ALFA: annotation landscape for aligned reads View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2019-12

AUTHORS

Mathieu Bahin, Benoit F. Noël, Valentine Murigneux, Charles Bernard, Leila Bastianelli, Hervé Le Hir, Alice Lebreton, Auguste Genovesio

ABSTRACT

BACKGROUND: The last 10 years have seen the rise of countless functional genomics studies based on Next-Generation Sequencing (NGS). In the vast majority of cases, whatever the species, whatever the experiment, the two first steps of data analysis consist of a quality control of the raw reads followed by a mapping of those reads to a reference genome/transcriptome. Subsequent steps then depend on the type of study that is being made. While some tools have been proposed for investigating data quality after the mapping step, there is no commonly adopted framework that would be easy to use and broadly applicable to any NGS data type. RESULTS: We present ALFA, a simple but universal tool that can be used after the mapping step on any kind of NGS experiment data for any organism with available genomic annotations. In a single command line, ALFA can compute and display distribution of reads by categories (exon, intron, UTR, etc.) and biotypes (protein coding, miRNA, etc.) for a given aligned dataset with nucleotide precision. We present applications of ALFA to Ribo-Seq and RNA-Seq on Homo sapiens, CLIP-Seq on Mus musculus, RNA-Seq on Saccharomyces cerevisiae, Bisulfite sequencing on Arabidopsis thaliana and ChIP-Seq on Caenorhabditis elegans. CONCLUSIONS: We show that ALFA provides a powerful and broadly applicable approach for post mapping quality control and to produce a global overview using common or dedicated annotations. It is made available to the community as an easy to install command line tool and from the Galaxy Tool Shed. More... »

PAGES

250

Identifiers

URI

http://scigraph.springernature.com/pub.10.1186/s12864-019-5624-2

DOI

http://dx.doi.org/10.1186/s12864-019-5624-2

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1113063179

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/30922228


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