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SEquence DAtaset builder
sequences fasta fasta-sequences bioinformatics java sequence-dataset-builder
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@hlfernandez
hlfernandez Change version to 1.0.0
Latest commit 55b2aae Dec 3, 2019
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README.md

SEDA license release

SEDA (SEquence DAtaset builder) is an open source application for processing FASTA files containing DNA and protein sequences. Please, visit the official web page of the project for downloads, a complete online manual and support.

SEDA Screenshot

Main features

Among other functions, SEDA allows you to:

  • Filter sequences based on different criteria (including text patterns).
  • Translate nucleic acid sequences into amino acid sequences.
  • Edit sequence headers in different ways.
  • Remove duplicated sequences.
  • Remove isoforms.
  • Sort, merge, split or reformat FASTA files.
  • Use BLAST to perform different types of queries.
  • Use Clustal Omega to perform multiple sequence alignments.
  • Perform gene annotation using different tools: Splign/Compart, ProSplign/ProCompart, or Augustus (as implemented in SAPP).

For programmers

Programmers can take advantage of the SEDA core to develop new operations to process FASTA files. In addition, SEDA has a plugin-based architecture, so new functions can be added to SEDA through plugins. Take a look at the manual for detailed information about this.

Works using SEDA

  • H. López-Fernández; P. Duque; S. Henriques; N. Vázquez; F. Fdez-Riverola; C.P. Vieira; M. Reboiro-Jato; J. Vieira (2018) A bioinformatics protocol for quickly creating large-scale phylogenetic trees. 12th International Conference on Practical Applications of Computational Biology & Bioinformatics: PACBB 2018. Toledo, Spain. 20 - June DOI
  • H. López-Fernández; P. Duque; S. Henriques; N. Vázquez; F. Fdez-Riverola; C.P. Vieira; M. Reboiro-Jato; J. Vieira (2018) Bioinformatics Protocols for Quickly Obtaining Large-Scale Data Sets for Phylogenetic Inferences. Interdisciplinary Sciences: Computational Life Sciences DOI
  • H. López-Fernández; P. Duque; N. Vázquez; F. Fdez-Riverola; M. Reboiro-Jato; C.P. Vieira; J. Vieira (2019) Inferring Positive Selection in Large Viral Datasets. 13th International Conference on Practical Applications of Computational Biology & Bioinformatics: PACBB 2019. Ávila, Spain. 26 - June DOI
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