Large-scale structure-informed multiple sequence alignment of proteins with SIMSApiper

Summary SIMSApiper is a Nextflow pipeline that creates reliable, structure-informed MSAs of thousands of protein sequences faster than standard structure-based alignment methods. Structural information can be provided by the user or collected by the pipeline from online resources. Parallelization wi...

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Podrobná bibliografie
Vydáno v:	Bioinformatics (Oxford, England) Ročník 40; číslo 5
Hlavní autoři:	Crauwels, Charlotte, Heidig, Sophie-Luise, Díaz, Adrián, Vranken, Wim F
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	England Oxford University Press 02.05.2024 Oxford Publishing Limited (England)
Témata:	Algorithms Alignment Amino Acid Sequence Applications Note Availability Computational Biology - methods Conserved sequence Databases, Protein Information processing Internet resources Nucleotide sequence Protein structure Proteins Proteins - chemistry Secondary structure Sequence Alignment - methods Sequence Analysis, Protein - methods Sequences Software
ISSN:	1367-4811, 1367-4803, 1367-4811
On-line přístup:	Získat plný text
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Popis
Shrnutí:	Summary SIMSApiper is a Nextflow pipeline that creates reliable, structure-informed MSAs of thousands of protein sequences faster than standard structure-based alignment methods. Structural information can be provided by the user or collected by the pipeline from online resources. Parallelization with sequence identity-based subsets can be activated to significantly speed up the alignment process. Finally, the number of gaps in the final alignment can be reduced by leveraging the position of conserved secondary structure elements. Availability and implementation The pipeline is implemented using Nextflow, Python3, and Bash. It is publicly available on github.com/Bio2Byte/simsapiper.
Bibliografie:	SourceType-Scholarly Journals-1 content type line 14 ObjectType-Report-1 ObjectType-Article-1 ObjectType-Feature-2 content type line 23 Charlotte Crauwels and Sophie-Luise Heidig Equal contribution.
ISSN:	1367-4811 1367-4803 1367-4811
DOI:	10.1093/bioinformatics/btae276