Genome Research Journal Impact Factor & Information

Publisher: Cold Spring Harbor Laboratory Press

Journal description

The journal focuses on genome studies in all species, and presents research that provides or aids in genome-based analyses of biological processes. The journal represents a nexus point where genomic information, applications, and technology come together with biological information to create a more global understanding of all biological systems.

Current impact factor: 13.85

Impact Factor Rankings

2015 Impact Factor Available summer 2015
2013 / 2014 Impact Factor 13.852
2012 Impact Factor 14.397
2011 Impact Factor 13.608
2010 Impact Factor 13.588
2009 Impact Factor 11.342
2008 Impact Factor 10.176
2007 Impact Factor 11.224
2006 Impact Factor 10.256
2005 Impact Factor 10.139
2004 Impact Factor 10.382
2003 Impact Factor 9.635
2002 Impact Factor 9.863
2001 Impact Factor 8.559
2000 Impact Factor 7.615
1999 Impact Factor 7.062

Impact factor over time

Impact factor
Year

Additional details

5-year impact 14.10
Cited half-life 5.70
Immediacy index 3.42
Eigenfactor 0.13
Article influence 7.49
Website Genome Research website
Other titles Genome research (Online), Genome research
ISSN 1088-9051
OCLC 37589079
Material type Online system or service, Periodical, Internet resource
Document type Internet Resource, Computer File, Journal / Magazine / Newspaper

Publisher details

Cold Spring Harbor Laboratory Press

  • Pre-print
    • Author can archive a pre-print version
  • Post-print
    • Author can archive a post-print version
  • Conditions
    • Author's pre-print on preprint server
    • Author's pre-print must be updated with citation, DOI and link to article upon publication
    • Publisher's version/PDF may be used after 6 months
    • Publisher's version/PDF and Author's post-print on author's personal website, institutional repository, funder's designated repository
    • Authors retain copyright
    • Content automatically sent to PubMed Central after 6 months
    • Publisher copyright and source must be acknowledged
    • Publisher last contacted on 15/07/2013
  • Classification
    ​ green

Publications in this journal

  • [Show abstract] [Hide abstract]
    ABSTRACT: DEAD-box RNA helicases eIF4A and Ded1 are believed to promote translation initiation by resolving mRNA secondary structures that impede ribosome attachment at the mRNA 5′ end or subsequent scanning of the 5′ UTR, but whether they perform unique or overlapping functions in vivo is poorly understood. We compared the effects of mutations in Ded1 or eIF4A on global translational efficiencies (TEs) in budding yeast Saccharomyces cerevisiae by ribosome footprint profiling. Despite similar reductions in bulk translation, inactivation of a cold-sensitive Ded1 mutant substantially reduced the TEs of >600 mRNAs, whereas inactivation of a temperature-sensitive eIF4A variant encoded by tif1-A79V (in a strain lacking the ortholog TIF2 ) yielded <40 similarly impaired mRNAs. The broader requirement for Ded1 did not reflect more pervasive secondary structures at low temperature, as inactivation of temperature-sensitive and cold-sensitive ded1 mutants gave highly correlated results. Interestingly, Ded1-dependent mRNAs exhibit greater than average 5′ UTR length and propensity for secondary structure, implicating Ded1 in scanning through structured 5′ UTRs. Reporter assays confirmed that cap-distal stem–loop insertions increase dependence on Ded1 but not eIF4A for efficient translation. While only a small fraction of mRNAs shows a heightened requirement for eIF4A, dependence on eIF4A is correlated with requirements for Ded1 and 5′ UTR features characteristic of Ded1-dependent mRNAs. Our findings suggest that Ded1 is critically required to promote scanning through secondary structures within 5′ UTRs, and while eIF4A cooperates with Ded1 in this function, it also promotes a step of initiation common to virtually all yeast mRNAs.
    Genome Research 08/2015; DOI:10.1101/gr.191601.115
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    ABSTRACT: Quantitative trait loci (QTLs) are being used to study genetic networks, protein functions, and systems properties that underlie phenotypic variation and disease risk in humans, model organisms, agricultural species, and natural populations. The challenges are many, beginning with the seemingly simple tasks of mapping QTLs and identifying their underlying genetic determinants. Various specialized resources have been developed to study complex traits in many model organisms. In the mouse, remarkably different pictures of genetic architectures are emerging. Chromosome Substitution Strains (CSSs) reveal many QTLs, large phenotypic effects, pervasive epistasis, and readily identified genetic variants. In contrast, other resources as well as genome-wide association studies (GWAS) in humans and other species reveal genetic architectures dominated with a relatively modest number of QTLs that have small individual and combined phenotypic effects. These contrasting architectures are the result of intrinsic differences in the study designs underlying different resources. The CSSs examine context-dependent phenotypic effects independently among individual genotypes, whereas with GWAS and other mouse resources, the average effect of each QTL is assessed among many individuals with heterogeneous genetic backgrounds. We argue that variation of genetic architectures among individuals is as important as population averages. Each of these important resources has particular merits and specific applications for these individual and population perspectives. Collectively, these resources together with high-throughput genotyping, sequencing and genetic engineering technologies, and information repositories highlight the power of the mouse for genetic, functional, and systems studies of complex traits and disease models.
    Genome Research 05/2015; 25(6). DOI:10.1101/gr.187450.114
  • Source
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    ABSTRACT: The mammalian genome harbors up to one million regulatory elements often located at great distances from their target genes. Long-range elements control genes through physical contact with promoters and can be recognized by the presence of specific histone modifications and transcription factor binding. Linking regulatory elements to specific promoters genome-wide is currently impeded by the limited resolution of high-throughput chromatin interaction assays. Here we apply a sequence capture approach to enrich Hi-C libraries for >22,000 annotated mouse promoters to identify statistically significant, long-range interactions at restriction fragment resolution, assigning long-range interacting elements to their target genes genome-wide in embryonic stem cells and fetal liver cells. The distal sites contacting active genes are enriched in active histone modifications and transcription factor occupancy, whereas inactive genes contact distal sites with repressive histone marks, demonstrating the regulatory potential of the distal elements identified. Furthermore, we find that coregulated genes cluster nonrandomly in spatial interaction networks correlated with their biological function and expression level. Interestingly, we find the strongest gene clustering in ES cells between transcription factor genes that control key developmental processes in embryogenesis. The results provide the first genome-wide catalog linking gene promoters to their long-range interacting elements and highlight the complex spatial regulatory circuitry controlling mammalian gene expression. © 2015 Schoenfelder et al.; Published by Cold Spring Harbor Laboratory Press.
    Genome Research 03/2015; 25(4). DOI:10.1101/gr.185272.114
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    ABSTRACT: Nascent strand sequencing (NS-seq) is used to discover DNA replication origins genome-wide, allowing identification of features for their specification. NS-seq depends on the ability of Lambda exonuclease (λ-exo) to efficiently digest parental DNA while leaving RNA-primer protected nascent strands intact. We took genomics and biochemical approaches to determine if λ-exo digests all parental DNA sequences equally. We report that λ-exo does not efficiently digest G-quadruplex (G4) structures in a plasmid. Moreover, λ-exo digestion of non-replicating genomic DNA (LexoG0) enriches GC-rich DNA and G4 motifs genome-wide. We used LexoG0 data to control for nascent strand independent λ-exo biases in NS-seq and validated this approach at the rDNA locus. The λ-exo-controlled NS-seq peaks are not GC-rich and only 35.5% overlap with 6.8% of all G4s, suggesting that G4s are not general determinants for origin specification, but may play a role for a subset. Interestingly, we observed a periodic spacing of G4 motifs and nucleosomes around the peak summits, suggesting that G4s may position nucleosomes at this subset of origins. Finally, we demonstrate that use of Na+ instead of K+ in the λ-exo digestion buffer reduced the effect of G4s on λ-exo digestion and discuss ways to increase both the sensitivity and specificity of NS-seq. Published by Cold Spring Harbor Laboratory Press.
    Genome Research 02/2015; 25(5). DOI:10.1101/gr.183848.114
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    ABSTRACT: In addition to mediating sister chromatid cohesion during the cell cycle, the cohesin complex associates with CTCF and with active gene regulatory elements to form long-range interactions between its binding sites. Genome-wide chromosome conformation capture had shown that cohesin's main role in interphase genome organization is in mediating interactions within architectural chromosome compartments, rather than specifying compartments per se. However, it remained unclear how cohesin-mediated interactions contribute to the regulation of gene expression. We have found that the binding of CTCF and cohesin is highly enriched at enhancers and in particular at enhancer arrays or 'super-enhancers' in mouse thymocytes. Using local and global chromosome conformation capture we demonstrate that enhancer elements associate not just in linear sequence, but also in 3-D, and that spatial enhancer clustering is facilitated by cohesin. The conditional deletion of cohesin from non-cycling thymocytes preserved enhancer position, H3K27ac, H4K4me1 and enhancer transcription, but weakened interactions between enhancers. Interestingly, ~50% of deregulated genes reside in the vicinity of enhancer elements, suggesting that cohesin regulates gene expression through spatial clustering of enhancer elements. We propose a model for cohesin-dependent gene regulation where spatial clustering of enhancer elements acts as a unified mechanism for both, enhancer-promoter 'connections' and 'insulation'. Published by Cold Spring Harbor Laboratory Press.
    Genome Research 02/2015; 25(4). DOI:10.1101/gr.184986.114