New chimeric RNAs in acute myeloid leukemia

Ruffle, Florence; Audoux, Jérôme; Boureux, Anthony; Beaumeunier, Sacha; Gaillard, Jean‐Baptiste; Samra, Elias Bou; Mégarbané, André; Cassinat, Bruno; Chomienne, Christine; Alves, Ronnie; Riquier, Sébastien; Gilbert, Nicolas; Lemaı̂tre, Jean-Marc; Bacq‐Daian, Delphine; Bougé, Anne Laure; Naveilhan, Philippe; Commes, Thérèse

doi:10.12688/f1000research.11352.1

Cited by 8 publications

(5 citation statements)

References 35 publications

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“…The expression of a new lncRNA was also quickly searched in the Leucegene dataset (see Figure 5c), we observe an homogeneous and low expression in CD34 normal cells compared to an heterogeneous one in AML subtypes. This lncRNA candidate was already described in Ruffle et al (18), using for the first time the "k-mer concept" for checking new biomarker candidates, and we have demonstrated a restricted expression of the NONE "chr2-p21" lncRNA in the haematopoietic lineage using Leucegene and ENCODE datasets. Hence, for lncRNA candidates, following their discovery in a tissue/disease type, their specificity could be easily evaluated through quantification in a wide range of RNA-seq data including normal and pathological conditions as recently described by Riquier et al (33).…”

Section: Detection Of Potential Contaminationsupporting

confidence: 52%

Kmerator Suite: design of specific k-mer signatures and automatic metadata discovery in large RNA-Seq datasets

Riquier

Bessière

Guibert

et al. 2021

Preprint

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The huge body of publicly available RNA-seq libraries is a treasure of functional information allowing to quantify the expression of known or novel transcripts in tissues. However, transcript quantification commonly relies on alignment methods requiring a lot of computational resources and processing time, which does not scale easily to large datasets. K-mer decomposition constitutes a new way to process RNA-seq data for the identification of transcriptional signatures, as k-mers can be used to quantify accurately gene expression in a less resource-consuming way. We present the Kmerator Suite, a set of three tools designed to extract specific k-mer signatures, quantify these k-mers into RNA-seq datasets and quickly visualize large datasets characteristics. The core tool, Kmerator, produces specific k-mers for 97% of human genes, enabling the measure of gene expression with high accuracy in simulated datasets. KmerExploR, a direct application of Kmerator, uses a set of predictor genes specific k-mers to infer metadata including library protocol, sample features or contaminations from RNA-seq datasets. KmerExploR results are visualised through a user-friendly interface. Moreover, we demonstrate that the Kmerator Suite can be used for advanced queries targeting known or new biomarkers such as mutations, gene fusions or long non coding-RNAs for human health applications.

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Section: Detection Of Potential Contaminationsupporting

confidence: 52%

Kmerator Suite: design of specific k-mer signatures and automatic metadata discovery in large RNA-Seq datasets

Riquier

Bessière

Guibert

et al. 2021

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…The expression of a new lncRNA was also quickly searched in the Leucegene dataset (see Figure 5C ); we observe a homogeneous and low expression in CD34 normal cells compared to a heterogeneous one in AML subtypes. This lncRNA candidate was already described in ( 23 ), using for the first time the ‘ k -mer concept’ for checking new biomarker candidates, and we have demonstrated a restricted expression of the NONE ‘chr2-p21’ lncRNA in the hematopoietic lineage using the Leucegene and ENCODE datasets. Hence, for lncRNA candidates, following their discovery in a tissue/disease type, their specificity could be easily evaluated through quantification in a wide range of RNA-seq data including normal and pathological conditions as recently described by Riquier et al.…”

Section: Resultsmentioning

confidence: 68%

“…The reference allele for each of these mutations is detected in almost all samples. (C) New lncRNA detection: NONE ‘chr2-p21’ lncRNA described in ( 23 ). This transcript is expressed in the whole dataset but shows different levels of expression depending on AML subtype.…”

Section: Resultsmentioning

confidence: 99%

“…For mutation detection, we manually designed 31 bp k -mers centered on the mutation for reference and alternative sequences of three genes currently used in acute myeloid leukemia (AML) diagnosis: TET2, KRAS and CEBPA. We finally designed k -mers with Kmerator at the transcript level for a new lncRNA previously published in ( 23 ) as NONE ‘chr2-p21’.…”

Section: Methodsmentioning

confidence: 99%

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Kmerator Suite: design of specific k-mer signatures and automatic metadata discovery in large RNA-seq datasets

Riquier

Bessière

Guibert

et al. 2021

NAR Genomics and Bioinformatics

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The huge body of publicly available RNA-sequencing (RNA-seq) libraries is a treasure of functional information allowing to quantify the expression of known or novel transcripts in tissues. However, transcript quantification commonly relies on alignment methods requiring a lot of computational resources and processing time, which does not scale easily to large datasets. K-mer decomposition constitutes a new way to process RNA-seq data for the identification of transcriptional signatures, as k-mers can be used to quantify accurately gene expression in a less resource-consuming way. We present the Kmerator Suite, a set of three tools designed to extract specific k-mer signatures, quantify these k-mers into RNA-seq datasets and quickly visualize large dataset characteristics. The core tool, Kmerator, produces specific k-mers for 97% of human genes, enabling the measure of gene expression with high accuracy in simulated datasets. KmerExploR, a direct application of Kmerator, uses a set of predictor gene-specific k-mers to infer metadata including library protocol, sample features or contaminations from RNA-seq datasets. KmerExploR results are visualized through a user-friendly interface. Moreover, we demonstrate that the Kmerator Suite can be used for advanced queries targeting known or new biomarkers such as mutations, gene fusions or long non-coding RNAs for human health applications.

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“…Most of published analyses highlighting new potential biomarkers of MSCs or fibroblasts have been restricted to a comparison between only few cell types and, as discussed, commonly described markers are not strictly distinctive. In order to assess the expression of Mlinc candidates in a large number of samples, we extracted specific 31nt k-mers from each of their sequences, as previously described [ 30 ]. These simplified but candidate-specific (oligonucleotide-like) probes allow a simple and fast presence/absence search on large-scale cohorts and a direct quantification in raw FASTQ data.…”

Section: Resultsmentioning

confidence: 99%

Long non-coding RNA exploration for mesenchymal stem cell characterisation

et al. 2021

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Background The development of RNA sequencing (RNAseq) and the corresponding emergence of public datasets have created new avenues of transcriptional marker search. The long non-coding RNAs (lncRNAs) constitute an emerging class of transcripts with a potential for high tissue specificity and function. Therefore, we tested the biomarker potential of lncRNAs on Mesenchymal Stem Cells (MSCs), a complex type of adult multipotent stem cells of diverse tissue origins, that is frequently used in clinics but which is lacking extensive characterization. Results We developed a dedicated bioinformatics pipeline for the purpose of building a cell-specific catalogue of unannotated lncRNAs. The pipeline performs ab initio transcript identification, pseudoalignment and uses new methodologies such as a specific k-mer approach for naive quantification of expression in numerous RNAseq data. We next applied it on MSCs, and our pipeline was able to highlight novel lncRNAs with high cell specificity. Furthermore, with original and efficient approaches for functional prediction, we demonstrated that each candidate represents one specific state of MSCs biology. Conclusions We showed that our approach can be employed to harness lncRNAs as cell markers. More specifically, our results suggest different candidates as potential actors in MSCs biology and propose promising directions for future experimental investigations.

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New chimeric RNAs in acute myeloid leukemia

Abstract: High-throughput next generation sequencing (NGS)

Cited by 8 publications

References 35 publications

Kmerator Suite: design of specific k-mer signatures and automatic metadata discovery in large RNA-Seq datasets

Kmerator Suite: design of specific k-mer signatures and automatic metadata discovery in large RNA-Seq datasets

Kmerator Suite: design of specific k-mer signatures and automatic metadata discovery in large RNA-seq datasets

Long non-coding RNA exploration for mesenchymal stem cell characterisation

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