Genome biology Journal Impact Factor & Information

Publisher: BioMed Central

Journal description

Genome Biology publishes articles from the full spectrum of biology. Subjects covered include any aspect of molecular, cellular, organismal or population biology studied from a genomic perspective, as well as genomics, proteomics, bioinformatics, genomic methods (including structure prediction), computational biology, sequence analysis (including large-scale and cross-genome analyses), comparative biology and evolution.

Current impact factor: 10.47

Impact Factor Rankings

2015 Impact Factor Available summer 2015
2013 / 2014 Impact Factor 10.465
2012 Impact Factor 10.288
2011 Impact Factor 9.036
2010 Impact Factor 6.885

Impact factor over time

Impact factor

Additional details

5-year impact 0.00
Cited half-life 0.00
Immediacy index 0.00
Eigenfactor 0.00
Article influence 0.00
Website Genome Biology website
Other titles Genome biology (Online), Genome biology,, GB. C
ISSN 1465-6914
OCLC 49210873
Material type Document, Periodical, Internet resource
Document type Internet Resource, Computer File, Journal / Magazine / Newspaper

Publisher details

BioMed Central

  • Pre-print
    • Author can archive a pre-print version
  • Post-print
    • Author can archive a post-print version
  • Conditions
    • Publisher's version/PDF may be used
    • Eligible UK authors may deposit in OpenDepot
    • Creative Commons Attribution License
    • Copy of License must accompany any deposit.
    • All titles are open access journals
    • 'BioMed Central' is an imprint of 'Springer Verlag (Germany)'
  • Classification
    ‚Äč green

Publications in this journal

  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: Average genome size is an important, yet often overlooked, property of microbial communities. We developed MicrobeCensus to rapidly and accurately estimate average genome size from shotgun metagenomic data and applied our tool to 1,352 human microbiome samples. We found that average genome size differs significantly within and between body sites and tracks with major functional and taxonomic differences. In the gut, average genome size is positively correlated with the abundance of Bacteroides and genes related to carbohydrate metabolism. Importantly, we found that average genome size variation can bias comparative analyses, and that normalization improves detection of differentially abundant genes.
    Genome biology 12/2015; 16(1):51. DOI:10.1186/s13059-015-0611-7
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    ABSTRACT: SNPs are the most abundant polymorphism type, and have been explored in many crop genomic studies, including rice and maize. SNP discovery in allotetraploid cotton genomes has lagged behind that of other crops due to their complexity and polyploidy. In this study, genome-wide SNPs are detected systematically using next-generation sequencing and efficient SNP genotyping methods, and used to construct a linkage map and characterize the structural variations in polyploid cotton genomes. We construct an ultra-dense inter-specific genetic map comprising 4,999,048 SNP loci distributed unevenly in 26 allotetraploid cotton linkage groups and covering 4,042 cM. The map is used to order tetraploid cotton genome scaffolds for accurate assembly of G. hirsutum acc. TM-1. Recombination rates and hotspots are identified across the cotton genome by comparing the assembled draft sequence and the genetic map. Using this map, genome rearrangements and centromeric regions are identified in tetraploid cotton by combining information from the publicly-available G. raimondii genome with fluorescent in situ hybridization analysis. We report the genotype-by-sequencing method used to identify millions of SNPs between G. hirsutum and G. barbadense. We construct and use an ultra-dense SNP map to correct sequence mis-assemblies, merge scaffolds into pseudomolecules corresponding to chromosomes, detect genome rearrangements, and identify centromeric regions in allotetraploid cottons. We find that the centromeric retro-element sequence of tetraploid cotton derived from the D subgenome progenitor might have invaded the A subgenome centromeres after allotetrapolyploid formation. This study serves as a valuable genomic resource for genetic research and breeding of cotton.
    Genome biology 05/2015; 16(1):108. DOI:10.1186/s13059-015-0678-1
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    ABSTRACT: Recent studies indicate that DNA methylation can be used to identify transcriptional enhancers, but no systematic approach has been developed for genome-wide identification and analysis of enhancers based on DNA methylation. We describe ELMER (Enhancer Linking by Methylation/Expression Relationships), an R-based tool that uses DNA methylation to identify enhancers and correlates enhancer state with expression of nearby genes to identify transcriptional targets. Transcription factor motif analysis of enhancers is coupled with expression analysis of transcription factors to infer upstream regulators. Using ELMER, we investigated more than 2,000 tumor samples from The Cancer Genome Atlas. We identified networks regulated by known cancer drivers such as GATA3 and FOXA1 (breast cancer), SOX17 and FOXA2 (endometrial cancer), and NFE2L2, SOX2 and TP63 (squamous cell lung cancer). We also identified novel networks with prognostic associations, including RUNX1 in kidney cancer. We propose ELMER as a powerful new paradigm for understanding the cis-regulatory interface between cancer-associated transcription factors and their functional target genes.
    Genome biology 05/2015; 16(1):105. DOI:10.1186/s13059-015-0668-3
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    ABSTRACT: We present a new pair-wise genome alignment method, based on a simple concept of finding an optimal set of local alignments. It gains accuracy by not masking repeats, and by using a statistical model to quantify the (un)ambiguity of each alignment part. Compared to previous animal genome alignments, it aligns thousands of locations differently and with much higher similarity, strongly suggesting that the previous alignments are non-orthologous. The previous methods suffer from an overly-strong assumption of long un-rearranged blocks. The new alignments should help find interesting and unusual features, such as fast-evolving elements and micro-rearrangements, which are confounded by alignment errors.
    Genome biology 05/2015; 16(1):106. DOI:10.1186/s13059-015-0670-9
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    ABSTRACT: Protein domains are commonly used to assess the functional roles and evolutionary relationships of proteins and protein families. Here, we use the Pfam protein family database to examine a set of candidate partial domains. Pfam protein domains are often thought of as evolutionarily indivisible, structurally compact, units from which larger functional proteins are assembled; however, almost 4% of Pfam27 PfamA domains are shorter than 50% of their family model length, suggesting that more than half of the domain is missing at those locations. To better understand the structural nature of partial domains in proteins, we examined 30,961 partial domain regions from 136 domain families contained in a representative subset of PfamA domains (RefProtDom2 or RPD2). We characterized three types of apparent partial domains: split domains, bounded partials, and unbounded partials. We find that bounded partial domains are over-represented in eukaryotes and in lower quality protein predictions, suggesting that they often result from inaccurate genome assemblies or gene models. We also find that a large percentage of unbounded partial domains produce long alignments, which suggests that their annotation as a partial is an alignment artifact; yet some can be found as partials in other sequence contexts. Partial domains are largely the result of alignment and annotation artifacts and should be viewed with caution. The presence of partial domain annotations in proteins should raise the concern that the prediction of the protein's gene may be incomplete. In general, protein domains can be considered the structural building blocks of proteins.
    Genome biology 05/2015; 16(1):99. DOI:10.1186/s13059-015-0656-7
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    ABSTRACT: Technologies for single-cell sequencing are improving steadily. A recent study describes a new method for interrogating all coding sequences of the human genome at single-cell resolution. See related research by Leung et al.,
    Genome biology 04/2015; 16(1):86. DOI:10.1186/s13059-015-0650-0
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    ABSTRACT: Aging and sun exposure are the leading causes of skin cancer. It has been shown that epigenetic changes, such as DNA methylation, are well established mechanisms for cancer, and also have emerging roles in aging and common disease. Here, we directly ask whether DNA methylation is altered following skin aging and/or chronic sun exposure in humans. We compare epidermis and dermis of both sun-protected and sun-exposed skin derived from younger subjects (under 35 years old) and older subjects (over 60 years old), using the Infinium HumanMethylation450 array and whole genome bisulfite sequencing. We observe large blocks of the genome that are hypomethylated in older, sun-exposed epidermal samples, with the degree of hypomethylation associated with clinical measures of photo-aging. We replicate these findings using whole genome bisulfite sequencing, comparing epidermis from an additional set of younger and older subjects. These blocks largely overlap known hypomethylated blocks in colon cancer and we observe that these same regions are similarly hypomethylated in squamous cell carcinoma samples. These data implicate large scale epigenomic change in mediating the effects of environmental damage with photo-aging.
    Genome biology 04/2015; 16(1):80. DOI:10.1186/s13059-015-0644-y