Large-Scale Phylogenetic Analysis on Current HPC Architectures

Phylogenetic inference is considered a grand challenge in Bioinformatics due to its immense computational requirements. The increasing popularity and availability of large multi-gene alignments as well as comprehensive datasets of single nucleotide polymorphisms (SNPs) in current biological studies, coupled with rapid accumulation of sequence data in general, pose new challenges for high performance computing. By example of RAxML, which is currently among the fastest and most accurate programs for phylogenetic inference under the Maximum Likelihood (ML) criterion, we demonstrate how the phylogenetic ML function can be efficiently scaled to current supercomputer architectures like the IBM BlueGene/L (BG/L) and SGI Altix. This is achieved by simultaneous exploitation of coarse- and fine-grained parallelism which is inherent to every ML-based biological analysis. Performance is assessed using datasets consisting of 270 sequences and 566,470 base pairs (haplotype map dataset), and 2,182 sequences and 51,089 base pairs, respectively. To the best of our knowledge, these are the largest datasets analyzed under ML to date. Experimental results indicate that the fine-grained parallelization scales well up to 1,024 processors. Moreover, a larger number of processors can be efficiently exploited by a combination of coarse- and fine-grained parallelism. We also demonstrate that our parallelization scales equally well on an AMD Opteron cluster with a less favorable network latency to processor speed ratio. Finally, we underline the practical relevance of our approach by including a biological discussion of the results from the haplotype map dataset analysis, which revealed novel biological insights via phylogenetic inference.

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Validation of variants using cost effective highresolution melting (HRM) analysis predicted from target re-sequencing in Eucalyptus

Acta Botanica Croatica ◽

10.37427/botcro-2020-019 ◽

2020 ◽

Vol 79 (2) ◽

pp. 105-113

Author(s):

Abdul Bari Muneera Parveen ◽

Divya Lakshmanan ◽

Modhumita Ghosh Dasgupta

Keyword(s):

Next Generation Sequencing ◽

Large Scale ◽

Sequence Data ◽

Cost Effective ◽

Nucleotide Polymorphisms ◽

Next Generation ◽

Time Saving ◽

Hrm Analysis ◽

The Cost ◽

Generation Sequencing

The advent of next-generation sequencing has facilitated large-scale discovery and mapping of genomic variants for high-throughput genotyping. Several research groups working in tree species are presently employing next generation sequencing (NGS) platforms for marker discovery, since it is a cost effective and time saving strategy. However, most trees lack a chromosome level genome map and validation of variants for downstream application becomes obligatory. The cost associated with identifying potential variants from the enormous amount of sequence data is a major limitation. In the present study, high resolution melting (HRM) analysis was optimized for rapid validation of single nucleotide polymorphisms (SNPs), insertions or deletions (InDels) and simple sequence repeats (SSRs) predicted from exome sequencing of parents and hybrids of Eucalyptus tereticornis Sm. ? Eucalyptus grandis Hill ex Maiden generated from controlled hybridization. The cost per data point was less than 0.5 USD, providing great flexibility in terms of cost and sensitivity, when compared to other validation methods. The sensitivity of this technology in variant detection can be extended to other applications including Bar-HRM for species authentication and TILLING for detection of mutants.

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TASUKE+: a web-based platform for exploring GWAS results and large-scale resequencing data

DNA Research ◽

10.1093/dnares/dsz022 ◽

2019 ◽

Vol 26 (6) ◽

pp. 445-452 ◽

Cited By ~ 3

Author(s):

Masahiko Kumagai ◽

Daiki Nishikawa ◽

Yoshihiro Kawahara ◽

Hironobu Wakimoto ◽

Ryutaro Itoh ◽

...

Keyword(s):

Phylogenetic Tree ◽

Large Scale ◽

Sequence Data ◽

Association Studies ◽

Data Retrieval ◽

Genome Wide Association Studies ◽

Web Based ◽

Annotation Information ◽

Effective Manner ◽

Biological Studies

Abstract Recent revolutionary advancements in sequencing technologies have made it possible to obtain mass quantities of genome-scale sequence data in a cost-effective manner and have drastically altered molecular biological studies. To utilize these sequence data, genome-wide association studies (GWASs) have become increasingly important. Hence, there is an urgent need to develop a visualization tool that enables efficient data retrieval, integration of GWAS results with diverse information and rapid public release of such large-scale genotypic and phenotypic data. We developed a web-based genome browser TASUKE+ (https://tasuke.dna.affrc.go.jp/), which is equipped with the following functions: (i) interactive GWAS results visualization with genome resequencing data and annotation information, (ii) PCR primer design, (iii) phylogenetic tree reconstruction and (iv) data sharing via the web. GWAS results can be displayed in parallel with polymorphism data, read depths and annotation information in an interactive and scalable manner. Users can design PCR primers for polymorphic sites of interest. In addition, a molecular phylogenetic tree of any region can be reconstructed so that the overall relationship among the examined genomes can be understood intuitively at a glance. All functions are implemented through user-friendly web-based interfaces so that researchers can easily share data with collaborators in remote places without extensive bioinformatics knowledge.

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Rare coding variants in 35 genes associate with circulating lipid levels – a multi-ancestry analysis of 170,000 exomes

10.1101/2020.12.22.423783 ◽

2020 ◽

Author(s):

George Hindy ◽

Peter Dornbos ◽

Mark D. Chaffin ◽

Dajiang J. Liu ◽

Minxian Wang ◽

...

Keyword(s):

Complex Traits ◽

Drug Target ◽

Large Scale ◽

Target Genes ◽

Genome Wide Association Study ◽

Sequence Data ◽

Blood Lipid ◽

Nucleotide Polymorphisms ◽

Lipid Levels ◽

Blood Lipid Levels

SummaryLarge-scale gene sequencing studies for complex traits have the potential to identify causal genes with therapeutic implications. We performed gene-based association testing of blood lipid levels with rare (minor allele frequency<1%) predicted damaging coding variation using sequence data from >170,000 individuals from multiple ancestries: 97,493 European, 30,025 South Asian, 16,507 African, 16,440 Hispanic/Latino, 10,420 East Asian, and 1,182 Samoan. We identified 35 genes associated with circulating lipid levels. Ten of these: ALB, SRSF2, JAK2, CREB3L3, TMEM136, VARS, NR1H3, PLA2G12A, PPARG and STAB1 have not been implicated for lipid levels using rare coding variation in population-based samples. We prioritize 32 genes identified in array-based genome-wide association study (GWAS) loci based on gene-based associations, of which three: EVI5, SH2B3, and PLIN1, had no prior evidence of rare coding variant associations. Most of the associated genes showed evidence of association in multiple ancestries. Also, we observed an enrichment of gene-based associations for low-density lipoprotein cholesterol drug target genes, and for genes closest to GWAS index single nucleotide polymorphisms (SNP). Our results demonstrate that gene-based associations can be beneficial for drug target development and provide evidence that the gene closest to the array-based GWAS index SNP is often the functional gene for blood lipid levels.

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An Integrated Pipeline of Open Source Software Adapted for Multi-CPU Architectures: Use in the Large-Scale Identification of Single Nucleotide Polymorphisms

Comparative and Functional Genomics ◽

10.1155/2007/35604 ◽

2007 ◽

Vol 2007 ◽

pp. 1-7 ◽

Cited By ~ 1

Author(s):

B. Jayashree ◽

Manindra S. Hanspal ◽

Rajgopal Srinivasan ◽

R. Vigneshwaran ◽

Rajeev K. Varshney ◽

...

Keyword(s):

Single Nucleotide Polymorphisms ◽

Open Source ◽

Open Source Software ◽

Large Scale ◽

Sequence Data ◽

Snp Genotyping ◽

Model Organisms ◽

Nucleotide Polymorphisms ◽

Single Nucleotide ◽

Web Interfaces

The large amounts of EST sequence data available from a single species of an organism as well as for several species within a genus provide an easy source of identification of intra- and interspecies single nucleotide polymorphisms (SNPs). In the case of model organisms, the data available are numerous, given the degree of redundancy in the deposited EST data. There are several available bioinformatics tools that can be used to mine this data; however, using them requires a certain level of expertise: the tools have to be used sequentially with accompanying format conversion and steps like clustering and assembly of sequences become time-intensive jobs even for moderately sized datasets. We report here a pipeline of open source software extended to run on multiple CPU architectures that can be used to mine large EST datasets for SNPs and identify restriction sites for assaying the SNPs so that cost-effective CAPS assays can be developed for SNP genotyping in genetics and breeding applications. At the International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), the pipeline has been implemented to run on a Paracel high-performance system consisting of four dual AMD Opteron processors running Linux with MPICH. The pipeline can be accessed through user-friendly web interfaces at http://hpc.icrisat.cgiar.org/PBSWeb and is available on request for academic use. We have validated the developed pipeline by mining chickpea ESTs for interspecies SNPs, development of CAPS assays for SNP genotyping, and confirmation of restriction digestion pattern at the sequence level.

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Towards Resolving and Redefining Amphipyrinae (Lepidoptera, Noctuoidea, Noctuidae): a Massively Polyphyletic Taxon

10.1101/271478 ◽

2018 ◽

Author(s):

Kevin L. Keegan ◽

J. Donald Lafontaine ◽

Niklas Wahlberg ◽

David L. Wagner

Keyword(s):

Large Scale ◽

Life History Traits ◽

Sequence Data ◽

Phylogenetic Study ◽

Base Pairs ◽

Molecular Phylogenetic Study ◽

Molecular Phylogenetic ◽

Genomic Scale ◽

The Many ◽

Scale Data

ABSTRACTAmphipyrinae have long been a catchall taxon for Noctuidae, with most members lacking discernible morphological synapomorphies that would allow their assignment to one of the many readily diagnosable noctuid subfamilies. Here data from seven gene regions (>5,500 base pairs) for more than 120 noctuid genera are used to infer a phylogeny for Amphipyrinae and related subfamilies. Sequence data for 57 amphipyrine genera—most represented by the type species of the genus—are examined. Presented here are: the first large-scale molecular phylogenetic study of Amphipyrinae and largest molecular phylogeny of Noctuidae to date; several proposed nomenclatural changes for well supported results; and the identification of areas of noctuid phylogeny where greater taxon sampling and/or genomic-scale data are needed. Adult and larval morphology, along with life history traits, for taxonomic groupings most relevant to the results are discussed. Amphipyrinae are significantly redefined; many former amphipyrines, excluded as a result of these analyses, are reassigned to other noctuid subfamily-level taxa. Four genera,ChamaecleaGrote,HeminocloaBarnes & Benjamin,HemioslariaBarnes & Benjamin, andThurberiphagaDyar are transferred to the tribe Chamaecleini Keegan & WagnerNew Tribein Acontiinae. Stiriina is elevated to StiriinaeRevised Status, Grotellina is elevated to GrotellinaeRevised Status, and Annaphilina is elevated to AnnaphiliniRevised Status.AcopaHarvey is transferred to Bryophilinae,AleptinaDyar is transferred to Condicinae,LeucocnemisHampson andOxycnemis gracillinea(Grote) are transferred to Oncocnemidinae,NacopaBarnes & Benjamin is transferred to Noctuinae, andNarthecophoraSmith is transferred to Stiriinae.AzeniaGrote (and its subtribe Azeniina),CropiaWalker,MetaponpneumataMöschler,SexserrataBarnes & Benjamin, andTristylaSmith are transferred to Noctuidaeincertae sedis.HemigrotellaBarnes & McDunnough (formerly in subtribe Grotellina) is retained in Amphipyrinae.This published work has been registered in ZooBank,http://zoobank.org/urn:lsid:zoobank.org:pub:4A140782-31BA-445A-B7BA-6EAB98ED43FA

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Single Nucleotide Polymorphisms Caused by Assembly Errors

Genomics Insights ◽

10.4137/gei.s3653 ◽

2010 ◽

Vol 3 ◽

pp. GEI.S3653

Author(s):

Jürgen Kleffe ◽

Robert Weißmann ◽

Florian F. Schmitzberger

Keyword(s):

Single Nucleotide Polymorphisms ◽

De Novo ◽

Sequence Data ◽

Bacterial Genome ◽

Sequence Assembly ◽

Nucleotide Polymorphisms ◽

Base Pairs ◽

Single Nucleotide ◽

Current Sequence ◽

Assembly Algorithms

We compare the results of three different assembler programs, Celera, Phrap and Mira2, for the same set of about a hundred thousand Sanger reads derived from an unknown bacterial genome. In difference to previous assembly comparisons we do not focus on speed of computation and numbers of assembled contigs but on how the different sequence assemblies agree by content. Threefold consistently assembled genome regions are identified in order to estimate a lower bound of erroneously identified single nucleotide polymorphisms (SNP) caused by nothing but the process of mathematical sequence assembly. We identified 509 sequence triplets common to all three de-novo assemblies spanning only 34% (3.3 Mb) of the bacterial genome with 175 of these regions (~1.5 Mb) including erroneous SNPs and insertion/deletions. Within these triplets this on average leads to one error per 7,155 base pairs. Replacing the assembler Mira2 by the most recent version Mira3, the letter number even drops to 5,923. Our results therefore suggest that a considerably high number of erroneous SNPs may be present in current sequence data and mathematicians should urgently take up research on numerical stability of sequence assembly algorithms. Furthermore, even the latest versions of currently used assemblers produce erroneous SNPs that depend on the order reads are used as input. Such errors will severely hamper molecular diagnostics as well as relating genome variation and disease. This issue needs to be addressed urgently as the field is moving fast into clinical applications.

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Making Sense of Genetic Information: The Promising Evolution of Clinical Stratification and Precision Oncology Using Machine Learning

Genes ◽

10.3390/genes12050722 ◽

2021 ◽

Vol 12 (5) ◽

pp. 722

Author(s):

Mahaly Baptiste ◽

Sarah Shireen Moinuddeen ◽

Courtney Lace Soliz ◽

Hashimul Ehsan ◽

Gen Kaneko

Keyword(s):

Machine Learning ◽

High Risk ◽

Genetic Information ◽

Large Scale ◽

Sequence Data ◽

Precision Oncology ◽

Nucleotide Polymorphisms ◽

Making Sense ◽

High Risk Populations ◽

Systematic Identification

Precision medicine is a medical approach to administer patients with a tailored dose of treatment by taking into consideration a person’s variability in genes, environment, and lifestyles. The accumulation of omics big sequence data led to the development of various genetic databases on which clinical stratification of high-risk populations may be conducted. In addition, because cancers are generally caused by tumor-specific mutations, large-scale systematic identification of single nucleotide polymorphisms (SNPs) in various tumors has propelled significant progress of tailored treatments of tumors (i.e., precision oncology). Machine learning (ML), a subfield of artificial intelligence in which computers learn through experience, has a great potential to be used in precision oncology chiefly to help physicians make diagnostic decisions based on tumor images. A promising venue of ML in precision oncology is the integration of all available data from images to multi-omics big data for the holistic care of patients and high-risk healthy subjects. In this review, we provide a focused overview of precision oncology and ML with attention to breast cancer and glioma as well as the Bayesian networks that have the flexibility and the ability to work with incomplete information. We also introduce some state-of-the-art attempts to use and incorporate ML and genetic information in precision oncology.

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bpRNA: Large-scale Automated Annotation and Analysis of RNA Secondary Structure

10.1101/271759 ◽

2018 ◽

Author(s):

Padideh Danaee ◽

Mason Rouches ◽

Michelle Wiley ◽

Dezhong Deng ◽

Liang Huang ◽

...

Keyword(s):

Secondary Structure ◽

Single Molecule ◽

Structure Prediction ◽

Rna Secondary Structure ◽

Large Scale ◽

Secondary Structure Prediction ◽

Sequence Data ◽

Secondary Structures ◽

Base Pairs ◽

Statistical Trends

ABSTRACTWhile RNA secondary structure prediction from sequence data has made remarkable progress, there is a need for improved strategies for annotating the features of RNA secondary structures. Here we present bpRNA, a novel annotation tool capable of parsing RNA structures, including complex pseudoknot-containing RNAs, to yield an objective, precise, compact, unambiguous, easily-interpretable description of all loops, stems, and pseudoknots, along with the positions, sequence, and flanking base pairs of each such structural feature. We also introduce several new informative representations of RNA structure types to improve structure visualization and interpretation. We have further used bpRNA to generate a web-accessible meta-database, “bpRNA-1m”, of over 100,000 single-molecule, known secondary structures; this is both more fully and accurately annotated and over 20-times larger than existing databases. We use a subset of the database with highly similar (≥90% identical) sequences filtered out to report on statistical trends in sequence, flanking base pairs, and length. Both the bpRNA method and the bpRNA-1m database will be valuable resources both for specific analysis of individual RNA molecules and large-scale analyses such as are useful for updating RNA energy parameters for computational thermodynamic predictions, improving machine learning models for structure prediction, and for benchmarking structure-prediction algorithms.

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Strategic resources and smallholder performance at the bottom of the pyramid

International Food and Agribusiness Management Review ◽

10.22434/ifamr2018.0111 ◽

2019 ◽

Vol 22 (3) ◽

pp. 365-380 ◽

Cited By ~ 1

Author(s):

Matthias Olthaar ◽

Wilfred Dolfsma ◽

Clemens Lutz ◽

Florian Noseleit

Keyword(s):

Large Scale ◽

Business Environment ◽

Agricultural Economics ◽

Local Market ◽

Bottom Of The Pyramid ◽

Fine Grained ◽

Strategic Resources ◽

Relative Contribution ◽

The Relationship ◽

Resource Based Theory

In a competitive business environment at the Bottom of the Pyramid smallholders supplying global value chains may be thought to be at the whims of downstream large-scale players and local market forces, leaving no room for strategic entrepreneurial behavior. In such a context we test the relationship between the use of strategic resources and firm performance. We adopt the Resource Based Theory and show that seemingly homogenous smallholders deploy resources differently and, consequently, some do outperform others. We argue that the ‘resource-based theory’ results in a more fine-grained understanding of smallholder performance than approaches generally applied in agricultural economics. We develop a mixed-method approach that allows one to pinpoint relevant, industry-specific resources, and allows for empirical identification of the relative contribution of each resource to competitive advantage. The results show that proper use of quality labor, storage facilities, time of selling, and availability of animals are key capabilities.

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PSI-40 Two mitochondrial lineages revealed in North American yak

Journal of Animal Science ◽

10.1093/jas/skaa278.833 ◽

2020 ◽

Vol 98 (Supplement_4) ◽

pp. 477-477

Author(s):

Leah K Treffer ◽

Edward S Rice ◽

Anna M Fuller ◽

Samuel Cutler ◽

Jessica L Petersen

Keyword(s):

Sequence Data ◽

Haplotype Network ◽

Ovis Aries ◽

Similar Species ◽

Nucleotide Polymorphisms ◽

Mt Dna ◽

Protein Coding ◽

Sister Clade ◽

Mtdna Sequence ◽

The Impact

Abstract Domestic yak (Bos grunniens) are bovids native to the Asian Qinghai-Tibetan Plateau. Studies of Asian yak have revealed that introgression with domestic cattle has contributed to the evolution of the species. When imported to North America (NA), some hybridization with B. taurus did occur. The objective of this study was to use mitochondrial (mt) DNA sequence data to better understand the mtDNA origin of NA yak and their relationship to Asian yak and related species. The complete mtDNA sequence of 14 individuals (12 NA yak, 1 Tibetan yak, 1 Tibetan B. indicus) was generated and compared with sequences of similar species from GeneBank (B. indicus, B. grunniens (Chinese), B. taurus, B. gaurus, B. primigenius, B. frontalis, Bison bison, and Ovis aries). Individuals were aligned to the B. grunniens reference genome (ARS_UNL_BGru_maternal_1.0), which was also included in the analyses. The mtDNA genes were annotated using the ARS-UCD1.2 cattle sequence as a reference. Ten unique NA yak haplotypes were identified, which a haplotype network separated into two clusters. Variation among the NA haplotypes included 93 nonsynonymous single nucleotide polymorphisms. A maximum likelihood tree including all taxa was made using IQtree after the data were partitioned into twenty-two subgroups using PartitionFinder2. Notably, six NA yak haplotypes formed a clade with B. indicus; the other four haplotypes grouped with B. grunniens and fell as a sister clade to bison, gaur and gayal. These data demonstrate two mitochondrial origins of NA yak with genetic variation in protein coding genes. Although these data suggest yak introgression with B. indicus, it appears to date prior to importation into NA. In addition to contributing to our understanding of the species history, these results suggest the two major mtDNA haplotypes in NA yak may functionally differ. Characterization of the impact of these differences on cellular function is currently underway.

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