A global initiative on addressing bioinformatics' grand challenges.

The Bioinformatics Grand Challenges Consortium (BGCC) is a collaborative effort to address the most pressing challenges in bioinformatics. Initially focusing on education and training, the consortium successfully defined seven key grand challenges and is actively developing actionable solutions for these challenges. Building on this foundation, the BGCC plans to broaden its focus to include additional grand challenges in emerging areas.

The Singapore National Precision Medicine Strategy.

Precision medicine promises to transform healthcare for groups and individuals through early disease detection, refining diagnoses and tailoring treatments. Analysis of large-scale genomic-phenotypic databases is a critical enabler of precision medicine. Although Asia is home to 60% of the world's population, many Asian ancestries are under-represented in existing databases, leading to missed opportunities for new discoveries, particularly for diseases most relevant for these populations. The Singapore National Precision Medicine initiative is a whole-of-government 10-year initiative aiming to generate precision medicine data of up to one million individuals, integrating genomic, lifestyle, health, social and environmental data. Beyond technologies, routine adoption of precision medicine in clinical practice requires social, ethical, legal and regulatory barriers to be addressed. Identifying driver use cases in which precision medicine results in standardized changes to clinical workflows or improvements in population health, coupled with health economic analysis to demonstrate value-based healthcare, is a vital prerequisite for responsible health system adoption.

A multi-task CNN learning model for taxonomic assignment of human viruses.

BACKGROUND: Taxonomic assignment is a key step in the identification of human viral pathogens. Current tools for taxonomic assignment from sequencing reads based on alignment or alignment-free k-mer approaches may not perform optimally in cases where the sequences diverge significantly from the reference sequences. Furthermore, many tools may not incorporate the genomic coverage of assigned reads as part of overall likelihood of a correct taxonomic assignment for a sample. RESULTS: In this paper, we describe the development of a pipeline that incorporates a multi-task learning model based on convolutional neural network (MT-CNN) and a Bayesian ranking approach to identify and rank the most likely human virus from sequence reads. For taxonomic assignment of reads, the MT-CNN model outperformed Kraken 2, Centrifuge, and Bowtie 2 on reads generated from simulated divergent HIV-1 genomes and was more sensitive in identifying SARS as the closest relation in four RNA sequencing datasets for SARS-CoV-2 virus. For genomic region assignment of assigned reads, the MT-CNN model performed competitively compared with Bowtie 2 and the region assignments were used for estimation of genomic coverage that was incorporated into a naïve Bayesian network together with the proportion of taxonomic assignments to rank the likelihood of candidate human viruses from sequence data. CONCLUSIONS: We have developed a pipeline that combines a novel MT-CNN model that is able to identify viruses with divergent sequences together with assignment of the genomic region, with a Bayesian approach to ranking of taxonomic assignments by taking into account both the number of assigned reads and genomic coverage. The pipeline is available at GitHub via https://github.com/MaHaoran627/CNN_Virus .

Towards precision medicine: interrogating the human genome to identify drug pathways associated with potentially functional, population-differentiated polymorphisms.

Drug response variations amongst different individuals/populations are influenced by several factors including allele frequency differences of single nucleotide polymorphisms (SNPs) that functionally affect drug-response genes. Here, we aim to identify drugs that potentially exhibit population differences in response using SNP data mining and analytics. Ninety-one pairwise-comparisons of >22,000,000 SNPs from the 1000 Genomes Project, across 14 different populations, were performed to identify 'population-differentiated' SNPs (pdSNPs). Potentially-functional pdSNPs (pf-pdSNPs) were then selected, mapped into genes, and integrated with drug-gene databases to identify 'population-differentiated' drugs enriched with genes carrying pf-pdSNPs. 1191 clinically-approved drugs were found to be significantly enriched (Z > 2.58) with genes carrying SNPs that were differentiated in one or more population-pair comparisons. Thirteen drugs were found to be enriched with such differentiated genes across all 91 population-pairs. Notably, 82% of drugs, which were previously reported in the literature to exhibit population differences in response were also found by this method to contain a significant enrichment of population specific differentiated SNPs. Furthermore, drugs with genetic testing labels, or those suspected to cause adverse reactions, contained a significantly larger number (P < 0.01) of population-pairs with enriched pf-pdSNPs compared with those without these labels. This pioneering effort at harnessing big-data pharmacogenomics to identify 'population differentiated' drugs could help to facilitate data-driven decision-making for a more personalized medicine.

Architecture of population-differentiated polymorphisms in the human genome.

Population variation in disease and other phenotype are partly attributed to single nucleotide polymorphisms (SNPs) in the human genome. Due to selection pressure, two individuals from the same ancestral population have more genetic similarity compared to individuals from further geographic regions. Here, we elucidated the genomic population differentiation pattern, by interrogating >22,000,000 SNPs. Majority of population-differentiated (pd) SNPs (~95%), including the potentially functional (pf) (~84%) subset reside in non-genic regions, compared to the proportion of all SNPs (58%) found in non-genic regions. This suggests that differences between populations are more likely due to differences in gene regulation rather than protein function. Actin Cytoskeleton, Axonal Guidance and Protein Kinase A signaling pathways are enriched with genes carrying at least three pdSNPs (enriched pdGenes), while Antigen Presentation, Hepatic Fibrosis and Huntington Disease Signalling pathways are over-represented by enriched pf-pdGenes. An inverse correlation between chromosome size and the proportion of pd-/pf-pdSNPs was observed. Smaller chromosomes have relatively more of such SNPs including genes carrying these SNPs. Genes associated with common diseases and enriched with these pd-/pfpdSNPs are localized to 11 different chromosomes, with immune-related disease pd/pf-pdGenes mainly residing in chromosome 6 while neurological disease pd/pf-pdGenes residing in smaller chromosomes including chromosome 21/22. The associated diseases were reported to show population differences in incidence, severity and/or etiology. In summary, this study highlights the non-sporadic nature of population differentiation footprint in the human genome, which can potentially lead to the identification of genomic regions that play roles in the manifestation of phenotypic differences, including in disease predisposition and drug response.

CEBPA mutational analysis in acute myeloid leukaemia by a laboratory-developed next-generation sequencing assay.

AIM: The presence of biallelic CEBPA mutations is a favourable prognostic feature in acute myeloid leukaemia (AML). CEBPA mutations are currently identified through conventional capillary sequencing (CCS). With the increasing adoption of next-generation sequencing (NGS) platforms, challenges with regard to amplification efficiency of CEBPA due to the high GC content may be encountered, potentially resulting in suboptimal coverage. Here, the performance of an amplicon-based NGS method using a laboratory-developed CEBPA-specific Nextera XT (CEBNX) was evaluated. METHODS: Mutational analyses of the CEBPA gene of 137 AML bone marrow or peripheral blood retrospective specimens were performed by the amplification of the CEBPA gene using the Expand Long Range dNTPack and the amplicons processed by CCS and NGS. CEBPA-specific libraries were then constructed using the Nextera XT V.2 kit. All FASTQ files were then processed with the MiSeq Reporter V.2.6.2.3 using the PCR Amplicon workflow via the customised CEBPA-specific manifest file. The variant calling format files were analysed using the Illumina Variant Studio V.2.2. RESULTS: A coverage per base of 3631X to 28184X was achieved. 22 samples (16.1%) were found to contain CEBPA mutations, with variant allele frequencies (VAF) ranging from 3.8% to 58.2%. Taking CCS as the 'gold standard', sensitivity and specificity of 97% and 97% was achieved. For the transactivation domain 2 polymorphism (c.584_589dupACCCGC/p.His195_Pro196dup), the CEBNX achieved 100% sensitivity and 100% specificity relative to CCS. CONCLUSIONS: Our laboratory-developed CEBNX workflow shows high coverage and thus overcomes the challenges associated with amplification efficiency and low coverage of CEBPA. Therefore, our assay is suitable for deployment in the clinical laboratory.

Predicting Zoonotic Risk of Influenza A Viruses from Host Tropism Protein Signature Using Random Forest.

Influenza A viruses remain a significant health problem, especially when a novel subtype emerges from the avian population to cause severe outbreaks in humans. Zoonotic viruses arise from the animal population as a result of mutations and reassortments, giving rise to novel strains with the capability to evade the host species barrier and cause human infections. Despite progress in understanding interspecies transmission of influenza viruses, we are no closer to predicting zoonotic strains that can lead to an outbreak. We have previously discovered distinct host tropism protein signatures of avian, human and zoonotic influenza strains obtained from host tropism predictions on individual protein sequences. Here, we apply machine learning approaches on the signatures to build a computational model capable of predicting zoonotic strains. The zoonotic strain prediction model can classify avian, human or zoonotic strains with high accuracy, as well as providing an estimated zoonotic risk. This would therefore allow us to quickly determine if an influenza virus strain has the potential to be zoonotic using only protein sequences. The swift identification of potential zoonotic strains in the animal population using the zoonotic strain prediction model could provide us with an early indication of an imminent influenza outbreak.

Single-cell genomic profiling of acute myeloid leukemia for clinical use: A pilot study.

Although bulk high-throughput genomic profiling studies have led to a significant increase in the understanding of cancer biology, there is increasing awareness that bulk profiling approaches do not completely elucidate tumor heterogeneity. Single-cell genomic profiling enables the distinction of tumor heterogeneity, and may improve clinical diagnosis through the identification and characterization of putative subclonal populations. In the present study, the challenges associated with a single-cell genomics profiling workflow for clinical diagnostics were investigated. Single-cell RNA-sequencing (RNA-seq) was performed on 20 cells from an acute myeloid leukemia bone marrow sample. Putative blasts were identified based on their gene expression profiles and principal component analysis was performed to identify outlier cells. Variant calling was performed on the single-cell RNA-seq data. The present pilot study demonstrates a proof of concept for clinical single-cell genomic profiling. The recognized limitations include significant stochastic RNA loss and the relatively low throughput of the current proposed platform. Although the results of the present study are promising, further technological advances and protocol optimization are necessary for single-cell genomic profiling to be clinically viable.

Exploring the transcriptome of non-model oleaginous microalga Dunaliella tertiolecta through high-throughput sequencing and high performance computing.

BACKGROUND: RNA-Seq technology has received a lot of attention in recent years for microalgal global transcriptomic profiling. It is widely used in transcriptome-wide analysis of gene expression., particularly for microalgal strains with potential as biofuel sources. However, insufficient genomic or transcriptomic information of non-model microalgae has limited the understanding of their regulatory mechanisms and hampered genetic manipulation to enhance biofuel production. As such, an optimal microalgal transcriptomic database construction is a subject of urgent investigation. RESULTS: Dunaliella tertiolecta, a non-model oleaginous microalgal species, was sequenced via Illumina MISEQ and HISEQ 4000 in RNA-Seq studies. The high quality high-throughout sequencing data were explored using high performance computing (HPC) in a petascale data center and subjected to de novo assembly and parallelized mpiBLASTX search with multiple species. As a result, a transcriptome database of 17,845 was constructed (~95% completeness). This enlarged database constructed fueled the RNA-Seq data analysis, which was validated by a nitrogen deprivation (ND) study that induces triacylglycerol (TAG) production. CONCLUSIONS: The new paralleled assembly and annotation method under HPC presented here allows the solution of large-scale data processing problems in acceptable computation time. There is significant increase in the number of transcriptomic data achieved and observable heterogeneity in the performance to identify differentially expressed genes in the ND treatment paradigm. The results provide new insights as to how response to ND treatment in microalgae is regulated. ND analyses highlight the advantages of this database generated in this study that could also serve as a useful resource for future gene manipulation and transcriptome-wide analysis. We thus demonstrate the usefulness of exploring the transcriptome as an informative platform for functional studies and genetic manipulations in similar species.

Development of a clinical decision support system for diabetes care: A pilot study.

Management of complex chronic diseases such as diabetes requires the assimilation and interpretation of multiple laboratory test results. Traditional electronic health records tend to display laboratory results in a piecemeal and segregated fashion. This makes the assembly and interpretation of results related to diabetes care challenging. We developed a diabetes-specific clinical decision support system (Diabetes Dashboard) interface for displaying glycemic, lipid and renal function results, in an integrated form with decision support capabilities, based on local clinical practice guidelines. The clinical decision support system included a dashboard feature that graphically summarized all relevant laboratory results and displayed them in a color-coded system that allowed quick interpretation of the metabolic control of the patients. An alert module informs the user of tests that are due for repeat testing. An interactive graph module was also developed for better visual appreciation of the trends of the laboratory results of the patient. In a pilot study involving case scenarios administered via an electronic questionnaire, the Diabetes Dashboard, compared to the existing laboratory reporting interface, significantly improved the identification of abnormal laboratory results, of the long-term trend of the laboratory tests and of tests due for repeat testing. However, the Diabetes Dashboard did not significantly improve the identification of patients requiring treatment adjustment or the amount of time spent on each case scenario. In conclusion, we have developed and shown that the use of the Diabetes Dashboard, which incorporates several decision support features, can improve the management of diabetes. It is anticipated that this dashboard will be most helpful when deployed in an outpatient setting, where physicians can quickly make clinical decisions based on summarized information and be alerted to pertinent areas of care that require additional attention.

Analysis of viral diversity for vaccine target discovery.

BACKGROUND: Viral vaccine target discovery requires understanding the diversity of both the virus and the human immune system. The readily available and rapidly growing pool of viral sequence data in the public domain enable the identification and characterization of immune targets relevant to adaptive immunity. A systematic bioinformatics approach is necessary to facilitate the analysis of such large datasets for selection of potential candidate vaccine targets. RESULTS: This work describes a computational methodology to achieve this analysis, with data of dengue, West Nile, hepatitis A, HIV-1, and influenza A viruses as examples. Our methodology has been implemented as an analytical pipeline that brings significant advancement to the field of reverse vaccinology, enabling systematic screening of known sequence data in nature for identification of vaccine targets. This includes key steps (i) comprehensive and extensive collection of sequence data of viral proteomes (the virome), (ii) data cleaning, (iii) large-scale sequence alignments, (iv) peptide entropy analysis, (v) intra- and inter-species variation analysis of conserved sequences, including human homology analysis, and (vi) functional and immunological relevance analysis. CONCLUSION: These steps are combined into the pipeline ensuring that a more refined process, as compared to a simple evolutionary conservation analysis, will facilitate a better selection of vaccine targets and their prioritization for subsequent experimental validation.

Elevated acetyl-CoA by amino acid recycling fuels microalgal neutral lipid accumulation in exponential growth phase for biofuel production.

Microalgal neutral lipids [mainly in the form of triacylglycerols (TAGs)], feasible substrates for biofuel, are typically accumulated during the stationary growth phase. To make microalgal biofuels economically competitive with fossil fuels, generating strains that trigger TAG accumulation from the exponential growth phase is a promising biological approach. The regulatory mechanisms to trigger TAG accumulation from the exponential growth phase (TAEP) are important to be uncovered for advancing economic feasibility. Through the inhibition of pyruvate dehydrogenase kinase by sodium dichloroacetate, acetyl-CoA level increased, resulting in TAEP in microalga Dunaliella tertiolecta. We further reported refilling of acetyl-CoA pool through branched-chain amino acid catabolism contributed to an overall sixfold TAEP with marginal compromise (4%) on growth in a TAG-rich D. tertiolecta mutant from targeted screening. Herein, a three-step α loop-integrated metabolic model is introduced to shed lights on the neutral lipid regulatory mechanism. This article provides novel approaches to compress lipid production phase and heightens lipid productivity and photosynthetic carbon capture via enhancing acetyl-CoA level, which would optimize renewable microalgal biofuel to fulfil the demanding fuel market.

T-Cell Epitope Prediction of Chikungunya Virus.

There has been a growing demand for vaccines against Chikungunya virus (CHIKV), and epitope-based vaccine is a promising solution. Identification of CHIKV T-cell epitopes is critical to ensure successful trigger of immune response for epitope-based vaccine design. Bioinformatics tools are able to significantly reduce time and effort in this process by systematically scanning for immunogenic peptides in CHIKV proteins. This chapter provides the steps in utilizing machine learning algorithms to train on major histocompatibility complex (MHC) class I peptide binding data and build prediction models for the classification of binders and non-binders. The models could then be used in the identification and prediction of CHIKV T-cell epitopes for future vaccine design.

Distinct Host Tropism Protein Signatures to Identify Possible Zoonotic Influenza A Viruses.

Zoonotic influenza A viruses constantly pose a health threat to humans as novel strains occasionally emerge from the avian population to cause human infections. Many past epidemic as well as pandemic strains have originated from avian species. While most viruses are restricted to their primary hosts, zoonotic strains can sometimes arise from mutations or reassortment, leading them to acquire the capability to escape host species barrier and successfully infect a new host. Phylogenetic analyses and genetic markers are useful in tracing the origins of zoonotic infections, but there are still no effective means to identify high risk strains prior to an outbreak. Here we show that distinct host tropism protein signatures can be used to identify possible zoonotic strains in avian species which have the potential to cause human infections. We have discovered that influenza A viruses can now be classified into avian, human, or zoonotic strains based on their host tropism protein signatures. Analysis of all influenza A viruses with complete proteome using the host tropism prediction system, based on machine learning classifications of avian and human viral proteins has uncovered distinct signatures of zoonotic strains as mosaics of avian and human viral proteins. This is in contrast with typical avian or human strains where they show mostly avian or human viral proteins in their signatures respectively. Moreover, we have found that zoonotic strains from the same influenza outbreaks carry similar host tropism protein signatures characteristic of a common ancestry. Our results demonstrate that the distinct host tropism protein signature in zoonotic strains may prove useful in influenza surveillance to rapidly identify potential high risk strains circulating in avian species, which may grant us the foresight in anticipating an impending influenza outbreak.

Coverage analysis in a targeted amplicon-based next-generation sequencing panel for myeloid neoplasms.

AIMS: PCR amplicon-based next-generation sequencing (NGS) panels are increasingly used for clinical diagnostic assays. Amplification bias is a well-known limitation of PCR amplicon-based approaches. We sought to characterise lower-performance amplicons in an off-the-shelf NGS panel (TruSight Myeloid Sequencing Panel) for myeloid neoplasms and attempted to patch the low read depth for one of the affected genes, CEBPA. METHODS: We performed targeted NGS of 158 acute myeloid leukaemia samples and analysed the amplicon read depths across 568 amplicons to identify lower-performance amplicons. We also correlated the amplicon read depths with the template GC content. Finally, we attempted to patch the low read depth for CEBPA using a parallel library preparation (Nextera XT) workflow. RESULTS: We identified 16 lower-performance amplicons affecting nine genes, including CEBPA. There was a slight negative correlation between the amplicon read depths and template GC content. Addition of the separate CEBPA library generated a minimum read depth per base across the CEBPA gene ranging from 268x to 758x across eight samples. CONCLUSIONS: The identification of lower-performance amplicons will be informative to laboratories intending to use this panel. We have also demonstrated proof-of-concept that different libraries (TruSight Myeloid and Nextera XT) can be combined and sequenced on the same flow cell to generate additional reads for CEBPA.

GIW and InCoB, two premier bioinformatics conferences in Asia with a combined 40 years of history.

Knowledge discovery in bioinformatics thrives on joint and inclusive efforts of stakeholders. Similarly, knowledge dissemination is expected to be more effective and scalable through joint efforts. Therefore, the International Conference on Bioinformatics (InCoB) and the International Conference on Genome Informatics (GIW) were organized as a joint conference for the first time in 13 years of coexistence. The Asia-Pacific Bioinformatics Network (APBioNet) and the Japanese Society for Bioinformatics (JSBi) collaborated to host GIW/InCoB2015 in Tokyo, September 9-11, 2015. The joint endeavour yielded 51 research articles published in seven journals, 78 poster and 89 oral presentations, showcasing bioinformatics research in the Asia-Pacific region. Encouraged by the results and reduced organizational overheads, APBioNet will collaborate with other bioinformatics societies in organizing co-located bioinformatics research and training meetings in the future. InCoB2016 will be hosted in Singapore, September 21-23, 2016.

RNA-Seq transcriptomic analysis with Bag2D software identifies key pathways enhancing lipid yield in a high lipid-producing mutant of the non-model green alga Dunaliella tertiolecta.

BACKGROUND: For many years, increasing demands for fossil fuels have met with limited supply. As a potential substitute and renewable source of biofuel feedstock, microalgae have received significant attention. However, few of the current algal species produce high lipid yields to be commercially viable. To discover more high yielding strains, next-generation sequencing technology is used to elucidate lipid synthetic pathways and energy metabolism involved in lipid yield. When subjected to manipulation by genetic and metabolic engineering, enhancement of such pathways may further enhance lipid yield. RESULTS: In this study, transcriptome profiling of a random insertional mutant with enhanced lipid production generated from a non-model marine microalga Dunaliella tertiolecta is presented. D9 mutant has a lipid yield that is 2- to 4-fold higher than that of wild type. Using novel Bag2D-workflow scripts developed and reported here, the non-redundant transcripts from de novo assembly were annotated based on the best hits in five model microalgae, namely Chlamydomonas reinhardtii, Coccomyxa subellipsoidea C-169, Ostreococcus lucimarinus, Volvox carteri, Chlorella variabilis NC64A and a high plant species Arabidopsis thaliana. The assembled contigs (~181 Mb) includes 481,381 contigs, covering 10,185 genes. Pathway analysis showed that a pathway from inositol phosphate metabolism to fatty acid biosynthesis is the most significantly correlated with higher lipid yield in this mutant. CONCLUSIONS: Herein, we described a pipeline to analyze RNA-Seq data without pre-existing transcriptomic information. The draft transcriptome of D. tertiolecta was constructed and annotated, which offered useful information for characterizing high lipid-producing mutants. D. tertiolecta mutant was generated with an enhanced photosynthetic efficiency and lipid production. RNA-Seq data of the mutant and wild type were compared, providing biological insights into the expression patterns of contigs associated with energy metabolism and carbon flow pathways. Comparison of D. tertiolecta genes with homologs of five other green algae and a model high plant species can facilitate the annotation of D. tertiolecta and lead to a more complete annotation of its sequence database, thus laying the groundwork for optimization of lipid production pathways based on genetic manipulation.

Heat shock protein 27 overexpression in CHO cells modulates apoptosis pathways and delays activation of caspases to improve recombinant monoclonal antibody titre in fed-batch bioreactors.

CHO cells are major production hosts for recombinant biologics including the rapidly expanding recombinant monoclonal antibodies (mAbs). Heat shock protein 27 (HSP27) expression was observed to be down-regulated towards the late-exponential and stationary phase of CHO fed-batch bioreactor cultures, whereas HSP27 was found to be highly expressed in human pathological cells and reported to have anti-apoptotic functions. These phenotypes suggest that overexpression of HSP27 is a potential cell line engineering strategy for improving robustness of CHO cells. In this work, HSP27 was stably overexpressed in CHO cells producing recombinant mAb and the effects of HSP27 on cell growth, volumetric production titer and product quality were assessed. Concomitantly, HSP27 anti-apoptosis functions in CHO cells were investigated. Stably transfected clones cultured in fed-batch bioreactors displayed 2.2-fold higher peak viable cell density, delayed loss of culture viability by two days and 2.3-fold increase in mAb titer without affecting the N-glycosylation profile, as compared to clones stably transfected with the vector backbone. Co-immunoprecipitation studies revealed HSP27 interactions with Akt, pro-caspase 3 and Daxx and caspase activity profiling showed delayed increase in caspase 2, 3, 8 and 9 activities. These results suggest that HSP27 modulates apoptosis signaling pathways and delays caspase activities to improve performance of CHO fed-batch bioreactor cultures.

Building MHC class II epitope predictor using machine learning approaches.

Identification of T-cell epitopes binding to MHC class II molecules is an important step in epitope-based vaccine development. This process has since been accelerated with the use of bioinformatics tools to aid in the prediction of peptide binding to MHC class II molecules and also to systematically scan for candidate peptides in antigenic proteins. There have been many prediction software developed over the years using various methods and algorithms and they are becoming increasingly sophisticated. Here, we illustrate the use of machine learning algorithms to train on MHC class II peptide data represented by feature vectors describing their amino acid physicochemical properties. The developed prediction model can then be used to predict new peptide data.