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Author

Carrie A. Davis

Bio: Carrie A. Davis is a academic researcher at Cold Spring Harbor Laboratory who has co-authored 50 publication(s) receiving 44756 citation(s). The author has an hindex of 35. Previous affiliations of Carrie A. Davis include University of California, Santa Cruz & Stanford University. The author has done significant research in the topic(s): Gene & Genome.

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Topics: Gene, Genome, Transcriptome ...read more
Papers
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Open accessJournal ArticleDOI: 10.1093/BIOINFORMATICS/BTS635
01 Jan 2013-Bioinformatics
Abstract: Motivation Accurate alignment of high-throughput RNA-seq data is a challenging and yet unsolved problem because of the non-contiguous transcript structure, relatively short read lengths and constantly increasing throughput of the sequencing technologies. Currently available RNA-seq aligners suffer from high mapping error rates, low mapping speed, read length limitation and mapping biases. Results To align our large (>80 billon reads) ENCODE Transcriptome RNA-seq dataset, we developed the Spliced Transcripts Alignment to a Reference (STAR) software based on a previously undescribed RNA-seq alignment algorithm that uses sequential maximum mappable seed search in uncompressed suffix arrays followed by seed clustering and stitching procedure. STAR outperforms other aligners by a factor of >50 in mapping speed, aligning to the human genome 550 million 2 × 76 bp paired-end reads per hour on a modest 12-core server, while at the same time improving alignment sensitivity and precision. In addition to unbiased de novo detection of canonical junctions, STAR can discover non-canonical splices and chimeric (fusion) transcripts, and is also capable of mapping full-length RNA sequences. Using Roche 454 sequencing of reverse transcription polymerase chain reaction amplicons, we experimentally validated 1960 novel intergenic splice junctions with an 80-90% success rate, corroborating the high precision of the STAR mapping strategy. Availability and implementation STAR is implemented as a standalone C++ code. STAR is free open source software distributed under GPLv3 license and can be downloaded from http://code.google.com/p/rna-star/.

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Topics: MRNA Sequencing (57%)

20,172 Citations


Open accessJournal ArticleDOI: 10.1038/NATURE11233
Sarah Djebali, Carrie A. Davis1, Angelika Merkel, Alexander Dobin1  +84 moreInstitutions (14)
06 Sep 2012-Nature
Abstract: Eukaryotic cells make many types of primary and processed RNAs that are found either in specific subcellular compartments or throughout the cells. A complete catalogue of these RNAs is not yet available and their characteristic subcellular localizations are also poorly understood. Because RNA represents the direct output of the genetic information encoded by genomes and a significant proportion of a cell's regulatory capabilities are focused on its synthesis, processing, transport, modification and translation, the generation of such a catalogue is crucial for understanding genome function. Here we report evidence that three-quarters of the human genome is capable of being transcribed, as well as observations about the range and levels of expression, localization, processing fates, regulatory regions and modifications of almost all currently annotated and thousands of previously unannotated RNAs. These observations, taken together, prompt a redefinition of the concept of a gene.

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Topics: Long non-coding RNA (59%), Human genome (54%), Gene (53%) ...read more

3,863 Citations


Open accessJournal ArticleDOI: 10.1101/GR.132159.111
01 Sep 2012-Genome Research
Abstract: The human genome contains many thousands of long noncoding RNAs (lncRNAs). While several studies have demonstrated compelling biological and disease roles for individual examples, analytical and experimental approaches to investigate these genes have been hampered by the lack of comprehensive lncRNA annotation. Here, we present and analyze the most complete human lncRNA annotation to date, produced by the GENCODE consortium within the framework of the ENCODE project and comprising 9277 manually annotated genes producing 14,880 transcripts. Our analyses indicate that lncRNAs are generated through pathways similar to that of protein-coding genes, with similar histone-modification profiles, splicing signals, and exon/intron lengths. In contrast to protein-coding genes, however, lncRNAs display a striking bias toward two-exon transcripts, they are predominantly localized in the chromatin and nucleus, and a fraction appear to be preferentially processed into small RNAs. They are under stronger selective pressure than neutrally evolving sequences-particularly in their promoter regions, which display levels of selection comparable to protein-coding genes. Importantly, about one-third seem to have arisen within the primate lineage. Comprehensive analysis of their expression in multiple human organs and brain regions shows that lncRNAs are generally lower expressed than protein-coding genes, and display more tissue-specific expression patterns, with a large fraction of tissue-specific lncRNAs expressed in the brain. Expression correlation analysis indicates that lncRNAs show particularly striking positive correlation with the expression of antisense coding genes. This GENCODE annotation represents a valuable resource for future studies of lncRNAs.

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Topics: GENCODE (57%), Human genome (52%), Gene expression profiling (50%)

3,709 Citations


Open access
01 Sep 2012-
Topics: ENCODE (68%), Human genome (63%)

2,767 Citations


Open accessJournal ArticleDOI: 10.1038/NATURE13182
27 Mar 2014-Nature
Abstract: Regulated transcription controls the diversity, developmental pathways and spatial organization of the hundreds of cell types that make up a mammal Using single-molecule cDNA sequencing, we mapped transcription start sites (TSSs) and their usage in human and mouse primary cells, cell lines and tissues to produce a comprehensive overview of mammalian gene expression across the human body We find that few genes are truly 'housekeeping', whereas many mammalian promoters are composite entities composed of several closely separated TSSs, with independent cell-type-specific expression profiles TSSs specific to different cell types evolve at different rates, whereas promoters of broadly expressed genes are the most conserved Promoter-based expression analysis reveals key transcription factors defining cell states and links them to binding-site motifs The functions of identified novel transcripts can be predicted by coexpression and sample ontology enrichment analyses The functional annotation of the mammalian genome 5 (FANTOM5) project provides comprehensive expression profiles and functional annotation of mammalian cell-type-specific transcriptomes with wide applications in biomedical research

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  • Figure 1. Promoter discovery and definition in FANTOM5 a, Samples profiled in FANTOM5. b, Reproducible cell-type-specific CAGE patterns observed for the 266 base CpG island associated B4GALT1 locus transcription initiation region hg19:chr9:33167138.33167403. CAGE profiles for CD4+ T cells (blue), CD14+ monocytes (gold), aortic smooth muscle cells (green) and the adrenal cortex adenocarcinoma cell line SW-13 (red) are shown. A combined pooled profile showing TSS distribution across the entire human collection is shown in black. Values on the y axis correspond to maximum normalized TPM for a single base in each track. c, Decompositionbased peak identification (DPI) finds 6 differentially used peaks within this composite transcription initiation region (note: peaks are labelled from p1@B4GALT1 with most tag support through to p7@B4GALT1 with the least tag support; p4@B4GALT1 is not shown and is in the 3′ UTR of the locus at position hg19::chr9:33111241.33111254−). Note in particular one large broad region on the left used in all samples and a sharp peak to the right, preferentially used in the aortic smooth muscle cells. d, Venn diagram showing DPI defined peaks expressed at ≥10 TPM in primary cells (red), tissues (blue) and cell lines (green). e, Fraction of unannotated peaks observed in subsets of d. P, primary cells, T, tissues, C, cell lines, PT, TC, PC and PTC correspond to peaks found in multiple sample types, for example, PT, found in primary cells and tissue samples.
    Figure 1. Promoter discovery and definition in FANTOM5 a, Samples profiled in FANTOM5. b, Reproducible cell-type-specific CAGE patterns observed for the 266 base CpG island associated B4GALT1 locus transcription initiation region hg19:chr9:33167138.33167403. CAGE profiles for CD4+ T cells (blue), CD14+ monocytes (gold), aortic smooth muscle cells (green) and the adrenal cortex adenocarcinoma cell line SW-13 (red) are shown. A combined pooled profile showing TSS distribution across the entire human collection is shown in black. Values on the y axis correspond to maximum normalized TPM for a single base in each track. c, Decompositionbased peak identification (DPI) finds 6 differentially used peaks within this composite transcription initiation region (note: peaks are labelled from p1@B4GALT1 with most tag support through to p7@B4GALT1 with the least tag support; p4@B4GALT1 is not shown and is in the 3′ UTR of the locus at position hg19::chr9:33111241.33111254−). Note in particular one large broad region on the left used in all samples and a sharp peak to the right, preferentially used in the aortic smooth muscle cells. d, Venn diagram showing DPI defined peaks expressed at ≥10 TPM in primary cells (red), tissues (blue) and cell lines (green). e, Fraction of unannotated peaks observed in subsets of d. P, primary cells, T, tissues, C, cell lines, PT, TC, PC and PTC correspond to peaks found in multiple sample types, for example, PT, found in primary cells and tissue samples.
  • Figure 2. Cell-type-restricted and housekeeping transcripts encoded in the mammalian genome a, Density plot summarizing the distribution of relative log expression (RLE) normalized maximum and median TPM expression values for the 185K robustly detected human peaks identified by FANTOM5 (colour bar on right indicates relative density). Box and whiskers plots above and to right show distribution of median and maximum values in the data set (box shows the interquartile range). Promoters of named genes are highlighted to show extremes of expression level and expression breadth, note the alternative promoters of IRF9 and TRMT5 have different maximums and breadths of expression (see Extended Data Fig. 10). Fraction on left of the red vertical dashed line corresponds to peaks detected in less than 50% of samples with non-ubiquitous (cell-type-restricted) expression patterns (median < 0.2 TPM). Fraction below the red diagonal dashed line corresponds to ubiquitous-uniform (housekeeping) expression profiles (maximum < 10× median). Fraction above diagonal and to the right of the vertical dashed lines corresponds to ubiquitous-non-uniform expression profiles (maximum > 10× median). b, Box and whisker plots showing the distribution of expression levels for the same peaks as in a across the 889 samples (box shows the interquartile range).
    Figure 2. Cell-type-restricted and housekeeping transcripts encoded in the mammalian genome a, Density plot summarizing the distribution of relative log expression (RLE) normalized maximum and median TPM expression values for the 185K robustly detected human peaks identified by FANTOM5 (colour bar on right indicates relative density). Box and whiskers plots above and to right show distribution of median and maximum values in the data set (box shows the interquartile range). Promoters of named genes are highlighted to show extremes of expression level and expression breadth, note the alternative promoters of IRF9 and TRMT5 have different maximums and breadths of expression (see Extended Data Fig. 10). Fraction on left of the red vertical dashed line corresponds to peaks detected in less than 50% of samples with non-ubiquitous (cell-type-restricted) expression patterns (median < 0.2 TPM). Fraction below the red diagonal dashed line corresponds to ubiquitous-uniform (housekeeping) expression profiles (maximum < 10× median). Fraction above diagonal and to the right of the vertical dashed lines corresponds to ubiquitous-non-uniform expression profiles (maximum > 10× median). b, Box and whisker plots showing the distribution of expression levels for the same peaks as in a across the 889 samples (box shows the interquartile range).
  • Figure 3. TSS conservation as a function of expression properties and functional annotation a, b, Human robust TSS coordinates were projected through EPO12 whole genome multiple sequence alignments (Supplementary Methods). The y-axis values show the fraction of human TSSs that align to an orthologous position in the indicated species. The x axis shows the relative divergence of macaque, dog and mouse genomes as the substitution rate at fourfold degenerate sites in protein coding sequence. The TSS locations were genome permuted (Supplementary Methods) and then projected through EPO12 alignments to give the null expectation (dashed blue line). The 95% confidence intervals of 1, 000 samples of 1, 000 TSS are shown (blue shading). a, TSS mapped to the 5′ ends of protein coding and noncoding transcripts are labelled (C and N, respectively), those that do not map to a known transcript 5′ end are shown as the ‘anonymous’ category. With the exception of anonymous, all robust TSSs represented in both panels are associated with the 59 ends of previously annotated transcripts. Non-ubiquitous (cell-type-restricted), ubiquitous-uniform (housekeeping) and non-uniform-ubiquitous were defined as in Fig. 2. Ultra-housekeeping TSSs were defined as those with less than fivefold difference between maximum and median. The category top 1000 UDE represents the 1,000 ubiquitous TSSs that are most differentially expressed4. There are 1,016 ultra-housekeeping TSSs, 276 ubiquitous-uniform non-coding TSSs and all other categories contain over 2, 000 TSSs. b, Same axes as panel a showing TSSs with expression that is biased towards a single expression facet (larger mutually exclusive grouping of the primary cell and tissue samples based on the sample ontologies CO and UBERON, defined in ref. 4). Only expression facets with greater than 250 enriched TSSs are shown. For clarity, only a subset of expression facets are coloured and labelled.
    Figure 3. TSS conservation as a function of expression properties and functional annotation a, b, Human robust TSS coordinates were projected through EPO12 whole genome multiple sequence alignments (Supplementary Methods). The y-axis values show the fraction of human TSSs that align to an orthologous position in the indicated species. The x axis shows the relative divergence of macaque, dog and mouse genomes as the substitution rate at fourfold degenerate sites in protein coding sequence. The TSS locations were genome permuted (Supplementary Methods) and then projected through EPO12 alignments to give the null expectation (dashed blue line). The 95% confidence intervals of 1, 000 samples of 1, 000 TSS are shown (blue shading). a, TSS mapped to the 5′ ends of protein coding and noncoding transcripts are labelled (C and N, respectively), those that do not map to a known transcript 5′ end are shown as the ‘anonymous’ category. With the exception of anonymous, all robust TSSs represented in both panels are associated with the 59 ends of previously annotated transcripts. Non-ubiquitous (cell-type-restricted), ubiquitous-uniform (housekeeping) and non-uniform-ubiquitous were defined as in Fig. 2. Ultra-housekeeping TSSs were defined as those with less than fivefold difference between maximum and median. The category top 1000 UDE represents the 1,000 ubiquitous TSSs that are most differentially expressed4. There are 1,016 ultra-housekeeping TSSs, 276 ubiquitous-uniform non-coding TSSs and all other categories contain over 2, 000 TSSs. b, Same axes as panel a showing TSSs with expression that is biased towards a single expression facet (larger mutually exclusive grouping of the primary cell and tissue samples based on the sample ontologies CO and UBERON, defined in ref. 4). Only expression facets with greater than 250 enriched TSSs are shown. For clarity, only a subset of expression facets are coloured and labelled.
  • Figure 4. Coexpression clustering of human promoters in FANTOM5
    Figure 4. Coexpression clustering of human promoters in FANTOM5
Topics: Mammalian promoter database (63%), Promoter (57%), Cap analysis gene expression (53%) ...read more

1,479 Citations


Cited by
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Open accessJournal ArticleDOI: 10.1093/BIOINFORMATICS/BTS635
01 Jan 2013-Bioinformatics
Abstract: Motivation Accurate alignment of high-throughput RNA-seq data is a challenging and yet unsolved problem because of the non-contiguous transcript structure, relatively short read lengths and constantly increasing throughput of the sequencing technologies. Currently available RNA-seq aligners suffer from high mapping error rates, low mapping speed, read length limitation and mapping biases. Results To align our large (>80 billon reads) ENCODE Transcriptome RNA-seq dataset, we developed the Spliced Transcripts Alignment to a Reference (STAR) software based on a previously undescribed RNA-seq alignment algorithm that uses sequential maximum mappable seed search in uncompressed suffix arrays followed by seed clustering and stitching procedure. STAR outperforms other aligners by a factor of >50 in mapping speed, aligning to the human genome 550 million 2 × 76 bp paired-end reads per hour on a modest 12-core server, while at the same time improving alignment sensitivity and precision. In addition to unbiased de novo detection of canonical junctions, STAR can discover non-canonical splices and chimeric (fusion) transcripts, and is also capable of mapping full-length RNA sequences. Using Roche 454 sequencing of reverse transcription polymerase chain reaction amplicons, we experimentally validated 1960 novel intergenic splice junctions with an 80-90% success rate, corroborating the high precision of the STAR mapping strategy. Availability and implementation STAR is implemented as a standalone C++ code. STAR is free open source software distributed under GPLv3 license and can be downloaded from http://code.google.com/p/rna-star/.

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Topics: MRNA Sequencing (57%)

20,172 Citations


Open access
28 Jul 2005-
Abstract: 抗原变异可使得多种致病微生物易于逃避宿主免疫应答。表达在感染红细胞表面的恶性疟原虫红细胞表面蛋白1(PfPMP1)与感染红细胞、内皮细胞、树突状细胞以及胎盘的单个或多个受体作用,在黏附及免疫逃避中起关键的作用。每个单倍体基因组var基因家族编码约60种成员,通过启动转录不同的var基因变异体为抗原变异提供了分子基础。

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18,940 Citations


Open accessJournal ArticleDOI: 10.1038/NATURE11247
06 Sep 2012-Nature
Abstract: The human genome encodes the blueprint of life, but the function of the vast majority of its nearly three billion bases is unknown. The Encyclopedia of DNA Elements (ENCODE) project has systematically mapped regions of transcription, transcription factor association, chromatin structure and histone modification. These data enabled us to assign biochemical functions for 80% of the genome, in particular outside of the well-studied protein-coding regions. Many discovered candidate regulatory elements are physically associated with one another and with expressed genes, providing new insights into the mechanisms of gene regulation. The newly identified elements also show a statistical correspondence to sequence variants linked to human disease, and can thereby guide interpretation of this variation. Overall, the project provides new insights into the organization and regulation of our genes and genome, and is an expansive resource of functional annotations for biomedical research.

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Topics: ENCODE (66%), Genome project (63%), Genome (59%) ...read more

11,598 Citations


Open accessJournal ArticleDOI: 10.1186/GB-2013-14-4-R36
Daehwan Kim1, Daehwan Kim2, Geo Pertea3, Cole Trapnell4  +5 moreInstitutions (7)
25 Apr 2013-Genome Biology
Abstract: TopHat is a popular spliced aligner for RNA-sequence (RNA-seq) experiments. In this paper, we describe TopHat2, which incorporates many significant enhancements to TopHat. TopHat2 can align reads of various lengths produced by the latest sequencing technologies, while allowing for variable-length indels with respect to the reference genome. In addition to de novo spliced alignment, TopHat2 can align reads across fusion breaks, which can occur after genomic translocations. TopHat2 combines the ability to identify novel splice sites with direct mapping to known transcripts, producing sensitive and accurate alignments, even for highly repetitive genomes or in the presence of pseudogenes. TopHat2 is available at http://ccb.jhu.edu/software/tophat.

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Topics: Reference genome (51%), Pseudogene (50%)

9,972 Citations


Open accessJournal ArticleDOI: 10.1038/NPROT.2012.016
Cole Trapnell1, Adam Roberts2, Loyal A. Goff3, Loyal A. Goff4  +11 moreInstitutions (7)
01 Mar 2012-Nature Protocols
Abstract: Recent advances in high-throughput cDNA sequencing (RNA-seq) can reveal new genes and splice variants and quantify expression genome-wide in a single assay. The volume and complexity of data from RNA-seq experiments necessitate scalable, fast and mathematically principled analysis software. TopHat and Cufflinks are free, open-source software tools for gene discovery and comprehensive expression analysis of high-throughput mRNA sequencing (RNA-seq) data. Together, they allow biologists to identify new genes and new splice variants of known ones, as well as compare gene and transcript expression under two or more conditions. This protocol describes in detail how to use TopHat and Cufflinks to perform such analyses. It also covers several accessory tools and utilities that aid in managing data, including CummeRbund, a tool for visualizing RNA-seq analysis results. Although the procedure assumes basic informatics skills, these tools assume little to no background with RNA-seq analysis and are meant for novices and experts alike. The protocol begins with raw sequencing reads and produces a transcriptome assembly, lists of differentially expressed and regulated genes and transcripts, and publication-quality visualizations of analysis results. The protocol's execution time depends on the volume of transcriptome sequencing data and available computing resources but takes less than 1 d of computer time for typical experiments and ∼1 h of hands-on time.

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Topics: MRNA Sequencing (61%), RNA-Seq (54%)

9,549 Citations


Performance
Metrics

Author's H-index: 35

No. of papers from the Author in previous years
YearPapers
20212
20203
20192
20183
20173
20165

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Author's top 5 most impactful journals

Nature

9 papers, 9.3K citations

Genome Research

7 papers, 5.1K citations

bioRxiv

6 papers, 30 citations

Genome Biology

5 papers, 666 citations

Nucleic Acids Research

3 papers, 1.2K citations

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