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  • 1
    Online Resource
    Online Resource
    Common Sequence Polymorphisms Shaping Genetic Diversity in Arabidopsis thaliana
    American Association for the Advancement of Science (AAAS) ; 2007
    In:  Science Vol. 317, No. 5836 ( 2007-07-20), p. 338-342
    Details
    In: Science, American Association for the Advancement of Science (AAAS), Vol. 317, No. 5836 ( 2007-07-20), p. 338-342
    Abstract: The genomes of individuals from the same species vary in sequence as a result of different evolutionary processes. To examine the patterns of, and the forces shaping, sequence variation in Arabidopsis thaliana , we performed high-density array resequencing of 20 diverse strains (accessions). More than 1 million nonredundant single-nucleotide polymorphisms (SNPs) were identified at moderate false discovery rates (FDRs), and ∼4% of the genome was identified as being highly dissimilar or deleted relative to the reference genome sequence. Patterns of polymorphism are highly nonrandom among gene families, with genes mediating interaction with the biotic environment having exceptional polymorphism levels. At the chromosomal scale, regional variation in polymorphism was readily apparent. A scan for recent selective sweeps revealed several candidate regions, including a notable example in which almost all variation was removed in a 500-kilobase window. Analyzing the polymorphisms we describe in larger sets of accessions will enable a detailed understanding of forces shaping population-wide sequence variation in A. thaliana .
    Type of Medium: Online Resource
    ISSN: 0036-8075 , 1095-9203
    URL: Article
    DOI: 10.1126/science.1138632
    RVK:
    TA 1000
    RVK:
    WA 15000
    Language: English
    Publisher: American Association for the Advancement of Science (AAAS)
    Publication Date: 2007
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    detail.hit.zdb_id: 2066996-3
    detail.hit.zdb_id: 2060783-0
    SSG: 11
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  • 2
    Online Resource
    Online Resource
    Sparse Gaussian Processes on Discrete Domains
    Institute of Electrical and Electronics Engineers (IEEE) ; 2021
    In:  IEEE Access Vol. 9 ( 2021), p. 76750-76758
    Details
    In: IEEE Access, Institute of Electrical and Electronics Engineers (IEEE), Vol. 9 ( 2021), p. 76750-76758
    Type of Medium: Online Resource
    ISSN: 2169-3536
    URL: Article
    DOI: 10.1109/ACCESS.2021.3082761
    Language: Unknown
    Publisher: Institute of Electrical and Electronics Engineers (IEEE)
    Publication Date: 2021
    detail.hit.zdb_id: 2687964-5
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  • 3
    Online Resource
    Online Resource
    Determination and Inference of Eukaryotic Transcription Factor Sequence Specificity
    Elsevier BV ; 2014
    In:  Cell Vol. 158, No. 6 ( 2014-09), p. 1431-1443
    Details
    In: Cell, Elsevier BV, Vol. 158, No. 6 ( 2014-09), p. 1431-1443
    Type of Medium: Online Resource
    ISSN: 0092-8674
    URL: Article
    DOI: 10.1016/j.cell.2014.08.009
    RVK:
    XA 36269
    RVK:
    WA 15000
    Language: English
    Publisher: Elsevier BV
    Publication Date: 2014
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    SSG: 12
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  • 4
    Online Resource
    Online Resource
    popSTR: population-scale detection of STR variants
    Oxford University Press (OUP) ; 2017
    In:  Bioinformatics Vol. 33, No. 24 ( 2017-12-15), p. 4041-4048
    Details
    In: Bioinformatics, Oxford University Press (OUP), Vol. 33, No. 24 ( 2017-12-15), p. 4041-4048
    Abstract: Microsatellites, also known as short tandem repeats (STRs), are tracts of repetitive DNA sequences containing motifs ranging from two to six bases. Microsatellites are one of the most abundant type of variation in the human genome, after single nucleotide polymorphisms (SNPs) and Indels. Microsatellite analysis has a wide range of applications, including medical genetics, forensics and construction of genetic genealogy. However, microsatellite variations are rarely considered in whole-genome sequencing studies, in large due to a lack of tools capable of analyzing them. Results Here we present a microsatellite genotyper, optimized for Illumina WGS data, which is both faster and more accurate than other methods previously presented. There are two main ingredients to our improvements. First we reduce the amount of sequencing data necessary for creating microsatellite profiles by using previously aligned sequencing data. Second, we use population information to train microsatellite and individual specific error profiles. By comparing our genotyping results to genotypes generated by capillary electrophoresis we show that our error rates are 50% lower than those of lobSTR, another program specifically developed to determine microsatellite genotypes. Availability and Implementation Source code is available on Github: https://github.com/DecodeGenetics/popSTR
    Type of Medium: Online Resource
    ISSN: 1367-4803 , 1367-4811
    URL: Article
    DOI: 10.1093/bioinformatics/btw568
    Language: English
    Publisher: Oxford University Press (OUP)
    Publication Date: 2017
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    SSG: 12
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  • 5
    Online Resource
    Online Resource
    HAPRAP: a haplotype-based iterative method for statistical fine mapping using GWAS summary statistics
    Oxford University Press (OUP) ; 2017
    In:  Bioinformatics Vol. 33, No. 1 ( 2017-01-01), p. 79-86
    Details
    In: Bioinformatics, Oxford University Press (OUP), Vol. 33, No. 1 ( 2017-01-01), p. 79-86
    Abstract: Fine mapping is a widely used approach for identifying the causal variant(s) at disease-associated loci. Standard methods (e.g. multiple regression) require individual level genotypes. Recent fine mapping methods using summary-level data require the pairwise correlation coefficients (r2) of the variants. However, haplotypes rather than pairwise r2, are the true biological representation of linkage disequilibrium (LD) among multiple loci. In this article, we present an empirical iterative method, HAPlotype Regional Association analysis Program (HAPRAP), that enables fine mapping using summary statistics and haplotype information from an individual-level reference panel. Results Simulations with individual-level genotypes show that the results of HAPRAP and multiple regression are highly consistent. In simulation with summary-level data, we demonstrate that HAPRAP is less sensitive to poor LD estimates. In a parametric simulation using Genetic Investigation of ANthropometric Traits height data, HAPRAP performs well with a small training sample size (N  & lt; 2000) while other methods become suboptimal. Moreover, HAPRAP’s performance is not affected substantially by single nucleotide polymorphisms (SNPs) with low minor allele frequencies. We applied the method to existing quantitative trait and binary outcome meta-analyses (human height, QTc interval and gallbladder disease); all previous reported association signals were replicated and two additional variants were independently associated with human height. Due to the growing availability of summary level data, the value of HAPRAP is likely to increase markedly for future analyses (e.g. functional prediction and identification of instruments for Mendelian randomization). Availability and Implementation The HAPRAP package and documentation are available at http://apps.biocompute.org.uk/haprap/ Supplementary information Supplementary data are available at Bioinformatics online.
    Type of Medium: Online Resource
    ISSN: 1367-4803 , 1367-4811
    URL: Article
    DOI: 10.1093/bioinformatics/btw565
    Language: English
    Publisher: Oxford University Press (OUP)
    Publication Date: 2017
    detail.hit.zdb_id: 1468345-3
    SSG: 12
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  • 6
    Online Resource
    Online Resource
    Rail-RNA: scalable analysis of RNA-seq splicing and coverage
    Oxford University Press (OUP) ; 2017
    In:  Bioinformatics Vol. 33, No. 24 ( 2017-12-15), p. 4033-4040
    Details
    In: Bioinformatics, Oxford University Press (OUP), Vol. 33, No. 24 ( 2017-12-15), p. 4033-4040
    Abstract: RNA sequencing (RNA-seq) experiments now span hundreds to thousands of samples. Current spliced alignment software is designed to analyze each sample separately. Consequently, no information is gained from analyzing multiple samples together, and it requires extra work to obtain analysis products that incorporate data from across samples. Results We describe Rail-RNA, a cloud-enabled spliced aligner that analyzes many samples at once. Rail-RNA eliminates redundant work across samples, making it more efficient as samples are added. For many samples, Rail-RNA is more accurate than annotation-assisted aligners. We use Rail-RNA to align 667 RNA-seq samples from the GEUVADIS project on Amazon Web Services in under 16 h for US$0.91 per sample. Rail-RNA outputs alignments in SAM/BAM format; but it also outputs (i) base-level coverage bigWigs for each sample; (ii) coverage bigWigs encoding normalized mean and median coverages at each base across samples analyzed; and (iii) exon–exon splice junctions and indels (features) in columnar formats that juxtapose coverages in samples in which a given feature is found. Supplementary outputs are ready for use with downstream packages for reproducible statistical analysis. We use Rail-RNA to identify expressed regions in the GEUVADIS samples and show that both annotated and unannotated (novel) expressed regions exhibit consistent patterns of variation across populations and with respect to known confounding variables. Availability and Implementation Rail-RNA is open-source software available at http://rail.bio. Supplementary information Supplementary data are available at Bioinformatics online.
    Type of Medium: Online Resource
    ISSN: 1367-4803 , 1367-4811
    URL: Article
    DOI: 10.1093/bioinformatics/btw575
    Language: English
    Publisher: Oxford University Press (OUP)
    Publication Date: 2017
    detail.hit.zdb_id: 1468345-3
    SSG: 12
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  • 7
    Online Resource
    Online Resource
    A Rapid Translational Immune Response Program in CD8 Memory T Lymphocytes
    The American Association of Immunologists ; 2022
    In:  The Journal of Immunology Vol. 209, No. 6 ( 2022-09-15), p. 1189-1199
    Details
    In: The Journal of Immunology, The American Association of Immunologists, Vol. 209, No. 6 ( 2022-09-15), p. 1189-1199
    Abstract: The activation of memory T cells is a very rapid and concerted cellular response that requires coordination between cellular processes in different compartments and on different time scales. In this study, we use ribosome profiling and deep RNA sequencing to define the acute mRNA translation changes in CD8 memory T cells following initial activation events. We find that initial translation enables subsequent events of human and mouse T cell activation and expansion. Briefly, early events in the activation of Ag-experienced CD8 T cells are insensitive to transcriptional blockade with actinomycin D, and instead depend on the translation of pre-existing mRNAs and are blocked by cycloheximide. Ribosome profiling identifies ∼92 mRNAs that are recruited into ribosomes following CD8 T cell stimulation. These mRNAs typically have structured GC and pyrimidine-rich 5′ untranslated regions and they encode key regulators of T cell activation and proliferation such as Notch1, Ifngr1, Il2rb, and serine metabolism enzymes Psat1 and Shmt2 (serine hydroxymethyltransferase 2), as well as translation factors eEF1a1 (eukaryotic elongation factor α1) and eEF2 (eukaryotic elongation factor 2). The increased production of receptors of IL-2 and IFN-γ precedes the activation of gene expression and augments cellular signals and T cell activation. Taken together, we identify an early RNA translation program that acts in a feed-forward manner to enable the rapid and dramatic process of CD8 memory T cell expansion and activation.
    Type of Medium: Online Resource
    ISSN: 0022-1767 , 1550-6606
    URL: Article
    DOI: 10.4049/jimmunol.2100537
    RVK:
    XA 54100
    RVK:
    XA 10000
    Language: English
    Publisher: The American Association of Immunologists
    Publication Date: 2022
    detail.hit.zdb_id: 1475085-5
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