MGUPGMA: A Fast UPGMA Algorithm With Multiple Graphics Processing Units Using NCCL

A phylogenetic tree is a visual diagram of the relationship between a set of biological species. The scientists usually use it to analyze many characteristics of the species. The distance-matrix methods, such as Unweighted Pair Group Method with Arithmetic Mean and Neighbor Joining, construct a phylogenetic tree by calculating pairwise genetic distances between taxa. These methods have the computational performance issue. Although several new methods with high-performance hardware and frameworks have been proposed, the issue still exists. In this work, a novel parallel Unweighted Pair Group Method with Arithmetic Mean approach on multiple Graphics Processing Units is proposed to construct a phylogenetic tree from extremely large set of sequences. The experimental results present that the proposed approach on a DGX-1 server with 8 NVIDIA P100 graphic cards achieves approximately 3-fold to 7-fold speedup over the implementation of Unweighted Pair Group Method with Arithmetic Mean on a modern CPU and a single GPU, respectively.

Pocket detection and interaction-weighted ligand-similarity search yields novel high-affinity binders for Myocilin-OLF, a protein implicated in glaucoma

Traditional structure and ligand based virtual screening approaches rely on the availability of structural and ligand binding information. To overcome this limitation, hybrid approaches were developed that relied on extraction of ligand binding information from proteins sharing similar folds and hence, evolutionarily relationship. However, they cannot target a chosen pocket in a protein. To address this, a pocket centric virtual ligand screening approach is required. Here, we employ a new, iterative implementation of a pocket and ligand-similarity based approach to virtual ligand screening to predict small molecule binders for the olfactomedin domain of human myocilin implicated in glaucoma. Small-molecule binders of the protein might prevent the aggregation of the protein, commonly seen during glaucoma. First round experimental assessment of the predictions using differential scanning fluorimetry with myoc-OLF yielded 7 hits with a success rate of 12.7%; the best hit had an apparent dissociation constant of 99nM. By matching to the key functional groups of the best ligand that were likely involved in binding, the affinity of the best hit was improved by almost 10,000 fold from the high nanomolar to the low picomolar range. Thus, this study provides preliminary validation of the methodology on a medically important glaucoma associated protein.

Embedded-Based Graphics Processing Unit Cluster Platform for Multiple Sequence Alignments

High-end graphics processing units (GPUs), such as NVIDIA Tesla/Fermi/Kepler series cards with thousands of cores per chip, are widely applied to high-performance computing fields in a decade. These desktop GPU cards should be installed in personal computers/servers with desktop CPUs, and the cost and power consumption of constructing a GPU cluster platform are very high. In recent years, NVIDIA releases an embedded board, called Jetson Tegra K1 (TK1), which contains 4 ARM Cortex-A15 CPUs and 192 Compute Unified Device Architecture cores (belong to Kepler GPUs). Jetson Tegra K1 has several advantages, such as the low cost, low power consumption, and high applicability, and it has been applied into several specific applications. In our previous work, a bioinformatics platform with a single TK1 (STK platform) was constructed, and this previous work is also used to prove that the Web and mobile services can be implemented in the STK platform with a good cost-performance ratio by comparing a STK platform with the desktop CPU and GPU. In this work, an embedded-based GPU cluster platform will be constructed with multiple TK1s (MTK platform). Complex system installation and setup are necessary procedures at first. Then, 2 job assignment modes are designed for the MTK platform to provide services for users. Finally, ClustalW v2.0.11 and ClustalWtk will be ported to the MTK platform. The experimental results showed that the speedup ratios achieved 5.5 and 4.8 times for ClustalW v2.0.11 and ClustalWtk, respectively, by comparing 6 TK1s with a single TK1. The MTK platform is proven to be useful for multiple sequence alignments.

Computing Platforms for Big Biological Data Analytics: Perspectives and Challenges

The last decade has witnessed an explosion in the amount of available biological sequence data, due to the rapid progress of high-throughput sequencing projects. However, the biological data amount is becoming so great that traditional data analysis platforms and methods can no longer meet the need to rapidly perform data analysis tasks in life sciences. As a result, both biologists and computer scientists are facing the challenge of gaining a profound insight into the deepest biological functions from big biological data. This in turn requires massive computational resources. Therefore, high performance computing (HPC) platforms are highly needed as well as efficient and scalable algorithms that can take advantage of these platforms. In this paper, we survey the state-of-the-art HPC platforms for big biological data analytics. We first list the characteristics of big biological data and popular computing platforms. Then we provide a taxonomy of different biological data analysis applications and a survey of the way they have been mapped onto various computing platforms. After that, we present a case study to compare the efficiency of different computing platforms for handling the classical biological sequence alignment problem. At last we discuss the open issues in big biological data analytics.

Can Physicians Affect Patient Adherence With Medication?

Non-compliance with medication therapy remains an unsolved and expensive problem for healthcare systems around the world, yet we know little about the factors that affect a patient's decision to follow treatment recommendations. In particular, there is little evidence on the extent to which doctors can influence patient adherence behavior. This study uses a unique panel dataset comprising all prescription drug users, physicians, and all prescription drug sales in Denmark over 7 years to analyze the contributions of doctor-specific, patient-specific, and drug-specific factors to the adherence decision. We find that physicians exert substantial influence on patient compliance. Further, the quality of the match between a doctor and a patient accounts for a substantial portion of the variation in adherence outcomes. This suggests that the sorting of patients across doctors is an important mechanism that affects patient adherence beyond the effects of individual patient-specific and physician-specific factors. Copyright © 2016 John Wiley & Sons, Ltd.

Disease-related gene module detection based on a multi-label propagation clustering algorithm

Detecting disease-related gene modules by analyzing gene expression data is of great significance. It is helpful for exploratory analysis of the interaction mechanisms of genes under complex disease phenotypes. The multi-label propagation algorithm (MLPA) has been widely used in module detection for its fast and easy implementation. The accuracy of MLPA greatly depends on the connections between nodes, and most existing research focuses on measuring the similarity between nodes. However, MLPA does not perform well with loose connections between disease-related genes. Moreover, the biological significance of modules obtained by MLPA has not been demonstrated. To solve these problems, we designed a double label propagation clustering algorithm (DLPCA) based on MLPA to study Huntington's disease. In DLPCA, in addition to category labels, we introduced pathogenic labels to supervise the process of multi-label propagation clustering. The pathogenic labels contain pathogenic information about disease genes and the hierarchical structure of gene expression data. Experimental results demonstrated the superior performance of DLPCA compared with other conventional gene-clustering algorithms.

FPGASW: Accelerating Large-Scale Smith-Waterman Sequence Alignment Application with Backtracking on FPGA Linear Systolic Array

The Smith-Waterman (SW) algorithm based on dynamic programming is a well-known classical method for high precision sequence matching and has become the gold standard to evaluate sequence alignment software. In this paper, we propose fine-grained parallelized SW algorithms using affine gap penalty and implement a parallel computing structures to accelerating the SW with backtracking on FPGA platform. We analysis the dynamic parallel computing features of anti-diagonal elements and storage expansion problem resulting from backtracking stage, and propose a series of optimization strategies to eliminate data dependency, reduce storage requirements, and overlap memory access latency. Our implementation is capable of supporting multi-type, large-scale biological sequence alignment applications. We obtain a speedup between 3.6 and 25.2 over the typical SW algorithm running on a general-purpose computer configured with an Intel Core i5 3.2 GHz CPU. Moreover, our work is superior to other FPGA implementations in both array size and clock frequency, and the experiment results show that it can get a performance closed to that of the latest GPU implementation, but the power consumption is only about 26% of that of the GPU platforms.

Integrated network analysis reveals potentially novel molecular mechanisms and therapeutic targets of refractory epilepsies

Epilepsy is a complex neurological disorder and a significant health problem. The pathogenesis of epilepsy remains obscure in a significant number of patients and the current treatment options are not adequate in about a third of individuals which were known as refractory epilepsies (RE). Network medicine provides an effective approach for studying the molecular mechanisms underlying complex diseases. Here we integrated 1876 disease-gene associations of RE and located those genes to human protein-protein interaction (PPI) network to obtain 42 significant RE-associated disease modules. The functional analysis of these disease modules showed novel molecular pathological mechanisms of RE, such as the novel enriched pathways (e.g., "presynaptic nicotinic acetylcholine receptors", "signaling by insulin receptor"). Further analysis on the relationships between current drug targets and the RE-related disease genes showed the rational mechanisms of most antiepileptic drugs. In addition, we detected ten potential novel drug targets (e.g., KCNA1, KCNA4-6, KCNC3, KCND2, KCNMA1, CAMK2G, CACNB4 and GRM1) located in three RE related disease modules, which might provide novel insights into the new drug discovery for RE therapy.

Genomic analyses of multidrug resistant Pseudomonas aeruginosa PA1 resequenced by single-molecule real-time sequencing

As a third-generation sequencing (TGS) method, single-molecule real-time (SMRT) technology provides long read length, and it is well suited for resequencing projects and de novo assembly. In the present study, Pseudomonas aeruginosa PA1 was characterized and resequenced using SMRT technology. PA1 was also subjected to genomic, comparative and pan-genomic analyses. The multidrug resistant strain PA1 possesses a 6,498,072 bp genome and a sequence type of ST-782. The genome of PA1 was also visualized, and the results revealed the details of general genome annotations, virulence factors, regulatory proteins (RPs), secretion system proteins, type II toxin–antitoxin (T–A) pairs and genomic islands. Whole genome comparison analysis suggested that PA1 exhibits similarity to other P. aeruginosa strains but differs in terms of horizontal gene transfer (HGT) regions, such as prophages and genomic islands. Phylogenetic analyses based on 16S rRNA sequences demonstrated that PA1 is closely related to PAO1, and P. aeruginosa strains can be divided into two main groups. The pan-genome of P. aeruginosa consists of a core genome of approximately 4,000 genes and an accessory genome of at least 6,600 genes. The present study presented a detailed, visualized and comparative analysis of the PA1 genome, to enhance our understanding of this notorious pathogen.

Efficient bit‐parallel subcircuit extraction using CUDA

Wafer processing technology has been improving rapidly. Moore's law has been exceeded as the number of transistors in a dense integrated circuit, now increases threefold or more, approximately every year. The integrated circuit has gone from very large scale to giga large scale. The extraction of subcircuits has therefore become computation‐intensive. In this paper, we propose an efficient bit‐parallel subcircuit extraction algorithm using graphic processing units. We conducted experimental trials and demonstrated that the proposed algorithm can achieve high throughput, suggesting practical applications in the extraction of subcircuits. Copyright © 2015 John Wiley & Sons, Ltd.

Gliptins in managing diabetes - Reviewing computational strategy

The pace of anti-diabetic drug discovery is very slow in spite of increasing rate of prevalence of Type 2 Diabetes which remains a major public health concern. Though extensive research steps are taken in the past decade, yet craves for better new treatment strategies to overcome the current scenario. One such general finding is the evolution of gliptins which discriminately inhibits DPP4 (Dipeptidyl peptidase-4) enzyme. Although the mechanism of action of gliptin is highly target oriented and accurate, still its long-term use stands unknown. This step calls for a fast, flexible, and cost-effective strategies to meet the demands of producing arrays of high-content lead compounds with improved efficiency for better clinical success. The present review highlights the available gliptins in the market and also other naturally occurring DPP4 enzyme inhibitors. Along with describing the known inhibitors and their origin in this review, we attempted to identify a probable new lead compounds using advanced computational techniques. In this context, computational methods that integrate the knowledge of proteins and drug responses were utilized in prioritizing targets and designing drugs towards clinical trials with better efficacy. The compounds obtained as a result of virtual screening were compared with the commercially available gliptin in the market to have better efficiency in the identification and validation of the potential DPP4 inhibitors. The combinatorial computational methods used in the present study identified Compound 1: 25022354 as promising inhibitor.

Characterization and Comparative Genomic Analyses of Pseudomonas aeruginosa Phage PaoP5: New Members Assigned to PAK_P1-like Viruses

As a potential alternative to antibiotics, phages can be used to treat multi-drug resistant bacteria. As such, the biological characteristics of phages should be investigated to utilize them as effective antimicrobial agents. In this study, phage PaoP5, a lytic virus that infects Pseudomonas aeruginosa PAO1, was isolated and genomically characterized. PaoP5 comprises an icosahedral head with an apex diameter of 69 nm and a contractile tail with a length of 120 nm. The PaoP5 genome is a linear dsDNA molecule containing 93,464 base pairs (bp) with 49.51% G + C content of 11 tRNA genes and a 1,200 bp terminal redundancy. A total of 176 protein-coding genes were predicted in the PaoP5 genome. Nine PaoP5 structural proteins were identified. Three hypothetical proteins were determined as structural. Comparative genomic analyses revealed that seven new Pseudomonas phages, namely, PaoP5, K8, C11, vB_PaeM_C2-10_Ab02, vB_PaeM_C2-10_Ab08, vB_PaeM_C2-10_Ab10, and vB_PaeM_C2-10_Ab15, were similar to PAK_P1-like viruses. Phylogenetic and pan-genome analyses suggested that the new phages should be assigned to PAK_P1-like viruses, which possess approximately 100 core genes and 150 accessory genes. This work presents a detailed and comparative analysis of PaoP5 to enhance our understanding of phage biology.

GPU-Acceleration of Sequence Homology Searches with Database Subsequence Clustering

Sequence homology searches are used in various fields and require large amounts of computation time, especially for metagenomic analysis, owing to the large number of queries and the database size. To accelerate computing analyses, graphics processing units (GPUs) are widely used as a low-cost, high-performance computing platform. Therefore, we mapped the time-consuming steps involved in GHOSTZ, which is a state-of-the-art homology search algorithm for protein sequences, onto a GPU and implemented it as GHOSTZ-GPU. In addition, we optimized memory access for GPU calculations and for communication between the CPU and GPU. As per results of the evaluation test involving metagenomic data, GHOSTZ-GPU with 12 CPU threads and 1 GPU was approximately 3.0- to 4.1-fold faster than GHOSTZ with 12 CPU threads. Moreover, GHOSTZ-GPU with 12 CPU threads and 3 GPUs was approximately 5.8- to 7.7-fold faster than GHOSTZ with 12 CPU threads.

Scalable metagenomics alignment research tool (SMART): a scalable, rapid, and complete search heuristic for the classification of metagenomic sequences from complex sequence populations

Background

Next generation sequencing technology has enabled characterization of metagenomics through massively parallel genomic DNA sequencing. The complexity and diversity of environmental samples such as the human gut microflora, combined with the sustained exponential growth in sequencing capacity, has led to the challenge of identifying microbial organisms by DNA sequence. We sought to validate a Scalable Metagenomics Alignment Research Tool (SMART), a novel searching heuristic for shotgun metagenomics sequencing results.

Results

After retrieving all genomic DNA sequences from the NCBI GenBank, over 1 × 10¹¹ base pairs of 3.3 × 10⁶ sequences from 9.25 × 10⁵ species were indexed using 4 base pair hashtable shards. A MapReduce searching strategy was used to distribute the search workload in a computing cluster environment. In addition, a one base pair permutation algorithm was used to account for single nucleotide polymorphisms and sequencing errors. Simulated datasets used to evaluate Kraken, a similar metagenomics classification tool, were used to measure and compare precision and accuracy. Finally using a same set of training sequences we compared Kraken, CLARK, and SMART within the same computing environment. Utilizing 12 computational nodes, we completed the classification of all datasets in under 10 min each using exact matching with an average throughput of over 1.95 × 10⁶ reads classified per minute. With permutation matching, we achieved sensitivity greater than 83 % and precision greater than 94 % with simulated datasets at the species classification level. We demonstrated the application of this technique applied to conjunctival and gut microbiome metagenomics sequencing results. In our head to head comparison, SMART and CLARK had similar accuracy gains over Kraken at the species classification level, but SMART required approximately half the amount of RAM of CLARK.

Conclusions

SMART is the first scalable, efficient, and rapid metagenomics classification algorithm capable of matching against all the species and sequences present in the NCBI GenBank and allows for a single step classification of microorganisms as well as large plant, mammalian, or invertebrate genomes from which the metagenomic sample may have been derived.

Receptor-ligand interactions: Advanced biomedical applications

Receptor-ligand interactions (RLIs) are at the base of all biological events occurring in living cells. The understanding of interactions between complementary macromolecules in biological systems represents a high-priority research area in bionanotechnology to design the artificial systems mimicking natural processes. This review summarizes and analyzes RLIs in some cutting-edge biomedical fields, in particular, for the preparation of novel stationary phases to separate complex biological mixtures in medical diagnostics, for the design of ultrasensitive biosensors for identification of biomarkers of various diseases at early stages, as well as in the development of innovative biomaterials and approaches for regenerative medicine. All these biotechnological fields are closely related, because their success depends on a proper choice, combination and spatial disposition of the single components of ligand-receptor pairs on the surface of appropriately designed support.

Using Genome-Wide SNP Discovery and Genotyping to Reveal the Main Source of Population Differentiation in Nothofagus dombeyi (Mirb.) Oerst. in Chile

Within a woody plant species, environmental heterogeneity has the potential to influence the distribution of genetic variation among populations through several evolutionary processes. In some species, a relationship between environmental characteristics and the distribution of genotypes can be detected, showing the importance of natural selection as the main source of differentiation. Nothofagus dombeyi (Mirb.) Oerst. (Nothofagaceae) is an endemic tree species occurring both in Chile and in Argentina temperate forests. Postglacial history has been studied with chloroplast DNA and evolutionary forces shaping genetic variation patterns have been analysed with isozymes but fine-scale genetic diversity studies are needed. The study of demographic and selection histories in Nothofagus dombeyi requires more informative markers such as single nucleotide polymorphisms (SNP). Genotyping-by-Sequencing tools now allow studying thousands of SNP markers at reasonable prices in nonmodel species. We investigated more than 10 K SNP loci for signatures of local adaptation and showed that interrogation of genomic resources can identify shifts in genetic diversity and putative adaptive signals in this nonmodel woody species.

Parallel algorithms for large-scale biological sequence alignment on Xeon-Phi based clusters

BACKGROUND

Computing alignments between two or more sequences are common operations frequently performed in computational molecular biology. The continuing growth of biological sequence databases establishes the need for their efficient parallel implementation on modern accelerators.

RESULTS

This paper presents new approaches to high performance biological sequence database scanning with the Smith-Waterman algorithm and the first stage of progressive multiple sequence alignment based on the ClustalW heuristic on a Xeon Phi-based compute cluster. Our approach uses a three-level parallelization scheme to take full advantage of the compute power available on this type of architecture; i.e. cluster-level data parallelism, thread-level coarse-grained parallelism, and vector-level fine-grained parallelism. Furthermore, we re-organize the sequence datasets and use Xeon Phi shuffle operations to improve I/O efficiency.

CONCLUSIONS

Evaluations show that our method achieves a peak overall performance up to 220 GCUPS for scanning real protein sequence databanks on a single node consisting of two Intel E5-2620 CPUs and two Intel Xeon Phi 7110P cards. It also exhibits good scalability in terms of sequence length and size, and number of compute nodes for both database scanning and multiple sequence alignment. Furthermore, the achieved performance is highly competitive in comparison to optimized Xeon Phi and GPU implementations. Our implementation is available at https://github.com/turbo0628/LSDBS-mpi .

CUDAMPF: a multi-tiered parallel framework for accelerating protein sequence search in HMMER on CUDA-enabled GPU

BACKGROUND

HMMER software suite is widely used for analysis of homologous protein and nucleotide sequences with high sensitivity. The latest version of hmmsearch in HMMER 3.x, utilizes heuristic-pipeline which consists of MSV/SSV (Multiple/Single ungapped Segment Viterbi) stage, P7Viterbi stage and the Forward scoring stage to accelerate homology detection. Since the latest version is highly optimized for performance on modern multi-core CPUs with SSE capabilities, only a few acceleration attempts report speedup. However, the most compute intensive tasks within the pipeline (viz., MSV/SSV and P7Viterbi stages) still stand to benefit from the computational capabilities of massively parallel processors.

RESULTS

A Multi-Tiered Parallel Framework (CUDAMPF) implemented on CUDA-enabled GPUs presented here, offers a finer-grained parallelism for MSV/SSV and Viterbi algorithms. We couple SIMT (Single Instruction Multiple Threads) mechanism with SIMD (Single Instructions Multiple Data) video instructions with warp-synchronism to achieve high-throughput processing and eliminate thread idling. We also propose a hardware-aware optimal allocation scheme of scarce resources like on-chip memory and caches in order to boost performance and scalability of CUDAMPF. In addition, runtime compilation via NVRTC available with CUDA 7.0 is incorporated into the presented framework that not only helps unroll innermost loop to yield upto 2 to 3-fold speedup than static compilation but also enables dynamic loading and switching of kernels depending on the query model size, in order to achieve optimal performance.

CONCLUSIONS

CUDAMPF is designed as a hardware-aware parallel framework for accelerating computational hotspots within the hmmsearch pipeline as well as other sequence alignment applications. It achieves significant speedup by exploiting hierarchical parallelism on single GPU and takes full advantage of limited resources based on their own performance features. In addition to exceeding performance of other acceleration attempts, comprehensive evaluations against high-end CPUs (Intel i5, i7 and Xeon) shows that CUDAMPF yields upto 440 GCUPS for SSV, 277 GCUPS for MSV and 14.3 GCUPS for P7Viterbi all with 100 % accuracy, which translates to a maximum speedup of 37.5, 23.1 and 11.6-fold for MSV, SSV and P7Viterbi respectively. The source code is available at https://github.com/Super-Hippo/CUDAMPF.

Inhibition of breast cancer with transdermal tamoxifen-encapsulated lipoplex

Background

Tamoxifen is currently used for the treatment of both early and advanced estrogen receptor (ER) positive breast cancer in pre- and post-menopausal women. However, using tamoxifen routinely to inhibit endogenous or exogenous estrogen effects is occasionally difficult because of its potential side effects.

Objectives

The aim of this study is to design a local drug delivery system to encapsulate tamoxifen for observing their efficacy of skin penetration, drug accumulation and cancer therapy.

Methods

A cationic liposome-PEG-PEI complex (LPPC) was used as a carrier for the encapsulation of tamoxifen and forming ‘LPPC/TAM’ for transdermal release. The cytotoxicity of LPPC/TAM was analyzed by MTT. The skin penetration, tumor growth inhibition and organ damages were measured in xenograft mice following transdermal treatment.

Results

LPPC/TAM had an average size less than 270 nm and a zeta-potential of approximately 40 mV. LPPC/TAM displayed dramatically increased the cytotoxic activity in all breast cancer cells, especially in ER-positive breast cancer cells. In vivo, LPPC drug delivery helped the fluorescent dye penetrating across the skim and accumulating rapidly in tumor area. Administration of LPPC/TAM by transdermal route inhibited about 86 % of tumor growth in mice bearing BT474 tumors. This local treatment of LPPC/TAM did not injury skin and any organs.

Conclusion

LPPC-delivery system provided a better skin penetration and drug accumulation and therapeutic efficacy. Therefore, LPPC/TAM drug delivery maybe a useful transdermal tool of drugs utilization for breast cancer therapy.

Computational allosteric ligand binding site identification on Ras proteins

A number of computational techniques have been proposed to expedite the process of allosteric ligand binding site identification in inherently flexible and hence challenging drug targets. Some of these techniques have been instrumental in the discovery of allosteric ligand binding sites on Ras proteins, a group of elusive anticancer drug targets. This review provides an overview of these techniques and their application to Ras proteins. A summary of molecular docking and binding site identification is provided first, followed by a more detailed discussion of two specific techniques for binding site identification in ensembles of Ras conformations generated by molecular simulations.

Mining Large Scale Tandem Mass Spectrometry Data for Protein Modifications Using Spectral Libraries

Experimental improvements in post-translational modification (PTM) detection by tandem mass spectrometry (MS/MS) has allowed the identification of vast numbers of PTMs. Open modification searches (OMSs) of MS/MS data, which do not require prior knowledge of the modifications present in the sample, further increased the diversity of detected PTMs. Despite much effort, there is still a lack of functional annotation of PTMs. One possibility to narrow the annotation gap is to mine MS/MS data deposited in public repositories and to correlate the PTM presence with biological meta-information attached to the data. Since the data volume can be quite substantial and contain tens of millions of MS/MS spectra, the data mining tools must be able to cope with big data. Here, we present two tools, Liberator and MzMod, which are built using the MzJava class library and the Apache Spark large scale computing framework. Liberator builds large MS/MS spectrum libraries, and MzMod searches them in an OMS mode. We applied these tools to a recently published set of 25 million spectra from 30 human tissues and present tissue specific PTMs. We also compared the results to the ones obtained with the OMS tool MODa and the search engine X!Tandem.

G2LC: Resources Autoscaling for Real Time Bioinformatics Applications in IaaS

Cloud computing has started to change the way how bioinformatics research is being carried out. Researchers who have taken advantage of this technology can process larger amounts of data and speed up scientific discovery. The variability in data volume results in variable computing requirements. Therefore, bioinformatics researchers are pursuing more reliable and efficient methods for conducting sequencing analyses. This paper proposes an automated resource provisioning method, G2LC, for bioinformatics applications in IaaS. It enables application to output the results in a real time manner. Its main purpose is to guarantee applications performance, while improving resource utilization. Real sequence searching data of BLAST is used to evaluate the effectiveness of G2LC. Experimental results show that G2LC guarantees the application performance, while resource is saved up to 20.14%.

Accelerating Smith-Waterman Alignment for Protein Database Search Using Frequency Distance Filtration Scheme Based on CPU-GPU Collaborative System

The Smith-Waterman (SW) algorithm has been widely utilized for searching biological sequence databases in bioinformatics. Recently, several works have adopted the graphic card with Graphic Processing Units (GPUs) and their associated CUDA model to enhance the performance of SW computations. However, these works mainly focused on the protein database search by using the intertask parallelization technique, and only using the GPU capability to do the SW computations one by one. Hence, in this paper, we will propose an efficient SW alignment method, called CUDA-SWfr, for the protein database search by using the intratask parallelization technique based on a CPU-GPU collaborative system. Before doing the SW computations on GPU, a procedure is applied on CPU by using the frequency distance filtration scheme (FDFS) to eliminate the unnecessary alignments. The experimental results indicate that CUDA-SWfr runs 9.6 times and 96 times faster than the CPU-based SW method without and with FDFS, respectively.

Evaluating the Cassandra NoSQL Database Approach for Genomic Data Persistency

Rapid advances in high-throughput sequencing techniques have created interesting computational challenges in bioinformatics. One of them refers to management of massive amounts of data generated by automatic sequencers. We need to deal with the persistency of genomic data, particularly storing and analyzing these large-scale processed data. To find an alternative to the frequently considered relational database model becomes a compelling task. Other data models may be more effective when dealing with a very large amount of nonconventional data, especially for writing and retrieving operations. In this paper, we discuss the Cassandra NoSQL database approach for storing genomic data. We perform an analysis of persistency and I/O operations with real data, using the Cassandra database system. We also compare the results obtained with a classical relational database system and another NoSQL database approach, MongoDB.

CUDA ClustalW: An efficient parallel algorithm for progressive multiple sequence alignment on Multi-GPUs

For biological applications, sequence alignment is an important strategy to analyze DNA and protein sequences. Multiple sequence alignment is an essential methodology to study biological data, such as homology modeling, phylogenetic reconstruction and etc. However, multiple sequence alignment is a NP-hard problem. In the past decades, progressive approach has been proposed to successfully align multiple sequences by adopting iterative pairwise alignments. Due to rapid growth of the next generation sequencing technologies, a large number of sequences can be produced in a short period of time. When the problem instance is large, progressive alignment will be time consuming. Parallel computing is a suitable solution for such applications, and GPU is one of the important architectures for contemporary parallel computing researches. Therefore, we proposed a GPU version of ClustalW v2.0.11, called CUDA ClustalW v1.0, in this work. From the experiment results, it can be seen that the CUDA ClustalW v1.0 can achieve more than 33× speedups for overall execution time by comparing to ClustalW v2.0.11.

GPU‐UPGMA: high‐performance computing for UPGMA algorithm based on graphics processing units

Constructing phylogenetic trees is of priority concern in computational biology, especially for developing biological taxonomies. As a conventional means of constructing phylogenetic trees, unweighted pair group method with arithmetic (UPGMA) is also an extensively adopted heuristic algorithm for constructing ultrametric trees (UT). Although the UT constructed by UPGMA is often not a true tree unless the molecular clock assumption holds, UT is still useful for the clocklike data. Moreover, UT has been successfully adopted in other problems, including orthologous‐domain classification and multiple sequence alignment. However, previous implementations of the UPGMA method have a limited ability to handle large taxa sets efficiently. This work describes a novel graphics processing unit (GPU)‐UPGMA approach, capable of providing rapid construction of extremely large datasets for biologists. Experimental results indicate that the proposed GPU‐UPGMA approach achieves an approximately 95× speedup ratio on NVIDIA Tesla C2050 GPU over the implementation with 2.13 GHz CPU. The developed techniques in GPU‐UPGMA also can be applied to solve the classification problem for large data set with more than tens of thousands items in the future.Copyright © 2014 John Wiley & Sons, Ltd.

PoLi: A Virtual Screening Pipeline Based on Template Pocket and Ligand Similarity

Often in pharmaceutical research the goal is to identify small molecules that can interact with and appropriately modify the biological behavior of a new protein target. Unfortunately, most proteins lack both known structures and small molecule binders, prerequisites of many virtual screening, VS, approaches. For such proteins, ligand homology modeling, LHM, that copies ligands from homologous and perhaps evolutionarily distant template proteins, has been shown to be a powerful VS approach to identify possible binding ligands. However, if we want to target a specific pocket for which there is no homologous holo template protein structure, then LHM will not work. To address this issue, in a new pocket-based approach, PoLi, we generalize LHM by exploiting the fact that the number of distinct small molecule ligand-binding pockets in proteins is small. PoLi identifies similar ligand-binding pockets in a holo template protein library, selectively copies relevant parts of template ligands, and uses them for VS. In practice, PoLi is a hybrid structure and ligand-based VS algorithm that integrates 2D fingerprint-based and 3D shape-based similarity metrics for improved virtual screening performance. On standard DUD and DUD-E benchmark databases, using modeled receptor structures, PoLi achieves an average enrichment factor of 13.4 and 9.6, respectively, in the top 1% of the screened library. In contrast, traditional docking-based VS using AutoDock Vina and homology-based VS using FINDSITE(filt) have an average enrichment of 1.6 (3.0) and 9.0 (7.9) on the DUD (DUD-E) sets, respectively. Experimental validation of PoLi predictions on dihydrofolate reductase, DHFR, using differential scanning fluorimetry, DSF, identifies multiple ligands with diverse molecular scaffolds, thus demonstrating the advantage of PoLi over current state-of-the-art VS methods.

Role of computer-aided drug design in modern drug discovery

Drug discovery utilizes chemical biology and computational drug design approaches for the efficient identification and optimization of lead compounds. Chemical biology is mostly involved in the elucidation of the biological function of a target and the mechanism of action of a chemical modulator. On the other hand, computer-aided drug design makes use of the structural knowledge of either the target (structure-based) or known ligands with bioactivity (ligand-based) to facilitate the determination of promising candidate drugs. Various virtual screening techniques are now being used by both pharmaceutical companies and academic research groups to reduce the cost and time required for the discovery of a potent drug. Despite the rapid advances in these methods, continuous improvements are critical for future drug discovery tools. Advantages presented by structure-based and ligand-based drug design suggest that their complementary use, as well as their integration with experimental routines, has a powerful impact on rational drug design. In this article, we give an overview of the current computational drug design and their application in integrated rational drug development to aid in the progress of drug discovery research.

MzJava: An open source library for mass spectrometry data processing

Mass spectrometry (MS) is a widely used and evolving technique for the high-throughput identification of molecules in biological samples. The need for sharing and reuse of code among bioinformaticians working with MS data prompted the design and implementation of MzJava, an open-source Java Application Programming Interface (API) for MS related data processing. MzJava provides data structures and algorithms for representing and processing mass spectra and their associated biological molecules, such as metabolites, glycans and peptides. MzJava includes functionality to perform mass calculation, peak processing (e.g. centroiding, filtering, transforming), spectrum alignment and clustering, protein digestion, fragmentation of peptides and glycans as well as scoring functions for spectrum-spectrum and peptide/glycan-spectrum matches. For data import and export MzJava implements readers and writers for commonly used data formats. For many classes support for the Hadoop MapReduce (hadoop.apache.org) and Apache Spark (spark.apache.org) frameworks for cluster computing was implemented. The library has been developed applying best practices of software engineering. To ensure that MzJava contains code that is correct and easy to use the library's API was carefully designed and thoroughly tested. MzJava is an open-source project distributed under the AGPL v3.0 licence. MzJava requires Java 1.7 or higher. Binaries, source code and documentation can be downloaded from http://mzjava.expasy.org and https://bitbucket.org/sib-pig/mzjava. This article is part of a Special Issue entitled: Computational Proteomics.

Influence of provider and urgent care density across different socioeconomic strata on outpatient antibiotic prescribing in the USA

OBJECTIVES

Despite a strong link between antibiotic use and resistance, and highly variable antibiotic consumption rates across the USA, drivers of differences in consumption rates are not fully understood. The objective of this study was to examine how provider density affects antibiotic prescribing rates across socioeconomic groups in the USA.

METHODS

We aggregated data on all outpatient antibiotic prescriptions filled in retail pharmacies in the USA in 2000 and 2010 from IMS Health into 3436 geographically distinct hospital service areas and combined this with socioeconomic and structural factors that affect antibiotic prescribing from the US Census. We then used fixed-effect models to estimate the interaction between poverty and the number of physician offices per capita (i.e. physician density) and the presence of urgent care and retail clinics on antibiotic prescribing rates.

RESULTS

We found large geographical variation in prescribing, driven in part by the number of physician offices per capita. For an increase of one standard deviation in the number of physician offices per capita there was a 25.9% increase in prescriptions per capita. However, the determinants of the prescription rate were dependent on socioeconomic conditions. In poorer areas, clinics substitute for traditional physician offices, reducing the impact of physician density. In wealthier areas, clinics increase the effect of physician density on the prescribing rate.

CONCLUSIONS

In areas with higher poverty rates, access to providers drives the prescribing rate. However, in wealthier areas, where access is less of a problem, a higher density of providers and clinics increases the prescribing rate, potentially due to competition.

Cloud computing-based TagSNP selection algorithm for human genome data

Single nucleotide polymorphisms (SNPs) play a fundamental role in human genetic variation and are used in medical diagnostics, phylogeny construction, and drug design. They provide the highest-resolution genetic fingerprint for identifying disease associations and human features. Haplotypes are regions of linked genetic variants that are closely spaced on the genome and tend to be inherited together. Genetics research has revealed SNPs within certain haplotype blocks that introduce few distinct common haplotypes into most of the population. Haplotype block structures are used in association-based methods to map disease genes. In this paper, we propose an efficient algorithm for identifying haplotype blocks in the genome. In chromosomal haplotype data retrieved from the HapMap project website, the proposed algorithm identified longer haplotype blocks than an existing algorithm. To enhance its performance, we extended the proposed algorithm into a parallel algorithm that copies data in parallel via the Hadoop MapReduce framework. The proposed MapReduce-paralleled combinatorial algorithm performed well on real-world data obtained from the HapMap dataset; the improvement in computational efficiency was proportional to the number of processors used.

Cytochrome P450 BM3 of Bacillus megaterium - a possible endosulfan biotransforming gene

Computing chemistry was applied to understand biotransformation mechanism of an organochlorine pesticide, endosulfan. The stereo specific metabolic activity of human CYP-2B6 (cytochrome P450) on endosulfan has been well demonstrated. Sequence and structural similarity search revealed that the bacterium Bacillus megaterium encodes CYP-BM3, which is similar to CYP-2B6. The functional similarity was studied at organism level by batch-scale studies and it was proved that B. megaterium could metabolize endosulfan to endosulfan sulfate, as CYP-2B6 does in human system. The gene expression analyses also confirmed the possible role of CYP-BM3 in endosulfan metabolism. Thus, our results show that the protein structure based in-silico approach can help us to understand and identify microbes for remediation strategy development. To the best of our knowledge this is the first report which has extrapolated the bacterial gene for endosulfan biotransformation through in silico prediction approach for metabolic gene identification.

Applications of the MapReduce programming framework to clinical big data analysis: current landscape and future trends

The emergence of massive datasets in a clinical setting presents both challenges and opportunities in data storage and analysis. This so called "big data" challenges traditional analytic tools and will increasingly require novel solutions adapted from other fields. Advances in information and communication technology present the most viable solutions to big data analysis in terms of efficiency and scalability. It is vital those big data solutions are multithreaded and that data access approaches be precisely tailored to large volumes of semi-structured/unstructured data. THE MAPREDUCE PROGRAMMING FRAMEWORK USES TWO TASKS COMMON IN FUNCTIONAL PROGRAMMING: Map and Reduce. MapReduce is a new parallel processing framework and Hadoop is its open-source implementation on a single computing node or on clusters. Compared with existing parallel processing paradigms (e.g. grid computing and graphical processing unit (GPU)), MapReduce and Hadoop have two advantages: 1) fault-tolerant storage resulting in reliable data processing by replicating the computing tasks, and cloning the data chunks on different computing nodes across the computing cluster; 2) high-throughput data processing via a batch processing framework and the Hadoop distributed file system (HDFS). Data are stored in the HDFS and made available to the slave nodes for computation. In this paper, we review the existing applications of the MapReduce programming framework and its implementation platform Hadoop in clinical big data and related medical health informatics fields. The usage of MapReduce and Hadoop on a distributed system represents a significant advance in clinical big data processing and utilization, and opens up new opportunities in the emerging era of big data analytics. The objective of this paper is to summarize the state-of-the-art efforts in clinical big data analytics and highlight what might be needed to enhance the outcomes of clinical big data analytics tools. This paper is concluded by summarizing the potential usage of the MapReduce programming framework and Hadoop platform to process huge volumes of clinical data in medical health informatics related fields.

Association of polymorphisms in AGTR1 and AGTR2 genes with primary aldosteronism in the Chinese Han population

HYPOTHESIS

Polymorphisms in angiotensin II type-1/2 receptor genes (AGTR1/AGTR2) may be involved in the pathogenesis of primary aldosteronism. The present study aims to reveal some loci susceptible to the disease on the genes in a group of Chinese Han nationality.

MATERIALS AND METHODS

A case-control study was conducted in 202 patients and 188 controls. Ten tagging SNPs on AGTR1/AGTR2 were genotyped for all subjects via the method of multiplex PCR-ligase detection reaction. Statistical analysis was performed with chi-square test and logistic regression analysis.

RESULTS

rs3772616 on the AGTR1 gene was a factor for susceptibility to primary aldosteronism (p<0.001), and the TT genotype significantly decreased the risk of primary aldosteronism compared with the CC homozygote (p=0.008, adjusted OR=0.13; 95%CI: 0.03-0.59). The rs3772616 polymorphism was associated with primary aldosteronism under the additive and dominant models. The female carriers of the G allele in rs5193 showed a significant difference compared with the T allele.

CONCLUSIONS

The AGTR1 rs3772616 polymorphism can be considered as a hereditary marker for primary aldosteronism, and in the Chinese Han population the rs5193 G allele seems to predispose to it only in women.

Big Data Usage Patterns in the Health Care Domain: A Use Case Driven Approach Applied to the Assessment of Vaccination Benefits and Risks. Contribution of the IMIA Primary Healthcare Working Group

BACKGROUND

Generally benefits and risks of vaccines can be determined from studies carried out as part of regulatory compliance, followed by surveillance of routine data; however there are some rarer and more long term events that require new methods. Big data generated by increasingly affordable personalised computing, and from pervasive computing devices is rapidly growing and low cost, high volume, cloud computing makes the processing of these data inexpensive.

OBJECTIVE

To describe how big data and related analytical methods might be applied to assess the benefits and risks of vaccines.

METHOD

We reviewed the literature on the use of big data to improve health, applied to generic vaccine use cases, that illustrate benefits and risks of vaccination. We defined a use case as the interaction between a user and an information system to achieve a goal. We used flu vaccination and pre-school childhood immunisation as exemplars.

RESULTS

We reviewed three big data use cases relevant to assessing vaccine benefits and risks: (i) Big data processing using crowdsourcing, distributed big data processing, and predictive analytics, (ii) Data integration from heterogeneous big data sources, e.g. the increasing range of devices in the "internet of things", and (iii) Real-time monitoring for the direct monitoring of epidemics as well as vaccine effects via social media and other data sources.

CONCLUSIONS

Big data raises new ethical dilemmas, though its analysis methods can bring complementary real-time capabilities for monitoring epidemics and assessing vaccine benefit-risk balance.

Cloud4Psi: cloud computing for 3D protein structure similarity searching

Summary: Popular methods for 3D protein structure similarity searching, especially those that generate high-quality alignments such as Combinatorial Extension (CE) and Flexible structure Alignment by Chaining Aligned fragment pairs allowing Twists (FATCAT) are still time consuming. As a consequence, performing similarity searching against large repositories of structural data requires increased computational resources that are not always available. Cloud computing provides huge amounts of computational power that can be provisioned on a pay-as-you-go basis. We have developed the cloud-based system that allows scaling of the similarity searching process vertically and horizontally. Cloud4Psi (Cloud for Protein Similarity) was tested in the Microsoft Azure cloud environment and provided good, almost linearly proportional acceleration when scaled out onto many computational units.

Availability and implementation: Cloud4Psi is available as Software as a Service for testing purposes at: http://cloud4psi.cloudapp.net/. For source code and software availability, please visit the Cloud4Psi project home page at http://zti.polsl.pl/dmrozek/science/cloud4psi.htm.

Contact:dariusz.mrozek@polsl.pl

Local alignment tool based on Hadoop framework and GPU architecture

With the rapid growth of next generation sequencing technologies, such as Slex, more and more data have been discovered and published. To analyze such huge data the computational performance is an important issue. Recently, many tools, such as SOAP, have been implemented on Hadoop and GPU parallel computing architectures. BLASTP is an important tool, implemented on GPU architectures, for biologists to compare protein sequences. To deal with the big biology data, it is hard to rely on single GPU. Therefore, we implement a distributed BLASTP by combining Hadoop and multi-GPUs. The experimental results present that the proposed method can improve the performance of BLASTP on single GPU, and also it can achieve high availability and fault tolerance.

Evolution of mitochondria reconstructed from the energy metabolism of living bacteria

The ancestors of mitochondria, or proto-mitochondria, played a crucial role in the evolution of eukaryotic cells and derived from symbiotic α-proteobacteria which merged with other microorganisms - the basis of the widely accepted endosymbiotic theory. However, the identity and relatives of proto-mitochondria remain elusive. Here we show that methylotrophic α-proteobacteria could be the closest living models for mitochondrial ancestors. We reached this conclusion after reconstructing the possible evolutionary pathways of the bioenergy systems of proto-mitochondria with a genomic survey of extant α-proteobacteria. Results obtained with complementary molecular and genetic analyses of diverse bioenergetic proteins converge in indicating the pathway stemming from methylotrophic bacteria as the most probable route of mitochondrial evolution. Contrary to other α-proteobacteria, methylotrophs show transition forms for the bioenergetic systems analysed. Our approach of focusing on these bioenergetic systems overcomes the phylogenetic impasse that has previously complicated the search for mitochondrial ancestors. Moreover, our results provide a new perspective for experimentally re-evolving mitochondria from extant bacteria and in the future produce synthetic mitochondria.

A novel approach for discovering condition-specific correlations of gene expressions within biological pathways by using cloud computing technology

Microarrays are widely used to assess gene expressions. Most microarray studies focus primarily on identifying differential gene expressions between conditions (e.g., cancer versus normal cells), for discovering the major factors that cause diseases. Because previous studies have not identified the correlations of differential gene expression between conditions, crucial but abnormal regulations that cause diseases might have been disregarded. This paper proposes an approach for discovering the condition-specific correlations of gene expressions within biological pathways. Because analyzing gene expression correlations is time consuming, an Apache Hadoop cloud computing platform was implemented. Three microarray data sets of breast cancer were collected from the Gene Expression Omnibus, and pathway information from the Kyoto Encyclopedia of Genes and Genomes was applied for discovering meaningful biological correlations. The results showed that adopting the Hadoop platform considerably decreased the computation time. Several correlations of differential gene expressions were discovered between the relapse and nonrelapse breast cancer samples, and most of them were involved in cancer regulation and cancer-related pathways. The results showed that breast cancer recurrence might be highly associated with the abnormal regulations of these gene pairs, rather than with their individual expression levels. The proposed method was computationally efficient and reliable, and stable results were obtained when different data sets were used. The proposed method is effective in identifying meaningful biological regulation patterns between conditions.

Cloud computing for protein-ligand binding site comparison

The proteome-wide analysis of protein-ligand binding sites and their interactions with ligands is important in structure-based drug design and in understanding ligand cross reactivity and toxicity. The well-known and commonly used software, SMAP, has been designed for 3D ligand binding site comparison and similarity searching of a structural proteome. SMAP can also predict drug side effects and reassign existing drugs to new indications. However, the computing scale of SMAP is limited. We have developed a high availability, high performance system that expands the comparison scale of SMAP. This cloud computing service, called Cloud-PLBS, combines the SMAP and Hadoop frameworks and is deployed on a virtual cloud computing platform. To handle the vast amount of experimental data on protein-ligand binding site pairs, Cloud-PLBS exploits the MapReduce paradigm as a management and parallelizing tool. Cloud-PLBS provides a web portal and scalability through which biologists can address a wide range of computer-intensive questions in biology and drug discovery.