple functional annotation-based association test of whole-genome sequencing data.

Despite ongoing large-scale population-based whole-genome sequencing (WGS) projects such as the NIH NHLBI TOPMed program and the NHGRI Genome Sequencing Program, WGS-based association analysis of complex traits remains a tremendous challenge due to the large number of rare variants, many of which are non-trait-associated neutral variants. External biological knowledge, such as functional annotations based on the ENCODE, Epigenomics Roadmap and GTEx projects, may be helpful in distinguishing causal rare variants from neutral ones; however, each functional annotation can only provide certain aspects of the biological functions. Our knowledge for selecting informative annotations a priori is limited, and incorporating non-informative annotations will introduce noise and lose power. We propose FunSPU, a versatile and adaptive test that incorporates multiple biological annotations and is adaptive at both the annotation and variant levels and thus maintains high power even in the presence of noninformative annotations. In addition to extensive simulations, we illustrate our proposed test using the TWINSUK cohort (n = 1,752) of UK10K WGS data based on six functional annotations: CADD, RegulomeDB, FunSeq, Funseq2, GERP++, and GenoSkyline. We identified genome-wide significant genetic loci on chromosome 19 near gene TOMM40 and APOC4-APOC2 associated with low-density lipoprotein (LDL), which are replicated in the UK10K ALSPAC cohort (n = 1,497). These replicated LDL-associated loci were missed by existing rare variant association tests that either ignore external biological information or rely on a single source of biological knowledge. We have implemented the proposed test in an R package “FunSPU”.

Bridging computer science and bioengineering for the multiscale modeling of biological systems

Computer science and biology have come together significantly in recent years. This trend dates back to the 1960s, with the publication of the first protein sequences, the subsequent application of information theory the first alignment algorithms in the 1970s and 1980s that culminated with its improvement and implementation in the 1990s with software such as BLAST, which became one of the most cited articles in the history of science. Already in this millennium there has been a rapid growth of large-scale sequencing projects (e.g. the Human Genome Project), as well as the widespread expansion of omics databases, e.g. with genomic, transcriptomic and metabolic information. This explosion is transforming medicine and the life sciences, whose progress is increasingly associated with a strong integration with Computer Science and Engineering, which are likely to represent, in the 21st century, a similar role to that of Mathematics in Physics of the 20th century. This talk will overview some of the progress at the interface highlighting some of the associated challenges.