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Sequence analysis (methods)

Section edited by Olivier Poch

This section incorporates all aspects of sequence analysis methodology, including but not limited to: sequence alignment algorithms, discrete algorithms, phylogeny algorithms, gene prediction and sequence clustering methods.

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  1. Content type: Methodology Article

    Geminiviruses infect a broad range of cultivated and non-cultivated plants, causing significant economic losses worldwide. The studies of the diversity of species, taxonomy, mechanisms of evolution, geographic...

    Authors: José Cleydson F. Silva, Thales F. M. Carvalho, Elizabeth P. B. Fontes and Fabio R. Cerqueira

    Citation: BMC Bioinformatics 2017 18:431

    Published on:

  2. Content type: Methodology Article

    Metagenomics sequencing provides deep insights into microbial communities. To investigate their taxonomic structure, binning assembled contigs into discrete clusters is critical. Many binning algorithms have b...

    Authors: Ying Wang, Kun Wang, Yang Young Lu and Fengzhu Sun

    Citation: BMC Bioinformatics 2017 18:425

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  3. Content type: Research Article

    Prediction of DNA-binding residue is important for understanding the protein-DNA recognition mechanism. Many computational methods have been proposed for the prediction, but most of them do not consider the re...

    Authors: Jiyun Zhou, Qin Lu, Ruifeng Xu, Yulan He and Hongpeng Wang

    Citation: BMC Bioinformatics 2017 18:379

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  4. Content type: Methodology Article

    Alignment-free methods for comparing protein sequences have proved to be viable alternatives to approaches that first rely on an alignment of the sequences to be compared. Much work however need to be done bef...

    Authors: Saghi Nojoomi and Patrice Koehl

    Citation: BMC Bioinformatics 2017 18:378

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  5. Content type: Methodology Article

    A multivariate genome-wide association test is proposed for analyzing data on multivariate quantitative phenotypes collected from related subjects. The proposed method is a two-step approach. The first step mo...

    Authors: James J. Yang, L Keoki Williams and Anne Buu

    Citation: BMC Bioinformatics 2017 18:376

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  6. Content type: Research Article

    Recently, many standalone applications have been proposed to correct sequencing errors in Illumina data. The key idea is that downstream analysis tools such as de novo genome assemblers benefit from a reduced err...

    Authors: Mahdi Heydari, Giles Miclotte, Piet Demeester, Yves Van de Peer and Jan Fostier

    Citation: BMC Bioinformatics 2017 18:374

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  7. Content type: Software

    Multi-label classification has recently gained great attention in diverse fields of research, e.g., in biomedical application such as protein function prediction or drug resistance testing in HIV. In this cont...

    Authors: Mona Riemenschneider, Alexander Herbst, Ari Rasch, Sergei Gorlatch and Dominik Heider

    Citation: BMC Bioinformatics 2017 18:371

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  8. Content type: Research Article

    Drug resistance in HIV treatment is still a worldwide problem. Predicting resistance to antiretrovirals (ARVs) before starting any treatment is important. Prediction accuracy is essential, as low-accuracy pred...

    Authors: Olivier Sheik Amamuddy, Nigel T. Bishop and Özlem Tastan Bishop

    Citation: BMC Bioinformatics 2017 18:369

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  9. Content type: Software

    Replication timing experiments that use label incorporation and high throughput sequencing produce peaked data similar to ChIP-Seq experiments. However, the differences in experimental design, coverage density...

    Authors: Gregory J. Zynda, Jawon Song, Lorenzo Concia, Emily E. Wear, Linda Hanley-Bowdoin, William F. Thompson and Matthew W. Vaughn

    Citation: BMC Bioinformatics 2017 18:362

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  10. Content type: Methodology Article

    Epigenetic heterogeneity within a tumour can play an important role in tumour evolution and the emergence of resistance to treatment. It is increasingly recognised that the study of DNA methylation (DNAm) patt...

    Authors: James E. Barrett, Andrew Feber, Javier Herrero, Miljana Tanic, Gareth A. Wilson, Charles Swanton and Stephan Beck

    Citation: BMC Bioinformatics 2017 18:354

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  11. Content type: Research article

    Large sample sets of whole genome sequencing with deep coverage are being generated, however assembling datasets from different sources inevitably introduces batch effects. These batch effects are not well und...

    Authors: Jennifer A. Tom, Jens Reeder, William F. Forrest, Robert R. Graham, Julie Hunkapiller, Timothy W. Behrens and Tushar R. Bhangale

    Citation: BMC Bioinformatics 2017 18:351

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  12. Content type: Methodology Article

    Detecting local correlations in expression between neighboring genes along the genome has proved to be an effective strategy to identify possible causes of transcriptional deregulation in cancer. It has been s...

    Authors: Eleni Ioanna Delatola, Emilie Lebarbier, Tristan Mary-Huard, François Radvanyi, Stéphane Robin and Jennifer Wong

    Citation: BMC Bioinformatics 2017 18:333

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  13. Content type: Software

    The major histocompatibility complex (MHC) region of the human genome, and specifically the human leukocyte antigen (HLA) genes, play a major role in numerous human diseases. With the recent progress of sequen...

    Authors: Marc Jeanmougin, Josselin Noirel, Cédric Coulonges and Jean-François Zagury

    Citation: BMC Bioinformatics 2017 18:334

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  14. Content type: Methodology Article

    For single-cell or metagenomic sequencing projects, it is necessary to sequence with a very high mean coverage in order to make sure that all parts of the sample DNA get covered by the reads produced. This lea...

    Authors: Axel Wedemeyer, Lasse Kliemann, Anand Srivastav, Christian Schielke, Thorsten B. Reusch and Philip Rosenstiel

    Citation: BMC Bioinformatics 2017 18:324

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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)

    Usage 
    3784657 downloads
    1405 Usage Factor


    Social Media Impact
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