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Comparative genomics

Section edited by Graziano Pesole

This section incorporates all aspects of comparative genomic analysis including but not limited to: methods and applications for the analysis of comparative phylogenetic and genomic data.

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  1. Content type: Methodology article

    Genomic-scale sequence alignments are increasingly used to infer phylogenies in order to better understand the processes and patterns of evolution. Different partitions within these new alignments (e.g., genes...

    Authors: James C. Wilgenbusch, Wen Huang and Kyle A. Gallivan

    Citation: BMC Bioinformatics 2017 18:85

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  2. Content type: Software

    Gene set analysis (in a form of functionally related genes or pathways) has become the method of choice for analyzing omics data in general and gene expression data in particular. There are many statistical me...

    Authors: Yasir Rahmatallah, Boris Zybailov, Frank Emmert-Streib and Galina Glazko

    Citation: BMC Bioinformatics 2017 18:61

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  3. Content type: Software

    With the advancement in high-throughput technologies, researchers can simultaneously investigate gene expression and copy number alteration (CNA) data from individual patients at a lower cost. Traditional anal...

    Authors: Yi-Pin Lai, Liang-Bo Wang, Wei-An Wang, Liang-Chuan Lai, Mong-Hsun Tsai, Tzu-Pin Lu and Eric Y. Chuang

    Citation: BMC Bioinformatics 2017 18:35

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  4. Content type: Software

    The analysis of DNA copy number variants (CNV) has increasing impact in the field of genetic diagnostics and research. However, the interpretation of CNV data derived from high resolution array CGH or NGS plat...

    Authors: Katrin Tebel, Vivien Boldt, Anne Steininger, Matthias Port, Grit Ebert and Reinhard Ullmann

    Citation: BMC Bioinformatics 2017 18:19

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  5. Content type: Database

    Accurate methods capable of predicting the impact of single nucleotide variants (SNVs) are assuming ever increasing importance. There exists a plethora of in silico algorithms designed to help identify and priori...

    Authors: Hashem A. Shihab, Mark F. Rogers, Michael Ferlaino, Colin Campbell and Tom R. Gaunt

    Citation: BMC Bioinformatics 2017 18:20

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  6. Content type: Methodology Article

    A large share of agriculturally and horticulturally important plant species are polyploid. Linkage maps are used to locate associations between genes and traits by breeders and geneticists. Linkage map creatio...

    Authors: Fabian Grandke, Soumya Ranganathan, Nikkie van Bers, Jorn R. de Haan and Dirk Metzler

    Citation: BMC Bioinformatics 2017 18:12

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  7. Content type: Methodology Article

    Feature selection, aiming to identify a subset of features among a possibly large set of features that are relevant for predicting a response, is an important preprocessing step in machine learning. In gene ex...

    Authors: Milos Radovic, Mohamed Ghalwash, Nenad Filipovic and Zoran Obradovic

    Citation: BMC Bioinformatics 2017 18:9

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  8. Content type: Methodology article

    In functional genomics studies, tests on mean heterogeneity have been widely employed to identify differentially expressed genes with distinct mean expression levels under different experimental conditions. Va...

    Authors: Weiwei Ouyang, Qiang An, Jinying Zhao and Huaizhen Qin

    Citation: BMC Bioinformatics 2016 17:497

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    The Erratum to this article has been published in BMC Bioinformatics 2017 18:89

  9. Content type: Methodology Article

    The analysis of DNA methylation is a key component in the development of personalized treatment approaches. A common way to measure DNA methylation is the calculation of beta values, which are bounded variable...

    Authors: Leonie Weinhold, Simone Wahl, Sonali Pechlivanis, Per Hoffmann and Matthias Schmid

    Citation: BMC Bioinformatics 2016 17:480

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  10. Content type: Methodology article

    In the biological experiments of soybean species, molecular markers are widely used to verify the soybean genome or construct its genetic map. Among a variety of molecular markers, insertions and deletions (In...

    Authors: Jianqiu Yang, Xinyi Shi, Lun Hu, Daipeng Luo, Jing Peng, Shengwu Xiong, Fanjing Kong, Baohui Liu and Xiaohui Yuan

    Citation: BMC Bioinformatics 2016 :1312

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  11. Content type: Software

    Molecular evolution studies involve many different hard computational problems solved, in most cases, with heuristic algorithms that provide a nearly optimal solution. Hence, diverse software tools exist for t...

    Authors: Jorge Álvarez-Jarreta and Eduardo Ruiz-Pesini

    Citation: BMC Bioinformatics 2016 17:436

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  12. Content type: Research Article

    Branching events in phylogenetic trees reflect bifurcating and/or multifurcating speciation and splitting events. In the presence of gene flow, a phylogeny cannot be described by a tree but is instead a direct...

    Authors: Hussein A. Hejase and Kevin J. Liu

    Citation: BMC Bioinformatics 2016 17:422

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  13. Content type: Research article

    Perfectly or highly conserved DNA elements were found in vertebrates, invertebrates, and plants by various methods. However, little is known about such elements in protists. The evolutionary distance between a...

    Authors: Lev I. Rubanov, Alexandr V. Seliverstov, Oleg A. Zverkov and Vassily A. Lyubetsky

    Citation: BMC Bioinformatics 2016 17:385

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  14. Content type: Research Article

    Studies exploring the potential of Chaos Game Representations (CGR) of genomic sequences to act as “genomic signatures” (to be species- and genome-specific) showed that CGR patterns of nuclear and organellar D...

    Authors: Rallis Karamichalis, Lila Kari, Stavros Konstantinidis, Steffen Kopecki and Stephen Solis-Reyes

    Citation: BMC Bioinformatics 2016 17:313

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  15. Content type: Software

    With the emerging interest in phytoplankton research, the need to establish genetic tools for the functional characterization of genes is indispensable. The CRISPR/Cas9 system is now well recognized as an effi...

    Authors: Achal Rastogi, Omer Murik, Chris Bowler and Leila Tirichine

    Citation: BMC Bioinformatics 2016 17:261

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2016 Journal Metrics

  • Citation Impact
    2.448 - 2-year Impact Factor
    3.450 - 5-year Impact Factor
    0.946 - Source Normalized Impact per Paper (SNIP)
    1.467 - SCImago Journal Rank (SJR)

    1405 Usage Factor

    Social Media Impact
    816 mentions