Literature DB >> 35650556

Correction: MGcount: a total RNA-seq quantification tool to address multi-mapping and multi-overlapping alignments ambiguity in non-coding transcripts.

Anna Alemany1, Sol Schvartzman2, Andrea Hita3,4, Gilles Brocart3, Ana Fernandez3,4, Marc Rehmsmeier4.   

Abstract

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Year:  2022        PMID: 35650556      PMCID: PMC9158184          DOI: 10.1186/s12859-022-04725-8

Source DB:  PubMed          Journal:  BMC Bioinformatics        ISSN: 1471-2105            Impact factor:   3.307


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Correction to: BMC Bioinformatics (2022) 23:39 https://doi.org/10.1186/s12859-021-04544-3

Following the publication of the original article [1], the authors identified an error in Fig. 2 and caption 2c. The correct figure is given below, and the caption has been updated from ‘’Reads ri (i = 1, 10)’’ to ‘’ Reads ri (i = 1, 11).’’
Fig. 2

MGcount strategy. a MGcount takes a set of genomic alignments (BAM files) and a GTF RNA feature annotations file as inputs. The algorithm assigns reads hierarchically and then models multi-mapping assignments in a graph using the Rosvall’s map equation [36, 37]. As output, MGcount provides an RNA expression count matrix (where feature communities are collapsed as new defined features), a feature metadata table and the graphs. b Illustration of how the hierarchical assignation can resolve multi-overlappers: reads that map to small-RNA and long-RNA features are assigned to small-RNA in the first round; reads that map to long-RNA introns and long-RNA exons are assigned to long-RNA exons in the second round; remaining reads are assigned in the last round. c Illustration of multi-mapping small-RNA and long-RNA exon graphs generation by MGcount. Reads ri (i = 1, 11) have been hierarchically assigned to S1, S2, S3, S4, S5 (small-RNA biotypes, yellow), and G1, G2 (long-RNA biotypes, blue). Each vertex in the directional multi-mapping graphs (right) corresponds to a feature and has a size proportional to the logarithm of the number of alignments. Edges connect vertices with common multi-mapping reads, with weights proportional to the number of common multi-mappers normalized by the total number of alignments of the source vertex. Hence, the weight of the edge connecting S1 with S2 becomes 3/4 (reads mapping both S1 and S2 divided by reads aligned to S1). (CB: Cell Barcode, UMI: Unique Molecular Identifier)

MGcount strategy. a MGcount takes a set of genomic alignments (BAM files) and a GTF RNA feature annotations file as inputs. The algorithm assigns reads hierarchically and then models multi-mapping assignments in a graph using the Rosvall’s map equation [36, 37]. As output, MGcount provides an RNA expression count matrix (where feature communities are collapsed as new defined features), a feature metadata table and the graphs. b Illustration of how the hierarchical assignation can resolve multi-overlappers: reads that map to small-RNA and long-RNA features are assigned to small-RNA in the first round; reads that map to long-RNA introns and long-RNA exons are assigned to long-RNA exons in the second round; remaining reads are assigned in the last round. c Illustration of multi-mapping small-RNA and long-RNA exon graphs generation by MGcount. Reads ri (i = 1, 11) have been hierarchically assigned to S1, S2, S3, S4, S5 (small-RNA biotypes, yellow), and G1, G2 (long-RNA biotypes, blue). Each vertex in the directional multi-mapping graphs (right) corresponds to a feature and has a size proportional to the logarithm of the number of alignments. Edges connect vertices with common multi-mapping reads, with weights proportional to the number of common multi-mappers normalized by the total number of alignments of the source vertex. Hence, the weight of the edge connecting S1 with S2 becomes 3/4 (reads mapping both S1 and S2 divided by reads aligned to S1). (CB: Cell Barcode, UMI: Unique Molecular Identifier) The original article [1] has been corrected.
  1 in total

1.  MGcount: a total RNA-seq quantification tool to address multi-mapping and multi-overlapping alignments ambiguity in non-coding transcripts.

Authors:  Anna Alemany; Sol Schvartzman; Andrea Hita; Gilles Brocart; Ana Fernandez; Marc Rehmsmeier
Journal:  BMC Bioinformatics       Date:  2022-01-14       Impact factor: 3.169

  1 in total

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