9 results on '"Statistical genomics"'
Search Results
2. OpenMendel: a cooperative programming project for statistical genetics
- Author
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Zhou, Hua, Sinsheimer, Janet S, Bates, Douglas M, Chu, Benjamin B, German, Christopher A, Ji, Sarah S, Keys, Kevin L, Kim, Juhyun, Ko, Seyoon, Mosher, Gordon D, Papp, Jeanette C, Sobel, Eric M, Zhai, Jing, Zhou, Jin J, and Lange, Kenneth
- Subjects
Biological Sciences ,Genetics ,Human Genome ,Networking and Information Technology R&D (NITRD) ,Algorithms ,Computational Biology ,Genome ,Human ,Genome-Wide Association Study ,Humans ,Models ,Statistical ,Polymorphism ,Single Nucleotide ,Programming Languages ,Software ,Statistical genomics ,GWAS ,Computational statistics ,Open source ,Collaborative programming ,stat.AP ,q-bio.GN ,Complementary and Alternative Medicine ,Paediatrics and Reproductive Medicine ,Genetics & Heredity ,Reproductive medicine - Abstract
Statistical methods for genome-wide association studies (GWAS) continue to improve. However, the increasing volume and variety of genetic and genomic data make computational speed and ease of data manipulation mandatory in future software. In our view, a collaborative effort of statistical geneticists is required to develop open source software targeted to genetic epidemiology. Our attempt to meet this need is called the OPENMENDEL project (https://openmendel.github.io). It aims to (1) enable interactive and reproducible analyses with informative intermediate results, (2) scale to big data analytics, (3) embrace parallel and distributed computing, (4) adapt to rapid hardware evolution, (5) allow cloud computing, (6) allow integration of varied genetic data types, and (7) foster easy communication between clinicians, geneticists, statisticians, and computer scientists. This article reviews and makes recommendations to the genetic epidemiology community in the context of the OPENMENDEL project.
- Published
- 2020
3. Linkage analysis between dominant and co-dominant makers in full-sib families of out-breeding species
- Author
-
Alexandre Alonso Alves, Leonardo Lopes Bhering, Cosme Damião Cruz, and Acelino Couto Alfenas
- Subjects
statistical genomics ,exogamic populations ,recombination frequency and maximum likelihood ,Genetics ,QH426-470 - Abstract
As high-throughput genomic tools, such as the DNA microarray platform, have lead to the development of novel genotyping procedures, such as Diversity Arrays Technology (DArT) and Single Nucleotide Polymorphisms (SNPs), it is likely that, in the future, high density linkage maps will be constructed from both dominant and co-dominant markers. Recently, a strictly genetic approach was described for estimating recombination frequency (r) between co-dominant markers in full-sib families. The complete set of maximum likelihood estimators for r in full-sib families was almost obtained, but unfortunately, one particular configuration involving dominant markers, segregating in a 3:1 ratio and co-dominant markers, was not considered. Here we add nine further estimators to the previously published set, thereby making it possible to cover all combinations of molecular markers with two to four alleles (without epistasis) in a full-sib family. This includes segregation in one or both parents, dominance and all linkage phase configurations.
- Published
- 2010
- Full Text
- View/download PDF
4. Statistical and Computational Methods for Microbiome Multi-Omics Data
- Author
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Vanni Bucci, Lingling An, and Himel Mallick
- Subjects
metagenomics ,statistical genomics ,Computer science ,microbiome ,biostatistics ,Computational biology ,multi-omics ,metabolomics ,Editorial ,computational biology ,Metagenomics ,Genetics ,Multi omics ,data science ,Microbiome - Published
- 2020
5. Grand Challenges in Statistical Genetics and Methodology
- Author
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Hemant K Tiwari and Nicholas J Schork
- Subjects
Epigenomics ,Proteomics ,bioinformatics ,statistical genomics ,Functional Genomics ,next generation sequencing ,Genetics ,QH426-470 - Published
- 2011
- Full Text
- View/download PDF
6. OPENMENDEL: a cooperative programming project for statistical genetics
- Author
-
Jing Zhai, Benjamin B. Chu, Janet S. Sinsheimer, Gordon D. Mosher, Christopher A. German, Kenneth Lange, Jin Zhou, Juhyun Kim, Eric M. Sobel, Douglas M. Bates, Hua Zhou, Sarah S. Ji, Jeanette C. Papp, Kevin L. Keys, and Seyoon Ko
- Subjects
FOS: Computer and information sciences ,Computer science ,Big data ,Cloud computing ,Genome-wide association study ,Software ,Models ,GWAS ,Genetics (clinical) ,Genetics & Heredity ,0303 health sciences ,Genome ,Data manipulation language ,030305 genetics & heredity ,Single Nucleotide ,Statistical ,Open source ,Variety (cybernetics) ,Networking and Information Technology R&D ,Networking and Information Technology R&D (NITRD) ,Statistical genetics ,q-bio.GN ,Algorithms ,Human ,Collaborative programming ,Context (language use) ,Statistics - Applications ,Polymorphism, Single Nucleotide ,Article ,Paediatrics and Reproductive Medicine ,03 medical and health sciences ,Complementary and Alternative Medicine ,Genetics ,Humans ,Statistical genomics ,Applications (stat.AP) ,Quantitative Biology - Genomics ,Polymorphism ,stat.AP ,030304 developmental biology ,Genomics (q-bio.GN) ,Models, Statistical ,business.industry ,Genome, Human ,Human Genome ,Computational Biology ,Data science ,Genetic epidemiology ,FOS: Biological sciences ,Computational statistics ,Programming Languages ,business ,Genome-Wide Association Study - Abstract
Statistical methods for genomewide association studies (GWAS) continue to improve. However, the increasing volume and variety of genetic and genomic data make computational speed and ease of data manipulation mandatory in future software. In our view, a collaborative effort of statistical geneticists is required to develop open source software targeted to genetic epidemiology. Our attempt to meet this need is called the OPENMENDELproject (https://openmendel.github.io). It aims to (1) enable interactive and reproducible analyses with informative intermediate results, (2) scale to big data analytics, (3) embrace parallel and distributed computing, (4) adapt to rapid hardware evolution, (5) allow cloud computing, (6) allow integration of varied genetic data types, and (7) foster easy communication between clinicians, geneticists, statisticians, and computer scientists. This article reviews and makes recommendations to the genetic epidemiology community in the context of the OPENMENDEL project., 16 pages, 2 figures, 2 tables
- Published
- 2019
7. Comparing the Statistical Fate of Paralogous and Orthologous Sequences
- Author
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Michael Sheinman, Florian Massip, Peter F. Arndt, Sophie Schbath, Statistique en grande dimension pour la génomique, Département PEGASE [LBBE] (PEGASE), Laboratoire de Biométrie et Biologie Evolutive - UMR 5558 (LBBE), Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Institut National de Recherche en Informatique et en Automatique (Inria)-VetAgro Sup - Institut national d'enseignement supérieur et de recherche en alimentation, santé animale, sciences agronomiques et de l'environnement (VAS)-Centre National de la Recherche Scientifique (CNRS)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Institut National de Recherche en Informatique et en Automatique (Inria)-VetAgro Sup - Institut national d'enseignement supérieur et de recherche en alimentation, santé animale, sciences agronomiques et de l'environnement (VAS)-Centre National de la Recherche Scientifique (CNRS)-Laboratoire de Biométrie et Biologie Evolutive - UMR 5558 (LBBE), and Université de Lyon-Université de Lyon-Institut National de Recherche en Informatique et en Automatique (Inria)-VetAgro Sup - Institut national d'enseignement supérieur et de recherche en alimentation, santé animale, sciences agronomiques et de l'environnement (VAS)-Centre National de la Recherche Scientifique (CNRS)
- Subjects
0301 basic medicine ,Genome evolution ,statistical genomics ,Sequence analysis ,[SDV]Life Sciences [q-bio] ,Sequence Homology ,Genomics ,Sequence alignment ,Computational biology ,comparative genomics ,Biology ,Investigations ,genome evolution ,01 natural sciences ,Genome ,Homologous Sequences ,Homology (biology) ,Evolution, Molecular ,03 medical and health sciences ,Segmental Duplications, Genomic ,Genetics ,0101 mathematics ,DNA duplications ,Probability ,030304 developmental biology ,Mathematics ,Segmental duplication ,Comparative genomics ,0303 health sciences ,Exact sequence ,Models, Genetic ,010102 general mathematics ,Computational Biology ,030104 developmental biology ,Exponent ,Sequence Alignment ,Orthologous Gene - Abstract
For several decades, sequence alignment has been a widely used tool in bioinformatics. For instance, finding homologous sequences with a known function in large databases is used to get insight into the function of nonannotated genomic regions. Very efficient tools like BLAST have been developed to identify and rank possible homologous sequences. To estimate the significance of the homology, the ranking of alignment scores takes a background model for random sequences into account. Using this model we can estimate the probability to find two exactly matching subsequences by chance in two unrelated sequences. For two homologous sequences, the corresponding probability is much higher, which allows us to identify them. Here we focus on the distribution of lengths of exact sequence matches between protein-coding regions of pairs of evolutionarily distant genomes. We show that this distribution exhibits a power-law tail with an exponent α=−5. Developing a simple model of sequence evolution by substitutions and segmental duplications, we show analytically and computationally that paralogous and orthologous gene pairs contribute differently to this distribution. Our model explains the differences observed in the comparison of coding and noncoding parts of genomes, thus providing a better understanding of statistical properties of genomic sequences and their evolution.
- Published
- 2016
8. Computational cancer biology: education is a natural key to many locks
- Author
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Emmert-Streib, Frank, Zhang, Shu-Dong, and Hamilton, Peter
- Subjects
EXPRESSION ,Cancer Research ,BIG DATA ,Genomics data ,CLASSIFICATION ,Computational biology ,Computational genomics ,SUBCLASSES ,Oncology ,CARCINOMAS ,Systems medicine ,Genetics ,Statistical genomics ,Computational oncology ,Cancer - Abstract
Background: Oncology is a field that profits tremendously from the genomic data generated by high-throughput technologies, including next-generation sequencing. However, in order to exploit, integrate, visualize and interpret such high-dimensional data efficiently, non-trivial computational and statistical analysis methods are required that need to be developed in a problem-directed manner.Discussion: For this reason, computational cancer biology aims to fill this gap. Unfortunately, computational cancer biology is not yet fully recognized as a coequal field in oncology, leading to a delay in its maturation and, as an immediate consequence, an under-exploration of high-throughput data for translational research.Summary: Here we argue that this imbalance, favoring 'wet lab-based activities', will be naturally rectified over time, if the next generation of scientists receives an academic education that provides a fair and competent introduction to computational biology and its manifold capabilities. Furthermore, we discuss a number of local educational provisions that can be implemented on university level to help in facilitating the process of harmonization.
- Published
- 2015
9. Linkage analysis between dominant and co-dominant makers in full-sib families of out-breeding species
- Author
-
Cosme Damião Cruz, Alexandre Alonso Alves, Acelino C. Alfenas, and Leonardo Lopes Bhering
- Subjects
Genetics ,statistical genomics ,lcsh:QH426-470 ,Diversity Arrays Technology ,Recombination frequency ,Single-nucleotide polymorphism ,Biology ,recombination frequency and maximum likelihood ,Plant Genetics ,lcsh:Genetics ,exogamic populations ,Genetic linkage ,Epistasis ,Allele ,DNA microarray ,Molecular Biology ,Genotyping ,Research Article ,Maximum likelihood ,Dominance (genetics) - Abstract
As high-throughput genomic tools, such as the DNA microarray platform, have lead to the development of novel genotyping procedures, such as Diversity Arrays Technology (DArT) and Single Nucleotide Polymorphisms (SNPs), it is likely that, in the future, high density linkage maps will be constructed from both dominant and co-dominant markers. Recently, a strictly genetic approach was described for estimating recombination frequency (r) between co-dominant markers in full-sib families. The complete set of maximum likelihood estimators for r in full-sib families was almost obtained, but unfortunately, one particular configuration involving dominant markers, segregating in a 3:1 ratio and co-dominant markers, was not considered. Here we add nine further estimators to the previously published set, thereby making it possible to cover all combinations of molecular markers with two to four alleles (without epistasis) in a full-sib family. This includes segregation in one or both parents, dominance and all linkage phase configurations.
- Published
- 2010
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